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codeparrot
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github-jupyter

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jochym/abinitio-workshop
notebooks/zapisz-trajektorie.ipynb
1
4050
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Warsztaty modelowania w nanofizyce\n", "----\n", "## Przetworzenie historii dynamiki molekularnej na trajektorie\n", "\n", "**Paweł T. Jochym**\n", "\n", "Zakład Komputerowych Badań Materiałów\n", "\n", "Instytut Fizyki Jądrowej PAN, Kraków" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "Wiele funkcji biblioteki ASE oczekuje danych w postaci trajektorii. Poniższy krótki kod przekształca surowe dane otrzymane z symulacji w zestaw trajektorii przydatnych np. przy wizualizacji." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "init_cell": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "import ase.io\n", "import pickle\n", "import gzip\n", "import nglview " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "run_control": { "frozen": false, "marked": false, "read_only": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[150, 300, 500, 600, 700, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1350, 1400, 1425, 1450, 1500, 1600, 1700, 2000]\n" ] } ], "source": [ "# Wczytanie danych wyliczonych przez program VASP przygotowanych wczesniej\n", "md={}\n", "for k,tr in pickle.load(gzip.open('data/md_PtFePt.p.gz','rb')).items():\n", " T=int(k.split('/')[-1][1:])\n", " md[T]=tr\n", " \n", "print(sorted(md.keys()))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for T in md:\n", " ase.io.write('data/md_T_%d.traj' % (T,), md[T][1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "hide_input": false, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" }, "toc": { "toc_cell": true, "toc_number_sections": false, "toc_threshold": 6, "toc_window_display": true }, "toc_position": { "height": "148px", "left": "1541px", "right": "27px", "top": "125px", "width": "352px" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false }, "widgets": { "state": { "02134e32b1a94802b3590f11424a0dd6": { "views": [] }, "65e8634afbad454cb7b4bd3df581a2d7": { "views": [] }, "7f8214e629e74b8fadf43f19cb28ac14": { "views": [] }, "bbd8d093002543fa8040764834f2d6bd": { "views": [] }, "c6201d0873454bdbad87e159c4d6648c": { "views": [] }, "e58e74ee097c4960a2daec7500477f3b": { "views": [] }, "f9242e8bf1ab4bbd97c343fbc346ba2f": { "views": [] }, "fcf722773fcb4c41a993093af94566a5": { "views": [ { "cell_index": 6 } ] } }, "version": "1.1.1" } }, "nbformat": 4, "nbformat_minor": 1 }
cc0-1.0
iurilarosa/thesis
codici/Archiviati/Notebook Completi/Codici per TEST/.ipynb_checkpoints/Hough Federico (rotto)-checkpoint.ipynb
1
181814
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy\n", "import scipy.io\n", "#from tensorflow.python.client import timeline\n", "import time\n", "\n", "percorsoDati = \"/home/protoss/hanford.mat\"\n", "\n", "struttura = scipy.io.loadmat(percorsoDati)['hanford']\n", "\n", "peakmap = struttura[9002].copy()\n", "\n", "del struttura\n", "peakmap = peakmap[:,25:80]\n", "from matplotlib import pyplot\n", "%matplotlib notebook\n", "pyplot.figure(figsize=(10, 8))\n", "#a = pyplot.scatter(numpy.arange(tempiUnici.size),tempiUnici/30, s = 0.5)\n", "a = pyplot.imshow(peakmap, origin = 'lower', interpolation = 'none')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for 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AADZCegTloA+z9oJ6O36KKADAMCbmgf01kGh1fZbB+QeQWjN/tfegjxDzRY6e8753Br37n2LfZYG9aWfCegAAGQnoG8IDDPMu9b+BfS5S0AX0AEAuB4C+obAMMO8a+1fQJ+7BHQBHQCA69H8W9zXnoSP2GemcFq7B/d/thS+nr58/rpGz6F3r0qPg3MXRma7sDDLRaQ94156XUAHACA7AV1AfzD8COgCesv97xn30usCOgAA2RUH9FahdqYAc+21NZzOsv6zHEtrxzHLeFuOcc/FgLWfE9ABAMhOQFe712K29Z/lWBLQ3/p5AR0AAB72OCJePXn2ovmJ/dIJdqtbbGe7pbjVnLLeot9qjLNdKJilZr3lvvRxgEuPXAjoAABkJ6AnKQG9z3Yz9G/vnGb5/RDQAQDgTQJ6khLQ+2w3Q//2zmmW3w8BHQAA3lQ1oNd4zrRGeFja1t2J/aVQcG7fM4SaWmtRcy5bt1X7W+JrHHsj12ypFzOMq3adu7C0di2W3iOgAwCQnYBesO9MYUlA73Pc1iwBXUAHAOC6Df076DUC/eiwccRaE6BGrEXNizg9etbjszPULOMX0AEAyE5AV6t6K6CX96zHZ2eoWcYvoAMAkJ2Arlb1VkAv71mPz85Qs4xfQAcAILvNAf3Ss9xbT+jPbWPGYDZD1bjQobf5qvRiSY31Xfu723ocSyWgAwCQnYB+gBLQr7ME9DdLQAcAILvigN7qVt6a3+bdKoBkrSPOqdU8j9yrrRfGZhzvUgnoAABkJ6BXnN+sdcQ5tZrnkXsloAMAwNwE9Irzm7WOOKdW8zxyrwR0AACYW9dvce91or5mWzMFkda9Kp1v1ufSZw3opd/NULv/LX5/Rm9jqQR0AACyE9APVgL6fOFRQG/f4/c/EtABAMhvdUDfGjBahZlZK/v496y/GrMmrY65ntuvsT0BHQCA7AT0hqFp9FhazGv0ONRb10RAvykBHQCA7B5HxKsnz14MDxsZ6ppD6jXPffYqDbo9b7nvedwI6AAAZCegbwhCo8dh7mppbQR0AR0AgNwE9A1BaPQ4zF0trY2ALqADAJBb1WfQa4SM0WEnUy31rMU3dl/zutT4PoXRc5i1avdWQAcAIDsBPXEJ6P16LKDP31sBHQCA7Ir/Dnrp7a7nbpOtHQBrBNbWYaxXaGt1W/LTl89fPX35vNkajujV6Lo/z0s9vXut51q0WMPa4xXQAQDITkAvCBQ1Pieg1+9p9hLQBXQAAIgQ0IsCRY3PCej1e5q9BHQBHQAAIjYE9NYn7TW3vxR6RgeWrePf8tnS4FcyjlYXAEb0ceaAW7OPe8JyaR9H9ENABwBgqz8ZET8SEb8YET8dEd8bEV987z2fGBHfGRE/GxH/LiL+akR89r33fH5E/M2I+OXb7XxrRLytYBwC+oQloPfto4AuoAMAcN2+LyK+MSJ+e0T8zrgJ2T8REZ9y8p4PR8S/jIj3RcSXRsTfj4i/d/L6x0fEP4qIH4iId0XEByLiZyLizxSMo2pAr3GCPvokf2/46Tn+tYHr7lboGuNvNc+sIbl0bjWPtdFzabH+e97/5NkLAR0AgCo+M25OLL/y9r8/LSJ+LSK+4eQ9v/X2PV9++98fiIiPxZv/qv7HIuIXIuITVu5XQK8cGAT0/es+w/q3WCcBvd6cBHQAAFp6Z9ycWP6O2/9+3+1/f/q99/1ERHzw9n9/S0T86L3Xf/Pt537Xyv0OucVdzVMzh8O9F0lK379lXjWDd6vtb51jq760Kre4AwBQw8dFxN+IiL978rM/EhG/uvDeH46IP3f7v78rIr7/3uufHDcnqB94YF9vj5uT17t6RwjoV10CuoC+5nOt165GCegAANTw4Yj48Yj4vJOftQroH7p9/Y0S0K+3BHQBfc3nWq9djRLQAQDY6zsi4l/Fza3pp1rd4l7tX9Bnea62NETcPY996ZnsGiEle2Wd+4gx13w2u1XfW/dladylFxGWXlv6e+9L7xPQAQDY6lHchPOfjIgvWnj97kvi/tDJz744lr8k7rNO3vNNcfMlcW9fOY7Nz6AL6MevrHMX0Mf0RUAHACCrvxARPx8RXxURn3NSn3Tyng/Hzb+Y/764+TNrP3Rbd+7+zNr3x82favvquPlb6MP+zNrokFQ6jqXXWt+yrOqu4ZHq3IWjEaH93D7XjufuPaUXxraUb3EHAGCrtzwHflvfePKeT4yI74yIfxMRvxQR3xM3If7Ub4qIvxURvxw3fwP92yLibQXjENAF9OnrWvouoO8rAR0AgOwEdAF9+rqWvgvo+0pABwAgu6oBfZZnlnuOQ6Cvu06nQW7v9xJsGUeN70J46DnsmZ8pLw3ctedUY1ueQQcAIDsBvWGwmKEXM5eA3u7Yq7EWrXrVai4COgAA2TX/FvelW1uXTvJbhdnZQsSIKg1el17vtXa15l1yAaXmXNZ++3jLdW3VvxoXM2rPRUAHACA7AX2iQNSqBHQBvXb/BHQAAKjvkLe4tw4po+fZM5hlCuEzjHHt8dIq/K7ZZ8+59ywBHQCA7AT0HUFHQB9fs41RQBfQAQBgKwF9R9AR0MfXbGMU0AV0AADY6nVALz25vhSMtp6o1w4uM4S2DLX271S3/lvWo0Pemn3vCb/nflda/M7s2caeix8jLmoJ6AAAZCegq1fvfySg399/6RgF9PPzFNABAOCy4lvc9wSRjDU6MO4Zb0nYPP1Z628fP1KNCKK957blol1Jz7aMZ2kbAjoAANkJ6JVDyugS0Mf0O+vxsmZuAjoAAPQhoFcOKaNLQB/T76zHy5q5CegAANBH1W9xn7mOEpqONPfRYXbvc9VHraV51px7q74L6AAAZCegX0HNOncBfc4S0AEAYIzpAvq521hHB7pW8xw9lmuq2n2fYVuzHEutxiGgAwBwLQT0CeY5eizXVAJ6nt6W9kVABwAguyoBvfUtsUeqmrf3nuv7pS/UqjnGmn3ZM+5e4z938Wj08VVzfqXrNXrcAjoAANkJ6BOGn7XvF9DHjF9AF9ABAKAFAX3C8LP2/QL6mPEL6AI6AAC08DgiXj159mL4yfW58FMzWOzZVoagm71qBr6ZwuNMla0vay8GCOgAAGQnoBdsS0Cfe/1bbutIla0vAjoAANfi9S3ua0/aS4Nr6a3LrcJDr0BSe/yzPT6QIditHWOvW+Frbmvptvqegbv1nJ6+fP66SvcloAMAkJ2A3ijACOjjSkAf39utcxLQAQC4ZgJ6owAjoI8rAX18b7fOSUAHAOCaVfkW99lO/DOMozRU7bmAsnX7a/d5F6hGr93WHtQMuJdCZ4u1q1F7gvHINTv9mYAOAEB2AvqgcQjo86yhgC6gAwDADKp8i3vprbY9A0DJeGa4QDCyVyPHMeJ27ay19yLMnve1usW99PdUQAcA4IgE9EcC+gzjENDbr4WADgAAc2t2i/tsIWu28cxcNcKSWu7ViLXrdXFlTw9qXBR48uyFgA4AQGoCutrUKwG9vJ8CuoAOAADnCOhqU68E9PJ+CugCOgAAnFP8DPqaE+nT12uEkz2f3/oN40cKnbOG6T2hreb+R2y/5lqUbqv2cVC6rVbf+u8ZdAAAshPQG+xzthLQx6yxgL5cAjoAACzbfIv7bGFv9Nhm7UXL3raY8+gLOiN7dv/vkM80h5brdOmC3trb9gV0AACyE9Ar73P03Hv2VkCv2zMBXUAHAOC6CeiV9zl67j17K6DX7ZmALqADAHDdigP62pPl1if5sweYHgFnz7b0fd4QXDq2HnMpucBw+nz5ufdf2t7SWp17v4AOAEB2AvqgEtDH933WuQjoD6+VgA4AwJG9DuitbllV+8NQrX4ecX0u3Sbd68JFjb63Xvetr9UYx1J4r10COgAA2QnoE5aAvm9OAvr6OQnoAAAwjyq3uPcKOq1DxIjxj6rWYWm2mu24zb4W525n33NrfunFjNOfCegAAGQnoA8e/6jKGAp7rKeAXjZmAR0AAOoR0AePf1RlDIU91lNALxuzgA4AAPU8johXT5692Bx4toSZ1s+9nttGtuewS59dHjmO0ZVhjDXHvfXvptd+Nn+WvgvoAABkJ6BPXgL6scZYc9wC+psloAMAkN3qW9xbn6CPvCV+z221NcLP1lp7a3TN8dS8HXuWiyVrv2G85lhP536/WvV2z/a3rvvSPlutuYAOAEB2AvoDP9s6RgF931qMKAG93boL6AAAsJ6A/sDPto5RQN+3FiNKQG+37gI6AACsV/wt7ltDwdIJec+AtnY/LULS6G3UXM9M298yjtLxtLhoM0vfR6zPnn0K6AAAZCegb3xfzX32Gk/PsDTD9reMQ0Afuz4COgAA16xqQO8ZlmYIdj1Cx+jxZhr3tVXWi06tSkAHACA7Ab3znEaPpeYaqDnWqdb7au5zRAnoAABkJ6B3ntPosdRcAzXHOtV6X819jigBHQCA7Jo/gz7byf0RLwDUHvfSNmbox1EvHFxLgN467qV1X/pWeQEdAIDsBPSJx1o76OzZxgz9END7bmuWHgjoAABciyp/B733SfzocYzuy1JguT+OGhciWvd7xvXcO+49cyr93MiLTTXWbs9xu1QCOgAA2QnolcY0qg8C+rg1F9AFdAAAqKnKLe5rb0HNGMZqh6XW+y79TKv3Z62R89x6DPU45nr1pfTix+nPBHQAALIT0BsECwE9bwnoY/sioAMAcM0E9AbBQkDPWwL62L4I6AAAXLPp/g76iCCyZ7uzBdfSkLd2/KMuPvTu2eixrBnPyLBcerys3X6NC0ZPnr0Q0AEASE1A37ndmYLdltAjoAvoJeMQ0AEAoJ3NAb00AO4JFFtP3mcLXjNX1l5lHXfPHtQI+zV/12usl7+DDgDAEQnoqto6GfecPRDQAQAgBwFdVVsn456zBwI6AADk0OwZ9K0n4QLX+j7u6dO5Cxit9jlDzy71ovW+1mxjxHj27PPcX2wovQCwZxwCOgAA2Qnok5eAXrdnl3rRel9rtiGgC+gAAFyn5re41wwuWWuWW+6XxjHLeEav0UzjHt2Xrbezl467xjZOS0AHACA7Ab1j4JkleM04ntFrNNO4R/dFQAcAgDGG/h302kEkY9jr2Wd9PEZtvbjS4nb8WXrwrm/+dgEdAID0BPQrKAH9WCWgC+gAAByTgH4FJaAfqwR0AR0AgGMqDujnnl/eE7h7nfivHeO595Ruo3avzo1x7TZbPD/can2uOYjW6GOLi2CXttti35dKQAcAIDsBfcN4BHQBffYS0AEAIJ+ut7gvndyfq9EhZ1SV9kLP3uxDq7WYbfw13j/iuNn6+39pLQR0AACyE9AnLAF9X99arcVs4xfQ3ywBHQCA7AT0CUtA39e3Vmsx2/gF9DdLQAcAILshAb1G2MhUGQL0pTG2Hn/rCxFrP1dznnvH2vN42XOBrEa4Lu3f0msCOgAA2QnoncPP6LFsHaOA3mZOsxwvAjoAAIz3OqCXnkC3ChEZwmyroKOPecfduh+lr63tcemFi6cvn796+vJ517VeO14BHQCA7AT0RoFq9DxG7//axt26H6Wvre2xgA4AAPNodot7aXhYOvFvfdtzzdCTrWrebtx7zKPHsWW8NYP2/ffv6UuNC0pbt7H0/j3jENABAMhOQN857qwloPcdr4C+bj0FdAAArpmAvnPcWUtA7zteAX3degroAABcs8cR8erdH/lglfCzdhutQ1ZpYLi7OFDr2dqMNcvaja4Z5lcjNNdY69Zzqn3MCegAAGQnoD8S0Gdau9E1w/wE9G1rIqADAJDd44h49eTZixRBJ9stzr1Cz56+z3Kr+8hxtA7Es/Tg9EJUq29jf2hMPXoroAMAkJ2AnrgEdAFdQP+NEtABAMhOQE9cArqALqD/RgnoAABk1+xb3EtPzGuEpBYBZ5bwpq5nfXr+Dqzt6V2gn7nHAjoAANkJ6AJ66jri+gjo20pABwAgu+YBvXWwWAoz53422/iz1NHn16N3ay8Gbdnumt+B0vFs3Xer/l1635NnLwR0AABSE9AHj1fcN3MAACAASURBVD9LHX1+PXonoO/b16X3CegAAGS3OaCvPUFvFU5mqRHj7rXPWW4bXxrDDOOapUavU6t9l27XLe4AAGQnoE8aTmbY5+jgd26+M4xrlhq9TgI6AADUIaBPGk5m2Ofo4HduvjOMa5YavU4COgAA1FHlGfSaz86OPsnfG35Gh6UR1Sugrb0YdIS59xjzlt/ZrdtYen/pxbtL/78hoAMAkJ2AXjnMCOjttiug1x2zgA4AAHNZHdDXnFz3DN6tg1SN8c/WAzV+zVpfYKjxOEDNHpT+f8Ke/w8R0AEAyE5AbxhSZuuBGr9mArqADgAADxHQG4aU2Xqgxq+ZgC6gAwDAQ6o+g/705fPXVeOEfuQ2MtSasNejBzUCYOvgWvN4Off+Lb8DGY73NRfoes33oRLQAQDITkBPXAJ63fEL6OvHKKADAEB9rwP6iBPu2rfEzxDKt9zK23NMNfs9Q/9mGe/avo/uackFnZrjrt2DpW0J6AAAZCegNwxDPQNLaZ9n6FWN/s0y3rV9H91TAR0AAOZV5Rb31kFHHbNGB9dZam8wHtHHGhf0ajwSc1oCOgAA2QnoaviaX/u6C+gCOgAARAjoaoI1v/Z1F9AFdAAAiJgooM9WGYJjhjFundPR5tViPdeG29Ltbn12/tz7l8J47QsRT569ENABANjkKyPir0fET8XNCeXX33v9L9/+/LS+7957PiMivjsi/m1E/HxE/MWI+NTCcQjolUKNMdad09Hm1WI9BfQ3S0AHAGCrD0TEn46IPxgPB/SXEfE5J/Uf3nvPy4j40Yj4PRHxnoj4sYj4K4XjqBrQs95q22us2WprUNuzr6zbn2WdWry/x7Fxbhtrt+8WdwAAangooH/vmc/8ttvP/e6Tn/2BiPj3EfG5BfsW0A8a2mr3NnuAPvJaC+g3JaADAFDDQwH95yPipyPin0XEhyPiPzp5/Y9GxM/d+8zbIuLX4+Zf5dcS0A8a2mr3NnuAPvJaC+g3JaADAFDDUkD/wxHxtRHx5Pa1fxIRPxwRH3/7+p+Km+B+309HxB8/s6+3x83J6129IyJevfsjH6x+cj0ycJ0LDdf4jHPNANWzd3fPLGddr1kuOpxbuxrbmKWPAjoAADUsBfT7fsvt+/7L2//eGtA/FG/98jkB/eAloOfte43tC+gAALDemoAeEfEzEfHNt/976y3ui/+CvuUW9y0n62tDddZAlrH2Ph5Qe50yrH/WY3XPuLe+v2d/BHQAAGpYE9A/L26+AO5rb//77kvivvTkPb8/On5JnIB+jBLQ941x9rHWGreADgDAkX1qRLzrtl5FxAdv//fn3772rRHx5RHxBXFzW/v/FRH/PG7+BfzOy4j4hxHxZRHxFbevp/0zazVP5JfGkSlI1Zj70fc521x63c7eo8+zXYBwizsAAK29NxaeBY+bb2//pIj4/rh5nvzXIuLHI+K7IuKz723jM+ImkP9iRPxCRPyluAn3JQT0g5WAPmYuAvr4NRHQAQDITkA/WAnoY+YioI9fEwEdAIDsqgb0mas0dMwQTHr24+4b05++fP76Pac/m6EfPQJj6+fqS4690f0+rfvHxmw9fv8jAR0AgPwE9M4hYpYS0Putu4DevsfvfySgAwCQX7OAXnoSfi7E7Dmhn/WW5axVM6jNshY1A/G547fVHC5dANg6np7jPvf66YWi+8ff6TYEdAAAshPQK3/u6CWgl29LQN+2fQEdAIBrI6BX/tzRS0Av35aAvm37AjoAANfmap5BV8t1pAsRLebSOqRuGf+sF51G90pABwAgOwH9yktAX7dNAX3+XgnoAABkVzWgl37r9+kJ/blbp9fcBrt2X62Dw+iqOc9zve0ZxmbpS6v5tg7crY6JkesvoAMAcEQC+sFKQG/XFwFdQAcAgJYE9IOVgN6uLwK6gA4AAC1N9wz66BP/7NU6iPb8FvfSizw15l5ygalWv3utdfbfq4999J2va+l1AR0AgOwE9IOVgC6gzzjWGiWgAwBwdEMC+gyh5tIt87PNs/WFi54XRnrvZ9RFn6V9rv3Z1vU5t/3Siw6l/RvxSMTpdgV0AACyE9Abjk1AH7v+AvpyH7b0TUAHAID2HkfEqyfPXnQJIjPUnqCTvZbmvuZiRZaLArOu2bm+1z4Ot4brVnPuuX0BHQCA7AT0QQFjlrkL6GP7LqDX276ADgBAdgL6oIAxy9wF9LF9F9DrbV9ABwAgu2Z/B31rEKkRKEaFkplqaS1a1bX3+lJfRoflGdb40txr9EVABwAgOwH9oCWgjy8BfbkXLbb//kcCOgAA+U3zd9BnCzEjb/0ePT81di0cX9tKQAcAIDsBfUPAmGWMreanxq6F42tbCegAAGQnoG8IGLOMsdX81Ni1cHxtKwEdAIDspgvorbdfeiFgtgsHW/qYYdwjjonZ1niWdWrxfRA9xi2gAwCQnYAuoE9bAvrYvgvoAADQV9WAPjrgHKVqBqPTbZx+s/vW7bcKs7McN7OM41pr6bhauyYCOgAA2QnoE5aAPr73o8dxrSWgAwBwzR5HxKsnz14MDy41tn/EcHVuTjVvZz/3CEDPno7Yd9YLS63GvPUxkNHHhIAOAEB2AvrkJaAL6FuOjZ5rIKADAEAdAvrkJaAL6FuOjZ5rIKADAEAdzb7FveTEvmcAKA1je8LbLAEzY/jc04dWPahx8WPpZzOsz9pjqHSsPfsuoAMAkJ2AXvn9rca9Z4wzBMAeJaC3P4YEdAAAaGf1Le6qXfhpFVhaj+Mo1fOizQzj37ONtRcWRvRHQAcAIDsBvUMJ6HOXgC6gAwDADAT0DiWgz10CuoAOAAAzGBLQ1z7rOjLsjNi3Gl8zrH/t7z1oEaT3BPpWz98L6AAAZCegVwos6hg1w/oL6NtKQAcAILtm3+I+azBaCgWjLwpsracvn796+vL5rrmrMbU2pJau8SzV+vb3pf4J6AAAZCegP/CzDCWg5y0BXUAHAID7Xgf0WcJb6ThKb7UdHVxG9/ShUHgpMLYOXLNUi7mU3tK9ZX3O1f2Qv2Vbax9LeWjcNXp8abwCOgAA2QnoV1ACenmvWvd/7fu3buO0BHQAAMhBQL+CEtDLe9W6/2vfv3UbpyWgAwBADlWeQV8TALeccNcYT83tqrp1hHUaOf61oXnWeV4aY+lrT569ENABAEhNQFfD6gjrJKC3W38BHQCAa7M5oB8hXC3N5Shzar2eNXqWIUTOvO9Z+r71OKl9Qc8t7gAAZCegbwgKRywBPd++Z+n71uNEQAcAgDcJ6BuCwhFLQM+371n6vvU4EdABAOBNxQF9zXOhe07Cs9Zsczt6v6+t1gbd1vtfO8aS8dcqAR0AgOwE9I4BZsR4ZhqTqrOeAvpyCegAAGRX5VvcZwgpPfdfey6z9WVpGzOE/Rrz23Mr/6xzGbkmD82p5oW6te8X0AEAyE5AHxSIBPQxPRPQ+6yPgA4AAOWaB/Qj3mp9jXOabb5Hvoiwpwe9xtHjYlnpdgV0AACyE9DNadWcZpuvgC6g3y8BHQCA7AR0c1o1p9nmK6AL6PdLQAcAILvHEfHqybMXaU7aa45x9FiUtVP1SkAHACA7AV2lKWunzpWADgBAdqm+xV1drrUBdmvQrRmQZ7mt/unL569r9Pr1XoM961Py/j0XV9Z+TkAHACA7Af1gJaCXl4C+bX1K3i+gAwDAZQL6wUpALy8Bfdv6lLxfQAcAgMu6BvQ1J9qeMy7v45ae7V2LHoFLlfWzZU/3rvXSGEt/dmlfAjoAANkJ6AlLQFe11qL1mgnoAACwXnFAny1crT3Jrzk/FxHqfGP/nlvLj9T/NX3c0+9zn2vVx73HxpaxCegAAGQnoG+Yn4AuoLfsZe1+C+gAAJDD6oBeerJ8F7x6htlWAadmX5bG02vfM19YaLU+M1SrC0a9wvWli2A1xrH1wtjpzwR0AACyE9Ab7rM0bLTe98zhV0AvX2MBXUAHAOBYBPSG+ywNG633PXP4FdDL11hAF9ABADiWzd/iXvsEfU2gzxbeRox15AUDVXcNR19EqrmtHseXgA4AQHYCesMS0NWeNRwdqmtuS0AHAIDLDv930LdeRDhiWC29+DE6tI94ZGG2WjPP1r8zl7bf+ndr7fsFdAAAshPQK4WIDCWg5ysBXUAHAOB6COiVQkSGEtDzlYAuoAMAcD2qBPTSE/prqew9OBfaM8wz63F4xN+jSxeASue89B4BHQCA7AT0DqFk9DhqrOu5Oc06z6zH4RF/jwR0AAC47HVAbxUGSk/ML52EzxZiMgWoPeHn3Gs1erB1G2svImToe4/9bx3Dud/hretZ+3dYQAcAIDsBvdL8Ruy7xlqUzklAb9f3HvsX0AEAYF5VvyRuliBSOsYa455h3ltCdWlIqjnPPWsxWwi/lmrd93PH46Vj48mzFwI6AACpCeiVxj3DvAV01boEdAAAaEdArzTuGeYtoKvWJaADAEA7Xf8OeukJ+ohtZAx+a8NM9vDbavxrL1KUXgBas61a2+vV+9LPPX35/NXTl8+brf/pzzyDDgBAdgJ6pSAyW88EdAG9Re9LPyegAwDAetMF9HPhZPR4Rgad2vvc2tue4695oSbTPGtcKFjbl95r0nLNBHQAALIT0AfNafQ+MwbXvcdPlnkK6AI6AADXSUAfNKfR+8wYXPceP1nmKaAL6AAAXKepA3rNYHHkMZ7u8+6Z3xohrPVcam9/9vnWHv/Iiw5rLyLcPy5Pn0df2/dzF5hOfyagAwCQnYB+gDEK6G9ub9b51h6/gC6gAwBwLF0D+pqAMDoktQgzo8d97mdr35+hWoy75/GYte+teuUWdwAAro2AXikczFICuoA+uv8COgAAbCOgVwoHs5SALqCP7r+ADgAA2zyOiFfv/sgHh5/cHzn07HnGufSiRo35zvJM9rUcL73XeO0z30vPjdes0+3X+L0Q0AEAyE5A7zgGAX3+EtAFdAAAGGW6W9yPuO+R8x11u3HpGFsE0Fnm2XqfawP3nmNjhscHLr1fQAcAIDsB/aAloAvoD21fQAcAgDk9johXT5696BpIZg4/swb5Hrc2t9jnNVeLCySjLly07E+tCwYCOgAA2Qnok4eYpTDTaxsCep01m239R/dlaU4COgAACOjTh5ilMNNrGwJ6nTWbbf1H92VpTgI6AAB0fga9VxA5erUKflvD+9ZwVfot4ddybMw8z63jWprTnmNoqQR0AACyE9ATloB+7GNj5nkK6AAA0M7VfIt7r/Bzaftr9r1njDVDz+igmHH8tUNn756t3cba43zpPa36IqADAJCdgN55+wJ6v+NFQG+3DQEdAADqE9A7b19A73e8COjttiGgAwBAfZsD+tLJd+1QMuu2ZqueofCIfRwdqnvNb20PavZq7ftr9F1ABwAgOwH9ACWg5+nfyPmt7UHNXgnoAACwXvNb3DOEnnMXG0aPrTTM9Bp3hnVt1ZeZL0T17udMF4MEdAAAshPQHxjjzGMW0Ov2bPTcM/Wx5/oL6AAAXJuqAX3PSXvGkNIjdGzdVo1biq8lhGeb59Y5jZxjjUdiLr1fQAcAIDsBfVBQab0tAb38c1nmuXVOAjoAAMxNQB8UVFpvS0Av/1yWeW6dk4AOAABzexwRr548ezE8ULQOBqPH0Sv81JjvLD0bMY4W+1y7Lmv3vffCy54x1uxn7X0+efZCQAcAIDUB/QAloM+9TwFdQAcAgDVW3+K+9/bUo1bWHhzxFvcjr13rtVj7SESLRzTWHodPXz5/XUvvc4s7AADZCeiVQsbocbQY99HXONPcBHQBHQCAdr4yIv56RPxU3JxQfv291x9FxLdExEcj4v+LiL8dEV907z2fERHfHRH/NiJ+PiL+YkR8auE4BPRKIWP0OFqM++hrnGluArqADgBAOx+IiD8dEX8wlgP6n4ib0P11EfElEfHXIuJfRMQnnrznZUT8aET8noh4T0T8WET8lcJxVPkW95IT+5kC0WzP67aYW48xlu5n5DEx49p97KPvfPWxj75z+DhG1tKalB4jAjoAADXcD+iP4uZfzv/Hk599WkT8SkT84dv//m23n/vdJ+/5AxHx7yPicwv2LaAL6N37KKC/WQK6gA4AwDzuB/Tfcvuzd91739+JiP/l9n//0Yj4uXuvvy0ifj1u/lX+IW+Pm5PXu3pHbAzoSyfOl25BXXMyPjoo1BzPbHOaeWw1j4k9FwxqzaNWb9f8PpWM6f64amy/5lqs/ezSuAV0AABquB/Qf+/tz/6Te+/7PyPi/7j9338qIv7ZwrZ+OiL++Jl9feh222+UgF43WMw6p5nHJqAvl4AuoAMA0FfPgN70X9BLT7hnvN24ZUjpETZrjnd032arNY9yjBzXqN+tGhdX1rzfl8QBANBDz1vc79v8DLqA3rZXW4NOzfGO7ttsJaCv74uADgBAVg99Sdz/cPKzx7H8JXFfevKe3x8dvyROQG/bq61Bp+Z4R/dtthLQ1/dFQAcAIJNPjZt/IX9X3JxQfvD2f3/+7et/Im7+hfxrI+JJRHxvLP+ZtX8YEV8WEV8REf88Bv2ZtbuT5pkD99pxzTr+NWOuHYhGr+cMa1Hag54XY7a+f6n29KjVc+x3Y1u7fQEdAICt3hsLX9YWEX/59vVHEfEtEfH/xM2/nP/tiPjP7m3jM+ImkP9iRPxCRPyluAn+JQT0naFnhhLQ5+mtgF6/9wI6AADXotnfQZ8hXI2u2kFoTW8z9b1VX/YE19E9yVK1b6uvccFIQAcAIDsBvVOIEdDP96dFv0eN4xpKQAcAgPoE9E4hRkA/358W/R41jmsoAR0AAOqrEtDXnlTXDJZrtiV4bVuntYGo15h6fW7vZ7f2LGOPL829xnbPbWvpZwI6AADZCejqLT0797MRY+r1ub2f3dqzjD2+NHcBHQAAyjUP6LOEiLXbOEqgb3278Yzrv/UCw7n3lfZx9EWNmddna//WBn8BHQCA7AT0hmFjhp4J6AL6LOuztX8COgAA1+JxRLx68uxF9WCxNxz2DDjXGKoylDV5ay/04+ES0AEAyE5AF36mLWvy1l7ox8MloAMAkJ2ALvxMW9bkrb3Qj4dLQAcAILvXAX1tQKgZNkaf0LeupXm2mPuW8Lbmmd5rrDV9ma1vrcYz2/cYXHoW/cmzFwI6AACpCegNS0DPVwJ6ne0K6AAAUO71t7jPdjJ+hICTodbOu3WgH9n/mvNaCpE9Hx8Z2cenL5+/rtLx1xi3W9wBAMhOQH8koAvoAnqNEtABAGAfAf2RgC6gC+g1SkAHAIB9Vj+DXjsItQx81xy4t/Zqy89mq9LguveC1NJnW4fyPfvcM577n62xr9IeXXq/gA4AQHYC+pWXgC6gb+mtgA4AAPW9vsW9ZmDoEbh6juMa6vT25Oz9LA3Je0J1zTDe+sLVLGvSah8COgAA2Qno6tX7HwnoAnq/NWm1DwEdAIDsNgf0o9bRgpEqW9cZ1r/2rd/n3j/iosDWfbrFHQCAoxPQHwgBo8ehxqzrDOsvoG+bp4AOAEB2AvoDIWD0ONSYdZ1h/QX0bfMU0AEAyK7Zt7jPEHTUurBT69npWeY0ehytx1Nzu+e21fNZ/hrzFdABAMhOQL/SEtDzjkdAXy4BHQCA7IpvcW99q3DPW233BoaetwPvGWPpNpbeP+L251l7W9r3EbeRn5vnnnGM/N10izsAAEcnoDcICi32tWeMAnrd3pb2XUDvc2wI6AAAZCegNwgKLfa1Z4wCet3elvZdQO9zbAjoAABkV/Vb3DOE6z1BJMPcRozx6cvnr56+fL55rGvH2+r4ar3d2u8/975WF8bWbLdnH5d+JqADAJCdgL4zFMxWAvr2ngnoAjoAAIxUJaBvDWit6kgXCkb05W49s/exRujc8/5z42jd23O31dfuy5p57hnv2hLQAQDITkC/ohLQl18v3V6NcQjo68e7tgR0AACya/Z30NXctTeEjR5jr3H0uEjxsY++89XHPvrO1fOsGfa3Buge6186RgEdAIDsBPQrLQG9bAwCuoAOAACtCehXWgJ62RgEdAEdAABaq/ot7mpM3T0zfvrceO19jAxoPSvj/ErXZe3z3qfHVelnR6yJgA4AQHYC+gFKQK8/z9Hj2Lo2W9ZSQAcAgDlUCeizhZqRQTJriB0RvNSbvZ9l+7ON5/7n3vXN3774lwYEdAAAshPQD7TvWuPOOP7MNVsgnm089z8noAMAcFQC+oH2XWvcGcefuWYLxLON5/7nBHQAAI7Kt7gftPYE7TVhfen5ZFXex6WeLm2j5nhabb/Hds+VgA4AQHYC+kFLQB9fArqADgAAJa7mW9yXwlKm27pHjHFpn7P3acY+jh7j7PNdO8ZLfRHQAQDITkAX0Iv2OXufZuzj6DHOPt+1YxTQAQA4OgFdQC/a5+x9mrGPo8c4+3zXjlFABwDg6Jp9i3uNUNAiWGQIaDNXz+eYZ5lfrW0epUcz9eV0GwI6AADZCehqirWepRxz/XstoAMAwI3XAV2IUGuqxa3XLbbX8ji+lt+VVvPcuk23uAMAcHQCuioqAV1Ar7Xd2uMR0AEAyO6QXxK3J1jsDXnZwtv9+a79oq4e4+jVx5pz3zLu++9vHYyzHaNr10JABwAgOwH9Qojpue/RvRLQBfQsJaADAHBEAvqFENNz36N7JaAL6FlKQAcA4IiqBvTRAaB1qBkdSnr3amnuR+rFUY7RHvscebw8ffn81dOXzy++T0AHACA7Ab1gTj3nMkOvBHT7nOF4EdABALgWmwN66W3BtU/aWwedIwXRlv3JdgGjdLyt5lnjMYrZetVqn2vXQEAHACA7AX1wCMpaAnrfcSx9drZetdqngA4AwLUQ0AeHoKwloPcdx9JnZ+tVq30K6AAAXIv03+KeLSBm6Mu5z7Xq9cg1bHUM3T07nXX7Sz1aeh58ad8j1lNABwAgOwH9oCWgjz+GBHQBHQAASjyOiFdPnr3YHA7WfLvyDDVLiJ9lHKXjOeJt1S32k+2C0db1H/GIg1vcAQA4OgH9SsdROp5ZQtjsayGgjzs2BHQAALJLcYv7iBCZKWT1CD+t9jVrn0eMa+8jCb0fS6g5xholoAMAkJ2A3mFbGUpAH7/+Avq+EtABAMhOQO+wrQwloI9ffwF9XwnoAABkVzWgXwpedz/r+e3T6vxajR7H7FX7GD33ezFiTq32XeP7KUrHJqADAJCdgH6lpe9lfRLQy0pABwCAcpsDeo/gUjPAzHKb/Gy3Tpf2pef4997y3aMvGeZZuo1zAb403C+9f+s2Ls1FQAcAIDsBvcG+1oSfGtvv2VsBPe88S7choAMAwBgCeoN9rQk/Nbbfs7cCet55lm5DQAcAgDGqPIM+InT2qtJQ2jvEtpxLz3n26tmlcY9cu1b7Pheye65x6xLQAQDITkBfOTcBve08BXQBfW8J6AAAZNfsFvdZTuzX3FabLYhsvVX43DZGz6lmX0aPI1PtPW5m6reADgBAdgL6irnMVgL6+TmNHkemEtABAGAeVf8Oes+gcPRxPDSupQsLl96/5uLE2h7M2p89485w63/NYDw6XLfat4AOAEB2Avqk43hoXAL6trUT0NuNseY67SkBHQCA7AT0Scfx0LgE9G1rJ6C3G2PNddpTAjoAANkNCeg1w0btUDBzAN06zxpzatGXkcfBjGuc6RgdfaFg6XMCOgAA2QnoE4af2vMU0Jf3OXqttvZ4hvEL6AAAUN/jiHj15NmL4eGk9IR+bRAtDQAzhJ9rqVa3g9ceW+9jotU+185plt+B0tv7BXQAALIT0CcNJ9dQAnqfvpTOaZbfAQEdAIBrI6BPGk6uoQT0Pn0pndMsvwMCOgAA1+b1M+hrQ8GaE/vT1zMEnHPjHh1SWveo1+dqb2PE9mede5bAvXYOWy+MCOgAAGQnoF8Y9+jg0rpHvT5Xexsjtj/r3AX0mxLQAQDIbugt7iPC+yw1y9wz3Mo9sk+zrNPouZfcGn+pV09fPn/19OXz6uMQ0AEAyE5AnyD8zDIOAX3edRo9dwEdAADaK/476LPe5ls7iIweU8YxHmF+pWM44lqUznPptRG38gvoAABkJ6A/miccZh/jEeYnoJfPU0AHAIA6BPRH84TD7GM8wvwE9PJ5CugAAFBHcUBfW6XPmY4IGKUhIkMYaz3GmmGsdRhvFfozjHvps1uP93PbuvSzVj1b2paADgBAdgK6gN69j/c/N3PQzTpuAR0AAPJpFtBLT8yXAv1SQGgRXEbULOOYdTw95zx6HHvWqlbgPkIJ6AAAZCegD6pZxjHreHrOefQ49qyVgP4bJaADAJCdgD6oZhnHrOPpOefR49izVgL6b5SADgBAdo8j4tWTZy+ahZkRz+vehf2lOmo4qdn3c5+r0bs12z99vfV6zXZMtBrPiDn23KeADgBAdgL6QUtA79OrTOMR0AEAYG7T3OI+IvyUBvqZ5lFr7qWfHT2HWcfTak6tL5aM6PfWi2VLv5enrwvoAABkJ6AL6EWfHT2HWcfTak4C+ls/J6ADAHBUxQG95+3GLW6nrrHdpTGeqxGhae3+Z7v1P9sFkZa3oq/d7iy9OjeOpddqr7WADgBAdgJ6pTEK6HOuf6/xtupBi/f3WLs1rwnoAADwJgG90hgF9DnXv9d4W/Wgxft7rN2a1wR0AAB40+Zn0GcJBVvG23vcpQG+9hjXBvmM61n6Wu31bDGXtWtSOoZM67ulBHQAALIT0AftW0BvP6ce69liLgL6thLQAQDIbvXfQd8bOnoEnSPWuWC/9K3zH/voO1997KPvHD5uNeYYqfn+Pb+fJRcYav3+C+gAAGQnoE9eAroqOUZqvl9ABwCAvgT0yUtAVyXHSM33C+gAANDX5mfQ19ZSiFxzQr8nFIwOTDVDUs2+LwX5WQL9LBd0zh2jPcext1e1t7V3n+cuNK1d90vbFdABAMhOQB9UAnr52gno63tVe1sCOgAAtNc8oLcIJllCeKa+3A/sr1lf7gAAFydJREFUl+rSBZcW85ulz6PHkWmMNS68LW1r6XUBHQCA7AT0A5SA3rfPo8eRaYwCOgAArJf276C3uM23x1xG3J7cep69gnq22vuoRo3HPUpvI1+7rxkfRRHQAQDITkDvPBcB/XpKQK/bl0sloAMA0MqH4uZE87T+6cnrnxgR3xkRPxsR/y4i/mpEfPaG/QjonecioF9PCeh1+3KpBHQAAFr5UET844j4nJP6j09e/3BE/MuIeF9EfGlE/P2I+Hsb9vM6oM94wt2rRl9syFQ1w+Olb5q/q9ZrszQXx0OdWrp4s7T+W9fptAR0AABa+VBE/OgDr31aRPxaRHzDyc9+a9ycmH554X4E9BUn/mq5VwK6ulQCOgAAR/ChiPiliPipiPgXEfHdEfH5t6+9L25OQj/93md+IiI+eGG7b4+bk9e7ekdM8i3uRwxE5+bkosD2fj5U979hvkePRzyyMPJC2szHrIAOAEArH4iI/yoiviQivjoifihuAvh/EBF/JCJ+deEzPxwRf+7Cdj8Ub322XUAfMCcBfXs/BXQBfakEdAAAevn0iPiFiPhvY19A9y/ok8xJQN/eTwFdQF8qAR0AgJ5+JCL+bOy7xf2+xxHx6smzF8NPrlWd2hvelp7D7hHI1lzMqLH9c8H+tEavY8ZjaPQ4BHQAAHr51Ij4NxHx38dvfEncHzp5/Ytjx5fECejHKQH98vwE9DbH0OhxCOgAALTybRHxVRHxBRHxeyPiByLiZyLiM29f/3Dc/Iv574ubP7P2Q7dVqmpArx3oZgkKGWvPWqy9Nb/1+oz8++qXbqd/qEave6++XOrZmvfv2efSzwR0AABa+d/j5hvcfzUi/vXtf3/hyeufGBHfGTf/qv5LEfE9cfO30ksJ6ActAb1e/wT08mNjzfv37FNABwDgiAT0g5aAXq9/Anr5sbHm/Xv2KaADAHBEj2OSb3FvHR5a7GdpX7OEtVnGsWW8LS4s9Kq7iwoP1dIYWz/3viZU75lnjTWv8X4BHQCA7AT0ysFilmA8yzi2jFdAb9Pbmj0T0AEAoL5DBfSawaXFfkaHxxFzP0Kves2z9NvkR/Rp7T5bj80t7gAAHJGALqBPVTP2qtc8BfR94xDQAQDIbpqAPlsoO3fL9aUvCtt7q7a6qfu3UPfo6Zrt174NP9NxMvPxLaADAJCdgL4hiAjofUpAn69mPr4FdAAAshPQNwQRAb1PCejz1czHt4AOAEB2zQJ6i5P3S+FgtsDQqo815rn1gkLNHrcKea2Og5prce7Cz6hjreR4ufT+EX/DXkAHACA7AX3yEtDnOPZqr4WAXr8EdAAAsmse0M+d1F+6fXxNcJkl5LW+9bdGoBsRwmv0e21vW1y46DnP0nVqXTX+1nnpGu4pAR0AgOwE9B1BqmcAEdAF9N4loAMAQF8C+o4g1TOACOgCeu8S0AEAoK/HEfHqybMXVU6+T0/oZwgYawPREes0CJWGrB5haqa1rjnP0osge/a59PlW++510WlPCegAAGQnoB+0BPRtvarZ99b7FNDfLAEdAIDsqtziPsPJvupTrddu6bbqazteagfXmrf+j+7NuRLQAQDITkBXTde6tAR0AX1rCegAAGT3OqCXhoKl26DPhau1t91mCz8j6yjzWDuXVmu3Z7ul4+59+/tRjo81JaADAJCdgJ64jjKPtXMR0PvMKWsJ6AAAZCegJ66jzGPtXAT0PnPKWgI6AADZNfs76CNqa1gqDVDXEoxmnsus4zpqjez32t9PAR0AgOwEdAE95VxmHddRS0AHAID2qvwd9LUn15lCwZHHqvpU6TGR9RhqPW4BHQCAayGgJwkpKl8J6HW3L6ADAHB0AnqSkKLylYBed/sCOgAAR1f1GfSZw3jW8DOitn7r+OhxzbbGI/oyes57+7J2Lvf/WsT7HwnoAADkJ6Crs+sooO8fb8++jJ7z3r4I6AAAXLPVt7jvDd6zhIca45ltTrP0ZU8tBa5Zq7RXS++veRzWeN/oi2s1tiGgAwCQnYA+aBuz1eg5Cejbx1HjfQI6AACMN+TvoC+djN8FtBG3VfcMpy3CUu1wNTKoj75QsLUHRz4eex4be+YuoAMAkJ2APmEgEtAF9JmOx57HhoAOAMA1E9AnDEQCuoA+0/HY89gQ0AEAuGZVAvoMgWrNCf+sYxzR2+x9adWLkteyrd1sFxFqH38COgAA2Qnok9dsIe+IJaDX7VnPbZyWgA4AQHZVA/rHPvrO11UzPOw5iT9iAN3a29JAdPfYQZZvV98731nGW3v9z+2v5vh79mnpdQEdAIDsBPSEJaC3me8s4629/uf2V3P8Pfu09LqADgBAdgJ6whLQ28x3lvHWXv9z+6s5/p59WnpdQAcAILtm3+KeIRhlqFYXLmZYzxkD9AzjmbEvI/uztM+lnwnoAABkJ6BPXgL6PP3uPYbR45ilPwI6AADXYsjfQVdtAsyl4NQq+LUOba1u/Z5l3D2Pjx7zXbuPpfcvPZ6wdpsCOgAA2QnoBygBfds4Zhl3z+NDQAcAgHlN83fQZwkz2WtLsLmGOteXnr2a7SJC696u/VyNcQjoAABkJ6AfrAT08r4I6O16u/ZzNcYhoAMAkJ2AfrAS0Mv7IqC36+3az9UYh4AOAEB2VZ9Bn+XvZp97jvXowXXtPEeG1LXjH93LWnOp9b7Zxr20Zi3ncuniioAOAEB2AvrBSkCfpwT09seGgA4AwJE8johX7/7IB6ucXM98q3CGObWqGS6aHLXOBcbZjpvS8bS+4OYWdwAAeJOAPtGcWpWA3q4E9P3jEdABAOCGgD7RnFqVgN6uBPT94xHQAQDgxpBvcd96Un7pGdTRAWj2yt6r7OM/ncPocSz19NyFhZEXeZbGs/QzAR0AgOwE9Cuq7L3KPv7TOYwex1JPBXQAABjrcUS8evLsxZAgsjZw1Qg1swSjmrdA19jWLH1pfQxtff/oY+Qo63nuQkCt3wEBHQCA7AT0CYLKyG3N0pfWx9DW948+Ro6yngI6AABcVvUW91Yn4TXG0yvgZAiAe+ZZc24jj5Frrh79HrGe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width=\"1000\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import tensorflow as tf\n", "import numpy\n", "import scipy.io\n", "#from tensorflow.python.client import timeline\n", "import time\n", "\n", "percorsoDati = \"/home/protoss/datiFederico4.mat\"\n", "index = 9002 #signal\n", "#index = 4 #signal meno bello\n", "#index = 5001\n", "\n", "struttura1 = scipy.io.loadmat(percorsoDati)['H']\n", "peakmap1 = struttura1[index].copy()\n", "del struttura1\n", "\n", "struttura2 = scipy.io.loadmat(percorsoDati)['L']\n", "peakmap2 = struttura2[index].copy()\n", "del struttura2\n", "\n", "struttura3 = scipy.io.loadmat(percorsoDati)['V']\n", "peakmap3 = struttura3[index].copy()\n", "del struttura3\n", "\n", "peakmapTOT = peakmap1+peakmap2+peakmap3\n", "\n", "\n", "\n", "\n", "#peakmap = peakmapTOT[:,25:80]\n", "peakmap = peakmapTOT\n", "from matplotlib import pyplot\n", "%matplotlib notebook\n", "pyplot.figure(figsize=(10, 8))\n", "#a = pyplot.scatter(numpy.arange(tempiUnici.size),tempiUnici/30, s = 0.5)\n", "a = pyplot.imshow(peakmap, origin = 'lower', interpolation = 'none')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.02e-11\n", "[495 496 497 498]\n" ] } ], "source": [ "#peakmap = struttura[0]\n", "\n", "sparsa = numpy.nonzero(peakmap)\n", "\n", "frequenze,tempi = sparsa\n", "tempi = tempi+1\n", "frequenze = frequenze / 8192 + 1\n", "pesi = numpy.ones(sparsa[0].size)\n", "\n", "tFft = 8192\n", "#tFft = 4096\n", "tObs = 1/4 #mesi\n", "tObs = tObs*30*24*60*60\n", "\n", "#headers vari\n", "securbelt = 15000\n", "\n", "\n", "#frequenze\n", "#frequenze\n", "stepFrequenza = 1/tFft\n", "enhancement = 10\n", "stepFreqRaffinato = stepFrequenza/enhancement\n", "\n", "freqMin = numpy.amin(frequenze)\n", "freqMax = numpy.amax(frequenze)\n", "freqIniz = freqMin- stepFrequenza/2 - stepFreqRaffinato\n", "freqFin = freqMax + stepFrequenza/2 + stepFreqRaffinato\n", "nstepFrequenze = numpy.ceil((freqFin-freqIniz)/stepFreqRaffinato)+securbelt\n", "\n", "#tempi\n", "#epoca definita come mediana di tempi di tutto il run\n", "#epoca = (57722+57990)/2 #0\n", "epoca = 30\n", "\n", "#spindowns\n", "spindownMin = -1e-8\n", "spindownMax = 1e-10\n", "\n", "\n", "nstepSpindown = 500#numpy.round((spindownMax-spindownMin)/stepSpindown).astype(numpy.int32)\n", "\n", "\n", "stepSpindown = (spindownMax-spindownMin)/nstepSpindown#stepFrequenza/tObs \n", "print(stepSpindown)\n", "\n", "# riarrangio gli array in modo che abbia i dati \n", "# nel formato che voglio io\n", "frequenze = frequenze-freqIniz\n", "frequenze = (frequenze/stepFreqRaffinato)-round(enhancement/2+0.001)\n", "\n", "tempi = tempi-epoca\n", "tempi = ((tempi)*3600*24/stepFreqRaffinato)\n", "#tempi = numpy.round(tempi/1e8)*1e8\n", "\n", "spindowns = numpy.arange(0, nstepSpindown)\n", "spindowns = numpy.multiply(spindowns,stepSpindown)\n", "spindowns = numpy.add(spindowns, spindownMin)\n", "# così ho i tre array delle tre grandezze, \n", "#più i pesi e la fascia di sicurezza\n", "indice0 = numpy.where(spindowns>0)[0]-1\n", "print(indice0)\n", "#spindowns = spindowns-spindowns[indice0]\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0820913314819336\n" ] } ], "source": [ "\n", "\n", "def mapnonVar(stepIesimo):\n", " sdTimed = tf.multiply(spindownsTF[stepIesimo], tempiTF, name = \"Tdotpert\")\n", " #sdTimed = tf.cast(sdTimed, dtype=tf.float32)\n", " \n", " appoggio = tf.round(frequenzeTF-sdTimed+securbeltTF/2, name = \"appoggioperindici\")\n", " appoggio = tf.cast(appoggio, dtype=tf.int64)\n", " \n", " valori = tf.unsorted_segment_sum(pesiTF, appoggio, nColumns)\n", "\n", "# zeriDopo = tf.zeros([nColumns - tf.size(valori)], dtype=tf.float32) \n", "# riga = tf.concat([valori,zeriDopo],0, name = \"rigadihough\")\n", " return valori\n", "\n", "\n", "#ora uso Tensorflow\n", "securbeltTF = tf.constant(securbelt,dtype=tf.float64)\n", "tempiTF = tf.constant(tempi,dtype=tf.float64)\n", "pesiTF = tf.constant(pesi,dtype=tf.float64)\n", "spindownsTF = tf.constant(spindowns, dtype=tf.float64)\n", "frequenzeTF = tf.constant(frequenze, dtype=tf.float64)\n", "\n", "nRowsTF = tf.constant(nstepSpindown, dtype=tf.int64)\n", "nColumns = nstepFrequenze\n", "\n", "pesiTF = tf.reshape(pesiTF,(1,tf.size(pesi)))\n", "pesiTF = pesiTF[0]\n", "\n", "imagenonVar = tf.map_fn(mapnonVar, tf.range(0, nRowsTF), dtype=tf.float64, parallel_iterations=4)\n", "\n", "#sessione = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n", "sessione = tf.Session()\n", "\n", "#run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n", "#run_metadata = tf.RunMetadata()\n", "\n", "start = time.time()\n", "#image = sessione.run(imagenonVar, options=run_options, run_metadata=run_metadata)\n", "image = sessione.run(imagenonVar)\n", "stop = time.time()\n", "print(stop-start)\n", "\n", "\n", " # Create the Timeline object, and write it to a json\n", "#tl = timeline.Timeline(run_metadata.step_stats)\n", "#ctf = tl.generate_chrome_trace_format()\n", "#with open('timelinenonVar.json', 'w') as f:\n", "#\tf.write(ctf)\n", "\n", "nColumns = nColumns.astype(int)\n", "semiLarghezza = numpy.round(enhancement/2+0.001).astype(int)\n", "image[:,semiLarghezza*2:nColumns]=image[:,semiLarghezza*2:nColumns]-image[:,0:nColumns - semiLarghezza*2]\n", "image = numpy.cumsum(image, axis = 1)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for 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width=\"1000\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib import pyplot\n", "%matplotlib notebook\n", "pyplot.figure(figsize=(10, 8))\n", "a = pyplot.imshow(image, aspect = 30, interpolation = 'none')\n", "#a = pyplot.imshow(image[94:98], aspect = 10000)\n", "pyplot.colorbar(shrink = 1,aspect = 10)\n", "#a = pyplot.imshow(image[191:192], aspect = 10000)\n", "pyplot.show()\n", "#\n", "#DA METTER IN LOG" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "146.0\n" ] } ], "source": [ "indice = 8960\n", "freq = (indice-securbelt/2)/10\n", "print(freq)" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "index 48000 is out of bounds for axis 0 with size 2533", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-119-622a1405b5ca>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0mfreqniu\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mstepFreqRaffinato\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mfreqIniziale\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfreqniu\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msecurbelt\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m52000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mIndexError\u001b[0m: index 48000 is out of bounds for axis 0 with size 2533" ] } ], "source": [ "primaFreq = freqIniz-(securbelt/2)*stepFreqRaffinato\n", "\n", "#freqIniziale = struttura['basic_info'][0,0]['frin'][0,0][0,0]\n", "#freqFinale = struttura['basic_info'][0,0]['frfi'][0,0][0,0]\n", "freqIniziale = freqIniz\n", "freqFinale = freqFin\n", "#QUI ANALOGO FUNZIONE CUT GD2\n", "#%time indexInizialewh = numpy.where(freqniu>freqIniziale)[0][0]\n", "#%time indexFinalewh = numpy.where(freqniu>freqFinale)[0][0]\n", "start = time.time()\n", "\n", "indexIniziale = ((freqIniziale-primaFreq)/stepFreqRaffinato).astype(numpy.int64)\n", "indexFinale = ((freqFinale-primaFreq)/stepFreqRaffinato+1).astype(numpy.int64)\n", "\n", "imageCand = image[:,indexIniziale:indexFinale]\n", "\n", "size = numpy.shape(imageCand)[1]\n", "freqniu = numpy.arange(0,size)*stepFreqRaffinato+freqIniziale\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
AllenDowney/ProbablyOverthinkingIt
negative_binomial.ipynb
1
44558
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Binomial and negative binomial distributions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Today's post is prompted by [this question from Reddit](https://www.reddit.com/r/statistics/comments/4ijzkm/number_of_selections_with_replacement_before/):\n", "\n", ">How do I calculate the distribution of the number of selections (with replacement) \n", "I need to make before obtaining `k`? For example, let's say I am picking marbles from \n", "a bag with replacement. There is a 10% chance of green and 90% of black. I want `k=5` green \n", "marbles. What is the distribution number of times I need to take a marble before getting 5? \n", "\n", ">I believe this is a geometric distribution. I see how to calculate the cumulative \n", ">probability given `n` picks, but I would like to generalize it so that for any value of `k` \n", ">(number of marbles I want), I can tell you the mean, 10% and 90% probability for the \n", ">number of times I need to pick from it.\n", "\n", ">Another way of saying this is, how many times do I need to pull on a slot machine \n", ">before it pays out given that each pull is independent?\n", "\n", "Note: I've changed the notation in the question to be consistent with convention.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import print_function, division\n", "\n", "import thinkplot\n", "from thinkstats2 import Pmf, Cdf\n", "\n", "from scipy import stats\n", "from scipy import special\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Solution\n", "\n", "There are two ways to solve this problem. One is to relate the desired distribution to the binomial distribution. \n", "\n", "If the probability of success on every trial is `p`, the probability of getting the `k`th success on the `n`th trial is\n", "\n", " PMF(n; k, p) = BinomialPMF(k-1; n-1, p) p\n", "\n", "That is, the probability of getting `k-1` successes in `n-1` trials, times the probability of getting the `k`th success on the `n`th trial.\n", "\n", "Here's a function that computes it:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def MakePmfUsingBinom(k, p, high=100):\n", " pmf = Pmf()\n", " for n in range(1, high):\n", " pmf[n] = stats.binom.pmf(k-1, n-1, p) * p\n", " return pmf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here's an example using the parameters in the question." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5d7c833f50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pmf = MakePmfUsingBinom(5, 0.1, 200)\n", "thinkplot.Pdf(pmf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can solve the same problem using the negative binomial distribution, but it requires some translation from the parameters of the problem to the conventional parameters of the binomial distribution.\n", "\n", "The negative binomial PMF is the probability of getting `r` non-terminal events before the `k`th terminal event. (I am using \"terminal event\" instead of \"success\" and \"non-terminal\" event instead of \"failure\" because in the context of the negative binomial distribution, the use of \"success\" and \"failure\" is often reversed.)\n", "\n", "If `n` is the total number of events, `n = k + r`, so\n", "\n", " r = n - k\n", "\n", "If the probability of a terminal event on every trial is `p`, the probability of getting the `k`th terminal event on the `n`th trial is\n", "\n", " PMF(n; k, p) = NegativeBinomialPMF(n-k; k, p) p\n", "\n", "That is, the probability of `n-k` non-terminal events on the way to getting the `k`th terminal event.\n", "\n", "Here's a function that computes it:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def MakePmfUsingNbinom(k, p, high=100):\n", " pmf = Pmf()\n", " for n in range(1, high):\n", " r = n-k\n", " pmf[n] = stats.nbinom.pmf(r, k, p)\n", " return pmf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here's the same example:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5d4b994610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pmf2 = MakePmfUsingNbinom(5, 0.1, 200)\n", "thinkplot.Pdf(pmf2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And confirmation that the results are the same within floating point error." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8.6736173798840355e-17" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diffs = [abs(pmf[n] - pmf2[n]) for n in pmf]\n", "max(diffs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the PMF, we can compute the mean and standard deviation:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(49.998064403376738, 21.207570382894403)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pmf.Mean(), pmf.Std()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To compute percentiles, we can convert to a CDF (which computes the cumulative sum of the PMF)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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SIuJxSRfZXpPrSPtY3H9Qm+5c0I1bd+iBxUd7VvEphrzU/mFCfjif+eFclssorZu1ko51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\nI+gBIHEEPQAk7v8BQAWBA2MLe/oAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5d4b614610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cdf = Cdf(pmf)\n", "scale = thinkplot.Cdf(cdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here are the 10th and 90th percentiles." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(26, 78)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cdf.Percentile(10), cdf.Percentile(90)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Copyright 2016 Allen Downey\n", "\n", "MIT License: http://opensource.org/licenses/MIT" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
ajrichards/phylogenetic-models
cat-model/dirichlet-process.ipynb
1
100984
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Gaussian Processes\n", "\n", "When we say Bayesian nonparametric it implies, not that there is an absence of distributions, but rather, that the number of parameters grows with the dataset. This means that Bayesian non-parametric models are *infinitely* parametric. The goal is to determine $k$ as a function of the data. \n", "\n", "If our model $p(y|\\theta)$ has a large number of parameters in $\\theta$ and to avoid the multidimensional integration over $\\theta$ when trying to make inference we can use MCMC or we can represent the model with Gaussians. \n", "\n", "$$p(x \\mid \\pi, \\Sigma) = (2\\pi)^{-k/2}|\\Sigma|^{-1/2} \\exp\\left\\{ -\\frac{1}{2} (x-\\mu)^{\\prime}\\Sigma^{-1}(x-\\mu) \\right\\}$$\n", "\n", "* marginals of multivariate normal distributions are normal\n", "\n", "$$p(x,y) = \\mathcal{N}\\left(\\left[{\n", "\\begin{array}{c}\n", " {\\mu_x} \\\\\n", " {\\mu_y} \\\\\n", "\\end{array}\n", "}\\right], \\left[{\n", "\\begin{array}{c}\n", " {\\Sigma_x} & {\\Sigma_{xy}} \\\\\n", " {\\Sigma_{xy}^T} & {\\Sigma_y} \\\\\n", "\\end{array}\n", "}\\right]\\right)$$\n", "\n", "$$p(x) = \\int p(x,y) dy = \\mathcal{N}(\\mu_x, \\Sigma_x)$$\n", "\n", "* conditionals of multivariate normals are normal\n", "\n", "$$p(x|y) = \\mathcal{N}(\\mu_x + \\Sigma_{xy}\\Sigma_y^{-1}(y-\\mu_y), \n", "\\Sigma_x-\\Sigma_{xy}\\Sigma_y^{-1}\\Sigma_{xy}^T)$$\n", "\n", "> A Gaussian process (GP) generalizes the multivariate normal to an infinite number of dimensions and any subset of this process has a Gaussian distribution. It is also a ***disribution over functions***. Just as a multivariate normal distribution is completely specified by a mean vector and covariance matrix, a GP is fully specified by a mean *function* and a covariance *function*:\n", "\n", "$$p(x) \\sim \\mathcal{GP}(m(x), k(x,x^{\\prime}))$$\n", "\n", "It is the marginalization property that makes working with a Gaussian process feasible: we can marginalize over the infinitely-many variables that we are not interested in, or have not observed.\n", "\n", "An example specification of a GP:\n", "\n", "$$\\begin{aligned}\n", "m(x) &=0 \\\\\n", "k(x,x^{\\prime}) &= \\theta_1\\exp\\left(-\\frac{\\theta_2}{2}(x-x^{\\prime})^2\\right)\n", "\\end{aligned}$$\n", "\n", "The covariance function is a squared exponential, for which values of $x$ and $x^{\\prime}$ that are close together result in values of $k$ closer to 1 and those that are far apart return values closer to zero.\n", "\n", "## Gaussian processes in machine learning\n", "\n", "GPs are used as a generic supervised learning that are generally used in the context of regression. The parameter nugget in this class is added to the diagonal of the correlation matrix between training points: in general this is a type of *Tikhonov regularization*. In the special case of a squared-exponential correlation function, this normalization is equivalent to specifying a fractional variance in the input.\n", "\n", "$$\\begin{equation}\n", "\\text{nugget}_{i} = \\left[ \\frac{\\sigma_{i}}{y_{i}} \\right]^{2}\n", "\\end{equation}$$\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the context of Gaussian Processes, the covariance matrix is referred to as the kernel (or Gram) matrix. The flexibility of GPs allows them to be used as prior distributions that typically would be fit using MCMC." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dirichlet Processes\n", "\n", "Usually used in the context of determining $k$ in a **data-driven** manner. Here we discuss two generative approaches for allocating samples to groups where the number of groups is not pre-determined.\n", "\n", "## Illustrating the DP with a Histogram\n", "One way to approximate an unknown density using sample observations is using a *histogram*. One way to parametrically describe a histogram is by specifying a series of knots that define the bins of a histogram:\n", "\n", "$$\\zeta = \\{\\zeta_i: \\zeta_1 \\lt \\zeta_2 \\lt \\ldots \\lt \\zeta_k \\}_{h=1}^k$$\n", "\n", "We can specify an associated probability model as:\n", "\n", "$$f(x) = \\sum_{h=i}^k I(\\zeta_{h-1} \\lt x \\le \\zeta_h) \\frac{\\pi_h}{\\zeta_h - \\zeta_{h-1}}$$\n", "\n", "where $I$ is the indicator function and $\\pi = \\pi_1, \\ldots, \\pi_k$ a probability simplex.\n", "\n", "We require a prior for the unknown probabilities, for which a natural choice is the *Dirichlet* distribution:\n", "\n", "$$f(\\mathbf{\\pi}) = \\frac{\\prod \\Gamma(\\alpha_h)}{\\Gamma(\\sum_{h=1}^k \\alpha_h)}\\prod_{h=1}^{k} \\pi_h^{\\alpha_h - 1}$$\n", "\n", "$$\\text{where } \\, E(\\pi|\\alpha) = \\pi_0 = \\frac{\\alpha_1}{\\sum_h \\alpha_h}, \\ldots , \\frac{\\alpha_k}{\\sum_h \\alpha_h}$$\n", "\n", "Notice that the Dirichlet is just a generalization of the beta distribution to $k \\gt 2$ classes.\n", "\n", "It is easy to show that the resulting posterior distribution for $\\pi$ is another Dirichlet:\n", "\n", "$$\\pi|x \\sim \\text{Dirichlet}(\\alpha_1 + n_i, \\ldots, \\alpha_k + n_k)$$\n", "\n", "where $n_h$ is the number of observations contained by the $h^{th}$ histogram bin.\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "from pymc import rbeta\n", "import matplotlib.pyplot as plt\n", "\n", "n = 100\n", "y = 0.75 * rbeta(1, 5, n) + 0.25 * rbeta(20, 2, n)\n", "\n", "counts, bins, patches = plt.hist(y, bins=10)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f7ba47c8110>" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use these bin counts to cacluate the expected value of the Dirichlet posterior" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from pymc import dirichlet_expval\n", "import numpy as np\n", "\n", "fig = plt.figure()\n", "ax1 = fig.add_subplot(1,2,1)\n", "counts, bins, patches = ax1.hist(y,bins=10)\n", "p = dirichlet_expval(1+counts)\n", "p = np.append(p, 1.-p.sum())\n", "y_exp = n*p\n", "ax1.step(bins, y_exp, color='red', where='post', linewidth=4)\n", "ax1.set_aspect(1./ax1.get_data_ratio())\n", "\n", "\n", "ax2 = fig.add_subplot(1,2,2)\n", "counts, bins, patches = plt.hist(y, bins=20)\n", "p = dirichlet_expval(1+counts) \n", "y_exp = n*np.append(p, 1.-p.sum())\n", "ax2.step(bins, y_exp, color='red', where='post', linewidth=4)\n", "ax2.set_aspect(1./ax2.get_data_ratio())\n", "\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f7b54dc9e50>" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Density estimation is great, but we are sensitive to the number of bins. So it is useful to generalize the DP some more.\n", "\n", "## Dirichlet Process Prior\n", "\n", "Consider a sample space $\\Omega$ that we may partition into $k$ non-overlapping subsets $\\{B_1,\\ldots,B_k\\} \\in \\mathcal{B}$. We can assign a probability measure $P$ to this partition:\n", "\n", "$$P(B_1),\\ldots,P(B_k) = \\int_{B_1} f(x) dx, \\ldots, \\int_{B_k} f(x) dx$$\n", "\n", "A Dirichlet distribition would be a natural conjugate prior on these partition (bin) probabilities:\n", "\n", "$$P(B_1),\\ldots,P(B_k) \\sim \\text{Dirichlet}(a P_0(B_1), \\ldots, a P_0(B_k))$$\n", "\n", "where $P_0$ is a base probability measure and $a > 0$ can be interpreted as prior sample size, which essentially controls the amount of prior shrinkage.\n", "\n", "However, we want our model to be insensitive to the choice of partition and to the number of bins. The important implication of specifying this prior is that although probabilities are assigned to each bin, it does not prescribe how that probability mass is distributed across any particular bin.\n", "\n", "It is easy to show that combining (or splitting) the elements of a Dirichlet distribution results in another Dirichlet:\n", "\n", "$$\\begin{aligned}\n", "\\pi_1, \\ldots, \\pi_k &\\sim \\text{Dirichlet}(\\alpha_1, \\ldots, \\alpha_k) \\\\\n", "\\Rightarrow \\pi_1 + \\pi_2, \\pi_3, \\ldots, \\pi_k &\\sim \\text{Dirichlet}(\\alpha_1 + \\alpha_2, \\alpha_3, \\ldots, \\alpha_k)\n", "\\end{aligned}$$\n", "\n", "or generally, for partition $\\{B_1,\\ldots,B_k\\} \\in \\mathcal{B}$:\n", "\n", "$$\\sum_{h \\in B_1} \\pi_h, \\ldots, \\sum_{h \\in B_k} \\pi_h \\sim \\text{Dirichlet}(\\sum_{h \\in B_1} \\alpha_h, \\ldots, \\sum_{h \\in B_k} \\alpha_h)$$\n", "\n", "Similarly, for $\\beta_1 + \\beta_2 = 1$,\n", "\n", "$$\\begin{aligned}\n", "\\pi_1, \\ldots, \\pi_k &\\sim \\text{Dirichlet}(\\alpha_1, \\ldots, \\alpha_k) \\\\\n", "\\tau_1, \\tau_2 &\\sim \\text{Dirichlet}(\\alpha_1 \\beta_1, \\alpha_1 \\beta_2) \\\\\n", "\\Rightarrow \\pi_1\\tau_1 + \\pi_1\\tau_2, \\pi_2, \\ldots, \\pi_k &\\sim \\text{Dirichlet}(\\alpha_1\\beta_1, \\alpha_1\\beta_2, \\alpha_2, \\alpha_3, \\ldots, \\alpha_k)\n", "\\end{aligned}$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Just as the Gaussian process is a distribution over functions, a Dirichlet process is a distribution over distributions (or, measure over measures).\n", "\n", "\\begin{equation}\n", "P\u223cDP(\\alpha,P0)\n", "\\end{equation}\n", "It is centered upon the baseline probability measure P0, with $\\alpha$ specifying the certainty in this baseline (i.e. inverse variance).\n", "\n", "The expectation of a DPP is:\n", "\n", "\\begin{equation}\n", "E[P(B)]=P0(B)\n", "\\end{equation}\n", "\n", "in other words, centered on the baseline measure, and the variance is:\n", "\n", "\\begin{equation}\n", "\\text{Var}(P(B))=P0(B)(1\u2212P0(B))1+\\alpha\n", "\\end{equation}\n", "\n", "It is essentially an infinitely decimated Dirichlet distribution. The marginal probability assigned to any subset B is beta distributed:\n", "\n", "\\begin{equation}\n", "P(B)\u223c\\text{Beta}(\\alpha P0(B),\\alpha(1\u2212P0(B)))\n", "\\end{equation}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stick-breaking Process\n", "\n", "The specification of the DP above is not necessarily intuitive in terms of what a DP realization looks like. A generative approach for allocating observations to groups is the stick-breaking process, which involves breaking the support of a particular variable into $k$ disjoint segments. Here, we start with a \"stick\" of unit length. To \"break\" the stick, we generate random points along the stick via the following algorithm:\n", "\n", "1. generate a random variable $\\beta_1 \\sim \\text{Beta}(1, \\alpha_0)$\n", "2. use this random variable (which is on the unit interval) to define a break point on the stick\n", "3. iterate $k-1$ times:\n", " - generate $\\beta_i \\sim Beta(1, \\alpha_0)$\n", " - identify next break point at $\\pi_i = \\beta_i \\prod_{j=1}^{i-1} (1-\\beta_j)$ (which is on the part of the stick that remains after the previous break)\n", "\n", "This results in $k$ \"pieces\" being created. Associated with each piece is a probability that is proportional to its length; these $k$ probabilities will have a Dirichlet distribution -- thus, the DP is a distribution over distributions. \n", "\n", "This process defines an **exchangeable** distribution on partitions of the stick.\n", "- though there is an order to the generation of the segments, the distribution is independent of order.\n", "\n", "Implementing a stick-breaking constructive process in Python is straightforward:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from numpy.random import beta\n", "\n", "def stick_breaking(alpha, k):\n", " betas = beta(1, alpha, k)\n", " remaining_pieces = np.append(1, np.cumprod(1 - betas[:-1]))\n", " p = betas * remaining_pieces\n", " return p/p.sum()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example, let's construct a DP with a baseline distribution that is standard normal:\n", "\n", "$$P_0 = N(0,1)$$\n", "\n", "We take a draw of $k$ values from the baseline distribution:\n", "\n", "$$ \\theta_1, \\theta_2, \\ldots \\theta_k \\sim P_0 $$\n", "\n", "then, using a stick breaking process, we can obtain a set of draws $\\beta_1, \\beta_2, \\ldots$ from a $\\text{Beta}(1,\\alpha)$. These are used to assign probabilities to the $\\theta_i$ values. As we established above, the probability of each $\\theta_i$ is calculated via:\n", "\n", "$$ \\pi_i = \\beta_i \\prod_{j=1}^{i-1} (1 - \\beta_j) $$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "k = 25\n", "alpha = 7\n", "theta = np.random.normal(0, 1, k)\n", "p = stick_breaking(alpha, k)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "These probabilities correspond to the set of draws from the baseline distribution, where each of the latter are point masses of probability. So, the DP density function is:\n", "\n", "$$ P(x) = \\sum_{i=1}^{n} \\pi_i I(x=\\beta_i) $$\n", "\n", "where $I$ is the indicator function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x = np.random.multinomial(k, p)\n", "dp = theta[x]\n", "print(dp)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ 1.45083515 0.36452561 0.43705663 0.43705663 0.46168919 1.45083515\n", " 1.45083515 1.45083515 0.43705663 0.46168919 0.43705663 0.7463551\n", " 1.45083515 0.43705663 0.43705663 1.26302955 1.45083515 0.43705663\n", " 0.43705663 0.43705663 1.45083515 0.43705663 0.43705663 0.43705663\n", " 0.43705663]\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "x = set(dp)\n", "f = [(dp==i).sum() for i in x]\n", "plt.bar(x, f, width=0.01)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "<Container object of 6 artists>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f7b54f97a50>" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, you can see that the Dirichlet process is discrete, despite the fact that its values may be non-integer. This can be generalized to a mixture of continuous distributions, which is called a DP mixture." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are several realizations with $k=20$ and $\\alpha=0.5$. So, there are 20 bars (sorted), and the height of the bar represents that group's probability." ] }, { "cell_type": "code", "collapsed": false, "input": [ "k = 20\n", "fig, axes = plt.subplots(2, 5, sharex=True, sharey=True, figsize=(10,6))\n", "for ax in np.ravel(axes):\n", " ax.bar(np.arange(k), np.sort(stick_breaking(alpha=0.5, k=k))[::-1])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f7b54b72650>" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, axes = plt.subplots(2, 5, sharex=True, sharey=True, figsize=(10,6))\n", "for ax in np.ravel(axes):\n", " ax.bar(np.arange(k), np.sort(stick_breaking(alpha=5, k=k))[::-1])\n", " ax.set_ylim(0,1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f7b54b9ee50>" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, axes = plt.subplots(2, 5, sharex=True, sharey=True, figsize=(10,6))\n", "for ax in np.ravel(axes):\n", " ax.bar(np.arange(k), np.sort(stick_breaking(alpha=25, k=k))[::-1])\n", " ax.set_ylim(0,1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f7b53c76290>" ] } ], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that as $\\alpha_0$ increases, the weights (stick sizes) are more even across the samples.\n", "\n", "We can use the stick-breaking process to induce $P \\sim DP(\\alpha P_0)$ for some $P_0$.\n", "\n", "$$P(\\cdot) = \\sum_{h=1}^k \\pi_h \\delta_{\\theta_h}(\\cdot)$$\n", "\n", "Where the $\\pi_h$ are geenrated by stick-breaking, $\\delta_{\\theta}$ is a degenerate distribution with the entire probability mass at $\\theta$, which in turn, are generated by $P_0$; here, we will use a standard normal." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from numpy.random import choice\n", "\n", "def dirichlet_process(p, n, P0=np.random.randn):\n", " theta = P0(len(p))\n", " return np.random.choice(theta, size=n, p=p)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "p = stick_breaking(alpha=1.0, k=1000)\n", "_ = plt.hist(dirichlet_process(p, 100))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f7b54c2de50>" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "p = stick_breaking(alpha=5, k=1000)\n", "_ = plt.hist(dirichlet_process(p, 100))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f7b53d94250>" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "p = stick_breaking(alpha=1000, k=10000)\n", "_ = plt.hist(dirichlet_process(p, 1000))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x7f7b539a10d0>" ] } ], "prompt_number": 14 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that, while the particular values of the DP realizations are continuous, the distribution is discrete. But, as $\\alpha \\rightarrow \\infty$, the likelihood of indexing the same $\\theta_h$ more than once goes to zero, and one is essentially drawing from $P_0$.\n", "\n", "So, while the DP is of limited use as a direct prior of a data distribution, it is extremely useful as a prior for an unknown mixture.\n", "\n", "If we generalize the above approach such that the DP is used as the mixture measure for some kernel $\\mathcal{K}(y|\\theta)$, then we can define the mixture model:\n", "\n", "$$f(y) = \\sum_{h=1}^{\\infty} \\pi_h \\mathcal{K}(y|\\theta_h)$$\n", "\n", "This is no different than other mixture models we have seen, except that the number of components is infinite. In practice, almost all the components are empty when we consider using it to model a finite dataset, but the model has the capacity to increase the number of mixture components as data are added.\n", "\n", "This model can be specified hierarchically by:\n", "\n", "$$\\begin{aligned}\n", "P &\\sim DP(\\alpha P_0) \\\\\n", "\\theta_i &\\sim P \\\\\n", "y_i &\\sim \\mathcal{K}(y|\\theta_i)\n", "\\end{aligned}$$\n", "\n", "The computational hurdle is in how to characterize the mixture when we cannot generate infinite mixture components. For this, we will use another generative model metaphor, the *Chinese restaurant process*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "Teh, Y. W., & Jordan, M. I. (2010). [Hierarchical Bayesian nonparametric models with applications](http://www.cs.berkeley.edu/~jordan/papers/teh-jordan-bnp.pdf). Bayesian nonparametrics, 158\u2013207.\n", "\n", "[Courses and materials by Chris Fonnesbeck] (https://github.com/fonnesbeck)\n", "\n", "---" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 14 } ], "metadata": {} } ] }
bsd-3-clause
brotherofken/national_data_science_bowl_2
alexcode/notebooks/Preprocessing.ipynb
1
6590
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import glob\n", "import dicom\n", "import numpy as np\n", "import pandas as pd\n", "from skimage.transform import resize, rescale" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[0m\u001b[01;34mlandmarks_v2\u001b[0m/ min_max_frame_idxs.csv validate-128x128-data.csv\r\n", "\u001b[01;31mlandmarks_v2.zip\u001b[0m \u001b[01;36mtrain\u001b[0m@ validate-label.csv\r\n", "local_test-128x128-data.csv train-128x128-data.csv X_train.npy\r\n", "local_test-label.csv train.csv y_train.npy\r\n", "local_train-128x128-data.csv train-label.csv\r\n", "local_train-label.csv \u001b[01;36mvalidate\u001b[0m@\r\n" ] } ], "source": [ "# run scripts/distribute_dataset.sh for the train directory\n", "# Download min_max_frame_idxs.csv from Slack and put it in ../input directory\n", "# Download landmarks_v2.zip from Slack and unpack to ../input\n", "# run all cells\n", "# files ../input/X_train.npy ../input/Y_train.npy ../input/X_valid.npy ../input/Y_valid.npy should appear" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "minmax = pd.read_csv(\"../input/min_max_frame_idxs.csv\", delim_whitespace=True, index_col=0, \n", " names=['min', 'max'])\n", "labels = pd.read_csv(\"../input/train.csv\", index_col=0)\n", "\n", "IMG_SIZE = 64\n", "MAX_SAXES = 15" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def crop_resize(filename, img_shape=(IMG_SIZE, IMG_SIZE)):\n", " \"\"\"\n", " Crop center and resize.\n", " :param img: image to be cropped and resized.\n", " \"\"\"\n", " dcm = dicom.read_file(filename)\n", " scale = map(float, dcm.PixelSpacing)\n", " img = dcm.pixel_array.astype(np.float) / dcm.LargestImagePixelValue\n", " img = rescale(img, scale)\n", " \n", " if img.shape[0] < img.shape[1]:\n", " img = img.T\n", " # we crop image from center\n", " short_edge = min(img.shape[:2])\n", " yy = int((img.shape[0] - short_edge) / 2)\n", " xx = int((img.shape[1] - short_edge) / 2)\n", " crop_img = img[yy: yy + short_edge, xx: xx + short_edge]\n", " img = crop_img\n", " img = resize(img, img_shape)\n", " return img[np.newaxis]\n", "\n", "def get_good_saxes(patient):\n", " fname = \"../input/landmarks_v2/%d_contour_areas.csv\" % patient\n", " saxes = []\n", " with open(fname, 'r') as f:\n", " for line in f:\n", " saxes.append(line.split()[0])\n", " return saxes\n", "\n", "def get_patient_slices(patient, min_idx, max_idx):\n", " mins = [min_idx - 1 if min_idx > 2 else 30 , min_idx, min_idx + 1 if min_idx < 30 else 1]\n", " maxs = [max_idx - 1 if max_idx > 2 else 30 , max_idx, max_idx + 1 if max_idx < 30 else 1]\n", " saxes = get_good_saxes(patient)\n", " sax_slices = []\n", " for sax in saxes:\n", " path = os.path.join('../input/train/', str(patient),'study', sax)\n", " slices_min = map(lambda x: glob.glob(path + \"/IM-*-%.4d*.dcm\" % x)[0], mins)\n", " slices_max = map(lambda x: glob.glob(path + \"/IM-*-%.4d*.dcm\" % x)[0], maxs)\n", " slices_min.extend(slices_min)\n", " sax_slices.append(np.vstack(map(crop_resize, slices_min))[np.newaxis])\n", " return np.vstack(sax_slices)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30.0 % \n", "40.0 % \n", "50.0 % \n", "60.0 % \n", "70.0 % \n", "80.0 % \n", "90.0 % \n", "100.0 % \n", "10.0 % \n" ] } ], "source": [ "val_images = []\n", "train_images = []\n", "val_y = []\n", "train_y = []\n", "\n", "for patient, minidx, max_idx in minmax.itertuples():\n", " if patient > 500:\n", " continue\n", " if (patient % 50) == 0:\n", " print \"%.1f %% \" % (100 * float(patient) / 500)\n", " systole, diastole = labels.loc[patient]\n", " r = get_patient_slices(patient, minidx + 1, max_idx + 1)\n", " n_saxes = r.shape[0]\n", " if n_saxes < MAX_SAXES:\n", " part = r[:(MAX_SAXES - n_saxes)].copy()\n", " r = np.vstack((r, part))\n", " else:\n", " r = r[:MAX_SAXES]\n", " assert r.shape[0] == MAX_SAXES\n", " if np.random.random() < 0.1:\n", " # validation\n", " val_images.append(r[:, :3].reshape(1, -1, IMG_SIZE, IMG_SIZE))\n", " val_images.append(r[:, 3:].reshape(1, -1, IMG_SIZE, IMG_SIZE))\n", " val_y.append(systole)\n", " val_y.append(diastole)\n", " else:\n", " train_images.append(r[:, :3].reshape(1, -1, IMG_SIZE, IMG_SIZE))\n", " train_images.append(r[:, 3:].reshape(1, -1, IMG_SIZE, IMG_SIZE))\n", " train_y.append(systole)\n", " train_y.append(diastole)\n", "\n", "\n", "X_train = np.vstack(train_images).astype(np.float32)\n", "Y_train = np.array(train_y)\n", "np.save(\"../input/X_train.npy\", X_train)\n", "np.save(\"../input/y_train.npy\", Y_train)\n", "\n", "X_valid = np.vstack(val_images).astype(np.float32)\n", "Y_valid = np.array(val_y)\n", "np.save(\"../input/X_valid.npy\", X_train)\n", "np.save(\"../input/y_valid.npy\", Y_train)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
pdamodaran/yellowbrick
examples/rebeccabilbro/dispersion_plots.ipynb
1
67398
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dispersion Plots" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys \n", "import json\n", "import codecs\n", "import requests\n", "\n", "# Modify the path \n", "sys.path.append(\"..\")\n", "\n", "import pandas as pd\n", "import yellowbrick as yb\n", "import matplotlib.pyplot as plt " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# yellowbrick.text.dispersion\n", "# Implementations of lexical dispersions for text visualization.\n", "#\n", "# Author: Larry Gray\n", "# Created: 2018-06-21 10:06\n", "#\n", "# Copyright (C) 2018 District Data Labs\n", "# For license information, see LICENSE.txt\n", "#\n", "# ID: dispersion.py [] [email protected] $\n", "\n", "\"\"\"\n", "Implementation of lexical dispersion for text visualization\n", "\"\"\"\n", "\n", "\n", "##########################################################################\n", "## Imports\n", "##########################################################################\n", "\n", "from collections import defaultdict\n", "import itertools\n", "\n", "from yellowbrick.text.base import TextVisualizer\n", "from yellowbrick.style.colors import resolve_colors\n", "from yellowbrick.exceptions import YellowbrickValueError\n", "\n", "import numpy as np\n", "\n", "##########################################################################\n", "## Dispersion Plot Visualizer\n", "##########################################################################\n", "\n", "class DispersionPlot(TextVisualizer):\n", " \"\"\"\n", " DispersionPlotVisualizer allows for visualization of the lexical dispersion\n", " of words in a corpus. Lexical dispersion is a measure of a word's\n", " homeogeneity across the parts of a corpus. This plot notes the occurences\n", " of a word and how many words from the beginning it appears.\n", "\n", " Parameters\n", " ----------\n", " target_words : list\n", " A list of target words whose dispersion across a corpus passed at fit\n", "\twill be visualized.\n", "\n", " ax : matplotlib axes, default: None\n", " The axes to plot the figure on.\n", "\n", " labels : list of strings\n", " The names of the classes in the target, used to create a legend.\n", " Labels must match names of classes in sorted order.\n", "\n", " colors : list or tuple of colors\n", " Specify the colors for each individual class\n", "\n", " colormap : string or matplotlib cmap\n", " Qualitative colormap for discrete target\n", "\n", " ignore_case : boolean, default: False\n", "\tSpecify whether input will be case-sensitive.\n", "\n", " annotate_docs : boolean, default: False\n", " Specify whether document boundaries will be displayed. Vertical lines\n", " are positioned at the end of each document.\n", "\n", " kwargs : dict\n", " Pass any additional keyword arguments to the super class.\n", "\n", " These parameters can be influenced later on in the visualization\n", " process, but can and should be set as early as possible.\n", " \"\"\"\n", "\n", " # NOTE: cannot be np.nan\n", " NULL_CLASS = None\n", "\n", " def __init__(self, target_words, ax=None, colors=None, ignore_case=False,\n", " annotate_docs=False, labels=None, colormap=None, **kwargs):\n", " super(DispersionPlot, self).__init__(ax=ax, **kwargs)\n", "\n", " self.labels = labels\n", " self.colors = colors\n", " self.colormap = colormap\n", "\n", " self.target_words = target_words\n", " self.ignore_case = ignore_case\n", " self.annotate_docs = annotate_docs\n", "\n", " def _compute_dispersion(self, text, y):\n", " self.boundaries_ = []\n", " offset = 0\n", "\n", "\n", " if y is None:\n", " y = itertools.repeat(None)\n", "\n", " for doc, target in zip(text, y):\n", " for word in doc:\n", " if self.ignore_case:\n", " word = word.lower()\n", "\n", " # NOTE: this will find all indices if duplicate words are supplied\n", " # In the case that word is not in target words, any empty list is\n", " # returned and no data will be yielded\n", " offset += 1\n", " for y_coord in (self.indexed_words_ == word).nonzero()[0]:\n", " y_coord = int(y_coord)\n", " yield (offset, y_coord, target)\n", " if self.annotate_docs:\n", " self.boundaries_.append(offset)\n", " self.boundaries_ = np.array(self.boundaries_, dtype=int)\n", "\n", " def _check_missing_words(self, points):\n", " for index in range(len(self.indexed_words_)):\n", " if index in points[:,1]:\n", " pass\n", " else:\n", " raise YellowbrickValueError((\n", " \"The indexed word '{}' is not found in \"\n", " \"this corpus\"\n", " ).format(self.indexed_words_[index]))\n", "\n", " def fit(self, X, y=None, **kwargs):\n", " \"\"\"\n", " The fit method is the primary drawing input for the dispersion\n", " visualization.\n", "\n", " Parameters\n", " ----------\n", " X : list or generator\n", " Should be provided as a list of documents or a generator\n", " that yields a list of documents that contain a list of \n", " words in the order they appear in the document.\n", "\n", " y : ndarray or Series of length n\n", " An optional array or series of target or class values for\n", " instances. If this is specified, then the points will be colored\n", " according to their class.\n", "\n", " kwargs : dict\n", " Pass generic arguments to the drawing method\n", "\n", " Returns\n", " -------\n", " self : instance\n", " Returns the instance of the transformer/visualizer\n", " \"\"\"\n", "\n", " if y is not None:\n", " self.classes_ = np.unique(y)\n", " elif y is None and self.labels is not None:\n", " self.classes_ = np.array([self.labels[0]])\n", " else:\n", " self.classes_ = np.array([self.NULL_CLASS])\n", "\n", " # Create an index (e.g. the y position) for the target words\n", " self.indexed_words_ = np.flip(self.target_words, axis=0)\n", " if self.ignore_case:\n", " self.indexed_words_ = np.array([w.lower() for w in self.indexed_words_])\n", "\n", " # Stack is used to create a 2D array from the generator\n", " try:\n", " points_target = np.stack(self._compute_dispersion(X, y))\n", " except ValueError:\n", " raise YellowbrickValueError((\n", " \"No indexed words were found in the corpus\"\n", " ))\n", " points = np.stack(zip(points_target[:,0].astype(int),\n", " points_target[:,1].astype(int)))\n", "\n", " self.target = points_target[:,2]\n", "\n", " self._check_missing_words(points)\n", "\n", " self.draw(points, self.target)\n", " return self\n", "\n", " def draw(self, points, target=None, **kwargs):\n", " \"\"\"\n", " Called from the fit method, this method creates the canvas and\n", " draws the plot on it.\n", " Parameters\n", " ----------\n", " kwargs: generic keyword arguments.\n", " \"\"\"\n", "\n", " # Resolve the labels with the classes\n", " labels = self.labels if self.labels is not None else self.classes_\n", " if len(labels) != len(self.classes_):\n", " raise YellowbrickValueError((\n", " \"number of supplied labels ({}) does not \"\n", " \"match the number of classes ({})\"\n", " ).format(len(labels), len(self.classes_)))\n", "\n", " # Create the color mapping for the labels.\n", " color_values = resolve_colors(\n", " n_colors=len(labels), colormap=self.colormap, colors=self.color)\n", " colors = dict(zip(labels, color_values))\n", "\n", " # Transform labels into a map of class to label\n", " labels = dict(zip(self.classes_, labels))\n", "\n", " # Define boundaries with a vertical line\n", " if self.annotate_docs:\n", " for xcoords in self.boundaries_:\n", " self.ax.axvline(x=xcoords, color='lightgray', linestyle='dashed')\n", "\n", " series = defaultdict(lambda: {'x':[], 'y':[]})\n", "\n", " if target is not None:\n", " for point, t in zip(points, target):\n", " label = labels[t]\n", " series[label]['x'].append(point[0])\n", " series[label]['y'].append(point[1])\n", " else:\n", " label = self.classes_[0]\n", " for x, y in points:\n", " series[label]['x'].append(x)\n", " series[label]['y'].append(y)\n", "\n", " for label, points in series.items():\n", " self.ax.scatter(points['x'], points['y'], marker='|',\n", " c=colors[label], zorder=100, label=label)\n", "\n", " self.ax.set_yticks(list(range(len(self.indexed_words_))))\n", " self.ax.set_yticklabels(self.indexed_words_)\n", "\n", " def finalize(self, **kwargs):\n", " \"\"\"\n", " The finalize method executes any subclass-specific axes\n", " finalization steps. The user calls poof & poof calls finalize.\n", " Parameters\n", " ----------\n", " kwargs: generic keyword arguments.\n", " \"\"\"\n", "\n", " self.ax.set_ylim(-1, len(self.indexed_words_))\n", " self.ax.set_title(\"Lexical Dispersion Plot\")\n", " self.ax.set_xlabel(\"Word Offset\")\n", " self.ax.grid(False)\n", "\n", " # Add the legend outside of the figure box.\n", " if not all(self.classes_ == np.array([self.NULL_CLASS])):\n", " box = self.ax.get_position()\n", " self.ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n", " self.ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", "\n", "##########################################################################\n", "## Quick Method\n", "##########################################################################\n", "\n", "def dispersion(words, corpus, y=None, ax=None, colors=None, colormap=None,\n", " labels=None, annotate_docs=False, ignore_case=False, **kwargs):\n", " \"\"\" Displays lexical dispersion plot for words in a corpus\n", "\n", " This helper function is a quick wrapper to utilize the DisperstionPlot\n", " Visualizer for one-off analysis\n", "\n", " Parameters\n", " ----------\n", "\n", " words : list\n", " A list of words whose dispersion will be examined within a corpus\n", "\n", " y : ndarray or Series of length n\n", " An optional array or series of target or class values for\n", " instances. If this is specified, then the points will be colored\n", " according to their class.\n", "\n", " corpus : list\n", " Should be provided as a list of documents that contain\n", " a list of words in the order they appear in the document.\n", "\n", " ax : matplotlib axes, default: None\n", " The axes to plot the figure on.\n", "\n", " labels : list of strings\n", " The names of the classes in the target, used to create a legend.\n", " Labels must match names of classes in sorted order.\n", "\n", " colors : list or tuple of colors\n", " Specify the colors for each individual class\n", "\n", " colormap : string or matplotlib cmap\n", " Qualitative colormap for discrete target\n", "\n", " annotate_docs : boolean, default: False\n", " Specify whether document boundaries will be displayed. Vertical lines\n", " are positioned at the end of each document.\n", "\n", " ignore_case : boolean, default: False\n", "\tSpecify whether input will be case-sensitive.\n", "\n", " kwargs : dict\n", " Pass any additional keyword arguments to the super class.\n", "\n", " Returns\n", " -------\n", " ax: matplotlib axes\n", " Returns the axes that the plot was drawn on\n", " \"\"\"\n", "\n", " # Instantiate the visualizer\n", " visualizer = DispersionPlot(\n", " words, ax=ax, colors=colors, colormap=colormap,\n", " ignore_case=ignore_case, labels=labels,\n", " annotate_docs=annotate_docs, **kwargs\n", " )\n", "\n", " # Fit and transform the visualizer (calls draw)\n", " visualizer.fit(corpus, y, **kwargs)\n", "\n", " # Return the axes object on the visualizer\n", " return visualizer.ax\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "URL = \"https://raw.githubusercontent.com/foxbook/atap/master/snippets/ch08/data/oz.json\"\n", "\n", "def fetch_data(fname='oz.json'):\n", " response = requests.get(URL)\n", " outpath = os.path.abspath(fname)\n", " with open(outpath, 'wb') as f:\n", " f.write(response.content)\n", " \n", " return outpath\n", "\n", "# Defining fetching data from the URL\n", "oz_json = fetch_data()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": 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Vz95EbK4ORGT7SoywvWy/2ZI6ky+XNK6SRilipyNsL9pK9d0N/Nz28y0oO6j05cya9FOBQ4HKD0xb4FXg6KJ9hqSngZ62P2h571tOa7Zf5mEb4B0icnRbQoT3QNtjJY0GdrX9jybq2AjY33a/We1PMvOMGXjFaIBOB+/fuTXqk3QssDfwUVXypYQo8TBJv6KOLI2khYlo6SMk9awyavYjRJafJfT3+jGtkTG304mimziL9Zxdxyira1TNBP2BC5pjJNbD9tXA1eU3+fqKMZ3MvaRxlcw2bO/4JVV9g+2KPlrlLm+IpLVt/8/2Bl9SuzPFl9D+OdVGpqSzgIuAXWey/NrAN1q5T8ns5xWgF1UR14Fv2B5WXv+VEF6ulYn5EaEXeA+hK/iQpK7A9sC3imDzBsBgSZsBpwHfBpYBnrG9b5HL+QOwJCGFs0+lckmrAdcShs5rhEjyMuXwEbb/JenQcvwtoGMj4ztE0jHEdWp/iiiz7WMkLUB45jYqUeIBTgTWknRyeb+TpN1K2yfZvqO8/xnwBfCY7eMbaXsaKp404GFCs3EF4A1gC9srlGynlLlbBNij/C1dvIRPEuLXFwPXlbKrAk/aPljSsmXO2gMGtrK92kz27TVCGuh54GzCwF4I+AQ4yPYbkg4nDO3JhHF2vqRewHGE+Pd/gN3reTmTlpHGVdIiihbXb4AewALAP4EjCLf1M4Rn5G5JpwObANsRHqVdbf9D0n7A0cSP3LvATwjv0zlAN2Ax4kf7ANt/bU7fbF9dDKw9gYslTQaWIz7vgynSHcBdtk+S1If4IWwDfL304ye2/yNpCUIMd13CSzQEOMb2REkTgNuA9YG9gB8QGmufAe8BfWy/VWnf9ruSTiptTQReBA6zPaYsKzwObErIjjxa+jAzP3ZDgN/WJko6iDgnXwBvExfST4DTgSUkXWV735moP5kLsf2n4smo5lVJPWw/THweF6lT9ACgL6G3N1DS120PL0LO19u+V9K+hOeqA/Bf29tIagM8J+nrxFLZ7bYvltQd+E6pW4QHbC/bLxUJmSG2B0paHbhK0v8RnrF1CR2/4dRnmO0zJO1IfL5/Ajwl6XjCEHywyrCCkMdZ1/bpxaP9pu0DJPUEjpX0V4qhaPtjSVdL2sb2AzXt/kzS7uX1v2wfXnXsIGCU7d0krQk8V3XsLtvXlLZ3tT1A0uG2Dym/MRXWIPQYPybOVyfCyPmz7YskbVOOzywrAt+y/Z6kG4Dzbd8j6buEnNAA4McUbUfgAUn3Eb9Dv7N9s6R9gMWBOeLdnx/JDe1JSzmeMBC62l6fuPM5w/ZY4kfwUkk7A32APauNBEnrE4bZ9rbXA24n7jo3Ju4IN7G9FnFnPFN3lnV4hvjxruZA4FXb3yJEXFcvxhOEUXNoaXc4cH5JPwcYbrsrsCFhmP2sHGsH3GFbhPFyFHEn/W3g/jKeKZQL1g4lz3qENtqgqiyrAj1Lv7ciDNcmkbQQ4TV4sCZ9K+ICuGU5P9cCfwb+DZwMPJqG1XzJvsAvJA0hNPLerT5YdPvWAc4iNP8mE0ZUY3wCdJR0HXAJsChxkyHiZgDbw2z/seTfAViYMOghPsv7lZuHy4Clic/5c7YnFFHoxpbcHin/hwGyPY7wHG1Xxjmj5b+K0Tam9Gk14ibr7tKftUpfajnbds/yd3jNsS6lP5T9Ve800V5jvGx7nO0vCM9dh+p6iRur5vCu7ffK63WBE8r4Tga+RpzvlYibsCGEJ2914ndsq6JD2Z0wdJNWIo2rpKV8n1hy+GfZU7Qz8WOF7fuBGwjR171sv1NT9rvAfbbfKPnPtd3P9uPEHoW+ks4klrlaut9rMnFnWM29wP+VvV99geNtf1iO3V/Zo0VcBLarGmffMsbhxB16tdFW+SF8kzDonip9f9r2n2va3wG4ynZlj8x5wHeLFxDCUJtULiIvExeievxU0tOlT38nvGS/qMmzPbFc+g6A7UGEV65zI3Um8wffI75z3yUuorVemQOAE21vb3t7wojfr3wGJzH1mlB5vQOwou09CBHohQiP8khgI4iN8FUix+cCPwX+UJbuXiCWsXsSy5HXAC8Ba0taqOSZsqm+hoo3bHOmijRfVsbQ0SEuXU11/yF+A6oZRSzHbVP683tCtLk5jCA88Uhalale8HrtQcxVLfXyTamX8Nw3h2qj6AXguDK+voTgtgkP25YlfRCxr+4g4FTbPUo/d2lmu0kT5LJg0lIWIJ5EugdA0qLEHRiSGghD623ih6L2TmwiVT8wxfuyEnEXeR5xV30b8UPRu4X92wi4qjrB9t8lrQxsTVxUnizetUqfKrRh6p33AsButkeWvi7JtD+O40vdkyT1IPambA2cI+lB20fW1EvN+wWZ+gP8SdWxydT/YYaaPVeNUO/GqYHwOiTzLy8R+w0/JpbN7oYpGoDfJ5aC1qtktv26pGeIG5kniGWkUYQXZTDwQ+AkSY8Qn8lXCe/yr4ErJfUu6ftT9l3ZfkDSrsRS1wDgirJEvThxMX9H0hmljXeYdkN+Nd0kDS3171fqfqLs6bqwTv6xQLti6H1Se7C0ezbwcDHqRgM3Njmb03MFMKjMx2vApzPI/7yka4C/zCDfGcSG9R8RqwCfN7NfFX5OLPV2IAzhI20/UzyZj0lqT3gK3yz/75Q0jvgdu7OFbSZ1SOMqaSn3AYeVL+1E4o5yPLH09lNir8e3gX9Iesj236vKPkgsXSxv+y3iDmsr4of7jrI/owPx47xAczsmaX9gFWp+OMsPeoPt4yTdRnig1iiHv1v2nrxJLJPcUTXOn0rqSywD3l7SBtTUXVl627gYcWOI5dFq7gP2lXRt8V4dATxie4Kk5g5zRtxH/MieWy4q+xIerpcJj0AaWfMBtkdT5emwfQdTP7vV+Sp7eFaoc6z6QZNLyv/+5Q+Kh6oOP6h5f2pVnX2r0neuyYftK4ErG6kX233qpZd9Xx8Rm8Jry3xKbMSvTX+BWG7H9jVMv8G/Ou+pTfWn7C+7wvb9ZQ9Z93K8Z1Xei6teb1mnuurz1a3UuyNwcvnt2BpYvpF+jKbGs2W7U9XrV5nqda/O8zvgdzXJdT8rSeuQxlUyIxaRNL4mbRPgl8CZxEb2ypM7R0vakFg+2Mj2m5KOAq4r6QA4nhY6Bri3GBVvEXemiwPXSnqW8Bw9QizjzWj5+seKp5omEx4bE6EPau8qzyWWK0YQcYCeIX6k9yD2Il0taXniqZuDSpkjCG/avwiD5C/U2Txe7g5vJIzJ8cSd8xE12a4gNp8+Wcb0MrERvtUp3oNzgKGlrXeA7xcP2+PAAEm32s6lgNlEa4Vg+KpSvM63Ekvr/5tD3XiV+D07hfg9OLSV6h1FeAInEr+ntb8dyTxGw+TJ9ZZ/k+SrQ3mSZ1fb35/TfUmSJEnmfXJDe5IkSZIkSSuSnqskSZIkSZJWJD1XSZIkSZIkrUgaV0mSJEmSJK3IV/JpweHDh7cnHi9+i6nxjJIkSWpZgHgs/u9du3adMKc7U0FSWyKUQWdCj+5Xtm8vMaAGEU/OjiBUB6rVEf7I1GCynxExlf5FBMhd0zOptVdV36mEzNR/iLn6hAhi+c8mylwP7GP7s0aO7wI8YXuGotGS1iae3l2YCDh8NxESogfQz/buNfnPJSKwvz7DwTXe5uZE9PO2RMiZq2xf1NL6aupemlCuuLY16kvmHF9J44owrJorMZAkyVeXzYHHZqWC/lv9fDTAr4ae2bkV+tMbeM/23uWC/DQRg+1soL/thyRdTKgo3FopZHsvmGIUjanEZKrRvmsuZ1fVsybwZ0kb1AmFUunD7vXSqziSiDXXpHFVAvpeD/Ry6BguQEQk70sEIK7X9lEzaLtJJK1CSGNtb/vtEgD5QUmv2r53VuourEcEbk3jah7nq2pcvQWwxhpr0K5duxnlTZLkK8pnn33Giy++COU3Yy7iJuDm8rqBqQoDXQn9PYB7CAHgW5k5upVI7ssBA21fWlQHBhAe/leAvkUPsC62X5D0FLCZpBeAgYRyw/KE0fdnSaOBNYGLiXhzncvxPuX/BsBgSZcDq9s+phhOTxPx8ypG207AUNsvlba/UAgQf0YE91xd0j1ARyI48alFc68fsDuwcjm2EvBT2/eVyPKHEl6pycAutqv1GfcGBtt+u7T5iaTtgPHFm3gVEcB4AcLovKHSZpmbfkAnwrt4HSHHsyrwpO2DCY3V9UtE++6EhNEyREy+EbYvlLQU8BeH3mkyl/JVNa6+AGjXrh3t27ef031JkmTuZ67aPmB7PICkxQgjqxJNvcF25RHwccASdYo3xudEdO+VCHHjywjlhc1sj5X0S8IAumwG9bxNaO6tCZxVvGjdgdMI8fBqXrPdV9KBwEG2+xXNzH6ERMtTko4ntDIfrPGGrUAE9ZxC1bxAGHU7E4bO61RFkC9MsL2DpG2AowlVgzWA79n+WNIlZT7+WFVmBcLIq27zw9LmwcA7tnuX8/JUUbBojDUI4/dj4FVJnQhDtl8xbLsTxuM5xWN2HSH7s2dNn5K5kNzQniRJMg8iaUVCSurqqj061SK+iwEfNKPKp4phNobYw7Qc4Um6sXhftiUMrxmxEqF48BYhen41YSzVk1yq7M16g6JNWqEImD9MGDj7ApfXlH2NUDyYgqSVJW1R3o6wPcH2x0yrHdpU22MJFYeriCW62j7Xa3P9okDRhVCVqPT9ecIrVU21XujLtsfZ/oKYqw5Mj0t9rwLjJK1FqDoMrpM3mYtI4ypJkmQeQ9LXgPuJzePVGn3/lNSzvN6B5u0trQ16+C5hJO1UtPMGAENn0K+1CdH2vxESWYNt700YgfWEyOsFWpzE1GvTZcABQEfbz9bkuxPYXtKqpe22xJ6zdZqou9G2JS1BeNd2L21+UqfP1wIHSFqulFmU0GNcHhhJ7M2reBTXJWRtPmWqVuC3Gmu/UD32yvsKlwEnAf+uWapM5kLSuEqSJJn3OAFYCjhJ0kPlbyFieeu0oh/ZjrIvS9L9kpq1wbQ8ZXgkcJekYcAhxBOItfystD8EOIuQkppI7As7U9IjwDbEUuHMMIzYc7W07SeA1aizDFb0BX8CXFY8a38j9iYNbMYwq/kf8FfgccIo/YQaoesinHwscEtp82HgD7bvBi4FlpH0GPAQcJrtscQG+Isk3ceMhehfAdYtmqy13ApsTWiUJnM5X8kI7cOHD+8MjFpnnXVyz1WSJI0yYcIERowYAbBy165dR89KXa38tOBXgiI6/ldguzko1jxXIGlhwpjbuDq8RjJ38lXd0J4kSTJbSaOqeUhamfDWXJWGlboTy4+npWE1b5DGVZIkSTLXYXsUEZbhK4/tYcQermQeIfdcJUmSJEmStCJpXCVJkiRJkrQiaVwlSZIkSZK0ImlcJUmSJEmStCK5oT1JkmQeowTMvJLQ5WsP/Mr27VXHzwFcEVSuSj+L0B/sRERhfxV4h5BV6TcTosq1/egDrGn7+Jr0W2z3auawqssPIgJuvk+MbxTwk6Z0Dec0kg6zfUFjczIT5U9lWjHtswmdwh8D183KfDbR5m+IYLNH2H6opPUBTic+GwsQgUz3sf1aK7T3TWB923fMal1zO+m5SpIkmQ1c2+vC0df2unB0K1XXG3jP9uaE7t4FAJKWK2LFP6xXyPbRJdr6GcC1tnva3q2V+lTdTmsYAseW/m1S3u/UCnV+mfSfcZYZI6lB0u8JUeldi4RPqxtWhd2ATSuGVRWVz8bmRADXY1qpva2ATVuprrma9FwlSZLMe9xEib5OSLRUtPMWJQSKd2hBnasXw6wjcIftUyWtS0QYbwDeA/arCBU3haQxtjsVzb3fE8LXnwIHEjf11xGafqsCT9o+uIm6FgAWJ3T/kHQ4IV48Gbje9vmSVie0B9sRQsi7A4sQ3r0FS94jbD8j6WUiCvwawBBC3Po7hKdv76LZeCmwEBGl/SDCu3djybswcKLt+6v6eCKwtKSLgCeBbpLuJ/QZBxYh5h6EhNAXRCT2vnU8cQ3AxaXP+1RiWlXN50OEcPQ6ZU52s/2apJOAXUo/FwZOqjaYGjkPfYgI9HdJ2s72J42cgqWq5n66MZR5uhxYstR3oe2Bkg4hIuhPAv4O/BQ4Hli4RPz/RvVx20cvBHA4AAAgAElEQVQ00v48SXqukiRJ5jFsj7c9rmjY3UzxmtgeVSRjWkIHYGdCH++wknYZcGjxdt1NSL80h8uAw2z3AC4itP8gDJv9CaNmR0md6pT9bTEmRhJiyc8U4eIfA5uVfu4sScCZwP8rXq7zgA1L2nm2tyBkfCqyMZ2J+docOKL0a2NgM0lLlnLnlzGfSXj5ViXke34A7EGNY8L2AOB924eUpM8JweldgKMkNZS56FXm4k3CuKnlhNLW12lcG/FJ21sDDwB7SFqfMKY3Is7f8nXKTHcebJ9OiHRvW8ew2rNIGv0D+AVwWxNjWI0wcrclxL1/VurYt7S5CXEOG5jqMb299rik+crZk8ZVkiTJPEjxsDwIXG372laockRZgvqYqZ6wLoQu3kPAfsRFvzmsYPvp8voRYO3y+mXb42x/AbxFGHa1VJYF1wBuI3QL1wFWIjxOQ4BlgNUBEZqA2L69eJW6lDYpfVix1Pue7deL1+gj28/bngx8WPqxLnBCGfPJwNdsP0dESL+OME5mdO18qtQ5hvAkLUcYPTeWerct46jltmI4jQdObKTuf5b/b5T+diEMri+KkfSPOmUaOw+NUVkW/DbQC/hTE2N4mzByryGM1raljn2BQyU9XPLVimDP6Pg8TRpXSZIk8xiSvgbcDxxn+8pWqraep8TE8lRPwmt1ZzPr/I+k9crrHsCLTbTVFG8QS34GngO2LH0aBDxLeEY2ApC0V1k6HEl4p5C0AWHozEzbLxDz2pNY9rqpLI8uZvt7xFLW7+uUqzYOatt4F/g3sFOpdwAwtE4dFWHsA4H9JfWsk6e27ueAjSS1kdSe8NrV0th5mBkqc9/YGI4GHrfdm1iurszDgcRDEj1Kn7oTS4Btmjg+3zBfueGSJEm+IpxA7IU5qey3AdihsX0zkgYD/W2/3sx2DgYGlyWbycRSXi0/kbR11fueVa8PBC4oS0oTGynfGL+VdDyxv2cBYr/Xq5KGAI8VQ+JJYnnqGOASSf2JPVe9gTuAyyT9nPCmzGzbPwcGSupA7Cc6EngJOEXSjwjj4OQ65Z4v3pu/1B6wPUnSkcT+pjbA/4B9GuuA7f9K+glwraSuTXXW9r8k3Q38jTCAPi9/1TT3POwpqVvJuxhhBDU2hsnA7yXtDnwATCzn5l/Ao5LGEefoiVLmRElPNXJ8vqFh8uTm3kDM+wwfPrwzMGqdddahffv2c7o7SZLMpUyYMIERI0YArNy1a9fRs1JX5UnBPW85tPMsdyxJCpIqTxVeVIya54CtWmBIJ61Ieq6SJElmA2lUJV8S7xLLgn8nvEiXp2E150njKkmSJEnmUUq4hn3ndD+SackN7UmSJEmSJK1IGldJkiRJkiStSBpXSZIkSZIkrUgaV0mSJEmSJK1IbmhPkiSZx5DUltDN6wy0B35l+/YSLLOiITeBCAD6dlW5s4CuQCcicvirhB7dhUQso91b0JfNibhPbQk9v6tsX9Ty0U1T99LA9q0UgR5JuwC/BX5v+/yq9MNsXyCpD7Cm7eNbUPeewKHE3LcBLrU9eBb72484V4MIiZlukq4nzutns1J38uXS6sZViSh7I/A8Eam1LXCu7Rtnoc5vAuvbvqOE3e9n+4VW6O5MsdkWJzJp0mQ6dVqSMWM+mObYafs+RAMNLLz41/h0fAQA7rBoJ7rvdgvDburFpx+NjbRFOvLpR2NZ7okuLDChHQ00QJviOJw0qW67bZdZms//+wFLfms9GhoaaLPYonTs/eMpx8decwOTxn/EH+58ifGffA4NbZhc6mpoM/X1r4aeOU29Z+4xgGU/WZQ2bdqwzy2Hc1vf+P7vdMnUmHbX7Rq/jeutswGfffDpNOXbLdmBtU/tzj+PGjrlPcBC31wSgFX2W4s3bnqZieM/nyZm8cp9ujBq0Mgprxtj1KCRU2MQN0TeXrv+FoBbbg5ps8o5adOmoUzhZJZbbjUWWKBtzFWbBiZNmhrDrfp9pUyF6vSbbtiLfofcCsDFF+1Cv0Nu5Z13PqJNmwa6rLUB77//OW3awJJLtuWDDz5nySXb8v77Ea/vgvM24OTTnuODD6bG76s+3qZN/VO97jqL0bbttE7kSvi5hgZYZJEFGfL0HkyaPLVwm4Y2LLVER35z7C1T0o77bS8AVlutI+M+fq/UE2UWX2Q5Dux1cf0Jr+H2vx8KwGILLwPAlmufDsDdT4fc3I4bXMCDz5085diDz53M4u2+Toe2cf4/m/gR1UGkh101mE/HjYvxtFkg6l6yIweeehdjHvi/yojotM1NjH20H5M+fSdSOixHx82jz2OfHcykz8bTpt2idFxvH8Y+G5/ZG3/5Z/43duyUtlZZaFHaNrShoWHqOW5o04Zd7ryDW7//gynfiV533zVTc9Ga/POIoaMBNjx/q86tUF1vQsZl72KAPA3cTujqHW77aUl9geOYqvWG7aMBao2IRiKBzxBJqxDCztvbflvSQsCDkl61fW+LRzeV9YAfAq1iXBHagD+zfUdNen/ggpZWKukHRGDOHW1/WObhZkmf2L6p5d2dnpYYwMns58vyXA2tfAAkLQo8LOnFKm2j5rIVsCYRcTdJkuSrzk2EYDPELUxFC3B322+V1wsCn9YWbILVJd0DdATusH1qkX05v7TxHhEl/cOqMnsDgyveMdufSNoOGF+8a1cBqxAR1s+2fUP1DXKNZ+Y6QmplVUIr72BCX299SQcR8ijLlL9nCC3ECyUtBfzF9pRI5kWA+Rpg8TIP/YFFgR2Bb0t61/bjJe+JwNKSLiIivneTdD+hpTfQ9qWSehByL18ArwB9izZhhcMJLcQPq+bhaEKP8CZJY2x3Ku1dD1wMPAVcDiwJrABcaHugpM0II/m/xHn9W/VJkjSauB5eTHgnOxOaf31sPyVpf0J4+33gM+AGYFg5FxMJr9qett8g+dL40vdc2R5PfMB2lXSWpCfK35EAkgZJukPSMElL1eaRtABwPBGO/4el2lMkDS15VpH0a0mHlvqWkjT8yx5XkiTJnML2eNvjJC1GGFn9S/pbAJK6ExfYc5pRbQdgZ0KP77CSdhlwaNGSu5vQF6xmBWJpsbpvHxZB5r7AO7a7A1sDv5K0bBPtr0F4f74D7CipE0W/zvalJc/QUt/vmCofsyfwx5q6+gMP2N4C2A24grg5v5cwgh6v6u8A4H3bh5Skz4HtgF2Ao4pkzGVAr6KD9ybQp6a9lWrnARhNGD6NsRqx1LctIYJc8TAOBPYoAs6jmigP8Jrt7Yil4IPK/B4HbFrqXKTk24YwHLcGTgGWmEG9ySwyuza0vw3sDqwMdAM2I4yldcvxyhdm09o8wFrAGYRK9+0l/122twLuAXYlrP+mvmhJkiTzFZJWBB4Erq7ekyTpx4RX43u232lGlSNsT7D9MVM9YV2Ai4q3aT/g6zVlXgNWrOnX+pI2LGUfAbA9jtgqsmpN+eo1+pdtjyuG2VuEsVeLS32vAuMkrQXsBdTubapu+01C065j40OfhqdsTyaEnhcmPFjLAzeWediWMKaqeY3w0FWzBmGI1VIZ89vAzkWPsD+xhQbga7Yrwsp/nUFf/1n+v0HM12rA87Y/LvM4rBy/gtD9u5cwnCfWVpS0LrPLuFoJ+APwqO3JxZ36N8JwgvKFIb4QjeWppuKZGgMsPBNftCRJkvkGSV8D7geOs31lVXpv4uLZs/wuNod6QrMmNk/3JLxWd9YcvxY4QNJypf1FiZWK5YGRhBeM4mFbl/DEfFqOA3xrBu1PYtrrVPWuxcuAk4B/2363plx1218nRK7fq1N/hWojr7Yf7wL/BnYq8zAAGFqT5wJCaHpxST0k3Qicy9R9XG0lLSqpHbB2STsaeNx2b2KZt9KHNyVVNqVu1ESf6/X1ZWBNSQsVceXvlPSdiGvrd0tbx82g3mQW+dKNK0mLE4rc/yO8UZUnXboTSuMw9QszspE8tV+wel/Cpr5oSZIk8xMnEAbDSZIeKn+LEPujFgNuKWmnAUgaXB4Mai4HA4MlPUasIDxbfdD2aMLouqV4dR4G/mD7buBSYJlS9iHgNNtjSx8vknQfsRerKV4B1pV0VJ1jtxLLXFfUOfZrYCtJjwB/Bg6y3ZS35vniQZqOIi9zJHCXpGHAIcCImjy3Eyso9xDG16qEsdS5ZDmXcBbcTHi5IJYpD5X0MHAUMLEIL/cl5nwI03vImqRc+34DPEp4qRYiljn/AZwuaSjQj1hGTL5EvqwN7VuVL9oXpY1TbN8iaUVJjwPtgBvL5rsphWzfKalnnTyTgRMlPdVEm7cSdwm9v6QxJUmSzBXYPpK44NeydCP596l5P6jm/UOEAVR536n8Hw70nEFf7ie8aLXpnwE/qZN+N7F/q5ZuVXm6VaU39mjxgsS+pgfqtPE+sX+sNr1PvYpsb1kn7VOKcdTYGGvyX0dsyp9C2fuG7V8Cv6xTbJ06aU9S32PVrdTVubzvU9X2vcC9khYEVrD97bJX7BHgDduvUBwXyeyhYfLkek6geQ9JCxN3TRuXO41GGT58eGdg1DrrrEP79u1nR/eSJJkHmTBhAiNGjABYuWvXrqPncHeSQjFaLiG8YTfPKP9XCUm/BrYnnhR8Ajiq7CFLZiPzRRDRmi9ak4ZVkiRJMm9jexixhyupwfYJxLJxMgeZL4yr/KIlSZIkSTK3kNqCSZIkSZIkrUgaV0mSJEmSJK1IGldJkiRJkiStyHyx5ypJkuSrRIkDeCURKqA98Cvbt5dAypcSMZZeAg6oje9UxJZ/C3wD+Bj4hJCEeU7SIOD6kvWbVbIzTfVlTeDiEmBzRnn/SER570w8zfYf4F9EYOgpQtLNQdLWwC+IeZhIhGc4skYDsbl1bk/oNPapSrsCuM/2jeX9SEJdpCK9NoiIqdUTOBsYTwha1xWdrsx1UwLXTZ2rlo6t1LsL8ITt/8wgX2cittlTxGeqPXCN7RaLXJd6l6bMzczMQynTEzh1Rp+z5p4n26/XqaMPLfw8VkjPVZIkyWxg1FUjR4+6auToVqquN/Ce7c2Jx+4rF7tfAyfY3rS8/0F1oRKy5nbgLNvdiozYacCF1fls3zszhlVzsb1XuTgOIi5sPW0f3tL6JK1PGB/72N6s1P0M02sgtgYPMDXq+6pEgNMeVcc3BYbYPqpcsNcDfjhdLTPJzJ6rFnIkIWo9MzxfzlMPYvw7SPrBjArNgJbMzRjCGJ8RzT1PXwrpuUqSJJn3uImI9g3hUah4p/7P9hdFZqUTUOu9+QFxF18tXPykpGmCaFbu3AmNwusI7bpVgSdtHyxpeULDtYG46FXK7QocSujkTQZ2aYZiRjdJ9xNafgNtXyqpBxHx/AviItm3SKNV6Ed47aZo+NmeIlYtaRvgV4TkznvAfrY/kHQWU4NqXmv7vCI5cyXwUfn7b03/hjDVaNuRMHx+WLyFnwBvOsS0Hyr9OhFYX9JBhAbk5URw7I8JrV2AvpKOJYSUD7b9ZFV7TZ6r4oFZpvz9gIjMviIhLXS77f4lzwTCU7g8EXh0eWADIgr81sA1pf2FgRNLwNS62P5c0nmElu8dko4uY5kIPGL7OEmnEuoqixJC3DvW5qmZm+nmofRvddvHSFoAeJqQ8jlSUgfgxib63NzztAxwFhHJ/mNCrxjqfB4bm5d6pOcqSZJkHsP2+HKBWIwwsvqX9C8krQQ8ByxLeHGqWZnQnwNA0m3lIvOCpG800twaxEXyO8COkjoRF8frSmTzP9fk/Z7tzQih5u2aMazPS/5dgKNKhPHLgF7Fa/ImVVHJa8cjaeUi+fOwpMdK+Uuryj8M9Jf0/VKuG2Fg7SlpXeB3wMm2t2aq4PEUHCLYkyQtAexASN3cU173JORmqhlAGEeXAmcC/8/2JsB5wIYlz/Dikfp9U2Mr46t3roba7k5IHv3N9nbEeepXVc9rJf33hAzQXYSxsg9hjC1LGGd7MHMOl7eBZcuc/YgwpLoDq5e5BRhZ+rVgI3mq56bePFxHiFovQHhmH7T9STkHqzbV5xacp50JY60HMJCQlYKaz+NMzMs0pHGVJEkyDyJpRcIjcnX1vh7br9lenfA6nV1T7A3iol3JWxEj/i+NX1hftj3O9hfAW0AHwoiqeFn+WpV3LPAHSVcRSz9tmzGkp0ok8TGER2I5wstyYzEqtmV6rb0p47E9qoxlO2KP0rLA/6q8Wo8QosldCBHjycUL9jdgrSbGVM0QQs9wWdtvEBft7sSFuak9QwIeL/28vcrTMrz8r4y57thKuXrnyuX/+8BGZU/bOcTeqAr/rKqvQ3UDZe/WJYQxcxEzZxOsRAhZr0kYdJ+X8/YoU0WpK/1qKk8108yD7XGEMbwdsC/h9WtOn5tznn4NrFDK7EoYVTD957FZpHGVJEkyjyHpa4TW3XG2r6xKv13S6uXtOEL0vprbgK0ldasqsxphjDQmkVIv/Xlgk/J6o1LPEsSeoN2BA4glmIZmDKu2nXeJi3jFqBgADK3JczHhjVq+Km3LUte7wOJVx3oALwIjKUuC5cGA7sTm/+nGVIcHCC/GQwC2XyX0HFdjei/hJKZeY0cydZ72klTZZ9aULM3MnKvK+e0DfGB7L2KJa+HiuWusjUlAm+J9Wsz29wgdyCYFnYuw9JHEQw8vABtLWrC0tQUxv9X9aixP9dw01sfLiM9RR9tTBMNnss/NOU+9gUHFC/scUFmqnCXJoNxzlSRJMu9xArF8cZKkk0raDsAZwCBJnxH7Rw4AkDQY6G/79bIZ+YxidCxI7Gf6qe3XJM1s+78C/ihpd2BUSfsf4fF5nNhf81/CI9AibE+SdCRwl6Q2pf5aAerhko4hvGVtgUWI5cNdbU+WdCBwi6RJpT99bL8rqaekx4k9UDfafqrsH/pDqe8dYp9WLY8BXYGTqtKeAZbw9Pp9rwDrSjoKOAa4RFJ/4rz0LvU0Nf7xzThXQ4BrJW1C7LF6iabnfhgwmNhU3lPSjwhj5+Q6edcqnsNJhCfyj7b/AiDpRuKctyHm5s/A+lVj+FcjeVaompvGxv9EMSZrN/C/BJwygz435zw9CVwu6aMyxoOYdgN8i5hvhJubQwo3J0kyM7SmcHPlScGV9+3SeZY7liTzOcWg/iuwne3/zen+NJf0XCVJkswG0qhKkplD0srArcBV86JhBWlcJUmSJEkyF2F7FBGOYZ4lN7QnSZIkSZK0ImlcJUmSJEmStCJpXCVJkiRJkrQiaVwlSZIkSZK0ImlcJUmSzGNIaivpakmPSnpS0g9rju9Z4jjVlusp6fqatDOKluCXiqQVJH0sabcWlu8k6aLW7leSfBmkcZUkSTIbGHXVyNGVWFetQG/gPdubE9prF1QOSNqQ0AJsTnT02cG+wPmEsHOzsT3G9iGt26Uk+XLIUAxJkiTzHjcRgs0QRtREAEnLEFppRxHyIc1C0lkUaRjgWtvnSRpE6K2tRGjWXU+I5n6TkKZ5RdL/AzYHFgDOtn1TTb0NwN4lz22S1rE9QtJCRKTwFQjtuy1sryCpB3AK4QBYFNgT+Ay43nY3Sc8S2nPrETIlO9n+sLnjTZIvi/RcJUmSzGPYHm97nKTFCCOrv6QFgCuAnxG6go2xlaSHKn+E4YKk7xNCwd0IA2vPouMGMNr2toRG3sq2dwT+BPxA0g4lbTNC1+9ESUvWtPld4F+23wGuZKr36iBglO1NgVOBr5X0tYHeRVPwFqB2KXFx4DrbPQi5mx2anrEkmb3MlOdK0vGEwnRbQnvn57aHN10qmRVePibkklb73emMveYGJo3/CCZPhoYGOvXbj7HX3DAl76Rx47nw5pHQ0IZf/uW3nLT1sUyeNFWvtaFNpFc4c48BfPD2f2lo04Zvr7oOH783noWXWZSdLplGtovb+g4G4JsLfQOAdkt24LP3P4U2077e8NyteO7UYQAsvnZHJo4vouI1sqEr79uFN256OY43wMp9ukw37inHCwsu2pYVd1ttmjy9do2xjB37IZMmTaZTpyW55eZjp6TfcvOxM57gwv/tdg0Abdo0sMwyC3PxRbtMl6ffIbcCcPFFu3Dyac/xwQefs+SSbQF4//3PS3k4/5xpY96dfNpzAJx+Sj0R+KnccNMbAPx4txWnvP/oo4kALLLIggx5eg8ALh3wGAAHnbjZNO/rcdkt/QA4sNfFzToG8OBzJ/Pp5x8A0KHtkmy59unc8/QRTGYSC7Vdmi3XPn2avBWWaPdNGhoaaLfgonw28SMA2i24CADDBg1m3AdjWWzJjhx46l1cdur3APjBph2mlG/TYTkAOm4+fb/GPhufxY7rTf2MXrBbL/43diwAi3fsyGE33cK9ffYFYPtBVwFMeV+hkj4/IGlFIor1RbavlfQdYHVgINCB0IQ713atfttQ27tX1XNGedkFeLRor30u6W/AWuXYU+X/B4QYL4RWXwdgXaBrMdQgrhOdgaer2jwQWFnSvYSe3/rlutIFuBfA9guS3in53wTOlzQe+Dohg1LLP8v/N0o/kmSuYYaeK0lrEeKO25S7hJ8Sdx5JkiTJHEDS14D7geNsXwlg+0nbaxdvz+7A83UMq6YYSVkSLCLI3QmRXJhyi1SXF4AHS7tbATcSosWVvi5LeMM2tr297a0Ib9RPgBHAJiXfqsCypdhlwL62+wD/of7+sa+eMG4yzzAznqsPibX1/STda/tpSd+RtDFwLmGgvQnsBXyH+uvkdwDvAXcD9xCbGhtK2n6E2vnvS/l2pY4Pgd+U8pcCYwgl9k+ryl0FDLD9D0kvACfYvkXS/cQX881ZmJskSZK5lROApYCTJJ1U0naw/Um9zJIGA/2bqtD2neVpwseJ3+EbbT8laUZ9uQPoKelR4nf/VtvVy5L7AH+y/UVV2mXEXqsNgUGSHgFeI37fAa4BHpX0EfA2sScrSeYZZmhc2X6zPOZ7GHCKpI+BE4GTgD1sj5S0P+HerayT/0fSCcQ6+R+BTkBX258VV/N+tp8v5Y4F/gEsa/s7kpYi9gwMATrY3rhshnwV2Kz050jih+JWYAdJ7wETgK0lVcqlYZUkyXyJ7SOBI5s4PprwFlXeV9ZTXwceqsl7fNXrn9epq08jec+tyvazJvpydp20J4E1JXUHrrB9v6TVCW8Zthurr1s53rlen5JkbmGGxpWk1YD/2d6vvP824X1awvZIANtXlGMrUn+dfJTtz8rrLsBF5W6oLeF2Hgc8Xur6L3E31hNwKbNs6UPFYHqEeCJmAHAb8C7h5foZsbHxjuZORJIkyZfJyvt26Tyn+zAX8ipwnaRTiOtBi8I0JMncxswsC64HHCTph8VAepHY1PhvSavbfknScSX9EmDV8hTLH5i6Tj6pqj4D+9h+XdKmwPLEY767AUhagliz/39V5d4FFpe0vO23gB7Ai7b/WzxpPwZ6AbsSd3O9WzQbSZIkyWzD9hjiCcMkma+YmWXBWyR1Af5ePFJtgGOIfVZXSpoEvEXsv5qZdfKDgcGSFiQ2JO5PeK+2lvRY6dNpNX2YLOlA4JbS3n+BPuXwbcT+qvcl3QccYvsVkiRJkiRJ5gANkyd/9R64GD58eGdg1DrrrEP79u3ndHeSJJlLmTBhAiNGjABYuWvXrqPncHeSJJlHyCCiSZIkSZIkrUgaV0mSJEmSJK1IagsmSZLMY5Qgn1cSkdDbA7+yfXvV8W2IkDkQ4Q2GlddHE7I1a9r+lGZQwuScCaxGPNn3OtD3y9L0k9QJONn2IZJG04w+l+jvQ4FnifBAl0s6FRhju740QZQ7zPYFkvqU9poV5qG0cRKwou3/lLSOxB7lA20Pak59jbQxxnanOu1OM7YyhverPxfJ7CONqyRJktlA981OGA0w7LFfd26F6noD79neW9LShNTMlIuo7QeAB2DKxbhn5dhMBAVtjOuAS2zfWur5KfGE+O5Nlmoh5UnCQ1pY9gwASZ2BA4DLZ7Jof+CClrRZxYvAj4iHvCCeZn99FutsNq1hyCUtJ42rJEmSeY+bCMFmiJA3E5tZfqCklcvrXYDxwMWENmEboL/thyqZJa0EdKoYVoXziYjsSNoLOIoI5vwSIci8F8X7I6kD8ILtzkWD8GlgHUKAeTfbr0nqD+xMXJcGAvcB19vuRg2SdiIk2Q4rXqrutn9Y+rESsAZwPfB/hMZiRQRzJ0m7AcsAJ9m+o6rOE4GlJV0EPAl0K2ofywEDbV8qqQcRX/ELQuKnr+2pYqjBDUxrXP2AEnuxxG/sV9F2rHihJA0qc9eZCE/Up0TH3594wn4B4HbbpwDtJV1LKKe8R4QgqoxhNeBawqDsRSibvAAcR6idrFLmdICkXiX9c0JiaHfb1WGTklkg91wlSZLMY9geX+IJLkYYWU1K29ThiuLNGg1sQ1yM37W9BbATcGFN/hWAUTV9+ML2h5KWIcLnbGV7MyIOYt8ZtP+k7a0J79oekjYkAkBvTMigrUF9PcEK9wNblNdbAF8v4X1+SOgWVhhAaCxW1MbftP1dwhA8uGY8A4hltIq37HNgO8L4PKoohVwG9Co6u28yNSRQNWOAjyStUoydN5gq69MUr9nejpCCO6gsJx4PbA58izCqFiUM2hPKXC9BSAgBiDCs9rL9bE3dKxGGZjdCFQVgD+B3pZ47CUM3aSXSuEqSJJkHKYoYDwJX2762mcWHl/9jgIX/P3vnHWZVdfXhl1EEe0ENalRQdEmLhdgQBVRsUUEUuwYbaDRq1FhR0Yif3dhRRLEXoiZiRxQQGxFEBeSHBSwYFMSCjf79sfaFw+VOH4YZZr3PM8+9Z5991t77zMw966699/oBrYH9UlTpCWDFJLic43Pg93nt10+Ros2AcRk9weG4FFqWfEfp3fT6BdAQdwxGJodttqSzKUGYOWkoTjSz7XEn6C3cydpE0oRyjLskRktakKm7Hh5Vejzdp71wp6UQj+DTpUfhEnDFkb0v+fdkM2CspF8lLZB0vqSfcAdwcoFx7JveZzUcc3wgaa6kn2VP9KIAACAASURBVIGc/uRZwO5mNgxflxdRqyoknKsgCIJahpn9Do/enCfpngqYyHdcJgCPpGjWvvi044zcySQ9Nj1Nx+U4A49yTcKn3lZN5e3xdUe/4c4IeOSltPa3M7Oi5LQNxhfql8RTwLW4g/kiLon2cl6d+Sz+nCstsWPW2cmvOx34Euic7lMffNF8IZ7A782uLK7luPCepKnWdUpo7xNcf7FBqv8vM9uohDH8E/gbcJ+ZrZB3rtA1PYDeKQpXD4/QBVVEOFdBEAS1jwuBtXEd1qHpZ2UzO9LMelTA3p34g3wYvrPwswLrb44BjjSz18zsbdxhOknSdOBS4FUzewvXgr0DeAFokpQ3DgV+LK5xSWNS/deBEXi0Z1YpfX4G2Bl3Ml9N/Xkyr843wEpmdnUptnKMN7MHi+njfNyhfNbM3sAX248tpu4PuCM2Ou8+vgN8n+7fZeRNtebZmIZr5g4zszeTrSnF1U/XDAbG42upSmMk8IyZDQEa4/czqCIiQ3tkaA+CoBiqMkN7Fe8WDIKgBhO7BYMgCKqBcKqCoO4Q04JBEARBEARVSDhXQRAEQRAEVUg4V0EQBEEQBFVIOFdBEARBEARVSDhXQRAEQRAEVUjsFgyCIKhlpCSR/fDM5gtwvbqxeXUm49p+v6XjrYC+WRHnCrbdBHgfGI0nn2wAPCipUoLHSYB6H0kPJ629RyW9UE4b5wN7AvXxBKLnSBpV8lXl7uc+uA5f97zy0yTdambdSZqK5bS7Mp4fbEM80/pUXLvw2yrpeFCtROQqCIKgGji424OTD+724OQqMncAgKRdcF3BPlVkt6yMl9QhZffeFdjXzA6opM0/4NqAFcLMWqTrO6V+/Q2oSPb6ilJefcd8jgOmStor6f2NAC4p5ZqghhKRqyAIglqGpH+bWS6j9qa4WHKZMbNOwBW4HMu3wPHAvUAfSe+Y2QRcHPhJM3sJOK647OCS5pjZTcCxwCAzOxvX1ZsLDJd0npn1xvXrVgNOAPbLrwNcBGydyTDf08zOxcWJTwG2AbaQ9PcUuRsDbJ+LzAE/AJsAx5vZC5LGmNkOabzt8SzyRakPRwL7AGtLuixJzLyHO3g90/kFePTsZjNrjjtqP6ef7/Lu50XAOmZ2O575fKd039YD7pB0V+pDH1z77xM8KjUnY+Zr4EQzex0Yhgs410v2T0j3YAXgaUmXmtlpQFdgVVya56DU7+PTOC/F5XXOSm2OkHS+ma0H3Aeslewfi2sgFvv7wRUBBGyVxvQlsD7wE/CmpHx5ozpPRK6CIAhqIZLmmtl9+EO4OHHgl3LyOMD9AGZWD7gL6JoiPMPwqMtTeASqKS49s6eZrQk0LE12BXcM1jWz1rjUTdv0s4WZ7Z/qfCipLf6lvlCdPsArku5K9UdJ2j2NrzsuhtwlOVb7AK9mHKuc/uGBwC7Am8lBzLXdEjg6TYk+CXQDHgAOTffjQFz+pRlwGNAOj8h1MTPDNQwvkbQnLg+0GJL64ILKf0lFc4C9cYfnzNRGv8w9n5LGlLXxBO7wnoDL4gwBmpvZ+sD5qT/bAQ3MbA2gEbCnpB3TPd0+mfouRb7exSV29kjHGyWnuhfuoLUFzgZ2KO33g+tNDsflhvbBZX/2SD8v5d+PIJyrIAiCWoukPwNbAv0ywslZ9krTdx3wCAW49t+PGYdpOO58DAI64Q/Pq/GH7r6pvDQ2xaMZWwFvSZojaQHwWrINHvmglDpZcmulpgKrSJqJO4J741Nod2crm1mzNK7jJW0CHA30TWu5pgA3p7VcHYH6kr7DHZB2uKNzN9AqjWVI+mmEOxdb4hEpcP3D0hidxjYVXz+1Hi7Y/HhydPdK7WT7vzMwRFIXPCo0IP1sBoyV9KukBZLOl/QjMBt4xMz6A7/H15nBovvcLLX7XGqzBbA5vk7vTQBJb0h6KO+64n4/T+IRrb3xKGMn3Cl9ogz3o84RzlUQBEEtw8yOMbML0uEv+OLtfKHl4pgOrGFmG6Tj9sDE5Gz8gkduXgA+x4WK88WQ8/vSINV7FJgA7GhmK6ZozW7AxFQ117/i6sxn8WdSIeHbfsCJwPqS3s879wfgVjNbKR1PxKdL56XrjkuL0L8iTbel8jOBlSVNwB2McUDH5JAOwBfvj8ejNrAoQpRPvcz7/L5Px53PzsluH+CVvDpH4PcRSfNSu7PwKcSt0n3GzP6Vphi7SDoM+Ct+33Lt5+7zJOALfA1aBzwC+BbwYW4MZrZbRtS6tN/PYPxvZV3gOaANsI2k/xZzP+o04VwFQRDUPp4EtjWz4cCLwJmSfjWzIzNrlgqSohEnAU+m9T17Av9Ip/+DR4lmJLurSPqkgJkWabrxFeBlYKCklyV9ADyOR3dGApOBf+e1X1ydT4DWZnZmCX1/G4/ILDENKulJPMry3zSuF4G/S/oBeBB4LZWvju/IQ9IwPFo1IB2/h0esRpjZO3jUago+fdbLzIYAOxbTvfFm9mAx/Z6PO07PmtkbwF/wqbUsFwGbm9mY1M+rgBMkTcMjicPM7E18l+Z/gZ9TvcHA/3JjyrQ5DbghXfc2HoWcCFwJdE7RrMuAO/OuK/j7kTQLd9ZGp/EIeLuYe1HnqbdgQaEvB0sHM9sMuAYPYf4C/AqcC/wd/9YDsElmzr0kWxXeVjxq1KgmwKRWrVrRoEGD8l4eBEEZGDf5MWbP/ZmVVlyVlk0Oq5Y2b+3WlQ1+nkVRURErr7suAPsMuBeAF7oft9hxWZg1axZjx44FaNqmTZvJlelbbqfgEwOPblIZO3UZMyvCH/p7p6mxIKiRVNtuQTNbBXgaOEnSm6lsB+A23DOmvDlNgiAIagvhVFWOtND+KeDecKyCmk51pmI4AN8J8mauQNJIM+uIbwEml3wN6IvvDPkCX4A3UtIpaY3AQ/jc8tScHTM7BDgVX9C3ADhI0vTqGFQQBEGw9JE0CU/HEAQ1nupcc9UU+Dh3YGb/SXO+E/Bpwny2xLek7gDsZ2aN8TnpRyR1ZPF5/C2BP6XtpuPx3QxBEARBEATVTnU6V1/gDhYAknK7Jr7Dd1Hk87GkmWnXxP+AhhS/HfYb4D4zuxffMVKfIAiCIAiCZUB1Olf/wZPS7ZQrSHlJfk9evo9EoZX2S2yHTUnuLsOzyZ6IL5KvV+DaIAiCIAiCpU61rbmS9FPSnroqrZ1aEc8/8jfgT2U0cwXwkJkdjufwAPgRj2K9iafq/468LalBEARBEATVRbWmYqgpRCqGIAjKQlWmYlgaJGmUUXiiyAmZ8k74GlVwCZOcZMvZeEbtrbLSMWVsa3bGTn1c5+6ItNC8ov0/GWgsqXem7Ho8QWVjPLv5p8A0fGf5yZIOr2h7ZexTb1xAuW8JdU6TdGtuE5ak88vZRnfgcnxsOW6Q9HT5e1xiO7sB3xdIuBosZUK4OQiCoBo47YwxkwFuvWmbJlVhz8zq4wkgf80/J2kwnlwSM5uazQfoUnkVYkaenZ64s3ZaRQ0WQtLZyX53Mo6LmXUo4bLqphdwayVtPFxep6wCHI/nkAznqpoJ5yoIgqB2ch2etuaC0ioW4I6UNwpcXPinZGsLfC1uL0lDS7GxKb4MAzM7CpeRmQV8BPQAVsb1+tbCl2rcJukOM2sH3JSunYtLspSVLczseVx7b5Ck3mnX+TfAOvgSk/64Ht8KeDTosVTnZEkTstEyM7s4jX8aHiW7OLXT2cy64dqCF0taqK9oZhcB65jZ7fgGq53M7CVcx+8OSXcleZo++NKXT4CekuaUNjgzWwvPJr8G/nzuJekVMxuLZ1efDfRMY2yULjtd0gdpQ1cz/L7fhK9R3gfYzszGS/q8zHc5qDQhfxMEQVDLSFGdaZJerKCJ/ikKNRkX4D0RmC5pN6AzPgWXzzpJ8ma0mU3Gd3BfbWaN8E1Fu6d0ON/jDkAz4FFJe+FCxWclO3fg04l7smjtbFlpCHQBdmXxiNkjyd5J+H1pi8v6XGFm6xYyZGZb45Iw2yebG2ROT5G0B+4wnpK9TlIfPIr3l1Q0B0//cxBwZtLj6wd0ldQel8/pXqALR6b7OdTMBqayXsDg9HvoBvRP9lYD/pGmRC/EBZ474k7sHWa2Oq4B2BV3qOZJGoVrRJ4bjlX1E85VEARB7eN4oFOKyGwD3J9yAZaVUel1Kh6xaY3nExyKr8lasYBTkpsW3B7X8Jst6Sc8SjRO0sxUbzjQEvga6JL09nqxKEXO7yTlxJyzKXXKwlhJsyT9gke9cii9Nk/tk/ozHk9EnaVepu5ISfMk/Qq8k6mTf39KYnTSa8zVXQ931B5P93MvCu+If1hSh/TTrUD/p+AbttbPG2Nr4Phkux+wThrrmcBdwGNALCZexoRzFQRBUMuQtJuk9snZGQMcK2lqKZdlyd/JNAGP/nTAozkDgRnFtD0Pj5gcZGZ/wqNPLcxs1VSlPT6FdTbwpqSjk72cUzPFzJqn99uXo8+F+p1jfnr9EI9qkaI5rVP/fmNRZGq79DoO2N7MisysAbBtGdrJkU33k193Op67MZfLsQ/wSin2cmT7vxGwNvBtOpcb4wTgxmT7UODBtAO/jaSD8KnRa8xsxXRNPOeXAXHTgyAIlhPM7Egz61GBS+8EtjKzYfiOwM8kzS+ucor0nAjcgi+ovxR41czeAtbFp/4GAacmm2cCc5MT0xOPtA2hcESnMtwFNDKzEcBQ4DJJ3wA3A7eb2Yv4WiwkfQA8h6/5egqf3it1XVRifIrILUG6b2cAz5rZG8BfgLFltHslsLuZDcdVSHpImptXpw9waIpcvZBsTwUap/YGA9el697G0x81J6hWIhVDpGIIgqAYqjIVQ1XvFgwqR0pjcYik25PTNw5fNxbrk4JKE7sFgyAIqoFwqmoc0/Fpwf/iU3t3h2MVVBXhXAVBEAR1jjR9d9yy7kewfBJrroIgCIIgCKqQcK6CIAiCIAiqkHCugiAIgiAIqpBwroIgCIIgCKqQWNAeBEFQCzGz0XgGb4BJko7LnDsGOAGXi2kBjE6njgJGSSpPNnfMrAku/puz0xDXI+wm6buKjmFpYGanAacCvSU9lsoaAkdLutvMegNTJfUth8318ESo4BnxJwK/AA/gGdlfkTSyFBvHADvnZHPM7E6graTW6bg7sI2kM8varwJtTC3v7zZYOoRzFQRBUA30v3fSZIATjmvapLK2krNQL2XpXgJJDwAPJKfo0Ww9M6tos+Pz7Pwf7sBdV1GDS4muwKEpSWiOxnjS07srYlDSNKADQFYEupxmXgb+njneHvjGzDaV9BnQEXikIv0Lah7hXAVBENQ+tgZWMbOX8M/xCyW9VcZrG5jZw8AmuLTKIbgmXn+gUapzep5zshhJTHhj4ON0/FfgSDxf1KOSbjazrsB5eNbzr4DDk/37gLVwCZlj8WjaVEl9zWwroK+kDmbWHs9GPg/4BOgpaU6mD02Ae9L4FwCnAzvi8jb9zewwSTlh6ItwiZ5L0nFnM+uW+nOxpEHp+KzU3ghJ55flZprZAOBR3IHbL93LzYGrJQ3I1ZP0PzNbYGbrABvhMjajcbma24EdgJPNbC3gQWCNNLZekl4xs07AFbiUz7e4vuRMPCt9y3SPGmT6NAfPgN8g9e8A/HfeGRfsvhP/HW4APC2pV7puFtAklXeXlItWBuUg1lwFQRDUPn7BI0Z7AycDDyUtubKwGu6MtQPWxDX1LgSGSOqI6wbeUeC6FmY21Mzex6fFPgbuM7MWwGFAO1wXr4t5eOwI4NrUzjO4s9ALf5C3xbUHdyjUweS89QO6SmoPTAG651W7DrhJ0m643Ex/SXexSGtxUqZuHzzydnk6niJpD1yW55Tk8FwG7JH6u1FyZsrLmpL2Bw4ECjlnQ4BdcP3G59PPvmbWFJcc+hW/R4PTuLrhjmI93InK3Y9hqd5BQENJOwEXsLjI9GRJe+F6hU0l7YeLch+AO1VvSdqb5NRlrvssld+C/y0EFSCcqyAIgtrHROBBSQskTcQjGRuUck2OGZImp/dT8Qdya+D4NOXVD1inwHW5acEdgc+Ar5N+XSs8QjIk/TQCtsCjQLsnbcG2uIiwAW8CSHpD0kN5beQEkddL43k89WkvltQhbA4MT7bG4A5DWRmVN/5mqc3nUnst8OhTeRmTXr/A16XlMxh3QPcGXpA0Dvg9PuX4QqqTHdcUfF3d+sCP6Zh0viWwJTAy1f08tZsjF3H6Hhif3n+X+jUDz07/EHAjKeKVeLeUMQRlIJyrIAiC2sfxwPUAZrYhHhX6XxmvLSQoOwG4MTlPh+LTUgVJ0ZWjgEvMbGtAuC5fx3T9AHzxew98UXl73Gk6CI+ibJ/6vZuZXY1Pc+Ucw+3S63TgS6BzstkHeCWvKx/ijgpmtg3uKBXHfBZ/3uXfg0m4M9EptXcLLuhcXkoT6x0O7AyslNZxgTtHJ7DIucqOayNgbdwZWsPMcvepPe5gj0/2cn8HG5WxL92B7yUdhf8drZKiY2UZQ1AGYs1VEARB7aM/MMDMRuAPw+MlzTWzs4CPJT1dTnt98OmnHrij1rukypK+NrNz8HU7bfGI1YgkgDwSn8YbCTxjZjPxnYXPAM8B95jZ0anfJ6TXx9Maq1HJ/nwzOwN41syK8OjNsXndOAfol/pRP9kqjm+AlZIz92uB8UwzsxuAYWa2Ar4m6fGS7kFFkPSzmc0hRaYSzwN7ZRbIX4nfo0OAlYEekuaY2UnAk2Y2H49Adccjlp3M7G08mji9jF0ZAjxsZjvja6w+Ajas3OiCLPUWLKh7TuqoUaOaAJNatWpFgwYNSqseBEEdZdasWYwdOxagaZs2bSZXxlZV7hYMgqBmE5GrIAiCaiCcqiCoO8SaqyAIgiAIgiqkRkWuzOx6oA2eL2QV4FNgmqRuBeo2AVpJeqa6+tf1kGuYOvX7xcouO24o9ajHnie8XmY7H554OsyfD0VF1F97LZpde/nCsvqNFm3SaXbt5Xz890tYZeONWHGdtVj/6MOWsPXNg49x76NjoKgeK6y6Kj9M+4E111uTcx65qMQ+XHdEH9aftQb1i1ZglUar0flOX87wn57388u3PwGwSqPVaLZBMwBa9m4LwLjebyw8zr4H+GLgxwBs3K3ZYm3ll08a8CEATbs3X3S8AKi3qCz/uq6HXAPAk/86F1j0uygqqseI4X1KHGtVcsll4wC4/NKWFSqbMWMORUVw843blNrWYwN9489h3TbmvGu6AnD1uU8u9r4s9HvSd1mf1LVviWUV5fkxpwOw7zY3l9yP3n/yNns/u7Dsm9dOZv5v31LUsBHr71p8X6a+czsAjf/4l4Lnb+3m9+S0gcXfkxe6H8ev06ez8rrrss+Ae0vsaxAEQWWoUc6VpLNhoQzAVqUkcdsTT3RWbc5VEARBEARBadQo56o4zOyfpO2muJbTXbiMQEMzexP4GrgJmIvvBDlJ0heFbAVBEARBECxNavyaKzPrgm8R3QnP/dEdT/h2LfCApGfxpHcnp3wq/dK5IAiCIAiCaqc2RK6aA69JWgDMTvk8mufV+V1GB2s4peRoCYIgqO2Y2QW4zMpKwO2S+mfOdcL19MDzUL2R3p+NS6BsJem3cra3Ni450wzPK/U5rvf3Q2XGUY7238L1CbuTtAhLqd+dAstLzOxJSV2XVj+DAGpB5ArPVtsOwMzq49ODH7F4xt2vzSy3cjiXuTYIgqDGcOKFbSefeGHbyVVhy8w64E7TLvhn3mLSL5IGS+qQso3PyL2XNGoJY2XnEeAZSe2TNuDbeBLRWkU4VkF1UBsiV/8B2pvZG7j+0cOS3jezlYDzzOxd4ESgr2uFMgeXhgiCIFhe2Rv4AHgKz6j+93Jef0cSCwaXpfkJ6ItrAhYBvSQNzVU2s02BxpKeyti4GReBxsyOwkWQc9m+e+DyMfvi2cS/BTpIGm1mo/Evyb2BP+JahO9JOs7MeuNO42p4xvWjgX1waZp1swMwsytxAebbUlTtZUltShu4mU2V1NjMtsVlbubhEjwnpbE/ktrbHBgp6ZTSbAZBPpGhPTK0B0FQDFWZoT0Xtbr7yjeaVLZfZtYPFzLeH2gKPI1PgS3xgZ5zJjLHk4GjJY0wswG4JE0joImk88ysETBcUsvMNTsD50o6qID9RngUa1tJM83sRuATXPx5Eq4R+E/gIXx39yW483WypGuSvM04fAf4ScDaks4wsz/iosLtcWfrI1w0ujuuI/gS8IikHc3sVKCBpBsy/epO4WnBnHP1DnCipDFm1hk4BpfUGYXvRP8FTwe0o6SSdAuDYAlqw7RgEARBsDjfAi9Kmi1JeORlvXJcn5senIrnFGwN7GdmQ/E1WSuaWTZS9Dnw+6wBM6ufIlabAeMkzUynhgMtgSeB/fDI00W483Rgsv8rsL6ZPYJPLa6Gr+MCF4IG2BJ4R9J8ST/ikbqFSPoUmGlmLXAh6fvLMX6ADSWNyeszuDbjTEnzcDHshuW0GwThXAVBENRCRgD7mFk9M9sQWBV3uMpKfoRrAh4F6oBP5Q0EZuROSpoCTE8RnhxnAJ3x6FQLM1s1lbcHJkoaizteO+DRsdVS/edSGxtLOgK4EBcorpeun59exwM7mFlRst2iwDj6ARcDX0oqq2hxjq/M7A/ZPqf3dW86J6hyasOaqyAIgiCDpGfMbDdgJP4l+VRJ88zsSGA1SXeV0+SdQD8zG4av4bpd0vy8OscAt5nZOfgOxU/wnII/mNmlwKtmNh/4GMhNxQ0Fmkqan2y3kPSzmY0ELjaz4bgz8ymecic7xjFm9jzwX+Ar4JsC/X4KuBVfm1WIP5vZnpnjDpn3JwG3mlk9PEfiCcXYCIJyE2uuYs1VEATFUFPXXAWOma0CDMPXReU7g0GwzIjIVRAEQTUQTlXVYmZt8YjbZeFYBTWNcK6CIAiCWoekN/CF+EFQ44gF7UEQBEEQBFVIOFdBEARBEARVSDhXQRAEQRAEVUg4V0EQBEEQBFVILGgPgiCoZSRpl+7psCGwDa799306fwyet6khnnxzdKp7FDAqK4dTgbYHAI9KeqGiNqqSlO/re0nvl6HuJsDWkgalbPQnS5pQzvZWxDPO74dnxgd4qAK5xfLttsalf4bnla8G9AG2xXOC/QicLWniklaCmkJEroIgCKqB6x84ePL1Dxw8uSpsSRogqUPKqD4KOD3nWKXzD6RzhwPjc3VTpvXljePJS0BaArsDu1SyvT54tvm2ktoDfwKOMrOtKmn3YIrPQv+xpN1Se72Af5vZmpVsL1iKROQqCIKglpLEjVtKOrUclzUws4eBTXDJnENwfcH+uIAzuLP2QTHX59quD9yLS9ysANwg6bFsRMjMTgYaAwOAQam95/CozxigFZ4Rvpukz8zsr8CReITmUTz7+kRgB0kzzOwUYHVJ16Q+tMG1C7czs/HArsCZwCxc6LmHpDmp7gp45vhVzOyNNIxLzex3uHzQEZI+NbP/S3ZyYxqYGfOKwGFAs6Q9iKSfzKyDpAVm1gG4GpgN3IVrMvYB5uEZ7XviUj93A2vhTuFtuPB2d2C2mY2WNDK1ty7QOskEkdp7z8wGAV3NrGHqD0AzYLCk40r6vQXVQ0SugiAIai8XApeV85rVgAsltQPWxKebLgSGSOoI9ADuKIOdnsA0SW1xUeYr8sSe82kM7JVzjICRkvYEBgNHJAHmw4B2uHPTBdgCeAiPwIHL3NyXMyhpFPACcC7wM34vdk9j+z71MVd3HnAV8LCkp1Pxs5J2B54HDjGzfXG5nnZAR+AiM1srM4Z1gRmS5gKY2SnJmXzHzM5MdRpK2hV4EI86dU0Rpym4A9UMn1bdC9gLOCtFFAfgztzITHub4U5ZPp8Cm0q6I0Uoz8EdubMK1A2WAeFcBUEQ1ELSQ98kvVrOS2dImpzeT8WjVq2B45Oj0A9YJ6+tlVOUJMcCoDkwHEDSTFxoefO8tupl3k+SNDtz/G56/QJfG9YK2BQYkn4a4c7VPcAxZtYK+FrS18WMazNgXOoLqW8ti6mbY1R6zd6HNuk+vADUB5pk6n8LNEpRMDLOTT88EgWg9LoesAHweLK3Vxrf10AXM3sQn+KrX0L/vkrX5LMF7kxhZs3xTPXdJH1XyniDaiKcqyAIgtrJbrgTUl4KCcpOAG5MjsKheNQlyxVAbmpqQ1xE+UM8woSZrY47JpPwRd4bpLrbZWzkS9Tk90PAOKBj6scA4H1Jn+FRqIvwqct85uPPsklACzNbNZW3x6cUC9Utrg8TgFdT+7sDj5OJHKUpxifwKF0RQHI6d8rYyo1zOvAl0DnZ6wO8ApwNvCnpaGAgixzQ/L4h6UvgEzNbOO1rZtsBBwBPmtmmwCPA0cvperpaSzhXQRAEtRPDp4cWFZidZWYHVsBWH+DQTMRmbN75O4EeZvY2Hmkag68pamRmI4ChuMbfN8DNwO1m9iK+bqlMSHoPdxZHmNk7eHQm5zD0wx25QjsU38an+9YDLgVeNbO38Cm8/OnND4DOZnY4hRkE/GRmr+FRrQWZSFiOc4GZwPB0v94A/gfcmDee+cAZwLNpjddf8Ps6CDjVzIbh68PmmlmD1N5pZtYxr71jgZZm9nay8w+gS9rAcDsecbvNzIaa2f3FjCuoZuotWFDoS8zyzahRo5oAk1q1akWDBg2WdXeCIKihzJo1i7FjxwI0bdOmzeTK2MrtFDz7mCeaVLpjdQwz64Yv7L5kWfclCMpC7BYMgiCoBsKpqhhmdiW+uHz/Zd2XICgr4VwFQRAENRZJFy7rPgRBeYk1V0EQBEEQBFVIOFdBEARBEARVSDhXQRAEQRAEVcgyW3NlZkOACySNNLOVgGnAFZKuTeeH4vlSDsQz+24taVAxtoZSAQHOquaVAe1gQX4ql0S9Ihquuj6//fwNDVddn/VHtlrsdLNrL1+qfeu1+zmpG0X84+VrSqldcb4Y+DEAG3drttTaqCzdDnuI+fMX7ZItKqpHo0arAND39oPodthDi9Uf4/QpiwAAIABJREFU+NhRXHLZOAC+/34OAGutVX+J9/PnQ1ERC1/XWqs+M2bMKbU/LVusTlER9DwpP/8i9LioHQB39RlRqp3zrulKo/VgxforsPoqjTipa1/6PXkyM3/5duFxIfo9eTJAsecryrjJjwHQsslhpdSsOm7t1pUNfp5FPfxvfcH8+dQrKuKgZwp+dARBECwVluWC9sF43pKR6fVFXG/q2pSUbVNgs6TXtDuwFZ4fJAiCoE6TdP3uw7OHzwNOyn65NLNOeNJNgLZ4LibwBJZPAFtJ+q2cbc7O2FkZ/8y+VFKF8/mY2VV44s7J+Bfk4vJP5eo3Ad4HRuNJOxviST+rZdF73j0Az0p/OXCJpL8Uc013/H6fn1f+FnB4Jlt+sByxrJ2ri4HrcafqbuDqpPS9HTAMmGRmLVlcbPNr4J/4lOYU4KhkbwkBzuocTBAEQUk8N+a0yQD7bXNrkyowtx+woqS2yZHqAxycOylpMP4Zi5lNTRnCSccVbXNGzo6Z1QP6AqcBt1TUYAUZn+lHEfC6mf1B0vvV0PaM7L3MUNCxCuouy9K5ehfYKv2T7oYLh76MC4D+Ac/E2wH/VnYV7vk/bWZjcOfpQzM7Ade3AhfgfNDMeuMq70tv7isIgmDZMhFYMTkXawClzz8vzh1m1jS9Pwj4CXeWtsC/uPaSNLS4i9OMwvW47t8tKcnnWfjn9QhJ55vZ7/EM6Q1xOZxekv5tZgfjmnrTgJXwyBUAZrYXHoXrlo5fxzXzviqmKw2BBsAvKap1D/5cWwCcLuk9M/sUz+K+OZ4h/UTgEnw2ZH1gbeCveKbz8rSd63MTXIh5JzNrjzu683DZnJ55dfsA++BZ7ksSuQ5qOctsQXuSBngP/0ObKmkWrky+C66K/lIxlzaW9GGy0V/S6FSeL8AZBEGwvPITPiU4AZeGubmc1/dPEZjJQCfc4ZguaTegM3BbGWx8DaxrZusAlwF7SGoHbJSiaVsB10vqBPTAJV/qAzfgX6L3Bn7JszkYaG1ma6dZi+kFnJsWSerlVeBp4CZJHwPXpfe74bIzOR3C3wMXS9oBWA3oksp/kbQ7cHQab1naXie1nftpkzuRAgX9gK6S2uMzK90z5/+IBxK2xyVtVi/tBge1l2WdRHQwHrF6JB2PwL9RLJA0IxO+zgpafmVmW0j6yMzOY5EwZ93T8QmCoK7yN+BFSReY2cbAK2bWuhzrqPK/jLYGdjWzHVP5ima2rqTpJdjYFBcmbobr+j2XPrNXx6NErwG90gzDAqB+qjdD0rcAaanHQlJE7EFcJHozCgs1L5wWzKM5MDzZGZPuC8DnyfkCXy+Ve7C8kuqOM7PGZWx7iWnBFLkijW0D4PF0H1bGn3G5trcE3kmBhR/N7IMC9oPlhGWdimEwHqV6DkDSbFz9fFhevazYZk/gniR6uW3u2iAIgjrEd8AP6f0M3HEps0gyS34ZnQA8khyHfYGByW5B0nTkOcCjwCR8mqtTuv4W4C1cYPh+SccArwL1gG+AtcxsvWRq+wLm7wW64VGe8ny+f4hvjsLMtsEdR/BIWuP0fhdgXHrfJtVtxSKB6Iq2DTAddzY7p/vQh+TAJcYDO5hZkZmtCrQop/2gFhHCzSHcHARBMVSlcHNVLmg3s9Xw9UUb4OuWbpL0sJkdCawm6a5M3amSGmeOJ5N2C2Z26z2CT2ltiq/hul1Sv7w2czvl5uPO3GDgHyniczS+qHsFfKrxOHx6sRfwLe50bC2ppZn9CXe8ZuBrxR4jb7egmT0NjJN0QV4fmpDWNxW4J03SGBqk/v1V0jtm9j2+nndj3Ok7E7iURWt6VwVOlTSqpLYL3cv8PqU1Y5fggYsf8em//dL9Pt/MeuFr3L5K/ekSuwWXT8K5CucqCIJiqKnO1fKOmT0DnJmZzquMrUIOUW98re8Syd2qsu2g7rKs11wFQRDUCcKpKh0zWxlfe/tKdTs3y7LtYPkjnKsgCIKgRiDpV9JaqCq02bhAWe/qaDuouyzrBe1BEARBEATLFeFcBUEQBEEQVCHhXAVBEARBEFQh4VwFQRAEQRBUIeFcBUEQBEEQVCGxWzAIgqAaqOIkog3wbOKb4ckqT5X0Ueb8McAJuLBxCyCnwXoUMKrQDroytLk5cD0udrwA14Y9T9LMSgyl0pjZacCpQG9Jj6WyhsDRku4uKadVKXZ7A0fiCT9znAvsjqdrGFnMdZNJSVozZfsAh0vqnilrgme3v0DSVZnyp4E1JHUws0eBY5N6SVCLCOcqCIKg9nES8FPKCm7ArbgQMgCSHgAeyGQP75A7l9FsLTNJrmUQcJykt1PZn/HM7vtXfBhVQlfgUElZrb7GuBj13ZW0fUMBp6ygU1VBPgEOBq4CMLNGwBa4KDa5jPVB7SOcqyAIgtpHC+B5AEkys+bluLaBmT0MbIJL0xyCizf3BxqlOqfnOSudgSE5xyq1e5+ZnWJmTXE5mUclvZCN0phZN+AsXGZmRJKAWbNQW2b2EfA6Lqz8NXCwpHm59pKjeA/+3FoAnA7sCGwH9DezwyRNStUvAlqY2SW5/qe+NAIuljSoUN/KcvPMbACuqTgE6Is7Q0VAL0lDM/Wap/7+nH6+K2BuOvCtmTWX9CFwKK7ruFuyMRnYCpfKuRuXOvoFOByPSt6FC0T/CvSQ9EVZxhAsfWLNVRAEQe1jDLC/mdUzs51wceKyCjevBlwoqR2wJrAtcCHuPHUEegB35F2zKfBpAVuTgSaFGjGzdYDLgD1SWxuZWacS2toMd3x2BtZjSVHn63ANxd2AM4D+SUNxDD51NilTtw8wXtLl6XiKpD1wXcFTSuhbPmeZ2dD0c0veuROB6ak/nYHb8s5fC1wiaU9ck7E4HsGdJZKdfxeocx3wf+ne3IT/zq4Dbk5RyetI0a+gZhCRqyAIgtrHPUBz4DU82jMqG+UphRkZseCpeNSqNbC7mR2WytfJu+YzYOcCtrYEpuSV1UuvzXAn6bk0Fbk6sHkJbU3PRF6+wCMzWZoDwwEkjTGzjUsYYz6j0mtuvMX1bXDedYWmBXO0BnY1sx3T8Ypmtm7m/JYsmkJ8PfW/EP8GXjOze1P/filQx4A3ASQ9DWBm/wQuNLPz8Hs+pxj7wTIgIldBEAS1j+3x6E87fBqpUFSpOBYUKJsA3JiiIIcCD+adfxrY08x2NLMiM3vGzG7GHaKJwG/ABqnudul1Eu4kdUp2bwHeKqGtQv3K8iGwK4CZbYM7IsUxn8Wfb/m2i+tbeZgAPJKu3xf/PczInB/PIoc0Pwq3EEk/AQKuAR4uptqHORtmdpSZ/TW1f15qv2dqP6ghhHMVBEFQ+/gIONPM3gT+ga8dwszOMrMDK2CvD3ComQ0FXgDGZk8mB2B/4AI8WrYhHv352cw2xdcD/c3MXgY2StdMA24AhpnZ27gDMrG0tkrgHOCvZjYcn0o8oYS63wArmdnVhU6W0LfycCewlZkNw6f9PpM0P3P+bKCXmQ3B14aVxENAO3wdVyH+DlyQ7tlRqf45wKWp/fuB98vZ/2ApUm/BgtK+LCx/jBo1qgkwqVWrVjRo0GBZdycIghrKrFmzGDt2LEDTNm3aTF7G3alxmNlmwC+SSooiBUGdI9ZcBUEQBBVCUnmmI4OgzhDTgkEQBEEQBFVIOFdBEARBEARVSDhXQRAEQRAEVUg4V0EQBEEQBFVILGgPgiCopaQEllfntAPNrBkwAM/rNBYXdJ6fqf8QniqhCTAbFyX+AE+yuVVZJWDy+rAnnqKhATAXz9p+hqQfKjisQm1MldTYzM6nBNHkStjfEU9vMFDSBZnyHrhA9i7AyRXR+jOzbfH0E2vh+cC+wyV/8pOvZq/pAdwrKRKD1lIichUEQVANvPtx/8nvftx/clXZM7Nz8fxS2UzmN+Aad7viWbs7Z6+RdFRyxAbg2cc7SPprJfqwNZ788lhJ7ZLt94BzK2qzJCRdVdWOVWJvXFrngrzyC4GyygotgZltgDttZ0lqK2l3PGnqNaVcWql2g2VPlUeuzKwD8CpwhKRHM+XvA6MldS+HraH4t4UJVdzNIKi1nHdNVwCuPvfJMpVXN+9+3B+AlVZcjZZNDiuldlAJPgG6Ag9kytoAw9L754G9gKfKaG8nM3sJl4W5Q9JdZtYej7rMS+31zIumnAxckY3CSLox9z4nPCzpNzO7Cs8q/gCegHNjPKv705J6JUHkRunnANwBaZnabZDsDcBFk9/AHcu18ISmt0m6Iz0zxgCtgDWAbpI+y/SnPh6J2gx3Xm7As7UfD8w2sy8lPZXqngA0Tu39E9jCzJ4H1gcGSeptZq2Bm3FH9lvg+LyI3bHA3dlnmKSnzOzfqY32uOh1Ea75eCSehT7Xbhcz+79UtgLuEA80s78Af8Yz0f9X0ukENYqlFbmawCIhStIf4KpLqa0gCII6h6QnWFJPrp6kXGbombgwc1mZg0dwDsKzv9cD+gFdJbXHNQS7513TFPgYwMyaJoHjYWY2ooR2NgbekrQ3sAPuoOV4RVJboCPQUNJO+JTjKnk2mgGPStoLdyDPypwbmcSSBwNH5F3XE5iW2tgTuAKXDhqAOy4LHVFJ/XGJndyzrCHQBXd0Tktl/fCp1w7AcywZscven5VzItC5Mtx5PDpd/yTuDC5s18z2BZommaOOwEVmthZwHHBaEnL+0MxiiU8NY2n9Qt4DzMzWTF780XhodBMzOwpXJp+FSzj0wNP574f/A22OryEYkDNmZgfg/zwH4f+Yi31TAM7DVc9vM7O1gZcltVlKYwuCIKipZOVXVge+L8e1oyUtMLOcuPF6eGTp8SRuvDJLCht/gTsQ70uaBHQws4b4F+x8coLOM4Dtzawj8CMpKpVQel0oeizpczP7gsX5GncAuyYb9TPn3s30rXHedc2Bl5PdmWY2Hn/mlIWxkmYBmNncjL3b0/2pjz/TsuTuD5J+BTqk63MZ7acAN5vZT/hauNfzrm8NtEkOWa6NJrhzdY6ZNcUFnesR1CiW5pqrJ4Cu6dvPDngYtxFwGbB78sS/x79JAKwpaX/gQCC7qLIr/i1hf0nfU/ibwt14+BU8rPrQUhxXEARBTeXdtDQDXC/vtXJcm6+FNh34EuicPm/7AK/k1emL6+dtkCnrmLH1G7BBeg5sk8q6A99LOgq4HlglnYdFzuFC0WMz25CkV5jhbOBNSUfjgsVZ56IkTbes+PPquPMyqYT6WQHoQnaFrzfrgD+Lnsk7fz9wkpltmSswszb4FCD48+y4tFzmq8w4cu1OAF5N9ncHHsenSU/Cl8y0B7YF2pYwhmAZsDRDiQ/j4pqfsugfvAgYJ2lmOh6Oh3TfxufJwT397ALNPfC581z4e4lvCpI+NbOZZtYCj4JVRLg0CIKgtnM20M/MVsIdiX8BpLVU+0uaXVZDkuab2RnAs2ZWhEeIjs2rM8rM/g7cl9YzrYpHYw5JVa7BvwRPxnfJgYsTP2xmO7NoBmPDvOb/A3RKosqf4Y5elkHALWZ2OP4lfa6ZlUUo9i78/ozAI3GXSfomPU8K8Vrq/2XFnD8FuD9Nyy0gT0xa0hdptub65Mw1xO9jbqPBg8BrZvYzHo3L3Ydcux3xaOBruEP2VIq4fZCum4nf77fLMPagGqly4eb0relkSYenP+Cv8TnzzfAIlAHbSPrZzP6J/+N8R9oGnAspS2qSW9COTyuumM6PBA5JoeJdgA0k/cvMDsPnw1eQdGhJfQzh5qA2Ewvaq4+qFG7O7RTcttkJTSrdsSAIajRLexHcY8AxkiYm9fTpeETrVTObjy/qO5/M4vdiuBwYaWbPUPw3haeAW3FHLAiCoEYRTlUQ1B2qPHK1rDCzVfAtyDtmk+YVIiJXQRCUhaqMXAVBUHdYLpKImllbfM756tIcqyAIgiAIgqXJcpEbQ9Ib+K6PIAiCIAiCZcpyEbkKgiAIgiCoKYRzFQRBEARBUIUsF9OCQRAEdREz2xFfa9ohr/xGQJL65pVfj+sPNsazsH8KTANuI6XQKWf73fHd3J9mij+ojBh0GdudKqlxXtnJQGNJvfPKW+L5tlbBc0U9B/QGtgYOlHS5mR0EvC3pqwr2Z0c8efXArPizma2GJ1/dFt/d/iNwtqSJFWknqD2EcxUEQVANXH/6dpMBzr55dJOqsGdm5wLHAD9nytbDs4JvCVybf42ks1O97qTcgum4QyW68nDOTk0j6fA9iusjfmRmK+AZ3XsmxzOXvPoMPKdihZwrXJPxJkm35JX3A96QdEbqz9bAv81s5zyB52A5I5yrIAiC2sknuDzYA5my1fCozL4VsLeFmT0PrA8MktTbzFqTp+VaVqcgZREfDvwBl3H5GtgNz8qe05Ltj8uiAZwu6QMz+yzVH5/O3wCsAKwLnJI2MOXaaAfchCeingu8ldeNzrgY9EcAkuaZ2bHA7FzCa/z+bYPnT7wb2ELS35MjNgbYXtJvqb36wL14UuwVUt8m4Rq3s83sy5z4s5mtC7SWtFA8WtJ7ZjYIl4ZrCOSy7DYDBks6riz3Nqj5xJqrIAiCWoikJ1gkC5YrmySpolIoDXGVi11xNQ0orOWaz5FmNjTzc0wqXx2Pau2abL4haTdgJaAlcCEwRFJHoAculwawMXCkpL+lemdL2gO4GhcsznIHcISkPSmsEbghi09ZIumnrAyQpGdxJ+pY4BGgS3Ks9sF1/X7LXN4TmCapLbAncEWyPwC4IedYJTbDHeB8PgU2lXRHuq/nAJ8DZxWoG9RSInIVBEEQAIyVNAvAzOamsiW0XAtcV9K04Oj0+j0eiQKPMjXE0+fsnqTLANZJr9MlfZveTwEuNrNfcWftxzz7v8usX3odjwBl+QzYLltgZk1xB24Jkm7fMHya7zh8PVmW5sDLmbrjgc0L2cKnGDctUL4F6V6YWXPgTnzt13cF6ga1lIhcBUEQBOALrvMRcGyKsJwLPFMFNnNMAG5Mtg/FRYwBsomgbwYulfRn4AN8ejLLlOSgAGxfoI1ngH3MbHNYOK13A9Aqr958Fj0P+wEnAutLej+v3od4FI4kxNyawhEzJH0JfGJmp+bKzGw74ADgSTPbFI+UHS1pSiEbQe0lIldBEATLOWZ2P9BL0uflvLQ4LdcsR5rZTpnjHyR1LoPtPkB/M+sBrIGvFcvnQWCgmX0HfImvu8rSM/XvR2AmHhVbiKQfzezPQD8zK8KjX4Pw6cT2mapvJDt7SXrbzJrhOyjzuSvZGgGsDFwm6ZsU2SvEscC1ZvY2MC/1r4uk783sIXzd2W2pb59LOrY4Q0HtYrnRFiwPoS0YBEFZqEptwareLRgsHZKj8zqwt6T8acggKBMRuQqCIKgGwqmq+aT1WE8B94ZjFVSGcK6CIAiCAN9tiadlCIJKEQvagyAIgiAIqpBwroIgCIIgCKqQcK6CIAiCIAiqkHCugiAIgiAIqpBY0B4EQVBLMbMdgatTIk7MrAWei6kenk39RElzM/WvB9oAjfEcS58C0/CcTidLOryc7XfHs5jnJGbWAl6XdGqxFwVBHSAiV0EQBNXA1MEHT546+ODJVWXPzM4F7salZHJcCVwoaZd0fED2GklnJ0fsKly2poOkbpXsSs5OB1xqZhsz+2MlbQZBrSYiV0EQBLWTT4CuwAOZsoMlzTOzlfDo1A/lsLeFmT0PrA8MktTbzFrjEjT1gG+B4yWVZHN1PHr1Q5Ka6Ytr6RXhGeKHmlkfoCP+/HlC0tUpAvfPVG8KcBTwPB5Nm2BmJwONU5/+ChyJZ4x/VNLN5RhjEFQLEbkKgiCohUh6ApiTVzYvadaNw6Vi3iuHyYZAF1w777RU1g84NUWlnsP1BfM50syGmdlEYAjQR9JHuD7fdEm7AZ1ZJCdzFO4c7YoLOoOLFx8vaUfgWVwgeQnStOdhQLt0fRcrQXsmCJYVEbkKgiBYjpD0GR6FOhEXKf5zGS8dK2kWgJnl1mk1B25P/kt9fB1XPg9LOj9lN38BmJjKWwO7pqgUwIpmti7uXF2FR9aeT+caS/ow9b9/6kO2jZxgcytgU9yJA1gbj4ypjGMMgmohIldBEATLCWb2tJltkQ5nAvPLcXkhoVkBx6bI1bnAM8VdnLKbn4oLLa8CTAAeSdfuCwxMfeoGHIFPDXZPkbavcv02s/PM7CDgN2CDZH67TH/GAR2T3QHA++UYYxBUC+FcBUEQLD9cBQwws1eBY4ELAczsfjPbpAL2TgHuN7MRyXaJjoykl4GXgcvwqb6tzGwY8AbwWYqMzQDeAl4FXgI+B3oC96S62+JTkDfjUbMXgRWS/ffwqNUIM3sHj1pNqcC4gmCpUm/BgkJfVpZvRo0atTnw8ZZbbslKK620rLsTBEENZfbs2UycOBGgWZs2bT6pjK3cTsHGnZ5oUvmeBUFQk6mra642AHIfmkEQBKWxAb47r8KEUxUEdYe66lz9F99p8j9g3jLuSxAENZcVcMfqv8u6I0EQ1B7q5LRgEARBEATB0iIWtAdBEARBEFQh4VwFQRAEQRBUIeFcBUEQBEEQVCF1dUF7EARBrSdlP786JdTMlh8J/FXSznnl1wNt8OzoqwCfAtNwaZqTJR1ezvY/BtpK+sbMNgC+BA6XNDCd/wTYHrhbUtcKDLG09h8F+koaminrDVwMbCzpq1S2Pp4P6yRJA8rZxlRJjauqz0HdIJyrEjCzIuB2YGtgFnCipI+Xba8qR/bD2Mya4RmOFwBjcQ2x+WZ2KfAnYC5wpqSR5alb7YMqhSQgew/QBGgAXAGMZzkfu5mtgGvDGd73k/Gs1wNYjsedIz1QRwGd8L4OYBmOe+o7t00GaPzHU5tUenCAmZ0LHAP8nFe+LXACiyRjFiLp7FSnO7CVpPPTcYcKduNlfOf1E8B+wL/S68AkhzNN0gxcYLo6mQgciotBg+sRfl7NfQjqMOFclUwXoKGknc1sJ+B6XIC0VlLgw/gGFinV9wU6m9lnQHtgR2Bj/ENz+3LWrWkcDXwr6RgzWwcYk36W97EfACBpl/Tw7IM/cJf3cecc6juBX1PR8vi3/gnutDyQKzCzRsCVwJm4Y10etjCz54H1gUGSeptZazxTej3gW1xc+YfMNYNZ3Lm6GHjSzOoBHXCtwYXRHzMbiv/vtQLWALpJ+szMLgYOwqNoqyQ77wL9gUaprdMlfWBmp+Ki0P9LfS3EYyzuXB0ADMrcp+tx8WdwbcSbzGwA/iW6CZ5+o7uk0ZlrrgTWxEWtDwHOwlP5jEjaiq8DPSSNM7N9U5sP4c+NOcAvwCGSZhbT52A5ItZclUw70oeDpLeAPy7b7lSa3IdxjjbAsPT+eWBPfMwvSVog6XNcbHW9ctataQzEP6zBHxJzqQNjl/RvoEc63BT4njow7sR1QF/gq3S83I1b0hP4QxtYGKnsjz/0K/IAb4h/odwVdyDAHbRT07Tjc7i+YJZXgLZmtiLQVNJ44ANcC7AD6fMzj5GS9sQdsyPMbGtce3D71H5OT/BCYIikjvjf8R1m9jvgDGAn/ItucRIbU4GfzWyzFIn8Ao/aYmb7A02TjXbAkcmJBJfo2Ru4hUX/O5jZdcCKkk7FxaIvA/aQ1A7YyMw6AXezSCT7+HTcBXgcd8zvSNcGdYBwrkpmDSD7LW1e+hCpleR/GAP1JOUSnc3Ev5XljzlXXp66NQpJP0maaWar49MWvag7Y59rZvfhD4uHqAPjTlNe0yS9mCle7seNO4Vb4A/xR4EWZvbPki9ZjLGSZkn6Bf8CAtAc1/cbijsMG2UvkPRdqrsv8Hoqfh7YBY9OFZo6fTe9foE7dM1xh2uepF+Bd9L51sDxqe1+wDrA5sC41M85xdjP8QhwOHAU/refoznwWnKU5+A6hy2K6RvA74A/AKul42bAesBzqW8tUr8eBw5M09G/T1GvK4ENcT3EQ1j88zdYjgnnqmR+BFbPHBdJmltc5VrI/Mz71fHIRv6Yc+XlqVvjMLONcaHYByQ9TB0au6Q/A1viD6iVM6eW13EfD3RKD75tgPtZfPpouRy3pJGSWqYo0+HAeElnlsNEoYzSAo5NNs8FnilQ55V07vl0/ALQDZgoaX6B+vntjAO2N7MiM2uACzcDTABuTG0fCjwIfAS0NLOVU6RuW4rnCTy6tSswNFP+IWlKME0ft012C/UN4Gtg79TuPsAk3PnqlPp2C/CWpJ/xz5ibUl/BlyQMSNG3cWSiYcHyTThXJfM6vo6AtObqg2XbnSrn3cxC1n2B1/Ax750+6DbBHcrp5axbo0hTCS8B50m6JxUv92M3s2PM7IJ0+AvuNLyzvI9b0m6S2qcH3xjgWOD55X3cJWFm96d+l5dTgPvNbARwFfB+gTqD8SUTrwKkHXqrAy8WqLsEkj7ApxzfAp7Coztz8DWChyYn+QU8sjYt9eMN3Jn7uZDNZPcHfPfi6KyTJ+kZYJKZvZna/Fd2bVUxthbgmwRuxf+PbgCGmdnb+N9ITqi2H+7Q5SJlI4G7zWwIsDvu6Ad1gJC/KQFbtFvwD/haneMkTVi2vaoc9v/tnXnYVWXVxn84kLMpWs6ipUsy/EwTBZlE1JyT0HAMB1BTE7Oc8ivMJEcsNUFJUVPEnAdwBEGQkMTPWe4Qwq8EB0Q/NVMU/P5Y6/huDue8A5wYXtfvurh4z977WfvZe7/v2fdez7PXbdYaGC5pFzMrZTNa4k9zfSTNM3+VeW9cfJ8maXxTtl3Sx9QQZvZ7/G2h4rU7FZ+o22yP3cxWB4bir92vjN+UXuFLcM1LxI35BPyGuFSPu9ZvCzYXYhitp6SrI3P1EtAt5rYtV5jZTngJjKOWdl+SpUuKqyRJkmSpEQ+x1+FztD4H7pJ04dLtVdMxs5Px7NYhkqY2tH3SvElxlSRJkiRJUkNyzlWSJEmSJEkNSXGVJEmSJElSQ1JcJUmSJEmS1JAUV0mSJEmSJDVkua02njRvzOxR4GpJd8fnS/FX6teVNDeWzQR2lfT3RYjfG+gqqXcQSUnrAAAUGUlEQVSFdYfjRRFXwl/h/zMwIF7HXxGvybMJcBJeQ6kj8CtJtzZh//sDW0ka2NS+J0kJKxixx+fv4IU+S2+rDZJ0W2H7W/Aq662Bubg90Au4wfUXRs5N2H9/4LCIsyLu5XimpP+Jv7E5ku5bxMNbKpjZrXgV9iOLpXfMbDfcRmsFvEzHHXiR0wbfCjOzvnhZlF2BEyT1amKfKl43Sac0Mc7GwFmSTjGzfwKti4Wxzey4WHZuU+ImC5PiKllWGYVXTr47PnfHC/51BEaHX9i/FkVY1UfcEH4KHCRpWljm3Ahci79mvTHQVtJGsf1o3Nx7bhN3tWPtep0sD1zQqcMMgF+Mm9C6FvFsYSN28N+rgZIuq9RG0uHRtj/whqTB8bn3YnRlYCHONsA9Zra9pBsWI+bSpLukBXwjzezbuAHzvpJmhQ3aIOBnwCWNiHkOi1FAtNp1W4Q4rwNNEmTJopHiKllWGU042sfT1ie4AfNesa4TXhm6VD3/97gX2GzgeEmvRgHJOcC2eBHRtriv4PvAa8CHFfbbH+gtaRpAeBIeC8w0s18D9wPrmdnT+NNjC2CSmR2Af9luEHHOk3RfiMBBQCu8SvopcSwnRN9fkzR0cU9W8qWkZMT+p8KyHQEzswPx7FU/SY01cd7FzB7BffMGSbrWzLrgldLnxf6ODz++ikiaYmbPAB3NrCNuoHwncBue8VkFz9w8a2bn4sbGK8X+rjGz03Hrns+AJySdGX9rPSXNMLOe+N/+L/HaWK1i1z+R9IKZvYYXC34Z2B9oJ2mOmZ0IrCnp4lJfzc2Wf4MbOr+D2yYNANY2s3slHVg4tBPw7PWsOM7Poq/PmNlV1Fn/rIQbQm8taXrs51j8e2E4/p22lZk9iFsy3S+pv7lx9BX498k7wDFRYb5ezKwlcA2wJZ45PFvSODN7Ca8a/xFu19MO90bsDVwvN5wGrx6/BTAr1hVj98O/N8Ftw65uqD9JHTnnKllWmQx8w8xWAfbE7WsewcUVQGfgkfhyGQ6cLOm/gMG4YWuJ5yUZ8BZwcbRrz4I+cQCY2frA5pSZwYY57Uv4jesAYKak70o6INZvD+wGzJC0I+4n1ima3wicIWkH3FdsuKSXo5+DU1gli4oWNmIH/939uaTOwHTgV00I+Sn+93UQ0M/MWuCV6ntI6gK8TtkNuApvAusVPrfDBcPe+FD66jF8uTewc6zfOgTGIXjGugMuQvbDRVSp4vnR0adzgFHh2dcXf4AB2BQ4TNJpuAVNafjtCPxvEYA4tmsLxzYWOFfSj/GhzKKwAhcv04oLJL0PrAZ8EsOyuwEzgBNLwiq2uw4XmaW+rIKLyk7AybFsCHBSxBmJT0toDMfj30ddImbpPKwN/LKU8cKHEDuwoG8mwFXRdiYuLgEws+3w34Nd8dGCH8aDYtJIUlwlyySS5uHDgN/Fv/AfiSHA1cxsHVwgPY4bEr8r6a/R7nbgm2a2doR6Kv7vAEyQ9GbMMbiZhSnNnaiU0W3ZQJcnAN83s3vwL6PzzWwNYCdgqJk9CwwD1jCzVvXESZLF4W5Jk0s/U7+xcTnPxPyhN3DRsD6wIfDnyALviT98NMTmuKdfiQdxb8Z7gV/jN3gDJkmaJ2mupNOBbXAD5E+jH+PwrPMwoKeZbQSsJelFPAt9TPRrCLBu7Gu2pHfi5+uBI2NI701Jbxb6tB7wfgyTATwR+6rG6/h8py8ws7WAuarzLbwSkKQh9Z8eXpT0iaSP8AwdQBvg6jieY/DpB42hLbB/tLsDaGlmX411Kmyn8obAR5Kejp8n4NekxLeBLfBRglHAOvg8tKSRpLhKlmVG4U9O7YC/xLLHcGPUdyJtXul3uAWeIgefYAsunIrbfkYZciPeabhw+wIzWw/4BvB0eZtC26n4zeEW/Il0UvThY0nbl/7hT+pzqsVJksXkYTNrFz/vjmeAG0v5xOzZuEg6MDIqF+A326qY2bbAt/AHoxJdgVmS9sSH4QbgQ3c7hBH2yvECy9+Anc1spcgsdQb+Fn/nk4HL8UnhRPvLo1+HUPewVDRofg14D/gFnv0qP7a1zGzD+NyFOvPlSgwCzjWzDeI4V8aH+AbF5/OBFpLOr9J+PnXfP5UmwAs4Ko7nDPylhMYwBbg52u2Dv3xTGk4sZqnKM1YAq4bwBP/OerEs7gvAbhH7xrL1SQOkuEqWZUbjwwEvFN5oeRQ4Pf4H/1JqFYapmNkhwGuSygXMeHxOycbhZfZDKnMu8Dsz2zLirQH8ER/Oq2okG75i50Xm7Mf4fIoWwFQzOyK22QN/QgYXdznnMak1JwKXRyZjV1zMYGalIfRGExmZU4ERZjYB/72udIP9qZmNMbNR+KTvnsU30IDngOOiT5cAv5X0LPAQntEaD9wi6TlcHDyJP5zMAO6JGEPwYcTSm48XAIdEzIeq9KvUrlNsUzy2z4E+wF1m9iT+wkw1YYSkZ/ChyNvMbHz0WcAl8d1zNj7XbUz861YWYhw+3Neiyi5OBG6K2BcCz1frSxmDgO3MbCx+3qY35u3F4N/A6WY2DvgqdcK1dLzjgPFmNhnP2s1sZNyE9BZMlnHM7A3gfEl/iM9r40+dXSRNiGXt8afI1fGsUN+YWDsG6C9pTGzXE/8C/Rc+4XW+Kpdi6Amchc+NWBGfwzUgJrG2BsZIah3bfi6pRQwR3Apshs9duUHSFfH21GB82GIuPh/jr2bWGX8aHCjpyhqesmQZpdZvCyYNY2YH42/3/nJp9yX5cpHiKkmSJGl2mNkAfJL5foV5WEmyREhxlSRJkiRJUkNyzlWSJEmSJEkNSXGVJEmSJElSQ1JcJUmSJEmS1JAUV0mSJEmSJDUk6+wkSZIsp5jZzsBFUegRM/saXttpHbyMyFEln8xYfxlu47QBXoV9OvA28Afc868XTSAMn4cC7SVNjGUr4151V0nq38R4w6PPDRqhhzXWDOAySRXNk6PkyXuSGqwbFbXtRgJPSfpRYflBuNNDS7ze3S6NOZYKsS8GNsH9/v6N22K91NRYZXH7AkOLfo9xTbaRdFZh2XDcbmvMIu5noZhJ/WTmKkmSZAlw1z77zrhrn31n1CqemZ2BF7hdpbD4YrwgZ2e8IO42xTaSTg8hdiEwTFJXSQcvZlemUOebB/A96qqENwlJvRojrIIf4L6ivaMwcCWOATZqZLyOwIiisApOBdZqZIyFMLPVgPtwEbiLpG7AebigXVzOoc6NIlmGyMxVkiTJ8sk0oAfwp8KyXYHnzewxPKtzahPibWVmD+LuAvdL6h9mylfglcXfAY4JO5oiDwJ7mdkKUdX9UArm6Wb2W9wjtBXwnKSjw1JqGPAVvNJ5N0nfNLMZuCDcFBeOLfFMTy9Jb5ft9zigX/R3H+ABM+sKXIQX7H0MF3o7mNnLRYeFyOB1jI/DcB/Gc3Dv0lcllWxt9gW2B27CzZ/XD//QDXFT+D5mtiluAr0qnpHqK+kfhX7uD4yWVLLwQtIkM9st9nFDnJtWwL64/U0nXDQNlHS7mXXBTbhXANYADottNsAF5vdpBMUMVGT+pkhqHQWXn8M9BT/Eq7PvhVdu3zOat48q/GvhxZlHlK6XpI/N7EJcaN+IC8fv4j6VWwD7S5rRmD42FzJzlSRJshwi6U7cDaBIa9zIvDvwv8CZTQi5Cn6T7gScHMuGACdFtmskfuMvZy7u/dnFzNbEb77/hC/Mjd+VtAd+s93FzDbG/f7ukdQFuJ2FH/QvxW1y2gO/p8yA2sy2AlYPy5zrgZOKxyGpk6TzcNubM8qE1X74DX8XXGAdBqxNXTZvUGlbSSOAZ3EbrrlxbEfj/qO7xzDspcAVcY4ujThFtgBeLez/3hAzU8xsk1g8WlKH6NMWkjriBVB/EUbM2wJHxD7uAg6WdB0uXioN5R5WsOIZA5Tb8VRikqTdccH7UVyzl3HfRXBni+64ALyqnmzhAUArSe2AY3Gh/KUjM1dJkiTNh3fwISiA+3EPvsbyoqRPAMys5A3YBrjazABWBqZWaTsMz1htht/8Sz6G/wa+Zma34hmRNSJOGzzDAZ4lKccIs3ZJ91VYfxywupk9hGfVOpjZN2OdGjjONsC48OD71Mwm4mbTjWG6pHcBzOwtfN5aW+AcMzsz+lIueP+BC0vieA6M9hOpuweX+twW2DEEEfi5ag28DlxhZh8CG+M+gvUxrMKcq3LKfQ6fif/fw0UVwLvUDTuPj3P2lpn9H55pqxSvDXXX7m0zm9JAX5slmblKkiRpPozHh8gAOgNNmTBdya5D+ATzrnjW6oEqbcfgWZeDgTsKy/cGNpV0KD7stip+E34Rz/4Q7cp5BSiZsR9uZqeUVsSE+V5AJ0nfk7QXni36cWwyvxBnPgvf514hhgQjVgeqi8byGJXO0RTgzDhHx+OZuCL3At3N7IvjDCG4SSFeqc9TgMcjVjfcyHoankE8OrxQZ1InZCodX318jA9pAuxQtq4hu5bS9dgAF8mzS/HMrAU+fAqFa2tm6wBbN6F/zYYUV0mSJM2H04GjzGwCPt9oAICZ3WRmmy1CvBOBm8xsPC5gKr51F3OtHsWHk94vrJoEbGlmT+Ciazo+wfxC4AAzexzow8LZnp8DZ0cG53DglsK6/YHJkuYUlg0FjsQzSUWeAi40szaFvj4A/N3M/gJMBO6Q9AzVmYDPuVq3yvqfAb8ys7Gx3QLnSNKH0ed+ZjbWzJ7EhzJPk/RaWaz7gQ/NbBwwGfhc0gfAzcC4aLsmdZP0xwEjQ9w0hoeA1nE9DwHeb2D7Iqua2Wg8M3p8ZLEuxoeLR+JZLoARwOz4HbwOnzNXfn2bPektmCRJsgQovSnYY+SI1ku3J0sfM9sHeFvSX82sO3BOvEWXLOeY2TbA9pKGm1krPHu6eWnI+ctCiqskSZJkiRKZpOuBz/C34n4i6eml26ukFpjZ6vgcvK/j1/YqSTfW36r5keIqSZIkSZKkhuScqyRJkiRJkhqS4ipJkiRJkqSGpLhKkiRJkiSpISmukiRJkiRJakhWaE+SJFlOMbOdgYui6GSpEvcGsbo1MFFSr8L2vQlvucKy4cBgSWMWsy9tgXUkPVH0nGtijLl4XakWeKHKyyXdXM/2M3AvwsHAcEkPLVLnK8feFHhY0rfi86G4j+NGkt4ys9a4hc/29YSpL/4N1LjP9eyrN2XXvZHtvvAf/E/0qzmTmaskSZIlwF377DujVOuqFpjZGbi5ccmeBEm9QmgdhNuYnFar/TWCH9B4G5lqzJHUNTwHuwGXNaFAZk0J8+UWYTINXvn+TrzqPLj3339cGCXLJ5m5SpIkWT6ZBvTAsynlnAdcKWlWY4OFFcxQYEu8PtFA3I7lAkn7mVkvvNjndma2K/AjSX2j7cZAb2CumZWqnQ8ysy3i54Nwb8HBwFb4g/25DWTLSqbPn4fB8SBcSG4Ybe9pzDFIus3MvgNcCczDLVv6RB9uxb3/voEbF59YFvIxoKOZ3Yf7/vXBq8ffCHQFhprZisA1uEHxhsB9ks41sx64cfanuGVNr6hkX5V6Yt1AZLnM7HsRq7eZTcV9Bg14Exe4LeMcbB4/l0y4dzGzR4D1gUGSrjWzLrj/5Dz89+l43Lj5FmAdCobTSdPIzFWSJMlyiKQ7qWArYmZfA3YHbqjS9DAzG1P6h2eIwG+sb0vqAHQHfoMbBm9uZl/BMzbzzezrwIG4QXOpL6/H/gZKmhSLr4ss2gxgD9xsebakztH+DxX6tm706wncRua2WL4NcJmkPYC+wElVjm2hY4jM0xDg5MiIXY0LR3Dfu2OBdsA+4ZtX5FGgE268PBl4GtjezFbAvfmexIXQxPA4bAecEG0PBS6R1BH3ZFyrSp+LVItVjS2B/5bUHhdNO0WbGbGsF7BzbPspsBcudPtFRnAI0CPOy+u4QD4BN/HujAu9ZBFIcZUkSdK86AkMkzSvyvphMfTWNcTP6FjeBngCIPzsXsYzOg/jQ2Cb4hmN7rjgGNVAPybH/2/gnn9tcQEzBh9eW6kw5FaiNCzYGc+8/MDMOgKzgOPN7E/4zX/lKvusdgwbSXo2tnkC2DZ+flXSB3GuZlEYYg3G4MbSewMjw09vIrAfME3Sp8AcYCczuwW4HM/8APwU6Baegx1Y0FC6GtViFSkOk86O4UvwDNwqeBbrL3EOpkr6Xax/Jvpfuh7r49mxP8c12RM/51vjnpBIeoovoS9gLUhxlSRJ0rzoDjy4CO1ewUUTZrYmLob+DtwNnIVnkh4GTsFFSflNdz4L3lPK7T+mALeGoNsbuB0XE9X4AJ831hI4H7hJ0pHA4ywoMBpzDDPNbLvYpgvwtyp9XIAwof4Ez7w9GosfBM6gbr5Vb+A9SYcDlwGrRVaoL9A/skIt8IxRQ1SL9TEuhMAzZiUq9f8VPIOFmW1pZsOqbDsb+CdwYFyTC3Ch/TLQPtp/h+pCNqmHnHOVJEnSvDBg+gILfK7Nfg20uxYYYmbjgVWB8+KtuNkR82JJz5vZZsBFFdpPBi4xs1eqxL8m4o/Fh8iurjAHad3IonyOZ2Em4WLq68ClZnY2LgjKM14NHUMf4KoQKp/hQ4GNZSywWwgtgEeAm4Ej4vMoYJiZtceF2FRgo+j7A2b2AT7f7IEKsa8ws1JcAQOqxPojcL2ZHU6dMKzGNbHtWHzeWT/g2+UbSZpvZqcCI2KY833gKPxtzZviHE6JfiRNJL0FkyRJlgClNwV7jBzReun2JEmS/zQprpIkSZIkSWpIzrlKkiRJkiSpISmukiRJkiRJakiKqyRJkiRJkhqS4ipJkiRJkqSGpLhKkiRJkiSpISmukiRJkiRJakiKqyRJkiRJkhqS4ipJkiRJkqSGpLhKkiRJkiSpISmukiRJkiRJakiKqyRJkiRJkhry/wWlsnSycO/lAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# oz.json contains a list of characters, reverse sorted by frequency\n", "# And a dict with {chapter title: chapter text} key-value pairs\n", "with codecs.open('oz.json', 'r', 'utf-8-sig') as data:\n", " text = json.load(data)\n", " chapters = text['chapters']\n", " titles = list(chapters.keys())\n", " \n", " target_characters = [\"Dorothy\", \"Scarecrow\", \"Glinda\", \"Toto\", \"Witch\", \"Monkey\"]\n", " chapter_text = [chap.split() for chap in chapters.values()]\n", " \n", " oz = DispersionPlot(target_characters, colormap='tab20b', labels=titles)\n", " oz.fit(chapter_text, titles)\n", " oz.poof()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
663project/fastica_lz
Source.ipynb
1
267449
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Algorithm Draft" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting Source/fastICA_0.py\n" ] } ], "source": [ "%%writefile Source/fastICA_0.py\n", "\n", "import numpy as np\n", "from sklearn import preprocessing\n", "\n", "def sym_decorrelation(W):\n", " \"\"\" Symmetric decorrelation \"\"\"\n", " K = np.dot(W, W.T)\n", " s, u = np.linalg.eigh(K) \n", " W = (u @ np.diag(1.0/np.sqrt(s)) @ u.T) @ W\n", " return W\n", "\n", "def g_logcosh(wx,alpha):\n", " \"\"\"derivatives of logcosh\"\"\"\n", " return np.tanh(alpha * wx)\n", "def gprime_logcosh(wx,alpha):\n", " \"\"\"second derivatives of logcosh\"\"\"\n", " return alpha * (1-np.square(np.tanh(alpha*wx)))\n", "# exp\n", "def g_exp(wx,alpha):\n", " \"\"\"derivatives of exp\"\"\"\n", " return wx * np.exp(-np.square(wx)/2)\n", "def gprime_exp(wx,alpha):\n", " \"\"\"second derivatives of exp\"\"\"\n", " return (1-np.square(wx)) * np.exp(-np.square(wx)/2)\n", "\n", "def fastICA_0(X, f,alpha=None, n_comp=None,maxit=200, tol=1e-04):\n", " \"\"\"FastICA algorithm for several units\"\"\"\n", " n,p = X.shape\n", " #check if n_comp is valid\n", " if n_comp is None:\n", " n_comp = min(n,p)\n", " elif n_comp > min(n,p):\n", " print(\"n_comp is too large\")\n", " n_comp = min(n,p)\n", " \n", " #centering\n", " #by subtracting the mean of each column of X (array).\n", " X = preprocessing.scale(X,axis = 0,with_std=False)\n", " X = X.T\n", "\n", " #whitening\n", " svd = np.linalg.svd(X @ (X.T) / n)\n", " k = np.diag(1/np.sqrt(svd[1])) @ (svd[0].T)\n", " k = k[:n_comp,:] \n", " X1 = k @ X\n", "\n", " # initial random weght vector\n", " w_init = np.random.normal(size=(n_comp, n_comp))\n", " W = sym_decorrelation(w_init)\n", " lim = 1\n", " it = 0\n", " \n", " \n", " # The FastICA algorithm\n", " if f == \"logcosh\":\n", " while lim > tol and it < maxit :\n", " wx = W @ X1\n", " gwx = g_logcosh(wx,alpha)\n", " g_wx = gprime_logcosh(wx,alpha)\n", " W1 = np.dot(gwx,X1.T)/X1.shape[1] - np.dot(np.diag(g_wx.mean(axis=1)),W)\n", " W1 = sym_decorrelation(W1)\n", " it = it +1\n", " lim = np.max(np.abs(np.abs(np.diag(W1 @ W.T)) - 1.0))\n", " W = W1\n", "\n", " S = W @ X1\n", " A = np.linalg.inv(W @ k)\n", " X_re = A @ S\n", " return{'X':X1.T,'X_re':X_re.T,'A':A.T,'S':S.T}\n", "\n", " elif f == \"exp\":\n", " while lim > tol and it < maxit :\n", " wx = W @ X1\n", " gwx = g_exp(wx,alpha)\n", " g_wx = gprime_exp(wx,alpha)\n", " W1 = np.dot(gwx,X1.T)/X1.shape[1] - np.dot(np.diag(g_wx.mean(axis=1)),W)\n", " W1 = sym_decorrelation(W1)\n", " it = it +1\n", " lim = np.max(np.abs(np.abs(np.diag(W1 @ W.T)) - 1.0))\n", " W = W1\n", "\n", " S = W @ X1\n", " A = np.linalg.inv(W @ k)\n", " X_re = A @ S\n", " return{'X':X1.T,'X_re':X_re.T,'A':A.T,'S':S.T}\n", "\n", " else:\n", " print(\"doesn't support this approximation negentropy function\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Better Algorithm" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting Source/fastICA_1.py\n" ] } ], "source": [ "%%writefile Source/fastICA_1.py\n", "\n", "import numpy as np\n", "\n", "def sym_decorrelation(W):\n", " \"\"\" Symmetric decorrelation \"\"\"\n", " K = np.dot(W, W.T)\n", " s, u = np.linalg.eigh(K) \n", " W = (u @ np.diag(1.0/np.sqrt(s)) @ u.T) @ W\n", " return W\n", "\n", "def fastICA_1(X, f,alpha=None, n_comp=None,maxit=200, tol=1e-04):\n", " \"\"\"FastICA algorithm for several units\"\"\"\n", " n,p = X.shape\n", " #check if n_comp is valid\n", " if n_comp is None:\n", " n_comp = min(n,p)\n", " elif n_comp > min(n,p):\n", " print(\"n_comp is too large\")\n", " n_comp = min(n,p)\n", " \n", " #centering\n", " #by subtracting the mean of each column of X (array).\n", " X = X - X.mean(axis=0)[None,:]\n", " X = X.T\n", "\n", " #whitening\n", " svd = np.linalg.svd(X @ (X.T) / n)\n", " k = np.diag(1/np.sqrt(svd[1])) @ (svd[0].T)\n", " k = k[:n_comp,:] \n", " X1 = k @ X\n", " del X\n", " \n", " # approximation negentropy function\n", " if f == \"logcosh\":\n", " def g(wx,alpha):\n", " return np.tanh(alpha * wx)\n", " def gprime(wx,alpha):\n", " return alpha * (1-np.square(np.tanh(alpha*wx)))\n", " elif f == \"exp\":\n", " def g(wx,alpha):\n", " return wx * np.exp(-np.square(wx)/2)\n", " def gprime(wx,alpha):\n", " return (1-np.square(wx)) * np.exp(-np.square(wx)/2)\n", " else:\n", " print(\"doesn't support this approximation negentropy function\")\n", " \n", " # initial random weght vector\n", " w_init = np.random.normal(size=(n_comp, n_comp))\n", " W = sym_decorrelation(w_init)\n", "\n", " lim = 1\n", " it = 0\n", " \n", " # The FastICA algorithm\n", " while lim > tol and it < maxit :\n", " wx = W @ X1\n", " gwx = g(wx,alpha)\n", " g_wx = gprime(wx,alpha)\n", " W1 = np.dot(gwx,X1.T)/X1.shape[1] - np.dot(np.diag(g_wx.mean(axis=1)),W)\n", " W1 = sym_decorrelation(W1)\n", " it = it +1\n", " lim = np.max(np.abs(np.abs(np.diag(W1 @ W.T)) - 1.0))\n", " W = W1\n", "\n", " S = W @ X1\n", " A = np.linalg.inv(W @ k)\n", " X_re = A @ S\n", " return{'X':X1.T,'X_re':X_re.T,'A':A.T,'S':S.T}" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting Source/fastICA_3.py\n" ] } ], "source": [ "%%writefile Source/fastICA_3.py\n", "import scipy.linalg\n", "import numpy as np\n", "\n", "\n", "def sym_decorrelation(W):\n", " \"\"\" Symmetric decorrelation \"\"\"\n", " K = np.dot(W, W.T)\n", " s, u = np.linalg.eigh(K) \n", " W = (u @ np.diag(1.0/np.sqrt(s)) @ u.T) @ W\n", " return W\n", "\n", "def g_logcosh(wx,alpha):\n", " \"\"\"derivatives of logcosh\"\"\"\n", " return np.tanh(alpha * wx)\n", "def gprime_logcosh(wx,alpha):\n", " \"\"\"second derivatives of logcosh\"\"\"\n", " return alpha * (1-np.square(np.tanh(alpha*wx)))\n", "# exp\n", "def g_exp(wx,alpha):\n", " \"\"\"derivatives of exp\"\"\"\n", " return wx * np.exp(-np.square(wx)/2)\n", "def gprime_exp(wx,alpha):\n", " \"\"\"second derivatives of exp\"\"\"\n", " return (1-np.square(wx)) * np.exp(-np.square(wx)/2)\n", "\n", "\n", "def fastICA_3(X, f,alpha=None,n_comp=None,maxit=200, tol=1e-04):\n", " \"\"\"FastICA algorithm for several units\"\"\"\n", " n,p = X.shape\n", " #check if n_comp is valid\n", " if n_comp is None:\n", " n_comp = min(n,p)\n", " elif n_comp > min(n,p):\n", " print(\"n_comp is too large\")\n", " n_comp = min(n,p)\n", " \n", " #centering\n", " #by subtracting the mean of each column of X (array).\n", " X = X - X.mean(axis=0)[None,:]\n", " X = X.T\n", " \n", " #whitening\n", " s = np.linalg.svd(X @ (X.T) / n)\n", " D = np.diag(1/np.sqrt(s[1]))\n", " k = D @ (s[0].T)\n", " k = k[:n_comp,:]\n", " X1 = k @ X\n", " \n", " # initial random weght vector\n", " w_init = np.random.normal(size=(n_comp, n_comp))\n", " W = sym_decorrelation(w_init)\n", " \n", " lim = 1\n", " it = 0\n", " \n", " # The FastICA algorithm\n", " while lim > tol and it < maxit :\n", " wx = W @ X1\n", " if f ==\"logcosh\":\n", " gwx = g_logcosh(wx,alpha)\n", " g_wx = gprime_logcosh(wx,alpha)\n", " elif f ==\"exp\":\n", " gwx = g_exp(wx,alpha)\n", " g_wx = gprimeg_exp(wx,alpha)\n", " else:\n", " print(\"doesn't support this approximation negentropy function\")\n", " W1 = np.dot(gwx,X1.T)/X1.shape[1] - np.dot(np.diag(g_wx.mean(axis=1)),W)\n", " W1 = sym_decorrelation(W1)\n", " it = it +1\n", " lim = np.max(np.abs(np.abs(np.diag(W1 @ W.T))) - 1.0)\n", " W = W1\n", " \n", " S = W @ X1\n", " A = scipy.linalg.pinv2(W @ k) \n", " return{'X':X1.T,'A':A.T,'S':S.T}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Scipy" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting Source/fastICA_scipy.py\n" ] } ], "source": [ "%%writefile Source/fastICA_scipy.py\n", "\n", "import scipy\n", "import scipy.linalg\n", "import numpy as np\n", "\n", "def sym_decorrelation_scipy(W):\n", " \"\"\" Symmetric decorrelation \"\"\"\n", " K = scipy.dot(W, W.T)\n", " s, u = scipy.linalg.eigh(K) \n", " W = scipy.dot(scipy.dot(scipy.dot(u,np.diag(1.0/np.sqrt(s)) ),u.T),W)\n", " return W\n", "\n", "def fastICA_scipy(X, f,alpha=None, n_comp=None,maxit=200, tol=1e-04):\n", " \"\"\"FastICA algorithm for several units\"\"\"\n", " n,p = X.shape\n", " #check if n_comp is valid\n", " if n_comp is None:\n", " n_comp = min(n,p)\n", " elif n_comp > min(n,p):\n", " print(\"n_comp is too large\")\n", " n_comp = min(n,p)\n", " \n", " #centering\n", " #by subtracting the mean of each column of X (array).\n", " X = X - X.mean(axis=0)[None,:]\n", " X = X.T\n", "\n", " #whitening\n", " svd = scipy.linalg.svd(scipy.dot(X,X.T) / n)\n", " k = scipy.dot(np.diag(1/np.sqrt(svd[1])),svd[0].T)\n", " k = k[:n_comp,:] \n", " X1 = scipy.dot(k,X)\n", " del X\n", " \n", " # approximation negentropy function\n", " if f == \"logcosh\":\n", " def g(wx,alpha):\n", " return scipy.tanh(alpha * wx)\n", " def gprime(wx,alpha):\n", " return alpha * (1-np.square(scipy.tanh(alpha*wx)))\n", " elif f == \"exp\":\n", " def g(wx,alpha):\n", " return wx * np.exp(-np.square(wx)/2)\n", " def gprime(wx,alpha):\n", " return (1-np.square(wx)) * np.exp(-np.square(wx)/2)\n", " else:\n", " print(\"doesn't support this approximation negentropy function\")\n", " \n", " # initial random weght vector\n", " w_init = np.random.normal(size=(n_comp, n_comp))\n", " W = sym_decorrelation_scipy(w_init)\n", "\n", " lim = 1\n", " it = 0\n", " \n", " # The FastICA algorithm\n", " while lim > tol and it < maxit :\n", " wx = scipy.dot(W,X1)\n", " gwx = g(wx,alpha)\n", " g_wx = gprime(wx,alpha)\n", " W1 = scipy.dot(gwx,X1.T)/X1.shape[1] - scipy.dot(np.diag(g_wx.mean(axis=1)),W)\n", " W1 = sym_decorrelation_scipy(W1)\n", " it = it +1\n", " lim = np.max(np.abs(np.abs(np.diag(scipy.dot(W1,W.T))) - 1.0))\n", " W = W1\n", "\n", " S = scipy.dot(W,X1)\n", " A = scipy.linalg.pinv2(scipy.dot(W,k))\n", " X_re = scipy.dot(A,S)\n", " return{'X':X1.T,'X_re':X_re.T,'A':A.T,'S':S.T}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Jit" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting Source/fastICA_jit.py\n" ] } ], "source": [ "%%writefile Source/fastICA_jit.py\n", "\n", "import scipy.linalg\n", "import numpy as np\n", "from numba import jit\n", "\n", "@jit\n", "def sym_decorrelation_jit(W):\n", " \"\"\" Symmetric decorrelation \"\"\"\n", " K = np.dot(W, W.T)\n", " s, u = np.linalg.eigh(K) \n", " W = (u @ np.diag(1.0/np.sqrt(s)) @ u.T) @ W\n", " return W\n", "\n", "def g_logcosh_jit(wx,alpha):\n", " \"\"\"derivatives of logcosh\"\"\"\n", " return np.tanh(alpha * wx)\n", "def gprime_logcosh_jit(wx,alpha):\n", " \"\"\"second derivatives of logcosh\"\"\"\n", " return alpha * (1-np.square(np.tanh(alpha*wx)))\n", "# exp\n", "def g_exp_jit(wx,alpha):\n", " \"\"\"derivatives of exp\"\"\"\n", " return wx * np.exp(-np.square(wx)/2)\n", "def gprime_exp_jit(wx,alpha):\n", " \"\"\"second derivatives of exp\"\"\"\n", " return (1-np.square(wx)) * np.exp(-np.square(wx)/2)\n", "\n", "\n", "def fastICA_jit(X, f,alpha=None,n_comp=None,maxit=200, tol=1e-04):\n", " \"\"\"FastICA algorithm for several units\"\"\"\n", " n,p = X.shape\n", " #check if n_comp is valid\n", " if n_comp is None:\n", " n_comp = min(n,p)\n", " elif n_comp > min(n,p):\n", " print(\"n_comp is too large\")\n", " n_comp = min(n,p)\n", " \n", " #centering\n", " #by subtracting the mean of each column of X (array).\n", " X = X - X.mean(axis=0)[None,:]\n", " X = X.T\n", " \n", " #whitening\n", " s = np.linalg.svd(X @ (X.T) / n)\n", " D = np.diag(1/np.sqrt(s[1]))\n", " k = D @ (s[0].T)\n", " k = k[:n_comp,:]\n", " X1 = k @ X\n", " \n", " # initial random weght vector\n", " w_init = np.random.normal(size=(n_comp, n_comp))\n", " W = sym_decorrelation_jit(w_init)\n", " \n", " lim = 1\n", " it = 0\n", " \n", " # The FastICA algorithm\n", " while lim > tol and it < maxit :\n", " wx = W @ X1\n", " if f ==\"logcosh\":\n", " gwx = g_logcosh_jit(wx,alpha)\n", " g_wx = gprime_logcosh_jit(wx,alpha)\n", " elif f ==\"exp\":\n", " gwx = g_exp_jit(wx,alpha)\n", " g_wx = gprimeg_exp_jit(wx,alpha)\n", " else:\n", " print(\"doesn't support this approximation negentropy function\")\n", " W1 = np.dot(gwx,X1.T)/X1.shape[1] - np.dot(np.diag(g_wx.mean(axis=1)),W)\n", " W1 = sym_decorrelation_jit(W1)\n", " it = it +1\n", " lim = np.max(np.abs(np.abs(np.diag(W1 @ W.T))) - 1.0)\n", " W = W1\n", " \n", " S = W @ X1\n", " A = scipy.linalg.pinv2(W @ k) \n", " return{'X':X1.T,'A':A.T,'S':S.T}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Numexpr\n", "\n", "note: numexpr can used only for element-wise operations" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting Source/fastICA_ne.py\n" ] } ], "source": [ "%%writefile Source/fastICA_ne.py\n", "\n", "import scipy.linalg\n", "import numpy as np\n", "import numexpr as ne\n", "\n", "def sym_decorrelation_ne(W):\n", " \"\"\" Symmetric decorrelation \"\"\"\n", " K = np.dot(W, W.T)\n", " s, u = np.linalg.eigh(K) \n", " return (u @ np.diag(1.0/np.sqrt(s)) @ u.T) @ W\n", "# logcosh\n", "def g_logcosh_ne(wx,alpha):\n", " \"\"\"derivatives of logcosh\"\"\"\n", " return ne.evaluate('tanh(alpha * wx)')\n", "def gprime_logcosh_ne(wx,alpha):\n", " \"\"\"second derivatives of logcosh\"\"\"\n", " return alpha * (1-ne.evaluate('tanh(alpha*wx)**2'))\n", "# exp\n", "def g_exp_ne(wx,alpha):\n", " \"\"\"derivatives of exp\"\"\"\n", " return ne.evaluate('wx * exp(-wx**2/2)')\n", "def gprime_exp_ne(wx,alpha):\n", " \"\"\"second derivatives of exp\"\"\"\n", " return (1-np.square(wx)) * ne.evaluate('exp(-wx**2/2)')\n", "\n", "\n", "def fastICA_ne(X, f,alpha=None,n_comp=None,maxit=200, tol=1e-04):\n", " n,p = X.shape\n", " #check if n_comp is valid\n", " if n_comp is None:\n", " n_comp = min(n,p)\n", " elif n_comp > min(n,p):\n", " print(\"n_comp is too large\")\n", " n_comp = min(n,p)\n", " \n", " #centering\n", " #by subtracting the mean of each column of X (array).\n", " X = X - X.mean(axis=0)[None,:]\n", " X = X.T\n", " \n", " #whitening\n", " s = np.linalg.svd(X @ (X.T) / n)\n", " D = np.diag(1/np.sqrt(s[1]))\n", " k = D @ (s[0].T)\n", " k = k[:n_comp,:]\n", " X1 = k @ X\n", " \n", " # initial random weght vector\n", " w_init = np.random.normal(size=(n_comp, n_comp))\n", " W = sym_decorrelation_ne(w_init)\n", " \n", " lim = 1\n", " it = 0\n", " \n", " # The FastICA algorithm\n", " while lim > tol and it < maxit :\n", " wx = W @ X1\n", " if f ==\"logcosh\":\n", " gwx = g_logcosh_ne(wx,alpha)\n", " g_wx = gprime_logcosh_ne(wx,alpha)\n", " elif f ==\"exp\":\n", " gwx = g_exp_ne(wx,alpha)\n", " g_wx = gprimeg_exp_ne(wx,alpha)\n", " else:\n", " print(\"doesn't support this approximation negentropy function\")\n", " W1 = np.dot(gwx,X1.T)/X1.shape[1] - np.dot(np.diag(g_wx.mean(axis=1)),W)\n", " W1 = sym_decorrelation_ne(W1)\n", " it = it +1\n", " lim = np.max(np.abs(np.abs(np.diag(W1 @ W.T))) - 1.0)\n", " W = W1\n", " \n", " S = W @ X1\n", " A = scipy.linalg.pinv2(W @ k) \n", " return{'X':X1.T,'A':A.T,'S':S.T}" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Cython" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%load_ext cython" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<!DOCTYPE html>\n", "<!-- Generated by Cython 0.23.5 -->\n", "<html>\n", 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class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">n</span> <span class=\"o\">=</span> <span class=\"n\">X</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">0</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_n = (__pyx_v_X.shape[0]);\n", "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">51</span>: <span class=\"k\">cdef</span> <span class=\"kt\">int</span> <span class=\"nf\">p</span> <span class=\"o\">=</span> <span class=\"n\">X</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"mf\">1</span><span class=\"p\">]</span></pre>\n", "<pre class='cython code score-0 '> __pyx_v_p = (__pyx_v_X.shape[1]);\n", "</pre><pre class=\"cython line score-0\">&#xA0;<span class=\"\">52</span>: <span class=\"c\">#check if n_comp is valid</span></pre>\n", "<pre class=\"cython line score-3\" onclick='toggleDiv(this)'>+<span class=\"\">53</span>: <span class=\"k\">if</span> <span class=\"n\">n_comp</span> <span class=\"ow\">is</span> <span class=\"bp\">None</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-3 '> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_PyInt_From_int</span>(__pyx_v_n_comp);<span class='error_goto'> if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 53; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);\n", " __pyx_t_2 = (__pyx_t_1 == Py_None);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;\n", " __pyx_t_3 = (__pyx_t_2 != 0);\n", " if (__pyx_t_3) {\n", "/* … */\n", " goto __pyx_L3;\n", " }\n", "</pre><pre class=\"cython line score-0\" onclick='toggleDiv(this)'>+<span class=\"\">54</span>: <span class=\"n\">n_comp</span> <span class=\"o\">=</span> <span class=\"nb\">min</span><span class=\"p\">(</span><span 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class='error_goto'> if (unlikely(!__pyx_t_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_10);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_7); __pyx_t_7 = 0;\n", " __pyx_t_7 = <span class='pyx_c_api'>__Pyx_PyObject_GetAttrStr</span>(__pyx_t_10, __pyx_n_s_normal);<span class='error_goto'> if (unlikely(!__pyx_t_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_7);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_10); __pyx_t_10 = 0;\n", " __pyx_t_10 = <span class='py_c_api'>PyDict_New</span>();<span class='error_goto'> if (unlikely(!__pyx_t_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 71; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_10);\n", " __pyx_t_1 = 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class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_10); __pyx_t_10 = 0;\n", " __pyx_v_w_init = __pyx_t_16;\n", " __pyx_t_16 = 0;\n", "</pre><pre class=\"cython line score-2\" onclick='toggleDiv(this)'>+<span class=\"\">72</span>: <span class=\"n\">W</span> <span class=\"o\">=</span> <span class=\"n\">sym_decorrelation_cython</span><span class=\"p\">(</span><span class=\"n\">w_init</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-2 '> __pyx_t_11 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dsds_double</span>(__pyx_v_w_init);\n", " if (unlikely(!__pyx_t_11.memview)) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 72; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " __pyx_t_16 = __pyx_f_46_cython_magic_8f9944ae9d4ade65cf1b126b673f1abc_sym_decorrelation_cython(__pyx_t_11);<span class='error_goto'> if (unlikely(!__pyx_t_16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 72; __pyx_clineno = __LINE__; goto 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class='pyx_c_api'>__Pyx_PyObject_Call</span>(__pyx_t_10, __pyx_t_8, NULL);<span class='error_goto'> if (unlikely(!__pyx_t_16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 78; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_16);\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_8); __pyx_t_8 = 0;\n", " <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_10); __pyx_t_10 = 0;\n", " <span class='pyx_macro_api'>__Pyx_XDECREF_SET</span>(__pyx_v_wx, __pyx_t_16);\n", " __pyx_t_16 = 0;\n", "</pre><pre class=\"cython line score-2\" onclick='toggleDiv(this)'>+<span class=\"\">79</span>: <span class=\"k\">if</span> <span class=\"n\">f</span> <span class=\"o\">==</span> <span class=\"s\">&quot;logcosh&quot;</span><span class=\"p\">:</span></pre>\n", "<pre class='cython code score-2 '> __pyx_t_3 = (<span class='pyx_c_api'>__Pyx_PyString_Equals</span>(__pyx_v_f, __pyx_n_s_logcosh, Py_EQ));<span class='error_goto'> if (unlikely(__pyx_t_3 &lt; 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 79; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " __pyx_t_2 = (__pyx_t_3 != 0);\n", " if (__pyx_t_2) {\n", "/* … */\n", " goto __pyx_L8;\n", " }\n", "</pre><pre class=\"cython line score-3\" onclick='toggleDiv(this)'>+<span class=\"\">80</span>: <span class=\"n\">gwx</span> <span class=\"o\">=</span> <span class=\"n\">g_logcosh_cython</span><span class=\"p\">(</span><span class=\"n\">wx</span><span class=\"p\">,</span><span class=\"n\">alpha</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-3 '> __pyx_t_11 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dsds_double</span>(__pyx_v_wx);\n", " if (unlikely(!__pyx_t_11.memview)) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " __pyx_t_16 = __pyx_f_46_cython_magic_8f9944ae9d4ade65cf1b126b673f1abc_g_logcosh_cython(__pyx_t_11, __pyx_v_alpha);<span class='error_goto'> if (unlikely(!__pyx_t_16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 80; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_16);\n", " __PYX_XDEC_MEMVIEW(&amp;__pyx_t_11, 1);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF_SET</span>(__pyx_v_gwx, __pyx_t_16);\n", " __pyx_t_16 = 0;\n", "</pre><pre class=\"cython line score-3\" onclick='toggleDiv(this)'>+<span class=\"\">81</span>: <span class=\"n\">g_wx</span> <span class=\"o\">=</span> <span class=\"n\">gprime_logcosh_cython</span><span class=\"p\">(</span><span class=\"n\">wx</span><span class=\"p\">,</span><span class=\"n\">alpha</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-3 '> __pyx_t_11 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dsds_double</span>(__pyx_v_wx);\n", " if (unlikely(!__pyx_t_11.memview)) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 81; 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class='error_goto'> if (unlikely(!__pyx_t_16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 83; __pyx_clineno = __LINE__; goto __pyx_L1_error;}</span>\n", " <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_16);\n", " __PYX_XDEC_MEMVIEW(&amp;__pyx_t_11, 1);\n", " <span class='pyx_macro_api'>__Pyx_XDECREF_SET</span>(__pyx_v_gwx, __pyx_t_16);\n", " __pyx_t_16 = 0;\n", "</pre><pre class=\"cython line score-3\" onclick='toggleDiv(this)'>+<span class=\"\">84</span>: <span class=\"n\">g_wx</span> <span class=\"o\">=</span> <span class=\"n\">gprime_exp_cython</span><span class=\"p\">(</span><span class=\"n\">wx</span><span class=\"p\">,</span><span class=\"n\">alpha</span><span class=\"p\">)</span></pre>\n", "<pre class='cython code score-3 '> __pyx_t_11 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dsds_double</span>(__pyx_v_wx);\n", " if (unlikely(!__pyx_t_11.memview)) <span class='error_goto'>{__pyx_filename = __pyx_f[0]; __pyx_lineno = 84; __pyx_clineno = __LINE__; goto 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g_logcosh_cython(double[:,:] wx, double alpha):\n", " \"\"\"derivatives of logcosh\"\"\"\n", " return np.tanh(np.multiply(alpha,wx))\n", "\n", "@cython.wraparound(False) \n", "@cython.boundscheck(False)\n", "cdef gprime_logcosh_cython(double[:,:] wx, double alpha):\n", " \"\"\"second derivatives of logcosh\"\"\"\n", " return np.multiply(alpha,(1-np.square(np.tanh(np.multiply(alpha,wx)))))\n", "\n", "@cython.cdivision(True)\n", "@cython.wraparound(False) \n", "@cython.boundscheck(False)\n", "cdef g_exp_cython(double[:,:] wx, double alpha):\n", " \"\"\"derivatives of exp\"\"\"\n", " return wx * np.exp(-np.square(wx)/2)\n", "\n", "@cython.cdivision(True)\n", "@cython.wraparound(False) \n", "@cython.boundscheck(False)\n", "cdef gprime_exp_cython(double[:,:] wx, double alpha):\n", " \"\"\"second derivatives of exp\"\"\"\n", " return (1-np.square(wx)) * np.exp(-np.square(wx)/2)\n", "\n", "@cython.cdivision(True)\n", "@cython.wraparound(False) \n", "@cython.boundscheck(False)\n", "def fastICA_cython(double[:,:] X, str f,double alpha, int n_comp,int maxit, double tol):\n", " \"\"\"FastICA algorithm for several units\"\"\"\n", " cdef int n = X.shape[0]\n", " cdef int p = X.shape[1]\n", " #check if n_comp is valid\n", " if n_comp is None:\n", " n_comp = min(n,p)\n", " elif n_comp > min(n,p):\n", " print(\"n_comp is too large\")\n", " n_comp = min(n,p)\n", " \n", " #centering\n", " #by subtracting the mean of each column of X (array).\n", " X = X - np.mean(X,axis=0)[None,:]\n", " X = X.T\n", "\n", " #whitening\n", " svd = np.linalg.svd(dot(X,X.T) / n)\n", " k = dot(np.diag(1/np.sqrt(svd[1])),svd[0].T)\n", " k = k[:n_comp,:] \n", " X1 = dot(k,X)\n", "\n", " # initial random weght vector\n", " w_init = np.random.normal(size=(n_comp, n_comp))\n", " W = sym_decorrelation_cython(w_init)\n", " lim = 1\n", " it = 0\n", " \n", " # The FastICA algorithm\n", " while lim > tol and it < maxit :\n", " wx = dot(W,X1)\n", " if f == \"logcosh\":\n", " gwx = g_logcosh_cython(wx,alpha)\n", " g_wx = gprime_logcosh_cython(wx,alpha)\n", " elif f == \"exp\":\n", " gwx = g_exp_cython(wx,alpha)\n", " g_wx = gprime_exp_cython(wx,alpha)\n", " else:\n", " print(\"doesn't support this approximation negentropy function\")\n", " \n", " W1 = np.dot(gwx,X1.T)/X1.shape[1] - np.dot(np.diag(g_wx.mean(axis=1)),W)\n", " W1 = sym_decorrelation_cython(W1)\n", " it = it +1\n", " lim = np.max(np.abs(np.abs(np.diag(dot(W1,W.T))) - 1.0))\n", " W = W1\n", "\n", " S = dot(W,X1)\n", " A = scipy.linalg.pinv2(dot(W,k))\n", " X_re = dot(A,S)\n", " return{'X':X1.T,'X_re':X_re.T,'A':A.T,'S':S.T}" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", 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mit
giacomov/3ML
docs/notebooks/Building_Plugins_from_TimeSeries.ipynb
1
340181
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:12:46.744508Z", "start_time": "2018-02-16T14:12:46.721660Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Configuration read from /home/ndilalla/.threeML/threeML_config.yml\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:keyring.backend:Loading KWallet\n", "INFO:keyring.backend:Loading SecretService\n", "INFO:keyring.backend:Loading Windows\n", "INFO:keyring.backend:Loading chainer\n", "INFO:keyring.backend:Loading macOS\n" ] } ], "source": [ "%matplotlib notebook\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "from threeML import *\n", "from threeML.io.package_data import get_path_of_data_file\n", "\n", "import warnings\n", "warnings.simplefilter('ignore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Constructing plugins from TimeSeries\n", "\n", "Many times we encounter event lists or sets of spectral histograms from which we would like to derive a single or set of plugins. For this purpose, we provide the **TimeSeriesBuilder** which provides a unified interface to time series data. Here we will demonstrate how to construct plugins from different data types.\n", "\n", "## Constructing time series objects from different data types\n", "\n", "The **TimeSeriesBuilder** currently supports reading of the following data type:\n", "* A generic PHAII data file\n", "* GBM TTE/CSPEC/CTIME files\n", "* LAT LLE files\n", "\n", "If you would like to build a time series from your own custom data, consider creating a TimeSeriesBuilder.from_your_data() class method.\n", "\n", "### GBM Data \n", "\n", "Building plugins from GBM is achieved in the following fashion" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:12:54.481423Z", "start_time": "2018-02-16T14:12:51.658348Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3638214c3e494df5a4fd11e7239432b1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=u'Loading PHAII spectra : '), HTML(value=u''), FloatProgress(value=0.0)))" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cspec_file = get_path_of_data_file('datasets/glg_cspec_n3_bn080916009_v01.pha')\n", "tte_file = get_path_of_data_file('datasets/glg_tte_n3_bn080916009_v01.fit.gz')\n", "gbm_rsp = get_path_of_data_file('datasets/glg_cspec_n3_bn080916009_v00.rsp2')\n", "\n", "\n", "gbm_cspec = TimeSeriesBuilder.from_gbm_cspec_or_ctime('nai3_cspec',\n", " cspec_or_ctime_file=cspec_file,\n", " rsp_file=gbm_rsp)\n", "\n", "gbm_tte = TimeSeriesBuilder.from_gbm_tte('nai3_tte',\n", " tte_file=tte_file,\n", " rsp_file=gbm_rsp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LAT LLE data\n", "\n", "LAT LLE data is constructed in a similar fashion" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T13:27:20.258228Z", "start_time": "2018-02-16T13:27:20.092339Z" } }, "outputs": [], "source": [ "lle_file = get_path_of_data_file('datasets/gll_lle_bn080916009_v10.fit')\n", "ft2_file = get_path_of_data_file('datasets/gll_pt_bn080916009_v10.fit')\n", "lle_rsp = get_path_of_data_file('datasets/gll_cspec_bn080916009_v10.rsp')\n", "\n", "lat_lle = TimeSeriesBuilder.from_lat_lle('lat_lle',\n", " lle_file=lle_file,\n", " ft2_file=ft2_file,\n", " rsp_file=lle_rsp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Viewing Lightcurves and selecting source intervals\n", "\n", "All time series objects share the same commands to get you to a plugin. \n", "Let's have a look at the GBM TTE lightcurve." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T13:27:22.507021Z", "start_time": "2018-02-16T13:27:22.264759Z" } }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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U7AkqakMQKWauAAMIUbg5h0zs+cE7lzmvoCSpElgACrVRAZg1d8nSVKdGIBKZQBKktQwBqBSGYCSJDWMAahUBqAkSQ1jACqVAShJUsMYgEplAEqS1DAGoFIZgJIkNYwBqFQGoCRJDWMAKpUBKElSwxiASmUASpLUMAagUhmAkiQ1jAGoVAagJEkNYwAqlQEoSVLDGIBKZQBKktQwBqBSGYCSJDWMAahUBqAkSQ1jACqVAShJUsMYgEplAEqS1DAGoFIZgJIkNYwBODxT456BETEAJUlqGAOwOucA1wKrgR3ALuBXwD8A/wV40vhmbagMQEmSGsYATPdK4E7gPuCzwMXAy4BlwGuADwPfArYCfwY8cTyzOTQGoCRJDWMApvsn4HeA3fvc7nDgj4F3DX2ORssAlCSpYQzAevoA8ANgGngA+BpwYu42K4kLLjv9We42RwJfB2ba9/Nx4HG52ywFbgG2AT8HLhxwXg1ASZIaxgAcrj2AZwIHDfh9NxJD7BTgVGLE3Q0sydxmJXA1cFhmyi7EPYCfAt9sz8M5wIPARzK3OQZ4FPgT4GTg7cBO4KUDzKsBKElSwxiA1boSeEv7/3sA3wFawGbimraFeiJxIZ2RuWxl+/G6OYd4IMqhmcsuATYBe7W/vgK4Lfd9XyEGaFkGoCRJDWMAVuvfgWe3//8K4F7gKcD/A3w34X6PJy6kp2UuW0lco/cQMeI+Cuybuf7DwI9y93NM+36e1f7628yPyDcRI7EsA1CSpIYxAKu1FTii/f+reSyujiEOCbMQuwN/R1ybmHURcVPt04E3EONzeeb6q4EVue/Zl7iwz2l/fSdxf8Os327fZp8u8zNFfLN0psMxACVJahQDsFp3Ay8hbv69h3h0MMR9+R5Z4H1eBazhsbDs5izigjyu/fWwAvBy5h98YgBKktQgBmC1Lgc2Av9GjMHO2UDeTBwuZlCfAtYS1yD2s4S4IDsHcAxrE7BrAA1ASVLDGYDVexVxrL/sGrsLgJcPcB+7EePvXuCEkt/zQuKCfEb7685BIIdkbnMRMe46YXoF8UjhrOvwIJDKv0+SpDoxAKvxBeA8YL+K7u8zxDWJZzJ3mJfOZtnjgP8KnAYcDZwL/IJ42rmOzjAwK4hDybyUOBZg0TAwHwNOAn4Ph4EZyvdJklQnBmA1/gD4IbAF+AbwNuKm0YWat49de7qwff2TibH3MPHAk7uIEZdfiEcBNxAHgn4Q+ATFA0HfShwI+hc4EPRQvk+SpDoxAKt1BHEt2gpimP2QGIfPHOdMDZkBKElSwxiAw/NrwGuALwEbiAeFfIp4RPAkMQAlSWoYA3A09gDOBj4J/O6Y56VqBqAkSQ1jAFZrH+aejeMo4J3EsQEnlQEoSVLDGIDV+nvi+XYBDgTWE8fx20I8MGQSGYCSJDWMAVith3hsH7/fBX5MPJXbq4mDQ08iA1CSpIYxAKs1AxzZ/v/fAB9q///J7esmkQEoSVLDGIDV+glwKTH4NgHPb19+GnD/uGZqyAxASZIaxgCs1quA7cRTsP195vIPEAeInkQGoCRJDWMAVu8w4FnEff86TgdOHM/sDJ0BKElSwxiA1foccQDovCXt6yaRAShJUsMYgNXaBRxScPnBwM4Rz8uoGICSJDWMAViN/YEDgBZwXPvrznQQcD6wbmxzN1wGoCRJDWMAVqNFXPvXbdoJ/Jexzd1wGYCSJDWMAViNM4GlxBB8ZfvrzvR84Eljm7PhMwAlSWoYA7BaRzH36N/FwACUJKlhDMDqHQi8BHgjcd+/7DSJDEBJkhrGAKzWy4BfETcFbwQeyUwbxjhfw2QASpLUMAZgte4ErgT2HfeMjJABKElSwxiA1XoUOHbcMzFiBqAkSQ1jAFZrOfCacc/EiBmAkiQ1jAFYrbcAdwOXA+cB5+amSWQASpLUMAZgtVo9pl1jnK9hMgAlSWoYA1CpDEBJkhrGAFQqA1CSpIYxAKv134FLCy5/O3F4mElkAEqS1DAGYLXuBU4ruPzXgX8f8byMigEoSVLDGIDV2gocX3D58e3rJpEBKElSwxiA1bqNuLk37x3AqhHPy6gs2gBcv3VDaLVaQ5xLSZKGwwCs1puBGeC/AWe2pw8TzxDy1jHO1zAt2gCcWr4sLF15mREoSWocA7B6byPu79cZ/281cP5Y52i4FlUAtlqtsHTlZXMi0E3BkqSmMQCH54nAfuOeiRFYVAEYQozA9Vs3GICSpMYyAJVq0QVgFd8vSdI4GYDpbgSeV+J2vwa8D/j94c7OyBmABqAkqWEMwHRvIY7/twq4Ang18ELieIDLiAND/w2wGfhr4MjxzObQGIAGoCSpYQzAakwBbwT+FniExw4A2QX8FPgEcPLY5m64DEADUJLUMAbgcBwAHAbsOe4ZGQEDcEwB2Gq1wuYdM2HzjhmHopEkDcQAVCoDcAwB2Gq1wpkrL52dh9NvvjhMG4KSpJIMQKUyAMcQgPkBqR2YWpI0CANQqQzAMQfgKSsucGBqSdJADEClMgDHHIDTO2YcmFqSNBADUKkMwDEHYOdAEANQklSWAVi9A4HfBT4KPL592a8Dh49tjobLADQAJUkNYwBW6xnAA8BdwA7g2Pblfwh8YVwzNWQGoAEoSWoYA7BaNwEfa/9/mscC8AXAmnHM0AgYgAagJKlhDMBqbQKOa/8/G4BHAVvHMkfD17gAbLVayQdNjDu4DEBJUgoDsFoPAM9q/z8bgL8JrB3LHA1fowIwP4CyAShJWowMwGr9JXA98RRw08AxwJHALcCVY5yvYWpUAOYHUF7owMnjDi4DUJKUwgCs1gHAN4FHgJ3APcB24B+AJWOcr2FqbACu37phwWfNGHdwGYCSpBQG4HC8EPg94L3AsjHPy7A1NgBTQmncwWUASpJSGIDVOh+YKrh8r/Z1k8gANAAlSQ1jAFZrF3BIweVPaF83iQxAA1CS1DAGYLVawBMLLj8V2DDieRkVA7BmATjd/rozLXQ/R0nS5DIAq3Er8UjfXcBP2v/vTD8GfgX8zdjmbrgMwJoF4HNuvqiSI50lSZPLAKzGh9pTC/h45usPAR8A/iNxP8BJZADWLACLJjcLS5KyDMBqXQDsPe6ZGLFFH4Apw8lU8fjdAnD15nUGoCSpkAGoVIs+AMexmbVMAKae7k6SNLkMwGrtAbwH+D5wP/HAj+w0iRZlALZarbB05WVj28yafx75+Vm68rIw7ZHBkqQuDMBqfRhYB7wb2AL838TTwz0EXDrG+RqmRRmAIcQIHNdatqLn0Wq15hz5O+79FCVJ9WUAVusXwO+0/z8NHNf+/6XAdWOZo+FbtAE4jPur8nENQElSNwZgtR4Fjmz//z7g19v/PxbYNJY5Gj4D0ACUJDWMAVitnwHPbf//O8D72/9/LfDAWOZo+AxAA1CS1DAGYLX+GPhg+/+vBXYAdwHb2tdNIgPQAJQkNYwBOFzPA/4z8LJxz8gQGYAGoCSpYQzA0dln3DMwJAbgCCOrc3RvmaOPDUBJUjcG4PBNEYeFuX/cMzIkBuCIIqvVaoUzV15a+jRvBqAkqRsDsBpTwEeBfwW+B7yiffmbiOMCrgXeN55ZGzoDcASRlR9zsMwZSAxASVI3BmA1rgA2Av+TGHw7gKuBnwCvI54hZFIZgEOOrKI1f+u3bpgd8Hmc8yZJaiYDsBqrgXPb/38a0AI+B+y2wPv7APAD4mDSDwBfA07M3WZv4NPAw8Bm4KvAobnbHAl8HZhp38/HgcflbrMUuIV4pPLPgQsHnFcDcMiRtdDzDpc5W4gkaXEyAKuxHTg88/UW4OkJ93cjMcROAU4lRtzdwJLMba4C7gHOAk4D/gn4bub6PYCfAt8EngmcAzwIfCRzm2OIg1f/CXAy8HZgJ/DSAebVABxhAK7fuqF0uBWdLzi7JrFsSEqSJo8BWI1dwBMzX08T46oqTyQupDPaXx9AjM5XZW5zUvs2z2t/fU57vrJrBS8hnpFkr/bXVwC35R7rK8QALcsAzEVW1WvYFjrP+XCczq1JdNOwJC1eBmA1WsS1dMvb0w5gRebrzrRQxxMX0tPaX5/V/vrA3O3uBt7V/v+HgR/lrj+m/X3Pan/9beDK3G3eRO/T1k0R3yyd6XAMwDC1fFmY3jEzlDVsVQTg1PJl4fSbLy4MQDcLS9LiYwBW4/Mlp4XYHfg74qnlOl5P3Gcv7/vEtXoQD0JZkbt+X+LCPqf99Z3E/Q2zfrt9m27jFl7evn7OZAAuKzxKd5ybmVutVli68rJ585SdhhWtkqR6MwDr7ypgDXBE5rJxBqBrALvcX90CMITuw8cMe54lSfVmANbbp4hjCOb3JxznJuA89wGscQDmv98AlCSFYADW1W7E+LsXOKHg+s5BIOdlLjuR4oNADsnc5iJi3E21v76CeKRw1nV4EMiC7m/15nVdYyq7n90g+9rl1+AZgJKkKhiA9fQZ4sDSZwKHZabsZtmriGv8XkwcBuZ77amjMwzMCuJQMi8ljgVYNAzMx4hHEf8eDgOz4PsrmoqGXymzr12r1QrTO2bmHbhhAEqSqmAA1tO8gyza04WZ23QGgt5AjLjlxEjMOgq4gTgQ9IPAJygeCPpW4j6Fv8CBoAdSdKBFNto6a/y6xWG3+yw65+9CD9AwACVJeQagUi3qAAxh/ubd6dxjdNtM3O3xi4ZvmU4YosUAlCTlGYBKtegDsN9jdDtQpNv+gdnbDHLmjzLzYwBKkkIwAJXOAOzzGL0CsNvm3lHtp2gAStLiZAAqlQHY4zHyp2DLr90rOj1b6j5/vebHAJQkhWAAKp0B2OMx8geF5IMrf91ChooZdH4MQEmSAahUBmBOr1OwTe+Y6XrdqII0O/Uau1CSNLkMQKUyAAt0OwVbZ83eKNe89Rur0ACUpMXHAFQqA7DEY+Ufs9d1o5iP/ADT+bELJUmTzQBUKgOwxGPVLQCnM/saFo1dKEmabAagUhmAJR6rbgGYf6xRvi6SpPEzAJXKACzxWAagJKlODEClMgBLPNakBWD+7CVVDVkjSRoNA1CpDMASj9WkAOycfq5b5BWdvaSqQaslSaNhACqVAdhFfjzAbCTVOQCnli8LZ668tGvkdRtWxk3HktQcBqBSGYA9ZNeiZdeQ1S0Aew1enf++7P1lB5I2ACWpOQxApTIAF6DX2sGqlY3NbgNU5yMvv7m4Dq+nJGkwBqBSGYAL1G3t4DAep2xsFt02P0agAShJzWcAKpUB2ACDxGb+tvnXrFsAdg4ekSTVnwGoVAbghCsbgB4NLEnNYQAqlQE44XoF4PSOmXkHj/i6SlL9GYBKZQBOuF4B2NlM7L6AktQsBqBSGYATrl8A5m/jvoCSVH8GoFIZgBNu0AB0X0BJqj8DUKkMwAmX3+evaHNv0UDSqa/vqIbJkaTFyABUKgNwwmVfs1NWXNA18qrcFzB/vmHXKEpStQxApTIAJ1y3c/8WRVlVr+8oT5VXlmskJU0SA1CpDMAJl9+8e/rNF4fpLhE0qQHoGklJk8YAVCoDcBEou/Yr5fXNPsZ0zQKwbkEqSakMQKUyADVroa9vfg3b6TdfXKvgMgAlTRoDUKkMQM1a6OvbbT/DugSXAShp0hiASmUAatagr29ns2/+nMIGoCQNlwGoVAagZg3y+uY3+xqAkjQ6BqBSGYCaNcjrWxRVRfv+1WF5GYCSJo0BqFQGoGYtNADXb93Q9ejfOiwvA1DSpDEAlcoA1KyFBmC3cwrXJbgMQEmTxgBUKgNQs8q+vt1OG2cAStJoGIBKZQBqVvb1nW4P6pwfQLro4I9xBeBCBrj2/SNpEhiASmUAalb29X3OzRfNCabO6dPyMZU9rdooA3CQ07sZgJImjQGoVAagZvUb0Lmzti178Ec2ukYZgINEnQEoadIYgEplAGpWUSit3ryuawDml4EBKEmjYQAqlQGoWa1WKyxdedmczarTudfcAJSk8TMAlcoA1Bz5Ayvyr7kBKEnjZwAqlQGonqoKwM5g0b2O1i0rPwyNAShpsTEAlcoAVDiYB1EAABoJSURBVE9VBWB2s3JKBPY6B3HRe2LQWJSkJjAAlcoAVE+pAVh0fuCFLLvO5uh8zGXvP3+/g8aiJDWFAahUBqB6Sg3AzoDSRWcOKatbyK3fumHeQSq95qdXLEpSkxiASmUAqqf8uH+9Qq7XvnYpy67ofosGpu41P/1iUZKaxABUKgNQPQ1yZO8oAjB/MEnZAOy39lKSmsQAVCoDUD3lxwbsdTDHKAKwX+T1us73j6RJYQAqlQGovrJjA2bHCMwzACVpNAxApTIAVRkDUJJGwwBUKgNQlTEAJWk0DEClMgBVGQNQkkbDAFQqA1CVMQAlaTQMQKUyAFUZA1CSRsMAVCoDUJXJDxmTHSrGAJSk6hiASmUAqlLZIWOyQ8UYgJJUHQNQqQxAjYQBKEnVMQCVygDUSBiAklQdA1CpDECNhAEoSdUxAJXKANRIGICSVB0DUKkMQI2EAShJ1TEAlcoA1Ehkl936rRvmHCE8yPdWFYDrt26Yd6SyJDWFAahUBqBGIj9IdHaMwEG+t6oAXMh8SFJdGIBKZQBqJPKDRA+yDKsKwKJ58L0kqYkMQKUyADUyrVYrrN+6YWwB2JmHzTtmFjQfklQXBqBSGYAaqYUswyoDMGU+JKkuDMD6OgP4W2AdcQG9Inf9Ne3Ls9ONuds8HvgS8CtgI/BZYL/cbZ4B/COwFVgLvHfA+TQANVIGoCSlMwDr6xzgD4FX0j0AvwEclpkOyt3mG8CPgOcCvwHcBVyXuX5/4H7gi8ApwOuAGeCiAebTANRIDTMA80cXG4CSJpUB2AzdAvBrPb7n5Pb3PTtz2W8BLeBJ7a/fBmwA9src5o+BOwaYNwNQIzXMAMwf1bvQcOzsJ9iZPEpYUt0YgM3QLQA3Ag8APwOuAp6Quf7NwCO573kcsJO4VhHgC8yPyBe3Hy+/NrEbA1AjVXUA9jq6eCHh2Gq1wpkrL3WoGEm1ZgA2Q1EAvg44F3h6+7pVwPeBPdrXf5AYhnkPENf8Afw98Oe565/afryTu8zLFPHN0pkOxwDUCFUdgCF0P7p4IeFYNFag7zdJdWMANkNRAOYd277d2e2vhxWAlzP/4BMDUCMz6DIsO3RM0f0uJByz37N68zrfb5JqyQBshjIBCPAgcHH7/8PaBOwaQI3VIMuwaHNslQFYdJv8voG+3yTVkQHYDGUC8AjiAR7ntr/uHARyWuY2L6H4IJA9M7f5CB4EohrLLsPpPgda9DrAo9dtDUBJk84ArK/9gGe2pwC8q/3/I9vXfRx4HnA0cbPvD4E7iWvoOr4B3AKcDrywfX12GJgDiMPAfIE4DMxrgUdxGBjVWHYZPufmi3rGXa8jdXvdrwEoadIZgPW1lIJ97YhH/+4DrCDuz7cdWANcDRyau4/HE4NvGtgEfI7eA0H/O/C+AefTANRIdTvIorNMs0OwTA+wvA1ASYuJAahUBqBGqlcATu+YmbPP3+k3XzzSAFy/dcOc6MwG4PqtGxwXUFJtGIBKZQBqpHoFYDa4Bh2GpYoAzEdnt/lxXEBJ42YAKpUBqJGqWwAWjQeYXSPZ7bpRvP/yZyRxDaSkDgNQqQxAjVTdAjCE+eMBdtsncZT7BBYNgeMaSEkdBqBSGYAaqVEEYGd/vUGCrWi+8t8zyvdfr9fJ978kA1CpDECN1CgCsKrvr0sAFgVtvzEUJU02A1CpDECN1LACsNe+fGU2mdY5AIs2aZ+y4gI3CUuLmAGoVAagRiofamWOui27vFMOmmhaALpJWFrcDEClMgA1ct0Ge04NwNR5yoZpvzOTjCMA8/OYXQvoz4O0uBiASmUAaqy6nXljHGu4smHa79zE4wjA/DwOcqYUSZPFAFSqWgVgnT6ANRrZZbp687pab+KsQwCOa34k1YsBqFS1CcD8uGf5TXD5sdr8wJsM/Y7erdPyNgAl1YUBqFS1CcBeO+EXDYrrB95kKDp6N3tgSJ2WtwEoqS4MQKVqRADmr3PYi8mSP3p3usd7YZwMQEl1YQAqVeMCcP3WDcbfhCszJMu458sAlDROBqBSNS4A/aCbfGWGZBkHA1BSXRiASjXyAMxv7ut8sBuAyup3RPg4lBmapap5NQAl9WIAKtVIA7DoYI7O2h0DUHXXbXDmXkevV/lYC7mNpMlkACrVSAOw25AfnbUnBqDqLPs+nG6/Z4sGr67iPWoASurFAFSqsQVgdtBfA1BNkH0fPufmiwr/mDEA1VRV7cpQx903JpEBqFRjC8DsmpP1Wzf0HPrDDzrVQa9Bq7NjF2Y3Dxft7zroYxmAo7fYIqaqXRmGtUuE5jMAlaoWAZj/ADUAVUdFAbh+64Z5Yxd2oqHb/q6DPpYB2F+ZYCsbdYsxYqoaeqmuQzhNIgNQqcYWgNM7ZuadAcIAVJ0NsptCr/1dB32sugRgyhrNYSoTbINE3WKMmMUQgJO2VtcAVKqxBWDnh7BoJ3oDUHXUa3zCXgGY3d91umRADRqAnTWRC/2A6xd3qWs0h6lMdAwSJlVGTFOiYxIDMPva72rtmri1ugagUo01APOXGYCqu24f6L0CMPtHTvbgkdNvvjhMZ+4ne9/Z7xkkVBbyAdcv7nr9oVaHn8e6BmC/tY51isNuuzcMOl91CcD8a1/X84unMACVaugBmP0ll99PKgQDUJOhbAB2i7X8GooyH1T5NZIL/YDrtbm6KA7zR/B3k1+rWMX+eWXnvzNfnfsdZLieQSOmzB8FRfNV9Rqpql/DhczXqNae9nuuvT5XRvFZMoq4NwCVqpIA7PZm7/VXWJkA7LaDvVQ3gwbgKSsumPdeX8gHcD6ysveT3dzcK8K6ba4uOjp/6crLSv08FoVjt+eTGkPdoqPbPBS9PmXDrd/z7LZbQJnfeym/26p8DfNrywaZr6LnlX8flpmvXs+nzHMdZwBWEfdlAtIAVKp5ATjoXy5Fkdf5gS8a2mWQAOwVjlKd5INvekfvAJzOxVr+/1Wsyek2VmH+A6lXrGZ/9jqbBMuskS+zZj+E7puXU6MjH+FFzyf7+pQNt7KPnXJdr/0xi34/L+Q1zD9G9v2af2+mLov8+7BMEPXaJF1m+YwzAFPjvmxAGoBKNScAF/KXS68ftKL9Lsr8IizzfVKd5N/H2TV83T6cu4VXynu8zAdf/jE25z78u21WLvqZLRNG67duKHx+vdbQVR2ARVsTBvmd1G1/uKoDsNf+mEW/nwfddaDVaoXpHTPzfsfm/8gus4zLLouFLN9u95NfA93t/sYVgMP8gybPAFSqOQG4kL9cst+T36xV5oev1yaDYZ1mS6par/3xxh2AnfjptpaxaAzDXj97gwZg0XMtWpPTb01/t30Kuz3nKvY5Lgqxfq93r9ew31qsbo/dbbD8Xmts869hv03i3eajqgAcZN/R7PPKx2qZz4VhBmDZXZ4W+lgGoEYlKQDzP6i9wm2QX7ZlrpPqpmjtSq81FqMKwH5hU3bf3KoCsOh16RZs3V7Xfq/vIM+r12167Q/XLfLu37Kh51aMsu+JbDBNLS/+A7vXGZX6RdHpN18c7t/S/4+TfjHeb//JQd7jRRHVa3eJss+11/IexCD7fHaL8fxr12tzfK95NQCVasEBWPSD2uuXbP5DcRw7SkujUPQLvmj8wFEHYL+jhnvdtteYh/3mo/M65B+71/nAO2vKyqy1yt5Pt/AqE4BFm4m77Q9Xdm3a1PJl4cyVl867bZm1wmUGy+/3GmYDrcxa2KL76bcmsdt7o9cfQmXeN2V+VrrdX5UB2G0ki17vrW4xnn/tit4bRaFvAGoYFhyA3X5Qu93PQo64MwA1KYrWmIw6APPz0e+DtMxanjL7xmU//HqNJ9hvLWEnKvJRlv89U3bzdbcp+wHcLYby91P0QZ4d5zH/3MsEYOd1z6/97BW5Rc+vs69g0XusTAD2Csle95f/nd/tPZFd+5V/jfr9rHT7makqAHuNZNHruZd935SdDEANQyUBmP8QSAnJMr+cpEkwjgBc6G17fV+/fePyUZlfq5U9yCF/Xbcg6HU/g/5O6XfQWb8P8uzaymzMlB1up99jdZ5v5/pdrV0DvYZTy+cfiTtIAJZ9ffOR2yvKOrfvtRY1+/3jCsBB7ic1APO7DmT/XzSUjgGoVAsKwEF+0Pv9sPXa1NTrOqnpmhqARZGRX6PT6/n0G+ak25qysvczaABmh60q2lWl3wd52eXVLyYGGX6lzFAxZcaW7BVzRSHZ7WCU6R0zXXcJKnr+3SKz2+/6Ogdgt/FqBw3A7JrS/P0VDaWzceNGA1BJ+gZgfjV+r33/ev3QlP1llg+8XtdJTTaMABzkj6aUP7CK/gjstkZn0OeT+rqkbFXot6m+zJquXq9ZPqq6jUuY+j7o9lwH2VrTLSSL1pjmb1NmrXB+OZdde9orAPvF7zACsOh1WWgADvLY6x6+3wBUkr4BmN0fpuiosUGGRpD0mGEEYAiD/dGU8gdWmTU6C1lrP84A7Hd/RUf4DrK8yhzxudDXbZDXITs//f4IKHPwULdN9d3mp1sglXku3QJwkANzqgjA1F0Huh0Y2euxs7sOGIBKtT8QNm7cWPqvpvwPfb8fdANQKjasAByV/Pznh3VZ6Fr71Nel7BHMC4mPqkOtzFq6Ku+76LmW+SOg1wE8ZZdTdn4GPdtIvwDsdvBP6kkFei37fuPV9gvAbgdG9nrs7GMZgEq1PxBe+PX5PyT5TRODnM7Hffek/iYpAPO/M1KeQxWvS5kjmMveb7dNt9M9gqmsYf6xXPV951+H/GkCBwnAQTd39wvAonNr91rDWjaGy2yy7fY6F23iHuS89gaghm1/IOz1xaXz4m5Xa9ecH4R+R7j1+0GSNFfTA7DXpsFxB2CZ+x7kfgf9/Tfs+RnXfXc7SGHQACy7MqHoe4uOoO52f70CLr+WumjIl6KBshcSgEXRPOgfHvlhkQxApZoTgCmbbSQNptuHWlMCMIRqzn2aN8zXpW67pwxzfoa9JWbQUE/Z3N0t5PLDpuTXyvZaa5lf4THoLlD9ArDfvpOD/uGRXyP58wfWGIBKMhuAbqaVRqvbh1qTAjCE6iNmmK/LYgrAEIa7JWbQAEw96rxo7MP8lqpu39u5TdEYilPL52+Szm9SLjsAdP66XkclL2Q5Zx9rry8uNQCVZH8grHv4fuNPGrFegxk3ybD3NavydVlsAThMC1lTmxKkVW2GLzNOYjYsu+1LWCYAO6paztmfDQNQqeYMAyNptIa1b9koDXtfs6bsczcJ8zOIpq/BHmSTdK/lVGYZVrk5vvOz4T6ASmUASkrSpIip2wgFTXrt8pq+BnuQ90K/zbxl7qfqzfGeCk6pDEBJSZoWMXUaoaBuQTqopq/BLvte6PceH8d7ygBUKgNQUpKmBWDd1ClIVayO73EDUKkMQElJ6vjhKFWpju9xA1CpDEBJSZq+GVPqxwDUJDIAJSVzM6YmWR3/yDEAlcoAlCSpj7r9kWMAKpUBKElSwxiASmUASpLUMAagUhmAkiQ1jAGoVAagJEkNYwAqlQEoSVLDGIBKZQBKktQwBqBS7Q+EtWvXhk2bNjk5OTk5OTk1YFq7dq0BqCSHE99ATk5OTk5OTs2bjkZagN2IEbh/g6ZOtDZtvn1+i+c5TvrzWwzP0efX/GnSn2Pn+e2PtEjsz2S/6Sf9+cHkP8dJf34w+c/R59d8k/4cJ/35SfNM+pt+0p8fTP5znPTnB5P/HH1+zTfpz3HSn580z6S/6Sf9+cHkP8dJf34w+c/R59d8k/4cJ/35SfNMAZe3/51Ek/78YPKf46Q/P5j85+jza75Jf46T/vwkSZIkSZIkSZIkSZIkSZIkSZIkNdHvA2uArcC/AKePdW4W7gPAD4Bp4AHga8CJudusZP5pf/5sdLOY5HLmz/sdmev3Bj4NPAxsBr4KHDraWUy2huJTM326ff3KguvqvPzOAP4WWEec11fkrt8N+DBwH7AFuAk4IXebxwNfAn4FbAQ+C+w3vFkeSK/ntydwBfBT4NH2bb4APCl3H2uYv0zfP8yZHlC/ZXgN8+f/xtxtmroMofvp0v6vzG3WFFxfl2VY5nOhzO/OI4GvAzPt+/k48LihzbU0Aq8FtgFvAp4KXA08AhwyzplaoBuBC4FTgFOJP6x3A0syt1lJfI6HZaamjPl0OXAbc+f94Mz1VwH3AGcBpwH/BHx3tLOY7InMfX7LiB8mS9vXr6RZy+8c4A+BV1L84fo+YhC8HHgG8P8Bq4kfSB3fAH4EPBf4DeAu4LqhznV5vZ7fAcA3gdcQP3CfR/wD819z97EG+K/MXaZLqI9+y/Aa4jLKzv9Buds0dRnC3Od1GPGzogUcm7nNGuq7DMt8LvT73bkH8Q+ZbwLPJL5mDwIfGe6sS8P1L8CnMl/vDtxLff56S/FE4i+0MzKXrQSuHMvcpLuc+CFS5ABgO/CqzGUnEZ//84Y7W0N1JfBz4poyaPbyy3+47kZc8/eezGUHENfEv6799cnt73t25ja/RfwAzq9JG7eieMh7Tvt2R2YuWwO8c0jzVLVuAfi1Ht8zacvwa8DNucvW0JxlmP9cKPO78xxgF3PXCl4CbAL2GubMSsOyF7CT+T/w1xLXRDTd8cQf4qdlLltJ/MvtIeLatI8C+458zhbmch7blLaauEmp80F6FvG5Hpj7nruBd41o/qq2F3E5fTBz2Uqau/zyH67Hti97Zu52/wB8sv3/NxPXyGc9jvhz+8ohzGOKMvGwjBg+2bW2a4D7iZvfbiVuWqzrprVuAbiRuFnwZ8S1SU/IXD9Jy/BQYAfw+tzla2jOMsx/LpT53flh5v/xfUz7+541nNmUhutJxDfw83OXf4y4ZrDJdgf+DvhO7vKLgJcCTwfeAPw7sHy0s7Zg5wCvJm4qfCnwPeIvqV8j/kLeVvA93yfuh9VEryF+SGbXkjR5+eU/XF/Qvuw/5G73N8Bft///QWJU5D0AvK3qGUzULx72Bn5I/MMl6z8TN/E/g7hW5RHgT4cwf1Uoeo6vA84lvidfAawi/tzt0b5+kpbhe4ENzN1FAZqzDIs+F8r87rwaWJG7fl/i63VOxfMojcQkB+BVxL9Kj+hzu85ff8cNe4aG4EDiJoi3MJkBuIK4c3ovTVp+izkA9wT+F3AL/ffZfDNxLVMdT8dVZi1nZ83u2e2vJ2UZQjzo7H+UuJ+6LsOizwUDUIvSpG4C/hSwlriKvp8lxB/ilw51jobnB8TNoJO2Cfgo4j43L+9zuyYtv8W6CXhP4Hrgx8zdNNrNKe37yh+pWQdlAhDibgoXt/8/CcsQ4EXt608tcT91XIbdPhfcBKxF61+Y+xfd7sTNak08CGQ34g/5vcwfSqObFxJ/iJ8xrJkaov2Im2Mu5bEdmc/LXH8izT0I5HLiARL99iNq0vLrdhDIuzOX7U/xQSCnZW7zEppzAEEn/m4j7nxfxhuI8Z8/krYOygTgEcTlc27766Yvw45rmH8Edzd1Wob9PhfK/O7sHASSHR3jIuIWmLqt5ZRKey3xA+cC4i+qPyf+tdq08eMAPkPcGftM5g5HsE/7+uOIQxWcBhxN/AX9C+Ialyb4BPG5HU3cfPhN4pqGzgfrVcS/Wl9MfI7fa09Nszvxefxx7vImLr/9iGv4nkn8QHlX+/+dg3feR/x56+xD9jWKh4G5hTg+5wuBO6nPECK9nt+exC0Ja4lrjbI/k50jJ59PPHr0VOIa0TcQN41eO7Jn0F+v57gfcTy45xHfk2cT93O8k7lh0NRl2LE/8QC0Swq+v+7LsN/nAvT/3dkZBmYF8Xm+lPgcHQZGjfd24pt/G3GN4HPHOzsL1m3A0gvb1z+ZGAsPE6P3LuL+jnUeRy7rK8QjgLcR19J+hbn7vnUGM91A/GW9nPiLrmleQlxuT8ld3sTlt5Ti9+Q17es7A0HfT3xONzH/eT+eGAvTxDUOn6M+gwgvpfvzO7rLddlxHX8d+GfiB/QW4gEUH6Bea1WW0v057kOMggeIa5HWEPcXy/8B3dRl2HERcQDkAwq+v+7LsN/nApT73XkUcAPxdXiQ+Ad5XY90liRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRpuJZSfML3UTkb+DfiKaX6+S3iied3H+ocSZIkNVi3Uz11psuJ5549jHjqtXH4IfEcqWX9APhPQ5oXSZKkxsue4P0y4rlWs5eN+5yrv0E8T+reA3zP7xMjUJIkSX1cSIytvKXM3QTcud3/AfyMeIL3/wnsC1wArAEeAf47czfbThFPAn8v8aTx/9K+714+Bfy/uctOBb4FTAO/Iq4hfHbm+iPb83tcn/uWJEla9C6kfABuB/4eeBZwBvAQsAL4a+CpxDjcBrw2cz9/AXwXeBExzt4DbAVO6DFPPwbel7vsNuCvgJPa3/tqYhRm3d+eT0mSJPVwIeUDML+G7c+Ia/Wym4xvbF8Oca3cTuBJufu+CfhIj3nayPz9+X5FXNPYyy3Ah/rcRpIkadG7kPIB+GjuNv8NuD132bXA8vb/f6d9H5tz0w7iWsNuthHX8GVd3v6+m4D3U7yp97vAFT3uV5IkSQy+D2DW5cThV7KuAb7W/v9riWsATwSOz02H9Zine4GLCi5/CvAu4mbobcArc9f/G3ETsyRJknq4kOEF4FPa9/GiAefp74Ar+9zmy8D/yny9N3EfxbMHfCxJkqRF50KGF4AAXwR+CfyfwDHA6cAHiJuHu3kH8K+Zr/chHhm8FDgKeCHwc+Zu7l1KPEJ43x73K0mSJIYfgHsS9xX8JXEN3TriPoJP7zFPjwe2EDcdQxyU+svAPcRNv/cC/4O54wT+OY8dfCJJkqQG+jgx6so4GHiYuIZRkiRJDXUg8EHKnd/32cwde1CSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJEmSJDXG/w8iNZPyjpyU3AAAAABJRU5ErkJggg==\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "threeML_config['lightcurve']['lightcurve color'] = '#07AE44'\n", "\n", "fig = gbm_tte.view_lightcurve(start=-20,stop=200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhaps we want to fit the time interval from 0-10 seconds. We make a selection like this:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:13:02.183218Z", "start_time": "2018-02-16T14:13:01.832320Z" } }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "threeML_config['lightcurve']['selection color'] = '#4C3CB7'\n", "\n", "gbm_tte.set_active_time_interval('0-10')\n", "fig = gbm_tte.view_lightcurve(start=-20,stop=200);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For event list style data like time tagged events, the selection is *exact*. However, pre-binned data in the form of e.g. PHAII files will have the selection automatically adjusted to the underlying temporal bins.\n", "\n", "Several discontinuous time selections can be made.\n", "\n", "## Fitting a polynomial background\n", "\n", "In order to get to a plugin, we need to model and create an estimated background in each channel ($B_i$) for our interval of interest. The process that we have implemented is to fit temporal off-source regions to polynomials ($P(t;\\vec{\\theta})$) in time. First, a polynomial is fit to the total count rate. From this fit we determine the best polynomial order via a likelihood ratio test, unless the user supplies a polynomial order in the constructor or directly via the polynomial_order attribute. Then, this order of polynomial is fit to every channel in the data.\n", "\n", "From the polynomial fit, the polynomial is integrated in time over the active source interval to estimate the count rate in each channel. The estimated background and background errors then stored for each channel.\n", "\n", "$$ B_i = \\int_{T_1}^{T_2}P(t;\\vec{\\theta}) {\\rm d}t $$\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:13:13.076018Z", "start_time": "2018-02-16T14:13:07.010630Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Auto-determined polynomial order: 0\n", "\n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3940a3dbd5614b368a0c227cc4bb5b6f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=u'Fitting GBM_NAI_03 background : '), HTML(value=u''), FloatProgress(value=0.0)))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Unbinned 0-order polynomial fit with the Nelder-Mead method\n", "\n", "\n" ] }, { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "threeML_config['lightcurve']['background color'] = '#FC2530'\n", "\n", "gbm_tte.set_background_interval('-24--5','100-200')\n", "gbm_tte.view_lightcurve(start=-20,stop=200);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For event list data, binned or unbinned background fits are possible. For pre-binned data, only a binned fit is possible. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T13:28:14.586494Z", "start_time": "2018-02-16T13:28:03.203889Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Auto-determined polynomial order: 4\n", "\n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dd9043bc8a734d579b4b6c1e228b79d3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=u'Fitting GBM_NAI_03 background : '), HTML(value=u''), FloatProgress(value=0.0)))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Binned 4-order polynomial fit with the Powell method\n", "\n", "\n" ] } ], "source": [ "gbm_tte.set_background_interval('-24--5','100-200',unbinned=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Saving the background fit\n", "\n", "The background polynomial coefficients can be saved to disk for faster manipulation of time series data.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T13:42:29.489135Z", "start_time": "2018-02-16T13:42:29.465282Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Saved fitted background to background_store.h5.\n", "\n" ] } ], "source": [ "gbm_tte.save_background('background_store',overwrite=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T13:43:25.412337Z", "start_time": "2018-02-16T13:43:25.057107Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully restored fit from background_store.h5\n" ] } ], "source": [ "gbm_tte_reloaded = TimeSeriesBuilder.from_gbm_tte('nai3_tte',\n", " tte_file=tte_file,\n", " rsp_file=gbm_rsp,\n", " restore_background='background_store.h5')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T13:43:56.994727Z", "start_time": "2018-02-16T13:43:56.718938Z" } }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = gbm_tte_reloaded.view_lightcurve(-10,200)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating a plugin\n", "\n", "With our background selections made, we can now create a plugin instance. In the case of GBM data, this results in a **DispersionSpectrumLike**\n", "plugin. Please refer to the Plugins documentation for more details." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T13:28:24.393400Z", "start_time": "2018-02-16T13:28:24.387255Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Auto-probed noise models:\n", "- observation: poisson\n", "- background: gaussian\n" ] } ], "source": [ "gbm_plugin = gbm_tte.to_spectrumlike()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T13:28:25.197988Z", "start_time": "2018-02-16T13:28:25.161537Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>n. channels</th>\n", " <td>128</td>\n", " </tr>\n", " <tr>\n", " <th>total rate</th>\n", " <td>2506.5</td>\n", " </tr>\n", " <tr>\n", " <th>total bkg. rate</th>\n", " <td>1287.47</td>\n", " </tr>\n", " <tr>\n", " <th>total bkg. rate error</th>\n", " <td>20.6602</td>\n", " </tr>\n", " <tr>\n", " <th>bkg. exposure</th>\n", " <td>9.95012</td>\n", " </tr>\n", " <tr>\n", " <th>bkg. is poisson</th>\n", " <td>False</td>\n", " </tr>\n", " <tr>\n", " <th>exposure</th>\n", " <td>9.95012</td>\n", " </tr>\n", " <tr>\n", " <th>is poisson</th>\n", " <td>True</td>\n", " </tr>\n", " <tr>\n", " <th>background</th>\n", " <td>profiled</td>\n", " </tr>\n", " <tr>\n", " <th>significance</th>\n", " <td>47.7858</td>\n", " </tr>\n", " <tr>\n", " <th>src/bkg area ratio</th>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>src/bkg exposure ratio</th>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>src/bkg scale factor</th>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>response</th>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0\n", "n. channels 128\n", "total rate 2506.5\n", "total bkg. rate 1287.47\n", "total bkg. rate error 20.6602\n", "bkg. exposure 9.95012\n", "bkg. is poisson False\n", "exposure 9.95012\n", "is poisson True\n", "background profiled\n", "significance 47.7858\n", "src/bkg area ratio 1\n", "src/bkg exposure ratio 1\n", "src/bkg scale factor 1\n", "response None" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gbm_plugin.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Time-resolved binning and plugin creation\n", "\n", "It is possible to temporally bin time series. There are up to four methods provided depending on the type of time series being used:\n", "\n", "* Constant cadence (all time series)\n", "* Custom (all time series)\n", "* Significance (all time series)\n", "* Bayesian Blocks (event lists)" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2018-02-16T13:31:48.588685Z", "start_time": "2018-02-16T13:31:48.357870Z" } }, "source": [ "### Constant Cadence\n", "\n", "Constant cadence bins are defined by a start and a stop time along with a time delta.\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:00:02.617448Z", "start_time": "2018-02-16T14:00:02.610843Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created 5 bins via constant\n" ] } ], "source": [ "gbm_tte.create_time_bins(start=0, stop=10, method='constant', dt=2.)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:00:16.617382Z", "start_time": "2018-02-16T14:00:16.594516Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Start</th>\n", " <th>Stop</th>\n", " <th>Duration</th>\n", " <th>Midpoint</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.000412</td>\n", " <td>2.000412</td>\n", " <td>2.0</td>\n", " <td>1.000412</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2.000412</td>\n", " <td>4.000412</td>\n", " <td>2.0</td>\n", " <td>3.000412</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.000412</td>\n", " <td>6.000412</td>\n", " <td>2.0</td>\n", " <td>5.000412</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>6.000412</td>\n", " <td>8.000412</td>\n", " <td>2.0</td>\n", " <td>7.000412</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>8.000412</td>\n", " <td>10.000412</td>\n", " <td>2.0</td>\n", " <td>9.000412</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Start Stop Duration Midpoint\n", "0 0.000412 2.000412 2.0 1.000412\n", "1 2.000412 4.000412 2.0 3.000412\n", "2 4.000412 6.000412 2.0 5.000412\n", "3 6.000412 8.000412 2.0 7.000412\n", "4 8.000412 10.000412 2.0 9.000412" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gbm_tte.bins.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Custom\n", "\n", "Custom time bins can be created by providing a contiguous list of start and stop times.\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:06:41.887324Z", "start_time": "2018-02-16T14:06:41.878126Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created 3 bins via custom\n" ] } ], "source": [ "time_edges = np.array([.5,.63,20.,21.])\n", "\n", "starts = time_edges[:-1]\n", "\n", "stops = time_edges[1:]\n", "\n", "gbm_tte.create_time_bins(start=starts, stop=stops, method='custom')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:06:49.019050Z", "start_time": "2018-02-16T14:06:48.998054Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Start</th>\n", " <th>Stop</th>\n", " <th>Duration</th>\n", " <th>Midpoint</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.50</td>\n", " <td>0.63</td>\n", " <td>0.13</td>\n", " <td>0.565</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.63</td>\n", " <td>20.00</td>\n", " <td>19.37</td>\n", " <td>10.315</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>20.00</td>\n", " <td>21.00</td>\n", " <td>1.00</td>\n", " <td>20.500</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Start Stop Duration Midpoint\n", "0 0.50 0.63 0.13 0.565\n", "1 0.63 20.00 19.37 10.315\n", "2 20.00 21.00 1.00 20.500" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gbm_tte.bins.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Significance\n", "\n", "Time bins can be created by specifying a significance of signal to background if a background fit has been performed." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:16:14.585605Z", "start_time": "2018-02-16T14:14:26.786230Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a7846e83541445b9a55bd46aa9a9bc9a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=u''), HTML(value=u''), FloatProgress(value=0.0)))" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Created 12 bins via significance\n" ] } ], "source": [ "gbm_tte.create_time_bins(start=0., stop=50., method='significance', sigma=25)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:16:21.577506Z", "start_time": "2018-02-16T14:16:21.553049Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Start</th>\n", " <th>Stop</th>\n", " <th>Duration</th>\n", " <th>Midpoint</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.000412</td>\n", " <td>1.334256</td>\n", " <td>1.333844</td>\n", " <td>0.667334</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1.334256</td>\n", " <td>1.810988</td>\n", " <td>0.476732</td>\n", " <td>1.572622</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1.810988</td>\n", " <td>2.411580</td>\n", " <td>0.600592</td>\n", " <td>2.111284</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>2.411580</td>\n", " <td>2.849876</td>\n", " <td>0.438296</td>\n", " <td>2.630728</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>2.849876</td>\n", " <td>3.454244</td>\n", " <td>0.604368</td>\n", " <td>3.152060</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>3.454244</td>\n", " <td>4.090328</td>\n", " <td>0.636084</td>\n", " <td>3.772286</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>4.090328</td>\n", " <td>4.724952</td>\n", " <td>0.634624</td>\n", " <td>4.407640</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>4.724952</td>\n", " <td>5.544472</td>\n", " <td>0.819520</td>\n", " <td>5.134712</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>5.544472</td>\n", " <td>6.033726</td>\n", " <td>0.489254</td>\n", " <td>5.789099</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>6.033726</td>\n", " <td>6.734410</td>\n", " <td>0.700684</td>\n", " <td>6.384068</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>6.734410</td>\n", " <td>8.443664</td>\n", " <td>1.709254</td>\n", " <td>7.589037</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>8.443664</td>\n", " <td>21.987320</td>\n", " <td>13.543656</td>\n", " <td>15.215492</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Start Stop Duration Midpoint\n", "0 0.000412 1.334256 1.333844 0.667334\n", "1 1.334256 1.810988 0.476732 1.572622\n", "2 1.810988 2.411580 0.600592 2.111284\n", "3 2.411580 2.849876 0.438296 2.630728\n", "4 2.849876 3.454244 0.604368 3.152060\n", "5 3.454244 4.090328 0.636084 3.772286\n", "6 4.090328 4.724952 0.634624 4.407640\n", "7 4.724952 5.544472 0.819520 5.134712\n", "8 5.544472 6.033726 0.489254 5.789099\n", "9 6.033726 6.734410 0.700684 6.384068\n", "10 6.734410 8.443664 1.709254 7.589037\n", "11 8.443664 21.987320 13.543656 15.215492" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gbm_tte.bins.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bayesian Blocks\n", "\n", "The Bayesian Blocks algorithm (Scargle et al. 2013) can be used to bin event list by looking for significant changes in the rate. \n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:20:54.460630Z", "start_time": "2018-02-16T14:19:42.981211Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Created 9 bins via bayesblocks\n" ] } ], "source": [ "gbm_tte.create_time_bins(start=0., stop=50., method='bayesblocks', p0=.01, use_background=True)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:21:52.527359Z", "start_time": "2018-02-16T14:21:52.501430Z" } }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Start</th>\n", " <th>Stop</th>\n", " <th>Duration</th>\n", " <th>Midpoint</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0.000412</td>\n", " <td>0.816854</td>\n", " <td>0.816442</td>\n", " <td>0.408633</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>0.816854</td>\n", " <td>6.983690</td>\n", " <td>6.166836</td>\n", " <td>3.900272</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>6.983690</td>\n", " <td>8.823971</td>\n", " <td>1.840281</td>\n", " <td>7.903831</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>8.823971</td>\n", " <td>21.723166</td>\n", " <td>12.899195</td>\n", " <td>15.273569</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>21.723166</td>\n", " <td>25.502056</td>\n", " <td>3.778890</td>\n", " <td>23.612611</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>25.502056</td>\n", " <td>30.894882</td>\n", " <td>5.392826</td>\n", " <td>28.198469</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>30.894882</td>\n", " <td>38.893854</td>\n", " <td>7.998972</td>\n", " <td>34.894368</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>38.893854</td>\n", " <td>48.517036</td>\n", " <td>9.623182</td>\n", " <td>43.705445</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>48.517036</td>\n", " <td>49.999594</td>\n", " <td>1.482558</td>\n", " <td>49.258315</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Start Stop Duration Midpoint\n", "0 0.000412 0.816854 0.816442 0.408633\n", "1 0.816854 6.983690 6.166836 3.900272\n", "2 6.983690 8.823971 1.840281 7.903831\n", "3 8.823971 21.723166 12.899195 15.273569\n", "4 21.723166 25.502056 3.778890 23.612611\n", "5 25.502056 30.894882 5.392826 28.198469\n", "6 30.894882 38.893854 7.998972 34.894368\n", "7 38.893854 48.517036 9.623182 43.705445\n", "8 48.517036 49.999594 1.482558 49.258315" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gbm_tte.bins.display()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Working with bins\n", "\n", "The light curve can be displayed by supplying the use_binner option to display the time binning\n" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:24:06.330584Z", "start_time": "2018-02-16T14:24:06.200602Z" } }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " this.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " // select the cell after this one\n", " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", " IPython.notebook.select(index + 1);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = gbm_tte.view_lightcurve(use_binner=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The bins can all be writted to a PHAII file for analysis via OGIPLike." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:25:10.554440Z", "start_time": "2018-02-16T14:25:08.262469Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "287f5c942927454fbacd341057c6e0e8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=u'Creating plugins : '), HTML(value=u''), FloatProgress(value=0.0)))" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gbm_tte.write_pha_from_binner(file_name='out', overwrite=True,\n", " force_rsp_write = False) # if you need to write the RSP to a file. We try to choose the best option for you." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, we can create a list of plugins directly from the time series." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2018-02-16T14:26:56.575245Z", "start_time": "2018-02-16T14:26:55.343468Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b94f0452e0614a0cb64d610b62151aa6", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value=u'Creating plugins : '), HTML(value=u''), FloatProgress(value=0.0)))" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "my_plugins = gbm_tte.to_spectrumlike(from_bins=True)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" }, "toc": { "base_numbering": 1, "nav_menu": { "height": "132px", "width": "254px" }, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": "block", "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
elivre/arfe
e2014/025-rede2014_codifica_receitas.ipynb
1
12578
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 025-rede_codifica_receitas" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "ano_eleicao = '2014'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "dbschema = f'rede{ano_eleicao}'\n", "table_receitas = f'{dbschema}.receitas_{ano_eleicao}'\n", "table_origem_receitas = f'{dbschema}.origem_receitas_{ano_eleicao}'\n", "table_fonte_receitas = f'{dbschema}.fonte_receitas_{ano_eleicao}'\n", "table_municipios = f\"{dbschema}.municipios_{ano_eleicao}\"\n", "table_partidos = f'{dbschema}.partidos_{ano_eleicao}'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "sys.path.append('../')\n", "import mod_tse as mtse\n", "home = os.environ[\"HOME\"]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "mtse.execute_query(f'CREATE SCHEMA IF NOT EXISTS {dbschema};')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# ATENÇÂO " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-2-27e3207f851a>, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-2-27e3207f851a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m atualizar receptor_cargo_ds (vice s suplentee 2014?????\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "atualizar receptor_cargo_ds (vice s suplentee 2014?????" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CODIFICA DESCRIÇÃO, CÓDIGO E SIGLA DA ORIGEM DA RECEITA" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "query_update_origem_receita = f\"\"\"\n", "update {table_receitas} as r\n", " set receita_origem_sg = o.sg_origem_receita,\n", " receita_origem_cd = o.cd_origem_receita,\n", " receita_origem_ds = o.ds_origem_receita\n", "from {table_origem_receitas} as o\n", "where \n", "(\n", " r.receita_origem_cd not in ('#NULO#','#NULO','#NE','') \n", " and\n", " r.receita_origem_sg in ('#NULO#','#NULO','#NE','') \n", " and\n", " upper(r.receita_origem_cd) = upper(o.cd_origem_receita)\n", ")\n", "or \n", "( \n", " r.receita_origem_ds not in ('#NULO#','#NULO','#NE','') \n", " and\n", " upper(r.receita_origem_cd) in ('#NULO#','#NULO','#NE','') \n", " and\n", " upper(r.receita_origem_ds) = upper(o.tx_origem_receita)\n", ")\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_update_origem_receita)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CODIFICA DESCRIÇÃO E CÓDIGO DA FONTE DA RECEITA" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "query_update_fonte_receita = f\"\"\"\n", "update {table_receitas} as r\n", " set receita_fonte_cd = f.cd_fonte_receita,\n", " receita_fonte_ds = f.ds_fonte_receita\n", "from {table_fonte_receitas} as f\n", "where \n", "(\n", " r.receita_fonte_cd not in ('#NULO#','#NULO','#NE','') \n", " and\n", " r.receita_fonte_ds in ('#NULO#','#NULO','#NE','')\n", " and\n", " upper(r.receita_fonte_cd) = upper(f.cd_fonte_receita)\n", ")\n", "or\n", "(\n", " receita_fonte_ds not in ('#NULO#','#NULO','#NE','') \n", " and\n", " receita_fonte_cd in ('#NULO#','#NULO','#NE','') \n", " and\n", " upper(r.receita_fonte_ds) = upper(f.tx_fonte_receita)\n", ")\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_update_fonte_receita)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CODIFICA UF E MUNICÍPIO DO DOADOR " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "siglas_ue = mtse.get_federacao_siglas_ue()\n", "\n", "query_update_uf_ue_doador = f\"\"\"\n", "update {table_receitas} as r\n", " set doador_uf = m.sg_uf,\n", " doador_ue_nome = m.nm_municipio,\n", " doador_ue = m.cd_municipio\n", "from {table_municipios} as m\n", "where \n", " (\n", " doador_ue not in ('#NULO#','#NULO','#NE','') \n", " and \n", " doador_ue = m.cd_municipio \n", " and \n", " doador_ue_nome in ('#NULO#','#NULO','#NE','') \n", " )\n", " or\n", " (\n", " doador_ue_nome not in ('#NULO#','#NULO','#NE','') \n", " and \n", " upper(doador_ue_nome) = upper(m.nm_municipio) \n", " and \n", " doador_ue in ('#NULO#','#NULO','#NE','') \n", " )\n", ";\n", "\n", "update {table_receitas} as r\n", " set doador_uf = doador_ue,\n", " doador_ue_nome = doador_ue\n", "where\n", " doador_uf in ('#NULO#','#NULO','#NE','','-1')\n", " and \n", " doador_ue in ({siglas_ue})\n", "; \n", "\n", "\"\"\"\n", "\n", "mtse.execute_query(query_update_uf_ue_doador)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CODIFICA UF E MUNICÍPIO DO RECEPTOR" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "siglas_ue = mtse.get_federacao_siglas_ue()\n", "\n", "query_update_uf_ue_receptor = f\"\"\"\n", "update {table_receitas} as r\n", " set receptor_uf = m.sg_uf,\n", " receptor_ue_nome = m.nm_municipio,\n", " receptor_ue = m.cd_municipio\n", "from {table_municipios} as m\n", "where \n", " (\n", " receptor_ue not in ('#NULO#','#NULO','#NE','') \n", " and \n", " receptor_ue = m.cd_municipio \n", " and \n", " receptor_ue_nome in ('#NULO#','#NULO','#NE','') \n", " )\n", " or\n", " (\n", " receptor_ue_nome not in ('#NULO#','#NULO','#NE','') \n", " and \n", " upper(receptor_ue_nome) = upper(m.nm_municipio) \n", " and \n", " receptor_ue in ('#NULO#','#NULO','#NE','') \n", " )\n", ";\n", "\n", "update {table_receitas} as r\n", " set receptor_uf = receptor_ue,\n", " receptor_ue_nome = receptor_ue\n", "where\n", " receptor_uf in ('#NULO#','#NULO','#NE','','-1')\n", " and \n", " receptor_ue in ({siglas_ue})\n", "; \n", "\n", "\"\"\"\n", "\n", "mtse.execute_query(query_update_uf_ue_receptor)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CODIFICA SIGLA/NUMERO DO PARTIDO DO DOADOR " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "query_update_doador_partido_sg = f\"\"\"\n", "\n", "update {table_receitas} \n", " set \n", " doador_partido_sg = sg_partido,\n", " doador_partido_nr = nr_partido \n", "from {table_partidos} \n", "where \n", " (\n", " doador_partido_sg not in ('#NULO#','#NULO','#NE','') \n", " and\n", " upper(doador_partido_sg) = upper(sg_partido)\n", " and\n", " doador_partido_nr in ('#NULO#','#NULO','#NE','') \n", " )\n", " or\n", " (\n", " doador_partido_nr not in ('#NULO#','#NULO','#NE','') \n", " and\n", " upper(doador_partido_nr) = upper(nr_partido)\n", " and\n", " doador_partido_sg in ('#NULO#','#NULO','#NE','') \n", " )\n", " or\n", " (\n", " doador_nome_rfb not in ('#NULO#','#NULO','#NE','') \n", " and\n", " receita_origem_sg ='RPP'\n", " and \n", " (\n", " upper(doador_nome_rfb) like '%'||upper(nm_partido)||'%' \n", " or\n", " upper(doador_nome_rfb) like '%'||upper(sg_partido)||'%' \n", " ) \n", " )\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_update_doador_partido_sg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CODIFICA SIGLA/NUMERO DO PARTIDO DO RECEPTOR" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "query_update_receptor_partido_sg = f\"\"\"\n", "update {table_receitas} \n", " set \n", " receptor_partido_sg = sg_partido,\n", " receptor_partido_nr = nr_partido \n", "from {table_partidos} \n", "where \n", " (\n", " receptor_partido_sg not in ('#NULO#','#NULO','#NE','') \n", " and\n", " upper(receptor_partido_sg) = upper(sg_partido)\n", " and\n", " receptor_partido_nr in ('#NULO#','#NULO','#NE','') \n", " )\n", " or\n", " (\n", " receptor_partido_nr not in ('#NULO#','#NULO','#NE','') \n", " and\n", " upper(receptor_partido_nr) = upper(nr_partido)\n", " and\n", " receptor_partido_sg in ('#NULO#','#NULO','#NE','') \n", " )\n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_update_receptor_partido_sg)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "table_esferas_partidarias = f'{dbschema}.esferas_partidarias_{ano_eleicao}'\n", "query_update_esfera_partidaria = f\"\"\"\n", "update {table_receitas} as r\n", " set receptor_esfera_partidaria_cd = e.cd,\n", " receptor_esfera_partidaria_ds = e.ds\n", "from {table_esferas_partidarias} as e\n", "where \n", " upper(r.receptor_nome) = upper(e.tipo)\n", " or\n", " upper(r.receptor_nome) = upper(e.ds) \n", ";\n", "\n", "update {table_receitas} as r\n", " set doador_esfera_partidaria_cd = e.cd,\n", " doador_esfera_partidaria_ds = e.ds\n", "from {table_esferas_partidarias} as e\n", "where \n", " upper(r.doador_nome) = upper(e.tipo)\n", " or\n", " upper(r.doador_nome) = upper(e.ds) \n", ";\n", "\"\"\"\n", "\n", "mtse.execute_query(query_update_esfera_partidaria)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "copia_receitas = f\"\"\"\n", "DROP TABLE IF EXISTS {table_receitas}_codificada CASCADE;\n", "create table {table_receitas}_codificada as\n", "select * from {table_receitas}\n", ";\n", "\"\"\"\n", "mtse.execute_query(copia_receitas)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-10-20 22:52:08.603597\n" ] } ], "source": [ "import datetime\n", "print(datetime.datetime.now())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
wd15/fmks
sandbox/ch-benchmark.ipynb
1
3798
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Dask with fMKS\n", "\n", "fMKS is currently being developed with Dask support. Currently the `generate_cahn_hilliard_data` function generates data using Dask. This is an embarrisegly parallel workflow as typically for MKS many Cahn-Hilliard simulations are required to calibrate the model. The following is tested using both the threaded and multiprocessing schedulers. Currently the author can not get the distributed scheduler working. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import dask.array as da\n", "from fmks.data.cahn_hilliard import generate_cahn_hilliard_data\n", "import dask.threaded\n", "import dask.multiprocessing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `time_ch` calls `generate_cahn_hilliard_data` to generate the data. `generate_cahn_hilliard_data` returns the microstructure and response as a tuple. `compute` is called on the response field with certain number of workers and with a scheduler." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def time_ch(num_workers,\n", " get,\n", " shape=(48, 200, 200),\n", " chunks=(1, 200, 200),\n", " n_steps=100):\n", " generate_cahn_hilliard_data(shape,\n", " chunks=chunks,\n", " n_steps=n_steps)[1].compute(num_workers=num_workers,\n", " get=get)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Threaded Timings" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8 thread(s)\n", "1 loop, best of 3: 7.15 s per loop\n", "4 thread(s)\n", "1 loop, best of 3: 9.87 s per loop\n", "2 thread(s)\n", "1 loop, best of 3: 17.9 s per loop\n", "1 thread(s)\n", "1 loop, best of 3: 33.6 s per loop\n" ] } ], "source": [ "for n_proc in (8, 4, 2, 1):\n", " print(n_proc, \"thread(s)\")\n", " %timeit time_ch(n_proc, dask.threaded.get)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multiprocessing Timings" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8 process(es)\n", "1 loop, best of 3: 6.41 s per loop\n", "4 process(es)\n", "1 loop, best of 3: 9.24 s per loop\n", "2 process(es)\n", "1 loop, best of 3: 17.6 s per loop\n", "1 process(es)\n", "1 loop, best of 3: 34.2 s per loop\n" ] } ], "source": [ "for n_proc in (8, 4, 2, 1):\n", " print(n_proc, \"process(es)\")\n", " %timeit time_ch(n_proc, dask.multiprocessing.get)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
nick-youngblut/SIPSim
ipynb/bac_genome/priming_exp/validation_sample/.ipynb_checkpoints/X12C.700.45.01_fracRichness-checkpoint.ipynb
1
275141
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Running SIPSim pipeline to simulate priming_exp gradient dataset\n", "\n", "* Basing simulation params off of priming_exp dataset\n", " * Basing starting community diversity on mean percent abundances in all fraction samples for the gradient\n", " * Other parameters are 'default'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setting variables" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "workDir = '/home/nick/notebook/SIPSim/dev/priming_exp/validation_sample/X12C.700.45_fracRichness/'\n", "genomeDir = '/home/nick/notebook/SIPSim/dev/priming_exp/genomes/'\n", "allAmpFrags = '/home/nick/notebook/SIPSim/dev/bac_genome1210/validation/ampFrags.pkl'\n", "otuTableFile = '/var/seq_data/priming_exp/data/otu_table.txt'\n", "metaDataFile = '/var/seq_data/priming_exp/data/allsample_metadata_nomock.txt'\n", "primerFile = '/home/nick/notebook/SIPSim/dev/515F-806R.fna'\n", "\n", "cdhit_dir = '/home/nick/notebook/SIPSim/dev/priming_exp/CD-HIT/'\n", "R_dir = '/home/nick/notebook/SIPSim/lib/R/'\n", "figureDir = '/home/nick/notebook/SIPSim/figures/'\n", "\n", "# simulation params\n", "comm_richness = 6901\n", "seq_per_fraction = ['lognormal', 10.096, 1.116]\n", "\n", "# for making genome_map file for genome fragment simulation\n", "taxonMapFile = os.path.join(cdhit_dir, 'target_taxa.txt')\n", "genomeFilterFile = os.path.join(cdhit_dir, 'genomeFile_seqID_filt.txt')\n", "abundFile = os.path.join('/home/nick/notebook/SIPSim/dev/priming_exp/exp_info', 'X12C.700.45_frac_OTU.txt')\n", "\n", "# misc\n", "nprocs = 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Init" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import glob\n", "import cPickle as pickle\n", "import copy\n", "from IPython.display import Image" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext rpy2.ipython" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: \n", "Attaching package: ‘dplyr’\n", "\n", "\n", " res = super(Function, self).__call__(*new_args, **new_kwargs)\n", "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: The following objects are masked from ‘package:stats’:\n", "\n", " filter, lag\n", "\n", "\n", " res = super(Function, self).__call__(*new_args, **new_kwargs)\n", "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: The following objects are masked from ‘package:base’:\n", "\n", " intersect, setdiff, setequal, union\n", "\n", "\n", " res = super(Function, self).__call__(*new_args, **new_kwargs)\n", "/opt/anaconda/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: Loading required package: grid\n", "\n", " res = super(Function, self).__call__(*new_args, **new_kwargs)\n" ] } ], "source": [ "%%R\n", "library(ggplot2)\n", "library(dplyr)\n", "library(tidyr)\n", "library(gridExtra)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if not os.path.isdir(workDir):\n", " os.makedirs(workDir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Creating a community file from the fraction relative abundances" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " OTUId mean_perc_abund median_perc_abund max_perc_abund sample\n", "1 OTU.1 2.191008528 2.109151822 3.455925277 X12C.700.45\n", "2 OTU.10 0.044722200 0.035513830 0.125434932 X12C.700.45\n", "3 OTU.100 0.234795756 0.201043020 0.469029886 X12C.700.45\n", "4 OTU.1000 0.003686293 0.002395669 0.014643969 X12C.700.45\n", "5 OTU.10000 0.002177551 0.002282115 0.002335085 X12C.700.45\n", "6 OTU.1001 0.023347018 0.021015762 0.057093919 X12C.700.45\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i abundFile\n", "# reading priming experiment OTU table\n", "tbl.abund = read.delim(abundFile, sep='\\t')\n", "tbl.abund %>% head" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " taxon_name rel_abund_perc library rank\n", "1 OTU.1 2.1910085 1 1\n", "2 OTU.3 1.3806520 1 2\n", "3 OTU.8 1.3443226 1 3\n", "4 OTU.2 1.2433854 1 4\n", "5 OTU.13 0.9717986 1 5\n", "6 OTU.22 0.9351165 1 6\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "tbl.comm = tbl.abund %>%\n", " rename('taxon_name' = OTUId,\n", " 'rel_abund_perc' = mean_perc_abund) %>%\n", " select(taxon_name, rel_abund_perc) %>%\n", " mutate(library = '1',\n", " rank = row_number(-rel_abund_perc)) %>%\n", " arrange(rank)\n", " \n", "tbl.comm %>% head" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Source: local data frame [6 x 4]\n", "\n", " library taxon_name rel_abund_perc rank\n", "1 1 OTU.1 1.8857213 1\n", "2 1 OTU.3 1.1882769 2\n", "3 1 OTU.8 1.1570095 3\n", "4 1 OTU.2 1.0701365 4\n", "5 1 OTU.13 0.8363917 5\n", "6 1 OTU.22 0.8048207 6\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "# rescaling rel_abund_perc so sum(rel_abund_perc) = 100\n", "tbl.comm = tbl.comm %>%\n", " group_by(library) %>%\n", " mutate(total = sum(rel_abund_perc)) %>% \n", " ungroup() %>%\n", " mutate(rel_abund_perc = rel_abund_perc * 100 / total) %>%\n", " select(library, taxon_name, rel_abund_perc, rank)\n", " \n", "tbl.comm %>% head" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Number of OTUs: 6901 \n", "Community richness = number of OTUs? TRUE \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i comm_richness\n", "# number of OTUs\n", "n.OTUs = tbl.comm$taxon_name %>% unique %>% length\n", "cat('Number of OTUs:', n.OTUs, '\\n')\n", "\n", "# assertion\n", "cat('Community richness = number of OTUs? ', comm_richness == n.OTUs, '\\n')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%R -i workDir\n", "\n", "commFile = paste(c(workDir, 'comm.txt'), collapse='/')\n", "write.table(tbl.comm, commFile, sep='\\t', quote=F, row.names=F)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting community distribution" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " library taxon_name rel_abund_perc rank\n", "1 1 OTU.1 1.8857213 1\n", "2 1 OTU.3 1.1882769 2\n", "3 1 OTU.8 1.1570095 3\n", "4 1 OTU.2 1.0701365 4\n", "5 1 OTU.13 0.8363917 5\n", "6 1 OTU.22 0.8048207 6\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i workDir\n", "\n", "commFile = paste(c(workDir, 'comm.txt'), collapse='/')\n", "comm = read.delim(commFile, sep='\\t')\n", "comm %>% head" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 900 -h 350\n", "\n", "ggplot(comm, aes(rank, rel_abund_perc)) +\n", " geom_point() +\n", " labs(x='Rank', y='% relative abundance', title='Priming experiment community abundance distribution') +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulating fragments" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Making a genome index file to map genome fasta files to OTUs\n", "\n", "* Will be used for community simulation\n", "* Just OTUs with association to genomes" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1] 236\n", " target_genome OTU\n", "1 CP001738_Thermomonospora_curvata_DSM_43183 OTU.8540\n", "2 CP001738_Thermomonospora_curvata_DSM_43183 OTU.9267\n", "3 CP001738_Thermomonospora_curvata_DSM_43183 OTU.1457\n", "----------------\n", "[1] 187\n", " V1\n", "1 CP003093_Pseudoxanthomonas_spadix_BD_a59\n", "2 CP000511_Mycobacterium_vanbaalenii_PYR_1\n", "3 CP003344_Desulfitobacterium_dichloroeliminans_LMG_P_21439\n", " V2\n", "1 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Pseudoxanthomonas_spadix_BD-a59.fasta\n", "2 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Mycobacterium_vanbaalenii_PYR-1.fasta\n", "3 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Desulfitobacterium_dichloroeliminans_LMG_P-21439.fasta\n", "----------------\n", "[1] 6901\n", " library taxon_name rel_abund_perc rank\n", "1 1 OTU.1 1.885721 1\n", "2 1 OTU.3 1.188277 2\n", "3 1 OTU.8 1.157009 3\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i taxonMapFile -i genomeFilterFile \n", "\n", "taxonMap = read.delim(taxonMapFile, sep='\\t') %>%\n", " select(target_genome, OTU) %>%\n", " distinct()\n", "taxonMap %>% nrow %>% print\n", "taxonMap %>% head(n=3) %>% print\n", "\n", "breaker = '----------------\\n'\n", "cat(breaker)\n", "\n", "genomeFilter = read.delim(genomeFilterFile, sep='\\t', header=F) \n", "genomeFilter %>% nrow %>% print\n", "genomeFilter %>% head(n=3) %>% print\n", "\n", "cat(breaker)\n", "\n", "comm = read.delim(commFile, sep='\\t') \n", "comm %>% nrow %>% print\n", "comm %>% head(n=3) %>% print" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ ".\n", " OTU.1 OTU.10 OTU.101 OTU.102 OTU.10237 OTU.1035 \n", " 1 1 1 1 1 1 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "taxonMap$OTU %>% table %>% sort(decreasing=T) %>% head" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " OTU\n", "1 OTU.8540\n", "2 OTU.9267\n", "3 OTU.1457\n", " fasta_file\n", "1 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Thermomonospora_curvata_DSM_43183.fasta\n", "2 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Thermomonospora_curvata_DSM_43183.fasta\n", "3 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Thermomonospora_curvata_DSM_43183.fasta\n", " target_genome\n", "1 CP001738_Thermomonospora_curvata_DSM_43183\n", "2 CP001738_Thermomonospora_curvata_DSM_43183\n", "3 CP001738_Thermomonospora_curvata_DSM_43183\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "\n", "tbl.j = inner_join(taxonMap, genomeFilter, c('target_genome' = 'V1')) %>%\n", " rename('fasta_file' = V2) %>%\n", " select(OTU, fasta_file, target_genome)\n", "\n", "tbl.j %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ ".\n", " OTU.1 OTU.10 OTU.101 OTU.102 OTU.10237 OTU.1035 \n", " 1 1 1 1 1 1 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "tbl.j$OTU %>% table %>% sort(decreasing=T) %>% head" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Number of target OTUs: 196 \n", "-------- \n", " OTU\n", "1 OTU.9267\n", "2 OTU.1457\n", "3 OTU.77\n", " fasta_file\n", "1 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Thermomonospora_curvata_DSM_43183.fasta\n", "2 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Thermomonospora_curvata_DSM_43183.fasta\n", "3 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Pseudoxanthomonas_spadix_BD-a59.fasta\n", " target_genome library rel_abund_perc rank\n", "1 CP001738_Thermomonospora_curvata_DSM_43183 1 0.003132433 4370\n", "2 CP001738_Thermomonospora_curvata_DSM_43183 1 0.011451281 1176\n", "3 CP003093_Pseudoxanthomonas_spadix_BD_a59 1 0.028904303 569\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "tbl.j2 = inner_join(tbl.j, comm, c('OTU' = 'taxon_name')) \n", "\n", "n.target.genomes = tbl.j2$OTU %>% unique %>% length\n", "cat('Number of target OTUs: ', n.target.genomes, '\\n')\n", "cat('--------', '\\n')\n", "tbl.j2 %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%R -i workDir\n", "\n", "outFile = paste(c(workDir, 'target_genome_index.txt'), collapse='/')\n", "write.table(tbl.j2, outFile, sep='\\t', quote=F, row.names=F, col.names=F)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting community abundance distribution of target genomes" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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XX301PDzcz89v48aNNburvLw8OTm5V69e/v7+MTExCxYsKCoqss4tLi6eM2dObGxsQEBA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\nfP89srOxdaulOaUS23sXJCSgoAA5OZb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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 900 -h 350\n", "\n", "ggplot(tbl.j2, aes(rank, rel_abund_perc)) +\n", " geom_point(size=3, shape='O', color='red') +\n", " labs(x='Rank', y='% relative abundance', title='Priming experiment community abundance distribution') +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulating fragments of genomes that match priming_exp bulk OTUs" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!cd $workDir; \\\n", " SIPSim fragments \\\n", " target_genome_index.txt \\\n", " --fp $genomeDir \\\n", " --fr $primerFile \\\n", " --fld skewed-normal,9000,2500,-5 \\\n", " --flr None,None \\\n", " --nf 10000 \\\n", " --np $nprocs \\\n", " 2> ampFrags.log \\\n", " > ampFrags.pkl " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Appending fragments from randomly selected genomes of total dataset (n=1210)\n", "\n", "* This is to obtain the richness of the bulk soil community\n", "* Random OTUs will be named after non-target OTUs in comm file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Making list of non-target OTUs" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%R -i workDir\n", "# loading files\n", "\n", "## target genome index (just OTUs with associated genome)\n", "inFile = paste(c(workDir, 'target_genome_index.txt'), collapse='/')\n", "tbl.target = read.delim(inFile, sep='\\t', header=F)\n", "colnames(tbl.target) = c('OTUId', 'fasta_file', 'genome_name')\n", "\n", "## comm file of total community OTUs \n", "commFile = paste(c(workDir, 'comm.txt'), collapse='/')\n", "tbl.comm = read.delim(commFile, sep='\\t')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Number of non-target genomes: 6705 \n", "---------\n", " library taxon_name rel_abund_perc rank\n", "1 1 OTU.8166 0.0004756572 6901\n", "2 1 OTU.8112 0.0004756572 6900\n", "3 1 OTU.7281 0.0004756572 6899\n", "4 1 OTU.7101 0.0004756572 6898\n", "5 1 OTU.6733 0.0004756572 6897\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "# just OTUs w/out an associated genome\n", "tbl.j = anti_join(tbl.comm, tbl.target, c('taxon_name' = 'OTUId'))\n", "n.nontarget.genomes = tbl.j$taxon_name %>% length\n", "cat('Number of non-target genomes: ', n.nontarget.genomes, '\\n')\n", "cat('---------\\n')\n", "tbl.j %>% head(n=5)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Target + nonTarget richness = total community richness?: TRUE \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i comm_richness\n", "# checking assumptions\n", "cat('Target + nonTarget richness = total community richness?: ',\n", " n.target.genomes + n.nontarget.genomes == comm_richness, '\\n')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -i workDir\n", "# writing out non-target OTU file\n", "outFile = paste(c(workDir, 'comm_nonTarget.txt'), collapse='/')\n", "write.table(tbl.j, outFile, sep='\\t', quote=F, row.names=F)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Randomly selecting amplicon fragment length-GC KDEs from total genome pool" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of non-target OTUs: 6705\n" ] }, { "data": { "text/plain": [ "['OTU.8166', 'OTU.8112', 'OTU.7281', 'OTU.7101']" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# List of non-target OTUs\n", "inFile = os.path.join(workDir, 'comm_nonTarget.txt')\n", "nonTarget = pd.read_csv(inFile, sep='\\t')['taxon_name'].tolist()\n", "\n", "print 'Number of non-target OTUs: {}'.format(len(nonTarget))\n", "nonTarget[:4]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target OTU richness: 196\n" ] } ], "source": [ "# loading amplicon fragments from full genome KDE dataset\n", "inFile = os.path.join(workDir, 'ampFrags.pkl')\n", "ampFrag_target = []\n", "with open(inFile, 'rb') as iFH:\n", " ampFrag_target = pickle.load(iFH)\n", "print 'Target OTU richness: {}'.format(len(ampFrag_target))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Count of frag-GC KDEs for all genomes: 1210\n" ] } ], "source": [ "# loading amplicon fragments from full genome KDE dataset\n", "ampFrag_all = []\n", "with open(allAmpFrags, 'rb') as iFH:\n", " ampFrag_all = pickle.load(iFH)\n", "print 'Count of frag-GC KDEs for all genomes: {}'.format(len(ampFrag_all)) " ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ 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20,108,42,171,146,154,146,70,104,98,122,152,86,161,105,89,70,193,141,2,193,96,1,0,1,2,175,27,49,54,32,54,97,101,12,14,29,89,96,159,85,32,46,169,99,160,184,102,72,123,162,130,148,41,11,65,54,158,12,144,190,139,82,172,12,41,74,174,152,23,100,141,73,105,15,23,26,165,47,86,62,50,99,9,68,4,15,114,26,19,86,87,59,15,126,151,147,22,59,81,82,176,191,51,35,41,153,171,108,161,74,157,59,75,143,73,153,167,146,139,115,83,44,174,94,148,113,168,70,61,163,113,60,128,49,46,107,112,70,128,30,171,147,124,93,179,33,152,56,137,154,33,186,176,12,120,127,154,98,80,35,192,35,58,135,122,86,61,62,29,47,35,9,94,93,117,117,21,7,61,178,57,52,91,55,58,184,20,58,74,143,175,122,82,172,34,92,171,139,118,91,1,195,2,55,30,37,36,144,194,33,48,160,74,180,110,189,66,0,139,14,195,36,184,72,32,36,76,132,132,45,43,150,139,101,139,160,90,39,181,76,65,36,58,63,176,2,15,37,127,7,140,56,166,33,171,142,46,89,78,174,150,140,118,181,88,69,188,155,3,167,175,1,64,152,88,195,128,40,134,82,21,6,37,12,120,85,105,24,1,140,190,183,17,2,1,162,177,81,168,70,35,91,77,98,150,156,115,12,161,62,109,35,18,6,75,75,13,117,61,124,179,87,185,43,189,54,83,53,84,91,72,105,91,33,117,97,169,178,70,24,74,45,135,81,156,62,43,125,116,74,119,88,59,45,57,164,27,45,8,41,37,177,59,86,147,1,88,14,55,29,188,136,148,137,190,185,1,67,4,182,69,81,21,21,156,66,79,185,41,51,94,42,185,66,169,65,54,160,151,70,36,21,149,159,187,97,92,120,170,45,19,93,116,54,145,65,158,178,45,194,35,90,131,111,181,52,117,157,161,25,115,24,88,154,61,46,106,134,107,145,118,146,190,152,121,69,2,180,137,5,135,52,136,9,49,88,89,119,25,112,71,44,22,15,104,187,97,31,49,58,162,72,82,39,10,122,65,91,92,20,42,55,13,5,176,117,122,164,133,175,127,58,193,92,187,148,131,46,79,118,146,106,179,178,195,104,122,147,55,25,156,24,46,86,160,70,143,88,65,12,140,129,172,73,63,179,103,187,44,85,55,30,48,60,9,47,160,149,16,158,35,96,10,93,54,116,156,28,92,107,49,63,74,175,80,105,106,64,63,128,42,107,167,72,123,99,125,60,155,31,186,105,35,128,93,170,119,32,30,41,60,175,170,62,109,96,51,97,103,97,121,76,164,128,31,150,10,78,39,109,122,159,84,108,71,99,43,60,175,56,179,108,120,120,64,95,123,3,31,49,8,114,147,44,145,20,176,124,153,143,86,119,58,53,130,73,87,152,25,158,128,134,138,155,46,69,48,117,121,151,60,57,138,6,107,188,0,7,28,161,60,56,169,65,22,178,148,112,166,52,68,46,57,51,14,59,172,114,168,112,79,133,195,58,113,183,67,76,87,100,118,34,117,75,161,63,46,15,117,132,154,39,44,97,159,56,2,111,82,162,195,160,66,17,87,156,85,50,146,167,146,57,143,182,51,51,154,194,122,135,175,192,194,40,73,19,17,20,127,126,137,90,148,106,156,83,124,133,41,137,6,72,24,193,113,51,70,8,119,33,114,143,152,167,22,143,30,155,81,176,121,149,90,63,45,40,152,160,116,24,100,147,132,84,83,62,186,28,138,144,163,190,152,47,143,163,148,169,184,67,104,15,3,186,159,18,165,74,86,56,39,150,54,161,138,189,122,176,18,180,74,153,153,130,159,143,70,179,56,29,66,84,133,61,99,22,46,107,47,53,173,168,195,100,188,142,124,99,31,103,162,184,55,172,10,84,169,88,105,17,190,147,118,12,156,120,65,67,79,107,34,73,95,173,131,191,0,82,170,116,55,130,188,18,178,68,8,12,193,67,103,84,14,45,143,85,60,59," ] }, { "name": "stdout", "output_type": "stream", "text": [ "Number of random taxa needed to reach richness: 6705\n" ] } ], "source": [ "# random selection from list\n", "#target_richness = len(ampFrag_target)\n", "\n", "target_richness = len(ampFrag_target)\n", "richness_needed = comm_richness - target_richness\n", "print 'Number of random taxa needed to reach richness: {}'.format(richness_needed)\n", "\n", "if richness_needed > 0:\n", " index = range(target_richness)\n", " index = np.random.choice(index, richness_needed)\n", " \n", " ampFrag_rand = []\n", " for i in index:\n", " sys.stderr.write('{},'.format(i))\n", " ampFrag_rand.append(copy.deepcopy(ampFrag_all[i]))\n", "else:\n", " ampFrag_rand = []" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# renaming randomly selected KDEs by non-target OTU-ID\n", "for i in range(len(ampFrag_rand)):\n", " ampFrag_rand[i][0] = nonTarget[i]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# appending random taxa to target taxa and writing\n", "outFile = os.path.join(workDir, 'ampFrags_wRand.pkl')\n", "\n", "with open(outFile, 'wb') as oFH:\n", " x = ampFrag_target + ampFrag_rand\n", " print 'Number of taxa in output: {}'.format(len(x))\n", " pickle.dump(x, oFH)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Converting fragments to kde object" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!cd $workDir; \\\n", " SIPSim fragment_kde \\\n", " ampFrags_wRand.pkl \\\n", " > ampFrags_wRand_kde.pkl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Adding diffusion" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!cd $workDir; \\\n", " SIPSim diffusion \\\n", " ampFrags_wRand_kde.pkl \\\n", " --np $nprocs \\\n", " > ampFrags_wRand_kde_dif.pkl " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Making an incorp config file" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!cd $workDir; \\\n", " SIPSim incorpConfigExample \\\n", " --percTaxa 0 \\\n", " --percIncorpUnif 100 \\\n", " > PT0_PI100.config" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Adding isotope incorporation to BD distribution" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!cd $workDir; \\\n", " SIPSim isotope_incorp \\\n", " ampFrags_wRand_kde_dif.pkl \\\n", " PT0_PI100.config \\\n", " --comm comm.txt \\\n", " --np $nprocs \\\n", " > ampFrags_wRand_kde_dif_incorp.pkl" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculating BD shift from isotope incorporation" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "!cd $workDir; \\\n", " SIPSim BD_shift \\\n", " ampFrags_wRand_kde_dif.pkl \\\n", " ampFrags_wRand_kde_dif_incorp.pkl \\\n", " --np $nprocs \\\n", " > ampFrags_wRand_kde_dif_incorp_BD-shift.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulating gradient fractions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!cd $workDir; \\\n", " SIPSim gradient_fractions \\\n", " comm.txt \\\n", " > fracs.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Simulating an OTU table" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!cd $workDir; \\\n", " SIPSim OTU_table \\\n", " ampFrags_wRand_kde_dif_incorp.pkl \\\n", " comm.txt \\\n", " fracs.txt \\\n", " --abs 1e9 \\\n", " --np $nprocs \\\n", " > OTU_abs1e9.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting taxon abundances" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -i workDir\n", "setwd(workDir)\n", "\n", "# loading file\n", "tbl = read.delim('OTU_abs1e9.txt', sep='\\t')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R\n", "## BD for G+C of 0 or 100\n", "BD.GCp0 = 0 * 0.098 + 1.66\n", "BD.GCp100 = 1 * 0.098 + 1.66" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 800 -h 300\n", "# plotting absolute abundances\n", "\n", "tbl.s = tbl %>%\n", " group_by(library, BD_mid) %>%\n", " summarize(total_count = sum(count))\n", "\n", "## plot\n", "p = ggplot(tbl.s, aes(BD_mid, total_count)) +\n", " geom_area(stat='identity', alpha=0.3, position='dodge') +\n", " geom_histogram(stat='identity') +\n", " geom_vline(xintercept=c(BD.GCp0, BD.GCp100), linetype='dashed', alpha=0.5) +\n", " labs(x='Buoyant density') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16) \n", " )\n", "p" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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+0bwo/9lkV3i9MxAJCJIy1guFzzx81t+Zv86Y+/NVc6yhvLR03y9um2jNNz1w\nyGTTnL2H3fXSqnMO+dFoS5BDl1tXLWTThCtW/sdVx5tVe3hi57PLpj3ZEmTs6IWrVy+Y7QqvdwYi\nAUFSxr50mL7Yend89xmTL95qDeexe0+ddPYqq3XXIfZV+MoZU/7sxW+WtrUE+dqke+y7d88pnbR4\ni1m12uNnH37qj4nrQWbTnxwx7fI+V3ijMxAFCJIr3jNezGlnugJBckRl+B/Oj23lbqyd6QsEyRHv\nGJ95K5+d6QsEyRP74vyTH2tn2gJBAGAAQQBgAEEAYABBAGAAQQBgAEEAYABBAGDw/0GzbMgkNzmP\nAAAAAElFTkSuQmCC\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 800 -h 300\n", "# plotting number of taxa at each BD\n", "\n", "tbl.nt = tbl %>%\n", " filter(count > 0) %>%\n", " group_by(library, BD_mid) %>%\n", " summarize(n_taxa = n())\n", "\n", "## plot\n", "p = ggplot(tbl.nt, aes(BD_mid, n_taxa)) +\n", " geom_area(stat='identity', alpha=0.3, position='dodge') +\n", " geom_histogram(stat='identity') +\n", " geom_vline(xintercept=c(BD.GCp0, BD.GCp100), linetype='dashed', alpha=0.5) +\n", " labs(x='Buoyant density', y='Number of taxa') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none'\n", " )\n", "p" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VFUCODC33eDzPPvus2VWQIGO60rhadqZ+99TTFYBPlPCPp7fGCfS5OS1WiqOs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\noLZXPT6KA3DKoj7LPefiWGV/aPdi6Cm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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 800 -h 250\n", "# plotting relative abundances\n", "\n", "## plot\n", "p = ggplot(tbl, aes(BD_mid, count, fill=taxon)) +\n", " geom_vline(xintercept=c(BD.GCp0, BD.GCp100), linetype='dashed', alpha=0.5) +\n", " labs(x='Buoyant density') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none'\n", " )\n", "p + geom_area(stat='identity', position='dodge', alpha=0.5)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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c+3ajPUqCXKF/9Siv8b5zOX8qM8w31mcx+gPWQ13ic1yE/KhGSyjFnxW6xR7Y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\nBx4onOA3f/M3nzB6/PHHX/nKV15yySUALr300pmZmUcffXRmZubSSy9d2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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -w 800 -h 250\n", "\n", "p + geom_area(stat='identity', position='fill')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Subsampling from the OTU table" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dist,loc,scale = seq_per_fraction\n", "\n", "!cd $workDir; \\\n", " SIPSim OTU_subsample \\\n", " --dist $dist \\\n", " --dist_params mean:$loc,sigma:$scale \\\n", " --walk 2 \\\n", " --min_size 10000 \\\n", " --max_size 200000 \\\n", " OTU_abs1e9.txt \\\n", " > OTU_abs1e9_sub.txt " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Testing/Plotting seq count distribution of subsampled fraction samples" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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LwI4hoKH7niktve+rr75iXQaAvUFAg0ms//LLLy9fvsy6DAC7goAGk/CRy//x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\niDyJDBgqz47hPmgAACuFgAY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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -h 300 -i workDir\n", "setwd(workDir)\n", "\n", "tbl = read.csv('OTU_abs1e9_sub.txt', sep='\\t') \n", "\n", "tbl.s = tbl %>% \n", " group_by(library, fraction) %>%\n", " summarize(total_count = sum(count)) %>%\n", " ungroup() %>%\n", " mutate(library = as.character(library))\n", "\n", "ggplot(tbl.s, aes(total_count)) +\n", " geom_density(fill='blue')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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dFhGfbqh6FMs4/i+Wg/0J0c0Sj3j+RRHFMo4lxCKkcHlf4UftJWlpP2zY8Mvu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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -h 300 -w 600\n", "setwd(workDir)\n", "\n", "tbl.s = tbl %>%\n", " group_by(fraction, BD_min, BD_mid, BD_max) %>%\n", " summarize(total_count = sum(count)) \n", "\n", "ggplot(tbl.s, aes(BD_mid, total_count)) +\n", " geom_point() +\n", " geom_line() +\n", " labs(x='Buoyant density', y='Total sequences') +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting list of target taxa" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Number of target OTUs: 196 \n", "----------\n", " OTUId\n", "1 OTU.9267\n", "2 OTU.1457\n", "3 OTU.77\n", " genome_file\n", "1 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Thermomonospora_curvata_DSM_43183.fasta\n", "2 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Thermomonospora_curvata_DSM_43183.fasta\n", "3 /home/nick/notebook/SIPSim/dev/bac_genome1210/genomes/Pseudoxanthomonas_spadix_BD-a59.fasta\n", " genome_ID X Y Z\n", "1 CP001738_Thermomonospora_curvata_DSM_43183 1 0.003132433 4370\n", "2 CP001738_Thermomonospora_curvata_DSM_43183 1 0.011451281 1176\n", "3 CP003093_Pseudoxanthomonas_spadix_BD_a59 1 0.028904303 569\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i workDir\n", "\n", "inFile = paste(c(workDir, 'target_genome_index.txt'), collapse='/')\n", "\n", "tbl.target = read.delim(inFile, sep='\\t', header=F)\n", "colnames(tbl.target) = c('OTUId', 'genome_file', 'genome_ID', 'X', 'Y', 'Z')\n", "tbl.target = tbl.target %>% distinct(OTUId)\n", "\n", "\n", "cat('Number of target OTUs: ', tbl.target$OTUId %>% unique %>% length, '\\n')\n", "cat('----------\\n')\n", "tbl.target %>% head(n=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting abundance distributions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -w 800 -h 250\n", "# plotting relative abundances\n", "\n", "tbl = tbl %>% \n", " group_by(fraction) %>%\n", " mutate(rel_abund = count / sum(count))\n", "\n", "\n", "## plot\n", "p = ggplot(tbl, aes(BD_mid, count, fill=taxon)) +\n", " geom_vline(xintercept=c(BD.GCp0, BD.GCp100), linetype='dashed', alpha=0.5) +\n", " labs(x='Buoyant density') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none'\n", " )\n", "p + geom_area(stat='identity', position='dodge', alpha=0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -w 800 -h 250\n", "\n", "p = ggplot(tbl, aes(BD_mid, rel_abund, fill=taxon)) +\n", " geom_vline(xintercept=c(BD.GCp0, BD.GCp100), linetype='dashed', alpha=0.5) +\n", " labs(x='Buoyant density') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none'\n", " )\n", "p + geom_area(stat='identity')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Abundance distribution of just target taxa" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R\n", "\n", "targets = tbl.target$OTUId %>% as.vector %>% unique \n", "\n", "tbl.f = tbl %>%\n", " filter(taxon %in% targets)\n", "\n", "tbl.f %>% head" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -w 800 -h 250\n", "# plotting absolute abundances\n", "\n", "## plot\n", "p = ggplot(tbl.f, aes(BD_mid, count, fill=taxon)) +\n", " geom_vline(xintercept=c(BD.GCp0, BD.GCp100), linetype='dashed', alpha=0.5) +\n", " labs(x='Buoyant density') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none'\n", " )\n", "p + geom_area(stat='identity', position='dodge', alpha=0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -w 800 -h 250\n", "# plotting relative abundances\n", "\n", "p = ggplot(tbl.f, aes(BD_mid, rel_abund, fill=taxon)) +\n", " geom_vline(xintercept=c(BD.GCp0, BD.GCp100), linetype='dashed', alpha=0.5) +\n", " labs(x='Buoyant density') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none'\n", " )\n", "p + geom_area(stat='identity')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting 'true' taxon abundance distribution (from priming exp dataset)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -i metaDataFile\n", "# loading priming_exp metadata file\n", "\n", "meta = read.delim(metaDataFile, sep='\\t')\n", "meta %>% head(n=4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -i otuTableFile\n", "# loading priming_exp OTU table \n", "\n", "tbl.otu.true = read.delim(otuTableFile, sep='\\t') %>%\n", " select(OTUId, starts_with('X12C.700.28')) \n", "tbl.otu.true %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R\n", "# editing table\n", "tbl.otu.true.w = tbl.otu.true %>%\n", " gather('sample', 'count', 2:ncol(tbl.otu.true)) %>%\n", " mutate(sample = gsub('^X', '', sample)) %>%\n", " group_by(sample) %>%\n", " mutate(rel_abund = count / sum(count)) %>%\n", " ungroup() %>%\n", " filter(count > 0)\n", "tbl.otu.true.w %>% head(n=5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R\n", "tbl.true.j = inner_join(tbl.otu.true.w, meta, c('sample' = 'Sample'))\n", "tbl.true.j %>% as.data.frame %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -w 800 -h 300 -i workDir\n", "# plotting number of taxa at each BD\n", "\n", "tbl = read.csv('OTU_abs1e9_sub.txt', sep='\\t') \n", "\n", "tbl.nt = tbl %>%\n", " filter(count > 0) %>%\n", " group_by(library, BD_mid) %>%\n", " summarize(n_taxa = n())\n", "\n", "## plot\n", "p = ggplot(tbl.nt, aes(BD_mid, n_taxa)) +\n", " geom_area(stat='identity', alpha=0.5) +\n", " geom_point() +\n", " geom_vline(xintercept=c(BD.GCp0, BD.GCp100), linetype='dashed', alpha=0.5) +\n", " labs(x='Buoyant density', y='Number of taxa') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none'\n", " )\n", "p" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -w 700 -h 350\n", "\n", "tbl.true.j.s = tbl.true.j %>%\n", " filter(count > 0) %>%\n", " group_by(sample, Density) %>%\n", " summarize(n_taxa = sum(count > 0))\n", "\n", "ggplot(tbl.true.j.s, aes(Density, n_taxa)) +\n", " geom_area(stat='identity', alpha=0.5) +\n", " geom_point() +\n", " geom_vline(xintercept=c(BD.GCp0, BD.GCp100), linetype='dashed', alpha=0.5) +\n", " labs(x='Buoyant density', y='Number of taxa') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none'\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting total counts for each sample" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -h 300 -w 600\n", "tbl.true.j.s = tbl.true.j %>%\n", " group_by(sample, Density) %>%\n", " summarize(total_count = sum(count)) \n", "\n", "ggplot(tbl.true.j.s, aes(Density, total_count)) +\n", " geom_point() +\n", " geom_line() +\n", " labs(x='Buoyant density', y='Total sequences') +\n", " theme_bw() +\n", " theme(\n", " text = element_text(size=16)\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting abundance distribution of target OTUs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R\n", "tbl.true.j.f = tbl.true.j %>%\n", " filter(OTUId %in% targets) %>%\n", " arrange(OTUId, Density) %>%\n", " group_by(sample)\n", "tbl.true.j.f %>% head(n=3) %>% as.data.frame" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R -w 800 -h 250\n", "# plotting relative abundances\n", "\n", "## plot\n", "ggplot(tbl.true.j.f, aes(Density, rel_abund, fill=OTUId)) +\n", " geom_area(stat='identity') +\n", " geom_vline(xintercept=c(BD.GCp0, BD.GCp100), linetype='dashed', alpha=0.5) +\n", " labs(x='Buoyant density') +\n", " theme_bw() +\n", " theme( \n", " text = element_text(size=16),\n", " legend.position = 'none'\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combining true and simulated OTU tables for target taxa" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R\n", "tbl.f.e = tbl.f %>%\n", " mutate(library = 'simulation') %>%\n", " rename('density' = BD_mid) %>%\n", " select(-BD_min, -BD_max)\n", "\n", "tbl.true.e = tbl.true.j.f %>% \n", " select('taxon' = OTUId,\n", " 'fraction' = sample,\n", " 'density' = Density,\n", " count, rel_abund) %>%\n", " mutate(library = 'true') \n", " \n", " \n", "tbl.sim.true = rbind(tbl.f.e, tbl.true.e) %>% as.data.frame\n", "tbl.f.e = data.frame()\n", "tbl.true.e = data.frame()\n", "\n", "tbl.sim.true %>% head(n=3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R\n", "# check\n", "cat('Number of target taxa: ', tbl.sim.true$taxon %>% unique %>% length, '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Abundance distributions of each target taxon" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "%%R -w 900 -h 3500\n", "\n", "tbl.sim.true.f = tbl.sim.true %>%\n", " ungroup() %>%\n", " filter(density >= 1.677) %>%\n", " filter(density <= 1.761) %>%\n", " group_by(taxon) %>%\n", " mutate(mean_rel_abund = mean(rel_abund)) %>%\n", " ungroup()\n", "\n", "tbl.sim.true.f$taxon = reorder(tbl.sim.true.f$taxon, -tbl.sim.true.f$mean_rel_abund)\n", "\n", "ggplot(tbl.sim.true.f, aes(density, rel_abund, color=library)) +\n", " geom_point() +\n", " geom_line() +\n", " theme_bw() +\n", " facet_wrap(~ taxon, ncol=4, scales='free_y')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%R\n", "tbl.otu.true.w %>% \n", " filter(OTUId == 'OTU.1') %>%\n", " as.data.frame()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, 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mit
albi3ro/Plots.jl_Examples
Labeling.ipynb
2
230608
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RGB{N0f8}(0.992,0.965,0.89) " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<svg xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\"\n", " width=\"120.0mm\" height=\"25.0mm\"\n", " shape-rendering=\"crispEdges\">\n", "<rect x=\"0.0mm\" y=\"0.0mm\"\n", " width=\"14.0mm\" height=\"25.0mm\"\n", " fill=\"#B58900\" stroke=\"none\" />\n", "<rect x=\"15.0mm\" y=\"0.0mm\"\n", " width=\"14.0mm\" height=\"25.0mm\"\n", " fill=\"#CB4B16\" stroke=\"none\" />\n", "<rect x=\"30.0mm\" y=\"0.0mm\"\n", " width=\"14.0mm\" height=\"25.0mm\"\n", " fill=\"#DC322F\" stroke=\"none\" />\n", "<rect x=\"45.0mm\" y=\"0.0mm\"\n", " width=\"14.0mm\" height=\"25.0mm\"\n", " fill=\"#D33682\" stroke=\"none\" />\n", "<rect x=\"60.0mm\" y=\"0.0mm\"\n", " width=\"14.0mm\" height=\"25.0mm\"\n", " fill=\"#6C71C4\" 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"base0=parse(Colorant,\"#839496\");\n", "base1=parse(Colorant,\"#839496\");\n", "base2=parse(Colorant,\"#eee8d5\");\n", "base3=parse(Colorant,\"#fdf6e3\");\n", "\n", "yellow=parse(Colorant,\"#b58900\");\n", "orange=parse(Colorant,\"#cb4b16\");\n", "red=parse(Colorant,\"#dc322f\");\n", "magenta=parse(Colorant,\"#d33682\");\n", "violet=parse(Colorant,\"#6c71c4\");\n", "blue=parse(Colorant,\"#268bd2\");\n", "cyan=parse(Colorant,\"#3aa198\");\n", "green=parse(Colorant,\"#859900\");\n", "\n", "display([base03, base02, base01,base00,base0,base1,base2,base3])\n", "display([yellow,orange,red,magenta,violet,blue,cyan,magenta])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x=collect(0:.1:4π);\n", "y1=sin.(x);\n", "y2=cos.(x);\n", "y3=sin.(2*x);\n", "y4=cos.(2*x);\n", "y5=sin.(x/2);" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<?xml version=\"1.0\" 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not support `formatter`, so I'm switching to pyplot at this point. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<img 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\" />" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pyplot()\n", "plot(x,y1,formatter=:scientific)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "mylabel (generic function with 1 method)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function mylabel(num::Real)\n", " if isapprox(num,π)\n", " return \"π\"\n", " elseif isapprox(num,0)\n", " return \"0\"\n", " end\n", " \n", " num2=num/π\n", " \n", " for ii in 1:10\n", " if isapprox(num2,ii)\n", " return \"$ii π\"\n", " elseif isapprox(num,-ii)\n", " return \"-$ii π\"\n", " end\n", " end\n", " \n", " return \"$num\"\n", "end" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<img 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mit
StewardObservatory/UaNoaoAstroStats
Chap3/Chapter3.ipynb
1
291483
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Image(url='http://www.astroml.org/_images/fig_gaussian_distribution_1.png',width=500)\n", "binomial_dist = Image(url='http://www.astroml.org/_images/fig_binomial_distribution_1.png',width=500)\n", "poisson_dist = Image(url='http://www.astroml.org/_images/fig_poisson_distribution_1.png',width=500)\n", "cauchy_dist = Image(url='http://www.astroml.org/_images/fig_cauchy_distribution_1.png',width=500)\n", "cauchy_dist2 = Image(url='http://www.astroml.org/_images/fig_cauchy_median_mean_1.png',width=500)\n", "laplace_dist = Image(url='http://www.astroml.org/_images/fig_laplace_distribution_1.png',width=500)\n", "chi_squared_dist = Image(url='http://www.astroml.org/_images/fig_chi2_distribution_1.png',width=500)\n", "student_t_dist = Image(url='http://www.astroml.org/_images/fig_student_t_distribution_1.png',width=500)\n", "fisher_f_dist = Image(url='http://www.astroml.org/_images/fig_fisher_f_distribution_1.png',width=500)\n", "beta_dist = Image(url='http://www.astroml.org/_images/fig_beta_distribution_1.png',width=500)\n", "gamma_dist = Image(url='http://www.astroml.org/_images/fig_gamma_distribution_1.png',width=500)\n", "weibull_dist = Image(url='http://www.astroml.org/_images/fig_weibull_distribution_1.png',width=500)\n", "\n", "display(intersection)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://www.astroml.org/_images/fig_prob_sum_1.png\" width=\"500\"/>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Image at 0x1134251d0>" ] } ], "prompt_number": 190 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Overview of Probability" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Definitions**\n", "\n", "* $A$, $B$ are events\n", "\n", "* $p(A)$ is the probability of $A$ occurring\n", "\n", "**Union**\n", "\n", "* $p(A \u222a B) = p(A) + p(B) \u2212 p(A \u2229 B)$\n", " \n", "**Intersection**\n", "\n", "* $p(A \u2229 B) = p(A|B) p(B) = p(B|A) p(A)$\n", " \n", "where $p(A|B)$ means the probability of event $A$ occuring \"given\" that event $B$ has occurred.\n", "\n", "**Law of Total Probability** (derives from intersection definition)\n", "\n", "* $p(A) = \\sum_i{p(A \u2229 B_i)} = \\sum_i{p(A|B_i) p(B_i)}$\n", "\n", "Given that events $B_i$, $i = 1,...,N$ are disjoint and their union is the set of all possible outcomes." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Overview of Random Numbers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Definitions**\n", "\n", "$x$, $y$ are random variables\n", "\n", "**Independent variables**\n", "\n", "* Joint probability = Intersection of two events\n", "* $p(x, y) = p(x) p(y)$\n", "\n", "**Dependent variables**\n", "\n", "* $p(x,y) = p(x \u2229 y) = p(x|y) p(y) = p(y|x) p(x)$ (see *intersection* above)\n", "\n", "**Marginal Probability Function**\n", "\n", "* $p(x) = \\int{p(x,y)dy}$ (see *law of total probability* above)\n", "\n", "**Law of Total Probability (continuous)**\n", "\n", "* $p(x) = \\int{p(x|y)p(y)dy}$ (see *law of total probability* above)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example of joint probability and conditional probability for two variables $x$ and $y$, see the image below." ] }, { "cell_type": "code", "collapsed": false, "input": [ "display(joint_probability)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://www.astroml.org/_images/fig_conditional_probability_1.png\" width=\"800\"/>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Image at 0x113410c10>" ] } ], "prompt_number": 191 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Bayes Rule (Theorem)** \n", "\n", "* $p(x|y) = \\frac{p(y|x) p(x)}{p(y)}$\n", "* $p(x|y) = \\frac{p(y|x) p(x)}{\\int{p(x|y)p(y)dy}}$\n", " \n", "We can derive this using the definitions above (*intersection* and *law of total probability*). \n", "But what does it *mean*?\n", "\n", "**Example: Monty Hall Problem**\n", "\n", "* 1000 boxes\n", "* 1 prize in the box\n", "* You guess which one has the prize with $p$(prize in your box) = 1/1000; $p$(prize not in your box) = 999/1000\n", "* Someone else who knows where the prize is, opens 998 of the non-prize boxes, so only 1 remains;\n", "* Do you switch boxes? or Keep the one you have? \n", "* Still $p$(prize in your box) = 1/1000; $p$(prize not in your box) = $p$(prize in other box) = 999/1000\n", "* Much different than probability if someone walked into room seeing two boxes.\n", "\n", "There is a full derivation in the text, but I'm not going to repeat it here, but I do have a numerical demonstration:" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "The Monty Hall Problem Tester" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "\n", "# Number of boxes and trials\n", "n_boxes = 3\n", "n_trials = 1000\n", "n_boxes_to_remove = n_boxes - 2 # n-2 leaves 1 box to choose from\n", "\n", "# Initializing vars\n", "keep_box_win = 0\n", "switch_box_win = 0\n", "no_win = 0\n", "debug = False\n", "\n", "for i in range(n_trials):\n", " \n", " available_boxes = range(n_boxes)\n", " prize_box = np.random.choice(n_boxes, 1)\n", " if debug: print \"Prize\", prize_box\n", "\n", " # We choose the first one\n", " our_choice = np.random.choice(n_boxes, 1)\n", " if debug: print \"Our choice\", our_choice\n", " available_boxes.remove(our_choice)\n", " if debug: print \"Available boxes\", available_boxes\n", " \n", " # We first temporarily remove the prize, so to make sure *Monty* doesn't remove it.\n", " if prize_box != our_choice:\n", " available_boxes.remove(prize_box)\n", "\n", " # Now Monty Hall removes all-but-one remaining box (none have the prize) \n", " # This must be done serially in order to assure no duplicate removals\n", " for j in range(n_boxes_to_remove):\n", " available_boxes.remove(np.random.choice(available_boxes, 1))\n", " if debug: print \"Monty removes %d box(es) leaving %d box(es).\" % (n_boxes_to_remove, n_boxes-n_boxes_to_remove-1)\n", " \n", " # Put the prize box back in the remaining boxes if possible\n", " if prize_box != our_choice:\n", " available_boxes.extend(prize_box)\n", " if debug: print \"Available boxes\", available_boxes\n", " \n", " # Do we win for keeping our box?\n", " if prize_box == our_choice:\n", " keep_box_win += 1.\n", " elif np.random.choice(available_boxes, 1) == prize_box:\n", " switch_box_win += 1.\n", " else:\n", " no_win += 1.\n", " \n", "n_wins = keep_box_win + switch_box_win\n", "print \"Wins by keeping our first choice box = %d / %d = %4.3f Percent of wins = %4.3f\" \\\n", " % (keep_box_win, n_trials, keep_box_win/n_trials, keep_box_win/n_wins)\n", "print \"Wins by switching to available box = %d / %d = %4.3f Percent of wins = %4.3f\" \\\n", " % (switch_box_win, n_trials, switch_box_win/n_trials, switch_box_win/n_wins)\n", "print \"No winner = %d / %d = %4.3f\" % (no_win, n_trials, no_win/n_trials)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Wins by keeping our first choice box = 336 / 1000 = 0.336 Percent of wins = 0.336\n", "Wins by switching to available box = 664 / 1000 = 0.664 Percent of wins = 0.664\n", "No winner = 0 / 1000 = 0.000\n" ] } ], "prompt_number": 192 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**The Effect of Transforming Random Variables**\n", "\n", "* $x$ is random variable\n", "* $y = \\Phi(x)$\n", "* $x = \\Phi^{-1}(y)$\n", "* If we know the probability distribution of $p(x)$, what is the probability distribution of $p(y)$?\n", "* Start with: $\\int{p(y)dy} = \\int{p(x)dx}$\n", "* to: $p(y) = p(x)\\frac{dx}{dy}$\n", "* to: $p(y) = p(\\Phi^{-1}(y)) \\frac{d\\Phi^{-1}(y)}{dy}$\n", "\n", "Using an example of $y = exp(x)$ -> $x = ln(y)$, where x is a uniform random distribution between (0,1). Plugging in:\n", "\n", "* $\\Phi = exp$\n", "* $\\Phi^{-1} = ln$\n", "* $\\frac{d\\Phi^{-1}(y)}{dy} = \\frac{d(ln(y))}{dy} = \\frac{1}{y}$\n", "* $p(y) = p(\\Phi^{-1}(y)) \\frac{d\\Phi^{-1}(y)}{dy} = \\frac{p(x)}{y} = \\frac{1}{y}$\n", "\n", "So the distribution will follow a $1/y$ profile from $y = \\Phi(x) = exp(x)$ for x = (0,1) -> $y = (1,e)$ as seen below:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "display(random_conversion)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://www.astroml.org/_images/fig_transform_distribution_1.png\" width=\"800\"/>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Image at 0x102258a90>" ] } ], "prompt_number": 193 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Definitions of Descriptive Statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For a distribution $h(x)$ of values from the *full* population set = **population statistics**\n", "For a partial sampling of $h(x)$, we refer to it as $f(x)$ = **sample statistics**\n", "\n", "**Arithemetic Mean** (also known as **expectation value**)\n", "\n", "* $\\mu = E(x) = \\int_{-\\inf}^{\\inf}x h(x) dx$\n", "* `np.mean()`\n", "\n", "**Variance**\n", "\n", "* $V = \\int_{-\\inf}^{\\inf}(x-\\mu)^2 h(x) dx$\n", "* Broadness of distribution\n", "* `np.var()`\n", "\n", "**Standard Deviation**\n", "\n", "* $\\sigma = \\sqrt V$\n", "* `np.std()`\n", "\n", "**Skewness**\n", "\n", "* $\\Sigma = \\int_{-\\inf}^{\\inf}(\\frac{x-\\mu}{\\sigma})^3 h(x) dx$\n", "* Asymmetry of distribution\n", "* Use carefully in cases with small sample size\n", "* `scipy.stats.skew()`\n", "\n", "**Kurtosis**\n", "\n", "* $K = \\int_{-\\inf}^{\\inf}(\\frac{x-\\mu}{\\sigma})^4 h(x) dx - 3$\n", "* Peakedness of distribution relative to Gaussian ($K_{gauss}$ = 0)\n", "* Use carefully in cases with small sample size\n", "* `scipy.stats.kurtosis()`\n", "\n", "**Absolute Deviation about $d$**\n", "\n", "* $\\delta = \\int_{-\\inf}^{\\inf}|x-d| h(x) dx$\n", "\n", "**Mode**\n", "\n", "* $x_m = (\\frac{dh(x)}{dx})_{x_m}$\n", "* `scipy.stats.mode()`\n", "\n", "**Quantiles** (also known as **percentiles** for $p$)\n", "\n", "* $\\frac{p}{100} = \\int_{-\\inf}^{q_p} h(x)dx$\n", "* `np.percentile()`" ] }, { "cell_type": "code", "collapsed": false, "input": [ "display(kurtosis_skew)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://www.astroml.org/_images/fig_kurtosis_skew_1.png\" width=\"500\"/>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Image at 0x1022589d0>" ] } ], "prompt_number": 194 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Median/Quartile estimates vs Mean/Sigma estimates for a sample**\n", "\n", "* Median and quartile values are much less susceptible to deviation from outliers.\n", "* But median and quartile values are more expensive to calculate for large samples than mean and standard deviation.\n", "\n", "* Can estimate sigma based on the interquartile range:\n", "\n", "$\\sigma_G = 0.7413 (q_{75} - q_{25})$\n", "\n", "This can be found in the stats package: `stats.sigmaG()`" ] }, { "cell_type": "heading", "level": 4, "metadata": {}, "source": [ "A function for plotting up any arbitrary distribution and identifying its characteristics" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from scipy import stats\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "def plot_dist(pop, dist=None):\n", "\n", " fig = plt.figure(figsize=(10,7))\n", " ax = fig.add_subplot(111)\n", " n, bins, patches = ax.hist(pop, 15, histtype='stepfilled', normed=True)\n", " #ax.hist(pop, 15, histtype='stepfilled', normed=True)\n", " #ax.plot(bins, y\n", "\n", " \n", " ylim = [0,10]\n", " mean = np.mean(pop)\n", " median = np.median(pop)\n", " std1 = (mean-np.std(pop))*np.ones(2)\n", " std2 = (mean+np.std(pop))*np.ones(2)\n", " q25 = np.percentile(pop,[25])*np.ones(2)\n", " q75 = np.percentile(pop,[75])*np.ones(2)\n", " var = np.var(pop)\n", " skew = stats.skew(pop)\n", " kurtosis = stats.kurtosis(pop)\n", " \n", " if dist:\n", " x = np.linspace(pop.min(), pop.max(), 1000)\n", " pdf = dist.pdf(x)\n", " ax.plot(x, pdf, '-r', linewidth=3, label='PDF')\n", " \n", " ax.plot(mean*np.ones(2), ylim, label='mean')\n", " ax.plot(median*np.ones(2), ylim, label='median')\n", " ax.plot(std1, ylim, label='mean - std')\n", " ax.plot(std2, ylim, label='mean + std')\n", " ax.plot(q25, ylim, label='1st quartile')\n", " ax.plot(q75, ylim, label='3rd quartile')\n", " ax.text(0.05,0.95,'Mean = %4.1f' % mean, transform=ax.transAxes, fontsize=15)\n", " ax.text(0.05,0.90,'Median = %4.1f' % median, transform=ax.transAxes, fontsize=15)\n", " ax.text(0.05,0.85,'Variance = %4.1f' % var, transform=ax.transAxes, fontsize=15)\n", " ax.text(0.05,0.80,'Skew = %4.1f' % skew, transform=ax.transAxes, fontsize=15)\n", " ax.text(0.05,0.75,'Kurtosis = %4.1f' % kurtosis, transform=ax.transAxes, fontsize=15)\n", "\n", " ax.set_ylim(0,1.4*max(n))\n", " ax.legend()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 280 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10000 nums from a uniform dist\n", "dist = stats.uniform(0,1)\n", "pop = dist.rvs(10000)\n", "# alternatively\n", "# pop = np.random.random(1000)\n", "plot_dist(pop, dist)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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KlStRWloKNzc3SCQSxMfHIyQkBIaGhti4cSMcHR0hEAjg6+sLoVCInJwcxMfH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\nDJVlkYuc9xZmNXffg0ajGL1ObbmuLFt2b1y48HT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"text": [ "<matplotlib.figure.Figure at 0x108bce990>" ] } ], "prompt_number": 281 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "All of the Distributions!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Normal Distribution (Top Hat)**\n", "\n", "* $p(x|\\mu,W) = \\frac{1}{W}$ for $|x-\\mu| \\le \\frac{W}{2}$\n", "* $p(x|\\mu,W) = 0$ elsewhere\n", "* for width of interval = W\n", "* `scipy.stats.uniform(left_edge, right_edge)`\n", "* `np.random.random()`\n", "* Continuous Distribution\n", "* When someone asks you for a random number between x and y, this is the standard distribution function.\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "display(uniform_dist)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://www.astroml.org/_images/fig_uniform_distribution_1.png\" width=\"500\"/>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Image at 0x102247ad0>" ] } ], "prompt_number": 282 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10000 nums from a uniform dist\n", "dist = stats.uniform(0,1)\n", "pop = dist.rvs(10000)\n", "# alternatively\n", "# pop = np.random.random(1000)\n", "plot_dist(pop, dist)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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zw8qVK/Hw4UOEh4fjjTfegI2NDV/O1dUVHMfh1KlT/LzU1FTk5eXh8uXLAICE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\nxYoVNQ0DALCUzfrne93d3dHd3T3jvr6+vunrK1e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"text": [ "<matplotlib.figure.Figure at 0x112eba850>" ] } ], "prompt_number": 283 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Gaussian Distribution (Normal Distribution)**\n", "\n", "* $p(x|\\mu, \\sigma) = \\frac{1}{\\sigma \\sqrt{2\\pi}}exp(\\frac{-(x-\\mu)^2}{2\\sigma^{2}})$\n", "* `scipy.stats.norm(mean, std)`\n", "* Continuous Distribution\n", "* Special distribution:\n", " * A convolution of two gaussians is also gaussian\n", " * Fourier transform of a gaussian is also gaussian\n", " * Sample mean and sample variance are independent\n", " * Central Limit Theorem tells us that the mean of samples drawn from an almost arbitrary distrubtion follow a Gaussian" ] }, { "cell_type": "code", "collapsed": false, "input": [ "display(gaussian_dist)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://www.astroml.org/_images/fig_gaussian_distribution_1.png\" width=\"500\"/>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Image at 0x102258ad0>" ] } ], "prompt_number": 284 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10000 nums from a gaussian dist\n", "dist = stats.norm(0,1)\n", "pop = dist.rvs(10000)\n", "# alternatively\n", "# pop = np.random.random(1000)\n", "plot_dist(pop, dist)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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SkpKYN29emep+2C7n5Qec0dHRPP/880Be8Hjw4EE+/vjj6myaEELUGAV7kPLP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\nZtGLrVirmJfmDM8wxnAiCXbZjkTZbN79rfeVV15xtWNjY4mNjfXq64s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"text": [ "<matplotlib.figure.Figure at 0x11278cdd0>" ] } ], "prompt_number": 285 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Binomial Distribution**\n", "\n", "* $p(k|b, N) = \\frac{N!}{k!(N-k)!}b^k(1-b)^{N-k}$\n", "* `scipy.stats.binom(N trials, prob b)`\n", "* Discrete Distribution\n", "* A variable can be either 0 or 1 with probability of success = b; N trials\n", "* Appropriate for a number of discrete boolean events (yes or no) which are independent\n", "* Example: How many heads do you get when you flip a coin N times?\n", "* Can be generalized to **multinomial distribution**" ] }, { "cell_type": "code", "collapsed": false, "input": [ "display(binomial_dist)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://www.astroml.org/_images/fig_binomial_distribution_1.png\" width=\"500\"/>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Image at 0x1134252d0>" ] } ], "prompt_number": 286 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10000 nums from a binomial dist\n", "dist = stats.binom(20,0.5) # 20 trials, probability 0.5\n", "pop = dist.rvs(10000)\n", "plot_dist(pop)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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KiooKlJSUICsrCyEhIQgMDISenl672/3uu+9i6dKleP311yGVSvHzzz8DeHK5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\nGhoa9PDDizVqVJYjWW40YsRD6ux0OgUAwDn/5W9/4vm/kn6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"text": [ "<matplotlib.figure.Figure at 0x108b51390>" ] } ], "prompt_number": 287 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Poisson Distribution**\n", "\n", "* $p(k|\\mu) = \\frac{\\mu^k exp(-\\mu)}{k!}$\n", "* `scipy.stats.poisson(mu)`\n", "* Discrete Distribution\n", "* Special case of **binomial distribution** when $N$ approaches infinity, so prob of success remains fixed.\n", "* \"Law of rare events\", p is small not \\mu\n", "* The mean $\\mu$ fully describes the Poisson Distribution:\n", " * mode = $\\mu - 1$\n", " * std = $\\sqrt \\mu$\n", " * skewness = $\\frac{1}{\\sqrt \\mu}$\n", " * kurtosis = $\\frac{1}{\\mu}$\n", "* As $\\mu$ increases, approximates a gaussian\n", "* Poisson distribution describes the distribution of the number of photons counted in a given interval.\n", " " ] }, { "cell_type": "code", "collapsed": false, "input": [ "display(poisson_dist)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://www.astroml.org/_images/fig_poisson_distribution_1.png\" width=\"500\"/>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Image at 0x113425290>" ] } ], "prompt_number": 288 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10000 nums from a poisson dist\n", "dist = stats.poisson(5) # mean = 20\n", "pop = dist.rvs(10000)\n", "plot_dist(pop)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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RkYGNGzfWGuejxo8fj5kzZ8Lb2xvBwcE4fPgw+vTpg0WLFtW7LkA9ASUioobn6OiIFStW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\nYAhFCwAAwJBY2wEA4MHrqS+//Fy/+1287SCSpD/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"text": [ "<matplotlib.figure.Figure at 0x108bc6490>" ] } ], "prompt_number": 289 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Cauchy (Lorentzian) Distribution**\n", "\n", "* $p(x|\\mu, \\gamma) = \\frac{1}{\\pi \\gamma} (\\frac{\\gamma^2}{\\gamma^2 + (x - \\mu)^2})$\n", "* `scipy.stats.cauchy(mu, gamma)`\n", "* Continuous Distribution\n", "* Symmetric distribution described by the location parameter $\\mu$ and the scale parameter $\\gamma$.\n", "* Median = mode = $\\mu$\n", "* Tails descrease slowly for large $|x|$, mean, variance, std, and higher moments do not exist\n", "* Can estimate std from interquartile range: $\\sigma_G = 1.483\\gamma$\n", "* Clipped mean approach for value estimates\n", " " ] }, { "cell_type": "code", "collapsed": false, "input": [ "display(cauchy_dist)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://www.astroml.org/_images/fig_cauchy_distribution_1.png\" width=\"500\"/>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Image at 0x102258b90>" ] } ], "prompt_number": 290 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10000 nums from a poisson dist\n", "dist = stats.cauchy(0,1)\n", "pop = dist.rvs(100) #increase N and see divergence\n", "plot_dist(pop)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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wc+ZMvkx+fj5EIhEMDQ1x6NAhzJkzB1OnToVUKlXYd9XTivn5+Xj99dcRERGBxMREzJ49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\nz33uc3nppZfe4OkDAIxudbUeHDdu3GE9SVVVrzt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"text": [ "<matplotlib.figure.Figure at 0x108bd89d0>" ] } ], "prompt_number": 293 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Exponential (Laplace) Distribution**\n", "\n", "* $p(x|\\mu, \\Delta) = \\frac{1}{2\\Delta}exp(\\frac{-|x-\\mu|}{\\Delta})$\n", "* `scipy.stats.laplace(mu, delta)`\n", "* Continuous Distribution\n", "* Symmetric distribution described by the location parameter $\\mu$ and the scale parameter $\\Delta$.\n", "* Median = mode = $\\mu$\n", "* It describes the time between two successive events which occur continuously and independently\n", "* Example: photons arriving at a detector" ] }, { "cell_type": "code", "collapsed": false, "input": [ "display(laplace_dist)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://www.astroml.org/_images/fig_laplace_distribution_1.png\" width=\"500\"/>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Image at 0x102258c10>" ] } ], "prompt_number": 294 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10000 nums from a laplace dist\n", "dist = stats.laplace(0, 10)\n", "pop = dist.rvs(10000) #increase N and see divergence\n", "plot_dist(pop)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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RFRWlrpt7/vx5zM3NZa7N/6/WBlsajYYBAwZw9epVDhw4ANwNpq5du8aAAQPK5D948CC9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\ndmOMMefOnTNTpkwxx44dM2vXrjV1dXXGGGM2b94cee8TzZY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"text": [ "<matplotlib.figure.Figure at 0x11342a110>" ] } ], "prompt_number": 295 }, { "cell_type": "markdown", "metadata": {}, "source": [ "**$\\chi^2$ Distribution**\n", "\n", "* $p(Q|k) = \\chi^2(Q|k) = \\frac{1}{2^{k/2}\\Gamma(k/2)} Q^{k/2-1} exp(\\frac{-Q}{2})$ for $Q>0$\n", "* `scipy.stats.chi2(k)`\n", "* Continuous Distribution\n", "* RAN OUT OF STEAM" ] }, { "cell_type": "code", "collapsed": false, "input": [ "display(chi_squared_dist)" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<img src=\"http://www.astroml.org/_images/fig_chi2_distribution_1.png\" width=\"500\"/>" ], "metadata": {}, "output_type": "display_data", "text": [ "<IPython.core.display.Image at 0x102258c50>" ] } ], "prompt_number": 296 }, { "cell_type": "code", "collapsed": false, "input": [ "# 10000 nums from a chi^2 dist\n", "dist = stats.chi2(10)\n", "pop = dist.rvs(10000) #increase N and see divergence\n", "plot_dist(pop)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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U2rVrq40bN1aXLVv22LoRaz7WSO8xMTQ0ZODAgXz77bd89NFHAGzYsAFnZ2ee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\n0Nq1axWLxVRdXa2CggLt3btXklRTU6OKigoFAgF5PB5lZma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"text": [ "<matplotlib.figure.Figure at 0x112ec1c50>" ] } ], "prompt_number": 297 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
OpenBookProjects/ipynb
101notebook/ipython-minibook/chapter4/402-world-map.ipynb
1
235474
{ "metadata": { "name": "9932_04_02" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Chapter 4, example 2\n", "====================\n", "\n", "In this example, we show how we can make visual representations of the world cities population data by displaying a map of human density. You need to execute the example 3 of Chapter 3 first, so that the data set is available." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "filename = '../chapter3/data/worldcitiespop.txt'\n", "data = pd.read_csv(filename)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the raw coordinates, with one pixel per position." ] }, { "cell_type": "code", "collapsed": false, "input": [ "plot(data.Longitude, data.Latitude, ',')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 2, "text": [ "[<matplotlib.lines.Line2D at 0x212cef90>]" ] }, { "output_type": "display_data", "png": 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hqHaG4ul3d3ejrq4OtbW1qKqqwjXXXIPOzs4wqiIiROT1JtHDLxG1l29ih6zX\n8sMqz0sMXnaOaAAkz9+dULJ3BgYGMGPGjPLvuVwO+/btO+m81tbW8s/5fB75fD4Mcwi4P2ReUzHd\npvR83rbsjZ5xGhTC8E691h0mlcx8CVqPyLmQhXwq2XbONuR/90uxWESxWAxsY4lQRD+j+VQ4RZ8I\nH5M7I0UCr/pMVV5chD9Otvqh0pvRgtTp5705OhvM/Nqj2pQV9LvyDvHdd9/tr6D/EUp4p6amBv39\n/eXf+/v7kcvlwqiK0MT0A61Kk/PqCcdlSh6H3Za2ESR8o3O96p7I+iU/6xQtEJvCxj4TiuhfeOGF\n6O3txcGDBzE2NoatW7di1apVYVRFRIwqjup1U42N2TE2YJNgmEbnfovCNXyoTZU2rJp5ikTZdB/k\nw5tRE0p4Z+LEiXj44Yfx3e9+F8ePH8e6devGZe4Q6cD50Pnx/sPMVAlKpR5kW76vX5yxbh5RzFun\nTVX9QFSX24DAC38Y99Wm+xhKyqZWxSGnbNokEHEgLAHjc/hVIiC61mZsFH3bdqY68bLAyn8PVVow\n35/cBhnRcZmddrajhSmbUWJzp487vEfEv35BhOhh1RVL20M9Ue/MlZGEvq/Tt0rn8eeXBgLddnCG\niGzub6ZInOgnocPbDO+t63hOXqfvxNd47c+29H/dMI3fHbaqkI2zbN2+53WDoRds6/OJE30iHEQi\n75bzL3pw/IiSbQ8N4Y4sm0bkgesIvmiWGcQWnfOTKvwk+gQAb2Ea3YdBtKBmU+cn7ESVxSVb4Oc3\nRflBFRIK0m9tmX2VINEnPCN6IN3ypKPu+GEuVBPe0Ym587NLp7MhGhh4x8SEp24qNGlT2iaJvgFs\nuZlBCSJgqvxnGzaokDjbiSzkA4hj9Kbr5uvTmb36tcMW4U+06HtpYBtuRhLQ2TTjt0wi2TgHAFUe\nvuoaL2sJovIA8dqVqT5oQ19OtOiHudjDQ4PGVzjT5WivRHDS0q9kWWCq/uM1Xdh5jU5oSZe43aNE\niz5PWAuLSRK2oN9FZ8ONqfIIMbaKkCz+DriLu9ORcDtfp0xR+TJbRefZ2sY6JFr03TpW0qZtUeCW\n7UAZO2bw0oa29kUTC/s6GTZufU4VOnITdtnGrzj18USLvq2dP0moZk8mUuiIrxG1tc1EbV/QRdcg\nMwWbSbToE/4w0YH5nbumyk0rfHjD5EJ5WERxv519TTUjCDIYyOrUscsGEiP6Yad3Ed4QiVISQhQ8\nle5ncWlRvG1QAAAP4UlEQVSXKNFZpDXdjm79wKb7lgjR57NE+E0cJuvhf6fBxR2bOrwf6B7bC+9c\niGZDqutMELf+nQjR95qb6wdnqEI0hSRhUBPn9rHtoY5zW5pGtAkQkIcURYNDUOIQanMSyh9RiYqw\nY8emXyRmM2E8FLYSp/0EtrdlFIieQ1WGDh9yjMu9N0WsRd9Uloiq7FK5omlj0juLLdvGw8bm+yjy\nXtOOLJyrOlf0v5c6VOiWx18TFYkI74SBaqoYtxStIKThe5KYxg9nmFXljMkcQpM7cv2cHyWxFn2/\nDa37kKve1VHqdGkQjDh1aD/Y+P10HQsbbQ8TkQcuEnZV0kXU+x2ivme+Rf+2227DnDlzsHDhQlx5\n5ZX45JNPyp+1tbVh1qxZaGhowO7du40YqovODfQyZZY9fKYXim0nLd/TK9Qu0SMSc9nue6fTFsa9\nk+mJTf3Et+ivWLEC7733Ht5++23U19ejra0NANDT04OtW7eip6cHXV1duPHGG3HixAljBjsxkZpl\n080g4kcYXiL1STmiEKvOmp4s6y4IcZ3l+xb9QqGACRO+unzp0qU4fPgwAKCzsxOrV69GVVUVamtr\nUVdXh+7ubjPWcujcPN2bG9cbmETiJHqmbXV7r4wusvfGxBG3MKroc7cMHf4av6FaLxl8tkQGjGTv\nPPHEE1i9ejUA4MiRI7jooovKn+VyOQwMDAiva21tLf+cz+eRz+c91+28oUGmbEFvRhpTvwg1fvZw\nyPqRbiaV6pw49k8v4Vpn2zkzdbyW5xXTMwieYrGIYrForDyl6BcKBQwNDZ10fNOmTVi5ciUAYOPG\njZg0aRLWrFkjLScjaWmn6PvFFrEOO1ZI2ItTkGWpvUHFRnS9qp/FvQ/K2lP3O/MeuCrW7vfeVCrn\nn3eI77777kDlKUV/z549youfeuop7Ny5Ey+88EL5WE1NDfr7+8u/Hz58GDU1NYGM1MVPg6vE2s9N\njPvDRgRDFlap1J6HJPQ/0exdt+1K58syfGQbuUwIf1za3ndMv6urC/fddx86OztxyimnlI+vWrUK\nW7ZswdjYGPr6+tDb24slS5YYMTZMTAi+81rCH3F5cLyQlk1uYePWN9yeWX5AEKVd+7lPceuzvmP6\nN910E8bGxlAoFAAAF198MTZv3ozGxkY0NzejsbEREydOxObNm6XhHZtRbc7yci2hR5LbLIbdP1J0\nd9s64WcHqrJM7sYV1WM7GcaiMTeTySCiqgmfUHqiGtFWe1Oeo9eYfhIIkqThFq51i/nL7oENbR5U\nO2O9IzcuJN3TiypjKonI2kQWo04ipZCLFy+dD9OohFxnAE3yIEuiXwGS0llkpCEO6oaJnZgUPvwa\nVSaO7Hy3mZUoF790TdIdMyck+oRn0iA6JvAiJjptasvmnrARibfKw1eVw//Ot2GaxL4EiX5MiLpz\nBs1wSDppCr9UAlEoRifkouu5+1ksTgok+j6IQvTS1jHjDg2M/tF57YIKPtTjLI//X6e8pEGi7wMS\nYDHULoQJVKmXXgSafxUD/5qGtPZXEn3CGCbj13GEspjCgY/xe83dF23Kcv5vgjjNFkj0CYJIDLK4\nv+7CsF/iNHDH+m/kEvEjTg+HH+iVC+bREWxRKMft3LRCok8QIeImMEkfBMNAZ7es6IVt/OtU0ir+\nJPoEESIk+P6QxfFFWTduufdhh3biBsX0iYpBIkf4RUfsS+hudEvrgEyiT4wjLC8oyQ+RH6g91AQV\nZLd3GKXZ2yfRJ8ZBYhQuac4PN0HQttMRe1P3x9aBhUSfcCVo502byPELiqJ3vhBqTLzWwob2tlH4\naSGXCA0bHrqoSPN3N4WJNrQhQ8eW9/CXIE+fIIhEQ7Os8ZDoE0p0/qJQnIja6yPsxmT/tvVZCSz6\n999/PyZMmICRkZHysba2NsyaNQsNDQ3YvXt30CoIi4i7aNr6IBL2EEYft+kPtQSK6ff392PPnj04\n55xzysd6enqwdetW9PT0YGBgAMuXL8eBAwcwYQJNKtIEiSthG6r4fprerx9IiX/2s5/h3nvvHXes\ns7MTq1evRlVVFWpra1FXV4fu7u5ARhLxgLJUCNtx9s+w+6stnj2Pb9Hv7OxELpfDggULxh0/cuQI\ncrlc+fdcLoeBgQH/FhKxgISeiDNhCLStf01NGd4pFAoYGho66fjGjRvR1tY2Ll7PFN8oI2nR1tbW\n8s/5fB75fN7FXMJWbEtLIwgVYadymiy7WCyiWCwaKy/DVGot4d1338WyZcvwjW98AwBw+PBh1NTU\nYN++fXjyyScBABs2bAAAXHbZZbj77ruxdOnS8RVnMsqBgrADnT9NR4JPxJGwMtN0/pZvsPKDaacv\n0ec599xz8eabb2Lq1Kno6enBmjVr0N3dXV7I/fvf/36St0+iHw/4Nx2G3aEJolJUSvRNPx9BtdPI\njlynoDc2NqK5uRmNjY2YOHEiNm/eLA3vEPHC+W7yEuTpE3GF78sm+nEc9rUY8fR9VWzY0yfhCQe3\nvzxEbU7EHVPaofMXvUwQVDspeZ5QIvsrRQSRFMJyXJypoTaRGNG3rWGTAgk8QbgTh7BOicSIPhEO\nqs5rc8cmCEIMiT7hCxJ8ghBj+7NBok/4wqYXSBFEEEz0Y1vj9yJI9AklJOxE0jEl1HEQfIBEn3BB\n9UKquHRygiC+hkSf8AUJfnKg2Vy6INEnXCFRSDY0gKcLEn3CMyQSBBFfjLx7hyCAk9/JQxCEfZCn\nT7hCAk4QyYFEn/BMyaOXvWCKBgmCsBcSfcI3tv45OIIg5JDoE1rwHnwlduRS1hBBmIdEn7AWmjkQ\nhHkoe4fwBAkxQcQb8vQJgiBSBIk+QRBEiggk+g899BDmzJmDefPmYf369eXjbW1tmDVrFhoaGrB7\n9+7ARkZJsViM2gQtyE6zkJ1miYOdcbDRBL5F/6WXXsKOHTvwzjvv4N1338Wtt94KAOjp6cHWrVvR\n09ODrq4u3HjjjThx4oQxgytNXDoC2WkWstMscbAzDjaawLfoP/LII/j5z3+OqqoqAMCZZ54JAOjs\n7MTq1atRVVWF2tpa1NXVobu724y1BEEQRCB8i35vby9eeeUVXHTRRcjn83jjjTcAAEeOHEEulyuf\nl8vlMDAwENxSgiAIIjhMwfLly9m8efNO+tfZ2cnmzZvHfvzjHzPGGOvu7mbnnnsuY4yxH/3oR+yZ\nZ54pl7Fu3Tr2+9///qSyAdA/+kf/6B/98/EvCMo8/T179kg/e+SRR3DllVcCABYvXowJEybgo48+\nQk1NDfr7+8vnHT58GDU1NSddzyjhmyAIouL4Du80NTXhxRdfBAAcOHAAY2NjOOOMM7Bq1Sps2bIF\nY2Nj6OvrQ29vL5YsWWLMYIIgCMI/vnfkXnfddbjuuuswf/58TJo0Cb/97W8BAI2NjWhubkZjYyMm\nTpyIzZs3I0MvUSEIgrCDQMEhTW699VbW0NDAFixYwK644go2Ojpa/mzTpk2srq6OzZ49m/3pT38q\nH3/jjTfYvHnzWF1dXXntIEy2bdvGGhsb2YQJE9ibb75ZPt7X18dOOeUUtmjRIrZo0SJ2ww03RGaj\nyk7G7GlLnrvuuovV1NSU23Dnzp2uNkfFrl272OzZs1ldXR1rb2+P2pxxnHPOOWz+/Pls0aJFbPHi\nxYwxxj7++GO2fPlyNmvWLFYoFNjRo0crbte1117Lpk2bxubNm1c+prIrqnsustO2vnno0CGWz+dZ\nY2Mjmzt3LnvwwQcZY2bbsyKiv3v3bnb8+HHGGGPr169n69evZ4wx9t5777GFCxeysbEx1tfXx2bO\nnMlOnDjBGGNs8eLFbN++fYwxxr73ve+xXbt2hWrj+++/z/72t7+xfD5/kug7O4mTStuostOmtuRp\nbW1l999//0nHRTaX+kkUfPnll2zmzJmsr6+PjY2NsYULF7Kenp7I7OGpra1lH3/88bhjt912G7vn\nnnsYY4y1t7eXn61K8sorr7D9+/ePe05kdkV5z0V22tY3BwcH2VtvvcUYY+zYsWOsvr6e9fT0GG3P\niryGoVAoYMKEr6paunQpDh8+DECc079v3z4MDg7i2LFj5bWAH/7wh9i+fXuoNjY0NKC+vl77/Chs\nBOR22tSWIphg4d62PR3d3d2oq6tDbW0tqqqqcM0116CzszMye0Tw7bhjxw60tLQAAFpaWiK5t5dc\ncgmmTJmiZVeU91xkJ2BX36yursaiRYsAAKeeeirmzJmDgYEBo+1Z8XfvPPHEE7j88ssByHP6+eM1\nNTWR5vr39fXhvPPOQz6fx6uvvgoAGBgYsMpG29vyoYcewsKFC7Fu3TqMjo4qbY6KgYEBzJgxwxp7\neDKZDJYvX44LL7wQjz76KABgeHgY2WwWAJDNZjE8PByliWVkdtl2zwF7++bBgwfx1ltvYenSpUbb\n09irlQuFAoaGhk46vmnTJqxcuRIAsHHjRkyaNAlr1qwxVa0ndGzkOeuss9Df348pU6Zg//79aGpq\nwnvvvWednVEjs3njxo244YYb8Itf/AIAcOedd+KWW27B448/LiwnykV/2xMO/vKXv2D69On45z//\niUKhgIaGhnGfZzIZK7+Dm11R2mxr3/zss89w1VVX4cEHH8Rpp512kh1B2tOY6Kty+gHgqaeews6d\nO/HCCy+Uj4ly+nO5HGpqasohoNJxUa6/aRtFTJo0CZMmTQIAnH/++Zg5cyZ6e3tDs9GvnZVuSx5d\nm6+//vrywKW7p6NS8Pb09/eP86KiZvr06QC+euXJFVdcge7ubmSzWQwNDaG6uhqDg4OYNm1axFZ+\nhcwu2+65s71s6ZtffPEFrrrqKqxduxZNTU0AzLZnRcI7XV1duO+++9DZ2YlTTjmlfFyW019dXY3T\nTz8d+/btA2MMTz/9dPnLVwJnjO+jjz7C8ePHAQAffvghent78X//93+YPn16pDbydtralsBX6x8l\n/vjHP2L+/PlKm6PiwgsvRG9vLw4ePIixsTFs3boVq1atisweJ59//jmOHTsGAPjXv/6F3bt3Y/78\n+Vi1ahU6OjoAAB0dHRW/tzJkdtl2z23rm4wxrFu3Do2Njbj55pvLx422ZyhL0Bx1dXXs7LPPFqY9\nbty4kc2cOZPNnj2bdXV1lY+X0gxnzpzJbrrpptBt/MMf/sByuRw75ZRTWDabZZdddhljjLHf/e53\nbO7cuWzRokXs/PPPZ88//3xkNqrsZMyetuRZu3Ytmz9/PluwYAH7/ve/z4aGhlxtjoqdO3ey+vp6\nNnPmTLZp06aozSnz4YcfsoULF7KFCxeyuXPnlm37+OOP2bJlyyJN2bzmmmvY9OnTWVVVFcvlcuyJ\nJ55Q2hXVPeftfPzxx63rm3/+859ZJpNhCxcuLOvlrl27jLZnhjF6HwJBEERaoL+cRRAEkSJI9AmC\nIFIEiT5BEESKINEnCIJIEST6BEEQKYJEnyAIIkX8P1LAw4bkRlesAAAAAElFTkSuQmCC\n" } ], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We easily recognize the world map!" ] }, { "cell_type": "code", "collapsed": false, "input": [ "locations = data[['Longitude','Latitude']].as_matrix()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we load a world map with `matplotlib.basemap`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "from mpl_toolkits.basemap import Basemap\n", "m = Basemap(projection='mill', llcrnrlat=-65, urcrnrlat=85, llcrnrlon=-180, urcrnrlon=180)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We specify the coordinates of the map's corners." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x0, y0 = m(-180, -65)\n", "x1, y1 = m(180, 85)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create a histogram of the human density. First, we retrieve the population of every city." ] }, { "cell_type": "code", "collapsed": false, "input": [ "population = data.Population" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We project the geographical coordinates into cartesian coordinates." ] }, { "cell_type": "code", "collapsed": false, "input": [ "x, y = m(locations[:,0], locations[:,1])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We handle cities which do not have a population value." ] }, { "cell_type": "code", "collapsed": false, "input": [ "weights = population.copy()\n", "weights[isnan(weights)] = 1000" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We create the 2D histogram with NumPy." ] }, { "cell_type": "code", "collapsed": false, "input": [ "h, _, _ = histogram2d(x, y, bins=(linspace(x0, x1, 500), linspace(y0, y1, 500)), weights=weights)\n", "h[h==0] = 1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We filter this histogram with a Gaussian kernel." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import scipy.ndimage.filters\n", "z = scipy.ndimage.filters.gaussian_filter(log(h.T), 1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We draw the coast lines in the world map, as well as the filtered human density." ] }, { "cell_type": "code", "collapsed": false, "input": [ "figure(figsize=(10,6))\n", "m.drawcoastlines()\n", "m.imshow(z, origin='lower', extent=[x0,x1,y0,y1], cmap=get_cmap('Reds'))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 11, "text": [ "<matplotlib.image.AxesImage at 0x6e417d0>" ] }, { "output_type": "display_data", "png": 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c3eZ9FiGhobidO8+1G7fYtXefdvnQgf0JfBXEkb27MLG2x97OjgtnjtPRtRcxsbHIZDJi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SsQeEip+0yOowKMqLsrpikIhQxCtoFpirLPTKHgkct7FB0f5YMtJLGoSU1TnHhzBXg+dxhj\nOeyFZbyay3OS38OQr5jUVMvaGy+jesZukKgOCNObhJOWktsLWVQuDUO9qM5VgEBsMx1qWxCh2L9G\nofXWEloR+JUTj2HivPs4edxoZNDyK732AL2eS7fjMr62Kvi7D3GfgX1C2cFbBcGu5WsSTB43lmvn\n7sdfHl/AntJgomXzqlvkd26G7Y0wn7XjGErR73n8LZ9jRqDmrBcGTtBVzPkeG32PV4czTC1d9Ae6\nbo12a5Jxfjyjg+UL1nNCOMZLxSLXZIbo9X0++8TLHLi+k2/lshgz50DjGJ0p968CPQFlQUWSiOao\nfj9JQ3M4/xsOeO+jtgKerfXPlxC6KyMNsELEo0l+fNU1qP4utp04nqMv/Q3jKm2m+iaO7zOYc1g6\nlGFGS72eW1fWaymwab+jIZ6eNzuBMmcQtWYZ1179S758xyMA3DpzOkc/8hLXbT+O43eahLXLzrDT\nXojaFt1+tkJ6tg5aZdPfpdU5a1ZorkdFDbJ9W4gmdODkf7nT6NtLBOSZMBO32xaAe7NZ7iXL2dEk\ne4fCOqQcUe7QlPYXBVyTGeJZp8B5iUr2sCPI4HdL6dRB/w0TTRIVAVCJGwbJqIUlJa7rkS16dBZ9\n0r6iTho6NgTI+zoCpRBsRLYMvFWERAGu75fjUa6qrCXv+2xn2rhKEeLN3OwSQDFNqYnRnt6UtWG0\nBjBKKTw/MN4MxlHaGdjANMA0BJapjQgNQxCNWcRqYtgVEZTr4Q5mcfNaPeR5ftBJ0snc2uRZAxe/\n6JEAjm+p4ZI13dzz3VNJTt+Jxm23x0gGadwB50wVhiA3zJrXXuXK62/CLxa54LTjiY8eo9VZjW36\nQUQDovbHNJoodUxrmlo4cbdp3LhsNSebNgXPRwj9HJsC7s3nOKCmgnhFIvBb+udGccpzRzymhnq1\nCrGi7u0dCiHZmM7jCjDjNpUheOz4o/AaW7ju7gc476Ul/K6qno1DGX6f118AWc9D6J4Znb5HTAoO\n3nNHZFuH9ut6n6BA+R5kUvirX8df8DzZriHWFR0eLeTZ1w5zfWUdd+Uy3JRL84/XVrPjnx5h1sSp\niCrNb/qXnrfKIpV/D3KyKpHJP6b37FbAs7U+uhJSe5hUN7Pv507mryGbQy/4P75WVcmhdpRlvsv4\nqjjh0WMQlXUjUtrNlkIVCzCwkd4XnuKOO//EAy8upm8wTV8qQ42UdIRsfvHMUgBOeXE5f1nTw+nd\nKWZFIzB6CCpr9Yy6WED1daEGe7UMvZBDVFbrjX6wB5UdQoSj788y/r+whJAoy2bCjB1IXXAalef9\nGh941XM4wowTCrgibtlJV+8xd+e0p8qXY0lmhqObRAuMxAyAHucIBIYhQYFtCOIhk7rqMEJAKlUk\n7/pckh4kLiTfSlRqc73AP8VHL4ym0HwiQ4gyIbY0AvNRjDdM/ED0Eti/6GmnMdKxMQwdbWCZklDC\nwA7rEajneLiO5tyA7uKYlsQwJVbIxAwZCAWFdCG4PUkoJInVxYmMrkOGLZyuAZycg+P4lJMhgmv3\nEOTyHsO9GaTrIqTgH33DfHPZegBkOExze4c+4efS5Pu7ueXW21n66qv0rO9kQ08/T6/v4XNt9Vy3\nciNn7Tmd+NgJiIZ2aC0lcH9MuUyl6AulBd6itoVzvvQFtjvhq5zS3orR45L3/SDEU/CqW+TQmnrt\nUvyhrk+hnKL2fUqnIBTTlPZN90ohEbEK2tpbueLR5+mMOJx38B5s96m5iFETuXrmLLaZeSS3iCKz\nY2HQTUF+Hqtig+vSpAwihsGTboFdaypIzJgBTR/AQkP5UMyTWrOCiUecwhsHb48pYZX0+E0mxZXp\nIfa0QpwRTZJVilvzGZ5e18usXFqT6n3/EzMj/Fir5GvlOqA87SZu2v9y4LMV8Gytj7RKagVR3ciO\nh87hhIef4LuPPsuhbe20KoOebIFHn3uJmdvM0KOUkhTlLaUcB7VxFX333MIO3/8lSU8xWprsZ4fY\nMVrF//kpHszlmBuO8duKWjIolhZdTvzzE3xp4XL6lCJnSr6w80RmJGPs+YdHeH4wzfhIiGW5AnNa\n67ht7p4YUybDmMn6WlBsWeOsoALVjWzuIDbnOLpHjaH2xO/wYCHHxclG/KyL62v1jSZmah7PJNvm\nWOIcF0sghNA5RqXY7KD80lgr+KYHpFyPdCZPX94hJKXOOFKKSypqyiCF4HsK3SUwDANToANqNwVf\nwX+9sr+KwEQgfYUpfCxfYNkCK3DxNQxByJaEYzbh6hh2bQIZtVEFBy9fxE0XcAez4CvMZARrVD3m\n2DZEdTWqv5/wy4uRK7pBKUzbRLoexfV9KNejkMqTDbhFJbAnN7nevOOTSjsYhibw75qI0rnDRG5N\nZ9jv+9fw3Sef554lq3m9sxfl+UxJRtklGWU7pdjTNvnZlDEo0+BKdwOV0ZA+pRvmxyxJDzaqYlaP\n1gwLDIOm8RM4dLvxPNA7wO6UXNrBQREVgqGiq/l26q3tmA9WwggED7VNiIqASLxJMDFCIiJxnl7X\ny/iaJMPZHIdMHhMEB0eRLR2cMPcwfvSbm7kvGuaAUIQHCzn+XMjxiJtnph1mhfIYWxvnyrNOxJix\nG6Kq4X0bDyrPQ6UH+N8fXERXKs2K/jTj2msZ5RTJrutCAP9wCuxfcNjTDJGOKCoUqO5OfRiLVX6s\nXbpPvPTJ5mO7u62AZ2t95FUKGCSW5JyjDuaKR59l6upVXFtdx4U11Zx08S95MRmlZre9oXEUGCZ+\nPofwXc0NilZo1L9xLRfdfC+72DbHigguEBECC8HqQKWxg2UTE5KQUuwmQ6w3XV5YN0CF1Kqiw5c/\nSlQIVnv693sLetwQcj06swVaaxs0d0f5+nT1cUnk/81KSEM/7+1TqG7uwHttX2YffSLfGxjgDNum\nIuvjUyIu6/V4qmUzww4RWPzhQxncGIEKRiq96WlWT9CZUYKir+j3XSwh3qQEs6Q2qispv3QApB6R\nKQUOYKgScVpvrIUAVOn8pJGccS8gQ7uu/mPTEsRjFtGaGEbYBCnwiw4yFkK2NiFjMaz+ftx13Sjf\nx6irQra3IZpH6bHtUAppmdghE+UrhCkpZosU+7MUiz6u6+MrpTtZgO8rPOVT8DUHyRQC15c4jo+U\nuptUKHoc4JtEq6q47ZEXOKKughljm/EkTK9KYCTDyIiNm8rhZwpcvaEXgJV5nxllztnHWSIYYUfA\nGPFzEtWNHHHAPlx1w20cFo+RSeVwFdyfz3FfPkt9Oow/0A+pAajLo/6p8bHmDIlknb7fUJBFtelz\nEJDwf/nXRwG4eNoYGEqhNm5ANHdAvIrvX3gh27a1MO+WO7n/tZUAPOLmAXjBK/LbY2ZxyJfOQLZP\n1q7Vm0tu30yVRm7PPfoI1z0wH4CGWXthbTeDXdavZ07/L7l7+ToAmkyDiJQcYsXYNxRGrVmNGuzV\nnW/rgypFSyC0oMNlQRs6mhbldqeU/16do5IFyiZxLh9HbQU8W+tfUsKwoLqRhn0PZPYOd3DfC4v4\n6mAvl4bq2cewOelH1/Dt/Z9h5x0mc9tzizlt3qPkXJ8541v5ylEHs+++e5Jf/DIvr+9mWwx89Js1\nJCURKdnguexmh3nGKTDRtEtBARwXiWMIQSmVZ04oxnK3yFjDIiolhiE5oGcDg47LDjc9zL3jJrPj\ndrsHBOct5FT1TiV0vIcKm4jGMdz2m6vZ48jjOWLtOh6pbyYXeC0JwERzcUCPLUpqrRLpM2IIYiGT\naNjA9xX5gkfB8Sn4qpxi7SkV8G8018cU4AjF5akhplgW+1qRYDymR1dSgCX0e8AUOiIgJzTpWgX2\nBzLglvpokrNC4eQVphREfAkJCxk2MWviyHgUUVkBDY2IplZwHNRQClUi9iiF6u1FbejE6+mn2D2E\nM5Qjn3XKcnRDCixLE6c1qdkjn/PwfCi6HnnfJ+srir4mWpuOx3AaUsMOedcvd762EyY7JquJCJNI\nUREKGTiZPL7vYxRd8Hxk1CYVsvneIXsxY/9ZWqH0QSIPPrK3iQg2U91ZUkpBvJJZhx3KiZdfR3hK\nDfFMkR7P47rcMHNjca7b2E/q13dyUySEKQRi7HaoSOIDgZ5yqKs0NGnZd3UkiilGbsdzUQPdfG6X\n7fjdM6/8f/bOPM6Ootz736pezn5m3zKTZLKHhAAJJCFENgPKjogCAS+ICKLeqyAoiIiIoiCLoC8q\nyAVEUERUlHtlFwhC2EFMgBCyb7PPnDn76e6q94/qMwmCkiBIvOT5fCbJ5Czdp7tO1VPP81u4b1UP\n+z7zMrNHtaLHTjL+VbEUH/v0Zzl6r5mcdsbX8Tb1c3OvkVLo+uonsA/7GHLCbtukBF9lj617+s/s\nefIZABR+dB7unvsjG1ppbulgwV5P8PvX1jMvEWNWbYJAa07o7uLKZUPc92Ijo2evx2oda0Qz2YZk\nUGvwPMgOoLtMAieax0AiHTI1jSCn/jtGzu9phG3rkRZp9RzfpfPckfDsiHcnhEBE4jijJ/M/995D\nuXs9L917J3t86RK+2FjPfQMZ9r7tAbjtgde9bOmabo753n9z4q2/557uIRq0YI9oCo1pD/gYxtAp\n8RSLKiUWOPFQEyasEAhTIZDhFyamNbtZ0bA6YfRdAO7rH+ajrXVc/rNf8+V0DXscenToHvz39Gre\nP1G14UiPm8yjN1xF/UEn8ICoME8bBWalTXLhakMvt4XAwlx3AyoWuELi2pJY1EwxtiWxywGOZxb+\nStiOApPoaMDXikpF80SpyG8LOb7FINfVNDLWcZAIbAFRSxKxDCC2WAlG3sOR0rS+wuQrCCtNJWV8\nk6JI4tIyVaGyb+QLWloQbaMgVWN+BNBQj9jQQ6VrgOL6AXxPUSn5+BUD2ZaWQKkQzCxMdhUEGs9X\neJ7C8zRBoEY6N0aPJsQxCVMBCwIDhI460oCoLfO3ZZnJXikolw2r0SoHMFTCcSW5hMuNy9Zz34nH\nQE1j6If23lckjdWERaKugb2njOMnAxmOdmw+O9jNrg1p9pjUztMvrOCh7kFuue1uTozHkOl6RNt4\ndKj6vU0Lse8ZlqdWZlGX1mZskArQmT66ewc4pbmeZZkC31+yhlvmDSBKBYTGJADRGDR38NDGflb2\nDXL4+DauPeUo7JmzjEinVlvdftPaqJqveGIRk448CYDC9d8gMmd/xKiJBpsSS/PZM7/I/X9ZRq6n\nj+5ml8Oeew2JqYw+sqKLT/R3G8PYlP+WNg9a6xBDBSP6YnUtyFS9+b8ReYJwB7A9JjuwhUdd2YjC\n2i7aibxrNhc7Ep4d8e6GtJDRONG2Mcw87Gj40iVc3TeABHaLRDjYjXF9fpgjatOcNrWduY8vZXo0\nwlVruqkRkqvrm6lCU3VYRcgqzb5ujAWRGLaQI3otcou/LQRDKuAXhSwfjiX4YyHPL4ubdTDqLYuD\nU0meq3gcfvbFHP7r/+XKiy8gOW4qxFNGZHF7nCD+RVF1U67pnADAxd193FXfYtpWAFrjK9N+DwRY\nwhhHihC0XCGglA3oz5WN8KQQWNJU3lxLIAOoiCrWw7CjSkozHARcnKjDkYJbSjlOy/RxRrKGgtYU\ntOKAaJypjgHpaiAqBVGESXikGSeB1lSUxlMKEQKdU1Gblo40yTENCClMZaCvDyxpqgPNo6ChDeEr\n7FIZVfYISoP4ZZ9y0TfVHEvgCAshwHWtETC0H2jK5SDUljG0dg0QgCMkSIWLhS0EMVsSj9pEoxaO\nbY2s01Wqu5FKCeny8vWJwKVruvnozCnssuc8wxranloUloWobeGnl1zAR04/izuH+zhwQju3nXcq\n9icvYJ/6NCdZUc7560o+/tpaErO6jYmqVQ4XOXcrQcGGmq8zvehCFlHTgGgajZapzSwqrbh35QYa\nbYtLU/WcNzRIpWeQWGHYVIVEBKSDaOrgN9deSd+GDew/fw6ivsV4rdmuUR7e2usb+Ojhfs659CoA\n+q+7kMjuH0C0hdghv4Ie7uO/r72e+5a8RjlQPLRpgHluhC8narmhlOXOTf2ckM+bhb+ayPydz68r\nRXSmD/JDpsVX02ysOmzXnPvWXMNqvNkcp6sNY9C+B5XyiHo0jvvOzo2i2iKNbMYubSmSOrJzeGeO\ntyPh2RHvfghpesotnaju1dz9h98x1fE59/Kf8N0lKxgbixDtaGDW8YfwaV9T7O7jPOny6lCRlG2b\nhQRT3SkEakTJ1Q7xH9UqQ7Wq4ynF1zL9PFQ2qr63FEyi0ygtvlXfwLxYjFjMJplw+FjS4audLZy9\ncjXHfepz3PWTyxDjd0anGwxN/X2d9NiQqqfvdz9l30+fxd1UOMKKUFKbMTS+1vjaYHakCIGqyvxf\nVclWhi1GRwoiUuIKo+dTChRlbZo6gTZg5oGQxp2wJCdGEtxTLnJVLjNyTiWlmZh2QkQQJGwL2zYL\n3QhQWFURRRKhTQssErGwXAvh2EgJOlDoQgl6+8DzEZGYMZbtmAD5LK5SWHVduN1D2F0ZhgeK+L5C\nEBCJ2CRqXCL1SYRj4WdLFAcLFAsevqfwfEW5rBBCYwujIC2EYYa5riQatYlETHtVa6MPFChDh5ch\nRb7KJpNSICzJq1rx6/X9LLngLGgda/Ar71V1p8rSqsLEpWWSlUSatj0/yMN/upeHH3qIA/aajbAk\nU9t+yGhLMt63abYsHly+jiMC31RlbGdENXqrQlqbJS2qVYwgMKQDSxpdmYZRbLjm65x15Y38cTCL\nRPP8K2uYt2wptI2HxlDkL17DrgcewQhZQW57FURrbYQgy0VuP+tTcFIXNNSbxTvwQRXQuUF6n3qE\n039y2+tee0o0SVfgM9uN8stSARqaRuQx/t710H4F3bsO/cwj6K51iNETYLe9ENEYpob9FuerAmOV\n4VfM9bMjmw2FtUYrf/PjQqBzg+iVS6CYR4wah2ifiI7XvAHEbdSvg5EEZlvwQlUzWjAtSmRgfg98\ncx5g7pcd+adZiDsSnh3xrwlRVWmu4eDjTgK/zK8+eCi7X/F9nnphCZ/+7MnImbtxxbTZzD/+s+zW\naHOsXUvfcImyUlxk20AAACAASURBVIgtcCIWZhFzw2QnFbOpq40ST7oEvuLyVV08VC5y0ZgWzpw9\niSDmYJd8Kn1ZCpkilYoy2ioRC6c+SWtHI1eNb2XUdXdz10+v57ATjkNO3R1qmwxdUhh8xrst5T+i\nOaRUyMB5jxMuy0Kk6qmft4CLv3YWX/7O91lcyvCZSJKmwLSloEo7B60NU6uq11NNOwQaD0E5UORD\n0UAFFJRJbgRQ1Iq/+BVGCZtW21RDdLjLvC7dSK2UWEIQlYKy1jgYXI4QjCQ8KlChYnPo8YU5l4pS\n5PMeek2GWE+eSNTGcS0sWyIdC7s+iasUOI7JmoaHEKPHYE+aiu15RDZuILn0VXLLNpAZLFEs+bgR\ni2jUwWmuxW7wsBLDRAZylHMV8gVvhJauddgClEb/JxKxcJxq68q0xqob6mqVx7KMeCFghp1j8bVl\nG/naUQto2nUPI5Jp2+/N2NDa7PqLWWM8GYmjE2mzoZEWIhIj3jqGQxaeFLZ6clxw0tHc9LPfEHEs\nZsSiLNrUxxGWbaoobmybPke13UosifAr5ly2rIjYLqJlLG1Hnch14yex14lfRCH48OMvMuvF1/jp\nsc8x+aRTkBN3MYDkfxYDFXjooV6+fv43+M5v7+fymRP5z/1mYfueScxiCXTXGpY//jgAN00fS5/S\n/Hh1NyJqcU5vL1mtObK1DhGJvBGE/WZhO9DUgkjXmCSkpvH1TLW3jL9t1W3BTt2SJikEIpqEtnEm\nEUnWgf1GFXod+FDIorP9oSxAGh1WyrZpjFa1LALD6NO5QSMSK21EQxskLJD/nHTIjoRnR/yLQ5iW\nghuD+ihf+fYlm7fmWpFK1nLt+f/FSedfxkfGjMbLFMlXwbLhd8cRgmioxJuI2jS3JUlPacNpSBEM\n5rj8yaVMjUc550MzkXNnI0aNgaEB3Jdfwl22Cq83g6r42FEHKx1D1NWwcm03AJ/59Z/42rpuDl6w\nF0FLO0EkiqehcVQ7bZN3Ml96N/bOe39Ve/JB8HqfoPe6wmTZkKjliBNP4ZADP8gvr7iMM2/9I6en\na5inHfxQOdkKcTgVRZiobGkSYVpWZaXCJMkkM38JKjyrPFapgB7PaLcAHJRO8UEcqlK9UkNZaWIS\nlBZ4SqOFOYKUEseW2I5ABYYB5fkKEYT6Qhg8lyVDQ9JKgBguI8LKk+tKkrkyacAeGkZXKoDAam1G\ndIyGhiZEPIFrS9LKg2VdZIYr5LIV7K4MVl0aa1wnkekJ3OFhoqvX467YiNVbIF/w0Nq0+qJRi5q6\nKKnGBDJi42VKFIdLhEMbaZkKkB2ap5rhYK7VfbkiGyo+p598AtS3hknCe4zdCTz0UA8agWxoQ9c0\nhaagW2LgJFgO++6zF8df8hMmjGnlgUKRxYftA+kGoxu0TQtitYRnGT8xJ4LwPbNJyPSa40aNoSrR\nBInOifzi9OPY+7vXcXNtE3eVCux34z3873CBmSediJw+G13bjHBCNtM2hlYmoet64iG+89v7GR9x\nOfv512h2XRZOGAf9G9HFPOqhe9ll1Tpe2W9XOqd3Yo1p53vfvIHFEciGn+nYOTshRhm9n39UuRO2\nCw3tiJomc862s012IkJaaDdmKiZ/KwAZbkq1ZUMkTIKiOsQ06XDD90aMo5AWOhJDUGeSnzer7oy0\nyd4ckCykNAKxVTyP1ohIbKTipCsl8/s/sA7amtiR8OyI9y5EqNRcDaXQTgTPjtAYixIos+bpEISq\nq9RjaWjKiahNY2OU9KQW3BlTobYWq6uL547ck4mxCLKtCVHfiKhrRMeTiNwwdiaDLlcIhotopfGH\n8ki7izlRi/wxe/PqpkFOee5VrnjypZBaDZYQdHs+R0wZw1c/dQwTP/hhaO00ffN3iiUT0jS1tLaY\nXLaPdpoI/cfsMVP4j4svY9b8eRx9zndZ48DpVhzfC8LlQuAKhachpwKjVougQVihno6ggOYRv8Q9\npQITapOc8qF9mH3AB5my53ywI2RWvEzzISdyD7C/GwVMsiMlxEJgNLB5PEhGgL8BCj8wVQAhwLaq\nFRPzIt/X+KFzucaMI6UtZKYMK3pwNwxAaEFhRdfg1i3FaW9CtjSC1sioixt3iXumkmTHI4imJkTn\nBGhsQSiF7NyI1fAi9gvLEZuGR44djVpEki52XQI7aRzrvVIFVQpCe4uqxpF43drgWYLz/rqOH3z+\nBNyxkxGJ9HtPLxaAsExynh82PniJmnAR3eJpQqCdCG177MuPLvwKfV2buHv+XCbsNT+kX2/dbl1r\nZSoM1TaYFeJrbAfyGX5yxeUsfmQRE+IRJk0Yyx7z92Ti3PmQrGH6vh8g8f2biCYdDtNx6oTkoDse\n4c5CgXmnesjd5odYom37HmsVQCnHqw/fzRGfP59Tmhr4D6Ls17uBE598iZ+t6eKHx61kYk2Cyisr\nsZNRxu+3J9bs+dA2lkVTZvONS6+CdT38cP/d+OjHDkM2tL41JV2Etj6hHcbbCVGtWG/N41szBwmB\ncKMGbPyG99HG97BcgHLR3PNIzCRuf5ukCZMgbz5uzAhxjtz3f97gVOiqtvObfg6Bzg/9UwfYltAj\n5SzffDDLfucWlB2xXcVI71uFYmSh5DylHL+69ofcftMvuMhJsKY3S8YPDE4EQcwytgT1jk1LU4x0\na4roxFHYE8dDIoHOZCA7DLEYRM0uhiAI2xUK+vrx12yk0jWIKvtYURe7PoHdVIOor4NkCl3xUBu7\nKa3cyPDGDLm8Tybw+WU+xy1DGT43bwYXfuNc5NRZkKp/X41RrRSU82SWPsMnP/9lNqzv4nPRJBMD\ni2f8MneU8izzK+QCRUcyzkCpQp1tMTcepaQ1fxrO89FdJ/GfJx7LbgsONKXqaNK08DyDT8j9+T5q\nTjmfLybTrPd8JkmbYTSvBT4L4wkapY0lBHEpiFsWdoh3gRDzKAQ6xMVUW0bG00uPiBmCSWQdy+Bq\nbFvgOha2I5FhwiSEwIlYRGpiyKhDkC9THCxQKgX4gTa2Es0p3IYUVlM9snMMYlQHDPajXniB4oou\nSkNF/IqPlAI37uLWxrGSEVSxgtebpVDw8cMyT/W4BsMjsR3JNb1DPKEUf/jp9xFTZprd9ja1L97p\nCN3Kizn0QBdoZQDUyToDoH2zSkM1g6te+G2kHRsPvYpZF8JWL9IyVab+jXzrnHP57X2L2C2wWOP7\nvOhVuHnBLA46/iOIxkaOPOs7HFL2GJdVBBoe9Uoskh6PXPYV7H0OQrR2miSCMJHxK8ZU2LbBcl9f\ntdIa7VfoW72c+2/7OVfccDvP9QzyWPtoIlpQsOH07i4GAsVVE9o5eEwj0YltyD33Qs6cj2jtNFUc\npaCY5aVnn2RqQwqRTJt7G0+ZVtgWi/uWS/T2sgna6qi2QHMD6OF+RDwFyXqTyPzdSs3IQHlrgPWb\nhEjU8vfSmu1vpq6a44HRDoD31YLyvgoV7txGAAymutHVN0AQKMoiMABYpfExWBFHCFKORVtbktrx\nDUhborM5gtdWGJ0O30fE48jGJmhqMe/f14culcyWPxFHNtZilyr4gwVUoFCeD7EYonMCTJ6BTNYg\nN60l9tTjsPgF1PoMUWVxVjrKaaMa2HnRC3x7wXF0L/oDTTvvgZbbWJr/Nw5T7UlSs/Mc7rj9Zq67\n+gdcded9rBjMMKEmwZeP3o/9DzuUlmkzkKk6lO+x5LnnWPTYYgIVcN2xH6Nh0rQwydksPqerVa1o\nnOSknbj1xEM541f30RsKRVZjAMXHowmm2y5FBSXlY/lmXDghG0yKze00ras4HqP/U31MVy0rAoUu\na4LAQkqNZYfjLaSZK6URPYURELGRPFEEgaJU8skOl4muGyTZPEDS87GTKWgdhZjjEp/QR2RDF/7a\nTfiDOQNRqPj4gz6qElAq+ZQrASrQBjMb0tytQOC6sCTwuXptN4u++fn3Hqg8EiHR3rIRsbixf3mr\nsT+CgXubR5QSvUVVY+S7Foplnrjw41x79yOk4g7n6RRPl4ocft/T9NbVUDtzMrNa63lx1QamS5ts\noDgwEuOawWEGhvM0V9lIWo3YQuhML/RvgvpWw4CqCjyGyU6pex37HPYxxsuAAy2bb7e1ExeSHhFw\nfSbDS+UKt+61M0cdsCfWlCmISTsjRo2HZG1Y2RAgFToSZ9qus9CDXehMP7prLaJltNHQiRu6/UgC\nFvhgO+h3ALj7Lw1h2m4k6xHxtKkM2m/Vqn99m+2djO0qk6iWQM2sUjbJj7TeVwvK+yWqFhSvc0sP\nUfmH7jqZM6++gYNbWmjWmqQliVqShCVJuTaNjXEadmrFnjQWPI+gZ4BgIEtQrKDLHjLmErEtRNto\nxJTdwI1AuQSlPBTzWL1diJqXYelyKl0ZVMkzFaH2TkT7RESyBmIprMwg0Q3rSedKFPIeUmhaIw6P\n7DuDfR/5K/fc9b98Ytwk00P/l8r7v8chBETiWB1T+Ox3ruCzF+RZv3Y1o1pbkYm0WSAsGyEkQmt2\nPXQ8ux7y0fC1b86EEUKYDU5dGyLVwMJLdmHykY+RGehn5pQJpPP9DDz4J1ouv5VnKmXua2yjFIKe\no0ISsy2SMQvXsQgCo4UjhMC2BIHSFEoBpSAw+CJhdIJijsRxjL+XUhov9NLS2tDMK75CaUMzt0PG\nVBBo/BBr5NpGcNDzFOWhIpGNfVgd3Yj2sYidZgFgD/djrXyVyJpVkMuhA0WQLVDeNDRyzErFOKpb\nthExtFyLV3XAwpfXcP0pRzHlsI8jmjoQkdh2kPCE1RrfQ+eGwQvtJWLqXXVmedP5X1oQT9E5bz+e\nve3HnPyFr/LN3gznx2qYUMiy928f4bsr17Nw8ig+8MQSPjmqjcxQmZIykolBPm/mhUrR/FSZRn4F\nrQJEKIanddhb98qQ6eOiCy9iasLlhuYaBvqK+L65Jtd09/OH4Rw//fAcPnL6yTh77BsCfd+Ecq+0\nSa4Gu9DrXjXfqXjKtAWlhRabW5yb1Yitd+36vpthvtsusO2A45FOgA7HV9Xd/W3GdpXwwBagqmqP\n8m1QBXfEv0eM3Ndw58RwP8Gy5/jfX/6amYkYE7VFUQZEhKQmYpOK2URd08LwBnKIDV1Ix0IXywSF\nMrpYQSuNdgJ0Lg/dG0wrq6HF7JxGjTeTyWA3UlhYGzbhbxhE+kZjQiRSCDcS0osCiEWxJo4jWVtD\nom8AbzCPjNrs2VDDVckEi559gU8MdkMshY5a7z2Q9F8YwvRewEpAJM7oGU3hAyN/bH4emMn6rd80\ndHI2oMo9PnzE5gkvP0xjYzvrOzqYcNZl1DfEyA2UKGtNQkpqEjbxmD3CgLJtCzfpYrkWXrbM4ECR\nwUyFYmDEChUYGnhA2Paq0sIDQ7cPDCPQ1xoZCBxttIOUJpRFMH5dTtg6q5QDij0ZrJdew5ESMXkq\nhDgVMWo0WBa6txuRySC9AGmb66ECTaliEi3LE7i+ZD0BJyzfyJXHfYjDT/0MomUMbBfJDuF3Qxnt\nmVLeAOxDmYj3IoQwYNeWMZ3cddaJfPCb1/K49Li6tpF7CnlOe3Y5J67p5cCaBP+dGeYIIhRUgK80\n3tKX0ROfAr8MsYS5uU4ohJisNQlOIQteme4Vy7j1ppu5+5HFLN3QwyOzJuGVfNIpd4Rt15GL0OF5\nHPfBuUTHToREDTjRN65fIf1bF3Po/k2QGYTOqcj2CYbMoQIo5c3m33ZH2m3vt9Bam+pWMYsuDBsg\nc6oBHU0YI9q3Meq2u4QHwrI51VK3Mj3jEKH4ngP2dsQ7HjrwYKiHwvOPMeOkL+EVK1xa04AONBEh\nSToWdQmXmrSLZQsjFmdJ8AO0Umg/QFgSkQwnFwF+Jo/T3QXJ1MjkrGsaEIm0kWGPxZDNjSQyOcOG\nGc6g17xmFhbbRq9bgV79GgQBcnQ7etJEIkYmF3yPfewoP/z5vVSWL8FN1iCc9q0T/fq/GO/WhkRr\ng6VQvgE6jp5EbtQa6mJRmmqiFAfLRAWkojbJuIPjGFaWbQmitVGi7fVYiSgbV3Sx11Mvc39bB1pr\n1vkevYFiRcHjiUqJQ+JxDk2lqK1xiacieBWfXLZCPu9T9JVhWglwwyRFeEbcMFCaYimgXAlGrCak\n7CYVsbAcG9pDrROloKkV0dAEvd0IvQLRPWTgCULg2hIPTaAUz5dLnNc9wDcPmc9xnz0d0T4pbKnI\nd1yE7e2HBh2YuTgaR0QToSbOu3heYcvzDSEwFRA3imxs4prD9+KDN9/HuJYmDhFJdo9EOLd/kJ2i\nERZXKqyQJU6MJOm0bO544iX+c8JY3LYxiKYO02YVEnSALhfJ92xiYO2LdFgeZ3/lO/Rt7OHjboTv\ntY8iUfCJNSVwmtIQMSyvSye18KPbHuHrd/6Jy3fdHdEyNgRYb6EuiQhB2BUoZGCoL9SeEajsILf8\n/IecMHsns0Ebu5MBd7+fqsdbhBACLaWhpQO6XIRYBXT8bVcTt8uEpxoGxByYsmkQgOOinei/Vw9z\nR/zD0IEPuSHUqpf50TXX050rcl9DGwVlduGWMO0ExxG4cRs7EUHGXSKj6qGtDcpl6BvA8j1EMoGI\nRtGFIjpXMJNkNA6NrQYoKC1CJ0mIxpBjRuPU14LjIBwXhEYXc0Z2XloIx0WTR5fKiJZ2xMQZkEij\nB7qYnnyeqU1PMPnT53H/dZcxed+D/mnK5I4wYYwQi5DLoPs3oks5RLoe7fn8+ld3cEhLLb2bchSV\nIm1btLTESbSmCQplCkNlKl4AmSKSAWTM4Y7XNtIXBDzvlfhUd/cbjvdYpcQlmSGejI/DKXlUPGMN\nYVuSGCGmPhQDVIFJOgQm6Skrhe9rhBCUlcKNOsSGi8hCAZEdNsB52zEVBCcOhTy4LlbUIRq1KJeN\ngrON5kdDQ9yRyfGTkw7niM98BjFmp800Zd8zVVAjPBRqNL0HYy0kkxBLm8qTCv4pxtBbhdGmqpj5\nP2RmvW7TK6QBwLaOQ6TqmT56Ej+2onzlF3czJxLh3GSaqxybhb1dAEyLOHwlN8AcJ8qX13ZxTN8g\nHUEATjTESAF+BZXP8uFPnMril1fwremdLHp1A5fU1jNVOzQkI9SOrcNtrUU4NiSTkE5j53IcPLqR\nB5etpfTyX4k2tSEbyvR3bSAqJfG6BjMOhERn+vl/P/wRsq+Lzy6YC9lBHrjnPj556bUMHbg7nzr+\naBItY0A3vmvX9t8hBAKtAnR2CD2wyYz7aMJ8B/6vVHiqMeK6LeIYYKvcsaD8HwqtAsgPMfjCYu64\n9nquWvQX/jNVS06pERpzRAgijsRNuLj1CezahKHuBgqRz6NzeYKhHNoPsDyFjBQBgWysQ+w0AzFx\nOqKuxeh2SAvKBXTPOhjqR5eKiGQKOjoRDa2Qrkck6ww4sK4F3dCM6NsEuQzEzXEpF6FnA3L9Om7c\ncwrNN93P6uefZdKsOQZj8U8KY+2IkG9eNRN0XITbYIwccxv5/ZN/oalYpmQlcYTAtQyDRpV9CDSx\nuE3ciQIaL1+mMlDgN5uMMWQ12bmwvoG5iRidEaPYvDhX4OSuLqYtW8HVdY18IBbHcQSWLZCWHLFV\n8n1N2TdA+iAEQ1eVpkETlDWiL49+aRNNxQp2+yasRAziMURDI7S2Q6oGOWYsVk8vek0fSmvWKp8v\ndffS0drA81ddSOu8/aGmybQyhAh9k4LNuEbbhWjcAFjfk2qPCCnE9maMybt2HiG7Sxvl7Dc/HYGw\nXXQ0AbEER+08nv2PmMfY2x7mgsZ6ZjiSJ9zR3J3Pc102w8J4il/nswCsfGUlo1YtR46eYNrZgYce\nHuT6H/4AazjD9RPGcPeqPpoQtGqj+ZSsieK2NyAaG2BwED2UgWwOlS+xX02Sc9b1kTzzCrLXOHzz\n9w9z+X1PEJWS9niEaQ01TKtLs3owy6/WbOKL08ag99oZ0dfDx797LQBn3P8sH5g3m5lzK8YbbERY\ncfuRqviXRTXBjsYNkDvUTPp7Bb+3iu064YEtsAI7YvuNt0EdBED56OEB7vzdXVz8wFOckkozR5sB\nHZWCuG0Rcy2SCYdIzEHGI1jpkDFhHBYRvm/yfClDY2CBdhxoaITR4xF1raaKNLDJqHeWC6aMjEa0\ntkPHeMToKUZHxIlg+mFlI2iWrkc7rkl4BnrQ65ab9tirr7BuyXI+cu+znLTTGPaf2GGYhYEfOhLL\nbabe7ohqGKsCXfUzcqOIWNIAmof6eGZ9DwDfakvjhoDibKZMseCZ1wqwZAUwmBytNYsLxmLkrNo6\nTkikAKPEXAWbznIiPNHawdG9m3ihUma2E8WyLaK2hbSEYQyWFWUvGFGHBigGiocrRR6tlKiTkhNi\nKfO+PQXKRZ+aTRmiNVEiTSnssodwHEQyhc5mUMUSlXLAeuWxcO1GLjx8Xz5z5heQ46e9QQF4hMxh\n2aaigtg6Rd53K8KWjA4NPAXCJGHvwjQtpIV25BsZWm94Iqb6U9sEnROpyQwzvf4ZXrI10z2B9jUH\nuDEKccVlWSO1cveksew+qs4IoYZO3dgRRDRO11CWmU01LLAi7F4RVDyFFAZzNdSdQ5VW4iQ3ov3A\nMP00BErx36tMJensBbP505IVXH7fEwDMTcRYXapw15ou7lpjnrNnXYrvHfMhxC5zQPnUug7ZkJUo\nclnoWW8sFoRJMEWqDh3bkt34N9fi33G+eau1QxgNMNEwyszROqz4KedtzbHbfcKzI7bj0Co0lysZ\n/xXHMWqrVerlP3ypgnIR1b2enz/4OAekk3xIRcn4AVIYj6RUwhmR4tdeQDBcREZdrJoEuC4kEqaU\n3BBqZsRiYclb4LsRDjr9XB55eeXIMROuzQNfOJbZu0yD+iZobEU0tRttCDdmeu1B6DVTKYG0kY3t\n0NCGStdDPgtda1HDGY6491kmp2M8urGfvb99La2j/sAxhx/CUQv2wY7HcWoaRtRgt1un4u06Qk2u\nYg5tOwilUJn+zY9GJDEtcFzjSWVJSRAog6kphyBgyygpV2OqsClsIZJoYRImKQW2kPyusY0A47we\ncSziCZtYzCFQmsxQmWIpqIo/A3BNYZgurehDkRHwyaFeTk2k+WQixcBwhWzOIzlYoklDsr6ANZyB\nShmdy4HSlFzJqcu7uejjH+K0c85BtI03GixvUsUWQhqvqPccz1HFERG2lqTZkL6L43vrvjvCJAJ1\nLTBjT6Tj8p8HruaLdz7CTR1tpDA6TEfGE/zZL/FcqcyABU4yDpGo0TaSlkkqI3EOXbAv83/+O74y\nfwbxuE3ED7WSwvFSKQf4XsEwApUi8DXLPY+cryhc+GncOXOxDzkVgDuOPwCpFH53P0G+iEglkKPb\nWXDkodjTd0fEkqg1r3DSbhP59qMvslN9miGt0WtehRefRA8O8cJAlnt78uy6227M33s+ta0hZlBr\nY8AWMeQBYb/X42PrYkRzzy+bJN52jW2FeRRgZONolJzjJr8rZM2PUiF4eetVpmFHwrMj3m5ojfYq\nkOlFrVsGXeugaRSycxp6i3L8343AQ/du4N5f3c7DqzZyeksrhdBnqcayqK+NkEw42HZYvtQaf7iI\nKvvI/ixOYxorEoFIBJJJw4SZMA3RPBosB53NUPrRnbTUpvF8n7Lnky9XmHf5rQC8ev7JjN9jNjpZ\ni0jUhyKIRiped61C96w3FNH2CYhUPdJ2UfmX0OvXcvdzy1iSybMkk+f7++zMrOY6zv/zUr749e/y\niXMuImpJOmpT/P7is5n6gX0R9W3oeHrbvWXej1E1plTKJM9t4w3mYWAjs046E4B1u00hm62MdL5K\nJd8kIiGQ2PONEnKgBT/OmN38h6MxpjnuiA6PIJRWQeOHuBwpBBHLtA18X1EuBTiujQzxOxodMrw0\nMSlZojzuPmofpu0xjS/d8SeWP/US8xuS1DkRymWfXCXALvkoBDi2+Wz5POTz+FpzypouDp2zM6ed\ndZYBuEZi23/LvspgrJSMY7fjIpwobAdEoioEQsTSUNvA8QvmYeWyfOK+Z/hFWxs1JSNTcFFjEweu\nW8edg8PsJh2m1DcaskK1d2nZdE6eiqcUox/9C717z6CS9whCmYJQkgfPV6jAKHP3eD4P5/MUtcKp\nqyGXy4+c106DWS54cQVLBnN4gSIVcXhiwV647WNCiwgQqRo+d9B8vv3oi3QXS+w7tROCAK9/gCOu\n+z0vD+Q4pKWW++5/jOO+dim/PHRPPjRpDCiFSCaxpk1D7DYfmjpM4iDlG6pAbylgWDUPrZQNhg5h\nWklO5F0iC23RvlYqlAQom+NbIVs7nDONTYw05JO1r0I0hqhrhlS92Vi60a3yHtyR8OyItx+WbUz8\nWkZDLAmpOjP4tsZnShsdiv93/2IuHjeK+qymqBQxS2JbEteRODEHJ+EiXdvQzSs+ygtQhQp+/zBS\ngqhNQySClhsQbgRtWYj6VqLxGI/fcg0UC5CqRaQaQCsee+Be9v702Uz+9o1c8uGlnH3YvqiJOyFG\nTzJGf14Fnc9Adojy0BD2+tVY0Rj4FW6/+yE++8t7yVT8kY/x+NK1PPziGh4dMpiAW5paKDiC0zZ2\nMf30rwPw5Pe+wh5HHYNobA/N93a0u/5+hFTUQhZdyJgydjSBiNdwzsIjuOJnd2DHXPRwhVIpCBMb\nM5FbwnhmGeq45vbhLNfmMxwbT/LFZC0ifI7rSOJxm0TCRUhBMV+hXAmIxR1iCYdywSObrTCc9cjl\nTYuh5CmygWlpSSHwtCKvNZPHt1NUihc29PCTnTs5si6NjLsUBwr09hYAUIUyqmcAUSig8kVygwW+\n+vJa4qk437vgHETrGPMZ/x0YqMJUUkjUGmPJUPJfvKs4nm05P+OmTn0roq6R4/bcmRWb+rlmbQ+X\n1zdSLPq0Ag+MGc1PilluePZlWocK9IvfcOGZn8ftGA+xJI3tHRy/31x+8fCTvBwETCS0uQnJFEGI\nLdJa4wGf3LCJVeUKvz/to4iJU3nwwT8zoz7FLCXZ7e4n6bBtUlKSCQIuGt2CWroMFXMQ61ch6pog\nVUPzvA/Q3nhgaAAAIABJREFUe30ra/oGTQV6aIDHVm7k/k2DzG1Mc2p7I6N9+M66Hp544TX2Fxrp\n2gg0L7z0Kj+47HqcdJqFBy9g/w9/GLu5PRT4NMm2MSYOzIYwTCR04BtSUKVk8EKBD9kB9IYVhgY+\nfgaivhXtxt7ReyzCcaQth9dTrkK/GCENcWTzC0wVvyoCuXENeqAHWkZD82hEunGzWv8/iB0Jz454\neyGM9xPJOojXQKvadlC5lEgpUcAdhRyHRePGeVtUd08Kuy6B094MlkT1D+H1DZu+udIERQ+rBkQ6\njahvNMBi32PjsqU8/sRTdK1aRVPE5qBDDqRml3mImgbmH/oRgiVzOeW/zubce59kj4hg3+EhKBfR\nyRRPv7CERxY/w7Mvr+DXL615wynXJ2KwRcJz18AwpXDjdFxDDYfs1IJTl2ChN5qLX1rLJau7mfuV\n77FoqB+7czJz99s/xAsZP6WRMnoo1LcjQmXWeArhRg2Ox3bBsjn2pJO4/La7eFAq9oxY5Ao+ZaVM\na0oIpDATqS3h5YrPD/MZPhKN88VkLVKAY0scS+K4kmjEwnVNqzFwTaIRdU2iHVihm7lWKN9YU1QC\nhaeMNk9EwEatyfgBl//Po3x9yWoAfpCuQWuNtASRmE1tbYQg0Ni2RKARErJeQPMfn2JcTZxnr/02\nzvhpEN8O/LG2IYSUIP85P6d3K0aqPKl6aBqF7t7AF+ZPZ9rS1axuVrQ7Et9TdEQcPpKo5RPPLYfn\nlgMwQxc49ujDDPYvmuDmC8/E+9KFvGbb7BSHylAJ3zeaO0KY9pZSmj4dsMnzefi/FjLnuOMQyRqe\nX/UrZiRjfCGIspOyOHd4gL3iMf6w93RaO5uxapPQ14seGkRHIoi2Dhg/lbpd51CnlPGeclz22WdP\nnhs3hpsffYHPLV1Ob6bI6nKF6ckY541pwW5v44L/fYxLF/2FE8Y2M6Vc5Nzv/oCui67k2L1mcvzR\nhzNr7/2Q6QZzv6zNc4xWgdG4WfsyetUrRoSxeRSiucOYuw72oLtWg1dCxFLoaNwkHE7snxL/2+Jm\njbSXR8L+OwKFQiDsiAHzT55l1LAjMUSi1iR1jrtV8+eOhGdH/NOxpW7SVodlI+pbWHjQfnzqBzfh\nKc0BkTixqimkME7YwrEhnYJUChmL4UQccpkcg0MFWrJFtNeNNZxDZnOQy/L84ieZ850bX3+sG+6i\n/zfXUDt2vCnbDvXy02MXcNNDT3LAH57kqqE8+2bL/OHF1/jGHx8fedlHx7fy25VdI78vu+ESxrY1\nET34FM7YfRJ3Ld/A0eNa+d5fVjJ47gkkbAuyw0YUSwWcH49wyWrDDDrmsp/h2pK2eJTxdWm0JQmk\nxcSOFs761HHU7TobmkdD5N9kp/9uRTWRltbmNokQaCGwGkdx5Tn/ybHnXMzNO3fSUfTxi2YBUlrj\nK3CExrYFS1QFgHNq6lHamM/aSiMcTLITsVFKU8hXyOU9fF+Ry3tYVhnfDyiFBqEAAVAKFOXQh0tr\nqA8n6WqyMysSoTFkEmqtTVKuNY4tcGpiyKZ6sq7NXrc9ymGTO7jl/C+QnrsA0o2v803aEe9ASMvQ\n+TsmgFch6fucN2cqFy9dwy8728hmPTxfsYdn5qzx8QgrC2VO+PXDjFUeczqaEGPGIGbOZdrM3bjx\n0cU8bEOD53NyUx1/3NTPBxNxmghVtpVi2A8IYjFEooa13X386L7HeGB6J1f+dR23FHN8MBLjNc8n\nqE9jz5+PmDHbmJaqADK96E1rYNNa7l/8LLc/9jx92TyddWkO3XUyB+y1O70PPsvTPZmRj7g0V+SE\n2x9mr0kdPLS2l3u/8kn2mdyJkx/mrJ5eXlm5jl8uWcVxZ12EbV3McbOmcvwh+zNp7p7QOtaAuyMm\ngRHtEyFRa6o8sYShfQ92ozetR6/7MypfQCuBaGpETp2BmDrTzFV/zzvt3YoqGy/daIQhq5YbnrE3\n0Y54y/N55xKet8sT2xHvzwg1lo6dtxuP3TuWR1ZspN6ySLk27aNTpDobkVEHgSZYtQ6Uws+VueGV\n9Xxh6eqRt7l+5gT2bW9g9s8eYCisvLiWJH/lGVAssKxrgJ2vup2Goz/PtYfNY+GEUVy5eCmHttSO\nvMcZi5bAoiUAnDppFFfvMRkr7rLBsvntdX9kww0X0zp7b0S6DtW9lguOWsBJu4zlspoUn7jlHgBS\nBxwECPS6FbBhHbq7B12s0GxbOAh+Vt+Mo+DxcomhrpxxfReCJet6mfrw2Zy37yz+60unY83Y02Cg\n/gacqqs9bnh/CHD+zVwipIWOp9jvqGO4MZfnPy76PjdOG8OkjGQ46xk9HKUJtMLRgv0iMdYlTDvK\n16YFoQKFFQjjsB6xQuhAhYqnKHtVKYTAVAnUZgtDMLjQz2R6uTBZR7tt02BZ3FzfTFFrVvs+x9Sm\nCTyFN1xCBYpC3ief91FK4/sDxIeLXN0zyOzmWn7+tc8h9tzPLHhbAfDfpjDW67wtVbb/I2Fsa1xE\nTRNiShTd1Manx0/hx6d+lasCn88nHEoDJQoVnxdGjyXRlmTGsy9z2oI92W3hQqTyIZlGjJrIcSed\nDMla2pMuZ15xLTd3DbFLexPnLlnBjJoE32pt4tsbDXNw7uRxICXRaJRKEPDHoSy3F3NEERwYidFf\nyvGzp17l3M7RuKPGIls7TVtnsAe99AVKz7/Mwbcvet1n+eETS1mecDlp/znc+tjz7NKY5sW+Ya4e\n28ZX13XxYq7EHz91CJMmtBuvwGQKmUozbVwn39p5El/763IeemE1v3puOfMXvcCYeITjZ0zghGMP\npWXvBYjWcZCoMS1/c/UM8y5RYyAKa19FPLeY0tN/Rf31NZxV63AKWZi7ANHY8a8XWxUCYTtobbSo\nCMJq+1ZWyN+2W7rRhgj1AYJQDbVq777D+2pH/IOo6u8Unn6YIz93Hv19Gc6OpulwHVprooyaWE90\n4ihEMoHO5/F7hqj0ZvGyJXpKHs+XSoxPx9nr6WWve99P7NzJLpPHccbRByEty4zLZJq7/vw0C6+6\nmZIXvOFcfjBrAidMaMMPFCWgOeqilUZIQTmd5NaMz2fOOgPZPBo92I1e/lcY6odEEtwIv1n0NNc9\n9DT33vwjZG0Tqn8jrHoFvfwVys+/wvqXNpAfLFHyFJ7WqJBUAaGDN5p1gc+VpWH2GNvKTy49D2eP\n/UyrMAThaa02Awm1NgA9N/p/P+n5mxhhdmT7eeC2Wzj+65fx4zEtjM5ocr5PRRs8j0AQkYKYlFhC\nUFZ6xA4iIgVRSxKptrMCTRACT4OwgiMwjB5vC2f1Vb7HyYO9fClRw1GJJHVJl3jMDk1EjQGoE3pz\nGZCleZ0fKIJAk1WKg9et5+FPHsa0k05E7jQHosl3bJ40juJls+uVlsGKvZfU9fcwRq6FVwpByA5o\nTc+KVzj8hFOYFFT4WixFKVNGCkGyMcb0vy5n+NZLceYeiKhpNK+xrFB5GdCaO+/6H3bfdQbtLY08\neM89nPn1i3hp9XrO3Gs63zrmIGIz58H4aQgnwtWXfo+LfnwjyUCxs3R5slJirG1Tb1ncNG8nWg/b\nD3ngEYimDvRQD/q5Rei+Hm5/ZR0t7aNIJ5N4w0PMv+Aa/t+RH+D0g+bzm5W9HHvZDa/7rMML98UG\nhGVhtzUgJkyElnYjtloqoJ99gsyiZ+l6pYf+bJmnKyX+VCnxtFfm5x/enQMWHoXYNXRxj8RHDJzR\nAQQ+Lz71OD++4kp29woc50qsxlrcPWch5u6H7JwOsdR2Nw/9I7f0bU94tDYLllcykthVgKoKDLXP\ncY0U9Pvwi7Yjti60V0ZvWM5rv7iJPS65gTsbWqkEipRj01wToakpjl0bR8bMgq+8AFX2QGusdAJ7\nVCOyqRHR1ALxFLuecwUbh4bp+f31iEgEBvugrwtSNYjOqQZQXS5y8Kln4JRL/ObwuRQrHsl0Ep3P\nE2zoQVUCrHQMWZs2/WnfNwywjg5E+1gz+Q32Gz+kaBTGT0U0tRktkr5uRsz/8ll013pYs5ryq+vI\nrB0gmylTrhgdl0BpJOa7FYQLtAJ8AV/NDjC6Mc2ph+9D6+5zmLzHPCItHZvVdrXCgPqs7W6S+ZeF\n1mjfQw928cgvf8bHvvkD7m9rZzBvfLIqYVLpSkFCWjjhdTbJizbK3UIgEVjCVNlsaZhZSmkqShkX\ndW0YXBqDD9Ia9undwEHROJc0NpFKO0QjlsH3eAqvEoywdizbYISiURulFJlhj5/0D7DRgVs/dQTu\nEUciJu1u7qv91sySrbosvgf5IXR2AOHGTMIcjb8vxsmbsY90VbiyKlgL6EqR/OplLPzUZyn09vPD\nlibUYIl1gcfn+/t49eLPIw84EtEx+c09sDYfELTmueeeRfoeu06daNbBqlZSMcvGxQ8x58Qv0OYp\n/iOW4hvDA2SUYrrj8utZU5hw+N7Yh3wEMXaaWTf9smEqVdubxRy6bwN6oMuss4UcpYF+VmZLTJs0\njiXreznxuz9mY98Ah00dyxGdzRwwcyrxWXMQo4zqtB7sQj3+EKVFi8m+1k2pFFDxjGzD4nyBrw0N\n8O2pYzj52A9hz5mHGDPJ4GH8CmtefYUGKvzq13dy2s/uYlZNgj8fvif25PHIceOgtQMxbhqiocOw\nuP7F6/0/Ypz9o4Rn21taoQgStmuywepP6BOyI9HZEW8ZUiISaSZM34mmWIReGxqUMFYStsRpSmHX\nxglyZfxsiWpZREYdrIY0ctJExM6zkGN2gngNfz3gY6aPq8yuRNe1QNsYU6pN1qJLBfRgL38865Po\nTesgnyMZj0EQIDwfqy6F3dSIaB0F0Rg6m4GhQcMKiEZNb7tcAttGjBkPdY2IpnZwI4hCHt3XjX51\nGcHAEKrsGTZZ2UcVKkSjhiEhcx6eH4QLr0CiR7oPErA1fDtRy3X9WS648X/oueEPtDWkeejGq3Gm\n7W5Kzv8mGhvvaoQlbWqb2e9jC5l20x0si0jGexaqrLFMpoIVAiIVVco5KC0MqzpMZhRGKVlqQZUP\n4ms2V3qEmSAloMNq+QtemVjcwpISzzPChZYUWDEbJ9BUKgFCgOMaULTnmcn53kKBb45pQRWKUMyD\nVzRaUnorGI1bE5ZtdttVMPxWgjj/raO6+fY902Goso9GKNnydc9FWiQamvjNZV/nCxdfxcHPvsQx\nySR392docixKS5YTG7fUXMfaZiP2CJs7GSZtMv+UFrN23+ON9y7wUV6ZTE8XxYqHJy10jcsNu+7M\nYMnjI+kkVtQyY6B3E7R0GoV3N7r5VJU5nmhqh5pGM6d1rSaychlTe3rR2R52bmrluavPZ3Xg8IfF\nz3H1A49w7O9+yqHznuWO751vTJKHB9HDGfx8mSBQ2LbAsizQsIcf43OJND96bSOz73mMaWvX0R2J\n8ttVmygFiq/d+yQz61Oszhqm4cnTxuDMm40YP8lsBrs3oJUy8iM1ze98a/YfhKn0Vsx9F9Iwvayt\nM29+Wxgeg9LfIaG/I95mCAsStfijx7EyV2RtayOjA005UAxnyiQ3DBHJFAnKHn4lwLIldszBirvI\nVALRPMr0nmMpkwTocDeXz6OzQ4AwZoCWjR7oQr+2BL3yVSgVEbEYJJPo4WHIZo0RaVMborEJYnHw\nPPOc1nbDWIjE0bkhyA0bocK6FgOYc6NG/AoB2SH0pk1UXllDqSeHE7HNucZdHMci0CAKPlIInHBS\nEEIg0CgtUJiqjyskn4unQUPEkpw82MdTixaxV+ekN6jvvq+j2sdPpDnmgA9w/I9u5Xft7dQIgdIa\nLTYnNGjD3nIQI7AWXcX0aEIhQYUMVZqr+0KBwKlWgaTAC/+/Jwio2IKkI439hCURAoJAUyz5+J7A\n8xX5vEe55OP5inXFCusDn1mpJDIahWRNKEr5zunuCCFGWp3vm6jKO1Q34WLzPX7TsBxI1uFMn8M1\nN1zP4cefzOVPvgBA3Bfs8bN7mHLHw+ze2cxRHz+I6QcfAZEoeqiXcjZLJF1jSAWRKKK+BR2v2SwE\nGWrFIC1Eqp62vRZw5IIHyXdtZO7sqczZb76hmnsVk+zYjmmLK99UdrYcBlU7BTdmsHy+B8laaGgy\nxselEuqVpYhVr9G580y+ePLxnHH2l3nwwT9x4MKTWfr4Y0yvi6HXraX8l+UMrxsgO1whUEZzquQF\nFJViorTpQLJg8UuMfeZVTmio5Zr+QdZWzGh/fsBIbXx0wiiO/MTRyL0PMA7wG1ei161Eb1iHKheQ\nO89D1zSZqti/Qp5gxGOzErYsKyPJLm8xR75tDM+O2BFvN7TWUCmi+zdy4/ev5Jwb7uC0dIoDVAQp\noNa1SSQcbMfQhCMxm0g6itNahz15PGKXWYhR44xqbX83ZAbRgYeIRE31pa3TaN4Iie5dj176FLqv\nCxFLmJarVwn1KGzz7yBAZ4ZgcNDsFMaOg+m7m2MEPrp/E2QHzaQTiZpJKFVngH2VMnrlEoJH7qH8\nzFIQArezFdlYD0FA0NVHYVU3wxsz5PI+nqdQyjCLAg1+iC/xR/AjRgHYlpKbgxzNU8dw0cVfR0yZ\nCcm690WLYqtCKXRhmA3PLOL/s3feYZZVVd5+9z7pprqVQ+cmdNMN3eSoAgaiGFCUMQ0mxIiOjJj9\ndNQxiwFFUWHGEWdQRwxjBhUUFAM5p4bOoXLdeNLe3x/r3FvV2ImhmzDWeh6e1ls3nrPD2mv9woLT\nzuLGw5fhjDSZrMc0jQjEtVpWWsk1VUxXfeIZ7UTT/lee72eJqafFydx1NUpZ/mHDRu6MIt7V38tb\n5/dT6Mrh5H15zyghrsfUaxG1WkyjkZCkwh5rGMObx0d49mA3H33N83BPfxF67wOl9TQbuyd2lTST\nbXfWGGxYY9PN1zPvxDMBeEO+g7VhwoQy3G5j3rBgDgs6Clw3PMZ3N4+yslTgpLn9vPuEw+k4cDlq\n/iJUsSxV4K5+VNcA5MuZ7k0qWlLr78OuuS+jey9E5TKcjONNSy5oB8gE+NJE1sbqBFQnRNwVBL+n\nHalYTwzDXbeI8vs+y1ErjhaF6STk2ae/mHvvuYc7nn8M4doRGuN1SbxjQ5oaosjQjFLi1LQxb7fG\nER+tjLNvMccvX3w8X9tS5dYtYxwyf4ijDjuIp5z6bBHGLIgtCxNbMHffgP3T7zCbhtFzBsS3cNmh\nYiibK8nhbA8lPtZmWl0tb7k0laqmnwMvhy737caW1mzMxqMMpZQIWfUv4NX/fD7PWrkfx5z3MZZ1\neyyKHZLU4nqazr4CuYEyuhCgtEI5CrtmLWbjZsHUNBqklQZKK/TcAfQBK9CLBLtgJ7bA6Cbs6vuw\nWzagOjqhswuRoO+Ezh6ZJOPiq4XrwuSkTB7Pl+QJIE1QXoDt6IZmXcxHg0JmFOpgJ0awax/AbtqM\nqUfgaNJNo9iJilQZaiE6TckVPFDQbKY0mylhbImNITKWJKs2AGgliU9kUvZJND+/6yGS++/BnbMI\nVShni+Oej2kMBI9cX+mxCK0gV2TeQUfyxtOezmUb1nHeYAmzoYJtxsTYdvKilZpxfcHJWu9JC6jc\nWpi1VILcDHSs1XSL3lrFxf2DnLBhHZ8cHuVZfR0clS/jFH1satBYnNTghi0/L5U5rGvKCj6S7+eN\nm7bw8a6SLMyzrf/dG7t6PVsVVseBXIme5Ydw9umn8IvfXsdXJyt8sLObqGEoJoofrtmMRuEC5+gS\nE3XLFfev587Vw3xg4C/ss28vHYctwVm6FPZZLiBx1weVrR1aTwvljW0BP4ftny+Cja12WdTEVsZh\nYgs0azMwslEmFGgksdm8AYodcODRqLl7Q0uhudwrmjlakzTqdHgOq0Ym+ew1t/HKYgmAYsnH75A1\n9PfrRvnlphprw4j3d/WSxoapOKTDdfjcSUfR8bzn8Y6Dnypq1dDGJrUTGGuxXQPopYdg4gj9+6to\n3nw3/tr16C0bUQdkh9Fy3wyMmt6t473FwsNxJfHJfAwJG3LddhCzCc9sPC7Rpo72zGHhsc/iwlfc\nxvmX/ZhLO/rEjToxOB053KEeMXCcrBKP1kS1NjZoL/NZcTXeYDe6pwvledhNa+DBuwVgPDUpLare\nfhiaL15fzQbki9DZh0oi7OSoyJf3D2EnJmAqS3qMyejJKTYOUeUe0avwcuIh1qhgN63G3nc76W23\nkGyeEOXVOCUallKwchTKddC+S76s8FyN68R4rsaPUqLIEMWGyEwzuFIsiYXIGEaTlLKD2BEkAtoW\n0bM9u1lOn6AylosXYL3cEyzpUShHPHbOP+csDvuHN/CaQ5aiMwqcBTLIKgBuJkrYZlAZi1VSU7NY\ntFK4TLOrWm0vEoMx8tqS0ryzs5uPTIzx3Y3j7I2bCRg6OI6AnpPYZO4Eio4On1J3HkfBl+5dz5Yo\nBs9D7bDvMhuPVSitCbp6+dqll7D5vrv44he+xJV//At3rd/MYM5hY9Owl+txqOORs4pF1mExLl+N\nKhy9dg2ldWu5IYxZ5Ho4jksyOc7vHljHaC3k+ccfhedo1j20Gm9ylEHfwYwNc+lf/pOXnnYChY6y\nzLGxYeyaVbB5k6xnQ3Ng3kKp/lQmYWpC5r7nwZz5qO4hsVPoXwC0EgkLYYNPffbzfO/qPwJw1fAE\nUTXkVT1deMblypEphtOEN97+YPv3P0DKcYUCZ3YV+VXc4E2//gs/OPQAFu27Ako9GRkpERsgpdrt\nKuUF2N456JVPwWDIpVeRjoxjH1wlyVn5ZmxXDwwtQA0tgt55osi/Gw9rSilQDtbLgeNnlR6p1u8o\ndnvC0yol7RbmQQZCbfmb7FG6+0wAHFaQ87uJQTEb2w5JejxUzxAvfNmZ/Nfv/szV1SYvUDmS1BJP\nNPCHJzAK4rEqaTUUdWbfkaO6zTYxz5WKjC9GncSRTMy99oW+uWBiWLcau3Y1JAlq7nxIYjklKC0J\nEKBKJcgF0D8k7atcEeUGIsRVKE/bZpg0M7Qbk9dh0UUfz3dQroOJEtJKE5vpAjlFH10I8FODN1Yj\nmmxgEkOSCKW50UhohilhYmgaQ2LFvuBWE3PovCGc+fOhWH7M5PvbJ6jWAqWfgBWeVijFwqF+Dhzo\n4lebJjgmVjQzTy2TQTsCpdqWEq3ERKfgaL2VWbNtAZatCNJ6WqMdlUFDFErDU4t5mICfV2ucW+um\n0RBsFhmUpFUVCgKXYsEj6C6AhSVdBU7TfYLdeKJey7/DUEqqMIP7H8K/XnSxtEnqFWyzRliZ5Ne/\nuZof/PzX/GHVGtaMTTBSazJYCDi6lOea4XH+snaM5n9dx4PJ1bxm0yZGsw13TuBx/tJ5nHfbQ3/z\nmc+b3ECuXOBn92/gP+5cy7pGyLpGRCHnc9dnz0cVOqT9XuoS6wQ/gHK36N6Uuv7Wk89arOvx/i9c\nDMBh+yxk+fx+rrjzAbROODcocsGdD/KHDIB80XErOeu1L+HPI3UuvOz7fG5snMuWLuQjazbzoa/9\nNxc5kDvmOFRnLzaso4Jihjsricly5ltFVx96xTGYNEXfeRsqEUyNWbOG+No/kdRjgn0X4Z14EmrF\nU8S3cBfsHx7Z/VOgNdZqSGeAyrcTuy3hsS2vjkQAT9b1dsnMa/vvl/Uz46aU9bxATtd7rC8o7t1U\nx7BhQ9oHHd179DNnIxOUK5Zh4RLOe8Xp/OMnvsYZfXmazZTx4RpxMyEIHLFW8RyU56CcbAPWCp33\nUZ6Lrdfg/ruxYxOYsXHQGnfpPmIKWK9iVz9I8tA6bCNEbx7BGR1GzV8Ag/ME4Kwd6J+HjZrTwLc4\nllaXU2pTTpV2sErLiaV3COYuQo9sQpkU22jK66YapLaBiVNUYiC1mIbMC9OMUZkycKsS4AeO6L4Y\n02YY/Slu8oBKufx1L0EvP0S0QR5D/I5c3yfBxmwMY6Nj/GHdMBcs35davS7KyhkLTltwrMVTkrAo\nLfUVB3AzVoe1osMjmJsM++OIpo7jTIPMtYZ9ghzfnTeXG2oNwihtSwsMpykFR7GoM0/vYIFcXwda\nK2yckDZijirmuXDNsKyPScJWmdZsPM6RVS+0I/O8UAZryQGnLT2E0855q7R3jSFq1ln30EPcetON\nHHXLzRx/9EqGJraQv2sVo9/4cfsdz9x/IX8aq7CsmGPQ0VwzVefwwW4ufd0LCeYP8fpLfsBv7lrF\nPoHPX0anePHh+/Px956Hc/gxYtXjtjAw2d6jZD5uk4mUVV1MZSzTzxFz13V33szTznwNP56os9EY\n5hVzPGPBAK971YvQRz6Tp3cPctSJJ/PMF7ycf7cpb+zv4YT7HuI77/kyN7zgRpYevD+6txfb0wul\nMrZYRvUJFolCWfbGvvnopz4HtfwwmBjBDq/H3HgjlU1TjDwwRmH1OPNVilMoo/Y9EFvonCZftObA\no91flcq0p/ydrpG7ucKjZnz5R/4jBDcgVE9phHvTm4/Se7baonS2uXkQjWPqU1JE6OjN+rKzSc+e\nCqUdKHZy9DOfRc9F3+ZWL+WAWDM2EdJopnSUPEodPrlSDr+/jO4qCbg4jqGjJIlLuRNGhklGV1O7\nZyNRM6GwajP+TXfi9pRQvodyNWmUwFQNJ0mkJ17qEr2SYhf0L5BR20rc0xgbG6nwzGzpWCMVojjE\nJhE2ikjHJonHquigRR2X04aJU0ycYGvC5DGpJUkMSSqtEkcrfF9jjUOUGHRqGSPlwtoUP335CXQc\neiSqZ3B2DG4zFHg+PXst4cD5g9yZ0xxU8DHViNRanKzi4rYo54klTY0sLY5CaxEejGJLlJp2a6t1\nna0Bm62luo3JURxWLrC/FxBGIjiogH+ZGuPmOOLOuftRmNuFv3AQW68TrR8jrIUsjC2VMOaOm+5g\nxdLlIjjneHuEeWdba2h2jWar1BJtsdwZe0n7sRboeeY+k60Bsjd44DkEuSL7HNzPPgcdzunWZg7f\nEfukCfGHL8BRYCdHePGrXs8Vqx8A4JC5vUQfegPOkcdiCyVece47+eM9D1FUigmluPrD53LsP54N\nnQNbR5fCAAAgAElEQVSoVpV6Z7/DJDNUhjOx3xYV3/HAzzH/8GP54zW/5qH77qZsQh64605OO/4Y\nnPlLBPfj+OQX5PneJV/i6NNfQffSeVy+7wLe+tAGVlzxBy780yrO2HeQ3qceiHPEUajeOaIO7gXT\n18lxxZYi3wEDi6BnCKfepHN0lJyrMGFCsmEL+t5bUf1zwcsLs7XljG5MBt4OHlUVuZ2w7iR222xT\nSvxu2jdLbWOiWSNJTYvCN+Pv1hhBojcq2DQVUGiukJXv9vxJU8r4gWi3YKE2JZWeoIHVTmaWNrtw\n7JFQGnIl9JzFvOWFJ/Nv3/kpH891UYsMaTPBcRSep/ETA4GP6uuFUql9UraTE9h160k3j9JYO0Jl\nokmtHjM1GdI12aBj3wG8oV50LkDnxXelhdNpndykHJDpS9lASttxKHfcFcCxbS1wzSp244PYB27H\nPng/ZssIyWSDdKqJ0+/hdhdRroNNU5IkJE0McWxIss1Ra+ELGSMqvNZYothgDfiO4tPVSd55xFKO\neNlL0Yv2E+bH/3VNlf9NKAWuj+6dy3lnvYivXHo5l/b24tUiASxrLYcWu3XCojOhwZYHV2wMiSzD\n4oNlhbmVpBY3G3uua9v4ILL2luMoLBptLAf6ATfHET/ZNM7LFOS3VHB9h6QeUa/GVCsR53SUOeGy\nX3JJnPKc8+ahFq+UamH2W3YLDMAaaenGWbXRy2H/HjR5dhQPF8t1fdHYUVo23UYFG4Wi75TvwPo5\naG3KaZKpDztIXTCL1v3Suq2P5SLXX+U7+N53L+eqH36ft3/qQppD/eily7FpBJtWc/l1N5H3XL70\nslM566yX4iw5SEDI2/BUm05eW4/bDKA2w2pGbXuzV9phzvwFzOnp5L4brmfu0AC6szeT8/CzJCHH\n/IOO4op/+yof/tRn+UZtnPWZDtC56zehevK8rl6HkS3Yrl5hmQV5MP60JED2WdbPCxPt2G78g5+C\nP7IBu+ou7AP3CNmjXkWlcUY4qWBHN0DYQPXOha4BrPL3eHK+G1taRrLhOMweCbAzSnC2xZ03CZA5\n2s483bSyxaCAsiYrTz22DtIqQ9Vb10eVumVAuX5W2p9NdvZYqBYANc+Zzz6Rd1/636zLddCBpZ6m\nuE1FseBKS8vR0mpyHHFIr1Ywq1bRuGctlfWT1OoJSWIygVVFUAxwF89HL1sOnos/vAU7OiIqykqj\nuvpRnf0yia2A/4QWOg6TI5J8984RNkTL1DKWRZLJcahMoqJIWiVKEU/WMXEqYoOBhx942CRFV5ok\nlYgoMpI8ZUmOSQVvkqYWreBy26S7p8R573kbev+joNy7RymeT/ZQmfDYC09+Bm/9zNdY09GJm9HM\nHWvRSuM4iImn77QVlY2oD6IAz1E4It7Tdly3WasqSbNKSZvlpXFcTYCD1godG6IoZVm26V01WeV5\nHSV5X88Rc8kwJUoMpwdF7ibl2ntXc1qjJpiHqA71Cng+tlAW7ZVtrTe7XP7P1lGbsYB2M2biSRmt\ng3grodDu9JquHfALWQU1S16y6oX1M1zgI6iSKaWF1dQzxImvegO3n3WOtJniSCReRjfS/PV/4nV0\nweBCWVe84G/2uTZxIGpCGvOyN53H3KFBPvKOt5IvFqXK4vqS7KgWDH5mYjT924122e+0l9FRKjJ6\n1424wQwFbqVQfo6jTnw2P3nWSSKzUZ3gx9//HuHYMGeccByq3C2fZRJsoyovK3Vh8x0yXtXMZCxL\n0BwHOrpgr/1QRXEzJ1+U695qQSmNDRvYyWF0rggFZ6c6Oo82dmOFRwtiumUm9rAWlFIKMtMvJQ88\n7PUqw+kEPK6hMiXXWVXbxzaUhkKZ/NKVvPO5x/HpX1zPJwqdhLEhTi1WaZxiDt1RkMR5agobJzA+\nRrJxlGiqibUQBA75nCwCnqdxij7KGKjXpO3VP5ixuTZib/4rVCexK49E9y8QmnmjAo0atuVJpNR0\nJahVBcoVhSUxOozauB7TbGKasVQMqk3Saoj2XXTggqvbQP5WtSEMk6y9kiU7VtR6HwjgO2MV/nrR\nh3APf4bo7sw6ae8wbLbIamuoJymdjmbS2rbGSGAtBTSe75LPuVggjg3WSMUmUI60GVNhwDmOwtGK\nJLWEYSJJqbXTbTBH4ToKRztYYwlDS2ws3Uo2kFVxjOc6BIErTEKtKGSvj2PDRD3l4IGerC0aYbas\nhjtuACxq/t6o+Uuw3QPZRqKxJsU0qvzyV1dy6onPksfbh7C/jXab4e9MpFIShExxWTttvF0rBJMW\nAA/bX1x/uwaYaiaG5pGGUnJQaX0FPy+g3+45+FlbTb7jdg702figPsltt97KmjVruPwHP+aCr3yd\n5xx7FM995rGsWHEAv7/xNmJjee4pJ3LgihWSWDh++32VdqillmKxyIErDuCK31zLmWe8cDvf14O8\nh8oVef7rzt1a2yiN24fBlmr1365LGWg4DmFqDFufQnV0i31GvpjR2z05O+RKqN65KM/Hho02C3Wb\nSdt2ol1xt3YGxmnHr31ks8LaGZ1htp20qB330Wb7ybOxzcgSYsp9nPvm1/ODa2/i5ybiNNcnMZaJ\nySbOvZspDU/h5lzcQoDuyGGjhGSqgesoOvoKuKWcYHWqoUiqV0PC21fhrNuEO9iLGhyQ00eawuQk\n9uabYHQEs/9B0DdXkl2tUfkSBHlZtLTOkp4ok+0PRG15aAF28zrU2Bg+4rdkqg3SarNF2cE0Y+Ja\nSBwm7Z8JgudJMh2esTTlXpvwhfEpLn75Kcw98lgBLs4mOzuObJOzk8N877LLmRf4qHpMZCUJQSt8\npJ3lOhrXlZO662SsKsAYS5xYtBa6v+QJCp0YZHlMSVJDI5KWpJeJYWpHte0nUizXhMKAOcgPSNJU\n5AO0tMKMq4kdTZIYVhby3LhplH/INhPl5bBhE3vf3dg7b0MtPQB10NGoeUuEKdeY4ry3vo0v/vfP\nuO1fz2X/Z50Mi5ZDqWtWhHJmWEvL8BKYNsF8As2fXcWZZM8mDCPyiw9sP9LTVea1L3wOWmsu+f5P\nWPWFrzEyMQXA1791OQ/++WpJdJzpw7q1hnPffj7GpJx/zqv48Gc/x4uffVLGtNqO52UrWZsZ2skk\nObpoJyUzWlryMiUt1O5BVEcPqtUOdP2tqtQKRAW/UJY11qRtHNIuJzvGQFiDyjg2aqCCAnT0YHci\n5rnLCc9W6obGYD1fqjHWyiBL462AxoLRaf+82ZiNnYbSGpsv4S45mEve/2ae8k8f56iBAcpNw2g9\nptpMyI80yAeaYl6AzEFPEafo4xTkVKMDD3SmZJAabJyS1EJMnJBONdDrtuAUA3Tel8Sn3kCtWQ2B\nL+7CpU45JSUJmBQb5EVN1XFlcnYNTCcirgelDtRAP6pUhChCOUJVdzoL6N5ubBTjrtuC2jBBmoTZ\nGqwyerTCVfD9sMZ/VCu842kH89yzzxYWxMOpp7PxNyGLXoNf/PAKzrvsp1y2ZAF6PKLgOCTK4mlF\n0XMoFlxyOQfPd8RTy1iSxBIn0o6KIlG/9jyN47r4nkb5msBYPFczVY2ohin1GLwQyPR6NOLF1TSW\nxdkGMZwkTFVisJDLJaTGEoYp9Ya0WtdFEeV6xjx1fVGmffrzUPsdiN2yXsZV2IDGFDYKsXffwF1/\nuYHn9ndx6r9+jdt6uil39UlCPpvwtEPpVoch2G14qMc1FPzmuusBeP2Zz+OzbzubfFePHLYKHeDn\nSKKI3/3xT5zy0lfzjre9GdXRMwO8nIWF2++6i2ccfSSnHXEA/zw+zk3XX8ehRx+TuaPvYru89Zyd\nPbdVKXJ23CGxIIfOsAFRmLW8SqB2kZzRyjUKHYL3bRGcHm2FR1DsM1H/TOcwaQpRA7P5QVh7v+iT\nDCxADSzE5ovyBVxf6OmPI8X14TLTMyfD1oh3tef1fmZjB6EyfZs8+6xYyfP3mceVw1OcTkDTpNQN\nVNIUP1T4lZiOyYi+ZkJHTwEn56IcLa7qgGnEmEhAzW6nuK+bKCEerxFuGEf7Lm5XAbdcwOnKyRjf\nvAG7ehXUqlCpYMMQlcth+/pR8xaglhwIXf1AlvynMeRyqN5+rDcBlQraczCug/J9VEcHOOLYrZTC\nrhsnDOsZMFaqpZ6jUUWP81YeyCc++UHUkoMh1/F3Of5sC+NnjSzE2zuBQlbyD4lGNvLKD32W751y\nGAdP1JkyNXKuJk4MWit836GQd0XaQEllLU2tYGuilGYzodFMSbEEkcbRmsDXBAUf7TkE+RitQVdi\njJHWo7EQJSnNVGwjQms4IGuB/zFqUk1TnLq0xVpYrWacYiwMOg6B50kpXinxZuovQd98VJZkg8U2\nqiQP3s1eLzmXqBnzhf4BNrsR1//lFk469GjonScaVn/PYOSHhXpYxeHJEjfdcis/+slP6e/r47Wv\n/EeCIOCO227jZWe/gY/985t555mnSFJgUzl85cvgerg5xc+uvo4kSXjJc04RpeYgvzX4VykqtToX\nfuKjOJ39HHLAMu5Z9ZAkPI9nMSKbv3Z8M3bDA4BC7b0SugbEkHQn93HaOy6Y0QXb+e/ZeYUnqktV\np6VRkCtOA5GxQA7dNYBtNrCjm7Cb12BHNggItKsPPWcvUW3kcZiY2cYiNOMoM2lzsI4zDUBtId5b\npdBs0lhj2s+fTYAe43BcVM8Q3YM9bJio0um40EhoNRk0itAa4mZEY2NKfqRB4Dvkcy5BzhHvI7I2\nuVakNTlRW2NJGjFxM8Gx8l5OOQ+Bj202IYpgfIJk/RaiLePYxOD1lfELRejqhe4BGQ/NKnZqDFud\nhGoVu3Ej6eq12HoDayw2NaipGpaNkFrS8QrRSIWoEZOmdlowUVlcB6opHLtgSLy5nL/Pyk6bHpxm\nCU/r8LHdFwBJxDVX/5a9Oosc6WiaUYLrKnI5Fzc1KMB1BbRsrSVNIU2lNZWkJqvyiK9WkjG2vDCh\nXPbwyjmsscQVkUZIU4vrioSAMRAlBosh0ArfOhSU5hgv4I9xyN1xzKHagTCVNqfJ6O5YDtQel67Z\nBPfdiV20DNXjZ5ic6VOxTWKoTnD9X29kQ7XJhUODLFUuKxyP6/56Gyc+8w7oHRIK83bwJ7PxxA9j\nDOe85W1c8s1vAfD0px7Dz37+C/77W5fw8c98jjNPO5l3nf0K2cfCOnZ4PbY6iepfgCp0YNKUX131\na3508QX0+gi+JtO/ayUMJk1Yt34DB+y3BJRmyaL5nPfhT/G1y6/gvy/7d3oHhh6X9UYphc3EfbFW\nNHxG16PyRazjoHa18jTDFHhXYucJj3bBzUBBSnpsNjuFtDn/XYOoch8qCiGsCQgJRL8kV+LxoNRa\nY7b22WhVqGxG59OuAKncYGu9H5C/x01pa7QED53Z8vFjEkrJpj+wkPe9++0c9pI3sX9vjpOtRz1K\nhdYNNC1E1mCTlNgY/CilGSbkQ5di0afcFeB1F0UHotoEneCWAgJHo5UijVPisSo2SXHyE1IdihLi\niTq1iQZhMyVXCugadKCvT5KdRhUzsgGqE7BlI3b9GsyadcRrNpFM1AWonPdBK5KoDhM1TDMmqoWE\n9TjDiig8X0Ms49F1FZVGSlfO/z8kRrfrwMNWtEkNM+bhjhdiYX3+8Krfcfo+cwhHqtSmQpJEqtGO\nVm2gsVLTXlrT7zvNxIotWdJjcBoJE5MR1lZIUsPkZESlHmd4dUcUl5XF1YoCTtszK0ktH+/q47qw\nwZDSVJMUnYoOkEZo7hboV5o7p2rYzRtR1UnoHGgnLbYFD6hPcsPVv+b493yGffIBxyifMDEsVx5X\nbhzDTIzjhPX/Q+Pl7zPO/ed3csk3v8XyZftx3S9/QjHnEwwuojC0uP2cD7/vXQwODqCSBFssC0sq\namCTiJ/+9jrGJiY49dRTUaVym7XV6qZYa9AmYdm+e3PXXXdx1BGH8/o3vIGnHXcc11z3R056/ou4\n4be/wPq5x5wcYdMUahOw7gHsHTdjJyfQlSnxKRzaS9pbe6ACtfOER+lpxgAZEj6sSzKhHfGxyFxf\nVd4TBdqtXr/nL6K1hjaCUB4QX42wLgOkUZHv7QeQL6M8fxrUlsQCdrNG7JLbeiweePm/7YnOxh6P\nFpan6+CncuU3PsOJ55zPTTmf1+ZylJqGhrWZBYOlCWgjho9+aigkFq01XYMlggV9WK1JRyaxgFMI\nhGbuOsTjNaJGQrM6hespXF+mQtRMiJopYZhgjCXYMIZz2x3osTGs60ppOYzEuHSiQrh5knC0Shyl\nKEJSW8P3NH7ORbkaE6ckUdpmAYkGjBZzX0/j+5ralKWrXHzSJ9Vta5Y0zphCGVvkEawBu3TatBab\nppiwzg+vuZ6rnn8kes0IrqdxXfB8jeNmityuRuc8nJxHWo9I61FWTYKwmaAnmiRTETYVdpbnKNLU\nMDkVEkWGepgSG0vgajo6PDq7c5g4ZXIqImympEZ0exSQ04qnBHkiYzBYlFWYrA5uESf2bzWrvGrZ\nItTKQ0V/xPMzPINQfm+/9rd8/eKv86Vf/QGAr5R7aaSG26OI8ydGKDoa5Ytkx2w8eeNHP/kpF33t\nGwBce+Uv6O7pliRgRlz8mY8xONAv+5Kfk8pO1BSdJa059sSTOOA7P+LUs17Pf/7b1+nvH/ib+VOt\nVHho7TrmDw2AVixcuJCFvR2cNK/IZ756CfHqe/Dm7Y0tdu5QBHNHsJBHGjZjoNnxLZj77iD+0w3U\n143TsXwDzpyF4vweFMTfZZff004XM3YQO8fwPHQ7qn8ettg1XQkJCvK/W2BlradbRI9hX7AtVlif\nwjZr4n2VaQOAEbZNoQR+gK1XMmHDKUhzsgL5M2j0rYtl7XRZ0HFme+SPU4jlRBd7P+M53HzVfnz+\nsxdw1hW/5LTeMv+oAgoNqBkj3keIB1KIhRQaUUqaGsjnUPk8rutkQl2GZKxCXG3SqEaEoeAqvESh\nw1RaXhmYFRRJYpjaVCGtriK4bwO64AuzJ0wwYULUjKlXI5rNFM/TFAouJjYkgJfLXLoNxJEhiUXM\nq21hkFqUBg/NVJJSLuSzdurjeNEfTbSwfm0tLpttynti/gjubnjjBsIoYmlvmebIFG4tJI6k/eQE\nGrcYoPMeOu+jc/KvP6DRfd2oQp7C6CTevetx1kxQq8coFK4nbLsoEkBzaiyOUuR9h86+Ap3L5oDn\nUlg3SnVThVolpNFICCNDakFh8bUSR3ZaBqZibXGPZ7h+KuSOt7wCvfJoUfnWLqQxV/7PD3ntP70L\nJ44ZAua7Ll/o7CVnswpRdhnffPhy9OJ9UZ09f3fU8yd9WMtvrr6GH/7PT7jwYkl2rvrJD+np6W7j\nZEfuu43AxhSLwhIVeRQ1DVB3Mryp49LVXeKn3/tPPvCRj7HfwUfy0jNewFve9HqWL13SxoJ95wc/\n4tijjmDevLkZfCPBxhGqXiXvuVTvupEu30e5HjZX3Cbzr01YymyjcP0MP/a/W6wUmXF0/1z0/ofi\nTkyQj29sq1ZjUmwmBLqz62lTKcDYyjgMr8euf2iHL9l5wnPvzZJdBoWMojvtOWJN5ke8p20ftv/t\n5GRUHccOrxeKarkH3TsHClniozOzR9cX7EUUYqNIKG2ejyr3tZUnt0KiP26/aTZaobTGBgU6lqzk\nA1/4Mm98y5186pOf5h9+8TvO7OzgdDdAxTZzGheXcWMsE82Y/PopLPfjduRwM02ctBbSGKnSmJKN\nEcD1HPycTIMkTnEwKOVAxpL0XC0KuwLGENdg38EJXHzfEZ2WRkIUpeRyDh0dHm4pQBczK4pKgyRM\nSFJJeER/UzZlbWV8TcUpneWO/wMicdnC7Hq7zv54FJ/l+wHGWsjlJalxNbaZEMaGJGkSRCle00NN\nCbZKaYXXXURZUK6HLuYIyjlKJQ+lIIpFEdtmujlKKQJH4bqKfN4Fa8U+JEt880UXV4ka8+aJhBO2\nrOP4IMfHO/twFSRZAvSgSflWs8bNUxGXvv5MOp9+KvTMkUOVNVCb5OOfvoD/d9hSnlNtML6xQqMo\ndHiTMat7PIe5OZ+PvuoMmLd4Vn37SRbWWn7yk5/w/JeetVW1ZG5fDzastfGiPT09AtxvCSW2iggt\nTSDHA9+2tYZcV/Hxf/l/nPv6s/naN7/Ns047nZXL9+Prn/5XFi6cz9e/+W0+8I63tdlrhJnQZb3K\nCXvP5XUf/wrf/UQXlDpRfm77zD/tgNNS33yU4y7TurOlHtQ+K3CUErTMxg0wPiI4pWJXW5xwm9cz\nK3bY4bXYB++E++8iffBBovvX7fCjdwG0HEJtSspofn6r9Xh3tHpmSmc/0gRDaQeb70DN2QfVvwAa\nVezYJszNv5f322cFanCRCMX5wsRR3gzMThoL0yxzeW3rCjjeIyqnzcaei5a2k82V6DvgUD518dd5\n6y1/5sMf/jiv/MsdfLG3n75I0TCmzSisxgmrRmqsH2vQ4bmU8g6uI8wdYyyB71AseuSKHsFAGX9e\nHwDxlgnikSnSRgxIa0xaMmBTiwkTdODidhVxBntRgU+wZZz8A5uojdba31cwtwqd93AVBHHaFp5L\nE6kkWQNk3Z6pJKGzo/jkphm35k4L69eyjtkjSY+8r5/LESZpdn8Mjla4riZJEuq1lGo1xnWlbSjq\nyeCONwg2TuDmfJSjMHGKFzh4kSFOTBsW4zoKvyBihYWuHMFQF/5AFzgO4YMbqGycQlmD62iCwGVx\nKcc/dXXx+YkJrmzWOSlf4L405pvNCncmMe844Ui+/6bXUVxxBHT2SVsdhY0j1t5+E7fe9xD/Necg\nGhMNlALfU2A1sRHhw8tqFV6+eBBVzNayXRBZm40nUFjL29/zAb78yY/ypne+D4D0/htkzjeqAg1x\nWxVeJVtimki1tOU84PoCvzAtUL9UUZXjMm/hYj70/vfynrefyycv+DwnveQsTj7+aWzctJlTnnI4\nJE35DJD9bmQTlz1tOf2X/or6mtUUFy2Fzn7xd9uWvt4eELNUjiNmoguXohpVbP032E3rUSMbxGrD\ny23l5vGwCwpxiNm0ls9f9G/8x7U38YtjV9C5O4QHbbMm7aBcsV35sC1mk0lnKFvOMFdsKxRuZ9Gz\nFttyQ08TcAOR8lZKvKzkxdPvsZ1oK2i6PtbJ6J4D8+WmBoWs1aZBz8DtiBugfG7YkEEV1gWD4Lio\nXCn7rU/iDej/WKgsEbWFTuYf+lQu/tpFPOuzn+Ktl/2Ui/oH6Q2VgJgtbdxEZCzVKCFKUjyl8FxN\nuTOgZ1EPhYX94DmY0QmitcMiqBynKKXQntNu0aRJIhtlzsPrLeH0daPLRSiXQWt0M8bJexgLYT0h\nji1+IyEIE9yiL87osVR0oiglilNA4TgW19X8oNFgoFSge5+lYmfyJE56RAtlWi19j23I2QIcdPaQ\nGst4vkQpH6AcjevqjIIuFhKupwmKPo7vkMaGpBkT1mKSMMXvCPC7iwRzu8lVmhQnGmhP43UUcDxH\ncrYMA6QLOazjklbqRGN1piZDHK1E1TuLs7u7uLre4INTY8zNebxnapT3Hn8wP3rDqykedDT0zhEd\nlYfdY19bUmMgjAmbKc1mSpxaIiNtMk9B7CgOWbYXeuHeT/px8vcWNk0hrDHU18OCwV6+fdHnOOXY\nY2SNiZvTHn5IFRnliJE1Gkw8gy2sQQXC6FNqa/awUmAMgav5wLnnsP9eC7l39Vq++cVP4wB2bDM0\na9gohNok9AyQP2B/9u65nvu3DHNQvSq+Yn7uMdz3MkuhYidq8fIsn0igd0hwwTtcPhQ4Dl/9n6s4\n/39+D8C3bnmIs7PD6/Zi5wlPEsPkCFSHxBK+JSiUJtIWihpS+QmKWBx5fqMiiYTjQrEsXiUPrwa1\nToMtsSDHAax4ebRKfJ4v770rOj5KPEHomYvqGpTHWl5erUHxsEXCOh74yHcwwgBq28zPloufkKG0\nxuYKqDl7ceY/n89otc4Xf/1nLurqYbIiLtmtMFnyE1uLBnKuJl/0CPrKOAvnooIAYxLCqWHxVkoN\nJkowcZJBfgSQ6gYu/kAZb7+9UZ1lbK0Go6OkExWiTRPUNk1RqQgmSGuxtMjVYny/2dZ+iSJDFKcZ\ni0hsJjxPc+HaEb517j/i7r0MSp1P+o3ssao6KO3gdnRz9svP5CM3PsQFB+4lIo9hTKm7hNNVBBSm\n0YQkRTkK5bmykbitDUPJ//d9XMchZ4ysQ4WC+LVVKtjJSUy1QbxpnGSyQTRRI0kMXZ1+Zi2R0mik\nRJFQ0L89bw5J4PDKDRv54guO5x/e8ibUPiuho2e7oND+3h568gH3N0K6UtHsEX0fGX9lx+HWMGTl\nEYfCnEWZQNvs+vRkisu++33Wb9rMkQetpH/OXHkwbAgGxfWzNnAmk6Klqt02Nm4VFbIEYbtVj2wf\nVUGeF734xdMwjbiJshYbR6BCOcwvWgJ9gyyZ/1P+dO9DHHjkJuifi/ILWO8xrB4qDV4O1TMEjoPd\n+KC03GqTWFdc37eFo1VaY/08rznndehmHbvuQV661wDl3h74w+3b/bidJzxdvYKHaS0S2EyjJtOt\nCYptmWphPYXYyRHsxGYICmh3LylNbQO8KDcvD5kDbLu91fLIiCPZtRxHZNZ3wfVXKj67thiIFLYP\n3kxVyNky8RM9xLctQA0s5BkvOJ3P/eqPdM8tk64ep9FMs71MERtLPRXqsdIKN06pV0Jy60dxtEEX\nc9hmRLCoH1XuwNYbROtGiIYr2DBudzndwMUb6ET19mIbdczmEdItY7IBVppYY8nlRNiuRRIIw5RG\nU3AYcSo6LzK8BYznKUW3FzAcJiw75CDo6MnMcmfH3y6FEvn7D73/vex/5NM457i3sOwQF5oNVN8A\nzJkH+QJOZQrWrMI++CAUi6j9lsPcRZDLSyth4zrsmoegUoHOTigWZZOp1TDD48Sbx0mmGpg4IW4m\nhFFKLucSdGSKvlNNGk0xB60ry5jr8J4NGzn0gCW8+P0fRA0t2okqskL5BY7cez63NCOe6ShSIONq\nqOYAACAASURBVLWy9CngziSi23PZr787UwA3O78+1k6zVzNm4NZ/zhiqSQTYdstkdvzthmjpvwGb\nNm/myt9czTs++K/89mc/on/v/ab3p3yHdDOUzljQbC01YDV42XP1TLr59HO2Nu7MEqQkhrQxrZ3n\n+tA9iOrsF7fyjJlsxzby5lOO5dVf/i9+d+8aPvbWs1l4/EmorkFZX3fjWLCtalaSkZzcoG03obSS\nVlpQwMYhdvW92Ilh1OL9Ub3zhDa/re/ieOQHF/CGd74bu/khmBjJzF6/sd3vsdOERx/ydKxJUH5+\netK2L2qmU6MdlFZgFTYoogYWofrmSvbmBrtwalVZW16Bn5eyuEmlBDc5KhWjQgeqe1B4+rv9FLzt\nlpv8aXYBeCKGuGS7LFi4gHWVOtoTnI7n2jZeQ6WpLCJKKj1JaoljS5KkmMSgMykD24ww4Rim1sQ2\nY3Sm3ZKmqVSTwwTTiCRlD2SjiytN6qM1ojDNzCWz15gUZcCiiFNDNUmppUKh95Qi0EqUl1FcU6vT\nW8zTOTQXFeRmT+2PNJSib2CId//TW3jXFb/hxx96K2xeD9VJqFXkkJYvyH+FAvT0wOB8KHdBow6V\nKZiaxE5MYSYr6HoDVSpi4wRbrZGOV4mnGjQmGkSRNEodR5MmhrSR4blcLZYUWvG+sRGu3dDgm2ed\nxsvf/T704ELwC9s9gNkWKyVqsHL+ALfcfh8nuXlcpXC14Dhcpbg2rHP6ogGpULVJ7tt6QzutP1YZ\nw06NAArVM4TtyIxoZzwXk0EKbKab5Lizbfz/bbQSzDThvnvu4dNf+BK/uub3VOsNjjzsUL532b9z\nwAEHbP2aGThRSQhCuXet7kfLsNj123uozYoKwpgStpMUG4zcy6gplO+xTdiJEcgVUH1zUZ39089r\nOQsYyzOPPYY75vTw6R/9hsPe9AHeeOqVvO/8t5FbvEzwNbsLt5NEJKOb+PQFn+e8V7wQf3AhlHun\nCyVKSzWnawA7vEk6RGFjhjno3+7Dot3lizpzqWtrvb3txM5/jR+gbNaXb2k/aCWZVJCTU2nbap7M\nIfZ/f5HaDqjGSCur3CNtMcfNFCT3/KbQ1hMxqZS4Z7jPzsYTKwqBMGYim+E1sAgcwtDMhOW0EnVm\nrTI4l6PR+QDV2y2pbr1OOjpFWg0xYYzN1HgbTWlVNKMU74HN5B2N8lzijeNUhmtMjjUJQ9HYiYwh\nyqo4gVJ4WpNm1Eo3E59zsjmbWEgwvHf9CF95zRmongGpgs4m1488FLzl7Ffy1Uv+nV/dcjcnLejB\nVitQrcDIFsEIjI7KSbK7D9U3B/rmCMOjUIJCCV0qo8ZGYGoKs2UEU2+iCwHeYBe6EIAzjh2tkaYG\n39O4vpaOQ5QQ1hOamRLzgsDn/Uvn8fLTT0bni9kX3Ik4oDWMr1/L16/+Kx9dMEhUTaXdmSEZc1rx\n27DB/zz3DPRhT0MNLkJtY6xYk0J9KpPoqGPHNsHYFsgVoFDKVLxnXLasJdDCW82yUv/3kSYJX/nq\nxfzm6t+SdzS/uvZ63nzGqfzygg+wZO/FUlkp92KTaMcCf1pn0I4M95pm7aw4BJBCgDxxutVlDRBk\nSXWLNCCimkRNqIlki50aFYNNraVKmMTYsA7lbkoHHsa/LN6bs0++m5d/5bsMfvki3nzeP6EWLM8K\nDLth78u+1/su+ibvu+ibrL3gHczZbz85jJgUOvvQg4uhsw/2OwiVKwqD2s/t+PMzxhfujr27WrEL\nSsuZDcNMWtvkCLY6DqVuycj83G6purQcUG1lXPqOfh6KndKz1u5jk3RkomTt1lrWhrAtRpdSkvjN\nsiSeAKFQQZ5i4JN2FuiohVQnm9TrCXEiVRWbPU8jbS5rIW3GJGMVlO+ie7uhrw/HGJLJGkmYEDUS\n4jhFK0UQuDiOIqqFcN9GsFCvhEyNh1TrCc1UEp1WwgPgK0XO2izRUeQ0GJSo7VpwNXzb1jluyQJO\nfcmLpRr6KHQt/p5DaY1f6uTTH/4A7/jwx7jxo2/BmZiQhGdgEIaGUIUS9sEHxCfNdWDpgaiB+aih\nRTCwAPY/ApWmMLwBdePv0atWZYc6H2U1frmBo0AFLm45jy4EmHpIPFpDZbgtFDzTz/PRe9bw3ltv\nxp+zCJ0rZOvmtsQX5WBna5O89WNf4JShHp6mPSbTZuuvANyTJuQ9hwMOPwTKPRl1+GHroLUQR5gt\na2H13VAso+YsRu21Ug6mXm6bJ/UWA3I2Hnm0hO5uv+02Xv+289DW8tKTj6M6MsyFF76fThtjx9di\n/7gK+gZgxZGZgrCzzWverlbMVN12kuzgbabHTwv20XIDMBkpB1cqN1EDW5sSHEx1CiZG5XWlDmyp\nLPu5SWUs9c0V+rfrYZs1FvYO8e4Nw3ziB7/mTSf+GQqdqP75GTv7Ua5NjovbPcCN3/93Dj3jVSw4\n7zNcc+ZxPGXFvqi+fugbxCiFHtoLNXffrKq1+/f8nSc8hTJklHFrTAY0zrR4WiytGdTyRxVKCYuh\nozvz1LHTlRYvwHrbyPYeLq/+aG+MypDjOgczk8YkE1lECX1wtuLzhIhmYgjTlGJPmVwjxMHiaA3E\nIg5oDBolGxaQJIbGVIg24/iTdbzOcZzOAjaMMY2IJE6JM3CxzRKU1FrCUGFtiLWWRk2wHImxmUBW\nptsiuock1kq1B8HqOCgsNhOmk4To+xNVrn3XOTBnoaiTP8nByo9fyKb9vNNO5Ytf/Trf+Os9vH7/\nBbB2NUyMyyEFMvmJBLtxPUqB3bRa5nCuAEOL0EOLoaMbOjph8CbsmlUwOoJthJhYDjrKc9CFHLqn\nE90RY1OLVw3xXAUa9jcOc7XDJb/8E2887gRZK7enNG2BNKay5gF+9JfbuemwZUxuqNCMUrG5yFKe\nq8MGz184IC3P7Y0RhYBVu/rALhW/w65+yJV2X0tiNiSsxZqEn//0Z/z+2uv4xn99jw+/41xe95IX\nil9aHGKnRmHzOqhXodnI9o1HFm1Wqs5EUzN1Yqn8CJtJOj2athREK2FyXUnyB+dD70DrDeXfrDKk\nip2ojl7RoDMJ1CawYZ0T9t+bV3/9+9yyag0r95/A7RnKdKke3b7agqsc/IyTuPxzH+Ulb38/x3/3\nd7z2vo189ZwzUJ3dUm3M9If2lNXFTmfDTLCb0hqCglR0uufScmjcXVnYzCy37SuTRtNU8hnR/nsS\nZQ7LWR+wBb57lN/p4TfYtkSgWsaG2+krzsZjE63W5x///Ff2H+ylFEckxpAr58n1FOiOU2qjdSqV\nuK1yrB2hpiuFPFZpYhoRbJlCAXGUEoWS7LQYWsZaHC3/RrEoJrdAqkkmdtgCmKZYImMxSqGMKH0n\nWVutYQzr0oRu1+WT1VGW9Xex6MADZeGZPWU/6lAoPvuON3PKa87lpee+kOKWLSSbJ0imGmDB6y3i\n7T0PPTAE5e7pA0umx2WrE6h8SfAu+x0IU5Mk9z1IvG6YtBGjHA3W0qwNw+phbGqIahHVSkQzTMXg\nHcWrurq49P51vNGa7HvtIKzlprvuZVlvGd/AZGoyEU3pPgWe5uqpBt896inoviGp7mxzzVEirNo1\nJN5cSm3NTp2N3RvGcNpLXwnALVf+mJWHHjat6xY1Re1/YCF2+REQhahcHgqdIpOyi5CMNrSjpXDc\nMtXNKnZ4PdnYUjO6DZ4oGOdL0DmIauF1oqYwn8OGtLKMkRZQEsn+CrJndnTjz1vAyQcv57APXQwf\nupg//fhyjnjacfKZu0FwED/Pma8+h+eedirvfOd7+fIPf8mb7ryfg3oHUPseJMm6u2eSHdhFHZ6t\nQilZoPdwgaNlJtjGA23zAsxApreKwFnP09pUst5MHVo9WhE0azOVykn5qGIZG5SEaTYbj3FkyW5j\nil//+rc8a9EASlncroK0qQIXm6RoR+PoOmGUkiYGxxFhOqVou2cbY7BWKMVJppWTptMsC8lrFWki\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Ur7Mh1NgXv3Emwl/ltUgJnkvUu5E7H36Ci5fOo7q+F8M2MdKDCNNARhGRGxDEBXO2Y2Db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\nXPnmdkmEACfJe48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} ], "prompt_number": 11 } ], "metadata": {} } ] }
mit
longyangking/ML
Deep Learning/Achievement of Operations.ipynb
1
703
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Achievement of Operations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
lgpl-3.0
seg/2016-ml-contest
SHandPR/Face_classification_SHPR_GradientBoost_Updated.ipynb
1
1672890
null
apache-2.0
ecervera/mindstorms-nb
task/navigation_teacher.ipynb
1
7449
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercici de navegació\n", "\n", "[<span title=\"Roomba navigating around furniture\"><img src=\"img/roomba.jpg\" align=\"right\" width=200></span>](https://www.youtube.com/watch?v=g3Qk2y38LZQ)\n", "Un robot mòbil com el Roomba de la imatge ha d'evitar xocar amb els obstacles del seu entorn, i si arriba a col·lisionar, ha de reaccionar per a no fer, ni fer-se mal.\n", "\n", "Amb el sensor de tacte no podem evitar el xoc, però si detectar-lo un cop es produeix, i reaccionar.\n", "\n", "L'objectiu d'aquest exercici és programar el següent comportament en el robot:\n", "\n", "* mentre no detecte res, el robot va cap avant\n", "* si el sensor detecta un xoc, el robot anirà cap enrere i girarà\n", "\n", "Connecteu el robot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from functions import connect, touch, forward, backward, left, right, stop, disconnect\n", "from time import sleep\n", "connect()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Versió 1.0\n", "\n", "Utilitzeu el codi de l'exemple anterior del bucle `while`: només heu d'afegir que, quan xoque, el robot vaja cap enrere, gire una mica (cap al vostre costat preferit), i pare." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "while not touch():\n", " forward()\n", "backward()\n", "sleep(1)\n", "left()\n", "sleep(1)\n", "stop()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Versió 2.0\n", "\n", "Se suposa que la maniobra del robot li permet evitar l'obstacle, i per tant tornar a anar cap avant. Com ho podem programar?\n", "\n", "Cal **repetir** tot el bloc d'instruccions del comportament, incloent el bucle. Cap problema, els llenguatges de programació permeten posar un bucle dins d'un altre, el que s'anomena **bucles anidats**.\n", "\n", "Utilitzeu un bucle `for` per a repetir 5 vegades el codi anterior." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for ...:\n", " while ...:\n", " ...\n", " ..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in range(5):\n", " while not touch():\n", " forward()\n", " backward()\n", " sleep(1)\n", " left()\n", " sleep(1)\n", " stop()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Versió 3.0\n", "\n", "<img src=\"img/interrupt.png\" align=\"right\">\n", "I si en lloc de repetir 10 o 20 vegades, volem que el robot continue fins que el parem nosaltres? Ho podem fer amb un **bucle infinit**, i indicarem al programa que pare amb el botó `interrupt kernel`.\n", "\n", "En Python, un bucle infinit s'escriu així:\n", "```python\n", "while True:\n", " statement\n", "```\n", "\n", "Quan s'interromp el programa, s'abandona la instrucció que s'estava executant en eixe moment, i cal parar el robot. En Python, aquest procés s'anomena **excepció** i es gestiona d'aquesta manera:\n", "```python\n", "try:\n", " while True:\n", " statement # ací anirà el comportament\n", "except KeyboardInterrupt:\n", " statement # ací pararem el robot\n", "```\n", "Utilitzeu un bucle infinit per a repetir el comportament del robot fins que el pareu." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "try:\n", " while True:\n", " while not touch():\n", " forward()\n", " backward()\n", " sleep(1)\n", " left()\n", " sleep(1)\n", "except KeyboardInterrupt:\n", " stop()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Versió 4.0\n", "\n", "El comportament del robot, girant sempre cap al mateix costat, és una mica previsible, no vos sembla?\n", "\n", "Anem a introduir un component d'atzar: en els llenguatges de programació, existeixen els [**generadors de números aleatoris**](https://en.wikipedia.org/wiki/Random_number_generation), que són com els *daus* dels ordinadors.\n", "\n", "Executeu el següent codi vàries vegades amb `Ctrl+Enter` i comproveu els resultats." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from random import random\n", "random()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La funció `random` és com llançar un dau, però en compte de donar una valor d'1 a 6, dóna un número real entre 0 i 1.\n", "\n", "Aleshores, el robot pot utilitzar eixe valor per a decidir si gira a esquerra o dreta. Com? Doncs si el valor és major que 0.5, gira a un costat, i si no, cap a l'altre. Aleshores, girarà a l'atzar, amb una probabilitat del 50% per a cada costat.\n", "\n", "Incorporeu la decisió a l'atzar per a girar al codi de la versió anterior: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "try:\n", " while True:\n", " while not touch():\n", " forward()\n", " backward()\n", " sleep(1)\n", " if random() > 0.5:\n", " left()\n", " else:\n", " right()\n", " sleep(1)\n", "except KeyboardInterrupt:\n", " stop()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Recapitulem\n", "\n", "Abans de continuar, desconnecteu el robot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "disconnect()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Tot el que hem vist en aquest exercici:\n", "\n", "* bucles anidats\n", "* excepcions\n", "* números aleatoris\n", "\n", "No està malament, quasi hem vist el temari d'un primer curs de programació, i això només amb un sensor!\n", "\n", "Passem a vore doncs el següent sensor.\n", "\n", "### [>>> Sensor de so](./sound.ipynb)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:py34]", "language": "python", "name": "conda-env-py34-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
rsignell-usgs/notebook
Barnegat.ipynb
1
39562
{ "metadata": { "name": "", "signature": "sha256:e4c65fabe7b193bcef5a140ec947d853dfbd30b4febd41e6aca3112aedb02967" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import pandas as pd\n", "from ulmo.usgs import nwis\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "#barnegat\n", "sta_id='394540074062901'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# download and cache site data (this will take a long time the first time)\n", "# currently downloads all available parameters\n", "nwis.hdf5.update_site_data(sta_id)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "INFO:ulmo.usgs.nwis.core:processing data from request: http://waterservices.usgs.gov/nwis/dv/?startDT=2015-05-18&site=394540074062901&format=waterml\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:ulmo.usgs.nwis.core:processing data from request: http://waterservices.usgs.gov/nwis/iv/?startDT=2015-05-18T10%3A49%3A13&site=394540074062901&format=waterml\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "sit = nwis.hdf5.get_site_data(sta_id, parameter_code='00035')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "{}" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "\n", "# wind speed and direction\n", "vars=['00035','00036']" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sit" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "{}" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "#Try reading discharge data from another site" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "sta_id='06043500'\n", "nwis.hdf5.update_site_data(sta_id)\n", "# read daily mean discharge data from cache (statistics code 00003)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "INFO:ulmo.usgs.nwis.core:processing data from request: http://waterservices.usgs.gov/nwis/dv/?startDT=1851-01-01&site=06043500&format=waterml\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "INFO:ulmo.usgs.nwis.core:processing data from request: http://nwis.waterservices.usgs.gov/nwis/iv/?startDT=2007-10-01T00%3A00%3A00&site=06043500&format=waterml\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "data = nwis.hdf5.get_site_data(sta_id, parameter_code='00060:00003')['00060:00003']\n", " \n", "# convert data to a pandas dataframe\n", "df = pd.DataFrame(data['values']).drop(['last_checked','last_modified','qualifiers'], axis=1).set_index('datetime')\n", "df.value = df.value.apply(np.float)\n", "df.index = pd.to_datetime(df.index).to_period('D')\n", " \n", "# mark bad data as NaN\n", "df[df.values == -999999] = np.nan" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# group the data by month, day & calculate means\n", "daily_groups = df.groupby((lambda d: d.month, lambda d: d.day))\n", "means = daily_groups.mean()\n", " \n", "print 'historic daily mean on March 23rd is %s' % means.ix[3,23].value" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "historic daily mean on March 23rd is 318.717647059\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "df.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f692aa67950>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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wwTusDumaazK+YdWyFqvO0jBR+eHtt4tvk09qxSgbIe8HY5+vLLRsCezYERyu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\nXZavri5j5ZO1dk6LZ5l+Q4Od/pdfTgNwIxoagLV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"text": [ "<matplotlib.figure.Figure at 0x7f692aa6e1d0>" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
Alykis/Donnees_manquantes
st-scenar-app-idm.ipynb
1
713087
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# [Scénarios d'Exploration Statistique](https://github.com/wikistat/Exploration)\n", "# Imputations de données manquantes avec R\n", "\n", "**Résumé** : Exemples d'[imputation de données manquantes](http://wikistat.fr/pdf/st-m-app-idm.pdf) sous R, sur deux jeux de données. Un premier dont les variables sont toutes quantitatives puis un deuxième avec des variables quantitatives et qualitatives. Plusieurs méthodes sont comparées, la robustesse des méthodes qui donne les meilleurs résultats est analysée en augmentant progressivement le taux de données manquantes.\n", "\n", "## 1. Objectif\n", "Tester des méthodes d'imputation de données manquantes sur des cas-types faciles à aborder. Comparer la précision des méthodes et la robustesse des meilleures. On commencera par un jeu de données quantitatif sur lesquelles toutes les méthodes d'imputation peuvent être testées. Nous passerons dans un second temps à des données hétérogènes, plus fréquemment rencontrées en cas concret.\n", "\n", "## 2. Données Quantitatives\n", "On s'intéresse dans un premier temps à un jeu de données quantitatives sur lesquelles on va pouvoir comparer un maximum de méthodes d'imputation. Ce jeu de données regroupe le [cours des actifs boursiers](http://Wikistat.fr/pdf/st-scenar-explo-bourse.pdf) sur la place de Paris de 2000 à 2005. On considère 349 cours d'entreprises ou indices régulièrement cotés sur cette période. \n", "\n", "Les données sont disponibles dans le fichier [Paris.dat](http://Wikistat.fr/data/Paris2005.dat); les charger avant d'exécuter les lignes de code R.\n", "\n", "### 2.1. Lecture des données" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# lecture des données\n", "dat=read.table(\"Paris2005.dat\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A noter que la plupart des méthodes d'imputation font des hypothèses de normalité. Une transformation des variables est souvent nécessaire :" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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S4Dx1kXujPyBKk77favefs62zvgjz343bij4WP4NvRGbM1CH22x4MGUPdeiPEUx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\n5eC68LBqC6NgAWwPyvf140UWErUi71opF2xTST0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6/iOeOg68A5bgJx7snmybhl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\nV8hDx/gK3obDoRHkwXLYFkLnU/G7nwsfg3n6C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GvALnQ27/0tjug3lgPF+HPSBX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\n/O5Mu68PLmdLH1zLdao6Qznmg7Peq/mFxvxd+Lu8V/PBE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lQuyViVO0+D/fc/VTVIV+rYlsk45qwqf1kEBQEE\nEECgOoFuaI/8LpFfPDwng+FM1X05o75I1dZaqEh7NJ2Wm6vIClkGAQQQQCBboEgHyTf6Pym/\nV36o/FOZqIxWdlG+qHxSGcxSVYPkj0X4m/Half9qgfvaLcR8BBBAAIEBCXRye7SIjmxm5Snl\namU55W4lLutror8vKLqtLVLciXy0yIIsgwACCCAwMIFhevivlfC3PWHojw0UvWkPbA/e/+jw\nN0jfU7UbJBd33LacPPb+H/5SiQvfX1X6lBukjQuudZKWcyNZRzlYG7msjg2xDQQQQKAmgU5r\nj2bRcb+huF30P1D/j+Jvq3NHaT7FZUXFn8DwMp9Xqiyd1B7xN0hVnmnWjQACHSPgv8n5hLK9\nspYSOicarbXQIBXjpoNUzImlEECg+wQ6pT2y3PTKCsqOiv/uyB/PfkFZWHE5SPG32PkbX6su\ndJCqFmb9CCDQ8wJ+Ja4vZSkt/GHF74Jcpfgrvm9S6i4va4PuJPnz2Msrbpg89Kt1wxWXzRR3\n5g5QzlYoCCCAAAK9I9Ap7ZFF/Q7Sv5Oc5AqVIYrfMXI5Xjlced4TFAQQQACB3hDwR9ouUnyz\nd/6m+N0jf5zgV0rolGh0UIsbpFBGaWTOMFHxsJNesYsPlXeQYg3GEUCgFwS6pT0aLOtOao+G\nCsHPGdYcLAy2iwACCPRHwP/HqEg5Ugv51Tr/vZE/KuDyqrKH4o8UbKx0Qgmv1nlfHlF4ta4T\nzgr7gAACCJQn0C3tUXlHzJoQQAABBGoVKNJB8jJbKbspZyj+eJuLOyNHKycpndJB0q5QEEAA\nAQR6VID2qEdPLIeFAAIIdJJAkb9Bmls7PKPyUM6Ou77ub7Lz3x8tlrM/WdX+WvK8/c9anjoE\nEEAAgc4ToD3qvHPCHiGAAAI9J1Ckg/S0jnqCsq3y/ZTAEE3vrPiPU+ss/v8S1/Rhg/6Shqyv\nAO/DKlgUAQQQQGCQBWiPBvkEsHkEEECgCQJFOkh28Lfv/EDxP4f1R+v8BQ3+b+A7KEso7iTV\nWa7VxnZSjlWuVH6mtCpPtprJPAQQQACBrhGgPeqaU8WOIoAAAr0t4M99H6L4673dQQp5VuPb\nK4NV3Enyf1Zfd7B2INluJ31rUEzBt9jFGowjgEAvCNAetT6LndQeDdWu+vkC32LX+pwxFwEE\nulxgHu2/OyNbK2so/rrVwS6XageuH+Sd6KQGKaaggxRrMI4AAr0kQHuUfTY7qT2ig5R9jqhF\nAIEOFyj6Ebsf6TguV/6hXKF0UvHfFi2i+Fje6qQdY18QQAABBEoXoD0qnZQVIoAAAgjEAv6o\nQrvid4n2VVZvt+Agzfc31PlLIugcDdIJYLMIIIBATQK0RzVBsxkEEECgyQJFOkj+v0f3K/7m\nOH9rHQUBBBBAAIHBEKA9Ggx1tokAAgg0TKDIR+z8B5b+triDlNsUv1uT/la421X3B4WCAAII\nIIBAVQK0R1XJsl4EEEAAgfcEinSQvPDXFX+D3cgkGryvXKApOkjvI2ECAQQQQKACAdqjClBZ\nJQIIIIDAVIGiHaRFpz6EMQQQQAABBAZNgPZo0OjZMAIIINAMgby/QZpbh7+DMqIZDBwlAggg\ngECHCtAedeiJYbcQQACBXhXI6yAtpgM+SRkVHficGv+R4nkUBBBAAAEE6hCgPapDmW0ggAAC\nCLwnkNdBem+BaMQdpP0V/88hCgIIIIAAAoMlQHs0WPJsFwEEEGiAQF86SA3g4BARQAABBBBA\nAAEEEECgyQJ0kJp89jl2BBBAAAEEEEAAAQQQeJ8AHaT3cTCBAAIIIIAAAggggAACTRagg9Tk\ns8+xI4AAAggggAACCCCAwPsE2v0fpKu09NvJI0Jn6lxNv/W+tUwzzXma3jFVxyQCCCCAAAJl\nCdAelSXJehBAAAEEWgrkdZCe1aN+3/KR75958/snmUIAAQQQQKAUAdqjUhhZCQIIIIBAUYG8\nDtIDWsF2RVfCcggggAACCFQkQHtUESyrRQABBBDIFggfm8ueSy0CCCCAAAIIIIAAAggg0CAB\nOkgNOtkcKgIIIIAAAggggAACCLQWoIPU2oe5CCCAAAIIIIAAAggg0CCBvL9BahBBy0PdSHMX\nbrnElJl2HF5gORZBAAEEEECgPwLf04OWKPDAGbTMiALLsQgCCCCAQI4AHaQcmKR6ew2Xab3I\n5LnT6efcBZZjEQQQQAABBPoj4HbGKVKGFFmIZRBAAAEEsgXoIGW7hNotw0ib4Uua/2ibZZiN\nAAIIIIBAfwUOKvjAz2q5CQWXZTEEEEAAgQwB/gYpA4UqBBBAAAEEEEAAAQQQaKYAHaRmnneO\nGgEEEEAAAQQQQAABBDIE6CBloFCFAAIIIIAAAggggAACzRSgg9TM885RI4AAAggggAACCCCA\nQIYAHaQMFKoQQAABBBBAAAEEEECgmQJ0kJp53jlqBBBAAAEEEEAAAQQQyBCgg5SBQhUCCCCA\nAAIIIIAAAgg0U4AOUjPPO0eNAAIIIIAAAggggAACGQJ0kDJQqEIAAQQQQAABBBBAAIFmCtBB\nauZ556gRQAABBBBAAAEEEEAgQ4AOUgYKVQgggAACCCCAAAIIINBMATpIzTzvHDUCCCCAAAII\nIIAAAghkCNBBykChCgEEEEAAAQQQQAABBJopQAepmeedo0YAAQQQQAABBBBAAIEMATpIGShU\nIYAAAggggAACCCCAQDMF6CA187xz1AgggAACCCCAAAIIIJAhQAcpA4UqBBBAAAEEEEAAAQQQ\naKYAHaRmnneOGgEEEEAAAQQQQAABBDIE6CBloFCFAAIIIIAAAggggAACzRSgg9TM885RI4AA\nAggggAACCCCAQIYAHaQMFKoQQAABBBBAAAEEEECgmQJ0kJp53jlqBBBAAAEEEEAAAQQQyBAY\nllHXbVXDtcNjlZHKfMq7yvPK7cq9ybQGFAQQQAABBCoVoD2qlJeVI4AAAvUIdHMHyft+kLKr\nMiKH60bV76zckTOfagQQQAABBAYqQHs0UEEejwACCHSQQDd/xO54OX5VOUFZR/mwMq8ySvE7\nSlsqzyg3K6sqFAQQQAABBKoQoD2qQpV1IoAAAgj0SWB2Lf22sn6BR52lZY4osNxAFnlJD964\n4Aomabki+11wdS0XO1hzL2u5BDMRQAABBAYiQHuUrzdUs/yx9zXzF2EOAggg0HkC3foO0iKi\n9E337wVI3UFYu8ByLIIAAggggEBfBWiP+irG8ggggECHC3RrB8lfwPCsslkbX38u3B+1u6fN\ncsxGAAEEEECgPwK0R/1R4zEIIIBABwu4A9GN5R3t9NHKaco2ygXKk8oEZXrFX9qwpLKtsriy\nukJBAAEEEECgbAHao7JFWR8CCCCAwIAENtCj71P8cbt03lSdO1D+woaqC3+DVLUw60cAAQQ6\nW4D26IPnh79B+qAJNQgg0AUC3foOUqC9WCNLKP7muoWV2RR3Vh5P8pqGAyk/14P9TlS7MlwL\n+Bv0KAgggAACzRSouj06RaxLF6CdUcssWGA5FkEAAQQQyBHo9g5SOKxHNOKUXR4ouMJPaTl/\nOx0FAQQQQKDZAlW1R+eJ9bYCtB/RMs8VWI5FEEAAAQRyBLq5g+QvmPBnv+Mykya2UPyuz1OK\nX9G7V+lvOabgA3fTci8WXJbFEEAAAQR6S6CO9uicgmQHarmBfnqi4KZYDAEEEOhNgW7tIM2q\n0+EOyVbKmcmpcafoIsVfuRrKWxo5QDk0VDBEAAEEEECgRAHaoxIxWRUCCCDQCQLd+jXfWXb+\nfLbfQdpTGan4H9OdqByibKpQEEAAAQQQqEOA9qgOZbaBAAIIVCTQre8gpTnmV8Uqyn7KUclM\nf+33NcqyyjbK+QoFAQQQQACBKgVoj6rUZd0IIIBADQK98g6Sv+Lbf4+U9RltfwRvqRos2QQC\nCCCAAAK0R1wDCCCAQJcLdHsHyX9vNLPiL2S4WllOSZf1VVHFN9ylt8M0AggggEBzBWiPmnvu\nOXIEEOgxgW7tIPkVOv8jWH/5gr+s4T+K/+7ol8p8isuKyiXKhspJCgUBBBBAAIGyBWiPyhZl\nfQgggMAgC3Tr3yC9LLdZlGWU5ZUVkqE7R/6nrS6bKZ9Q/C12ZysUBBBAAAEEyhagPSpblPUh\ngAACCJQqMCRa2yiNzxlNVzn6kla+ccENTNJy/thfHeVgbeSyOjbENhBAAAEE3idAezTNNEMl\n4nfY/K2yFAQQQKBrBLr1HaQ8YN+IQ+HvjoIEQwQQQACBugVoj+oWZ3sIIIBASQLd+jdIJR0+\nq0EAAQQQQAABBBBAAAEEpgrQQZpqwRgCCCCAAAIIIIAAAgg0XIAOUsMvAA4fAQQQQAABBBBA\nAAEEpgrQQZpqwRgCCCCAAAIIIIAAAgg0XIAOUsMvAA4fAQQQQAABBBBAAAEEpgrQQZpqwRgC\nCCCAAAIIIIAAAgg0XIAOUsMvAA4fAQQQQAABBBBAAAEEpgrQQZpqwRgCCCCAAAIIIIAAAgg0\nXIAOUsMvAA4fAQQQQAABBBBAAAEEpgrQQZpqwRgCCCCAAAIIIIAAAgg0XIAOUsMvAA4fAQQQ\nQAABBBBAAAEEpgrQQZpqwRgCCCCAAAIIIIAAAgg0XIAOUsMvAA4fAQQQQAABBBBAAAEEpgrQ\nQZpqwRgCCCCAAAIIIIAAAgg0XIAOUsMvAA4fAQQQQAABBBBAAAEEpgrQQZpqwRgCCCCAAAII\nIIAAAgg0XIAOUsMvAA4fAQQQQAABBBBAAAEEpgoMmzrKWA8KzKBjmlNZt6Zje13bubambbEZ\nBBBAAAEELLCSMkfFFK9p/ddUvA1WjwACHSLQCx2k4bIcq4xU5lPeVZ5XblfuTaY1aGRZRUe9\no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"text/plain": [ "Plot with title “Histogram of dat[, 50]”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Les données ne sont visiblement pas gaussiennes :\n", "layout(matrix(c(1,1,2,3),ncol=2,byrow=T))\n", "boxplot(dat)\n", "hist(dat[,1],xlab=names(dat)[1])\n", "hist(dat[,50],xlab=names(dat)[1])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VWC/49WprBzez/4ILO90fAo6HGuTz9uBvfG4bADHAXfQu7XB8TPgsqwKiwLyntP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V4p5hrGOCXhPmuzD+HOZwdsV0atBHQpsV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\n8ubkvDsYVooUe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7deTG8fIkg39xXbk6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\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VldjvXipD4ke2hyXXJE0uXy3DS+ftE+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\nAABJRU5ErkJggg==", "text/plain": [ "Plot with title “Histogram of dat[, 50]”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Passage au log pour s'en approcher :\n", "dat=log(dat+1)\n", "\n", "# Vérification visuelle :\n", "layout(matrix(c(1,1,2,3),ncol=2,byrow=T))\n", "boxplot(dat)\n", "hist(dat[,1],xlab=names(dat)[1])\n", "hist(dat[,50],xlab=names(dat)[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.2 Création de données manquantes\n", "En se limitant au cas MCAR, on crée artificiellement des données manquantes. On pourra ensuite comparer les résultats de la complétion avec les données retirées." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# initialisation du générateur\n", "set.seed(42) \n", "# Ratio de données manquantes\n", "test.ratio=0.1\n", "# Indices de l'échantillon test\n", "IND=which(!is.na(dat),arr.ind=TRUE)\n", "ntest=ceiling(dim(dat)[1]*test.ratio)\n", "ind.test=IND[sample(1:dim(IND)[1],ntest),]\n", "# Création des données manquantes\n", "dat.test=dat[ind.test]\n", "dat.train=dat\n", "dat.train[ind.test]=NA " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.3 Imputation\n", "### 2.3.1 LOCF" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# chargement de la bibliothèque\n", "if(!(\"zoo\" %in% rownames(installed.packages())))install.packages(\"zoo\")\n", "suppressMessages(library(zoo))\n", "dat.locf=na.locf(dat.train,na.rm=FALSE)\n", "dat.locf=na.locf(dat.locf,na.rm=FALSE,\n", " fromLast=TRUE)\n", "# calcul de l'erreur\n", "err.locf=abs(dat.test-dat.locf[ind.test])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.3.2 Par la moyenne" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "# chargement de la bibliothèque\n", "if(!(\"Hmisc\" %in% rownames(installed.packages())))install.packages(\"Hmisc\")\n", "suppressMessages(library(Hmisc))\n", "dat.moy=impute(dat.train, fun=mean)\n", "err.moy=abs(dat.test-as.matrix(dat.moy)[ind.test])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3.3 Par la médiane" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "med=apply(dat.train,1,median,na.rm=TRUE)\n", "dat.med=dat.train\n", "ind.na=which(is.na(dat.med),arr.ind=TRUE)\n", "dat.med[ind.na]=med[ind.na[,1]]\n", "err.med=abs(dat.test-dat.med[ind.test])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3.4 K plus proches voisins" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if(!(\"VIM\" %in% rownames(installed.packages())))install.packages(\"VIM\")\n", "suppressMessages(library(VIM))\n", "dat.kNN=kNN(dat.train, k=5, imp_var=FALSE)\n", "err.kNN=abs(dat.test-dat.kNN[ind.test])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3.5 LOESS" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if(!(\"locfit\" %in% rownames(installed.packages())))install.packages(\"locfit\")\n", "suppressMessages(library(locfit))\n", "dat.imputed=rbind(colnames(dat.train),dat.train)\n", "indices=1:nrow(dat.train)\n", "dat.loess= apply(dat.imputed, 2, function(j) {\n", " predict(locfit(j[-1] ~ indices), indices)\n", "})\n", "err.loess=abs(dat.test-dat.loess[ind.test])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3.6 SVD" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# chargement de la bibliothèque\n", "if(!(\"bcv\" %in% rownames(installed.packages())))install.packages(\"bcv\")\n", "suppressMessages(library(bcv))\n", "dat.SVD=impute.svd(dat.train,k=3,maxiter=1000)$x\n", "err.svd=abs(dat.test-dat.SVD[ind.test])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3.7 missForest" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n" ] } ], "source": [ "# chargement de la bibliothèque\n", "if(!(\"missForest\" %in% rownames(installed.packages())))install.packages(\"missForest\")\n", "suppressMessages(library(missForest))\n", "dat.mF<-missForest(dat.train,maxiter=10,ntree = 200, variablewise = TRUE)$ximp\n", "err.mF=abs(dat.test-dat.mF[ind.test])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3.8 AmeliaII" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-- Imputation 1 --\n", "\n", " 1 2 3\n", "\n" ] } ], "source": [ "if(!(\"Amelia\" %in% rownames(installed.packages())))install.packages(\"Amelia\")\n", "suppressMessages(library(Amelia))\n", "dat.amelia=amelia(dat.train,m=1)$imputations$imp1\n", "err.amelia=abs(dat.test-dat.amelia[ind.test])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2.4 Comparaison des résultats" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AECBAgQmJaABGlasg0d9wHRbyZCR63o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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "boxplot(data.frame(err.locf,err.moy,err.med,err.kNN,err.loess,err.svd,err.mF,err.amelia),las=2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Identifier les méthodes les plus précises: SVD, missForest et AmeliaII, dont le comportement est ensuite étudié lorsque la quantité de données manquantes augmente." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.5 Robustesse des méthodes" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-- Imputation 1 --\n", "\n", " 1 2 3\n", "\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", "-- Imputation 1 --\n", "\n", " 1 2 3 4\n", "\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", " missForest iteration 5 in progress...done!\n", " missForest iteration 6 in progress...done!\n", " missForest iteration 7 in progress...done!\n", " missForest iteration 8 in progress...done!\n", "-- Imputation 1 --\n", "\n", " 1 2 3\n", "\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", "-- Imputation 1 --\n", "\n", " 1 2 3 4 5 6 7 8 9 10 11 12 13 14\n", "\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", "-- Imputation 1 --\n", "\n", " 1 2 3 4 5 6 7 8 9\n", "\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", "-- Imputation 1 --\n", "\n", " 1 2 3 4 5 6 7\n", "\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", " missForest iteration 5 in progress...done!\n", "-- Imputation 1 --\n", "\n", " 1 2 3 4 5 6 7 8 9 10 11\n", "\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", " missForest iteration 5 in progress...done!\n", "-- Imputation 1 --\n", "\n", " 1 2 3 4 5 6 7 8\n", "\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n" ] } ], "source": [ "# de 10 à 80% de données manquantes\n", "TEST.RATIO=seq(0.1,0.8,by=0.1)\n", "# initialisation des matrices d'erreur\n", "err.amelia=matrix(NA,nrow=length(TEST.RATIO),ncol=280)\n", "err.mf=matrix(NA,nrow=length(TEST.RATIO),ncol=280)\n", "err.svd=matrix(NA,nrow=length(TEST.RATIO),ncol=280)\n", "tmp=1\n", "for (test.ratio in TEST.RATIO){\n", " IND=which(!is.na(dat),arr.ind=TRUE)\n", " ntest=ceiling(dim(dat)[1]*test.ratio)\n", " ind.test=IND[sample(1:dim(IND)[1],ntest),]\n", " dat.test=dat[ind.test]\n", " dat.train=dat\n", " dat.train[ind.test]=NA \n", " \n", " dat.amelia=amelia(dat.train,m=1)$imputations$imp1\n", " err.amelia[tmp,1:length(ind.test[,2])]=abs(dat.test-dat.amelia[ind.test])\n", " \n", " dat.SVD=impute.svd(dat.train,k=3,maxiter=1000)$x\n", " err.svd[tmp,1:length(ind.test[,2])]=abs(dat.test-dat.SVD[ind.test])\n", " \n", " dat.mf<-missForest(dat.train, maxiter=10, \n", " ntree = 200, variablewise = TRUE)$ximp\n", " err.mf[tmp,1:length(ind.test[,2])]=abs(dat.test-dat.mf[ind.test])\n", " \n", " tmp=tmp+1\n", "}" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<ol class=list-inline>\n", "\t<li>0.1</li>\n", "\t<li>0.2</li>\n", "\t<li>0.3</li>\n", "\t<li>0.4</li>\n", "\t<li>0.5</li>\n", "\t<li>0.6</li>\n", "\t<li>0.7</li>\n", "\t<li>0.8</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 0.1\n", "\\item 0.2\n", "\\item 0.3\n", "\\item 0.4\n", "\\item 0.5\n", "\\item 0.6\n", "\\item 0.7\n", "\\item 0.8\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 0.1\n", "2. 0.2\n", "3. 0.3\n", "4. 0.4\n", "5. 0.5\n", "6. 0.6\n", "7. 0.7\n", "8. 0.8\n", "\n", "\n" ], "text/plain": [ "[1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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1Og6XUh42EyO7sqQ5VNf7OLVKWnuXk6B7+xkoFGrzTCprgILh25lt4/hamP1LJ2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bEAKZnrNrQwNSJIshAQn0iEA65xO5MbE3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\nl+L6+UT/21Gk+bP7ilTpd7uyrtsKWRdIeRCAAAQgAIFOJtC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"text/plain": [ "Plot with title “missForest”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Affichage des erreurs\n", "# ratio de données manquantes en abscisse\n", "TEST.RATIO\n", "par(mfrow=c(2,2))\n", "boxplot(\n", " data.frame(t(err.amelia)),\n", " na.action=na.omit,ylim=c(0,0.5),\n", " xlab=\"ratio de données manquantes\",\n", " ylab=\"erreur completion\",\n", " main=\"AmeliaII\"\n", ")\n", "boxplot(\n", " data.frame(t(err.svd)),na.action=na.omit,\n", " ylim=c(0,0.5),xlab=\"ratio de données manquantes\",\n", " ylab=\"erreur completion\",\n", " main=\"SVD\"\n", ")\n", "boxplot(\n", " data.frame(t(err.mf)),na.action=na.omit,\n", " ylim=c(0,0.5),xlab=\"ratio de données manquantes\",\n", " ylab=\"erreur completion\",\n", " main=\"missForest\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparer les résultats. Si AmeliaII semble donner les meilleurs résultats, remarquer que l'erreur de complétion reste relativement stable pour les trois méthodes présentées malgré l'augmentation du taux de valeurs manquantes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Données hétérogènes\n", "Si la plupart des méthodes de complétion privilégient les données quantitatives, certaines peuvent être utilisées pour imputer des données qualitatives voire hétérogènes (*i.e.* un mélange de données qualitatives et quantitatives). Nous développerons cette possibilité en utilisant un jeu de données hétérogène propice à l'imputation.\n", "\n", "Les données ont été acquises par [Detrano et al. (1989)](http://www.ajconline.org/article/0002-9149%2889%2990524-9/abstract) et mises à disposition par [Bache et Lichman (2013)](https://scholar.google.com/citations?view_op=view_citation&hl=fr&user=RSMBQOgAAAAJ&citation_for_view=RSMBQOgAAAAJ:zYLM7Y9cAGgC). On considère donc un ensemble de 270 relevés médicaux liés à la présence de maladie coronarienne. Sur les 14 variables relevées, 5 sont des variables quantitatives (age, pression, cholestérol, fréquence cardiaque maximale, *oldpeak*) et 9 sont qualitatives (sexe, douleur, sucre, cardio, angine, pente du pic, nombre de vaisseaux cardiaques, thalassémie,présence de maladie cardiaque). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1 Lecture des données\n", "La lecture des données nécessite un pré-traitement afin de définir les variables qualitatives comme des facteurs :" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Lecture des données\n", "heart=read.table(\"heart.dat\")\n", "\n", "# recodage des classes et nom des variables\n", "heart=data.frame(\n", " Age=heart[,1],\n", " Sexe=factor(as.factor(heart[,2]),labels=c(\"sxF\",\"sxM\")),\n", " Douleur=factor(as.factor(heart[,3]),labels=c(\"dlA\",\"dlB\",\"dlC\",\"dlD\")),\n", " Pression=heart[,4],\n", " Cholest=heart[,5],\n", " Sucre=factor(as.factor(heart[,6]),labels=c(\"scN\",\"scO\")),\n", " Cardio=factor(as.factor(heart[,7]),labels=c(\"cdA\",\"cdB\",\"cdC\")),\n", " Taux_max=heart[,8],\n", " Ang_ind=factor(as.factor(heart[,9]),labels=c(\"tmA\",\"tmB\")),\n", " Pic_ind=heart[,10],\n", " Pente_ind=factor(as.factor(heart[,11]),labels=c(\"piA\",\"piB\",\"piC\")),\n", " Nvais=factor(as.factor(heart[,12]),labels=c(\"flA\",\"flB\",\"flC\",\"flD\")),\n", " Thal=factor(as.factor(heart[,13]),labels=c(\"thN\",\"thF\",\"thR\")),\n", " Classe=factor(as.factor(heart[,14]),labels=c(\"hdA\",\"hdP\"))\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2 Comparaison de méthodes d'imputation\n", "LOCF, kNN et missForest sont trois méthodes de complétion qui permettent d'imputer des données hétérogènes. Ces méthodes sont testées et comparées entre elles au fur et à mesure que la quantité de données manquantes augmente. L'erreur d'imputation des données quantitatives est calculée comme précédemment, c'est à dire que l'on prendra la valeur absolue de la différence avec l'échantillon test. Pour les variables qualitatives, on utilisera la distance de Hamming définie par\n", "$$err=100*\\frac{\\sum_i \\mathbb{1}_{x_i^* \\neq x_i^{test}}}{\\# x_i^{test}}$$\n", "avec $x_i^{test}$ la valeur de test et $x_i^*$ la valeur imputée.\n", "\n", "Dans R, on définit la fonction d'erreur par :" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "err.model<-function(heart,heart.model,ind.test){\n", "err={}\n", "for (i in sort(unique(ind.test[,2]))){\n", " test=heart[ind.test[ind.test[,2]==i,1],i]\n", " if (length(test)>0){\n", " if (is.factor(heart[,i])){\n", " #distance de Hamming (pourcentage de mauvais choix)\n", " err=rbind(err,100*length(which(heart.model[,i]!=heart[,i]))/length(test))\n", " } else {\n", " #moyenne de l'erreur en valeur absolue\n", " err=rbind(err,mean(abs(as.numeric(heart[ind.test[ind.test[,2]==i,1],i])-as.numeric(heart.model[ind.test[ind.test[,2]==i,1],i])))) \n", " }\n", " }\n", "}\n", "err\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.3 Création de données manquantes\n", "En se limitant toujours au cas MCAR, on crée artificiellement de plus en plus de données manquantes à imputer : de 10 à 80%, ratio donné par `TEST.RATIO`. On initialise les matrices d'erreur, une ligne par ratio de données manquantes, une colonne par variable :" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "TEST.RATIO=seq(0.1,0.8,by=0.1)\n", "# initialisation des matrices d'erreur\n", "err.locf=matrix(NA,nrow=length(TEST.RATIO),ncol=14)\n", "colnames(err.locf)=names(heart)\n", "err.kNN=matrix(NA,nrow=length(TEST.RATIO),ncol=14)\n", "colnames(err.kNN)=names(heart)\n", "err.missForest=matrix(NA,nrow=length(TEST.RATIO),ncol=14)\n", "colnames(err.missForest)=names(heart)\n", "err.amelia=matrix(NA,nrow=length(TEST.RATIO),ncol=14)\n", "colnames(err.amelia)=names(heart)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.4 Imputation" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", " missForest iteration 5 in progress...done!\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", " missForest iteration 5 in progress...done!\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", " missForest iteration 5 in progress...done!\n", " missForest iteration 6 in progress...done!\n", " missForest iteration 7 in progress...done!\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", " missForest iteration 5 in progress...done!\n", " missForest iteration 6 in progress...done!\n", " missForest iteration 7 in progress...done!\n", " missForest iteration 8 in progress...done!\n", " missForest iteration 1 in progress...done!\n", " missForest iteration 2 in progress...done!\n", " missForest iteration 3 in progress...done!\n", " missForest iteration 4 in progress...done!\n", " missForest iteration 5 in progress...done!\n", " missForest iteration 6 in progress...done!\n" ] } ], "source": [ "for(test.ratio in TEST.RATIO){\n", " # création de l'échantillon test\n", " IND=which(!is.na(heart),arr.ind=TRUE)\n", " ntest=ceiling(dim(heart)[1]*test.ratio)\n", " ind.test=IND[sample(1:dim(IND)[1],ntest),]\n", " heart.test=heart[ind.test]\n", " heart.train=heart\n", " heart.train[ind.test]=NA \n", " \n", " # par LOCF\n", " heart.locf=na.locf(heart.train,na.rm=FALSE)\n", " heart.locf=na.locf(heart.locf,na.rm=FALSE,fromLast=TRUE)\n", " err.locf[,1:length(unique(ind.test[,2]))]=err.model(heart,heart.locf,ind.test)\n", " \n", " # par kNN\n", " heart.kNN=kNN(heart.train, k=5, imp_var=FALSE)\n", " err.kNN[,1:length(unique(ind.test[,2]))]=err.model(heart,heart.kNN,ind.test)\n", " \n", " # par missForest\n", " heart.missForest<-missForest(heart.train,maxiter=10,ntree=200,variablewise=TRUE)$ximp\n", " err.missForest[,1:length(unique(ind.test[,2]))]=err.model(heart,heart.missForest,ind.test)\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.5 Comparaison des résultats\n", "Pour une variable quantitative et une variable qualitative, on affiche l'évolution de l'erreur avec la proportion de données manquantes." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Zg2EzABEzABE+gzgQfYg2bUqFSn9PnIle3As9hVxsu5TcAEqkvAs9iVx1PfytPz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29w0tgwAEIACBEgkk0/H2lrN0vkaYXi/8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RYsI26O0ZTsDV2e5783ZJirq6pyoyKxKDVQ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\nTkHNZWGNI8/xYhrC/WjmuKTVRne5sG6ai59f67wJFoEPCe6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lqXiLQFsERkyKfoVmpdOb/xf2va+GyNl8/JjK2rZtsYufFaZ7U3CSNs+M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\nFdaWGFmH9iloApJlnEXjkO/Z7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ycOOt\nQtCCCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiACIiA\nCJScwP8DKQyk0vTEHi8AAAAASUVORK5CYII=", "text/plain": [ "Plot with title “Variable qualitative”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "err.pression=cbind(err.locf[,4],err.kNN[,4],err.missForest[,4])\n", "err.nvais=cbind(err.locf[,12],err.kNN[,12],err.missForest[,12])\n", "\n", "par(mfrow=c(2,1))\n", "matplot(TEST.RATIO,err.nvais,type='l',lwd=2,lty=1,col=1:3,xlab=\"test.ratio\",main=\"Variable quantitative\",ylim=c(0,100))\n", "legend(\"top\",legend=c(\"locf\",\"knn\",\"mF\"),col=1:3,lwd=2,lty=1)\n", "matplot(TEST.RATIO,err.pression,type='l',lwd=2,lty=1,col=1:3,xlab=\"test.ratio\",main=\"Variable qualitative\",ylim=c(0,100))\n", "legend(\"top\",legend=c(\"locf\",\"knn\",\"mF\"),col=1:3,lwd=2,lty=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparer les erreurs de complétion sur l'échantillon test par LOCF, KNN et missForest quand la quantité de valeurs manquantes augmente, pour une variable qualitative et une quantitative." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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kBgX/LwEzSrDlINOPTMwjYs+Tv0cvQq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", "text/plain": [ "Plot with title “MF error”" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "par(mfrow=c(3,1))\n", "matplot(TEST.RATIO,data.frame(err.nvais[,1],err.pression[,1]),type='l',lwd=2,lty=1,col=4:5,ylab=\"err\",xlab=\"test.ratio\",main=\"LOCF error\",ylim=c(0,100))\n", "legend(\"top\",legend=c(\"quant\",\"qual\"),col=4:5,lwd=2,lty=1)\n", "\n", "matplot(TEST.RATIO,data.frame(err.nvais[,2],err.pression[,2]),type='l',lwd=2,lty=1,col=4:5,ylab=\"err\",xlab=\"test.ratio\",main=\"KNN error\",ylim=c(0,100))\n", "legend(\"top\",legend=c(\"quant\",\"qual\"),col=4:5,lwd=2,lty=1)\n", "\n", "matplot(TEST.RATIO,data.frame(err.nvais[,3],err.pression[,3]),type='l',lwd=2,lty=1,col=4:5,ylab=\"err\",xlab=\"test.ratio\",main=\"MF error\",ylim=c(0,100))\n", "legend(\"top\",legend=c(\"quant\",\"qual\"),col=4:5,lwd=2,lty=1)" ] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.3.2" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
Ledoux/ShareYourSystem
Pythonlogy/ShareYourSystem/Standards/Interfacers/Interfacer/Readme.ipynb
1
3451
{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Interfacer\n\n##Doc\n----\n\n\n> \n> The Interfacer\n> \n> \n\n----\n\n<small>\nView the Interfacer notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Interfacer.ipynb)\n</small>\n\n", "cell_type": "markdown", "prompt_number": 0, "metadata": { "slideshow": { "slide_type": "slide" } } }, { "source": "\n<!--\nFrozenIsBool False\n-->\n\n##Code\n\n----\n\n<ClassDocStr>\n\n----\n\n```python\n# -*- coding: utf-8 -*-\n\"\"\"\n\n\n<DefineSource>\n@Date : Fri Nov 14 13:20:38 2014 \\n\n@Author : Erwan Ledoux \\n\\n\n</DefineSource>\n\n\nThe Interfacer\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Standards.Objects.Rebooter\"\nDecorationModuleStr=\"ShareYourSystem.Standards.Classors.Classer\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nimport os\n#</ImportSpecificModules>\n\n#<DefineLocals>\n#</DefineLocals>\n\n#<DefineClass>\n@DecorationClass()\nclass InterfacerClass(BaseClass):\n\t\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t\t]\n\n\n\tdef default_init(self,\n\t\t\t\t\t\t**_KwargVariablesDict\n\t\t\t\t\t):\n\n\t\t#Call the parent __init__ method\n\t\tBaseClass.__init__(self,**_KwargVariablesDict)\n\n\t#@Argumenter.ArgumenterClass()\n\tdef do_interface(self,**_KwargVariablesDict):\n\n\t\tpass\n\n\t\t#Return self\n\t\t#return self\n\t\n#</DefineClass>\n\n\n```\n\n<small>\nView the Interfacer sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Interfacers/Interfacer\" target=\"_blank\">Github</a>\n</small>\n\n", "cell_type": "markdown", "prompt_number": 1, "metadata": { "slideshow": { "slide_type": "subslide" } } }, { "source": "\n<!---\nFrozenIsBool True\n-->\n\n##Example\n\nLet's create an empty class, which will automatically receive\nspecial attributes from the decorating ClassorClass,\nspecially the NameStr, that should be the ClassStr\nwithout the TypeStr in the end.", "cell_type": "markdown", "prompt_number": 2, "metadata": { "slideshow": { "slide_type": "subslide" } } }, { "source": "```python\n#ImportModules\nimport ShareYourSystem as SYS\nfrom ShareYourSystem.Standards.Interfacers import Interfacer\n\n#Definition \nMyInterfacer=Interfacer.InterfacerClass()\n\n#Definition the AttestedStr\nSYS._attest(\n [\n 'MyInterfacer is '+SYS._str(\n MyInterfacer,\n **{\n 'RepresentingAlineaIsBool':False\n })\n ]\n) \n\n#Print\n\n\n\n```\n", "cell_type": "markdown", "metadata": {} }, { "source": "```console\n>>>\n\n\n*****Start of the Attest *****\n\nMyInterfacer is < (InterfacerClass), 4540175632>\n /{ \n / '<New><Instance>IdInt' : 4540175632\n /}\n\n*****End of the Attest *****\n\n\n\n```\n", "cell_type": "markdown", "metadata": {} } ] } ], "metadata": { "name": "", "signature": "" }, "nbformat_minor": 0 }
mit
adrn/TriandRRLyrae
notebooks/RR Lyrae Velocity Fits.ipynb
1
1026803
{ "metadata": { "name": "", "signature": "sha256:2c4f27db5fe7a30f821ab2e2c5b56e1123091db9f61a83e843ae8065eb8c7c6c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import emcee\n", "from scipy.interpolate import interp1d\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "more_info = np.genfromtxt(\"/Users/adrian/projects/triand-rrlyrae/data/mdm-targets.txt\", \n", " names=True, dtype=None)\n", "more_info" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 163, "text": [ "array([ ('TriAndRRL1', 35.804598, 31.551122, 0.6297892, 55889.67436, 0.724, 17.902, 112, 0.181, 18.131),\n", " ('TriAndRRl2', 30.59351, 33.377335, 0.6043698, 55763.907589, 0.515, 16.902, 105, 0.208, 16.965),\n", " ('TriAndRRl3', 28.380832, 30.584897, 0.5817013, 55899.726836, 0.966, 17.151, 116, 0.14, 17.597),\n", " ('TriAndRRl4', 24.505426, 32.027878, 0.5610494, 55892.613891, 0.68, 17.373, 107, 0.149, 17.595),\n", " ('TriAndRRl5', 22.322702, 32.72186, 0.5129024, 55853.589624, 0.7, 16.891, 116, 0.148, 17.177),\n", " ('TriAndRRl6', 12.488351, 42.598328, 0.6189352, 55820.651431, 0.357, 16.523, 101, 0.222, 16.486),\n", " ('TriAndRRl7', 11.310251, 42.705384, 0.5819742, 55757.857652, 0.499, 16.482, 104, 0.219, 16.524),\n", " ('TriAndRRl8', 17.992175, 35.466819, 0.6438835, 56272.808192, 0.347, 17.201, 101, 0.151, 17.23),\n", " ('TriAndRRl9', 11.491072, 40.199252, 0.5191218, 55774.793032, 0.775, 16.636, 116, 0.18, 16.934),\n", " ('TriAndRRl10', 16.841753, 32.235145, 0.4767494, 56258.826942, 0.973, 16.217, 116, 0.167, 16.64),\n", " ('TriAndRRl11', 9.262168, 38.824378, 0.6286307, 55899.583961, 0.367, 16.68, 103, 0.151, 16.727),\n", " ('TriAndRRl12', 12.887871, 34.28071, 0.5708534, 56231.918816, 0.831, 17.026, 118, 0.279, 17.237),\n", " ('TriAndRRl13', 8.323077, 37.502618, 0.605721, 56265.64237, 0.433, 18.191, 100, 0.168, 18.235),\n", " ('TriAndRRl14', 7.316877, 37.832822, 0.5361401, 56233.871843, 0.776, 17.176, 112, 0.161, 17.453),\n", " ('TriAndRRl15', 8.828356, 36.304111, 0.5994482, 56237.939283, 0.798, 16.461, 110, 0.168, 16.725),\n", " ('TriAndRRl16', 10.495088, 33.834552, 0.5099844, 56258.839841, 0.834, 16.439, 112, 0.244, 16.663),\n", " ('TriAndRRl17', 11.420589, 31.895553, 0.5194712, 56203.614781, 0.771, 16.282, 112, 0.236, 16.481),\n", " ('TriAndRRl18', 9.956522, 32.429326, 0.5740692, 56272.769061, 0.752, 16.876, 119, 0.223, 17.113),\n", " ('TriAndRRl19', 351.051857, 32.899191, 0.4542834, 55046.805814, 1.067, 17.296, 119, 0.375, 17.559),\n", " ('TriAndRRl20', 5.576572, 36.203326, 0.6826233, 56229.917435, 0.537, 16.545, 105, 0.186, 16.641),\n", " ('TriAndRRl21', 352.155077, 33.73084, 0.5913201, 56239.726193, 0.548, 17.362, 110, 0.297, 17.367),\n", " ('TriAndRRl22', 353.523696, 34.806522, 0.634024, 56226.528309, 0.686, 17.01, 107, 0.281, 17.104),\n", " ('TriAndRRl23', 352.977933, 34.21691, 0.6201623, 56197.71663, 0.469, 17.138, 104, 0.315, 17.07),\n", " ('TriAndRRl24', 353.000072, 33.44693, 0.5104113, 55782.967115, 0.934, 16.293, 114, 0.247, 16.581),\n", " ('TriAndRRl25', 353.576571, 33.945158, 0.5268401, 55519.664379, 0.73, 16.881, 106, 0.236, 17.049),\n", " ('TriAndRRl26', 351.509509, 30.571749, 0.5281412, 56232.636689, 0.75, 17.395, 116, 0.539, 17.319),\n", " ('TriAndRRl27', 355.466314, 33.808053, 0.5673953, 55813.943244, 0.768, 16.994, 112, 0.199, 17.229),\n", " ('TriAndRRl28', 2.822702, 34.243251, 0.5633701, 55514.634149, 0.69, 16.391, 116, 0.131, 16.688),\n", " ('TriAndRRl29', 0.350222, 36.141364, 0.5029317, 56207.678085, 1.135, 17.276, 106, 0.309, 17.574),\n", " ('TriAndRRl30', 1.605007, 34.567764, 0.6251911, 56204.766799, 0.453, 18.159, 106, 0.156, 18.26),\n", " ('TriAndRRl31', 354.620544, 30.140181, 0.558377, 56234.646836, 0.73, 17.402, 116, 0.258, 17.596),\n", " ('TriAndRRl32', 356.154789, 30.168917, 0.5399409, 56234.597284, 0.798, 17.194, 109, 0.364, 17.27),\n", " ('TriAndRRl33', 1.014094, 32.019259, 0.6935978, 56239.834007, 0.597, 16.898, 104, 0.135, 17.073),\n", " ('TriAndRRl34', 359.193996, 31.672904, 0.5732184, 55771.935045, 0.581, 17.05, 113, 0.141, 17.254)], \n", " dtype=[('name', 'S11'), ('ra', '<f8'), ('dec', '<f8'), ('period', '<f8'), ('hjd0', '<f8'), ('amp', '<f8'), ('mag0', '<f8'), ('template', '<i8'), ('rExt', '<f8'), ('Vmag', '<f8')])" ] } ], "prompt_number": 163 }, { "cell_type": "code", "collapsed": false, "input": [ "_phase,_vtemplate = np.loadtxt(\"/Users/adrian/projects/triand-rrlyrae/data/RVtemplates/Halpha.txt\").T\n", "template = interp1d(_phase, _vtemplate) # define an interpolator to get template value at arbitrary phases" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# observed parameters\n", "a = 35.6 # \u00b1 2.5\n", "b = 78.2 # \u00b1 2.4" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def model(phase, vsys, a, b, AV):\n", " Arv_Ha = a*AV + b\n", " return Arv_Ha*template(phase) + vsys\n", "\n", "def ln_likelihood(p, phase, v, sigma, a, a_err, b, b_err, AV):\n", " (vsys,) = p\n", " var = (sigma**2 + template(phase)**2 * (AV**2*a_err**2 + b_err**2))\n", " lnB = -0.5 * var / (sigma*a_err*b_err)\n", " lnL = -0.5 * (v - model(phase, vsys, a, b, AV))**2 / var\n", " return lnB + lnL\n", "\n", "def ln_posterior(p, phase, v, sigma, a, a_err, b, b_err, AV):\n", " return ln_likelihood(p, phase, v, sigma, a, a_err, b, b_err, AV).sum()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test for RRL14 -- from Ally's fit:\n", "\n", "$$v_\\gamma = -228. \\pm 25.$$\n", "\n", "rv phase:\n", "- -256.0 0.19\n", "- -210.0 0.26\n", "\n", "$$ A_R=0.453 $$" ] }, { "cell_type": "code", "collapsed": false, "input": [ "phase = np.array([0.19, 0.26])\n", "rv = np.array([-256., -210.])\n", "rv_err = np.array([15., 18.])\n", "AV = 1.21*0.453\n", "\n", "lls = []\n", "vsyss = np.linspace(-250,-150,100)\n", "for vsys in vsyss:\n", " lls.append(ln_likelihood([vsys], phase, rv, rv_err, a, 2.5, b, 2.4, AV).sum())\n", "\n", "plt.plot(vsyss, lls)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "[<matplotlib.lines.Line2D at 0x109ce1cd0>]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x109c4be50>" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "sampler = emcee.EnsembleSampler(nwalkers=32, dim=1, lnpostfn=ln_posterior, \n", " args=(phase, rv, rv_err, a, 2.5, b, 2.4, AV))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 30 }, { "cell_type": "code", "collapsed": false, "input": [ "p0 = np.random.normal(-200, 10., size=(sampler.k,1))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "pos,prob,state = sampler.run_mcmc(p0, 100)\n", "sampler.reset()\n", "pos,prob,state = sampler.run_mcmc(pos, 256)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "vhel = model(0.27, sampler.flatchain[:,0], a, b, AV)\n", "v = np.median(vhel)\n", "verr = np.sqrt((a*AV + b + 3.4)**2 * (0.06**2 + (0.1*1.47)**2))\n", "v, verr" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 64, "text": [ "(-230.52609847811124, 16.054125122225098)" ] } ], "prompt_number": 64 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Fit all velocities" ] }, { "cell_type": "code", "collapsed": false, "input": [ "ally_d = np.genfromtxt(\"/Users/adrian/projects/triand-rrlyrae/data/TriAnd_RRL_26mar15.csv\", \n", " skiprows=0, dtype=None, names=True, delimiter=',')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "d = np.genfromtxt(\"/Users/adrian/projects/triand-rrlyrae/data/TriAnd_RRL_RV_intermediate.csv\", \n", " delimiter=',', names=True, dtype=None)\n", "d.dtype.names" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "('oname', 'name', 'RV_hel', 'Phase', 'Err', 'A_R')" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "def fit_v(phase, rv, rv_err, A_R):\n", " sampler = emcee.EnsembleSampler(nwalkers=32, dim=1, lnpostfn=ln_posterior, \n", " args=(phase, rv, rv_err, a, 2.5, b, 2.4, AV))\n", " p0 = np.random.normal(-200, 10., size=(sampler.k,1))\n", " \n", " pos,prob,state = sampler.run_mcmc(p0, 100)\n", " sampler.reset()\n", " pos,prob,state = sampler.run_mcmc(pos, 256)\n", " \n", " extra = np.sqrt(np.sum((-(phase-0.5)**2/3 + 0.06)**2))\n", " \n", " vsys = sampler.flatchain[:,0]\n", " vhel = model(0.27, vsys, a, b, AV)\n", " v = np.median(vhel)\n", " verr = np.sqrt((a*AV + b + 3.4)**2 * (extra**2 + (0.1*1.47)**2))\n", " return vsys, v, verr" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 26 }, { "cell_type": "code", "collapsed": false, "input": [ "ally_d" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 61, "text": [ "array([('RRL2', 'TriAndRRl26', 351.509509, 30.571749, 0.75, -199, 20, 22.07),\n", " ('RRL3', 'TriAndRRl21', 352.155077, 33.73084, 0.548, -269, 16, 22.56),\n", " ('RRL4', 'TriAndRRl23', 352.977933, 34.21691, 0.469, -84, 23, 19.68),\n", " ('RRL5', 'TriAndRRl24', 353.000072, 33.44693, 0.934, -305, 25, 15.71),\n", " ('RRL8', 'TriAndRRl31', 354.620544, 30.140181, 0.73, -318, 24, 25.07),\n", " ('RRL9', 'TriAndRRl27', 355.466314, 33.808053, 0.768, -231, 18, 21.17),\n", " ('RRL10', 'TriAndRRl32', 356.154789, 30.168917, 0.798, -176, 25, 21.57),\n", " ('RRL13', 'TriAndRRl33', 1.014094, 32.019259, 0.597, -89, 20, 19.71),\n", " ('RRL14', 'TriAndRRl30', 1.605007, 34.567764, 0.453, -228, 25, 34.04),\n", " ('RRL16', 'TriAndRRl20', 5.576572, 36.203326, 0.537, -129, 25, 16.15),\n", " ('RRL19', 'TriAndRRl15', 8.828356, 36.304111, 0.798, -88, 16, 16.79),\n", " ('RRL20', 'TriAndRRl11', 9.262168, 38.824378, 0.367, -133, 22, 16.8),\n", " ('RRL22', 'TriAndRRl16', 10.495088, 33.834552, 0.834, -194, 25, 16.32),\n", " ('RRL25', 'TriAndRRl9', 11.491072, 40.199252, 0.775, -157, 18, 18.48),\n", " ('RRL27', 'TriAndRRl12', 12.887871, 34.28071, 0.831, -25, 22, 21.25),\n", " ('RRL28', 'TriAndRRl10', 16.841753, 32.235145, 0.973, -287, 18, 16.14),\n", " ('RRL29', 'TriAndRRl8', 17.992175, 35.466819, 0.347, -128, 19, 21.18),\n", " ('RRL30', 'TriAndRRl5', 22.322702, 32.72186, 0.7, -212, 20, 20.67),\n", " ('RRL33', 'TriAndRRl2', 30.59351, 33.377335, 0.515, -245, 19, 18.75),\n", " ('RRL34', 'TriAndRRL1', 35.804598, 31.551122, 0.724, -159, 22, 32.08)], \n", " dtype=[('oname', 'S5'), ('name', 'S11'), ('ra', '<f8'), ('dec', '<f8'), ('amp', '<f8'), ('Vsys', '<i8'), ('Err', '<i8'), ('dist', '<f8')])" ] } ], "prompt_number": 61 }, { "cell_type": "code", "collapsed": false, "input": [ "ally_dd.dtype.names" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "('oname', 'name', 'ra', 'dec', 'amp', 'Vsys', 'Err', 'dist')" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "rows = []\n", "for name in np.unique(d['name']):\n", " dd = d[d['name'] == name]\n", " ally_dd = ally_d[ally_d['name'] == name]\n", " info = more_info[more_info['name'] == name]\n", " \n", " print(\"---------------\\n{0}:\".format(name))\n", " AV = 1.21 * dd['A_R'][0]\n", " \n", " # data to fit\n", " phase = (dd['Phase'] + 0.5/info['period']) % 1.\n", " print(\"Phase shift: {0}\".format((phase - dd['Phase'])[0]))\n", " \n", " # phase = dd['phase']\n", " ix = (phase > 0.05) & (phase < 0.85)\n", " if ix.sum() == 0:\n", " continue\n", " \n", " phase = phase[ix]\n", " RV_hel = dd['RV_hel'][ix]\n", " RV_err = dd['Err'][ix]\n", "\n", " print(\"\\t Phases: {0}\".format(phase))\n", " print(\"{0} measurements initially, {1} with good phase\".format(len(dd), len(phase)))\n", " vsys,v,verr = fit_v(phase, RV_hel, RV_err, AV)\n", " \n", " plt.figure()\n", " plt.title(name)\n", " plt.errorbar(phase, RV_hel, RV_err, marker='o', ecolor='#666666', linestyle='none')\n", " \n", " phase = np.linspace(0.,0.94,100)\n", " for i in range(128):\n", " plt.plot(phase, model(phase, vsys[np.random.randint(len(vsys))], a, b, AV), \n", " marker=None, color='k', alpha=0.05)\n", " \n", " rows.append((name, v, verr, ally_dd['Vsys'], ally_dd['Err'], ally_dd['ra'], ally_dd['dec'], ally_dd['dist']))\n", " \n", "apw = np.array(rows, dtype=[('name','S12'),('v_apw','f8'),('verr_apw','f8'),('v_ally','f8'),('verr_ally','f8'),\n", " ('ra','f8'), ('dec','f8'), ('dist','f8')])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "---------------\n", "TriAndRRL1:\n", "Phase shift: -0.206083559388\n", "\t Phases: [ 0.26391644 0.48391644 0.22391644]\n", "3 measurements initially, 3 with good phase\n", "---------------\n", "TriAndRRl10:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: 0.048769017853\n", "\t Phases: [ 0.49876902 0.26876902]\n", "2 measurements initially, 2 with good phase\n", "---------------\n", "TriAndRRl11:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.204620455221\n", "\t Phases: [ 0.06537954 0.30537954]\n", "2 measurements initially, 2 with good phase\n", "---------------\n", "TriAndRRl12:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.124118381357\n", "\t Phases: [ 0.56588162 0.21588162]\n", "2 measurements initially, 2 with good phase\n", "---------------\n", "TriAndRRl15:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.165899572307\n", "\t Phases: [ 0.39410043 0.59410043]\n", "2 measurements initially, 2 with good phase\n", "---------------\n", "TriAndRRl16:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.019577853754\n", "\t Phases: [ 0.67042215]\n", "1 measurements initially, 1 with good phase\n", "---------------\n", "TriAndRRl2:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.172691951186\n", "\t Phases: [ 0.07730805 0.48730805]\n", "2 measurements initially, 2 with good phase\n", "---------------\n", "TriAndRRl20:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.267531594658\n", "\t Phases: [ 0.35246841]\n", "2 measurements initially, 1 with good phase\n", "---------------\n", "TriAndRRl21:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.154434290328\n", "\t Phases: [ 0.42556571]\n", "2 measurements initially, 1 with good phase\n", "---------------\n", "TriAndRRl23:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.193759440069\n", "\t Phases: [ 0.30624056]\n", "1 measurements initially, 1 with good phase\n", "---------------\n", "TriAndRRl24:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.0203978634486\n", "\t Phases: [ 0.30960214 0.76960214]\n", "2 measurements initially, 2 with good phase\n", "---------------\n", "TriAndRRl26:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.053283477979\n", "\t Phases: [ 0.43671652 0.55671652]\n", "2 measurements initially, 2 with good phase\n", "---------------\n", "TriAndRRl27:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.118780152039\n", "\t Phases: [ 0.41121985]\n", "2 measurements initially, 1 with good phase\n", "---------------\n", "TriAndRRl30:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: 0.799755466768\n", "\t Phases: [ 0.05975547]\n", "2 measurements initially, 1 with good phase\n", "---------------\n", "TriAndRRl31:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.104547644333\n", "\t Phases: [ 0.21545236]\n", "1 measurements initially, 1 with good phase\n", "---------------\n", "TriAndRRl32:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.0739727255335\n", "\t Phases: [ 0.24602727]\n", "1 measurements initially, 1 with good phase\n", "---------------\n", "TriAndRRl33:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.279121127547\n", "\t Phases: [ 0.34087887 0.09087887]\n", "2 measurements initially, 2 with good phase\n", "---------------\n", "TriAndRRl5:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.0251556631437\n", "\t Phases: [ 0.48484434 0.18484434]\n", "2 measurements initially, 2 with good phase\n", "---------------\n", "TriAndRRl8:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.223462008267\n", "\t Phases: [ 0.43653799]\n", "2 measurements initially, 1 with good phase\n", "---------------\n", "TriAndRRl9:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Phase shift: -0.0368349007882\n", "\t Phases: [ 0.0531651 0.5031651]\n", "2 measurements initially, 2 with good phase\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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dLi6GLmpxn5bZTqczNYzIiz5fh/oH5zyy2SzOnj2LfD6PdruN9fV11Ot1qTw4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"text": [ "<matplotlib.figure.Figure at 0x110b30550>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Rmkg0NOYE06xMzpLCqtfrqNVGI3wymQzC4bB4YHW7XaTTaSwvL0sJbyAQQLFY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\nFyzVpeexvLyMZ8+eoVwuu07wbDbrpMtXVlZc2GpqaqqlQY6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Q6XRQLpfnhlfxTr7b7Uoqj4tkPp+XRZvpJgCyX27HOgEbGVl8p3KLx9Iko12E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\ns9ksUqmUOalSlOdqWbuEZgvngUDAlHrYeWUL55VKBel0Gjs7O9jd3TU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"text": [ "<matplotlib.figure.Figure at 0x10f341ad0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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E3g22EuHio93luqVIJpMBcCig0wHe7XZF/yiVSigWi4hGoxgOh7h//z52dnak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\nrbUfNHl+dXFx0eU7ALiubVZkATX/q83NTZc/4e6CzYPs8aCrbjKZRBA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ZzWYTW1tbaLfbsCwL8/PzKJVK+PTTTwEACwsLuHjxIpLJ5BR5MFaeVQfv+tmu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"text": [ "<matplotlib.figure.Figure at 0x10fa47910>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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SkUjEM2XxRWl3ol/vdYIlEguLlxTT6RT9fl+K0BdR/2DqikX6UCiEZDK58jmGwyHa7Tbq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\nq1gshv39/aXGRD5ezwEB5k7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jqz4PyZbL5UKpVMqUy+WWz99/DcCsXC5/j8crlUrfLZfL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"text": [ "<matplotlib.figure.Figure at 0x10bee0910>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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gMEJQH3x/f9QybdgW03QkPf47n8/P4JueLU6FSJjSeoKXHlj7pqbI/Lh58yZu3br1BG+p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"text": [ "<matplotlib.figure.Figure at 0x11378a290>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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h8NHk92/k8/kfFQqF7177mVrMDfofZFkK5hpnsVs/DWYKzC19xfSWzrczguh2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\n4ZCEsewm+Prrr5s6/2aziZOTE2NF0i/XYSdY9/f3sbe3h+3tbZw/fx7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"text": [ "<matplotlib.figure.Figure at 0x10eca1290>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Ypxfrx5vOMb4usxvqOJcRlkgsLM4AM/vg9rNh+37oWVN6dEkmkwkdXaLLV2bn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\n6gO3fElE7qjqt/1s78UGA1ouwu5RbklyAAemT/fHdQAHcx1+jcC/gVV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"text": [ "<matplotlib.figure.Figure at 0x10ecc6590>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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JIJ1Ow3VdlEolhEIhdLtdPH782Be98KbJ4/DwUBZP7th1xbleWEkidKrF43Gk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\nMzMuK6tWq+Hhw4cuTsAhUWw78vbbb+PUqVMdLrWw62pzcxP1ev1A1hX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"text": [ "<matplotlib.figure.Figure at 0x110d26a10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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yIupxwSiECxjv7Jl24qJPC3YAsnilUikhinA4LKI7I5nNzU2pwOr1ekilUlhf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\nSEpRke6JRALNZhNzc3Nid3YJRONMFArFYTGJra0igE8jbr8A4C6/CFY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JYk+S6na78uj1ehgOhxIxMKWVzWZF99CEoxd4iu/m4q/TWLPKdTW0sVGTkyYwRktBWoyO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\nbP+Xf/kXGWfKhZgRCn0geiGhXqFLcgm2SediRlBQpf6hF15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MwX39618/3z/Qc4AhEgODC4LXwsTPA4uLia7AYgPdaDSSBXRZBRab2Zg+0s67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\n9eTJE9RqNekoZ+c4JwrSYzh79izS6bSc49ramkwvTKVSaDQaePTokYy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"text": [ "<matplotlib.figure.Figure at 0x10ed46c50>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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9QHxkH+12W0mSOOEaokN3wDXl23VzuZzq9XpqLAoOtFarlerTQO+APCqVihqN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\npClx2BHsbJKVNZLE9ncwzwpLcqVS0b1791zZzNc7IEZf77AzrchqbMn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"text": [ "<matplotlib.figure.Figure at 0x10b2aae10>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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+1atXj/Gv8XhgicTC4gmA+gazG+x5awcWr5DNxLPOT5gC+srKiizSsVgMzWYT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"text": [ "<matplotlib.figure.Figure at 0x1137b9b50>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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s2xbi8Hq9ojdwNJbVAbUZ+kdYWZCAuAgrl8tdiIDnNBarDTrV1SpEJYdlcAYx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"text": [ "<matplotlib.figure.Figure at 0x10e9b39d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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GteJwkg4tw7OIAXh4sp1E1G63RfvpdrtCuu+8887z/Os6EzSRaGhoPHM4tQ9eXXMmgzMc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"text": [ "<matplotlib.figure.Figure at 0x10e974150>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Gf/30i+r+Ci21ksv/uQA8pOKvECNpnTfOg0guvPxXQ0Pj5cN4PBY7ES6Q3W4XrVZL9Aym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\nimmWYrEonh9s4aX3h8/nk24kewAukUjIokv/DwYQ1j6YmioUClL/oCg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"text": [ "<matplotlib.figure.Figure at 0x114c81710>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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i5xRBie5sgiZAOD/PtosQHWcfk5OTWlpaKhXOadqjbOaB1/UaXufR0VEoeTFa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\nREgHoqWHhKDv5oR4ECI60SAHVdnU3quYQ1XWM3KZhsOEhISrxa0iknq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"text": [ "<matplotlib.figure.Figure at 0x112cc8d50>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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9Oct/og7qg96Tm4brIpId0zQ/sCxL3TNSsCyrbZpmAScLr1R8AeBDAOfuMdHQ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"text": [ "<matplotlib.figure.Figure at 0x113010710>" ] } ], "prompt_number": 190 }, { "cell_type": "code", "collapsed": false, "input": [ "apw.dtype.names" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 191, "text": [ "('name', 'v_apw', 'verr_apw', 'v_ally', 'verr_ally', 'ra', 'dec', 'dist')" ] } ], "prompt_number": 191 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure(figsize=(6,6))\n", "plt.plot(apw['v_apw'], apw['v_ally'], linestyle='none')\n", "plt.plot(np.linspace(apw['v_apw'].min(),apw['v_apw'].max(),100),\n", " np.linspace(apw['v_apw'].min(),apw['v_apw'].max(),100), marker=None)\n", "plt.xlabel('Adrian')\n", "plt.ylabel('Ally')\n", "plt.title(\"Best-fit velocity\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 192, "text": [ "<matplotlib.text.Text at 0x1119a4490>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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TpANJZyaUbxVbPQB1Iozao/V9SCOsSnqWs/4wlj2eUA6gAwijdulqIC2NKTsh6fiE8lze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\nOBODDcPIe9+L65T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"text": [ "<matplotlib.figure.Figure at 0x10f767b90>" ] } ], "prompt_number": 192 }, { "cell_type": "code", "collapsed": false, "input": [ "plt.figure(figsize=(6,6))\n", "plt.plot(apw['verr_apw'], apw['verr_ally'], linestyle='none')\n", "plt.plot(np.linspace(13,27,100), np.linspace(13,27,100), marker=None)\n", "plt.xlim(13,27)\n", "plt.ylim(13,27)\n", "plt.xlabel('Adrian')\n", "plt.ylabel('Ally')\n", "plt.title(\"Velocity uncertainty\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 193, "text": [ "<matplotlib.text.Text at 0x111012150>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"text": [ "<matplotlib.figure.Figure at 0x10eee5910>" ] } ], "prompt_number": 193 }, { "cell_type": "code", "collapsed": false, "input": [ "from astropy.table import Table" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 194 }, { "cell_type": "code", "collapsed": false, "input": [ "tbl = Table(apw)\n", "tbl.rename_column('v_apw', 'Vsys')\n", "tbl.rename_column('verr_apw', 'Err')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 195 }, { "cell_type": "code", "collapsed": false, "input": [ "tbl.write(\"/Users/adrian/projects/triand-rrlyrae/data/apw_velocities.csv\", format='ascii', delimiter=',')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 196 }, { "cell_type": "code", "collapsed": false, "input": [ "!cat /Users/adrian/projects/triand-rrlyrae/data/apw_velocities.csv" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "name,Vsys,Err,v_ally,verr_ally,ra,dec,dist\r\n", "TriAndRRL1,-130.85487474138452,18.910268086849097,-159.0,22.0,35.804598,31.551122,32.08\r\n", "TriAndRRl10,-293.2393952686294,20.290690174660256,-287.0,18.0,16.841753,32.235145,16.14\r\n", "TriAndRRl11,-110.21222346621133,15.047109038479922,-133.0,22.0,9.262168,38.824378,16.8\r\n", "TriAndRRl12,-7.622035501769265,18.97776114397998,-25.0,22.0,12.887871,34.28071,21.25\r\n", "TriAndRRl15,-62.70110044420859,19.416283601942077,-88.0,16.0,8.828356,36.304111,16.79\r\n", "TriAndRRl16,-191.91409384280064,18.2603504887519,-194.0,25.0,10.495088,33.834552,16.32\r\n", "TriAndRRl2,-223.04871707478003,16.476122497928984,-245.0,19.0,30.59351,33.377335,18.75\r\n", "TriAndRRl20,-110.24892867804371,16.35661959775641,-129.0,25.0,5.576572,36.203326,16.15\r\n", "TriAndRRl21,-242.96579819932728,16.63140990128772,-269.0,16.0,352.155077,33.73084,22.56\r\n", "TriAndRRl23,-59.41835657984708,15.726415435825837,-84.0,23.0,352.977933,34.21691,19.68\r\n", "TriAndRRl24,-303.27771404734347,19.334464121414793,-305.0,25.0,353.000072,33.44693,15.71\r\n", "TriAndRRl26,-193.88573572816819,19.237495987409744,-199.0,20.0,351.509509,30.571749,22.07\r\n", "TriAndRRl27,-214.18498593649738,18.096799949460543,-231.0,18.0,355.466314,33.808053,21.17\r\n", "TriAndRRl30,-188.04085683291544,14.870965634886057,-228.0,25.0,1.605007,34.567764,34.04\r\n", "TriAndRRl31,-304.0424444147709,17.031538022552102,-318.0,24.0,354.620544,30.140181,25.07\r\n", "TriAndRRl32,-165.41084709322047,17.62326074451958,-176.0,25.0,356.154789,30.168917,21.57\r\n", "TriAndRRl33,-52.06527512875746,16.723852802894605,-89.0,20.0,1.014094,32.019259,19.71\r\n", "TriAndRRl5,-212.661111197825,17.992964178273137,-212.0,20.0,22.322702,32.72186,20.67\r\n", "TriAndRRl8,-104.03853233446037,15.28064781440207,-128.0,19.0,17.992175,35.466819,21.18\r\n", "TriAndRRl9,-152.12563066664936,18.27178895174345,-157.0,18.0,11.491072,40.199252,18.48\r\n" ] } ], "prompt_number": 197 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Table to go with publication" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pub_tbl = Table(apw)\n", "\n", "# fix names\n", "pub_tbl['name'] = np.array([name.replace('l','L') for name in pub_tbl['name']])\n", "pub_tbl['sort_name'] = np.array([int(name[9:]) for name in pub_tbl['name']])\n", "\n", "pub_tbl.rename_column('v_apw', 'vgsr')\n", "pub_tbl.rename_column('verr_apw', 'verr')\n", "\n", "pub_tbl.remove_column('v_ally')\n", "pub_tbl.remove_column('verr_ally')\n", "\n", "pub_tbl.sort('sort_name')\n", "pub_tbl.remove_column('sort_name')\n", "\n", "pub_tbl" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "&lt;Table masked=False length=20&gt;\n", "<table id=\"table4620285008\">\n", "<thead><tr><th>name</th><th>vgsr</th><th>verr</th><th>ra</th><th>dec</th><th>dist</th></tr></thead>\n", "<thead><tr><th>string96</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n", "<tr><td>TriAndRRL1</td><td>-130.854874741</td><td>18.9102680868</td><td>35.804598</td><td>31.551122</td><td>32.08</td></tr>\n", "<tr><td>TriAndRRL2</td><td>-223.048717075</td><td>16.4761224979</td><td>30.59351</td><td>33.377335</td><td>18.75</td></tr>\n", "<tr><td>TriAndRRL5</td><td>-212.661111198</td><td>17.9929641783</td><td>22.322702</td><td>32.72186</td><td>20.67</td></tr>\n", "<tr><td>TriAndRRL8</td><td>-104.038532334</td><td>15.2806478144</td><td>17.992175</td><td>35.466819</td><td>21.18</td></tr>\n", "<tr><td>TriAndRRL9</td><td>-152.125630667</td><td>18.2717889517</td><td>11.491072</td><td>40.199252</td><td>18.48</td></tr>\n", "<tr><td>TriAndRRL10</td><td>-293.239395269</td><td>20.2906901747</td><td>16.841753</td><td>32.235145</td><td>16.14</td></tr>\n", "<tr><td>TriAndRRL11</td><td>-110.212223466</td><td>15.0471090385</td><td>9.262168</td><td>38.824378</td><td>16.8</td></tr>\n", "<tr><td>TriAndRRL12</td><td>-7.62203550177</td><td>18.977761144</td><td>12.887871</td><td>34.28071</td><td>21.25</td></tr>\n", "<tr><td>TriAndRRL15</td><td>-62.7011004442</td><td>19.4162836019</td><td>8.828356</td><td>36.304111</td><td>16.79</td></tr>\n", "<tr><td>TriAndRRL16</td><td>-191.914093843</td><td>18.2603504888</td><td>10.495088</td><td>33.834552</td><td>16.32</td></tr>\n", "<tr><td>TriAndRRL20</td><td>-110.248928678</td><td>16.3566195978</td><td>5.576572</td><td>36.203326</td><td>16.15</td></tr>\n", "<tr><td>TriAndRRL21</td><td>-242.965798199</td><td>16.6314099013</td><td>352.155077</td><td>33.73084</td><td>22.56</td></tr>\n", "<tr><td>TriAndRRL23</td><td>-59.4183565798</td><td>15.7264154358</td><td>352.977933</td><td>34.21691</td><td>19.68</td></tr>\n", "<tr><td>TriAndRRL24</td><td>-303.277714047</td><td>19.3344641214</td><td>353.000072</td><td>33.44693</td><td>15.71</td></tr>\n", "<tr><td>TriAndRRL26</td><td>-193.885735728</td><td>19.2374959874</td><td>351.509509</td><td>30.571749</td><td>22.07</td></tr>\n", "<tr><td>TriAndRRL27</td><td>-214.184985936</td><td>18.0967999495</td><td>355.466314</td><td>33.808053</td><td>21.17</td></tr>\n", "<tr><td>TriAndRRL30</td><td>-188.040856833</td><td>14.8709656349</td><td>1.605007</td><td>34.567764</td><td>34.04</td></tr>\n", "<tr><td>TriAndRRL31</td><td>-304.042444415</td><td>17.0315380226</td><td>354.620544</td><td>30.140181</td><td>25.07</td></tr>\n", "<tr><td>TriAndRRL32</td><td>-165.410847093</td><td>17.6232607445</td><td>356.154789</td><td>30.168917</td><td>21.57</td></tr>\n", "<tr><td>TriAndRRL33</td><td>-52.0652751288</td><td>16.7238528029</td><td>1.014094</td><td>32.019259</td><td>19.71</td></tr>\n", "</table>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 202, "text": [ "<Table masked=False length=20>\n", " name vgsr verr ra dec dist \n", " string96 float64 float64 float64 float64 float64\n", "----------- -------------- ------------- ---------- --------- -------\n", " TriAndRRL1 -130.854874741 18.9102680868 35.804598 31.551122 32.08\n", " TriAndRRL2 -223.048717075 16.4761224979 30.59351 33.377335 18.75\n", " TriAndRRL5 -212.661111198 17.9929641783 22.322702 32.72186 20.67\n", " TriAndRRL8 -104.038532334 15.2806478144 17.992175 35.466819 21.18\n", " TriAndRRL9 -152.125630667 18.2717889517 11.491072 40.199252 18.48\n", "TriAndRRL10 -293.239395269 20.2906901747 16.841753 32.235145 16.14\n", "TriAndRRL11 -110.212223466 15.0471090385 9.262168 38.824378 16.8\n", "TriAndRRL12 -7.62203550177 18.977761144 12.887871 34.28071 21.25\n", "TriAndRRL15 -62.7011004442 19.4162836019 8.828356 36.304111 16.79\n", "TriAndRRL16 -191.914093843 18.2603504888 10.495088 33.834552 16.32\n", "TriAndRRL20 -110.248928678 16.3566195978 5.576572 36.203326 16.15\n", "TriAndRRL21 -242.965798199 16.6314099013 352.155077 33.73084 22.56\n", "TriAndRRL23 -59.4183565798 15.7264154358 352.977933 34.21691 19.68\n", "TriAndRRL24 -303.277714047 19.3344641214 353.000072 33.44693 15.71\n", "TriAndRRL26 -193.885735728 19.2374959874 351.509509 30.571749 22.07\n", "TriAndRRL27 -214.184985936 18.0967999495 355.466314 33.808053 21.17\n", "TriAndRRL30 -188.040856833 14.8709656349 1.605007 34.567764 34.04\n", "TriAndRRL31 -304.042444415 17.0315380226 354.620544 30.140181 25.07\n", "TriAndRRL32 -165.410847093 17.6232607445 356.154789 30.168917 21.57\n", "TriAndRRL33 -52.0652751288 16.7238528029 1.014094 32.019259 19.71" ] } ], "prompt_number": 202 }, { "cell_type": "code", "collapsed": false, "input": [ "pub_tbl.write(\"/Users/adrian/projects/triand-rrlyrae/data/publication_data.csv\", format='ascii', delimiter=',')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 204 }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n" ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Figure 1" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# _cache = dict()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 187 }, { "cell_type": "code", "collapsed": false, "input": [ "fig,axes = plt.subplots(2, 2, figsize=(10,8), sharex=True)\n", "\n", "for i,name in enumerate(['TriAndRRl2','TriAndRRl26']):\n", " dd = d[d['name'] == name]\n", " info = more_info[more_info['name'] == name]\n", " print(\"{0}: {1} measurements\".format(name, len(dd)))\n", " print(info['period'])\n", " print(info['hjd0'])\n", " AV = 1.21 * dd['A_R'][0]\n", " phase = np.linspace(0.,0.94,100)\n", " \n", " if name not in _cache:\n", " vsys,v,verr = fit_v(dd['Phase'], dd['RV_hel'], dd['Err'], AV)\n", " lines = np.array([model(phase, vsys[np.random.randint(len(vsys))], a, b, AV) for j in range(1000)])\n", " _cache[name] = dict(vsys=vsys, v=v, verr=verr, lines=lines)\n", " else:\n", " vsys = _cache[name]['vsys']\n", " v = _cache[name]['v']\n", " verr = _cache[name]['verr']\n", " lines = _cache[name]['lines']\n", " \n", " # fit to RV curve\n", " col = axes[:,i]\n", " col[0].set_title(name.replace('l','L'))\n", " \n", " quantiles = np.percentile(lines, [16, 84, 50], axis=0)\n", " col[0].fill_between(phase, quantiles[0], quantiles[1], color='#dddddd')\n", " \n", " col[0].plot(phase, quantiles[2], marker=None, lw=2., color='k')\n", " col[0].errorbar(dd['Phase'], dd['RV_hel'], dd['Err'], marker='o', ecolor='#666666', linestyle='none')\n", "\n", " # light curve\n", " path = \"/Users/adrian/projects/triand-rrlyrae/data/lc_{0}.dat\".format(name.replace('l','L'))\n", " lc = np.genfromtxt(path, names=True, dtype=None, skiprows=20)\n", " lc = lc[(lc['filterID'] == 2) & (lc['sextractorFlags'] == 0) & (lc['magerr'] < 0.15)]\n", " \n", " col[1].set_xlabel('Phase')\n", "\n", " phase = ((lc['epoch'] - info['hjd0'] + 0.5) % info['period']) / info['period']\n", "# if '10' in name:\n", "# phase = ((lc['epoch'] - info['hjd0'] + 0.08) % info['period']) / info['period']\n", " col[1].errorbar(phase, lc['mag'], lc['magerr'], linestyle='none', \n", " ecolor='#666666', marker='o', alpha=0.75, ms=4)\n", "\n", " axes[1,1].set_xlim(-0.02, 1.02)\n", "axes[0,0].set_ylabel(r'$v_r$ [${\\rm km~s}^{-1}$]')\n", "axes[1,0].set_ylabel(r'$R$ [${\\rm mag}$]')\n", "\n", "# left column, top\n", "axes[0,0].set_ylim(-300, -150)\n", "axes[0,0].set_yticks([-160, -200, -240, -280])\n", "\n", "# left column, bottom\n", "axes[1,0].set_ylim(17.65, 16.75)\n", "axes[1,0].set_yticks([17.5, 17.3, 17.1, 16.9])\n", "\n", "# right column, top\n", "axes[0,1].set_ylim(-275, -90)\n", "# axes[0,1].set_yticks([-200, -240, -280, -320])\n", "\n", "axes[1,1].set_ylim(18.4, 17.2)\n", "axes[1,1].set_yticks([18.2, 17.9, 17.6, 17.3])\n", "\n", "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0.)\n", "\n", "fig.savefig(\"/Users/adrian/projects/triand-rrlyrae/plots/rv_lc.pdf\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "TriAndRRl2: 2 measurements\n", "[ 0.6043698]\n", "[ 55763.907589]\n", "TriAndRRl26: 2 measurements" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[ 0.5281412]\n", "[ 56232.636689]\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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r9VJaWorL5QopkHs8HgoKCtizZw8Ac+fO5YEHHrCtlyEwzzg9PV3KtQkhIl5L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\nJtcFZ5SzvapqLjBleweIaGFT6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"text": [ "<matplotlib.figure.Figure at 0x10f23e2d0>" ] } ], "prompt_number": 189 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
ericmjl/Network-Analysis-Made-Simple
archive/7-game-of-thrones-case-study-instructor.ipynb
1
21543
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##### Let's change gears and talk about Game of thrones or shall I say Network of Thrones.\n", "\n", "It is suprising right? What is the relationship between a fatansy TV show/novel and network science or python(it's not related to a dragon).\n", "\n", "If you haven't heard of Game of Thrones, then you must be really good at hiding. Game of Thrones is the hugely popular television series by HBO based on the (also) hugely popular book series A Song of Ice and Fire by George R.R. Martin. In this notebook, we will analyze the co-occurrence network of the characters in the Game of Thrones books. Here, two characters are considered to co-occur if their names appear in the vicinity of 15 words from one another in the books." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](images/got.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Andrew J. Beveridge, an associate professor of mathematics at Macalester College, and Jie Shan, an undergraduate created a network from the book A Storm of Swords by extracting relationships between characters to find out the most important characters in the book(or GoT).\n", "\n", "The dataset is publicly avaiable for the 5 books at https://github.com/mathbeveridge/asoiaf. This is an interaction network and were created by connecting two characters whenever their names (or nicknames) appeared within 15 words of one another in one of the books. The edge weight corresponds to the number of interactions. \n", "\n", "Credits:\n", "\n", "Blog: https://networkofthrones.wordpress.com\n", "\n", "Math Horizons Article: https://www.maa.org/sites/default/files/pdf/Mathhorizons/NetworkofThrones%20%281%29.pdf" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "import community\n", "import numpy as np\n", "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Let's load in the datasets" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "book1 = pd.read_csv('datasets/game_of_thrones_network/asoiaf-book1-edges.csv')\n", "book2 = pd.read_csv('datasets/game_of_thrones_network/asoiaf-book2-edges.csv')\n", "book3 = pd.read_csv('datasets/game_of_thrones_network/asoiaf-book3-edges.csv')\n", "book4 = pd.read_csv('datasets/game_of_thrones_network/asoiaf-book4-edges.csv')\n", "book5 = pd.read_csv('datasets/game_of_thrones_network/asoiaf-book5-edges.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The resulting DataFrame book1 has 5 columns: Source, Target, Type, weight, and book. Source and target are the two nodes that are linked by an edge. A network can have directed or undirected edges and in this network all the edges are undirected. The weight attribute of every edge tells us the number of interactions that the characters have had over the book, and the book column tells us the book number.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "book1.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have the data loaded as a pandas DataFrame, it's time to create a network. We create a graph for each book. It's possible to create one MultiGraph instead of 5 graphs, but it is easier to play with different graphs." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "G_book1 = nx.Graph()\n", "G_book2 = nx.Graph()\n", "G_book3 = nx.Graph()\n", "G_book4 = nx.Graph()\n", "G_book5 = nx.Graph()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's populate the graph with edges from the pandas DataFrame." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for row in book1.iterrows():\n", " G_book1.add_edge(row[1]['Source'], row[1]['Target'], weight=row[1]['weight'], book=row[1]['book'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for row in book2.iterrows():\n", " G_book2.add_edge(row[1]['Source'], row[1]['Target'], weight=row[1]['weight'], book=row[1]['book'])\n", "for row in book3.iterrows():\n", " G_book3.add_edge(row[1]['Source'], row[1]['Target'], weight=row[1]['weight'], book=row[1]['book'])\n", "for row in book4.iterrows():\n", " G_book4.add_edge(row[1]['Source'], row[1]['Target'], weight=row[1]['weight'], book=row[1]['book'])\n", "for row in book5.iterrows():\n", " G_book5.add_edge(row[1]['Source'], row[1]['Target'], weight=row[1]['weight'], book=row[1]['book'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "books = [G_book1, G_book2, G_book3, G_book4, G_book5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's have a look at these edges." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "list(G_book1.edges(data=True))[16]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "list(G_book1.edges(data=True))[400]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Finding the most important node i.e character in these networks.\n", "\n", "Is it Jon Snow, Tyrion, Daenerys, or someone else? Let's see! Network Science offers us many different metrics to measure the importance of a node in a network as we saw in the first part of the tutorial. Note that there is no \"correct\" way of calculating the most important node in a network, every metric has a different meaning.\n", "\n", "First, let's measure the importance of a node in a network by looking at the number of neighbors it has, that is, the number of nodes it is connected to. For example, an influential account on Twitter, where the follower-followee relationship forms the network, is an account which has a high number of followers. This measure of importance is called degree centrality.\n", "\n", "Using this measure, let's extract the top ten important characters from the first book (book[0]) and the fifth book (book[4])." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "deg_cen_book1 = nx.degree_centrality(books[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "deg_cen_book5 = nx.degree_centrality(books[4])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sorted(deg_cen_book1.items(), key=lambda x:x[1], reverse=True)[0:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sorted(deg_cen_book5.items(), key=lambda x:x[1], reverse=True)[0:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot a histogram of degree centrality\n", "plt.hist(list(nx.degree_centrality(G_book4).values()))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d = {}\n", "for i, j in dict(nx.degree(G_book4)).items():\n", " if j in d:\n", " d[j] += 1\n", " else:\n", " d[j] = 1\n", "x = np.log2(list((d.keys())))\n", "y = np.log2(list(d.values()))\n", "plt.scatter(x, y, alpha=0.9)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Create a new centrality measure, weighted_degree(Graph, weight) which takes in Graph and the weight attribute and returns a weighted degree dictionary. Weighted degree is calculated by summing the weight of the all edges of a node and find the top five characters according to this measure." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def weighted_degree(G, weight):\n", " result = dict()\n", " for node in G.nodes():\n", " weight_degree = 0\n", " for n in G.edges([node], data=True):\n", " weight_degree += n[2]['weight']\n", " result[node] = weight_degree\n", " return result" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(list(weighted_degree(G_book1, 'weight').values()))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sorted(weighted_degree(G_book1, 'weight').items(), key=lambda x:x[1], reverse=True)[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's do this for Betweeness centrality and check if this makes any difference\n", "\n", "Haha, evil laugh" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# First check unweighted, just the structure\n", "\n", "sorted(nx.betweenness_centrality(G_book1).items(), key=lambda x:x[1], reverse=True)[0:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Let's care about interactions now\n", "\n", "sorted(nx.betweenness_centrality(G_book1, weight='weight').items(), key=lambda x:x[1], reverse=True)[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### PageRank\n", "The billion dollar algorithm, PageRank works by counting the number and quality of links to a page to determine a rough estimate of how important the website is. The underlying assumption is that more important websites are likely to receive more links from other websites." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# by default weight attribute in pagerank is weight, so we use weight=None to find the unweighted results\n", "sorted(nx.pagerank_numpy(G_book1, weight=None).items(), key=lambda x:x[1], reverse=True)[0:10]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sorted(nx.pagerank_numpy(G_book1, weight='weight').items(), key=lambda x:x[1], reverse=True)[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Is there a correlation between these techniques?\n", "\n", "#### Exercise\n", "\n", "Find the correlation between these four techniques.\n", "\n", "- pagerank\n", "- betweenness_centrality\n", "- weighted_degree\n", "- degree centrality" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cor = pd.DataFrame.from_records([nx.pagerank_numpy(G_book1, weight='weight'), nx.betweenness_centrality(G_book1, weight='weight'), weighted_degree(G_book1, 'weight'), nx.degree_centrality(G_book1)])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# cor.T" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cor.T.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evolution of importance of characters over the books" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to degree centrality the most important character in the first book is Eddard Stark but he is not even in the top 10 of the fifth book. The importance changes over the course of five books, because you know stuff happens ;)\n", "\n", "Let's look at the evolution of degree centrality of a couple of characters like Eddard Stark, Jon Snow, Tyrion which showed up in the top 10 of degree centrality in first book.\n", "\n", "We create a dataframe with character columns and index as books where every entry is the degree centrality of the character in that particular book and plot the evolution of degree centrality Eddard Stark, Jon Snow and Tyrion.\n", "We can see that the importance of Eddard Stark in the network dies off and with Jon Snow there is a drop in the fourth book but a sudden rise in the fifth book" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "evol = [nx.degree_centrality(book) for book in books]\n", "evol_df = pd.DataFrame.from_records(evol).fillna(0)\n", "evol_df[['Eddard-Stark', 'Tyrion-Lannister', 'Jon-Snow']].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "set_of_char = set()\n", "for i in range(5):\n", " set_of_char |= set(list(evol_df.T[i].sort_values(ascending=False)[0:5].index))\n", "set_of_char" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Exercise\n", "\n", "Plot the evolution of weighted degree centrality of the above mentioned characters over the 5 books, and repeat the same exercise for betweenness centrality." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "evol_df[list(set_of_char)].plot(figsize=(29,15))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "evol = [nx.betweenness_centrality(graph, weight='weight') for graph in [G_book1, G_book2, G_book3, G_book4, G_book5]]\n", "evol_df = pd.DataFrame.from_records(evol).fillna(0)\n", "\n", "set_of_char = set()\n", "for i in range(5):\n", " set_of_char |= set(list(evol_df.T[i].sort_values(ascending=False)[0:5].index))\n", "\n", "\n", "evol_df[list(set_of_char)].plot(figsize=(19,10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### So what's up with Stannis Baratheon?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nx.draw(nx.barbell_graph(5, 1), with_labels=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sorted(nx.degree_centrality(G_book5).items(), key=lambda x:x[1], reverse=True)[:5]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sorted(nx.betweenness_centrality(G_book5).items(), key=lambda x:x[1], reverse=True)[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Community detection in Networks\n", "A network is said to have community structure if the nodes of the network can be easily grouped into (potentially overlapping) sets of nodes such that each set of nodes is densely connected internally.\n", "\n", "We will use louvain community detection algorithm to find the modules in our graph." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(15, 15))\n", "\n", "partition = community.best_partition(G_book1)\n", "size = float(len(set(partition.values())))\n", "pos = nx.kamada_kawai_layout(G_book1)\n", "count = 0\n", "colors = ['red', 'blue', 'yellow', 'black', 'brown', 'purple', 'green', 'pink']\n", "for com in set(partition.values()):\n", " list_nodes = [nodes for nodes in partition.keys()\n", " if partition[nodes] == com]\n", " nx.draw_networkx_nodes(G_book1, pos, list_nodes, node_size = 20,\n", " node_color = colors[count])\n", " count = count + 1\n", "\n", "\n", "\n", "nx.draw_networkx_edges(G_book1, pos, alpha=0.2)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d = {}\n", "for character, par in partition.items():\n", " if par in d:\n", " d[par].append(character)\n", " else:\n", " d[par] = [character]\n", "d" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nx.draw(nx.subgraph(G_book1, d[3]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nx.draw(nx.subgraph(G_book1, d[1]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nx.density(G_book1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nx.density(nx.subgraph(G_book1, d[4]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nx.density(nx.subgraph(G_book1, d[4]))/nx.density(G_book1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise \n", "\n", "Find the most important node in the partitions according to degree centrality of the nodes." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "max_d = {}\n", "deg_book1 = nx.degree_centrality(G_book1)\n", "\n", "for group in d:\n", " temp = 0\n", " for character in d[group]:\n", " if deg_book1[character] > temp:\n", " max_d[group] = character\n", " temp = deg_book1[character]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "max_d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A bit about power law in networks" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "G_random = nx.erdos_renyi_graph(100, 0.1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nx.draw(G_random)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "G_ba = nx.barabasi_albert_graph(100, 2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nx.draw(G_ba)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Plot a histogram of degree centrality\n", "plt.hist(list(nx.degree_centrality(G_random).values()))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.hist(list(nx.degree_centrality(G_ba).values()))\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "G_random = nx.erdos_renyi_graph(2000, 0.2)\n", "G_ba = nx.barabasi_albert_graph(2000, 20)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d = {}\n", "for i, j in dict(nx.degree(G_random)).items():\n", " if j in d:\n", " d[j] += 1\n", " else:\n", " d[j] = 1\n", "x = np.log2(list((d.keys())))\n", "y = np.log2(list(d.values()))\n", "plt.scatter(x, y, alpha=0.9)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d = {}\n", "for i, j in dict(nx.degree(G_ba)).items():\n", " if j in d:\n", " d[j] += 1\n", " else:\n", " d[j] = 1\n", "x = np.log2(list((d.keys())))\n", "y = np.log2(list(d.values()))\n", "plt.scatter(x, y, alpha=0.9)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "nams", "language": "python", "name": "nams" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
simonward86/MySJcLqwwx
LSTM_test.ipynb
1
172396
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "pylab.rcParams['figure.figsize'] = (10, 6)\n", "from tensorflow.contrib import learn\n", "from sklearn.metrics import mean_squared_error\n", "from lstm import lstm_model, load_csvdata\n", "from datetime import datetime\n", "import Predictors as predictors\n", "import stock_tools as st\n", "import matplotlib.pyplot as plt\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Testing Deep learning" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This is a test of a LSTM memory deep learning model to predict the SPY. It is written using tensorflow and is the process of being updated to tensrflow 1.0 The memory modules and the number of nodes have not be tuned, so predictions can be made better. At the moment only a small subset of the data is used due to the computational constraints.\n", "\n", "In the future more predictors should be introduced as this will aid precission." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Create a template with the available variables\n", "interest = 'SPY'\n", "start_date = datetime.strptime('2000-01-01', '%Y-%m-%d')\n", "end_date = datetime.strptime('2010-12-31', '%Y-%m-%d')\n", "\n", "# Get the data and correct for fluctuations\n", "data = st.get_data(start_date, end_date, from_file=True)\n", "corr_data = st.ohlc_adj(data)\n", "# Create a predictors class which we will base our decisions from\n", "pred = predictors.Predictors(corr_data)\n", "\n", "# The data is far too noisy to make accurate predictions.\n", "# We apply a 5 day exponential rolling filter. This should preserve\n", "# shape and reduce noise.\n", "pred.e_filter(5)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Setup the default vairables\n", "LOG_DIR = '.opt_logs/lstm_stock'\n", "TIMESTEPS = 20\n", "RNN_LAYERS = [{'num_units': 5}]\n", "DENSE_LAYERS = [10, 10]\n", "TRAINING_STEPS = 100000\n", "BATCH_SIZE = 100\n", "PRINT_STEPS = TRAINING_STEPS / 100\n", "LEARNING_RATE= 0.01\n", " \n", "# We want to predict the Closing price. Not too much data\n", "close = pred.data.Close.ix[(len(pred.props)-252*2):]\n", "\n", "# Split the data into test and train\n", "X, y = load_csvdata(close, TIMESTEPS, seperate=False)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INFO:tensorflow:Using default config.\n", "INFO:tensorflow:Using config: {'_task_type': None, '_save_checkpoints_secs': 600, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x10369ad30>, '_tf_config': gpu_options {\n", " per_process_gpu_memory_fraction: 1\n", "}\n", ", '_environment': 'local', '_keep_checkpoint_max': 5, '_save_summary_steps': 100, '_num_ps_replicas': 0, '_is_chief': True, '_task_id': 0, '_tf_random_seed': None, '_evaluation_master': '', '_save_checkpoints_steps': None, '_keep_checkpoint_every_n_hours': 10000, '_master': ''}\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:322: BaseMonitor.__init__ (from tensorflow.contrib.learn.python.learn.monitors) is deprecated and will be removed after 2016-12-05.\n", "Instructions for updating:\n", "Monitors are deprecated. Please use tf.train.SessionRunHook.\n", "WARNING:tensorflow:From <ipython-input-7-5632e25cd73d>:14: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Estimator is decoupled from Scikit Learn interface by moving into\n", "separate class SKCompat. Arguments x, y and batch_size are only\n", "available in the SKCompat class, Estimator will only accept input_fn.\n", "Example conversion:\n", " est = Estimator(...) -> est = SKCompat(Estimator(...))\n", "WARNING:tensorflow:From <ipython-input-7-5632e25cd73d>:14: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Estimator is decoupled from Scikit Learn interface by moving into\n", "separate class SKCompat. Arguments x, y and batch_size are only\n", "available in the SKCompat class, Estimator will only accept input_fn.\n", "Example conversion:\n", " est = Estimator(...) -> est = SKCompat(Estimator(...))\n", "WARNING:tensorflow:From <ipython-input-7-5632e25cd73d>:14: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with batch_size is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Estimator is decoupled from Scikit Learn interface by moving into\n", "separate class SKCompat. Arguments x, y and batch_size are only\n", "available in the SKCompat class, Estimator will only accept input_fn.\n", "Example conversion:\n", " est = Estimator(...) -> est = SKCompat(Estimator(...))\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py:247: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " equality = a == b\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.mean_squared_error instead.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:530: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.compute_weighted_loss instead.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.add_loss instead.\n", "INFO:tensorflow:Create CheckpointSaverHook.\n", "INFO:tensorflow:Saving checkpoints for 339006 into .opt_logs/lstm_stock/model.ckpt.\n", "INFO:tensorflow:step = 339006, loss = 0.806717\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Estimator is decoupled from Scikit Learn interface by moving into\n", "separate class SKCompat. Arguments x, y and batch_size are only\n", "available in the SKCompat class, Estimator will only accept input_fn.\n", "Example conversion:\n", " est = Estimator(...) -> est = SKCompat(Estimator(...))\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Estimator is decoupled from Scikit Learn interface by moving into\n", "separate class SKCompat. Arguments x, y and batch_size are only\n", "available in the SKCompat class, Estimator will only accept input_fn.\n", "Example conversion:\n", " est = Estimator(...) -> est = SKCompat(Estimator(...))\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.mean_squared_error instead.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:530: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.compute_weighted_loss instead.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.add_loss instead.\n", "INFO:tensorflow:Starting evaluation at 2017-04-09-09:21:17\n", "INFO:tensorflow:Finished evaluation at 2017-04-09-09:21:20\n", "INFO:tensorflow:Saving dict for global step 339006: global_step = 339006, loss = 0.25423\n", "WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.\n", "INFO:tensorflow:Validation (step 339006): loss = 0.25423, global_step = 339006\n", "INFO:tensorflow:global_step/sec: 5.17287\n", "INFO:tensorflow:step = 339106, loss = 0.582741\n", "INFO:tensorflow:global_step/sec: 106.13\n", "INFO:tensorflow:step = 339206, loss = 0.528766\n", "INFO:tensorflow:global_step/sec: 91.9155\n", "INFO:tensorflow:step = 339306, loss = 0.273415\n", "INFO:tensorflow:global_step/sec: 102.148\n", "INFO:tensorflow:step = 339406, loss = 0.919193\n", "INFO:tensorflow:global_step/sec: 115.209\n", "INFO:tensorflow:step = 339506, loss = 0.513935\n", "INFO:tensorflow:global_step/sec: 114.399\n", "INFO:tensorflow:step = 339606, loss = 0.670143\n", "INFO:tensorflow:global_step/sec: 114.766\n", "INFO:tensorflow:step = 339706, loss = 0.435021\n", "INFO:tensorflow:global_step/sec: 113.334\n", "INFO:tensorflow:step = 339806, loss = 0.454065\n", "INFO:tensorflow:global_step/sec: 111.879\n", "INFO:tensorflow:step = 339906, loss = 0.580047\n", "INFO:tensorflow:global_step/sec: 102.983\n", "INFO:tensorflow:step = 340006, loss = 0.546571\n", "INFO:tensorflow:global_step/sec: 113.181\n", "INFO:tensorflow:step = 340106, loss = 0.614448\n", "INFO:tensorflow:global_step/sec: 115.903\n", "INFO:tensorflow:step = 340206, loss = 0.560771\n", "INFO:tensorflow:global_step/sec: 111.747\n", "INFO:tensorflow:step = 340306, loss = 0.629022\n", "INFO:tensorflow:global_step/sec: 116.741\n", "INFO:tensorflow:step = 340406, loss = 0.44038\n", "INFO:tensorflow:global_step/sec: 115.986\n", "INFO:tensorflow:step = 340506, loss = 0.408515\n", "INFO:tensorflow:global_step/sec: 112.924\n", "INFO:tensorflow:step = 340606, loss = 0.662987\n", "INFO:tensorflow:global_step/sec: 116.818\n", "INFO:tensorflow:step = 340706, loss = 0.531829\n", "INFO:tensorflow:global_step/sec: 117.568\n", "INFO:tensorflow:step = 340806, loss = 0.438657\n", "INFO:tensorflow:global_step/sec: 117.674\n", "INFO:tensorflow:step = 340906, loss = 0.620047\n", "INFO:tensorflow:global_step/sec: 117.091\n", "INFO:tensorflow:step = 341006, loss = 0.349382\n", "INFO:tensorflow:global_step/sec: 99.7272\n", "INFO:tensorflow:step = 341106, loss = 0.439398\n", "INFO:tensorflow:global_step/sec: 78.0838\n", "INFO:tensorflow:step = 341206, loss = 0.596738\n", "INFO:tensorflow:global_step/sec: 75.8052\n", "INFO:tensorflow:step = 341306, loss = 0.470497\n", "INFO:tensorflow:global_step/sec: 106.383\n", "INFO:tensorflow:step = 341406, loss = 0.601673\n", "INFO:tensorflow:global_step/sec: 117.009\n", "INFO:tensorflow:step = 341506, loss = 0.532679\n", "INFO:tensorflow:global_step/sec: 111.718\n", "INFO:tensorflow:step = 341606, loss = 0.556467\n", "INFO:tensorflow:global_step/sec: 115.078\n", "INFO:tensorflow:step = 341706, loss = 0.391023\n", "INFO:tensorflow:global_step/sec: 116.364\n", "INFO:tensorflow:step = 341806, loss = 0.460295\n", "INFO:tensorflow:global_step/sec: 113.886\n", "INFO:tensorflow:step = 341906, loss = 0.548109\n", "INFO:tensorflow:global_step/sec: 116.403\n", "INFO:tensorflow:step = 342006, loss = 0.524565\n", "INFO:tensorflow:global_step/sec: 116.934\n", "INFO:tensorflow:step = 342106, loss = 0.511981\n", "INFO:tensorflow:global_step/sec: 114.017\n", "INFO:tensorflow:step = 342206, loss = 0.398324\n", "INFO:tensorflow:global_step/sec: 115.184\n", "INFO:tensorflow:step = 342306, loss = 0.281603\n", "INFO:tensorflow:global_step/sec: 114.725\n", "INFO:tensorflow:step = 342406, loss = 0.39462\n", "INFO:tensorflow:global_step/sec: 118.718\n", "INFO:tensorflow:step = 342506, loss = 0.480679\n", "INFO:tensorflow:global_step/sec: 111.672\n", "INFO:tensorflow:step = 342606, loss = 0.57041\n", "INFO:tensorflow:global_step/sec: 114.754\n", "INFO:tensorflow:step = 342706, loss = 0.411844\n", "INFO:tensorflow:global_step/sec: 116.828\n", "INFO:tensorflow:step = 342806, loss = 0.533774\n", "INFO:tensorflow:global_step/sec: 114.179\n", "INFO:tensorflow:step = 342906, loss = 0.455098\n", "INFO:tensorflow:global_step/sec: 117.01\n", "INFO:tensorflow:step = 343006, loss = 0.42254\n", "INFO:tensorflow:global_step/sec: 115.726\n", "INFO:tensorflow:step = 343106, loss = 0.510424\n", "INFO:tensorflow:global_step/sec: 116.358\n", "INFO:tensorflow:step = 343206, loss = 0.500688\n", "INFO:tensorflow:global_step/sec: 108.862\n", "INFO:tensorflow:step = 343306, loss = 0.46724\n", "INFO:tensorflow:global_step/sec: 105.158\n", "INFO:tensorflow:step = 343406, loss = 0.66938\n", "INFO:tensorflow:global_step/sec: 115.92\n", "INFO:tensorflow:step = 343506, loss = 0.387522\n", "INFO:tensorflow:global_step/sec: 117.361\n", "INFO:tensorflow:step = 343606, loss = 0.364447\n", "INFO:tensorflow:global_step/sec: 114.268\n", "INFO:tensorflow:step = 343706, loss = 0.514702\n", "INFO:tensorflow:global_step/sec: 113.073\n", "INFO:tensorflow:step = 343806, loss = 0.469244\n", "INFO:tensorflow:global_step/sec: 116.972\n", "INFO:tensorflow:step = 343906, loss = 0.534099\n", "INFO:tensorflow:global_step/sec: 116.398\n", "INFO:tensorflow:step = 344006, loss = 0.409695\n", "INFO:tensorflow:global_step/sec: 116.199\n", "INFO:tensorflow:step = 344106, loss = 0.54489\n", "INFO:tensorflow:global_step/sec: 110.191\n", "INFO:tensorflow:step = 344206, loss = 0.609699\n", "INFO:tensorflow:global_step/sec: 117.537\n", "INFO:tensorflow:step = 344306, loss = 0.647423\n", "INFO:tensorflow:global_step/sec: 112.543\n", "INFO:tensorflow:step = 344406, loss = 0.620821\n", "INFO:tensorflow:global_step/sec: 98.5311\n", "INFO:tensorflow:step = 344506, loss = 0.453426\n", "INFO:tensorflow:global_step/sec: 103.522\n", "INFO:tensorflow:step = 344606, loss = 0.499177\n", "INFO:tensorflow:global_step/sec: 110.217\n", "INFO:tensorflow:step = 344706, loss = 0.411452\n", "INFO:tensorflow:global_step/sec: 108.275\n", "INFO:tensorflow:step = 344806, loss = 0.425225\n", "INFO:tensorflow:global_step/sec: 108.11\n", "INFO:tensorflow:step = 344906, loss = 0.390474\n", "INFO:tensorflow:global_step/sec: 113.261\n", "INFO:tensorflow:step = 345006, loss = 0.313468\n", "INFO:tensorflow:global_step/sec: 108.572\n", "INFO:tensorflow:step = 345106, loss = 0.434078\n", "INFO:tensorflow:global_step/sec: 104.522\n", "INFO:tensorflow:step = 345206, loss = 0.512227\n", "INFO:tensorflow:global_step/sec: 81.0007\n", "INFO:tensorflow:step = 345306, loss = 0.351638\n", "INFO:tensorflow:global_step/sec: 116.67\n", "INFO:tensorflow:step = 345406, loss = 0.472098\n", "INFO:tensorflow:global_step/sec: 117.19\n", "INFO:tensorflow:step = 345506, loss = 0.347732\n", "INFO:tensorflow:global_step/sec: 117.762\n", "INFO:tensorflow:step = 345606, loss = 0.558673\n", "INFO:tensorflow:global_step/sec: 112.362\n", "INFO:tensorflow:step = 345706, loss = 0.49848\n", "INFO:tensorflow:global_step/sec: 117.734\n", "INFO:tensorflow:step = 345806, loss = 0.425987\n", "INFO:tensorflow:global_step/sec: 113.684\n", "INFO:tensorflow:step = 345906, loss = 0.543236\n", "INFO:tensorflow:global_step/sec: 111.97\n", "INFO:tensorflow:step = 346006, loss = 0.401278\n", "INFO:tensorflow:global_step/sec: 117.85\n", "INFO:tensorflow:step = 346106, loss = 0.411407\n", "INFO:tensorflow:global_step/sec: 94.3647\n", "INFO:tensorflow:step = 346206, loss = 0.49146\n", "INFO:tensorflow:global_step/sec: 72.0722\n", "INFO:tensorflow:step = 346306, loss = 0.719834\n", "INFO:tensorflow:global_step/sec: 113.715\n", "INFO:tensorflow:step = 346406, loss = 0.387193\n", "INFO:tensorflow:global_step/sec: 113.196\n", "INFO:tensorflow:step = 346506, loss = 0.415186\n", "INFO:tensorflow:global_step/sec: 108.784\n", "INFO:tensorflow:step = 346606, loss = 0.435098\n", "INFO:tensorflow:global_step/sec: 96.7805\n", "INFO:tensorflow:step = 346706, loss = 0.515897\n", "INFO:tensorflow:global_step/sec: 117.198\n", "INFO:tensorflow:step = 346806, loss = 0.488466\n", "INFO:tensorflow:global_step/sec: 115.551\n", "INFO:tensorflow:step = 346906, loss = 0.57207\n", "INFO:tensorflow:global_step/sec: 114.11\n", "INFO:tensorflow:step = 347006, loss = 0.494605\n", "INFO:tensorflow:global_step/sec: 115.721\n", "INFO:tensorflow:step = 347106, loss = 0.612438\n", "INFO:tensorflow:global_step/sec: 116.584\n", "INFO:tensorflow:step = 347206, loss = 0.489791\n", "INFO:tensorflow:global_step/sec: 107.971\n", "INFO:tensorflow:step = 347306, loss = 0.468933\n", "INFO:tensorflow:global_step/sec: 112.272\n", "INFO:tensorflow:step = 347406, loss = 0.496476\n", "INFO:tensorflow:global_step/sec: 114.758\n", "INFO:tensorflow:step = 347506, loss = 0.506092\n", "INFO:tensorflow:global_step/sec: 117.463\n", "INFO:tensorflow:step = 347606, loss = 0.489552\n", "INFO:tensorflow:global_step/sec: 116.831\n", "INFO:tensorflow:step = 347706, loss = 0.603145\n", "INFO:tensorflow:global_step/sec: 100.971\n", "INFO:tensorflow:step = 347806, loss = 0.551206\n", "INFO:tensorflow:global_step/sec: 75.9792\n", "INFO:tensorflow:step = 347906, loss = 0.303643\n", "INFO:tensorflow:global_step/sec: 74.6989\n", "INFO:tensorflow:step = 348006, loss = 0.37927\n", "INFO:tensorflow:global_step/sec: 92.6601\n", "INFO:tensorflow:step = 348106, loss = 0.920131\n", "INFO:tensorflow:global_step/sec: 116.881\n", "INFO:tensorflow:step = 348206, loss = 0.413982\n", "INFO:tensorflow:global_step/sec: 114.421\n", "INFO:tensorflow:step = 348306, loss = 0.498875\n", "INFO:tensorflow:global_step/sec: 112.332\n", "INFO:tensorflow:step = 348406, loss = 0.452476\n", "INFO:tensorflow:global_step/sec: 118.374\n", "INFO:tensorflow:step = 348506, loss = 0.452039\n", "INFO:tensorflow:global_step/sec: 117.065\n", "INFO:tensorflow:step = 348606, loss = 0.477456\n", "INFO:tensorflow:global_step/sec: 116.62\n", "INFO:tensorflow:step = 348706, loss = 0.398244\n", "INFO:tensorflow:global_step/sec: 117.225\n", "INFO:tensorflow:step = 348806, loss = 0.952247\n", "INFO:tensorflow:global_step/sec: 115.254\n", "INFO:tensorflow:step = 348906, loss = 0.431787\n", "INFO:tensorflow:global_step/sec: 116.098\n", "INFO:tensorflow:step = 349006, loss = 0.662923\n", "INFO:tensorflow:global_step/sec: 114.305\n", "INFO:tensorflow:step = 349106, loss = 0.498748\n", "INFO:tensorflow:global_step/sec: 114.518\n", "INFO:tensorflow:step = 349206, loss = 0.602874\n", "INFO:tensorflow:global_step/sec: 117.794\n", "INFO:tensorflow:step = 349306, loss = 0.647044\n", "INFO:tensorflow:global_step/sec: 113.615\n", "INFO:tensorflow:step = 349406, loss = 0.560512\n", "INFO:tensorflow:global_step/sec: 113.496\n", "INFO:tensorflow:step = 349506, loss = 0.461952\n", "INFO:tensorflow:global_step/sec: 115.568\n", "INFO:tensorflow:step = 349606, loss = 0.597699\n", "INFO:tensorflow:global_step/sec: 115.952\n", "INFO:tensorflow:step = 349706, loss = 0.742347\n", "INFO:tensorflow:global_step/sec: 116.308\n", "INFO:tensorflow:step = 349806, loss = 0.486255\n", "INFO:tensorflow:global_step/sec: 117.033\n", "INFO:tensorflow:step = 349906, loss = 0.374514\n", "INFO:tensorflow:global_step/sec: 105.601\n", "INFO:tensorflow:step = 350006, loss = 0.473089\n", "INFO:tensorflow:global_step/sec: 115.441\n", "INFO:tensorflow:step = 350106, loss = 0.498085\n", "INFO:tensorflow:global_step/sec: 117.259\n", "INFO:tensorflow:step = 350206, loss = 0.513201\n", "INFO:tensorflow:global_step/sec: 116.014\n", "INFO:tensorflow:step = 350306, loss = 0.443927\n", "INFO:tensorflow:global_step/sec: 113.88\n", "INFO:tensorflow:step = 350406, loss = 0.452892\n", "INFO:tensorflow:global_step/sec: 115.601\n", "INFO:tensorflow:step = 350506, loss = 0.436417\n", "INFO:tensorflow:global_step/sec: 116.503\n", "INFO:tensorflow:step = 350606, loss = 0.420372\n", "INFO:tensorflow:global_step/sec: 118.287\n", "INFO:tensorflow:step = 350706, loss = 0.36123\n", "INFO:tensorflow:global_step/sec: 115.155\n", "INFO:tensorflow:step = 350806, loss = 0.537954\n", "INFO:tensorflow:global_step/sec: 115.585\n", "INFO:tensorflow:step = 350906, loss = 0.480799\n", "INFO:tensorflow:global_step/sec: 112.515\n", "INFO:tensorflow:step = 351006, loss = 0.492548\n", "INFO:tensorflow:global_step/sec: 109.685\n", "INFO:tensorflow:step = 351106, loss = 0.551821\n", "INFO:tensorflow:global_step/sec: 91.4695\n", "INFO:tensorflow:step = 351206, loss = 0.567941\n", "INFO:tensorflow:global_step/sec: 107.787\n", "INFO:tensorflow:step = 351306, loss = 0.747511\n", "INFO:tensorflow:global_step/sec: 117.029\n", "INFO:tensorflow:step = 351406, loss = 0.369468\n", "INFO:tensorflow:global_step/sec: 114.139\n", "INFO:tensorflow:step = 351506, loss = 0.695197\n", "INFO:tensorflow:global_step/sec: 114.321\n", "INFO:tensorflow:step = 351606, loss = 0.461856\n", "INFO:tensorflow:global_step/sec: 111.252\n", "INFO:tensorflow:step = 351706, loss = 0.47705\n", "INFO:tensorflow:global_step/sec: 93.3355\n", "INFO:tensorflow:step = 351806, loss = 0.507072\n", "INFO:tensorflow:global_step/sec: 86.4301\n", "INFO:tensorflow:step = 351906, loss = 0.620717\n", "INFO:tensorflow:global_step/sec: 116.494\n", "INFO:tensorflow:step = 352006, loss = 0.72283\n", "INFO:tensorflow:global_step/sec: 115.707\n", "INFO:tensorflow:step = 352106, loss = 0.328128\n", "INFO:tensorflow:global_step/sec: 116.606\n", "INFO:tensorflow:step = 352206, loss = 0.434188\n", "INFO:tensorflow:global_step/sec: 114.608\n", "INFO:tensorflow:step = 352306, loss = 0.782123\n", "INFO:tensorflow:global_step/sec: 111.28\n", "INFO:tensorflow:step = 352406, loss = 0.588399\n", "INFO:tensorflow:global_step/sec: 115.001\n", "INFO:tensorflow:step = 352506, loss = 0.543554\n", "INFO:tensorflow:global_step/sec: 116.058\n", "INFO:tensorflow:step = 352606, loss = 0.430322\n", "INFO:tensorflow:global_step/sec: 114.072\n", "INFO:tensorflow:step = 352706, loss = 0.579322\n", "INFO:tensorflow:global_step/sec: 115.354\n", "INFO:tensorflow:step = 352806, loss = 0.84962\n", "INFO:tensorflow:global_step/sec: 117.043\n", "INFO:tensorflow:step = 352906, loss = 0.865846\n", "INFO:tensorflow:global_step/sec: 115.53\n", "INFO:tensorflow:step = 353006, loss = 0.631429\n", "INFO:tensorflow:global_step/sec: 116.876\n", "INFO:tensorflow:step = 353106, loss = 0.338776\n", "INFO:tensorflow:global_step/sec: 114.599\n", "INFO:tensorflow:step = 353206, loss = 0.561563\n", "INFO:tensorflow:global_step/sec: 110.706\n", "INFO:tensorflow:step = 353306, loss = 0.312941\n", "INFO:tensorflow:global_step/sec: 104.683\n", "INFO:tensorflow:step = 353406, loss = 0.54279\n", "INFO:tensorflow:global_step/sec: 109.309\n", "INFO:tensorflow:step = 353506, loss = 0.540346\n", "INFO:tensorflow:global_step/sec: 117.541\n", "INFO:tensorflow:step = 353606, loss = 0.322703\n", "INFO:tensorflow:global_step/sec: 110.23\n", "INFO:tensorflow:step = 353706, loss = 0.51748\n", "INFO:tensorflow:global_step/sec: 110.669\n", "INFO:tensorflow:step = 353806, loss = 0.490543\n", "INFO:tensorflow:global_step/sec: 115.189\n", "INFO:tensorflow:step = 353906, loss = 0.538233\n", "INFO:tensorflow:global_step/sec: 115.57\n", "INFO:tensorflow:step = 354006, loss = 0.593269\n", "INFO:tensorflow:global_step/sec: 116.221\n", "INFO:tensorflow:step = 354106, loss = 0.435844\n", "INFO:tensorflow:global_step/sec: 117.413\n", "INFO:tensorflow:step = 354206, loss = 0.388676\n", "INFO:tensorflow:global_step/sec: 116.299\n", "INFO:tensorflow:step = 354306, loss = 0.453993\n", "INFO:tensorflow:global_step/sec: 116.168\n", "INFO:tensorflow:step = 354406, loss = 0.421964\n", "INFO:tensorflow:global_step/sec: 114.938\n", "INFO:tensorflow:step = 354506, loss = 0.705114\n", "INFO:tensorflow:global_step/sec: 92.8791\n", "INFO:tensorflow:step = 354606, loss = 0.679216\n", "INFO:tensorflow:global_step/sec: 89.7701\n", "INFO:tensorflow:step = 354706, loss = 0.611321\n", "INFO:tensorflow:global_step/sec: 81.0525\n", "INFO:tensorflow:step = 354806, loss = 0.439996\n", "INFO:tensorflow:global_step/sec: 94.6417\n", "INFO:tensorflow:step = 354906, loss = 0.724999\n", "INFO:tensorflow:global_step/sec: 109.918\n", "INFO:tensorflow:step = 355006, loss = 0.409076\n", "INFO:tensorflow:global_step/sec: 115.881\n", "INFO:tensorflow:step = 355106, loss = 0.482952\n", "INFO:tensorflow:global_step/sec: 116.942\n", "INFO:tensorflow:step = 355206, loss = 0.358182\n", "INFO:tensorflow:global_step/sec: 116.024\n", "INFO:tensorflow:step = 355306, loss = 0.386676\n", "INFO:tensorflow:global_step/sec: 115.828\n", "INFO:tensorflow:step = 355406, loss = 0.521929\n", "INFO:tensorflow:global_step/sec: 114.783\n", "INFO:tensorflow:step = 355506, loss = 0.540193\n", "INFO:tensorflow:global_step/sec: 111.375\n", "INFO:tensorflow:step = 355606, loss = 0.428702\n", "INFO:tensorflow:global_step/sec: 111.721\n", "INFO:tensorflow:step = 355706, loss = 0.560208\n", "INFO:tensorflow:global_step/sec: 113.518\n", "INFO:tensorflow:step = 355806, loss = 0.504133\n", "INFO:tensorflow:global_step/sec: 106.573\n", "INFO:tensorflow:step = 355906, loss = 0.544022\n", "INFO:tensorflow:global_step/sec: 115.898\n", "INFO:tensorflow:step = 356006, loss = 0.40819\n", "INFO:tensorflow:global_step/sec: 114.021\n", "INFO:tensorflow:step = 356106, loss = 0.540408\n", "INFO:tensorflow:global_step/sec: 114.626\n", "INFO:tensorflow:step = 356206, loss = 0.517392\n", "INFO:tensorflow:global_step/sec: 115.214\n", "INFO:tensorflow:step = 356306, loss = 0.667194\n", "INFO:tensorflow:global_step/sec: 115.663\n", "INFO:tensorflow:step = 356406, loss = 0.519743\n", "INFO:tensorflow:global_step/sec: 116.276\n", "INFO:tensorflow:step = 356506, loss = 0.582115\n", "INFO:tensorflow:global_step/sec: 116.239\n", "INFO:tensorflow:step = 356606, loss = 0.465168\n", "INFO:tensorflow:global_step/sec: 116.434\n", "INFO:tensorflow:step = 356706, loss = 0.537055\n", "INFO:tensorflow:global_step/sec: 97.193\n", "INFO:tensorflow:step = 356806, loss = 0.412585\n", "INFO:tensorflow:global_step/sec: 113.247\n", "INFO:tensorflow:step = 356906, loss = 0.563576\n", "INFO:tensorflow:global_step/sec: 114.862\n", "INFO:tensorflow:step = 357006, loss = 0.455356\n", "INFO:tensorflow:global_step/sec: 113.803\n", "INFO:tensorflow:step = 357106, loss = 0.386652\n", "INFO:tensorflow:global_step/sec: 112.421\n", "INFO:tensorflow:step = 357206, loss = 0.565708\n", "INFO:tensorflow:global_step/sec: 91.5738\n", "INFO:tensorflow:step = 357306, loss = 0.507282\n", "INFO:tensorflow:global_step/sec: 69.1222\n", "INFO:tensorflow:step = 357406, loss = 0.517668\n", "INFO:tensorflow:global_step/sec: 89.9787\n", "INFO:tensorflow:step = 357506, loss = 0.41188\n", "INFO:tensorflow:global_step/sec: 113.1\n", "INFO:tensorflow:step = 357606, loss = 0.737142\n", "INFO:tensorflow:global_step/sec: 115.822\n", "INFO:tensorflow:step = 357706, loss = 0.421028\n", "INFO:tensorflow:global_step/sec: 100.12\n", "INFO:tensorflow:step = 357806, loss = 0.374103\n", "INFO:tensorflow:global_step/sec: 76.6795\n", "INFO:tensorflow:step = 357906, loss = 0.520725\n", "INFO:tensorflow:global_step/sec: 68.2123\n", "INFO:tensorflow:step = 358006, loss = 0.733428\n", "INFO:tensorflow:global_step/sec: 97.6556\n", "INFO:tensorflow:step = 358106, loss = 0.715023\n", "INFO:tensorflow:global_step/sec: 114.072\n", "INFO:tensorflow:step = 358206, loss = 0.38664\n", "INFO:tensorflow:global_step/sec: 91.9725\n", "INFO:tensorflow:step = 358306, loss = 0.473888\n", "INFO:tensorflow:global_step/sec: 100.346\n", "INFO:tensorflow:step = 358406, loss = 0.365555\n", "INFO:tensorflow:global_step/sec: 114.73\n", "INFO:tensorflow:step = 358506, loss = 0.841251\n", "INFO:tensorflow:global_step/sec: 109.482\n", "INFO:tensorflow:step = 358606, loss = 0.42148\n", "INFO:tensorflow:global_step/sec: 116.343\n", "INFO:tensorflow:step = 358706, loss = 0.298296\n", "INFO:tensorflow:global_step/sec: 115.505\n", "INFO:tensorflow:step = 358806, loss = 0.499517\n", "INFO:tensorflow:global_step/sec: 111.901\n", "INFO:tensorflow:step = 358906, loss = 0.49119\n", "INFO:tensorflow:global_step/sec: 112.331\n", "INFO:tensorflow:step = 359006, loss = 0.613291\n", "INFO:tensorflow:global_step/sec: 109.004\n", "INFO:tensorflow:step = 359106, loss = 0.459996\n", "INFO:tensorflow:global_step/sec: 82.0152\n", "INFO:tensorflow:step = 359206, loss = 0.974221\n", "INFO:tensorflow:global_step/sec: 88.9082\n", "INFO:tensorflow:step = 359306, loss = 0.408903\n", "INFO:tensorflow:global_step/sec: 98.4797\n", "INFO:tensorflow:step = 359406, loss = 0.381257\n", "INFO:tensorflow:global_step/sec: 100.715\n", "INFO:tensorflow:step = 359506, loss = 0.714971\n", "INFO:tensorflow:global_step/sec: 110.567\n", "INFO:tensorflow:step = 359606, loss = 0.394651\n", "INFO:tensorflow:global_step/sec: 100.536\n", "INFO:tensorflow:step = 359706, loss = 0.47018\n", "INFO:tensorflow:global_step/sec: 99.4939\n", "INFO:tensorflow:step = 359806, loss = 0.321963\n", "INFO:tensorflow:global_step/sec: 109.407\n", "INFO:tensorflow:step = 359906, loss = 0.346378\n", "INFO:tensorflow:global_step/sec: 81.7777\n", "INFO:tensorflow:step = 360006, loss = 0.377356\n", "INFO:tensorflow:global_step/sec: 94.2963\n", "INFO:tensorflow:step = 360106, loss = 0.641642\n", "INFO:tensorflow:global_step/sec: 89.7458\n", "INFO:tensorflow:step = 360206, loss = 0.522802\n", "INFO:tensorflow:global_step/sec: 99.4068\n", "INFO:tensorflow:step = 360306, loss = 0.630444\n", "INFO:tensorflow:global_step/sec: 114.412\n", "INFO:tensorflow:step = 360406, loss = 0.457534\n", "INFO:tensorflow:global_step/sec: 112.986\n", "INFO:tensorflow:step = 360506, loss = 0.433585\n", "INFO:tensorflow:global_step/sec: 104.476\n", "INFO:tensorflow:step = 360606, loss = 0.560122\n", "INFO:tensorflow:global_step/sec: 113.59\n", "INFO:tensorflow:step = 360706, loss = 0.429952\n", "INFO:tensorflow:global_step/sec: 99.0058\n", "INFO:tensorflow:step = 360806, loss = 0.502104\n", "INFO:tensorflow:global_step/sec: 61.2156\n", "INFO:tensorflow:step = 360906, loss = 0.48818\n", "INFO:tensorflow:global_step/sec: 72.3553\n", "INFO:tensorflow:step = 361006, loss = 0.412807\n", "INFO:tensorflow:global_step/sec: 77.579\n", "INFO:tensorflow:step = 361106, loss = 0.375867\n", "INFO:tensorflow:global_step/sec: 104.867\n", "INFO:tensorflow:step = 361206, loss = 0.56726\n", "INFO:tensorflow:global_step/sec: 111.547\n", "INFO:tensorflow:step = 361306, loss = 0.904301\n", "INFO:tensorflow:global_step/sec: 114.148\n", "INFO:tensorflow:step = 361406, loss = 0.694944\n", "INFO:tensorflow:global_step/sec: 103.042\n", "INFO:tensorflow:step = 361506, loss = 0.388004\n", "INFO:tensorflow:global_step/sec: 116.166\n", "INFO:tensorflow:step = 361606, loss = 0.531125\n", "INFO:tensorflow:global_step/sec: 116.451\n", "INFO:tensorflow:step = 361706, loss = 0.639938\n", "INFO:tensorflow:global_step/sec: 113.633\n", "INFO:tensorflow:step = 361806, loss = 0.329817\n", "INFO:tensorflow:global_step/sec: 111.406\n", "INFO:tensorflow:step = 361906, loss = 0.479359\n", "INFO:tensorflow:global_step/sec: 115.648\n", "INFO:tensorflow:step = 362006, loss = 0.518256\n", "INFO:tensorflow:global_step/sec: 114.789\n", "INFO:tensorflow:step = 362106, loss = 0.379107\n", "INFO:tensorflow:global_step/sec: 112.549\n", "INFO:tensorflow:step = 362206, loss = 0.374788\n", "INFO:tensorflow:global_step/sec: 108.196\n", "INFO:tensorflow:step = 362306, loss = 0.634651\n", "INFO:tensorflow:global_step/sec: 115.379\n", "INFO:tensorflow:step = 362406, loss = 0.743446\n", "INFO:tensorflow:global_step/sec: 116.006\n", "INFO:tensorflow:step = 362506, loss = 0.384704\n", "INFO:tensorflow:global_step/sec: 89.3002\n", "INFO:tensorflow:step = 362606, loss = 0.38781\n", "INFO:tensorflow:global_step/sec: 81.7567\n", "INFO:tensorflow:step = 362706, loss = 0.509994\n", "INFO:tensorflow:global_step/sec: 89.7892\n", "INFO:tensorflow:step = 362806, loss = 0.481287\n", "INFO:tensorflow:global_step/sec: 96.8089\n", "INFO:tensorflow:step = 362906, loss = 0.468156\n", "INFO:tensorflow:global_step/sec: 114.328\n", "INFO:tensorflow:step = 363006, loss = 0.671575\n", "INFO:tensorflow:global_step/sec: 113.136\n", "INFO:tensorflow:step = 363106, loss = 0.615688\n", "INFO:tensorflow:global_step/sec: 96.0356\n", "INFO:tensorflow:step = 363206, loss = 0.614301\n", "INFO:tensorflow:global_step/sec: 75.2654\n", "INFO:tensorflow:step = 363306, loss = 0.517174\n", "INFO:tensorflow:global_step/sec: 82.8963\n", "INFO:tensorflow:step = 363406, loss = 0.473946\n", "INFO:tensorflow:global_step/sec: 85.887\n", "INFO:tensorflow:step = 363506, loss = 0.413526\n", "INFO:tensorflow:global_step/sec: 76.7159\n", "INFO:tensorflow:step = 363606, loss = 0.371268\n", "INFO:tensorflow:global_step/sec: 94.947\n", "INFO:tensorflow:step = 363706, loss = 0.432136\n", "INFO:tensorflow:global_step/sec: 72.8634\n", "INFO:tensorflow:step = 363806, loss = 0.560483\n", "INFO:tensorflow:global_step/sec: 65.2025\n", "INFO:tensorflow:step = 363906, loss = 0.337906\n", "INFO:tensorflow:global_step/sec: 63.7278\n", "INFO:tensorflow:step = 364006, loss = 0.581399\n", "INFO:tensorflow:global_step/sec: 97.0644\n", "INFO:tensorflow:step = 364106, loss = 0.313138\n", "INFO:tensorflow:global_step/sec: 88.3304\n", "INFO:tensorflow:step = 364206, loss = 0.355037\n", "INFO:tensorflow:global_step/sec: 96.1141\n", "INFO:tensorflow:step = 364306, loss = 0.42799\n", "INFO:tensorflow:global_step/sec: 106.924\n", "INFO:tensorflow:step = 364406, loss = 0.588652\n", "INFO:tensorflow:global_step/sec: 98.5002\n", "INFO:tensorflow:step = 364506, loss = 0.464241\n", "INFO:tensorflow:global_step/sec: 96.2835\n", "INFO:tensorflow:step = 364606, loss = 0.398349\n", "INFO:tensorflow:global_step/sec: 80.3412\n", "INFO:tensorflow:step = 364706, loss = 0.557676\n", "INFO:tensorflow:global_step/sec: 59.0903\n", "INFO:tensorflow:step = 364806, loss = 0.355101\n", "INFO:tensorflow:global_step/sec: 100.182\n", "INFO:tensorflow:step = 364906, loss = 0.635606\n", "INFO:tensorflow:global_step/sec: 107.377\n", "INFO:tensorflow:step = 365006, loss = 0.402679\n", "INFO:tensorflow:global_step/sec: 111.267\n", "INFO:tensorflow:step = 365106, loss = 0.512721\n", "INFO:tensorflow:global_step/sec: 115.199\n", "INFO:tensorflow:step = 365206, loss = 0.811941\n", "INFO:tensorflow:global_step/sec: 110.388\n", "INFO:tensorflow:step = 365306, loss = 0.470325\n", "INFO:tensorflow:global_step/sec: 103.514\n", "INFO:tensorflow:step = 365406, loss = 0.347053\n", "INFO:tensorflow:global_step/sec: 106.625\n", "INFO:tensorflow:step = 365506, loss = 0.307461\n", "INFO:tensorflow:global_step/sec: 103.963\n", "INFO:tensorflow:step = 365606, loss = 0.840517\n", "INFO:tensorflow:global_step/sec: 111.796\n", "INFO:tensorflow:step = 365706, loss = 0.618616\n", "INFO:tensorflow:global_step/sec: 99.8\n", "INFO:tensorflow:step = 365806, loss = 0.497677\n", "INFO:tensorflow:global_step/sec: 112.447\n", "INFO:tensorflow:step = 365906, loss = 0.501001\n", "INFO:tensorflow:global_step/sec: 107.364\n", "INFO:tensorflow:step = 366006, loss = 0.349695\n", "INFO:tensorflow:global_step/sec: 79.2341\n", "INFO:tensorflow:step = 366106, loss = 0.299495\n", "INFO:tensorflow:global_step/sec: 96.988\n", "INFO:tensorflow:step = 366206, loss = 0.414866\n", "INFO:tensorflow:global_step/sec: 112.072\n", "INFO:tensorflow:step = 366306, loss = 0.525106\n", "INFO:tensorflow:global_step/sec: 114.201\n", "INFO:tensorflow:step = 366406, loss = 0.443463\n", "INFO:tensorflow:global_step/sec: 108.394\n", "INFO:tensorflow:step = 366506, loss = 0.510108\n", "INFO:tensorflow:global_step/sec: 115.792\n", "INFO:tensorflow:step = 366606, loss = 0.690211\n", "INFO:tensorflow:global_step/sec: 75.4912\n", "INFO:tensorflow:step = 366706, loss = 0.433921\n", "INFO:tensorflow:global_step/sec: 89.0011\n", "INFO:tensorflow:step = 366806, loss = 0.431971\n", "INFO:tensorflow:global_step/sec: 76.8565\n", "INFO:tensorflow:step = 366906, loss = 0.651394\n", "INFO:tensorflow:global_step/sec: 90.5047\n", "INFO:tensorflow:step = 367006, loss = 0.328572\n", "INFO:tensorflow:global_step/sec: 102.834\n", "INFO:tensorflow:step = 367106, loss = 0.405934\n", "INFO:tensorflow:global_step/sec: 110.737\n", "INFO:tensorflow:step = 367206, loss = 0.507407\n", "INFO:tensorflow:global_step/sec: 114.318\n", "INFO:tensorflow:step = 367306, loss = 0.448925\n", "INFO:tensorflow:global_step/sec: 114.114\n", "INFO:tensorflow:step = 367406, loss = 0.563856\n", "INFO:tensorflow:global_step/sec: 100.19\n", "INFO:tensorflow:step = 367506, loss = 0.461348\n", "INFO:tensorflow:global_step/sec: 108.707\n", "INFO:tensorflow:step = 367606, loss = 0.444303\n", "INFO:tensorflow:global_step/sec: 100.644\n", "INFO:tensorflow:step = 367706, loss = 0.53237\n", "INFO:tensorflow:global_step/sec: 111.62\n", "INFO:tensorflow:step = 367806, loss = 0.632266\n", "INFO:tensorflow:global_step/sec: 103.236\n", "INFO:tensorflow:step = 367906, loss = 0.405548\n", "INFO:tensorflow:global_step/sec: 113.127\n", "INFO:tensorflow:step = 368006, loss = 0.826931\n", "INFO:tensorflow:global_step/sec: 110.09\n", "INFO:tensorflow:step = 368106, loss = 0.441889\n", "INFO:tensorflow:global_step/sec: 114.948\n", "INFO:tensorflow:step = 368206, loss = 0.408063\n", "INFO:tensorflow:global_step/sec: 101.211\n", "INFO:tensorflow:step = 368306, loss = 0.47915\n", "INFO:tensorflow:global_step/sec: 113.949\n", "INFO:tensorflow:step = 368406, loss = 0.817547\n", "INFO:tensorflow:global_step/sec: 111.769\n", "INFO:tensorflow:step = 368506, loss = 0.406165\n", "INFO:tensorflow:global_step/sec: 110.701\n", "INFO:tensorflow:step = 368606, loss = 0.575006\n", "INFO:tensorflow:global_step/sec: 91.9781\n", "INFO:tensorflow:step = 368706, loss = 0.83134\n", "INFO:tensorflow:global_step/sec: 43.1266\n", "INFO:tensorflow:step = 368806, loss = 0.555549\n", "INFO:tensorflow:global_step/sec: 31.3476\n", "INFO:tensorflow:step = 368906, loss = 1.11554\n", "INFO:tensorflow:global_step/sec: 18.2088\n", "INFO:tensorflow:step = 369006, loss = 0.72701\n", "INFO:tensorflow:global_step/sec: 25.7042\n", "INFO:tensorflow:step = 369106, loss = 0.46521\n", "INFO:tensorflow:global_step/sec: 40.7444\n", "INFO:tensorflow:step = 369206, loss = 0.406284\n", "INFO:tensorflow:global_step/sec: 68.2664\n", "INFO:tensorflow:step = 369306, loss = 0.566859\n", "INFO:tensorflow:global_step/sec: 83.1579\n", "INFO:tensorflow:step = 369406, loss = 0.389359\n", "INFO:tensorflow:global_step/sec: 65.9385\n", "INFO:tensorflow:step = 369506, loss = 0.537176\n", "INFO:tensorflow:global_step/sec: 86.4792\n", "INFO:tensorflow:step = 369606, loss = 0.555678\n", "INFO:tensorflow:global_step/sec: 92.2497\n", "INFO:tensorflow:step = 369706, loss = 0.392416\n", "INFO:tensorflow:global_step/sec: 112.931\n", "INFO:tensorflow:step = 369806, loss = 0.401592\n", "INFO:tensorflow:global_step/sec: 76.5004\n", "INFO:tensorflow:step = 369906, loss = 0.368342\n", "INFO:tensorflow:global_step/sec: 102.302\n", "INFO:tensorflow:step = 370006, loss = 0.657361\n", "INFO:tensorflow:global_step/sec: 65.297\n", "INFO:tensorflow:step = 370106, loss = 0.530682\n", "INFO:tensorflow:global_step/sec: 86.1733\n", "INFO:tensorflow:step = 370206, loss = 0.456874\n", "INFO:tensorflow:global_step/sec: 86.8029\n", "INFO:tensorflow:step = 370306, loss = 0.460774\n", "INFO:tensorflow:global_step/sec: 44.6874\n", "INFO:tensorflow:step = 370406, loss = 0.481309\n", "INFO:tensorflow:global_step/sec: 32.2417\n", "INFO:tensorflow:step = 370506, loss = 0.591823\n", "INFO:tensorflow:global_step/sec: 52.8557\n", "INFO:tensorflow:step = 370606, loss = 0.563673\n", "INFO:tensorflow:global_step/sec: 57.1618\n", "INFO:tensorflow:step = 370706, loss = 0.586539\n", "INFO:tensorflow:global_step/sec: 32.503\n", "INFO:tensorflow:step = 370806, loss = 0.382462\n", "INFO:tensorflow:global_step/sec: 43.3426\n", "INFO:tensorflow:step = 370906, loss = 0.514546\n", "INFO:tensorflow:global_step/sec: 56.578\n", "INFO:tensorflow:step = 371006, loss = 0.688749\n", "INFO:tensorflow:global_step/sec: 99.4563\n", "INFO:tensorflow:step = 371106, loss = 0.607606\n", "INFO:tensorflow:global_step/sec: 102.243\n", "INFO:tensorflow:step = 371206, loss = 0.660682\n", "INFO:tensorflow:global_step/sec: 97.7741\n", "INFO:tensorflow:step = 371306, loss = 0.552725\n", "INFO:tensorflow:global_step/sec: 66.1034\n", "INFO:tensorflow:step = 371406, loss = 0.506364\n", "INFO:tensorflow:global_step/sec: 104.673\n", "INFO:tensorflow:step = 371506, loss = 0.521884\n", "INFO:tensorflow:global_step/sec: 76.4861\n", "INFO:tensorflow:step = 371606, loss = 0.392521\n", "INFO:tensorflow:global_step/sec: 59.7726\n", "INFO:tensorflow:step = 371706, loss = 0.496586\n", "INFO:tensorflow:global_step/sec: 109.109\n", "INFO:tensorflow:step = 371806, loss = 0.484375\n", "INFO:tensorflow:global_step/sec: 114.778\n", "INFO:tensorflow:step = 371906, loss = 0.329978\n", "INFO:tensorflow:global_step/sec: 115.392\n", "INFO:tensorflow:step = 372006, loss = 0.518678\n", "INFO:tensorflow:global_step/sec: 115.235\n", "INFO:tensorflow:step = 372106, loss = 0.593999\n", "INFO:tensorflow:global_step/sec: 116.431\n", "INFO:tensorflow:step = 372206, loss = 0.504442\n", "INFO:tensorflow:global_step/sec: 116.13\n", "INFO:tensorflow:step = 372306, loss = 0.332552\n", "INFO:tensorflow:global_step/sec: 60.5573\n", "INFO:tensorflow:step = 372406, loss = 0.506939\n", "INFO:tensorflow:global_step/sec: 47.103\n", "INFO:tensorflow:step = 372506, loss = 0.385431\n", "INFO:tensorflow:global_step/sec: 56.126\n", "INFO:tensorflow:step = 372606, loss = 0.522054\n", "INFO:tensorflow:global_step/sec: 40.4452\n", "INFO:tensorflow:step = 372706, loss = 0.362984\n", "INFO:tensorflow:global_step/sec: 37.1223\n", "INFO:tensorflow:step = 372806, loss = 0.349559\n", "INFO:tensorflow:global_step/sec: 32.6249\n", "INFO:tensorflow:step = 372906, loss = 0.482582\n", "INFO:tensorflow:global_step/sec: 49.4579\n", "INFO:tensorflow:step = 373006, loss = 0.589609\n", "INFO:tensorflow:global_step/sec: 89.607\n", "INFO:tensorflow:step = 373106, loss = 0.34628\n", "INFO:tensorflow:global_step/sec: 106.536\n", "INFO:tensorflow:step = 373206, loss = 0.552656\n", "INFO:tensorflow:global_step/sec: 76.0007\n", "INFO:tensorflow:step = 373306, loss = 1.1616\n", "INFO:tensorflow:global_step/sec: 92.0309\n", "INFO:tensorflow:step = 373406, loss = 0.592554\n", "INFO:tensorflow:global_step/sec: 55.823\n", "INFO:tensorflow:step = 373506, loss = 0.508718\n", "INFO:tensorflow:global_step/sec: 66.9084\n", "INFO:tensorflow:step = 373606, loss = 0.506246\n", "INFO:tensorflow:global_step/sec: 99.9224\n", "INFO:tensorflow:step = 373706, loss = 0.376887\n", "INFO:tensorflow:global_step/sec: 105.78\n", "INFO:tensorflow:step = 373806, loss = 0.439415\n", "INFO:tensorflow:global_step/sec: 102.653\n", "INFO:tensorflow:step = 373906, loss = 0.590443\n", "INFO:tensorflow:global_step/sec: 102.743\n", "INFO:tensorflow:step = 374006, loss = 0.38206\n", "INFO:tensorflow:global_step/sec: 110.337\n", "INFO:tensorflow:step = 374106, loss = 0.411511\n", "INFO:tensorflow:global_step/sec: 101.601\n", "INFO:tensorflow:step = 374206, loss = 0.417852\n", "INFO:tensorflow:global_step/sec: 118.937\n", "INFO:tensorflow:step = 374306, loss = 0.463536\n", "INFO:tensorflow:global_step/sec: 109.554\n", "INFO:tensorflow:step = 374406, loss = 0.451343\n", "INFO:tensorflow:global_step/sec: 95.1583\n", "INFO:tensorflow:step = 374506, loss = 0.469439\n", "INFO:tensorflow:global_step/sec: 103.167\n", "INFO:tensorflow:step = 374606, loss = 0.613322\n", "INFO:tensorflow:global_step/sec: 58.156\n", "INFO:tensorflow:step = 374706, loss = 0.432542\n", "INFO:tensorflow:global_step/sec: 51.6874\n", "INFO:tensorflow:step = 374806, loss = 0.449231\n", "INFO:tensorflow:global_step/sec: 78.8243\n", "INFO:tensorflow:step = 374906, loss = 0.574736\n", "INFO:tensorflow:global_step/sec: 51.8593\n", "INFO:tensorflow:step = 375006, loss = 0.527558\n", "INFO:tensorflow:global_step/sec: 51.4942\n", "INFO:tensorflow:step = 375106, loss = 0.557404\n", "INFO:tensorflow:global_step/sec: 32.2928\n", "INFO:tensorflow:step = 375206, loss = 0.621539\n", "INFO:tensorflow:global_step/sec: 57.4225\n", "INFO:tensorflow:step = 375306, loss = 0.52713\n", "INFO:tensorflow:global_step/sec: 66.0151\n", "INFO:tensorflow:step = 375406, loss = 0.460037\n", "INFO:tensorflow:global_step/sec: 42.6926\n", "INFO:tensorflow:step = 375506, loss = 0.425695\n", "INFO:tensorflow:global_step/sec: 82.974\n", "INFO:tensorflow:step = 375606, loss = 0.496867\n", "INFO:tensorflow:global_step/sec: 62.8989\n", "INFO:tensorflow:step = 375706, loss = 0.693525\n", "INFO:tensorflow:global_step/sec: 46.2377\n", "INFO:tensorflow:step = 375806, loss = 0.474493\n", "INFO:tensorflow:global_step/sec: 75.5642\n", "INFO:tensorflow:step = 375906, loss = 0.351381\n", "INFO:tensorflow:global_step/sec: 86.8906\n", "INFO:tensorflow:step = 376006, loss = 0.42278\n", "INFO:tensorflow:global_step/sec: 103.296\n", "INFO:tensorflow:step = 376106, loss = 0.347039\n", "INFO:tensorflow:global_step/sec: 109.961\n", "INFO:tensorflow:step = 376206, loss = 0.421514\n", "INFO:tensorflow:global_step/sec: 110.405\n", "INFO:tensorflow:step = 376306, loss = 0.483162\n", "INFO:tensorflow:global_step/sec: 103.134\n", "INFO:tensorflow:step = 376406, loss = 0.383999\n", "INFO:tensorflow:global_step/sec: 77.6414\n", "INFO:tensorflow:step = 376506, loss = 0.54582\n", "INFO:tensorflow:global_step/sec: 100.202\n", "INFO:tensorflow:step = 376606, loss = 0.579292\n", "INFO:tensorflow:global_step/sec: 100.626\n", "INFO:tensorflow:step = 376706, loss = 0.441035\n", "INFO:tensorflow:global_step/sec: 110.825\n", "INFO:tensorflow:step = 376806, loss = 0.823949\n", "INFO:tensorflow:global_step/sec: 115.409\n", "INFO:tensorflow:step = 376906, loss = 0.505792\n", "INFO:tensorflow:global_step/sec: 114.968\n", "INFO:tensorflow:step = 377006, loss = 0.626471\n", "INFO:tensorflow:global_step/sec: 114.395\n", "INFO:tensorflow:step = 377106, loss = 0.327142\n", "INFO:tensorflow:global_step/sec: 116.406\n", "INFO:tensorflow:step = 377206, loss = 0.775088\n", "INFO:tensorflow:global_step/sec: 118.156\n", "INFO:tensorflow:step = 377306, loss = 0.597418\n", "INFO:tensorflow:global_step/sec: 118.713\n", "INFO:tensorflow:step = 377406, loss = 0.576139\n", "INFO:tensorflow:global_step/sec: 119.591\n", "INFO:tensorflow:step = 377506, loss = 0.378929\n", "INFO:tensorflow:global_step/sec: 100.406\n", "INFO:tensorflow:step = 377606, loss = 0.414595\n", "INFO:tensorflow:global_step/sec: 88.6105\n", "INFO:tensorflow:step = 377706, loss = 0.484988\n", "INFO:tensorflow:global_step/sec: 77.5634\n", "INFO:tensorflow:step = 377806, loss = 0.331551\n", "INFO:tensorflow:global_step/sec: 117.024\n", "INFO:tensorflow:step = 377906, loss = 0.389457\n", "INFO:tensorflow:global_step/sec: 120.076\n", "INFO:tensorflow:step = 378006, loss = 0.536181\n", "INFO:tensorflow:global_step/sec: 116.956\n", "INFO:tensorflow:step = 378106, loss = 0.537166\n", "INFO:tensorflow:global_step/sec: 112.007\n", "INFO:tensorflow:step = 378206, loss = 0.524786\n", "INFO:tensorflow:global_step/sec: 119\n", "INFO:tensorflow:step = 378306, loss = 0.528516\n", "INFO:tensorflow:global_step/sec: 119.695\n", "INFO:tensorflow:step = 378406, loss = 0.435281\n", "INFO:tensorflow:global_step/sec: 119.095\n", "INFO:tensorflow:step = 378506, loss = 0.565628\n", "INFO:tensorflow:global_step/sec: 118.008\n", "INFO:tensorflow:step = 378606, loss = 0.437732\n", "INFO:tensorflow:global_step/sec: 119.066\n", "INFO:tensorflow:step = 378706, loss = 0.476584\n", "INFO:tensorflow:global_step/sec: 112.978\n", "INFO:tensorflow:step = 378806, loss = 0.45328\n", "INFO:tensorflow:global_step/sec: 118.528\n", "INFO:tensorflow:step = 378906, loss = 0.720383\n", "INFO:tensorflow:global_step/sec: 120.037\n", "INFO:tensorflow:step = 379006, loss = 0.490189\n", "INFO:tensorflow:global_step/sec: 119.595\n", "INFO:tensorflow:step = 379106, loss = 0.406839\n", "INFO:tensorflow:global_step/sec: 117.278\n", "INFO:tensorflow:step = 379206, loss = 0.501699\n", "INFO:tensorflow:global_step/sec: 102.947\n", "INFO:tensorflow:step = 379306, loss = 0.331875\n", "INFO:tensorflow:global_step/sec: 118.932\n", "INFO:tensorflow:step = 379406, loss = 0.478926\n", "INFO:tensorflow:global_step/sec: 116.891\n", "INFO:tensorflow:step = 379506, loss = 0.467781\n", "INFO:tensorflow:global_step/sec: 118.21\n", "INFO:tensorflow:step = 379606, loss = 0.33497\n", "INFO:tensorflow:global_step/sec: 118.543\n", "INFO:tensorflow:step = 379706, loss = 0.38063\n", "INFO:tensorflow:global_step/sec: 118.242\n", "INFO:tensorflow:step = 379806, loss = 0.456673\n", "INFO:tensorflow:global_step/sec: 105.957\n", "INFO:tensorflow:step = 379906, loss = 0.646754\n", "INFO:tensorflow:global_step/sec: 109.42\n", "INFO:tensorflow:step = 380006, loss = 0.591002\n", "INFO:tensorflow:global_step/sec: 118.723\n", "INFO:tensorflow:step = 380106, loss = 0.334872\n", "INFO:tensorflow:global_step/sec: 118.712\n", "INFO:tensorflow:step = 380206, loss = 0.490124\n", "INFO:tensorflow:global_step/sec: 116.117\n", "INFO:tensorflow:step = 380306, loss = 0.408442\n", "INFO:tensorflow:global_step/sec: 119.599\n", "INFO:tensorflow:step = 380406, loss = 0.498566\n", "INFO:tensorflow:global_step/sec: 119.957\n", "INFO:tensorflow:step = 380506, loss = 0.392774\n", "INFO:tensorflow:global_step/sec: 119.551\n", "INFO:tensorflow:step = 380606, loss = 0.529818\n", "INFO:tensorflow:global_step/sec: 119.674\n", "INFO:tensorflow:step = 380706, loss = 0.562171\n", "INFO:tensorflow:global_step/sec: 119.484\n", "INFO:tensorflow:step = 380806, loss = 0.43141\n", "INFO:tensorflow:global_step/sec: 117.855\n", "INFO:tensorflow:step = 380906, loss = 0.45203\n", "INFO:tensorflow:global_step/sec: 101.306\n", "INFO:tensorflow:step = 381006, loss = 0.448674\n", "INFO:tensorflow:global_step/sec: 72.1571\n", "INFO:tensorflow:step = 381106, loss = 0.659196\n", "INFO:tensorflow:global_step/sec: 87.1296\n", "INFO:tensorflow:step = 381206, loss = 0.351614\n", "INFO:tensorflow:global_step/sec: 101.185\n", "INFO:tensorflow:step = 381306, loss = 0.381443\n", "INFO:tensorflow:global_step/sec: 119.168\n", "INFO:tensorflow:step = 381406, loss = 0.499513\n", "INFO:tensorflow:global_step/sec: 119.037\n", "INFO:tensorflow:step = 381506, loss = 0.485337\n", "INFO:tensorflow:global_step/sec: 115.165\n", "INFO:tensorflow:step = 381606, loss = 0.47329\n", "INFO:tensorflow:global_step/sec: 118.653\n", "INFO:tensorflow:step = 381706, loss = 0.564508\n", "INFO:tensorflow:global_step/sec: 119.22\n", "INFO:tensorflow:step = 381806, loss = 0.468723\n", "INFO:tensorflow:global_step/sec: 119.308\n", "INFO:tensorflow:step = 381906, loss = 0.516377\n", "INFO:tensorflow:global_step/sec: 118.739\n", "INFO:tensorflow:step = 382006, loss = 0.42499\n", "INFO:tensorflow:global_step/sec: 118.937\n", "INFO:tensorflow:step = 382106, loss = 0.457166\n", "INFO:tensorflow:global_step/sec: 118.223\n", "INFO:tensorflow:step = 382206, loss = 0.579174\n", "INFO:tensorflow:global_step/sec: 104.848\n", "INFO:tensorflow:step = 382306, loss = 0.686682\n", "INFO:tensorflow:global_step/sec: 105.143\n", "INFO:tensorflow:step = 382406, loss = 0.515517\n", "INFO:tensorflow:global_step/sec: 100.643\n", "INFO:tensorflow:step = 382506, loss = 0.389967\n", "INFO:tensorflow:global_step/sec: 109.42\n", "INFO:tensorflow:step = 382606, loss = 0.412178\n", "INFO:tensorflow:global_step/sec: 95.8852\n", "INFO:tensorflow:step = 382706, loss = 0.44355\n", "INFO:tensorflow:global_step/sec: 96.0917\n", "INFO:tensorflow:step = 382806, loss = 0.534399\n", "INFO:tensorflow:global_step/sec: 107.697\n", "INFO:tensorflow:step = 382906, loss = 0.554399\n", "INFO:tensorflow:global_step/sec: 107.913\n", "INFO:tensorflow:step = 383006, loss = 0.490167\n", "INFO:tensorflow:global_step/sec: 89.6477\n", "INFO:tensorflow:step = 383106, loss = 0.484643\n", "INFO:tensorflow:global_step/sec: 65.8837\n", "INFO:tensorflow:step = 383206, loss = 0.383646\n", "INFO:tensorflow:global_step/sec: 47.9689\n", "INFO:tensorflow:step = 383306, loss = 0.328062\n", "INFO:tensorflow:global_step/sec: 54.4778\n", "INFO:tensorflow:step = 383406, loss = 0.472838\n", "INFO:tensorflow:global_step/sec: 34.3446\n", "INFO:tensorflow:step = 383506, loss = 0.514248\n", "INFO:tensorflow:global_step/sec: 57.7484\n", "INFO:tensorflow:step = 383606, loss = 0.590927\n", "INFO:tensorflow:global_step/sec: 29.4674\n", "INFO:tensorflow:step = 383706, loss = 0.523987\n", "INFO:tensorflow:global_step/sec: 49.6168\n", "INFO:tensorflow:step = 383806, loss = 0.574431\n", "INFO:tensorflow:global_step/sec: 55.3299\n", "INFO:tensorflow:step = 383906, loss = 0.315646\n", "INFO:tensorflow:global_step/sec: 54.5231\n", "INFO:tensorflow:step = 384006, loss = 0.400056\n", "INFO:tensorflow:global_step/sec: 50.338\n", "INFO:tensorflow:step = 384106, loss = 0.515504\n", "INFO:tensorflow:global_step/sec: 78.161\n", "INFO:tensorflow:step = 384206, loss = 0.491597\n", "INFO:tensorflow:global_step/sec: 80.2718\n", "INFO:tensorflow:step = 384306, loss = 0.377297\n", "INFO:tensorflow:global_step/sec: 82.4471\n", "INFO:tensorflow:step = 384406, loss = 0.343897\n", "INFO:tensorflow:global_step/sec: 87.8854\n", "INFO:tensorflow:step = 384506, loss = 0.393786\n", "INFO:tensorflow:global_step/sec: 82.6583\n", "INFO:tensorflow:step = 384606, loss = 0.485856\n", "INFO:tensorflow:global_step/sec: 111.961\n", "INFO:tensorflow:step = 384706, loss = 0.461298\n", "INFO:tensorflow:global_step/sec: 78.4229\n", "INFO:tensorflow:step = 384806, loss = 0.407846\n", "INFO:tensorflow:global_step/sec: 99.1838\n", "INFO:tensorflow:step = 384906, loss = 0.541162\n", "INFO:tensorflow:global_step/sec: 98.9235\n", "INFO:tensorflow:step = 385006, loss = 0.41547\n", "INFO:tensorflow:global_step/sec: 59.2678\n", "INFO:tensorflow:step = 385106, loss = 0.424858\n", "INFO:tensorflow:global_step/sec: 99.9649\n", "INFO:tensorflow:step = 385206, loss = 0.596552\n", "INFO:tensorflow:global_step/sec: 97.0856\n", "INFO:tensorflow:step = 385306, loss = 0.567831\n", "INFO:tensorflow:global_step/sec: 117.444\n", "INFO:tensorflow:step = 385406, loss = 0.364882\n", "INFO:tensorflow:global_step/sec: 108.632\n", "INFO:tensorflow:step = 385506, loss = 0.481766\n", "INFO:tensorflow:global_step/sec: 116.346\n", "INFO:tensorflow:step = 385606, loss = 0.475818\n", "INFO:tensorflow:global_step/sec: 116.285\n", "INFO:tensorflow:step = 385706, loss = 0.423154\n", "INFO:tensorflow:global_step/sec: 116.912\n", "INFO:tensorflow:step = 385806, loss = 0.567895\n", "INFO:tensorflow:global_step/sec: 108.971\n", "INFO:tensorflow:step = 385906, loss = 0.732621\n", "INFO:tensorflow:global_step/sec: 58.6307\n", "INFO:tensorflow:step = 386006, loss = 0.398121\n", "INFO:tensorflow:global_step/sec: 36.1394\n", "INFO:tensorflow:step = 386106, loss = 0.372611\n", "INFO:tensorflow:global_step/sec: 56.7783\n", "INFO:tensorflow:step = 386206, loss = 0.412003\n", "INFO:tensorflow:global_step/sec: 93.2437\n", "INFO:tensorflow:step = 386306, loss = 0.555516\n", "INFO:tensorflow:global_step/sec: 93.0212\n", "INFO:tensorflow:step = 386406, loss = 0.482065\n", "INFO:tensorflow:global_step/sec: 85.7707\n", "INFO:tensorflow:step = 386506, loss = 0.469064\n", "INFO:tensorflow:global_step/sec: 44.7564\n", "INFO:tensorflow:step = 386606, loss = 0.481325\n", "INFO:tensorflow:global_step/sec: 107.685\n", "INFO:tensorflow:step = 386706, loss = 0.400064\n", "INFO:tensorflow:global_step/sec: 117.671\n", "INFO:tensorflow:step = 386806, loss = 0.730189\n", "INFO:tensorflow:global_step/sec: 98.0446\n", "INFO:tensorflow:step = 386906, loss = 0.472098\n", "INFO:tensorflow:global_step/sec: 109.901\n", "INFO:tensorflow:step = 387006, loss = 0.45731\n", "INFO:tensorflow:global_step/sec: 109.564\n", "INFO:tensorflow:step = 387106, loss = 0.528994\n", "INFO:tensorflow:global_step/sec: 116.445\n", "INFO:tensorflow:step = 387206, loss = 0.499117\n", "INFO:tensorflow:global_step/sec: 89.8502\n", "INFO:tensorflow:step = 387306, loss = 0.324342\n", "INFO:tensorflow:global_step/sec: 84.1684\n", "INFO:tensorflow:step = 387406, loss = 0.694533\n", "INFO:tensorflow:global_step/sec: 81.6077\n", "INFO:tensorflow:step = 387506, loss = 0.388515\n", "INFO:tensorflow:global_step/sec: 78.8264\n", "INFO:tensorflow:step = 387606, loss = 0.465186\n", "INFO:tensorflow:global_step/sec: 53.7486\n", "INFO:tensorflow:step = 387706, loss = 0.525449\n", "INFO:tensorflow:global_step/sec: 50.5706\n", "INFO:tensorflow:step = 387806, loss = 0.384899\n", "INFO:tensorflow:global_step/sec: 58.9592\n", "INFO:tensorflow:step = 387906, loss = 0.364296\n", "INFO:tensorflow:global_step/sec: 68.8129\n", "INFO:tensorflow:step = 388006, loss = 0.441541\n", "INFO:tensorflow:global_step/sec: 57.4465\n", "INFO:tensorflow:step = 388106, loss = 0.500398\n", "INFO:tensorflow:global_step/sec: 46.4508\n", "INFO:tensorflow:step = 388206, loss = 0.398707\n", "INFO:tensorflow:global_step/sec: 75.4235\n", "INFO:tensorflow:step = 388306, loss = 0.605762\n", "INFO:tensorflow:global_step/sec: 52.463\n", "INFO:tensorflow:step = 388406, loss = 0.478451\n", "INFO:tensorflow:global_step/sec: 55.3954\n", "INFO:tensorflow:step = 388506, loss = 0.382462\n", "INFO:tensorflow:global_step/sec: 38.8498\n", "INFO:tensorflow:step = 388606, loss = 0.595269\n", "INFO:tensorflow:global_step/sec: 75.6649\n", "INFO:tensorflow:step = 388706, loss = 0.257353\n", "INFO:tensorflow:global_step/sec: 85.5813\n", "INFO:tensorflow:step = 388806, loss = 0.393585\n", "INFO:tensorflow:global_step/sec: 81.0885\n", "INFO:tensorflow:step = 388906, loss = 0.412343\n", "INFO:tensorflow:global_step/sec: 68.8203\n", "INFO:tensorflow:step = 389006, loss = 0.503414\n", "INFO:tensorflow:global_step/sec: 76.7401\n", "INFO:tensorflow:step = 389106, loss = 0.489611\n", "INFO:tensorflow:global_step/sec: 116.1\n", "INFO:tensorflow:step = 389206, loss = 0.399781\n", "INFO:tensorflow:global_step/sec: 110.076\n", "INFO:tensorflow:step = 389306, loss = 0.461814\n", "INFO:tensorflow:global_step/sec: 70.7897\n", "INFO:tensorflow:step = 389406, loss = 0.504079\n", "INFO:tensorflow:global_step/sec: 70.274\n", "INFO:tensorflow:step = 389506, loss = 0.451159\n", "INFO:tensorflow:global_step/sec: 66.6445\n", "INFO:tensorflow:step = 389606, loss = 0.572699\n", "INFO:tensorflow:global_step/sec: 76.5977\n", "INFO:tensorflow:step = 389706, loss = 0.486634\n", "INFO:tensorflow:global_step/sec: 91.7861\n", "INFO:tensorflow:step = 389806, loss = 0.439432\n", "INFO:tensorflow:global_step/sec: 94.7557\n", "INFO:tensorflow:step = 389906, loss = 0.417397\n", "INFO:tensorflow:global_step/sec: 110.992\n", "INFO:tensorflow:step = 390006, loss = 0.378733\n", "INFO:tensorflow:global_step/sec: 103.646\n", "INFO:tensorflow:step = 390106, loss = 0.5153\n", "INFO:tensorflow:global_step/sec: 117.597\n", "INFO:tensorflow:step = 390206, loss = 0.456069\n", "INFO:tensorflow:global_step/sec: 106.083\n", "INFO:tensorflow:step = 390306, loss = 0.447019\n", "INFO:tensorflow:global_step/sec: 110.825\n", "INFO:tensorflow:step = 390406, loss = 0.521564\n", "INFO:tensorflow:global_step/sec: 110.191\n", "INFO:tensorflow:step = 390506, loss = 0.524356\n", "INFO:tensorflow:global_step/sec: 85.4958\n", "INFO:tensorflow:step = 390606, loss = 0.322122\n", "INFO:tensorflow:global_step/sec: 66.4122\n", "INFO:tensorflow:step = 390706, loss = 0.400309\n", "INFO:tensorflow:global_step/sec: 59.075\n", "INFO:tensorflow:step = 390806, loss = 0.487829\n", "INFO:tensorflow:global_step/sec: 76.3594\n", "INFO:tensorflow:step = 390906, loss = 0.703757\n", "INFO:tensorflow:global_step/sec: 92.3058\n", "INFO:tensorflow:step = 391006, loss = 0.382804\n", "INFO:tensorflow:global_step/sec: 92.6568\n", "INFO:tensorflow:step = 391106, loss = 0.566826\n", "INFO:tensorflow:Saving checkpoints for 391144 into .opt_logs/lstm_stock/model.ckpt.\n", "INFO:tensorflow:global_step/sec: 7.98702\n", "INFO:tensorflow:step = 391206, loss = 0.434566\n", "INFO:tensorflow:global_step/sec: 66.808\n", "INFO:tensorflow:step = 391306, loss = 0.536504\n", "INFO:tensorflow:global_step/sec: 62.853\n", "INFO:tensorflow:step = 391406, loss = 0.364899\n", "INFO:tensorflow:global_step/sec: 58.5329\n", "INFO:tensorflow:step = 391506, loss = 0.470208\n", "INFO:tensorflow:global_step/sec: 95.6628\n", "INFO:tensorflow:step = 391606, loss = 0.332662\n", "INFO:tensorflow:global_step/sec: 111.603\n", "INFO:tensorflow:step = 391706, loss = 0.49798\n", "INFO:tensorflow:global_step/sec: 110.665\n", "INFO:tensorflow:step = 391806, loss = 0.498388\n", "INFO:tensorflow:global_step/sec: 114.772\n", "INFO:tensorflow:step = 391906, loss = 0.436898\n", "INFO:tensorflow:global_step/sec: 114.465\n", "INFO:tensorflow:step = 392006, loss = 0.673943\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Estimator is decoupled from Scikit Learn interface by moving into\n", "separate class SKCompat. Arguments x, y and batch_size are only\n", "available in the SKCompat class, Estimator will only accept input_fn.\n", "Example conversion:\n", " est = Estimator(...) -> est = SKCompat(Estimator(...))\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/monitors.py:712: calling BaseEstimator.evaluate (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Estimator is decoupled from Scikit Learn interface by moving into\n", "separate class SKCompat. Arguments x, y and batch_size are only\n", "available in the SKCompat class, Estimator will only accept input_fn.\n", "Example conversion:\n", " est = Estimator(...) -> est = SKCompat(Estimator(...))\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.mean_squared_error instead.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:530: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.compute_weighted_loss instead.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.add_loss instead.\n", "INFO:tensorflow:Starting evaluation at 2017-04-09-09:31:35\n", "INFO:tensorflow:Finished evaluation at 2017-04-09-09:31:39\n", "INFO:tensorflow:Saving dict for global step 391144: global_step = 391144, loss = 0.208015\n", "WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32.\n", "INFO:tensorflow:Validation (step 392006): loss = 0.208015, global_step = 391144\n", "INFO:tensorflow:global_step/sec: 7.39509\n", "INFO:tensorflow:step = 392106, loss = 0.335708\n", "INFO:tensorflow:global_step/sec: 116.441\n", "INFO:tensorflow:step = 392206, loss = 0.382963\n", "INFO:tensorflow:global_step/sec: 115.807\n", "INFO:tensorflow:step = 392306, loss = 0.537882\n", "INFO:tensorflow:global_step/sec: 115.245\n", "INFO:tensorflow:step = 392406, loss = 0.625242\n", "INFO:tensorflow:global_step/sec: 117.556\n", "INFO:tensorflow:step = 392506, loss = 0.366225\n", "INFO:tensorflow:global_step/sec: 105.651\n", "INFO:tensorflow:step = 392606, loss = 0.344535\n", "INFO:tensorflow:global_step/sec: 96.672\n", "INFO:tensorflow:step = 392706, loss = 0.490773\n", "INFO:tensorflow:global_step/sec: 74.9781\n", "INFO:tensorflow:step = 392806, loss = 0.611885\n", "INFO:tensorflow:global_step/sec: 43.7497\n", "INFO:tensorflow:step = 392906, loss = 0.436357\n", "INFO:tensorflow:global_step/sec: 79.2152\n", "INFO:tensorflow:step = 393006, loss = 0.482824\n", "INFO:tensorflow:global_step/sec: 116.383\n", "INFO:tensorflow:step = 393106, loss = 0.486694\n", "INFO:tensorflow:global_step/sec: 108.878\n", "INFO:tensorflow:step = 393206, loss = 0.627389\n", "INFO:tensorflow:global_step/sec: 98.1852\n", "INFO:tensorflow:step = 393306, loss = 0.591142\n", "INFO:tensorflow:global_step/sec: 117.282\n", "INFO:tensorflow:step = 393406, loss = 0.429641\n", "INFO:tensorflow:global_step/sec: 115.905\n", "INFO:tensorflow:step = 393506, loss = 0.485474\n", "INFO:tensorflow:global_step/sec: 103.198\n", "INFO:tensorflow:step = 393606, loss = 0.415822\n", "INFO:tensorflow:global_step/sec: 111.503\n", "INFO:tensorflow:step = 393706, loss = 0.504345\n", "INFO:tensorflow:global_step/sec: 64.8767\n", "INFO:tensorflow:step = 393806, loss = 0.743686\n", "INFO:tensorflow:global_step/sec: 49.6055\n", "INFO:tensorflow:step = 393906, loss = 0.453747\n", "INFO:tensorflow:global_step/sec: 50.937\n", "INFO:tensorflow:step = 394006, loss = 0.683122\n", "INFO:tensorflow:global_step/sec: 53.9478\n", "INFO:tensorflow:step = 394106, loss = 0.418972\n", "INFO:tensorflow:global_step/sec: 39.0643\n", "INFO:tensorflow:step = 394206, loss = 0.492652\n", "INFO:tensorflow:global_step/sec: 48.5584\n", "INFO:tensorflow:step = 394306, loss = 0.467116\n", "INFO:tensorflow:global_step/sec: 73.1647\n", "INFO:tensorflow:step = 394406, loss = 0.550699\n", "INFO:tensorflow:global_step/sec: 67.8354\n", "INFO:tensorflow:step = 394506, loss = 0.534082\n", "INFO:tensorflow:global_step/sec: 99.4983\n", "INFO:tensorflow:step = 394606, loss = 0.370517\n", "INFO:tensorflow:global_step/sec: 83.7151\n", "INFO:tensorflow:step = 394706, loss = 0.300084\n", "INFO:tensorflow:global_step/sec: 105.824\n", "INFO:tensorflow:step = 394806, loss = 0.473405\n", "INFO:tensorflow:global_step/sec: 50.1923\n", "INFO:tensorflow:step = 394906, loss = 0.32583\n", "INFO:tensorflow:global_step/sec: 110.751\n", "INFO:tensorflow:step = 395006, loss = 0.559909\n", "INFO:tensorflow:global_step/sec: 111.81\n", "INFO:tensorflow:step = 395106, loss = 0.560547\n", "INFO:tensorflow:global_step/sec: 94.0539\n", "INFO:tensorflow:step = 395206, loss = 0.310815\n", "INFO:tensorflow:global_step/sec: 84.3184\n", "INFO:tensorflow:step = 395306, loss = 0.438703\n", "INFO:tensorflow:global_step/sec: 105.287\n", "INFO:tensorflow:step = 395406, loss = 0.60348\n", "INFO:tensorflow:global_step/sec: 95.8173\n", "INFO:tensorflow:step = 395506, loss = 0.5214\n", "INFO:tensorflow:global_step/sec: 96.7383\n", "INFO:tensorflow:step = 395606, loss = 0.428374\n", "INFO:tensorflow:global_step/sec: 95.5123\n", "INFO:tensorflow:step = 395706, loss = 0.403604\n", "INFO:tensorflow:global_step/sec: 108.802\n", "INFO:tensorflow:step = 395806, loss = 0.480228\n", "INFO:tensorflow:global_step/sec: 61.61\n", "INFO:tensorflow:step = 395906, loss = 0.578693\n", "INFO:tensorflow:global_step/sec: 51.7796\n", "INFO:tensorflow:step = 396006, loss = 0.445174\n", "INFO:tensorflow:global_step/sec: 42.6104\n", "INFO:tensorflow:step = 396106, loss = 0.52254\n", "INFO:tensorflow:global_step/sec: 54.1196\n", "INFO:tensorflow:step = 396206, loss = 0.893658\n", "INFO:tensorflow:global_step/sec: 70.1982\n", "INFO:tensorflow:step = 396306, loss = 0.538302\n", "INFO:tensorflow:global_step/sec: 85.9776\n", "INFO:tensorflow:step = 396406, loss = 0.449723\n", "INFO:tensorflow:global_step/sec: 48.8699\n", "INFO:tensorflow:step = 396506, loss = 0.643234\n", "INFO:tensorflow:global_step/sec: 61.8005\n", "INFO:tensorflow:step = 396606, loss = 0.293744\n", "INFO:tensorflow:global_step/sec: 69.8891\n", "INFO:tensorflow:step = 396706, loss = 0.4705\n", "INFO:tensorflow:global_step/sec: 88.0424\n", "INFO:tensorflow:step = 396806, loss = 0.507166\n", "INFO:tensorflow:global_step/sec: 110.043\n", "INFO:tensorflow:step = 396906, loss = 0.395285\n", "INFO:tensorflow:global_step/sec: 113.784\n", "INFO:tensorflow:step = 397006, loss = 0.417568\n", "INFO:tensorflow:global_step/sec: 117.253\n", "INFO:tensorflow:step = 397106, loss = 0.485271\n", "INFO:tensorflow:global_step/sec: 68.3422\n", "INFO:tensorflow:step = 397206, loss = 0.531123\n", "INFO:tensorflow:global_step/sec: 55.926\n", "INFO:tensorflow:step = 397306, loss = 0.370861\n", "INFO:tensorflow:global_step/sec: 95.0851\n", "INFO:tensorflow:step = 397406, loss = 0.486647\n", "INFO:tensorflow:global_step/sec: 92.3384\n", "INFO:tensorflow:step = 397506, loss = 0.5503\n", "INFO:tensorflow:global_step/sec: 98.4269\n", "INFO:tensorflow:step = 397606, loss = 0.555602\n", "INFO:tensorflow:global_step/sec: 104.588\n", "INFO:tensorflow:step = 397706, loss = 0.523503\n", "INFO:tensorflow:global_step/sec: 117.354\n", "INFO:tensorflow:step = 397806, loss = 0.401698\n", "INFO:tensorflow:global_step/sec: 112.943\n", "INFO:tensorflow:step = 397906, loss = 0.53062\n", "INFO:tensorflow:global_step/sec: 97.0996\n", "INFO:tensorflow:step = 398006, loss = 0.515456\n", "INFO:tensorflow:global_step/sec: 83.9036\n", "INFO:tensorflow:step = 398106, loss = 0.598588\n", "INFO:tensorflow:global_step/sec: 65.7725\n", "INFO:tensorflow:step = 398206, loss = 0.468295\n", "INFO:tensorflow:global_step/sec: 103.088\n", "INFO:tensorflow:step = 398306, loss = 0.391292\n", "INFO:tensorflow:global_step/sec: 117.075\n", "INFO:tensorflow:step = 398406, loss = 0.478551\n", "INFO:tensorflow:global_step/sec: 108.62\n", "INFO:tensorflow:step = 398506, loss = 0.454265\n", "INFO:tensorflow:global_step/sec: 89.2762\n", "INFO:tensorflow:step = 398606, loss = 0.364046\n", "INFO:tensorflow:global_step/sec: 78.8166\n", "INFO:tensorflow:step = 398706, loss = 0.443109\n", "INFO:tensorflow:global_step/sec: 95.8935\n", "INFO:tensorflow:step = 398806, loss = 0.420566\n", "INFO:tensorflow:global_step/sec: 77.8957\n", "INFO:tensorflow:step = 398906, loss = 0.471803\n", "INFO:tensorflow:global_step/sec: 113.086\n", "INFO:tensorflow:step = 399006, loss = 0.411357\n", "INFO:tensorflow:global_step/sec: 88.8211\n", "INFO:tensorflow:step = 399106, loss = 0.476511\n", "INFO:tensorflow:global_step/sec: 103.429\n", "INFO:tensorflow:step = 399206, loss = 0.510633\n", "INFO:tensorflow:global_step/sec: 109.615\n", "INFO:tensorflow:step = 399306, loss = 0.28937\n", "INFO:tensorflow:global_step/sec: 118.717\n", "INFO:tensorflow:step = 399406, loss = 0.503807\n", "INFO:tensorflow:global_step/sec: 117.958\n", "INFO:tensorflow:step = 399506, loss = 0.529118\n", "INFO:tensorflow:global_step/sec: 116.352\n", "INFO:tensorflow:step = 399606, loss = 0.441333\n", "INFO:tensorflow:global_step/sec: 116.639\n", "INFO:tensorflow:step = 399706, loss = 0.646827\n", "INFO:tensorflow:global_step/sec: 112.972\n", "INFO:tensorflow:step = 399806, loss = 0.391756\n", "INFO:tensorflow:global_step/sec: 105.006\n", "INFO:tensorflow:step = 399906, loss = 0.437857\n", "INFO:tensorflow:global_step/sec: 97.54\n", "INFO:tensorflow:step = 400006, loss = 0.400544\n", "INFO:tensorflow:global_step/sec: 106.825\n", "INFO:tensorflow:step = 400106, loss = 0.346333\n", "INFO:tensorflow:global_step/sec: 116.733\n", "INFO:tensorflow:step = 400206, loss = 0.463668\n", "INFO:tensorflow:global_step/sec: 106.663\n", "INFO:tensorflow:step = 400306, loss = 0.532467\n", "INFO:tensorflow:global_step/sec: 92.3885\n", "INFO:tensorflow:step = 400406, loss = 0.494708\n", "INFO:tensorflow:global_step/sec: 112.41\n", "INFO:tensorflow:step = 400506, loss = 0.621454\n", "INFO:tensorflow:global_step/sec: 75.6945\n", "INFO:tensorflow:step = 400606, loss = 0.44368\n", "INFO:tensorflow:global_step/sec: 91.8458\n", "INFO:tensorflow:step = 400706, loss = 0.670259\n", "INFO:tensorflow:global_step/sec: 102.374\n", "INFO:tensorflow:step = 400806, loss = 0.454248\n", "INFO:tensorflow:global_step/sec: 68.8062\n", "INFO:tensorflow:step = 400906, loss = 0.461421\n", "INFO:tensorflow:global_step/sec: 102.718\n", "INFO:tensorflow:step = 401006, loss = 0.503687\n", "INFO:tensorflow:global_step/sec: 107.911\n", "INFO:tensorflow:step = 401106, loss = 0.449828\n", "INFO:tensorflow:global_step/sec: 79.5957\n", "INFO:tensorflow:step = 401206, loss = 0.51874\n", "INFO:tensorflow:global_step/sec: 77.123\n", "INFO:tensorflow:step = 401306, loss = 0.364971\n", "INFO:tensorflow:global_step/sec: 99.2958\n", "INFO:tensorflow:step = 401406, loss = 0.472288\n", "INFO:tensorflow:global_step/sec: 97.944\n", "INFO:tensorflow:step = 401506, loss = 0.431314\n", "INFO:tensorflow:global_step/sec: 83.0777\n", "INFO:tensorflow:step = 401606, loss = 0.45232\n", "INFO:tensorflow:global_step/sec: 93.9801\n", "INFO:tensorflow:step = 401706, loss = 0.434075\n", "INFO:tensorflow:global_step/sec: 51.2784\n", "INFO:tensorflow:step = 401806, loss = 0.422774\n", "INFO:tensorflow:global_step/sec: 54.0319\n", "INFO:tensorflow:step = 401906, loss = 0.487717\n", "INFO:tensorflow:global_step/sec: 69.1924\n", "INFO:tensorflow:step = 402006, loss = 0.487424\n", "INFO:tensorflow:global_step/sec: 117.124\n", "INFO:tensorflow:step = 402106, loss = 0.305266\n", "INFO:tensorflow:global_step/sec: 98.951\n", "INFO:tensorflow:step = 402206, loss = 0.370469\n", "INFO:tensorflow:global_step/sec: 116.988\n", "INFO:tensorflow:step = 402306, loss = 0.569091\n", "INFO:tensorflow:global_step/sec: 88.7954\n", "INFO:tensorflow:step = 402406, loss = 0.500529\n", "INFO:tensorflow:global_step/sec: 107.199\n", "INFO:tensorflow:step = 402506, loss = 0.436976\n", "INFO:tensorflow:global_step/sec: 78.8449\n", "INFO:tensorflow:step = 402606, loss = 0.548398\n", "INFO:tensorflow:global_step/sec: 81.5959\n", "INFO:tensorflow:step = 402706, loss = 0.688496\n", "INFO:tensorflow:global_step/sec: 64.0378\n", "INFO:tensorflow:step = 402806, loss = 0.404446\n", "INFO:tensorflow:global_step/sec: 54.1595\n", "INFO:tensorflow:step = 402906, loss = 0.420469\n", "INFO:tensorflow:global_step/sec: 92.4421\n", "INFO:tensorflow:step = 403006, loss = 0.413106\n", "INFO:tensorflow:global_step/sec: 94.7454\n", "INFO:tensorflow:step = 403106, loss = 0.419622\n", "INFO:tensorflow:global_step/sec: 110.63\n", "INFO:tensorflow:step = 403206, loss = 0.481133\n", "INFO:tensorflow:global_step/sec: 113.034\n", "INFO:tensorflow:step = 403306, loss = 0.608908\n", "INFO:tensorflow:global_step/sec: 108.557\n", "INFO:tensorflow:step = 403406, loss = 0.68986\n", "INFO:tensorflow:global_step/sec: 82.5319\n", "INFO:tensorflow:step = 403506, loss = 0.363275\n", "INFO:tensorflow:global_step/sec: 97.8894\n", "INFO:tensorflow:step = 403606, loss = 0.623024\n", "INFO:tensorflow:global_step/sec: 104.592\n", "INFO:tensorflow:step = 403706, loss = 0.469535\n", "INFO:tensorflow:global_step/sec: 104.857\n", "INFO:tensorflow:step = 403806, loss = 0.461719\n", "INFO:tensorflow:global_step/sec: 107.689\n", "INFO:tensorflow:step = 403906, loss = 0.594725\n", "INFO:tensorflow:global_step/sec: 75.5144\n", "INFO:tensorflow:step = 404006, loss = 0.331376\n", "INFO:tensorflow:global_step/sec: 53.273\n", "INFO:tensorflow:step = 404106, loss = 0.416641\n", "INFO:tensorflow:global_step/sec: 72.0402\n", "INFO:tensorflow:step = 404206, loss = 0.570839\n", "INFO:tensorflow:global_step/sec: 77.6376\n", "INFO:tensorflow:step = 404306, loss = 0.537538\n", "INFO:tensorflow:global_step/sec: 85.6715\n", "INFO:tensorflow:step = 404406, loss = 0.337554\n", "INFO:tensorflow:global_step/sec: 85.2359\n", "INFO:tensorflow:step = 404506, loss = 0.482005\n", "INFO:tensorflow:global_step/sec: 96.9699\n", "INFO:tensorflow:step = 404606, loss = 0.478884\n", "INFO:tensorflow:global_step/sec: 113.991\n", "INFO:tensorflow:step = 404706, loss = 0.403557\n", "INFO:tensorflow:global_step/sec: 94.4143\n", "INFO:tensorflow:step = 404806, loss = 0.432545\n", "INFO:tensorflow:global_step/sec: 70.7315\n", "INFO:tensorflow:step = 404906, loss = 0.370167\n", "INFO:tensorflow:global_step/sec: 112.589\n", "INFO:tensorflow:step = 405006, loss = 0.385547\n", "INFO:tensorflow:global_step/sec: 116.171\n", "INFO:tensorflow:step = 405106, loss = 0.458629\n", "INFO:tensorflow:global_step/sec: 69.1668\n", "INFO:tensorflow:step = 405206, loss = 0.411348\n", "INFO:tensorflow:global_step/sec: 62.4484\n", "INFO:tensorflow:step = 405306, loss = 0.416474\n", "INFO:tensorflow:global_step/sec: 80.2474\n", "INFO:tensorflow:step = 405406, loss = 0.510911\n", "INFO:tensorflow:global_step/sec: 116.595\n", "INFO:tensorflow:step = 405506, loss = 0.586084\n", "INFO:tensorflow:global_step/sec: 117.157\n", "INFO:tensorflow:step = 405606, loss = 0.340949\n", "INFO:tensorflow:global_step/sec: 117.219\n", "INFO:tensorflow:step = 405706, loss = 0.571199\n", "INFO:tensorflow:global_step/sec: 117.554\n", "INFO:tensorflow:step = 405806, loss = 0.341207\n", "INFO:tensorflow:global_step/sec: 104.983\n", "INFO:tensorflow:step = 405906, loss = 0.401108\n", "INFO:tensorflow:global_step/sec: 117.361\n", "INFO:tensorflow:step = 406006, loss = 0.47326\n", "INFO:tensorflow:global_step/sec: 116.317\n", "INFO:tensorflow:step = 406106, loss = 0.386925\n", "INFO:tensorflow:global_step/sec: 115.099\n", "INFO:tensorflow:step = 406206, loss = 0.301503\n", "INFO:tensorflow:global_step/sec: 107.705\n", "INFO:tensorflow:step = 406306, loss = 0.592171\n", "INFO:tensorflow:global_step/sec: 101.851\n", "INFO:tensorflow:step = 406406, loss = 0.385737\n", "INFO:tensorflow:global_step/sec: 107.749\n", "INFO:tensorflow:step = 406506, loss = 0.414853\n", "INFO:tensorflow:global_step/sec: 109.035\n", "INFO:tensorflow:step = 406606, loss = 0.487771\n", "INFO:tensorflow:global_step/sec: 95.694\n", "INFO:tensorflow:step = 406706, loss = 0.422073\n", "INFO:tensorflow:global_step/sec: 96.7356\n", "INFO:tensorflow:step = 406806, loss = 0.463222\n", "INFO:tensorflow:global_step/sec: 114.436\n", "INFO:tensorflow:step = 406906, loss = 0.350539\n", "INFO:tensorflow:global_step/sec: 90.1783\n", "INFO:tensorflow:step = 407006, loss = 0.361962\n", "INFO:tensorflow:global_step/sec: 103.503\n", "INFO:tensorflow:step = 407106, loss = 0.540805\n", "INFO:tensorflow:global_step/sec: 117.848\n", "INFO:tensorflow:step = 407206, loss = 0.474398\n", "INFO:tensorflow:global_step/sec: 115.648\n", "INFO:tensorflow:step = 407306, loss = 0.329449\n", "INFO:tensorflow:global_step/sec: 81.9448\n", "INFO:tensorflow:step = 407406, loss = 0.370165\n", "INFO:tensorflow:global_step/sec: 70.1046\n", "INFO:tensorflow:step = 407506, loss = 0.300879\n", "INFO:tensorflow:global_step/sec: 64.5091\n", "INFO:tensorflow:step = 407606, loss = 0.426417\n", "INFO:tensorflow:global_step/sec: 81.7825\n", "INFO:tensorflow:step = 407706, loss = 0.60729\n", "INFO:tensorflow:global_step/sec: 87.0292\n", "INFO:tensorflow:step = 407806, loss = 0.614959\n", "INFO:tensorflow:global_step/sec: 105.655\n", "INFO:tensorflow:step = 407906, loss = 0.393347\n", "INFO:tensorflow:global_step/sec: 114.245\n", "INFO:tensorflow:step = 408006, loss = 0.523645\n", "INFO:tensorflow:global_step/sec: 113.42\n", "INFO:tensorflow:step = 408106, loss = 0.5615\n", "INFO:tensorflow:global_step/sec: 95.7717\n", "INFO:tensorflow:step = 408206, loss = 0.44691\n", "INFO:tensorflow:global_step/sec: 119.278\n", "INFO:tensorflow:step = 408306, loss = 0.467671\n", "INFO:tensorflow:global_step/sec: 113.609\n", "INFO:tensorflow:step = 408406, loss = 0.649268\n", "INFO:tensorflow:global_step/sec: 117.366\n", "INFO:tensorflow:step = 408506, loss = 0.485989\n", "INFO:tensorflow:global_step/sec: 105.394\n", "INFO:tensorflow:step = 408606, loss = 0.33541\n", "INFO:tensorflow:global_step/sec: 92.5111\n", "INFO:tensorflow:step = 408706, loss = 0.523931\n", "INFO:tensorflow:global_step/sec: 90.5399\n", "INFO:tensorflow:step = 408806, loss = 0.61181\n", "INFO:tensorflow:global_step/sec: 102.912\n", "INFO:tensorflow:step = 408906, loss = 0.510409\n", "INFO:tensorflow:global_step/sec: 91.5863\n", "INFO:tensorflow:step = 409006, loss = 0.577686\n", "INFO:tensorflow:global_step/sec: 106.806\n", "INFO:tensorflow:step = 409106, loss = 0.36276\n", "INFO:tensorflow:global_step/sec: 77.5078\n", "INFO:tensorflow:step = 409206, loss = 0.564566\n", "INFO:tensorflow:global_step/sec: 96.8325\n", "INFO:tensorflow:step = 409306, loss = 0.537525\n", "INFO:tensorflow:global_step/sec: 118.221\n", "INFO:tensorflow:step = 409406, loss = 0.417209\n", "INFO:tensorflow:global_step/sec: 116.394\n", "INFO:tensorflow:step = 409506, loss = 0.414354\n", "INFO:tensorflow:global_step/sec: 117.242\n", "INFO:tensorflow:step = 409606, loss = 0.51769\n", "INFO:tensorflow:global_step/sec: 102.434\n", "INFO:tensorflow:step = 409706, loss = 0.535554\n", "INFO:tensorflow:global_step/sec: 112.236\n", "INFO:tensorflow:step = 409806, loss = 0.477071\n", "INFO:tensorflow:global_step/sec: 115.686\n", "INFO:tensorflow:step = 409906, loss = 0.493522\n", "INFO:tensorflow:global_step/sec: 78.6793\n", "INFO:tensorflow:step = 410006, loss = 0.573935\n", "INFO:tensorflow:global_step/sec: 70.8052\n", "INFO:tensorflow:step = 410106, loss = 0.549597\n", "INFO:tensorflow:global_step/sec: 99.7808\n", "INFO:tensorflow:step = 410206, loss = 0.434716\n", "INFO:tensorflow:global_step/sec: 115.125\n", "INFO:tensorflow:step = 410306, loss = 0.399273\n", "INFO:tensorflow:global_step/sec: 114.613\n", "INFO:tensorflow:step = 410406, loss = 0.570051\n", "INFO:tensorflow:global_step/sec: 105.547\n", "INFO:tensorflow:step = 410506, loss = 0.57195\n", "INFO:tensorflow:global_step/sec: 103.57\n", "INFO:tensorflow:step = 410606, loss = 0.402485\n", "INFO:tensorflow:global_step/sec: 101.925\n", "INFO:tensorflow:step = 410706, loss = 0.337177\n", "INFO:tensorflow:global_step/sec: 79.4864\n", "INFO:tensorflow:step = 410806, loss = 0.513869\n", "INFO:tensorflow:global_step/sec: 98.6207\n", "INFO:tensorflow:step = 410906, loss = 0.429587\n", "INFO:tensorflow:global_step/sec: 63.0951\n", "INFO:tensorflow:step = 411006, loss = 0.6864\n", "INFO:tensorflow:global_step/sec: 49.6859\n", "INFO:tensorflow:step = 411106, loss = 0.325141\n", "INFO:tensorflow:global_step/sec: 88.7371\n", "INFO:tensorflow:step = 411206, loss = 0.386168\n", "INFO:tensorflow:global_step/sec: 109.987\n", "INFO:tensorflow:step = 411306, loss = 0.496252\n", "INFO:tensorflow:global_step/sec: 105.574\n", "INFO:tensorflow:step = 411406, loss = 0.494634\n", "INFO:tensorflow:global_step/sec: 75.5268\n", "INFO:tensorflow:step = 411506, loss = 0.646116\n", "INFO:tensorflow:global_step/sec: 64.003\n", "INFO:tensorflow:step = 411606, loss = 0.415556\n", "INFO:tensorflow:global_step/sec: 103.336\n", "INFO:tensorflow:step = 411706, loss = 0.296865\n", "INFO:tensorflow:global_step/sec: 108.316\n", "INFO:tensorflow:step = 411806, loss = 0.37458\n", "INFO:tensorflow:global_step/sec: 112.227\n", "INFO:tensorflow:step = 411906, loss = 0.471315\n", "INFO:tensorflow:global_step/sec: 67.5667\n", "INFO:tensorflow:step = 412006, loss = 0.34065\n", "INFO:tensorflow:global_step/sec: 62.5492\n", "INFO:tensorflow:step = 412106, loss = 0.503045\n", "INFO:tensorflow:global_step/sec: 114.934\n", "INFO:tensorflow:step = 412206, loss = 0.646014\n", "INFO:tensorflow:global_step/sec: 117.041\n", "INFO:tensorflow:step = 412306, loss = 0.561491\n", "INFO:tensorflow:global_step/sec: 115.191\n", "INFO:tensorflow:step = 412406, loss = 0.322631\n", "INFO:tensorflow:global_step/sec: 117.559\n", "INFO:tensorflow:step = 412506, loss = 0.30119\n", "INFO:tensorflow:global_step/sec: 115.87\n", "INFO:tensorflow:step = 412606, loss = 0.314966\n", "INFO:tensorflow:global_step/sec: 96.8106\n", "INFO:tensorflow:step = 412706, loss = 0.347287\n", "INFO:tensorflow:global_step/sec: 100.441\n", "INFO:tensorflow:step = 412806, loss = 0.528177\n", "INFO:tensorflow:global_step/sec: 89.0149\n", "INFO:tensorflow:step = 412906, loss = 0.392006\n", "INFO:tensorflow:global_step/sec: 103.994\n", "INFO:tensorflow:step = 413006, loss = 0.428011\n", "INFO:tensorflow:global_step/sec: 97.1763\n", "INFO:tensorflow:step = 413106, loss = 0.41557\n", "INFO:tensorflow:global_step/sec: 114.883\n", "INFO:tensorflow:step = 413206, loss = 0.434117\n", "INFO:tensorflow:global_step/sec: 114.383\n", "INFO:tensorflow:step = 413306, loss = 0.344988\n", "INFO:tensorflow:global_step/sec: 117.754\n", "INFO:tensorflow:step = 413406, loss = 0.590606\n", "INFO:tensorflow:global_step/sec: 116.803\n", "INFO:tensorflow:step = 413506, loss = 0.4598\n", "INFO:tensorflow:global_step/sec: 111.964\n", "INFO:tensorflow:step = 413606, loss = 0.439139\n", "INFO:tensorflow:global_step/sec: 116.928\n", "INFO:tensorflow:step = 413706, loss = 0.474991\n", "INFO:tensorflow:global_step/sec: 117.092\n", "INFO:tensorflow:step = 413806, loss = 0.412982\n", "INFO:tensorflow:global_step/sec: 117.556\n", "INFO:tensorflow:step = 413906, loss = 0.38924\n", "INFO:tensorflow:global_step/sec: 115.294\n", "INFO:tensorflow:step = 414006, loss = 0.412\n", "INFO:tensorflow:global_step/sec: 116.971\n", "INFO:tensorflow:step = 414106, loss = 0.538725\n", "INFO:tensorflow:global_step/sec: 99.5923\n", "INFO:tensorflow:step = 414206, loss = 0.458266\n", "INFO:tensorflow:global_step/sec: 90.8598\n", "INFO:tensorflow:step = 414306, loss = 0.489416\n", "INFO:tensorflow:global_step/sec: 109.964\n", "INFO:tensorflow:step = 414406, loss = 0.427159\n", "INFO:tensorflow:global_step/sec: 110.182\n", "INFO:tensorflow:step = 414506, loss = 0.339135\n", "INFO:tensorflow:global_step/sec: 104.89\n", "INFO:tensorflow:step = 414606, loss = 0.347388\n", "INFO:tensorflow:global_step/sec: 99.018\n", "INFO:tensorflow:step = 414706, loss = 0.574903\n", "INFO:tensorflow:global_step/sec: 87.0639\n", "INFO:tensorflow:step = 414806, loss = 0.473315\n", "INFO:tensorflow:global_step/sec: 77.5272\n", "INFO:tensorflow:step = 414906, loss = 0.401416\n", "INFO:tensorflow:global_step/sec: 117.051\n", "INFO:tensorflow:step = 415006, loss = 0.427428\n", "INFO:tensorflow:global_step/sec: 99.4964\n", "INFO:tensorflow:step = 415106, loss = 0.398215\n", "INFO:tensorflow:global_step/sec: 100.194\n", "INFO:tensorflow:step = 415206, loss = 0.434329\n", "INFO:tensorflow:global_step/sec: 53.3463\n", "INFO:tensorflow:step = 415306, loss = 0.379673\n", "INFO:tensorflow:global_step/sec: 91.8203\n", "INFO:tensorflow:step = 415406, loss = 0.594126\n", "INFO:tensorflow:global_step/sec: 86.4454\n", "INFO:tensorflow:step = 415506, loss = 0.656012\n", "INFO:tensorflow:global_step/sec: 58.4565\n", "INFO:tensorflow:step = 415606, loss = 0.646204\n", "INFO:tensorflow:global_step/sec: 48.0631\n", "INFO:tensorflow:step = 415706, loss = 0.372051\n", "INFO:tensorflow:global_step/sec: 36.4255\n", "INFO:tensorflow:step = 415806, loss = 0.724293\n", "INFO:tensorflow:global_step/sec: 61.8105\n", "INFO:tensorflow:step = 415906, loss = 0.476395\n", "INFO:tensorflow:global_step/sec: 40.4704\n", "INFO:tensorflow:step = 416006, loss = 0.573318\n", "INFO:tensorflow:global_step/sec: 63.4465\n", "INFO:tensorflow:step = 416106, loss = 0.381573\n", "INFO:tensorflow:global_step/sec: 64.0808\n", "INFO:tensorflow:step = 416206, loss = 0.526631\n", "INFO:tensorflow:global_step/sec: 71.4501\n", "INFO:tensorflow:step = 416306, loss = 0.420762\n", "INFO:tensorflow:global_step/sec: 112.961\n", "INFO:tensorflow:step = 416406, loss = 0.381024\n", "INFO:tensorflow:global_step/sec: 117.615\n", "INFO:tensorflow:step = 416506, loss = 0.436105\n", "INFO:tensorflow:global_step/sec: 117.637\n", "INFO:tensorflow:step = 416606, loss = 0.537828\n", "INFO:tensorflow:global_step/sec: 115.951\n", "INFO:tensorflow:step = 416706, loss = 0.55923\n", "INFO:tensorflow:global_step/sec: 117.829\n", "INFO:tensorflow:step = 416806, loss = 0.505462\n", "INFO:tensorflow:global_step/sec: 117.336\n", "INFO:tensorflow:step = 416906, loss = 0.53274\n", "INFO:tensorflow:global_step/sec: 108.899\n", "INFO:tensorflow:step = 417006, loss = 0.742025\n", "INFO:tensorflow:global_step/sec: 69.8829\n", "INFO:tensorflow:step = 417106, loss = 0.63275\n", "INFO:tensorflow:global_step/sec: 110.1\n", "INFO:tensorflow:step = 417206, loss = 0.380732\n", "INFO:tensorflow:global_step/sec: 113.222\n", "INFO:tensorflow:step = 417306, loss = 0.43718\n", "INFO:tensorflow:global_step/sec: 112.592\n", "INFO:tensorflow:step = 417406, loss = 0.636627\n", "INFO:tensorflow:global_step/sec: 92.5952\n", "INFO:tensorflow:step = 417506, loss = 0.484322\n", "INFO:tensorflow:global_step/sec: 100.54\n", "INFO:tensorflow:step = 417606, loss = 0.563857\n", "INFO:tensorflow:global_step/sec: 113.197\n", "INFO:tensorflow:step = 417706, loss = 0.469623\n", "INFO:tensorflow:global_step/sec: 102.781\n", "INFO:tensorflow:step = 417806, loss = 0.330349\n", "INFO:tensorflow:global_step/sec: 101.925\n", "INFO:tensorflow:step = 417906, loss = 0.436512\n", "INFO:tensorflow:global_step/sec: 107.442\n", "INFO:tensorflow:step = 418006, loss = 0.794276\n", "INFO:tensorflow:global_step/sec: 49.7978\n", "INFO:tensorflow:step = 418106, loss = 0.406383\n", "INFO:tensorflow:global_step/sec: 43.6719\n", "INFO:tensorflow:step = 418206, loss = 0.528364\n", "INFO:tensorflow:global_step/sec: 57.6107\n", "INFO:tensorflow:step = 418306, loss = 0.374951\n", "INFO:tensorflow:global_step/sec: 61.5903\n", "INFO:tensorflow:step = 418406, loss = 0.52853\n", "INFO:tensorflow:global_step/sec: 82.8972\n", "INFO:tensorflow:step = 418506, loss = 0.504747\n", "INFO:tensorflow:global_step/sec: 76.5579\n", "INFO:tensorflow:step = 418606, loss = 0.592249\n", "INFO:tensorflow:global_step/sec: 101.154\n", "INFO:tensorflow:step = 418706, loss = 0.58335\n", "INFO:tensorflow:global_step/sec: 98.8048\n", "INFO:tensorflow:step = 418806, loss = 0.342805\n", "INFO:tensorflow:global_step/sec: 95.8911\n", "INFO:tensorflow:step = 418906, loss = 0.50278\n", "INFO:tensorflow:global_step/sec: 103.199\n", "INFO:tensorflow:step = 419006, loss = 0.436523\n", "INFO:tensorflow:global_step/sec: 85.8788\n", "INFO:tensorflow:step = 419106, loss = 0.42115\n", "INFO:tensorflow:global_step/sec: 107.57\n", "INFO:tensorflow:step = 419206, loss = 0.441032\n", "INFO:tensorflow:global_step/sec: 113.916\n", "INFO:tensorflow:step = 419306, loss = 0.570843\n", "INFO:tensorflow:global_step/sec: 112.658\n", "INFO:tensorflow:step = 419406, loss = 0.430384\n", "INFO:tensorflow:global_step/sec: 114.743\n", "INFO:tensorflow:step = 419506, loss = 0.555416\n", "INFO:tensorflow:global_step/sec: 73.6488\n", "INFO:tensorflow:step = 419606, loss = 0.324258\n", "INFO:tensorflow:global_step/sec: 76.6124\n", "INFO:tensorflow:step = 419706, loss = 0.603813\n", "INFO:tensorflow:global_step/sec: 53.4647\n", "INFO:tensorflow:step = 419806, loss = 0.648136\n", "INFO:tensorflow:global_step/sec: 54.8067\n", "INFO:tensorflow:step = 419906, loss = 0.417836\n", "INFO:tensorflow:global_step/sec: 71.6612\n", "INFO:tensorflow:step = 420006, loss = 0.540526\n", "INFO:tensorflow:global_step/sec: 71.9118\n", "INFO:tensorflow:step = 420106, loss = 0.538695\n", "INFO:tensorflow:global_step/sec: 33.103\n", "INFO:tensorflow:step = 420206, loss = 0.500875\n", "INFO:tensorflow:global_step/sec: 49.8387\n", "INFO:tensorflow:step = 420306, loss = 0.280485\n", "INFO:tensorflow:global_step/sec: 48.1987\n", "INFO:tensorflow:step = 420406, loss = 0.51666\n", "INFO:tensorflow:global_step/sec: 67.5003\n", "INFO:tensorflow:step = 420506, loss = 0.478623\n", "INFO:tensorflow:global_step/sec: 96.5487\n", "INFO:tensorflow:step = 420606, loss = 0.429893\n", "INFO:tensorflow:global_step/sec: 62.7126\n", "INFO:tensorflow:step = 420706, loss = 0.549632\n", "INFO:tensorflow:global_step/sec: 45.2684\n", "INFO:tensorflow:step = 420806, loss = 0.453726\n", "INFO:tensorflow:global_step/sec: 28.7167\n", "INFO:tensorflow:step = 420906, loss = 0.638019\n", "INFO:tensorflow:global_step/sec: 52.7535\n", "INFO:tensorflow:step = 421006, loss = 0.573441\n", "INFO:tensorflow:global_step/sec: 54.8315\n", "INFO:tensorflow:step = 421106, loss = 0.461695\n", "INFO:tensorflow:global_step/sec: 54.3166\n", "INFO:tensorflow:step = 421206, loss = 0.437009\n", "INFO:tensorflow:global_step/sec: 23.3161\n", "INFO:tensorflow:step = 421306, loss = 0.49744\n", "INFO:tensorflow:global_step/sec: 26.5325\n", "INFO:tensorflow:step = 421406, loss = 0.525337\n", "INFO:tensorflow:global_step/sec: 49.163\n", "INFO:tensorflow:step = 421506, loss = 0.573535\n", "INFO:tensorflow:global_step/sec: 64.0805\n", "INFO:tensorflow:step = 421606, loss = 0.484636\n", "INFO:tensorflow:global_step/sec: 56.3936\n", "INFO:tensorflow:step = 421706, loss = 0.292208\n", "INFO:tensorflow:global_step/sec: 33.761\n", "INFO:tensorflow:step = 421806, loss = 0.510519\n", "INFO:tensorflow:global_step/sec: 20.6473\n", "INFO:tensorflow:step = 421906, loss = 0.529513\n", "INFO:tensorflow:global_step/sec: 59.4794\n", "INFO:tensorflow:step = 422006, loss = 0.471067\n", "INFO:tensorflow:global_step/sec: 75.2916\n", "INFO:tensorflow:step = 422106, loss = 0.370604\n", "INFO:tensorflow:global_step/sec: 58.8928\n", "INFO:tensorflow:step = 422206, loss = 0.594427\n", "INFO:tensorflow:global_step/sec: 87.5781\n", "INFO:tensorflow:step = 422306, loss = 0.490108\n", "INFO:tensorflow:global_step/sec: 92.6497\n", "INFO:tensorflow:step = 422406, loss = 0.409757\n", "INFO:tensorflow:global_step/sec: 107.943\n", "INFO:tensorflow:step = 422506, loss = 0.455557\n", "INFO:tensorflow:global_step/sec: 106.529\n", "INFO:tensorflow:step = 422606, loss = 0.64797\n", "INFO:tensorflow:global_step/sec: 114.23\n", "INFO:tensorflow:step = 422706, loss = 0.498126\n", "INFO:tensorflow:global_step/sec: 114.123\n", "INFO:tensorflow:step = 422806, loss = 0.503534\n", "INFO:tensorflow:global_step/sec: 115.884\n", "INFO:tensorflow:step = 422906, loss = 0.340305\n", "INFO:tensorflow:global_step/sec: 117.842\n", "INFO:tensorflow:step = 423006, loss = 0.459481\n", "INFO:tensorflow:global_step/sec: 110.014\n", "INFO:tensorflow:step = 423106, loss = 0.47673\n", "INFO:tensorflow:global_step/sec: 106.219\n", "INFO:tensorflow:step = 423206, loss = 0.582803\n", "INFO:tensorflow:global_step/sec: 112.108\n", "INFO:tensorflow:step = 423306, loss = 0.470261\n", "INFO:tensorflow:global_step/sec: 110.477\n", "INFO:tensorflow:step = 423406, loss = 0.327331\n", "INFO:tensorflow:global_step/sec: 115.742\n", "INFO:tensorflow:step = 423506, loss = 0.373291\n", "INFO:tensorflow:global_step/sec: 118.416\n", "INFO:tensorflow:step = 423606, loss = 0.630655\n", "INFO:tensorflow:global_step/sec: 118.046\n", "INFO:tensorflow:step = 423706, loss = 0.411714\n", "INFO:tensorflow:global_step/sec: 118.874\n", "INFO:tensorflow:step = 423806, loss = 0.310494\n", "INFO:tensorflow:global_step/sec: 117.859\n", "INFO:tensorflow:step = 423906, loss = 0.367473\n", "INFO:tensorflow:global_step/sec: 117.833\n", "INFO:tensorflow:step = 424006, loss = 0.320105\n", "INFO:tensorflow:global_step/sec: 118.003\n", "INFO:tensorflow:step = 424106, loss = 0.494732\n", "INFO:tensorflow:global_step/sec: 116.882\n", "INFO:tensorflow:step = 424206, loss = 0.32872\n", "INFO:tensorflow:global_step/sec: 104.324\n", "INFO:tensorflow:step = 424306, loss = 0.566186\n", "INFO:tensorflow:global_step/sec: 92.6228\n", "INFO:tensorflow:step = 424406, loss = 0.558004\n", "INFO:tensorflow:global_step/sec: 95.7526\n", "INFO:tensorflow:step = 424506, loss = 0.46077\n", "INFO:tensorflow:global_step/sec: 106.357\n", "INFO:tensorflow:step = 424606, loss = 0.35891\n", "INFO:tensorflow:global_step/sec: 106.978\n", "INFO:tensorflow:step = 424706, loss = 0.398465\n", "INFO:tensorflow:global_step/sec: 97.3041\n", "INFO:tensorflow:step = 424806, loss = 0.312876\n", "INFO:tensorflow:global_step/sec: 73.6504\n", "INFO:tensorflow:step = 424906, loss = 0.797811\n", "INFO:tensorflow:global_step/sec: 77.21\n", "INFO:tensorflow:step = 425006, loss = 0.429547\n", "INFO:tensorflow:global_step/sec: 112.121\n", "INFO:tensorflow:step = 425106, loss = 0.539678\n", "INFO:tensorflow:global_step/sec: 111.162\n", "INFO:tensorflow:step = 425206, loss = 0.451492\n", "INFO:tensorflow:global_step/sec: 117.87\n", "INFO:tensorflow:step = 425306, loss = 0.302857\n", "INFO:tensorflow:global_step/sec: 104.866\n", "INFO:tensorflow:step = 425406, loss = 0.412508\n", "INFO:tensorflow:global_step/sec: 107.019\n", "INFO:tensorflow:step = 425506, loss = 0.324712\n", "INFO:tensorflow:global_step/sec: 111.079\n", "INFO:tensorflow:step = 425606, loss = 0.338704\n", "INFO:tensorflow:global_step/sec: 95.8107\n", "INFO:tensorflow:step = 425706, loss = 0.357042\n", "INFO:tensorflow:global_step/sec: 45.6431\n", "INFO:tensorflow:step = 425806, loss = 0.533395\n", "INFO:tensorflow:global_step/sec: 61.0767\n", "INFO:tensorflow:step = 425906, loss = 0.634868\n", "INFO:tensorflow:global_step/sec: 118.922\n", "INFO:tensorflow:step = 426006, loss = 0.364024\n", "INFO:tensorflow:global_step/sec: 118.658\n", "INFO:tensorflow:step = 426106, loss = 0.70469\n", "INFO:tensorflow:global_step/sec: 83.6482\n", "INFO:tensorflow:step = 426206, loss = 0.596489\n", "INFO:tensorflow:global_step/sec: 118.636\n", "INFO:tensorflow:step = 426306, loss = 0.621488\n", "INFO:tensorflow:global_step/sec: 114.693\n", "INFO:tensorflow:step = 426406, loss = 0.385896\n", "INFO:tensorflow:global_step/sec: 90.7509\n", "INFO:tensorflow:step = 426506, loss = 0.449903\n", "INFO:tensorflow:global_step/sec: 100.92\n", "INFO:tensorflow:step = 426606, loss = 0.585428\n", "INFO:tensorflow:global_step/sec: 91.7549\n", "INFO:tensorflow:step = 426706, loss = 0.379231\n", "INFO:tensorflow:global_step/sec: 102.073\n", "INFO:tensorflow:step = 426806, loss = 0.305658\n", "INFO:tensorflow:global_step/sec: 86.8891\n", "INFO:tensorflow:step = 426906, loss = 0.486742\n", "INFO:tensorflow:global_step/sec: 116.115\n", "INFO:tensorflow:step = 427006, loss = 0.448885\n", "INFO:tensorflow:global_step/sec: 113.674\n", "INFO:tensorflow:step = 427106, loss = 0.497082\n", "INFO:tensorflow:global_step/sec: 76.3542\n", "INFO:tensorflow:step = 427206, loss = 0.561545\n", "INFO:tensorflow:global_step/sec: 40.7942\n", "INFO:tensorflow:step = 427306, loss = 0.401827\n", "INFO:tensorflow:global_step/sec: 55.3136\n", "INFO:tensorflow:step = 427406, loss = 0.365532\n", "INFO:tensorflow:global_step/sec: 81.5264\n", "INFO:tensorflow:step = 427506, loss = 0.400427\n", "INFO:tensorflow:global_step/sec: 73.1214\n", "INFO:tensorflow:step = 427606, loss = 0.475435\n", "INFO:tensorflow:global_step/sec: 85.4748\n", "INFO:tensorflow:step = 427706, loss = 0.442511\n", "INFO:tensorflow:global_step/sec: 98.347\n", "INFO:tensorflow:step = 427806, loss = 0.554806\n", "INFO:tensorflow:global_step/sec: 100.024\n", "INFO:tensorflow:step = 427906, loss = 0.415691\n", "INFO:tensorflow:global_step/sec: 117.088\n", "INFO:tensorflow:step = 428006, loss = 0.397549\n", "INFO:tensorflow:global_step/sec: 118.519\n", "INFO:tensorflow:step = 428106, loss = 0.394291\n", "INFO:tensorflow:global_step/sec: 119.371\n", "INFO:tensorflow:step = 428206, loss = 0.423157\n", "INFO:tensorflow:global_step/sec: 103.546\n", "INFO:tensorflow:step = 428306, loss = 0.55074\n", "INFO:tensorflow:global_step/sec: 116.016\n", "INFO:tensorflow:step = 428406, loss = 0.425674\n", "INFO:tensorflow:global_step/sec: 116.804\n", "INFO:tensorflow:step = 428506, loss = 0.402643\n", "INFO:tensorflow:global_step/sec: 113.162\n", "INFO:tensorflow:step = 428606, loss = 0.534744\n", "INFO:tensorflow:global_step/sec: 105.82\n", "INFO:tensorflow:step = 428706, loss = 0.354176\n", "INFO:tensorflow:global_step/sec: 115.101\n", "INFO:tensorflow:step = 428806, loss = 0.685939\n", "INFO:tensorflow:global_step/sec: 111.404\n", "INFO:tensorflow:step = 428906, loss = 0.487807\n", "INFO:tensorflow:global_step/sec: 85.874\n", "INFO:tensorflow:step = 429006, loss = 0.366385\n", "INFO:tensorflow:global_step/sec: 106.414\n", "INFO:tensorflow:step = 429106, loss = 0.580176\n", "INFO:tensorflow:global_step/sec: 82.3936\n", "INFO:tensorflow:step = 429206, loss = 0.529077\n", "INFO:tensorflow:global_step/sec: 102.036\n", "INFO:tensorflow:step = 429306, loss = 0.482415\n", "INFO:tensorflow:global_step/sec: 109.876\n", "INFO:tensorflow:step = 429406, loss = 0.406685\n", "INFO:tensorflow:global_step/sec: 119.543\n", "INFO:tensorflow:step = 429506, loss = 0.56468\n", "INFO:tensorflow:global_step/sec: 111.397\n", "INFO:tensorflow:step = 429606, loss = 0.436728\n", "INFO:tensorflow:global_step/sec: 109.839\n", "INFO:tensorflow:step = 429706, loss = 0.487103\n", "INFO:tensorflow:global_step/sec: 105.931\n", "INFO:tensorflow:step = 429806, loss = 0.482126\n", "INFO:tensorflow:global_step/sec: 117.545\n", "INFO:tensorflow:step = 429906, loss = 0.470042\n", "INFO:tensorflow:global_step/sec: 119.026\n", "INFO:tensorflow:step = 430006, loss = 0.512768\n", "INFO:tensorflow:global_step/sec: 106.063\n", "INFO:tensorflow:step = 430106, loss = 0.65634\n", "INFO:tensorflow:global_step/sec: 94.0685\n", "INFO:tensorflow:step = 430206, loss = 0.462901\n", "INFO:tensorflow:global_step/sec: 92.7027\n", "INFO:tensorflow:step = 430306, loss = 0.360102\n", "INFO:tensorflow:global_step/sec: 95.9051\n", "INFO:tensorflow:step = 430406, loss = 0.421326\n", "INFO:tensorflow:global_step/sec: 103.234\n", "INFO:tensorflow:step = 430506, loss = 0.496619\n", "INFO:tensorflow:global_step/sec: 95.0533\n", "INFO:tensorflow:step = 430606, loss = 0.551563\n", "INFO:tensorflow:global_step/sec: 92.0804\n", "INFO:tensorflow:step = 430706, loss = 0.429046\n", "INFO:tensorflow:global_step/sec: 119.763\n", "INFO:tensorflow:step = 430806, loss = 0.370887\n", "INFO:tensorflow:global_step/sec: 93.3423\n", "INFO:tensorflow:step = 430906, loss = 0.384966\n", "INFO:tensorflow:global_step/sec: 111.896\n", "INFO:tensorflow:step = 431006, loss = 0.450823\n", "INFO:tensorflow:global_step/sec: 117.256\n", "INFO:tensorflow:step = 431106, loss = 0.467991\n", "INFO:tensorflow:global_step/sec: 118.708\n", "INFO:tensorflow:step = 431206, loss = 0.492158\n", "INFO:tensorflow:global_step/sec: 114.346\n", "INFO:tensorflow:step = 431306, loss = 0.369902\n", "INFO:tensorflow:global_step/sec: 118.309\n", "INFO:tensorflow:step = 431406, loss = 0.52271\n", "INFO:tensorflow:global_step/sec: 117.736\n", "INFO:tensorflow:step = 431506, loss = 0.565111\n", "INFO:tensorflow:global_step/sec: 102.908\n", "INFO:tensorflow:step = 431606, loss = 0.451215\n", "INFO:tensorflow:global_step/sec: 106.906\n", "INFO:tensorflow:step = 431706, loss = 0.525432\n", "INFO:tensorflow:global_step/sec: 112.931\n", "INFO:tensorflow:step = 431806, loss = 0.482871\n", "INFO:tensorflow:global_step/sec: 112.481\n", "INFO:tensorflow:step = 431906, loss = 0.365422\n", "INFO:tensorflow:global_step/sec: 110.639\n", "INFO:tensorflow:step = 432006, loss = 0.538592\n", "INFO:tensorflow:global_step/sec: 112.068\n", "INFO:tensorflow:step = 432106, loss = 0.306204\n", "INFO:tensorflow:global_step/sec: 112.721\n", "INFO:tensorflow:step = 432206, loss = 0.334636\n", "INFO:tensorflow:global_step/sec: 94.1291\n", "INFO:tensorflow:step = 432306, loss = 0.415131\n", "INFO:tensorflow:global_step/sec: 108.886\n", "INFO:tensorflow:step = 432406, loss = 0.458054\n", "INFO:tensorflow:global_step/sec: 109.498\n", "INFO:tensorflow:step = 432506, loss = 0.513917\n", "INFO:tensorflow:global_step/sec: 108.601\n", "INFO:tensorflow:step = 432606, loss = 0.512376\n", "INFO:tensorflow:global_step/sec: 111.294\n", "INFO:tensorflow:step = 432706, loss = 0.317374\n", "INFO:tensorflow:global_step/sec: 111.756\n", "INFO:tensorflow:step = 432806, loss = 0.299387\n", "INFO:tensorflow:global_step/sec: 110.081\n", "INFO:tensorflow:step = 432906, loss = 0.510377\n", "INFO:tensorflow:global_step/sec: 113.748\n", "INFO:tensorflow:step = 433006, loss = 0.251561\n", "INFO:tensorflow:global_step/sec: 112.519\n", "INFO:tensorflow:step = 433106, loss = 0.531615\n", "INFO:tensorflow:global_step/sec: 113.012\n", "INFO:tensorflow:step = 433206, loss = 0.516377\n", "INFO:tensorflow:global_step/sec: 111.531\n", "INFO:tensorflow:step = 433306, loss = 0.498984\n", "INFO:tensorflow:global_step/sec: 89.0442\n", "INFO:tensorflow:step = 433406, loss = 0.416305\n", "INFO:tensorflow:global_step/sec: 81.585\n", "INFO:tensorflow:step = 433506, loss = 0.393011\n", "INFO:tensorflow:global_step/sec: 75.6112\n", "INFO:tensorflow:step = 433606, loss = 0.397339\n", "INFO:tensorflow:global_step/sec: 98.3975\n", "INFO:tensorflow:step = 433706, loss = 0.430039\n", "INFO:tensorflow:global_step/sec: 110.766\n", "INFO:tensorflow:step = 433806, loss = 0.417845\n", "INFO:tensorflow:global_step/sec: 108.742\n", "INFO:tensorflow:step = 433906, loss = 0.528167\n", "INFO:tensorflow:global_step/sec: 106.831\n", "INFO:tensorflow:step = 434006, loss = 0.490619\n", "INFO:tensorflow:global_step/sec: 99.6313\n", "INFO:tensorflow:step = 434106, loss = 0.49169\n", "INFO:tensorflow:global_step/sec: 107.99\n", "INFO:tensorflow:step = 434206, loss = 0.448148\n", "INFO:tensorflow:global_step/sec: 94.5385\n", "INFO:tensorflow:step = 434306, loss = 0.342449\n", "INFO:tensorflow:global_step/sec: 97.6068\n", "INFO:tensorflow:step = 434406, loss = 0.392912\n", "INFO:tensorflow:global_step/sec: 85.9497\n", "INFO:tensorflow:step = 434506, loss = 0.416774\n", "INFO:tensorflow:global_step/sec: 104.088\n", "INFO:tensorflow:step = 434606, loss = 0.43352\n", "INFO:tensorflow:global_step/sec: 105.132\n", "INFO:tensorflow:step = 434706, loss = 0.424444\n", "INFO:tensorflow:global_step/sec: 103.279\n", "INFO:tensorflow:step = 434806, loss = 0.395388\n", "INFO:tensorflow:global_step/sec: 44.0608\n", "INFO:tensorflow:step = 434906, loss = 0.513787\n", "INFO:tensorflow:global_step/sec: 55.7899\n", "INFO:tensorflow:step = 435006, loss = 0.525513\n", "INFO:tensorflow:global_step/sec: 49.0053\n", "INFO:tensorflow:step = 435106, loss = 0.495579\n", "INFO:tensorflow:global_step/sec: 112.45\n", "INFO:tensorflow:step = 435206, loss = 0.351461\n", "INFO:tensorflow:global_step/sec: 62.0605\n", "INFO:tensorflow:step = 435306, loss = 0.39851\n", "INFO:tensorflow:global_step/sec: 72.2697\n", "INFO:tensorflow:step = 435406, loss = 0.376165\n", "INFO:tensorflow:global_step/sec: 83.5916\n", "INFO:tensorflow:step = 435506, loss = 0.354302\n", "INFO:tensorflow:global_step/sec: 44.0844\n", "INFO:tensorflow:step = 435606, loss = 0.337326\n", "INFO:tensorflow:global_step/sec: 59.8255\n", "INFO:tensorflow:step = 435706, loss = 0.322885\n", "INFO:tensorflow:global_step/sec: 41.313\n", "INFO:tensorflow:step = 435806, loss = 0.382056\n", "INFO:tensorflow:global_step/sec: 39.553\n", "INFO:tensorflow:step = 435906, loss = 0.432241\n", "INFO:tensorflow:global_step/sec: 53.5287\n", "INFO:tensorflow:step = 436006, loss = 0.451678\n", "INFO:tensorflow:global_step/sec: 46.9819\n", "INFO:tensorflow:step = 436106, loss = 0.385194\n", "INFO:tensorflow:global_step/sec: 53.2343\n", "INFO:tensorflow:step = 436206, loss = 0.532502\n", "INFO:tensorflow:global_step/sec: 45.8845\n", "INFO:tensorflow:step = 436306, loss = 0.420789\n", "INFO:tensorflow:global_step/sec: 39.4492\n", "INFO:tensorflow:step = 436406, loss = 0.452752\n", "INFO:tensorflow:global_step/sec: 75.1014\n", "INFO:tensorflow:step = 436506, loss = 0.43155\n", "INFO:tensorflow:global_step/sec: 38.4569\n", "INFO:tensorflow:step = 436606, loss = 0.38609\n", "INFO:tensorflow:global_step/sec: 60.0908\n", "INFO:tensorflow:step = 436706, loss = 0.548012\n", "INFO:tensorflow:global_step/sec: 41.2633\n", "INFO:tensorflow:step = 436806, loss = 0.419649\n", "INFO:tensorflow:global_step/sec: 60.0169\n", "INFO:tensorflow:step = 436906, loss = 0.466492\n", "INFO:tensorflow:global_step/sec: 64.1797\n", "INFO:tensorflow:step = 437006, loss = 0.366808\n", "INFO:tensorflow:global_step/sec: 88.9339\n", "INFO:tensorflow:step = 437106, loss = 0.498089\n", "INFO:tensorflow:global_step/sec: 92.7881\n", "INFO:tensorflow:step = 437206, loss = 0.382282\n", "INFO:tensorflow:global_step/sec: 84.775\n", "INFO:tensorflow:step = 437306, loss = 0.505018\n", "INFO:tensorflow:global_step/sec: 105.173\n", "INFO:tensorflow:step = 437406, loss = 0.507503\n", "INFO:tensorflow:global_step/sec: 68.7711\n", "INFO:tensorflow:step = 437506, loss = 0.529242\n", "INFO:tensorflow:global_step/sec: 41.5779\n", "INFO:tensorflow:step = 437606, loss = 0.552276\n", "INFO:tensorflow:global_step/sec: 53.8395\n", "INFO:tensorflow:step = 437706, loss = 0.447926\n", "INFO:tensorflow:global_step/sec: 55.9332\n", "INFO:tensorflow:step = 437806, loss = 0.362302\n", "INFO:tensorflow:global_step/sec: 74.2658\n", "INFO:tensorflow:step = 437906, loss = 0.463032\n", "INFO:tensorflow:global_step/sec: 43.4752\n", "INFO:tensorflow:step = 438006, loss = 0.436154\n", "INFO:tensorflow:global_step/sec: 46.2918\n", "INFO:tensorflow:step = 438106, loss = 0.412675\n", "INFO:tensorflow:global_step/sec: 50.3416\n", "INFO:tensorflow:step = 438206, loss = 0.373837\n", "INFO:tensorflow:global_step/sec: 31.7823\n", "INFO:tensorflow:step = 438306, loss = 0.36443\n", "INFO:tensorflow:global_step/sec: 67.9552\n", "INFO:tensorflow:step = 438406, loss = 0.581339\n", "INFO:tensorflow:global_step/sec: 44.9161\n", "INFO:tensorflow:step = 438506, loss = 0.351752\n", "INFO:tensorflow:Saving checkpoints for 438596 into .opt_logs/lstm_stock/model.ckpt.\n", "INFO:tensorflow:global_step/sec: 7.02513\n", "INFO:tensorflow:step = 438606, loss = 0.574096\n", "INFO:tensorflow:global_step/sec: 35.0276\n", "INFO:tensorflow:step = 438706, loss = 0.368276\n", "INFO:tensorflow:global_step/sec: 40.3259\n", "INFO:tensorflow:step = 438806, loss = 0.377791\n", "INFO:tensorflow:global_step/sec: 75.692\n", "INFO:tensorflow:step = 438906, loss = 0.418554\n", "INFO:tensorflow:Saving checkpoints for 439005 into .opt_logs/lstm_stock/model.ckpt.\n", "INFO:tensorflow:Loss for final step: 0.258574.\n" ] }, { "data": { "text/plain": [ "Estimator(params=None)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create an regression model based on LSTM architecture\n", "regressor = learn.Estimator(model_fn=lstm_model(TIMESTEPS, RNN_LAYERS, DENSE_LAYERS,\n", " learning_rate=LEARNING_RATE, optimizer=\"Adagrad\"),\n", " model_dir=LOG_DIR)\n", "\n", "# create a lstm instance and validation monitor\n", "validation_monitor = learn.monitors.ValidationMonitor(X['val'], y['val'],\n", " every_n_steps=PRINT_STEPS,\n", " early_stopping_rounds=1000)\n", "# Fit the data to the models\n", "regressor.fit(X['train'], y['train'],\n", " monitors=[validation_monitor],\n", " batch_size=BATCH_SIZE,\n", " steps=TRAINING_STEPS)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From <ipython-input-8-d096f593ed28>:2: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Estimator is decoupled from Scikit Learn interface by moving into\n", "separate class SKCompat. Arguments x, y and batch_size are only\n", "available in the SKCompat class, Estimator will only accept input_fn.\n", "Example conversion:\n", " est = Estimator(...) -> est = SKCompat(Estimator(...))\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/anaconda/lib/python3.5/site-packages/tensorflow/python/util/deprecation.py:247: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n", " equality = a == b\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/models.py:107: mean_squared_error_regressor (from tensorflow.contrib.learn.python.learn.ops.losses_ops) is deprecated and will be removed after 2016-12-01.\n", "Instructions for updating:\n", "Use `tf.contrib.losses.mean_squared_error` and explicit logits computation.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/ops/losses_ops.py:39: mean_squared_error (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.mean_squared_error instead.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:530: compute_weighted_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.compute_weighted_loss instead.\n", "WARNING:tensorflow:From /anaconda/lib/python3.5/site-packages/tensorflow/contrib/losses/python/losses/loss_ops.py:151: add_loss (from tensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30.\n", "Instructions for updating:\n", "Use tf.losses.add_loss instead.\n", "MSE: 0.157704\n" ] } ], "source": [ "# Make the prediction\n", "predicted = list(regressor.predict(X['test']))\n", "\n", "# Calculate the error\n", "score = mean_squared_error(predicted, y['test'])\n", "print (\"MSE: %f\" % score)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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NidJaZ9Z+nwXUbZv5AigFMoHDwAta61OTOyGEEOKc7Moo4uvN6dw4NJb2wT6N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\nKYf5f9JFoELHfxOWAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11d1d20f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot the data\n", "actual = close.ix[len(close)-len(predicted):]\n", "predicted = pd.Series(predicted, index=actual.index)\n", "\n", "comp = pd.DataFrame({\"Actual\": actual,\"Pred\":predicted})\n", "\n", "plot_predicted = pred.props.price.ix[len(pred.props.price)-len(predicted):].plot(label='SPY', legend=True)\n", "plot_test = comp.Pred.plot(label='Predicted SPY', legend=True)\n", "plt.ylabel(\"Share Price [$]\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "We can see that the preciction is the shape of the real data, however there is a delay. This is probably due to using the delayed exponential mean.\n", "\n", "I have not made trading predictions using this model as the computation time is too long. But the trading model is presented in DL_BuySell.py" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
csc-training/python-introduction
notebooks/examples/Extra Scikit-learn.ipynb
1
5135
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Classification with Scikit-learn\n", "\n", "First we use pandas to read in the csv file and separate the Y (target class, final column in CSV) from the X, the predicting values." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "data = pd.read_csv(\"../data/iris.data\")\n", "\n", "# convert to NumPy arrays because they are the easiest to handle in sklearn\n", "variables = data.drop([\"class\"], axis=1).as_matrix()\n", "classes = data[[\"class\"]].as_matrix().reshape(-1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# import cross-validation scorer and KNeighborsClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "train_X, test_X, train_Y, test_Y = train_test_split(variables, classes)\n", "\n", "# initialize classifier object\n", "classifier = KNeighborsClassifier()\n", "\n", "# fit the object using training data and sample labels\n", "classifier.fit(train_X, train_Y)\n", "\n", "# evaluate the results for held-out test sample\n", "classifier.score(test_X, test_Y)\n", "# value is the mean accuracy " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# if we wanted to predict values for unseen data, we would use the predict()-method\n", "\n", "classifier.predict(test_X) # note no known Y-values passed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "* Import the classifier object ``sklearn.svm.SVC```\n", "* initialize it\n", "* fit it with the training data (no need to split a second time)\n", "* evaluate the quality of the created classifier using ``score()``" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pipelining and cross-validation\n", "\n", "It's common to want to preprocess data somehow or in general have several steps. This can be easily done with the Pipeline class. \n", "\n", "There are typically parameters involved and you might want to select the best possible parameter." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.decomposition import PCA # pca is a subspace method that projects the data into a lower-dimensional space\n", "\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "\n", "pca = PCA(n_components=2)\n", "knn = KNeighborsClassifier(n_neighbors=3)\n", "\n", "from sklearn.pipeline import Pipeline\n", "\n", "pipeline = Pipeline([(\"pca\", pca), (\"kneighbors\", knn)])\n", "\n", "parameters_grid = dict(\n", " pca__n_components=[1,2,3,4],\n", " kneighbors__n_neighbors=[1,2,3,4,5,6]\n", " )\n", "grid_search = GridSearchCV(pipeline, parameters_grid)\n", "grid_search.fit(train_X, train_Y)\n", "grid_search.best_estimator_" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# you can now test agains the held out part\n", "grid_search.best_estimator_.score(test_X, test_Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercise\n", "\n", "There is another dataset, \"breast-cancer-wisconsin.data\". For a description see [here] (https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/) . \n", "\n", "It contains samples with patient ID (that you should remove), measurements and as last the doctors judgment of the biopsy: malignant or benign.\n", "\n", "Read in the file and create a classifier.\n", "\n", "You can alternately just split the input and use some classifier or do a grid cross-validation over a larger space of potential parameters.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
surchs/Logbooks
10_23_14.ipynb
2
3722295
null
gpl-3.0
Danghor/Formal-Languages
ANTLR4-Python/Earley-Parser/Earley-Parser.ipynb
1
27008
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.core.display import HTML\n", "with open('../../style.css', 'r') as file:\n", " css = file.read()\n", "HTML(css)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementing an Earley Parser" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A Grammar for Grammars" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Earley's algorithm has two inputs:\n", "- a grammar $G$ and\n", "- a string $s$.\n", "\n", "It then checks whether the string $s$ can be parsed with the given grammar.\n", "\n", "In order to input the grammar in a natural way, we first have to develop a parser for grammars.\n", "An example grammar that we want to parse is stored in the file `simple.g`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!cat simple.g" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use <span style=\"font-variant:small-caps;\">Antlr</span> to develop a parser for this Grammar. \n", "The pure grammar to parse this type of grammar is stored in\n", "the file `Pure.g4`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!cat Pure.g4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The annotated grammar is stored in the file `Grammar.g4`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!cat -n Grammar.g4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We start by generating both scanner and parser. " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!antlr4 -Dlanguage=Python3 Grammar.g4" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from GrammarLexer import GrammarLexer\n", "from GrammarParser import GrammarParser\n", "import antlr4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `parse_grammar` takes a `filename` as its argument and returns the grammar that is stored in the given file. The grammar is represented as list of rules. Each rule is represented as a tuple. The example below will clarify this structure." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def parse_grammar(filename):\n", " input_stream = antlr4.FileStream(filename)\n", " lexer = GrammarLexer(input_stream)\n", " token_stream = antlr4.CommonTokenStream(lexer)\n", " parser = GrammarParser(token_stream)\n", " grammar = parser.start()\n", " return grammar.g" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "parse_grammar('simple.g')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Earley's Algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given a context-free grammar $G = \\langle V, \\Sigma, R, S \\rangle$ and a string $s = x_1x_2 \\cdots x_n \\in \\Sigma^*$ of length $n$, \n", "an *Earley item* is a pair of the form\n", "$$\\langle A \\rightarrow \\alpha \\bullet \\beta, k \\rangle$$\n", "such that \n", "- $(A \\rightarrow \\alpha \\beta) \\in R\\quad$ and\n", "- $k \\in \\{0,1,\\cdots,n\\}$. \n", "\n", "The class `EarleyItem` represents a single *Earley item*. \n", "- `mVariable` is the variable $A$,\n", "- `mAlpha` is $\\alpha$,\n", "- `mBeta` is $\\beta$, and\n", "- `mIndex` is $k$.\n", "\n", "Since we later have to store objects of class `EarleyItem` in sets, we have to implement the functions\n", "- `__eq__`,\n", "- `__ne__`,\n", "- `__hash__`.\n", "\n", "It is easiest to implement `__hash__` by first converting the object into a string. Hence we also\n", "implement the function `__repr__`, that converts an `EarleyItem` into a string." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class EarleyItem():\n", " def __init__(self, variable, alpha, beta, index):\n", " self.mVariable = variable\n", " self.mAlpha = alpha\n", " self.mBeta = beta\n", " self.mIndex = index\n", " \n", " def __eq__(self, other):\n", " return isinstance(other, EarleyItem) and \\\n", " self.mVariable == other.mVariable and \\\n", " self.mAlpha == other.mAlpha and \\\n", " self.mBeta == other.mBeta and \\\n", " self.mIndex == other.mIndex\n", " \n", " def __ne__(self, other):\n", " return not self.__eq__(other)\n", " \n", " def __hash__(self):\n", " return hash(self.__repr__())\n", " \n", " def __repr__(self):\n", " alphaStr = ' '.join(self.mAlpha)\n", " betaStr = ' '.join(self.mBeta)\n", " return f'<{self.mVariable} → {alphaStr} • {betaStr}, {self.mIndex}>'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given an Earley item `self`, the function `isComplete` checks, whether the Earley item `self` has the form\n", "$$\\langle A \\rightarrow \\alpha \\bullet, k \\rangle,$$\n", "i.e. whether the $\\bullet$ is at the end of the grammar rule." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def isComplete(self):\n", " return self.mBeta == ()\n", "\n", "EarleyItem.isComplete = isComplete\n", "del isComplete" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `sameVar(self, C)` checks, whether the item following the dot is the same as the variable \n", "given as argument, i.e. `sameVar(self, C)` returns `True` if `self` is an Earley item of the form\n", "$$\\langle A \\rightarrow \\alpha \\bullet C\\beta, k \\rangle.$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def sameVar(self, C):\n", " return len(self.mBeta) > 0 and self.mBeta[0] == C\n", "\n", "EarleyItem.sameVar = sameVar\n", "del sameVar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `scan(self, t)` checks, whether the item following the dot matches the token `t`, \n", "i.e. `scan(self, t)` returns `True` if `self` is an Earley item of the form\n", "$$\\langle A \\rightarrow \\alpha \\bullet t\\beta, k \\rangle.$$\n", "The argument $t$ can either be the name of a token or a literal." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def scan(self, t):\n", " if len(self.mBeta) > 0:\n", " return self.mBeta[0] == t or self.mBeta[0] == \"'\" + t + \"'\"\n", " return False\n", "\n", "EarleyItem.scan = scan\n", "del scan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given an Earley item, this function returns the name of the variable following the dot. If there is no variable following the dot, the function returns `None`. The function can distinguish variables from token names because variable names consist only of lower case letters." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def nextVar(self):\n", " if len(self.mBeta) > 0:\n", " var = self.mBeta[0]\n", " if var[0] != \"'\" and var.islower():\n", " return var\n", " return None\n", "\n", "EarleyItem.nextVar = nextVar\n", "del nextVar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `moveDot(self)` moves the $\\bullet$ in the Earley item `self`, where `self` has the form \n", "$$\\langle A \\rightarrow \\alpha \\bullet \\beta, k \\rangle$$\n", "over the next variable, token, or literal in $\\beta$. It assumes that $\\beta$ is not empty." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def moveDot(self):\n", " return EarleyItem(self.mVariable, \n", " self.mAlpha + (self.mBeta[0],), \n", " self.mBeta[1:], \n", " self.mIndex)\n", "\n", "EarleyItem.moveDot = moveDot\n", "del moveDot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The class `Grammar` represents a context free grammar. It stores a list of the rules of the grammar.\n", "Each grammar rule of the form\n", "$$ a \\rightarrow \\beta $$\n", "is stored as the tuple $(a,) + \\beta$. The start symbol is assumed to be the variable on the left hand side of\n", "the first rule. To distinguish syntactical variables form tokens, variables contain only lower case letters,\n", "while tokens either contain only upper case letters or they start and end with a single quote character \"`'`\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class Grammar():\n", " def __init__(self, Rules):\n", " self.mRules = Rules " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `startItem` returns the Earley item\n", "$$ \\langle\\hat{S} \\rightarrow \\bullet S, 0\\rangle $$\n", "where $S$ is the start variable of the given grammar and $\\hat{S}$ is a new variable." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def startItem(self):\n", " return EarleyItem('Start', (), (self.startVar(),), 0)\n", "\n", "Grammar.startItem = startItem\n", "del startItem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `finishItem` returns the Earley item\n", "$$ \\langle\\hat{S} \\rightarrow S \\bullet, 0\\rangle $$\n", "where $S$ is the start variable of the given grammar and $\\hat{S}$ is a new variable." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def finishItem(self):\n", " return EarleyItem('Start', (self.startVar(),), (), 0)\n", "\n", "Grammar.finishItem = finishItem\n", "del finishItem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `startVar` returns the start variable of the grammar. It is assumed that\n", "the first rule grammar starts with the start variable of the grammar." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def startVar(self):\n", " return self.mRules[0][0]\n", "\n", "Grammar.startVar = startVar\n", "del startVar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `toString` creates a readable presentation of the grammar rules." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def toString(self):\n", " result = ''\n", " for head, *body in self.mRules:\n", " result += f'{head}: {body};\\n'\n", " return result\n", "\n", "Grammar.__str__ = toString\n", "del toString" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The class `EarleyParser` implements the [parsing algorithm of Jay Earley](https://en.wikipedia.org/wiki/Earley_parser).\n", "The class maintains the following member variables:\n", "- `mGrammar` is the grammar that is used to parse the given token string.\n", "- `mString` is the list of tokens and literals that has to be parsed.\n", "\n", " As a hack, the first element of this list in `None`. \n", " Therefore, `mString[i]` is the `i`th token.\n", "- `mStateList` is a list of sets of *Earley items*. If $n$ is the length of the given token string\n", " (excluding the first element `None`), then $Q_i = \\texttt{mStateList}[i]$. \n", " The idea is that the set $Q_i$ is the set of those *Earley items* that the parser could be in \n", " when it has read the tokens `mString[1]`, $\\cdots$, `mString[n]`. $Q_0$ is initialized as follows:\n", " $$ Q_0 = \\bigl\\{\\langle\\hat{S} \\rightarrow \\bullet S, 0\\rangle\\bigr\\}. $$\n", " \n", "The *Earley items* are interpreted as follows: If we have\n", "$$ \\langle C \\rightarrow \\alpha \\bullet \\beta, k\\rangle \\in Q_i, $$\n", "then we know the following:\n", "- After having read the tokens `mString[:k+1]` the parser tries to parse the variable $C$\n", " in the token string `mString[k+1:]`.\n", "- After having read the token string `mString[k+1:i+1]` the parser has already recognized $\\alpha$\n", " and now needs to recognize $\\beta$ in the token string `mString[i+1:]` in order to parse the variable $C$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class EarleyParser():\n", " def __init__(self, grammar, TokenList):\n", " self.mGrammar = grammar \n", " self.mString = [None] + TokenList # dirty hack so mString[1] is first token\n", " self.mStateList = [set() for i in range(len(TokenList)+1)] \n", " print('Grammar:\\n')\n", " print(self.mGrammar)\n", " print(f'Input: {self.mString}\\n')\n", " self.mStateList[0] = { self.mGrammar.startItem() }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The method `parse` implements Earley's algorithm. For all states \n", "$Q_1$, $\\cdots$, $Q_n$ we proceed as follows:\n", "- We apply the completion operation followed by the prediction operation.\n", " This is done until no more states are added to $Q_i$. \n", " \n", " (The inner `while` loop is not necessary if the grammar does not contain $\\varepsilon$-rules.)\n", "- Finally, the scanning operation is applied to $Q_i$.\n", "\n", "After $Q_i$ has been computed, we proceed to compute $Q_{i+1}$.\n", "Parsing is successful iff\n", "$$ \\langle\\hat{S} \\rightarrow S \\bullet, 0\\rangle \\in Q_n $$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def parse(self):\n", " \"run Earley's algorithm\"\n", " n = len(self.mString) - 1 # mString[0] = None\n", " for i in range(0, n+1):\n", " if i + 1 <= n:\n", " next_token = self.mString[i+1]\n", " else:\n", " next_token = 'EOF'\n", " print('_' * 80)\n", " print(f'next token = {next_token}')\n", " print('_' * 80)\n", " change = True\n", " while change:\n", " change = self.complete(i)\n", " change = self.predict(i) or change\n", " self.scan(i)\n", " # print states\n", " print(f'\\nQ{i}:')\n", " Qi = self.mStateList[i]\n", " for item in Qi: \n", " print(item)\n", " if i + 1 <= n:\n", " print(f'\\nQ{i+1}:')\n", " Qip1 = self.mStateList[i+1]\n", " for item in Qip1: \n", " print(item)\n", " if self.mGrammar.finishItem() in self.mStateList[-1]:\n", " print('Parsing successful!')\n", " else:\n", " print('Parsing failed!')\n", "\n", "EarleyParser.parse = parse\n", "del parse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The method `complete(self, i)` applies the completion operation to the state $Q_i$:\n", "If we have\n", "- $\\langle C \\rightarrow \\gamma \\bullet, j\\rangle \\in Q_i$ and\n", "- $\\langle A \\rightarrow \\beta \\bullet C \\delta, k\\rangle \\in Q_j$,\n", "then the parser tried to parse the variable $C$ after having read `mString[:j+1]`\n", "and we know that \n", "$$ C \\Rightarrow^* \\texttt{mString[j+1:i+1]}, $$\n", "i.e. the parser has recognized $C$ after having read `mString[j+1:i+1]`.\n", "Therefore the parser should proceed to recognize $\\delta$ in state $Q_i$.\n", "Therefore we add the *Earley item* $\\langle A \\rightarrow \\beta C \\bullet \\delta,k\\rangle$ to the set $Q_i$:\n", "$$\\langle C \\rightarrow \\gamma \\bullet, j\\rangle \\in Q_i \\wedge\n", " \\langle A \\rightarrow \\beta \\bullet C \\delta, k\\rangle \\in Q_j \\;\\rightarrow\\;\n", " Q_i := Q_i \\cup \\bigl\\{ \\langle A \\rightarrow \\beta C \\bullet \\delta, k\\rangle \\bigr\\}\n", "$$\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def complete(self, i):\n", " change = False\n", " added = True\n", " Qi = self.mStateList[i]\n", " while added:\n", " added = False\n", " newQi = set()\n", " for item in Qi:\n", " if item.isComplete():\n", " C = item.mVariable\n", " j = item.mIndex\n", " Qj = self.mStateList[j]\n", " for newItem in Qj:\n", " if newItem.sameVar(C):\n", " moved = newItem.moveDot()\n", " newQi.add(moved)\n", " if not (newQi <= Qi):\n", " change = True\n", " added = True\n", " print(\"completion:\")\n", " for newItem in newQi:\n", " if newItem not in Qi:\n", " print(f'{newItem} added to Q{i}')\n", " self.mStateList[i] |= newQi\n", " Qi = self.mStateList[i]\n", " return change\n", " \n", "EarleyParser.complete = complete\n", "del complete" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The method `self.predict(i)` applies the prediction operation to the state $Q_i$: \n", "If $\\langle A \\rightarrow \\beta \\bullet C \\delta, k \\rangle \\in Q_j$, then\n", "the parser tries to recognize $C\\delta$ after having read `mString[:j+1]`. To this end\n", "it has to parse $C$ in the string `mString[j+1:]`.\n", "Therefore, if $C \\rightarrow \\gamma$ is a rule of our grammar,\n", "we add the *Earley item* $\\langle C \\rightarrow \\bullet \\gamma, j\\rangle$ to the set $Q_j$:\n", "$$ \\langle A \\rightarrow \\beta \\bullet C \\delta, k\\rangle \\in Q_j \n", " \\wedge (C \\rightarrow \\gamma) \\in R \n", " \\;\\rightarrow\\;\n", " Q_j := Q_j \\cup\\bigl\\{ \\langle C \\rightarrow \\bullet\\gamma, j\\rangle\\bigr\\}.\n", "$$\n", "As the right hand side $\\gamma$ might start with a variable, the function uses a fix point iteration\n", "until no more *Earley items* are added to $Q_j$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def predict(self, i):\n", " change = False\n", " added = True\n", " Qi = self.mStateList[i]\n", " while added:\n", " added = False\n", " newQi = set()\n", " for item in Qi:\n", " c = item.nextVar()\n", " if c != None:\n", " for rule in self.mGrammar.mRules:\n", " if c == rule[0]:\n", " newQi.add(EarleyItem(c, (), rule[1:], i))\n", " if not (newQi <= Qi):\n", " change = True\n", " added = True\n", " print(\"prediction:\")\n", " for newItem in newQi:\n", " if newItem not in Qi:\n", " print(f'{newItem} added to Q{i}')\n", " self.mStateList[i] |= newQi\n", " Qi = self.mStateList[i]\n", " return change\n", "\n", "EarleyParser.predict = predict\n", "del predict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `self.scan(i)` applies the scanning operation to the state $Q_i$.\n", "\n", "If $\\langle A \\rightarrow \\beta \\bullet a \\gamma, k\\rangle \\in Q_i$ and $a$ is a token,\n", "then the parser tries to recognize the right hand side of the grammar rule\n", "$$ A \\rightarrow \\beta a \\gamma$$ \n", "and after having read `mString[k+1:i+1]` it has already recognized $\\beta$.\n", "If we now have `mString[i+1] == a`, then the parser still has to recognize $\\gamma$ in `mString[i+2:]`.\n", "Therefore, the *Earley object* $\\langle A \\rightarrow \\beta a \\bullet \\gamma, k\\rangle$ is added to\n", "the set $Q_{i+1}$:\n", "$$\\langle A \\rightarrow \\beta \\bullet a \\gamma, k\\rangle \\in Q_i \\wedge x_{i+1} = a\n", " \\;\\rightarrow\\;\n", " Q_{i+1} := Q_{i+1} \\cup \\bigl\\{ \\langle A \\rightarrow \\beta a \\bullet \\gamma, k\\rangle \\bigr\\}\n", "$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def scan(self, i):\n", " Qi = self.mStateList[i]\n", " n = len(self.mString) - 1 # remember mStateList[0] == None\n", " if i + 1 <= n:\n", " a = self.mString[i+1]\n", " for item in Qi:\n", " if item.scan(a):\n", " self.mStateList[i+1].add(item.moveDot())\n", " print('scanning:')\n", " print(f'{item.moveDot()} added to Q{i+1}')\n", "\n", "EarleyParser.scan = scan\n", "del scan" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import re" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `tokenize` transforms the string `s` into a list of tokens. See below for an example." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def tokenize(s):\n", " '''Transform the string s into a list of tokens. The string s\n", " is supposed to represent an arithmetic expression.\n", " '''\n", " lexSpec = r'''([ \\t]+) | # blanks and tabs\n", " ([1-9][0-9]*|0) | # number\n", " ([()]) | # parentheses \n", " ([-+*/]) | # arithmetical operators\n", " (.) # unrecognized character\n", " '''\n", " tokenList = re.findall(lexSpec, s, re.VERBOSE)\n", " result = []\n", " for ws, number, parenthesis, operator, error in tokenList:\n", " if ws: # skip blanks and tabs\n", " continue\n", " elif number:\n", " result += [ 'NUMBER' ]\n", " elif parenthesis:\n", " result += [ parenthesis ]\n", " elif operator:\n", " result += [ operator ]\n", " else:\n", " result += [ f'ERROR({error})']\n", " return result" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tokenize('1 + 2 * 3')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function `test` takes two arguments.\n", "- `file` is the name of a file containing a grammar,\n", "- `word` is a string that should be parsed.\n", "\n", "`word` is first tokenized. Then the resulting token list is parsed using *Earley's algorithm*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def test(file, word): \n", " Rules = parse_grammar(file)\n", " grammar = Grammar(Rules)\n", " TokenList = tokenize(word)\n", " ep = EarleyParser(grammar, TokenList)\n", " ep.parse()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "test('simple.g', '1 + 2 * 3')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The command below cleans the directory. If you are running windows, you have to replace `rm`with `del`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!rm GrammarLexer.* GrammarParser.* Grammar.tokens GrammarListener.py Grammar.interp\n", "!rm -r __pycache__" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!ls" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.0" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }
gpl-2.0
sophie63/FlyLFM
Notebooks/.ipynb_checkpoints/100160-checkpoint.ipynb
1
4123289
null
bsd-2-clause
itoledoc/python_coffee
itoledoc_pycoffee2.ipynb
2
11479
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# An introduction to Pandas Library\n", "\n", "## Python Coffee February 18th, 2016\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as pl\n", "import datetime as dt\n", "%matplotlib notebook" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pd.options.display.max_rows = 20\n", "pd.options.display.max_columns = 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pwv = pd.read_csv('pwv_APEX_3h.csv', na_values='NaN')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "measurements = pd.read_csv('sc_measurements.csv')\n", "sources = pd.read_csv('sc_sources.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "targets = pd.read_csv('sg_targets.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execblocks = pd.read_csv('aqua_exeblock.csv', index_col=0)\n", "execblocks.rename(columns={'SB_UID.1': 'SB_UID'}, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inspecting Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pwv.values" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pwv" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get a quick summary of the dataframe\n", "pwv.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the first columns\n", "pwv.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Get the last rows\n", "pwv.tail()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Extract/Slice data (rows)\n", "pwv[:3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pwv.ix[:3,1:3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pwv[['Month', 'Day']][:3]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pwv['PWV']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "?pd.read_csv" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print pwv.PWV.min()\n", "print pwv.PWV.max()\n", "print pwv.PWV.mean()\n", "print pwv.PWV.median()\n", "print pwv.PWV.std()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "pwv.PWV.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pwv.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "pl.figure(1)\n", "pwv.PWV.hist(bins=40)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pl.figure(2)\n", "pwv.PWV.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "pl.figure(3)\n", "pwv.plot(y='PWV')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Selecting/Querying Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execblocks.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execblocks.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execblocks[['QA0STATUS', 'SE_STATUS']][:7]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execblocks['QA0STATUS'].unique()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execblocks['QA0STATUS'].value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "pl.figure(7)\n", "execblocks['QA0STATUS'].value_counts().plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execblocks['SB_UID'].value_counts()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "pl.figure()\n", "execblocks['SB_UID'].value_counts()[:10].plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execblocks['QA0STATUS'] == \"Pass\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "execblocks[execblocks['QA0STATUS'] == \"Pass\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "execblocks.query('QA0STATUS == \"Pass\"')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "execblocks.query('QA0STATUS == \"Pass\" and SE_STATUS == \"FAIL\"')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sblist = execblocks['SB_UID'].value_counts()[:10].index.values\n", "sblist" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mostobserved = execblocks.query('SB_UID in @sblist')\n", "mostobserved.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mostobserved.groupby(['SB_UID', 'QA0STATUS']).aggregate({'EXECBLOCKUID': pd.np.count_nonzero, 'delta': pd.np.mean})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "table1 = mostobserved.groupby(['SB_UID', 'QA0STATUS']).aggregate({'EXECBLOCKUID': pd.np.count_nonzero, 'delta': pd.np.mean})\n", "table1.unstack()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "table1.to_excel('table1.xls')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execblocks.dropna().apply(lambda x: x['delta'] * 45., axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "execblocks['day'] = execblocks.apply(lambda x: x['STARTTIME'][:10], axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "execblocks" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "effi = execblocks.groupby(['day', 'QA0STATUS']).aggregate({'EXECBLOCKUID': pd.np.count_nonzero})\n", "effi" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "effipl = effi.unstack()['EXECBLOCKUID'].reset_index()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "effipl['Date'] = effipl.apply(lambda x: dt.datetime.strptime(x['day'], '%Y-%m-%d'), axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "effipl" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ax = effipl.plot(x='Date', y='Pass')\n", "effipl.plot(x='Date', y='Fail', ax=ax)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pwv['Date'] = pwv.apply(lambda x: \n", " dt.datetime(int(x['Year']), int(x['Month']), int(x['Day']), int(x['Hour'])), \n", " axis=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pwv.set_index('Date', inplace=True, drop=False)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
bbglab/adventofcode
2016/ferran/day18.ipynb
1
4951
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Challenge 18" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Challenge 18.1" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "myinput = '/home/fmuinos/projects/adventofcode/2016/ferran/inputs/input18.txt'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "safe tiles (.) and traps (^)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": true }, "outputs": [], "source": [ "trap_dict = {True: '^', False: '.'}" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def parse_input(myinput):\n", " with open(myinput, 'rt') as f:\n", " return next(f).rstrip()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'.^^^.^.^^^.^.......^^.^^^^.^^^^..^^^^^.^.^^^..^^.^.^^..^.^..^^...^.^^.^^^...^^.^.^^^..^^^^.....^....'" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parse_input(myinput)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def trap(left, center, right):\n", " cond = []\n", " cond.append((left and center) and not right)\n", " cond.append((center and right) and not left)\n", " cond.append(left and ((not center) and (not right)))\n", " cond.append(right and ((not left) and (not center)))\n", " for item in cond:\n", " if item:\n", " return True\n", " return False\n", "\n", "def next_row(row):\n", " new_row = []\n", " for i in range(len(row)):\n", " if i == 0:\n", " left = False\n", " else:\n", " left = (row[i-1] == '^')\n", " center = (row[i] == '^')\n", " if i == len(row) - 1:\n", " right = False\n", " else:\n", " right = (row[i+1] == '^')\n", " new_row.append(trap_dict[trap(left, center, right)])\n", " return ''.join(new_row)\n", "\n", "def count_safe(row, n):\n", " safe = row.count('.')\n", " for i in range(n-1):\n", " row = next_row(row)\n", " safe += row.count('.')\n", " return safe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tests" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "count_safe('..^^.', 3)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "38" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "count_safe('.^^.^.^^^^', 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Result" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2013" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "row = parse_input(myinput)\n", "count_safe(row, 40)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Challenge 18.2" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "20006289" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "row = parse_input(myinput)\n", "count_safe(row, 400000)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:adventofcode]", "language": "python", "name": "conda-env-adventofcode-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
AllenDowney/ThinkBayes2
examples/normal.ipynb
1
511017
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Think Bayes\n", "\n", "Second Edition\n", "\n", "Copyright 2020 Allen B. Downey\n", "\n", "License: [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# If we're running on Colab, install empiricaldist\n", "# https://pypi.org/project/empiricaldist/\n", "\n", "import sys\n", "IN_COLAB = 'google.colab' in sys.modules\n", "\n", "if IN_COLAB:\n", " !pip install empiricaldist" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Get utils.py and create directories\n", "\n", "import os\n", "\n", "if not os.path.exists('utils.py'):\n", " !wget https://github.com/AllenDowney/ThinkBayes2/raw/master/soln/utils.py\n", " \n", "if not os.path.exists('figs'):\n", " !mkdir figs" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from empiricaldist import Pmf, Cdf\n", "from utils import decorate, savefig" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Univariate normal\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 9.19413254, 7.93174418, 9.26052972, 11.09719199, 8.40716297,\n", " 10.28738107, 9.40427944, 7.29124596, 9.94310494, 10.34056147,\n", " 8.25335541, 9.52849993, 8.49452561, 11.50807398, 9.39053403,\n", " 8.14435859, 15.22433046, 6.37571855, 13.72639858, 9.00185182])" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy.stats import norm\n", "\n", "data = norm(10, 2).rvs(20)\n", "data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(20, 9.640249062837732, 4.073028457273443)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = len(data)\n", "xbar = np.mean(data)\n", "s2 = np.var(data)\n", "\n", "n, xbar, s2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grid algorithm" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "mus = np.linspace(8, 12, 101)\n", "prior_mu = Pmf(1, mus)\n", "prior_mu.index.name = 'mu'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "sigmas = np.linspace(0.01, 5, 100)\n", "ps = sigmas**-2\n", "prior_sigma = Pmf(ps, sigmas)\n", "prior_sigma.index.name = 'sigma'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from utils import make_joint\n", "\n", "prior = make_joint(prior_mu, prior_sigma)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "from utils import normalize\n", "\n", "def update_norm(prior, data):\n", " \"\"\"Update the prior based on data.\n", " \n", " prior: joint distribution of mu and sigma\n", " data: sequence of observations\n", " \"\"\"\n", " X, Y, Z = np.meshgrid(prior.columns, prior.index, data)\n", " likelihood = norm(X, Y).pdf(Z).prod(axis=2)\n", "\n", " posterior = prior * likelihood\n", " normalize(posterior)\n", "\n", " return posterior" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "posterior = update_norm(prior, data)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from utils import marginal\n", "\n", "posterior_mu_grid = marginal(posterior, 0)\n", "posterior_sigma_grid = marginal(posterior, 1)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "posterior_mu_grid.plot()\n", "decorate(title='Posterior distribution of mu')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "posterior_sigma_grid.plot(color='C1')\n", "decorate(title='Posterior distribution of sigma')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Update\n", "\n", "Mostly following notation in Murphy, [Conjugate Bayesian analysis of the Gaussian distribution](https://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "m0 = 0\n", "kappa0 = 0\n", "alpha0 = 0\n", "beta0 = 0" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9.640249062837732" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_n = (kappa0 * m0 + n * xbar) / (kappa0 + n)\n", "m_n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kappa_n = kappa0 + n\n", "kappa_n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10.0" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alpha_n = alpha0 + n/2\n", "alpha_n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "40.73028457273443" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "beta_n = beta0 + n*s2/2 + n * kappa0 * (xbar-m0)**2 / (kappa0 + n) / 2\n", "beta_n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "def update_normal(prior, summary):\n", " m0, kappa0, alpha0, beta0 = prior\n", " n, xbar, s2 = summary\n", "\n", " m_n = (kappa0 * m0 + n * xbar) / (kappa0 + n)\n", " kappa_n = kappa0 + n\n", " alpha_n = alpha0 + n/2\n", " beta_n = (beta0 + n*s2/2 + \n", " n * kappa0 * (xbar-m0)**2 / (kappa0 + n) / 2)\n", "\n", " return m_n, kappa_n, alpha_n, beta_n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(9.640249062837732, 20, 10.0, 40.73028457273443)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prior = 0, 0, 0, 0\n", "summary = n, xbar, s2\n", "update_normal(prior, summary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posterior distribution of sigma" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import invgamma\n", "\n", "dist_sigma2 = invgamma(alpha_n, scale=beta_n)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.52558717474827" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist_sigma2.mean()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.6000366900576855" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist_sigma2.std()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.002242322922353" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sigma2s = np.linspace(0.01, 20, 101)\n", "ps = dist_sigma2.pdf(sigma2s)\n", "posterior_sigma2_invgammas = Pmf(ps, sigma2s)\n", "posterior_sigma2_invgammas.normalize()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "posterior_sigma2_invgammas.plot()\n", "decorate(xlabel='$\\sigma^2$',\n", " ylabel='PDF',\n", " title='Posterior distribution of variance')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5.002242322922353" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sigmas = np.sqrt(sigma2s)\n", "posterior_sigma_invgammas = Pmf(ps, sigmas)\n", "posterior_sigma_invgammas.normalize()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "posterior_sigma_grid.make_cdf().plot(color='gray', label='grid')\n", "posterior_sigma_invgammas.make_cdf().plot(color='C1', label='invgamma')\n", "\n", "decorate(xlabel='$\\sigma$',\n", " ylabel='PDF',\n", " title='Posterior distribution of standard deviation')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2.0978773059173537, 2.0974647660359413)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "posterior_sigma_invgammas.mean(), posterior_sigma_grid.mean()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.3514983795094244, 0.35127637938193573)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "posterior_sigma_invgammas.std(), posterior_sigma_grid.std()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.3244428422615251" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2 / np.sqrt(2 * (n-1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posterior distribution of mu" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import t as student_t\n", "\n", "def make_student_t(df, loc, scale):\n", " return student_t(df, loc=loc, scale=scale)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "df = 2 * alpha_n\n", "precision = alpha_n * kappa_n / beta_n\n", "dist_mu = make_student_t(df, m_n, 1/np.sqrt(precision))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9.640249062837732" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist_mu.mean()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.47568829997952805" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist_mu.std()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.4472135954999579" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sqrt(4/n)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "24.980970615100787" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mus = np.linspace(8, 12, 101)\n", "ps = dist_mu.pdf(mus)\n", "posterior_mu_student = Pmf(ps, mus)\n", "posterior_mu_student.normalize()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "posterior_mu_student.plot()\n", "decorate(xlabel='$\\mu$',\n", " ylabel='PDF',\n", " title='Posterior distribution of mu')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "posterior_mu_grid.make_cdf().plot(color='gray', label='grid')\n", "posterior_mu_student.make_cdf().plot(label='invgamma')\n", "decorate(xlabel='$\\mu$',\n", " ylabel='CDF',\n", " title='Posterior distribution of mu')" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "def make_posterior_mu(m_n, kappa_n, alpha_n, beta_n):\n", " df = 2 * alpha_n\n", " loc = m_n\n", " precision = alpha_n * kappa_n / beta_n\n", " dist_mu = make_student_t(df, loc, 1/np.sqrt(precision))\n", " return dist_mu" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posterior joint distribution" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "mu_mesh, sigma2_mesh = np.meshgrid(mus, sigma2s)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "joint = (dist_sigma2.pdf(sigma2_mesh) * \n", " norm(m_n, sigma2_mesh/kappa_n).pdf(mu_mesh))\n", "joint_df = pd.DataFrame(joint, columns=mus, index=sigma2s)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from utils import plot_contour\n", "\n", "plot_contour(joint_df)\n", "decorate(xlabel='$\\mu$',\n", " ylabel='$\\sigma^2$',\n", " title='Posterior joint distribution')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling from posterior predictive" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "sample_sigma2 = dist_sigma2.rvs(1000)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "sample_mu = norm(m_n, sample_sigma2 / kappa_n).rvs()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "sample_pred = norm(sample_mu, np.sqrt(sample_sigma2)).rvs()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cdf_pred = Cdf.from_seq(sample_pred)\n", "cdf_pred.plot()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(9.516734504437707, 4.577270204875544)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_pred.mean(), sample_pred.var()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analytic posterior predictive" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "df = 2 * alpha_n\n", "precision = alpha_n * kappa_n / beta_n / (kappa_n+1)\n", "dist_pred = make_student_t(df, m_n, 1/np.sqrt(precision))" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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z3HPP8ctf/pINGzZw00038eijj5Kdnc3kyZO58sorWbNmDb169eK55577Qt9S8+bN4+c//znl5eV0796dv/3tb8THx3v0vyDSPDjnmLMuj2c+3sWGvYX4HcSFVdEt7DCz+sXxpWu/pt9DD4VsQP3i7U1szjvWoMvs27ElP7/20tO2qa3LjQkTJpy8aextt93GO++8w7XXXgtAVFQUS5cu5fHHH2fGjBmsWrWKxMREunfvzve//30Atm3bxtNPP83w4cO56667+OMf/8h99913cp2HDh3i0UcfZf78+cTFxfHrX/+aP/zhD7XeqFZEGkZpRRVPLsriiUVZ9GwXz9Ru0UTlraFjXBjTpl3DJZdc4nWJzZ7ut1FD//79mT9/Pvfffz8fffQRrVq1YtGiRVx55ZX079+fhQsXsmnTppPtp0+ffvJ5l156KSkpKURHR9OtWzdycgIdDaelpTF8+HAAZs2axccff3zKOj/77DM2b97M8OHDGTBgAM8++yy7d+++SFss0vx8trOAwY9+yBOLsrg0JZ6bE7NJzvuEoX268M1vfkPhFCJCdg/qTHs6F0ptXW48+eSTZGZmkpaWxsMPP3xKFxsnussICws7peuMsLAwfD4f8MUuOGqOO+eYMGECL7744oXaLBEJWvZ5AXfNXklsVDj3j05h36oP2Z9XwfTp0xkwYIC+zxRCtAdVQ15eHi1atGDWrFncd999rF69GoCkpCSKiop49dVXz3qZe/bsYdmyZQC8+OKLjBgx4pT5Q4cO5ZNPPiErKwuAkpIStm/ffp5bIiLVFZZU8q0XVnP7MytIbRPLT66IYvcnb5EQF8vXvvY1Bg4cqHAKMSG7B+WV2rrcePPNN+nfvz/p6elcccUVZ73MPn368Oyzz3LPPffQs2dPvvGNb5wyPzk5mdmzZ3PzzTdTXh64G/Kjjz5Kr169GmSbRJo75xzffWkNS7bnc1mnllzX9gCblm/i0ksvZfr06bo9UYhSdxsXWHZ2NtOmTWPjxo0XZX1N8f9Q5Hy9vjqXH7yyjntGdCZ5/3L27t3L+PHjGTZsmPaaQoC62xCRZunAsTL+690tdE2MIWr7fA6WlXLTTTfpQohGQAF1gaWnp1+0vScROVXe0VLu/NtKjpZUkMEeCPdz5513kpKS4nVpUg8hF1DOOe1ynyOvDteKhBq/3/H88t08/PZmqvyOEdE59Gvt47bbvkrr1q29Lk/qKaQCKiYmhoKCAtq2bauQOkvOOQoKCoiJifG6FBHPbNt/nNmf7mLNnqNs3X+cHm2j6F+ylks6tGTWrLuIi4vzukQ5CyEVUJ06dSI3N5f8/HyvS2mUYmJi6NSpk9dliHjinfV53PfPdQBc2rEV92QkUr7pQzqndeKWW27RH2+NUEgFVGRkJF27dvW6DBFpZPYUlPCDV9bRP7UVf7p1EAf3ZPH666/TpXMat9xyyylfopfGI6QCSkTkbDnn+PX7WwF48pZBFOzdxeuvv07nzp255ZZb9B2nRkx3khCRRu2X723l3Q37+Mbo7hQf2strr71Gp06dFE5NgPagRKRRqvD5+f28bTy1dCfDurdlRs9oXnrheZKSkhROTYQCSkQanT0FJdz78hrW7DnKjRmd+M6w9rzw92dJSEhg1qxZuiCiiVBAiUijsq+wlPH/s4QKn58fTerNbYPb8cwzzxAREcFtt92mDgabEAWUiDQqr63KpcLn56nbBjOmZyKzZ8+mpKSEO++8U1/CbWJ0kYSINBqlFVX8/bPdpCXGcvUlybz66qvs37+fG264QbcvaoIUUCLSaPxxcRYHjpXzkyl9WLBgATt27GDKlCn07NnT69LkAlBAiUij8PGOQzyxKIuJfdvToeoAy5YtIyMj45z6aJPGQQElIiFvx4HjfP0fq+iS2IL7Rrbj3XfeIT09ncmTJ3tdmlxA9QooM5tsZtvMLMvMflzL/M5mtsjM1pjZejOb2vClikhz9YcPt+N3jj/O7Mc7b7xKQkICN9xwA+Hh4V6XJhfQGQPKzMKBJ4EpQF/gZjPrW6PZz4BXnHMDgZnAHxu6UBFpnt5Yk8t7G/dzy5DOZC56j9LSQIeDLVq08Lo0ucDqswc1BMhyzu10zlUALwEzarRxQMvgcCsgr+FKFJHmauv+Y/zon+vp2CqGgVH72LVrF1OnTqVDhw5elyYXQX0CKhXIqTaeG5xW3cPALDPLBeYC36ltQWZ2t5llmlmmutQQkdPZuLeQ6U98QpgZPxvTjsxlHzNw4EAGDhzodWlykdQnoGrrObBm1603A7Odc52AqcDfzewLy3bOPeWcy3DOZSQnJ599tSLSbDz98S4iwow37x7Exo/eo3379kyZMsXrsuQiqk9A5QJp1cY78cVDeF8FXgFwzi0DYoCkhihQRJqfj3cc4q21e7n2shRWLn4fn8/HDTfcQGRkpNelyUVUn4BaCfQ0s65mFkXgIog5NdrsAa4GMLM+BAJKx/BE5Kw453h55R6+8+JqEuOiGNnqCNnZ2UydOpW2bdt6XZ5cZGcMKOecD/g28AGwhcDVepvM7BEzmx5s9kPga2a2DngRuMM5V/MwoIjIaa3ec4T7X9vA0dJKvj8yhdXLltKvXz8uv/xyr0sTD9TrZrHOubkELn6oPu2hasObgeENW5qINCe5R0q4428riY4IY8kPR/Dyc8/QqlUrrrnmGsxqOxUuTZ3uJCEintu6/xg3/fkzSiqqeOaOK1j58WKOHj3Kddddp76dmjEFlIh4qrjcx9eey6Tc5+fp2zNo546wevVqhg0bRufOnb0uTzykgBIRzxSV+/jm86vJPVLKn2YNYkhaPHPmzKFdu3aMHTvW6/LEYwooEfHMt55fzZLt+Xzv6l5ckZ7Ie++9R0lJCddddx0REepPtblTQImIJ95el8eS7fl8d1wP7h3fk61bt7Jx40ZGjRqlWxkJoIASEQ9syC3key+v5fJOrfjaqG6Ulpby7rvv0r59e0aMGOF1eRIiFFAictE9vmAHcVHh/PX2K0iIiWTevHkUFxczY8YMdaEhJymgROSieiUzh/lbDnDzkM4kJ0Tz+eefs3btWoYPH05KSorX5UkIUUCJyEWzp6CEn7+1iau6teWHE3tTUVHB22+/TVJSEqNHj/a6PAkxCigRuWh+/+E2qpzjDzddTlREGEuWLKGwsJBp06bpqj35AgWUiFwUL63Yw1tr87hjWDoprWI5cOAAy5YtY+DAgXTp0sXr8iQEKaBE5IKr8Pn5fwuz6Jfakh9O7IVzjrfffpvY2FgmTJjgdXkSohRQInLBvbdxH3uPlnLv1b2IjggnMzOTvXv3MmnSJGJjY70uT0KUAkpELqjSiir+8tFOIsONET2SKC4uZuHChaSnp9O/f3+vy5MQpoASkQvqiUU72Lj3GN8b34vYqHDmz59PRUUFU6dOVTcacloKKBG5YJxzvLdhP4M6t+ZbY3uQk5PD2rVrGTp0KMnJyV6XJyFOASUiF8yq3UfYeaiY6Zd3xO/3M3fuXBISEvSdJ6kXBZSIXDBPLMoiITqC6wd3IjMzk/379zNp0iSioqK8Lk0aAQWUiFwQOYdLWLwtn38bmEqEv5JFixbRtWtX+vbt63Vp0kgooETkgvjj4s8B+MpVXVi8eDHl5eVMnjxZF0ZIvSmgRKTBvb0ujxdX7OGmjDRaUkJmZiYZGRm0a9fO69KkEVFAiUiD2rr/GN95cQ0prWL42bQ+vP/++8TExKgLdzlrCigRaTDOOb774hoAnrtrCLm7ssjOzmbMmDG6Y4ScNQWUiDSYB17fwPYDRdw3sRdd28by4YcfkpycTEZGhtelSSOkgBKRBpGZfZiXVuYwsmcSdwzvysqVKzly5AgTJ04kLEwfNXL29K4RkfNWWeXnu8HzTv87cyBhVRUsWbKEHj160KNHD6/Lk0ZKASUi5+2F5XvIKyzjP2f0o01cFEuWLKGiokJdach5UUCJyHnx+x3/M387l3RI4Oo+7Th06BArV65k0KBBuqxczosCSkTOy85DRRwtqeT2YemYGQsWLCAyMlKXlct5U0CJyDlzzvHgm5sAGNI1kd27d7N161aGDx9OXFycx9VJY6eAEpFz9s76fSzbWcDMK9LolhTHhx9+SEJCAldddZXXpUkToIASkXPinONX720lzODe8T3ZsmULe/fuZezYsURGRnpdnjQBCigROSeZu4+w92gpD03rS7v4KBYsWEBycjKXX36516VJE6GAEpGz5qvy883nVwMw9pJ2rFq1isOHDzN+/Hh9KVcaTL3eSWY22cy2mVmWmf24jjY3mtlmM9tkZi80bJkiEkpWZB8m/3g5P7umDykJkSxdupQuXbrQs2dPr0uTJiTiTA3MLBx4EpgA5AIrzWyOc25ztTY9gQeA4c65I2amLz+INFFF5T5+8PI6UlrFMHNIZ5Yt+4Ti4mJmzpypvp6kQdVnD2oIkOWc2+mcqwBeAmbUaPM14Enn3BEA59zBhi1TRELFq5k57D9WxuMzB2K+cj799FP69OlDp06dvC5Nmpj6BFQqkFNtPDc4rbpeQC8z+8TMPjOzybUtyMzuNrNMM8vMz88/t4pFxDNVfserq3Pp0rYFQ7omsnTpUiorKxk3bpzXpUkTVJ+Aqm2f3dUYjwB6AmOAm4G/mlnrLzzJuaeccxnOuYzk5OSzrVVEPPbyyhw27j3GbUO7cOTIETIzMxk4cCBJSUlelyZNUH0CKhdIqzbeCcirpc1bzrlK59wuYBuBwBKRJqKwpJKH52witXUsX7kqncWLFxMWFsbo0aO9Lk2aqPoE1Eqgp5l1NbMoYCYwp0abN4GxAGaWROCQ386GLFREvLV53zEqqvz87Jo+HD18iPXr1zNkyBBatmzpdWnSRJ0xoJxzPuDbwAfAFuAV59wmM3vEzKYHm30AFJjZZmAR8CPnXMGFKlpELr431uQSFxXOqF7JLFy4kOjoaIYPH+51WdKEnfEycwDn3Fxgbo1pD1UbdsAPgg8RaWLKKqtYuPUgYy5px5H8/Wzbto2xY8fSokULr0uTJkxf+RaRM3pq6U4OFVVwU0YnFixYQFxcHEOHDvW6LGniFFAiclrlvipeX51L/9RWpIYfJzs7m5EjRxIVFeV1adLEKaBE5LTe37if7IISbr+qCwsXLqRVq1YMHjzY67KkGVBAichpvbU2j5RWMfSNKyIvL4/Ro0cTEVGv09ci50UBJSJ1OlxcwdLt+Vx7WQqLFy2ibdu26k5DLhoFlIjU6b2N+/D5HX3jS8jPz2fs2LHqTkMuGr3TRKROLyzfQ5e2Ldi99lM6dOhA3759vS5JmhEFlIjUyu93bD9wnAFJxtGjRxg7dqy605CLSgElIrVavuswlVWO43k76dSpkzojlItOASUiX1Duq+Inb2wg3KB95T7GjRunvSe56BRQIvIFf1r8ObsOFTMqbh99uqXRtWtXr0uSZkgBJSKneHHFHh6bv4MB7SLo4tvL2LFjvS5JmikFlIic5JzjiYVZpLdtwRUV6+jVqydpaWlnfqLIBaCAEpGTNuUdY+/RUka39+ErL9Xek3hKASUiJ722OpeIMIOcNfTt25eUlBSvS5JmTAElIkDg8N6ctXlcmghhvlLGjBnjdUnSzCmgRASAhVsPUlBcQcLxPVx22WUkJyd7XZI0cwooEaGwpJIfvLKO+EjoFpbP6NGjvS5JRAElIvD7D7dRWFrJ0PCdXDFoAImJiV6XJKKAEmnuXsnM4blluxne3k96RCGjRo3yuiQRQAEl0qz5qvz87oNtdGsbS7dj6xg8eDCtWrXyuiwRQAEl0qwt2Z7PwePljGpTSGxkGCNHjvS6JJGTFFAizdiLK3JIiovE5W5gyJAhxMfHe12SyEkKKJFmavWeI8zfcoAesSXExkQxfPhwr0sSOYUCSqSZ+tXcrbSPj6Rb0UauuuoqYmNjvS5J5BQKKJFmaMn2fFZkH2ZQ3FHaxEUzdOhQr0sS+QIFlEgzU1BUzk9e30BSiwiSCrcyYsQIoqOjvS5L5AsUUCLNSJXf8cDrG9h7tJRpiQdo0zKejIwMr8sSqZUCSqQZ+a93tzBv8wGuu7Q1VrCL0aNHExkZ6XVZIrWK8LoAEbk4MrMP88wnu8jo0obux7gkKOEAABWGSURBVNfjb9OGAQMGeF2WSJ20ByXSDBSX+3jwrU0kREdwb0YLCvIPMHbsWMLDw70uTaROCiiRZuCVzBy27DvGw9P7smbZUtq3b0+/fv28LkvktBRQIk1chc/PXz/aRZe2Lejs38+RI0cYN24cZuZ1aSKnVa+AMrPJZrbNzLLM7MenafdlM3NmpsuCRELEM5/sYu/RUu6b0JOlS5eSlpZGz549vS5L5IzOGFBmFg48CUwB+gI3m1nfWtolAN8Fljd0kSJybp5a+jm/+2Ab/VJb0vr4LoqKirj66qu19ySNQn32oIYAWc65nc65CuAlYEYt7f4T+A1Q1oD1icg52lNQwq/e28qoXsn8aeZlfPrpp/To0YMuXbp4XZpIvdQnoFKBnGrjucFpJ5nZQCDNOffO6RZkZnebWaaZZebn5591sSJSf88v302YGb+8vj9b166grKyM8ePHe12WSL3VJ6BqOxbgTs40CwP+B/jhmRbknHvKOZfhnMtITk6uf5Uiclbyjpby7LJsxvROJsaVs3z5ci6//HLat2/vdWki9VafgMoF0qqNdwLyqo0nAP2AxWaWDQwF5uhCCRHvPL98N2WVfn44sTeLFy8GYMyYMZ7WJHK26hNQK4GeZtbVzKKAmcCcEzOdc4XOuSTnXLpzLh34DJjunMu8IBWLyGlt3X+M/1uyk/F92tE2vIx169ZxxRVX0Lp1a69LEzkrZwwo55wP+DbwAbAFeMU5t8nMHjGz6Re6QBE5O8t3HqbK7/jJ1D4sWLCAqKgodeUujVK97sXnnJsLzK0x7aE62o45/7JE5Fx9sGk/3ZPjsKJ8tm/fzrhx42jRooXXZYmcNd1JQqQJqazyk7n7CGN6JTN//nxatmypzgil0VJAiTQhb6zeS4XPT8fIYvLy8hg7dqy605BGSwEl0kQcOFbGg29tpH9qS45tXUb79u257LLLvC5L5JwpoESagLLKKr7zwhqq/I47LzGOFR5lwoQJhIXpV1waL717RZqA+/65jhXZh/nvGX3YsfoTunfvTvfu3b0uS+S8KKBEGrnich+Lth7kmv4pJBzeSnl5ORMnTvS6LJHzpoASaeRmf5pNcUUV1/drw8qVKxk0aBDt2rXzuiyR86aAEmnkFm09yKUdW3Jw82dERUUxduxYr0sSaRAKKJFG7MCxMjJ3H2Fwhyi2b9/OyJEjiYuL87oskQZRrztJiEjoeebjXTy5KAszsLz1tG7dmiuvvNLrskQajPagRBqhA8fKeOSdzaS2ieUnw1oSVpjHpEmTiIjQ35zSdCigRBqhOWsDPd78dFIPDm/+lG7dutG7d2+PqxJpWAookUZm2/7j/G7eNgZ3acPBrZmUl5czefJkzGrrW1Sk8VJAiTQiR4oruPWvy4mLjuA/Rndk9epVDBkyBPVQLU2RAkqkEXk5M4dDReX86vr+bPpsIS1atGD06NFelyVyQSigRBqJwpJK/m/J5/RJaUli+T5ycnIYP348sbGxXpcmckEooEQaiV+8s4mjJZX8fGpPFi2YT1paGgMGDPC6LJELRgEl0ghkZh/m9dV7uXlIGgXbV1FaWsrUqVN1YYQ0aQookRBX5Xf84JV1JMREcNOlLcnMzOSKK66gQ4cOXpcmckEpoERC3MKtB9lzuIRfXNuX5Ys/ID4+Xvfbk2ZBASUSwg4VlXPvS2to3SKS5LIc9u/fz+TJk4mJifG6NJELTgElEsIWbj1ISUUVD03qzkdLFtOzZ0/69u3rdVkiF4UCSiREOed45uNd9GofT0nWcgBdGCHNigJKJEQt2Z7P1v3HuaZ7DJ9n7WDs2LG0bt3a67JELhoFlEiIenzBDlrGRFC6dSkdO3ZUVxrS7CigRELQ7oJi1uw5ypA2pVSWlzFjxgzCwvTrKs2L3vEiIcY5x0/e2ECYQevDWxg9ejTt2rXzuiyRi04BJRJi5m0+wCdZBfSMKqRnx0SGDx/udUkinlBAiYSQrINF3PfKOlJi/VwVvpPp06cTHh7udVkinlBAiYSQ/3xnM2WVPkb4NzBuzChSUlK8LknEMxFeFyAigYsi7vn7KrbuP87AmHwuSWvPiBEjvC5LxFPagxLxmHOOn76xkdwjpUxpX8ygyH1cd911umpPmj39Boh47LXVe/k46xAzekTS4dgWpkyaQGJiotdliXhOASXioSPFFfzi7U30To4lYtenXHLJJQwePNjrskRCQr0Cyswmm9k2M8sysx/XMv8HZrbZzNab2QIz69LwpYo0LR9uPsC43y+muNzHUNtO64Q4pk+frnvtiQSdMaDMLBx4EpgC9AVuNrOat1NeA2Q45y4DXgV+09CFijQlFT4/97+2njZxUXyrTyXhRQe5/vrriY2N9bo0kZBRnz2oIUCWc26nc64CeAmYUb2Bc26Rc64kOPoZ0KlhyxRpOpxz/PK9LRwuruC6HpEU7VrHqFGj6NJFBx5EqqtPQKUCOdXGc4PT6vJV4L3aZpjZ3WaWaWaZ+fn59a9SpAl5eM4m/vZJNlf3bM2R9Yvo1q0bo0aN8roskZBTn4Cq7YC4q7Wh2SwgA/htbfOdc0855zKccxnJycn1r1Kkicg5XMLfP9vNtf3b0/94JgnxcVx//fW6pFykFvX5rcgF0qqNdwLyajYys/HAT4HpzrnyhilPpGn5zQfbiAgLo69vB8ePH+OGG24gLi7O67JEQlJ9Amol0NPMuppZFDATmFO9gZkNBP5MIJwONnyZIo3f6j1HeHtdHld1MA5mb2fy5Ml06qTTtSJ1OWNAOed8wLeBD4AtwCvOuU1m9oiZTQ82+y0QD/zTzNaa2Zw6FifSbD21ZCfhBikFqxg0aBAZGRlelyQS0up1Lz7n3Fxgbo1pD1UbHt/AdYk0KTmHS1i49QC9wvO5JD2VqVOn6vtOImegM7MiF1hxuY8fvbIG5/cxLLGYG2+8UV1oiNSD7mYucgHtOlTM1MeXUlrpZ1B0AffMuoEWLVp4XZZIo6A9KJEL6OmPPqe00s+oqGx+e7u6bhc5G9qDErkAnHO8vHIPzy/fQ+ewo/zohlF079bN67JEGhUFlEgDe3/jfv60OIt1uYW0DyviZxO60L9/f6/LEml0FFAiDejVVbk88Pp6EqMdV0bu5t9H92LC2JFelyXSKCmgRBrAkeIKfv/hNv7x2R7SW4YxrGI1I64axPirr/a6NJFGSwElcp5KK6r4wStrWbQtn4Htwul7LJMhA/szZcoUfddJ5DwooETOw5HiCqY/+TE5h0sZlxZOl0PLGTBwANdee63CSeQ8KaBEzsODb20k53ApM3uFEbNnOYMGD2LatGkKJ5EGoIASOUeLtx3knfX7GNy2iticTIZcOYTJkycrnEQaiAJK5Cw553h7/T4efHMDSdFV9C1ey9hxYxk5cqTCSaQBKaBEzsLGvYU88PoGNuwtpEN0BVfZDv7t2msYPHiw16WJNDkKKJF6Wp97lK88swLn9zMhYR/pls+Xv3Q9vXv39ro0kSZJASVSD/M27edHr64nKszPiLDNdIkL4+ab76J9+/ZelybSZCmgRM5gxa7D3P33VSTFwEjfBvp1acdNN92krtpFLjAFlEgd1uYc5ffztvHRjkOE4RjrNjB+2CDGjx+v/pxELgIFlEgNJRU+7vvnOt7fuJ8WkcZlUfn0ij7KXdf/G3369PG6PJFmQwElUsNTS3cyd8N+RrTzkXpsE5d0SeG66+6gdevWXpcm0qwooESqeX/jPh6bv4O0yCJ6FW1n3PixDBs2jLAw9e0pcrEpoESCdu09wH0vZZJoZcxMK+LLM+7WVXoiHlJASbNXUFjMIy9/xKJdxZS4KH42tiM3TviS9ppEPKaAkmartLyS/371U57fcAw/YXRLiOK/Jvfn2sHpXpcmIiigpBny+/3848OV/H7pPgqrIkmJruIHE3tww3BdoScSShRQ0mxUVlYy+4MVPLdiHzkVscSEhfHIhI7cNm6AbvIqEoIUUNLkHT9+nNcWruDpzEPkVMaTEB7FVzMS+e41g2kVG+V1eSJSBwWUNEl+v5/Pd+7kvU/X8dzWKg7544i0OL55VXvunTqQ6EjdCUIk1CmgpEkoqfCRf7ycjzfvYcH63WzYV8xhXxRVxBAbAfeP68otw3vSKjbS61JFpJ4UUNLolFVWcay0kmNllazPLeTlz3ayYs9xXLU23eLCGdGzNZf3SGVSvxRSWsV6Vq+InBsFlDQKB46V8frqvTz98S4OFZWfMi/eyukfcZhuyXH06ZrG+Iw+dO/Y1qNKRaShKKAkJJVVVrFsZwHrco6yeOsB1u89ht9BTFgVl0bk09LKiYs0eqclc/WA7vTuNUndX4g0MQooCQmFJZV8nl/E5pxDLM86wNJdxygsDxy0a2vF9Aorpl/MEfp3TiI9vQfdunUjNTVVd3sQacIUUHJRHS6uYENOAVtyDpG1v5DdBcXsPlrJwbJ/fQ8pgio6hB1nXNtSMrok0r1zZzp16kRKSor6YRJpRuoVUGY2GXgcCAf+6pz7VY350cBzwGCgALjJOZfdsKVKqMk9UsKhogqOFFdQUFxBQVEZa3YfJu9oCWUVPsoqq6jw+Sn3+ams8lPph3K/AYEwCsNPgpXTJtLH+OQwerSL45LURC7vlkrHlPZER0d7u4Ei4qkzBpSZhQNPAhOAXGClmc1xzm2u1uyrwBHnXA8zmwn8GrjpQhQstfP7HSXBQDj5qKqiwueoqAqMVwZ/lvv8VFT5qfT5KfdVUVpRSVm5j/JKH2WVPioqqyj3+SivrKKi0keFr4ryyioqfVWU+/yUV1aRW+w4WvHFw2sRVNE2rIQI/ITjJ9b8tAoPIyYmghbRUbSNj6Z/Shy9UtrQs1MyyUltFUQiUqv67EENAbKcczsBzOwlYAZQPaBmAA8Hh18FnjAzc85Vv/K3QZVWVPHgWxtxDhyO4D9OrDIw/K9p7sREAu2d4+Rz3cnpBIfdyedS7fmuxjrg1GWcrKVaW2pdx6nLo0bNfufn2LHjtT//xHhwns9Bqc8orQJHw9yux3CEnfLwE2YnhiEyDBIj/fRt7ad7K0iMCwRPu5YtaNsqjri4OOLj40lISCA+Pp7ISH33SETOXn0CKhXIqTaeC1xZVxvnnM/MCoG2wKHqjczsbuBugM6dO59jyQE+v59lnxdUW3bwgXHitmoWWGfgY9v+Nf6veae2p1r76ss7ufzggFUfr/kc/jXDAAsDI+zk8gLTq9VY7bknnuP3+yk6eIx/LcpOri+wHDtZS0QYxET6iY1wxIQ5osKNiHAjMiwwLyo8jKiIsH/9DD6iI8KJjgwnJjKCmKgIYqMjiYkKPqKjiIyMJCoq6uQjOjqaqKgoIiJ02lJELo76fNrU9md5zT2j+rTBOfcU8BRARkbGee1dJcRE8smPx53PIkLcUK8LEBHxVH2u0c0F0qqNdwLy6mpjZhFAK+BwQxQoIiLNU30CaiXQ08y6mlkUMBOYU6PNHOD24PCXgYUX8vyTiIg0fWc8xBc8p/Rt4AMCl5k/45zbZGaPAJnOuTnA08DfzSyLwJ7TzAtZtIiINH31OuPtnJsLzK0x7aFqw2XADQ1bmoiINGe6T4yIiIQkBZSIiIQkBZSIiIQkBZSIiIQkBZSIiIQk8+rrSmaWD+z2ZOWQRI3bMDVy2p7Q19S2SdsT+hrTNnVxziXXnOhZQHnJzDKdcxle19FQtD2hr6ltk7Yn9DWFbdIhPhERCUkKKBERCUnNNaCe8rqABqbtCX1NbZu0PaGv0W9TszwHJSIioa+57kGJiEiIU0CJiEhIatIBZWaTzWybmWWZ2Y9rmR9tZi8H5y83s/SLX2X9mFmamS0ysy1mtsnM7q2lzRgzKzSztcHHQ7UtK1SYWbaZbQjWmlnLfDOz/w2+PuvNbJAXddaXmfWu9n+/1syOmdn3arQJ6dfIzJ4xs4NmtrHatEQz+9DMdgR/tqnjubcH2+wws9tra3Ox1bE9vzWzrcH31Btm1rqO5572/emVOrbpYTPbW+19NbWO5572MzHkOOea5INA31WfA92AKGAd0LdGm28C/xccngm87HXdp9meFGBQcDgB2F7L9owB3vG61rPYpmwg6TTzpwLvAQYMBZZ7XfNZbFs4sJ/AFxAbzWsEjAIGARurTfsN8OPg8I+BX9fyvERgZ/Bnm+BwmxDdnolARHD417VtT3Dead+fIbZNDwP3neF5Z/xMDLVHU96DGgJkOed2OucqgJeAGTXazACeDQ6/ClxtZnYRa6w359w+59zq4PBxYAuQ6m1VF9wM4DkX8BnQ2sxSvC6qnq4GPnfOeXW3lHPinFtKoNPR6qr/njwL/FstT50EfOicO+ycOwJ8CEy+YIXWU23b45yb55zzBUc/Azpd9MLOQx2vUX3U5zMxpDTlgEoFcqqN5/LFD/STbYJv2EKg7UWp7jwED0UOBJbXMvsqM1tnZu+Z2aUXtbCz54B5ZrbKzO6uZX59XsNQNRN4sY55jek1AmjvnNsHgT+UgHa1tGmsr9VdBPbSa3Om92eo+XbwsOUzdRyGbXSvUVMOqNr2hGpeU1+fNiHFzOKB14DvOeeO1Zi9msAhpcuB/we8ebHrO0vDnXODgCnAt8xsVI35je71ATCzKGA68M9aZje216i+Gt1rZWY/BXzA83U0OdP7M5T8CegODAD2Ab+vpU2je42ackDlAmnVxjsBeXW1MbMIoBXntut8UZhZJIFwet4593rN+c65Y865ouDwXCDSzJIucpn15pzLC/48CLxB4BBEdfV5DUPRFGC1c+5AzRmN7TUKOnDi0Grw58Fa2jSq1yp4Ecc04FYXPEFTUz3enyHDOXfAOVflnPMDf6H2WhvVawRNO6BWAj3NrGvwL9qZwJwabeYAJ642+jKwsK43q9eC58aeBrY45/5QR5sOJ86hmdkQAq9vwcWrsv7MLM7MEk4MEzhxvbFGsznAV4JX8w0FCk8cagpxN1PH4b3G9BpVU/335HbgrVrafABMNLM2wcNLE4PTQo6ZTQbuB6Y750rqaFOf92fIqHFu9jpqr7U+n4mhxeurNC7kg8BVYNsJXLny0+C0Rwi8MQFiCByGyQJWAN28rvk02zKCwO74emBt8DEV+Drw9WCbbwObCFyd8xkwzOu6T7M93YJ1rgvWfOL1qb49BjwZfP02ABle112P7WpBIHBaVZvWaF4jAsG6D6gk8Bf3Vwmcl10A7Aj+TAy2zQD+Wu25dwV/l7KAO73eltNsTxaBczEnfo9OXMnbEZh7uvdnKDzq2Ka/B39H1hMInZSa2xQc/8JnYig/dKsjEREJSU35EJ+IiDRiCigREQlJCigREQlJCigREQlJCigREQlJCigREQlJCigREQlJ/x+6znmWgcnbEQAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "xs = np.linspace(2, 16, 101)\n", "ys = dist_pred.cdf(xs)\n", "\n", "plt.plot(xs, ys, color='gray', label='student t')\n", "cdf_pred.plot(label='sample')\n", "\n", "decorate(title='Predictive distribution')" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "def make_posterior_pred(m_n, kappa_n, alpha_n, beta_n):\n", " df = 2 * alpha_n\n", " loc = m_n\n", " precision = alpha_n * kappa_n / beta_n / (kappa_n+1)\n", " dist_pred = make_student_t(df, loc, 1/np.sqrt(precision))\n", " return dist_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multivariate normal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate data" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[4, 1.7999999999999998], [1.7999999999999998, 9]]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean = [10, 20]\n", "\n", "sigma_x = 2\n", "sigma_y = 3\n", "rho = 0.3\n", "cov = rho * sigma_x * sigma_y \n", "\n", "Sigma = [[sigma_x**2, cov], [cov, sigma_y**2]]\n", "Sigma" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 9.09443543, 17.71994515],\n", " [16.44233547, 23.6472648 ],\n", " [12.01135574, 24.03660932],\n", " [ 9.0748985 , 21.66032914],\n", " [13.11185908, 18.83369963],\n", " [13.63682486, 21.49850919],\n", " [ 8.24152444, 22.92781604],\n", " [13.56010439, 29.07263218],\n", " [11.65768605, 20.22149536],\n", " [ 9.63956732, 16.85039916],\n", " [ 9.31995128, 15.62154768],\n", " [ 7.95094601, 14.22500863],\n", " [ 8.72949594, 20.7225257 ],\n", " [10.01041019, 21.53598675],\n", " [10.2989316 , 16.31756551],\n", " [ 7.95686713, 18.08908348],\n", " [ 6.84997664, 16.56372388],\n", " [ 8.88606479, 18.42782995],\n", " [10.0890154 , 21.17141536],\n", " [11.62347859, 17.63973527]])" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy.stats import multivariate_normal\n", "\n", "n = 20\n", "data = multivariate_normal(mean, Sigma).rvs(n)\n", "data" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = len(data)\n", "n" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([10.40928644, 19.83915611])" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xbar = np.mean(data, axis=0)\n", "xbar" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 5.77766323, 4.71029497],\n", " [ 4.71029497, 12.38587614]])" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "S = np.cov(data.transpose())\n", "S" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1. , 0.55681205],\n", " [0.55681205, 1. ]])" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.corrcoef(data.transpose())" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([2.40367702, 3.51935735])" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stds = np.sqrt(np.diag(S))\n", "stds" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1. , 0.55681205],\n", " [0.55681205, 1. ]])" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corrcoef = S / np.outer(stds, stds)\n", "corrcoef" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "def unpack_cov(S):\n", " stds = np.sqrt(np.diag(S))\n", " corrcoef = S / np.outer(stds, stds)\n", " return stds[0], stds[1], corrcoef[0][1]" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2.4036770231837497, 3.519357348098385, 0.5568120515289984)" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sigma_x, sigma_y, rho = unpack_cov(S)\n", "sigma_x, sigma_y, rho" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "def pack_cov(sigma_x, sigma_y, rho):\n", " cov = sigma_x * sigma_y * rho\n", " return np.array([[sigma_x**2, cov], [cov, sigma_y**2]])" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 5.77766323, 4.71029497],\n", " [ 4.71029497, 12.38587614]])" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pack_cov(sigma_x, sigma_y, rho)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 5.77766323, 4.71029497],\n", " [ 4.71029497, 12.38587614]])" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "S" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Update\n", "\n" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "m_0 = 0\n", "Lambda_0 = 0\n", "nu_0 = 0\n", "kappa_0 = 0" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([10.40928644, 19.83915611])" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_n = (kappa_0 * m_0 + n * xbar) / (kappa_0 + n)\n", "m_n" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([10.40928644, 19.83915611])" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xbar" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[108.35324426, 206.51145873],\n", " [206.51145873, 393.59211512]])" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "diff = (xbar - m_0)\n", "D = np.outer(diff, diff)\n", "D" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 5.77766323, 4.71029497],\n", " [ 4.71029497, 12.38587614]])" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Lambda_n = Lambda_0 + S + n * kappa_0 * D / (kappa_0 + n)\n", "Lambda_n" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 5.77766323, 4.71029497],\n", " [ 4.71029497, 12.38587614]])" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "S" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nu_n = nu_0 + n\n", "nu_n" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kappa_n = kappa_0 + n\n", "kappa_n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posterior distribution of covariance" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import invwishart\n", "\n", "def make_invwishart(nu, Lambda):\n", " d, _ = Lambda.shape\n", " return invwishart(nu, scale=Lambda * (nu - d - 1))" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "dist_cov = make_invwishart(nu_n, Lambda_n)" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 5.77766323, 4.71029497],\n", " [ 4.71029497, 12.38587614]])" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist_cov.mean()" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 5.77766323, 4.71029497],\n", " [ 4.71029497, 12.38587614]])" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "S" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 5.75009385, 4.73096796],\n", " [ 4.73096796, 12.48177402]])" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_Sigma = dist_cov.rvs(1000)\n", "np.mean(sample_Sigma, axis=0)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [], "source": [ "res = [unpack_cov(Sigma) for Sigma in sample_Sigma]" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2.3607897260964137, 3.4767864662050987, 0.5480438257464233)" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_sigma_x, sample_sigma_y, sample_rho = np.transpose(res)\n", "sample_sigma_x.mean(), sample_sigma_y.mean(), sample_rho.mean()" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2.4036770231837497, 3.519357348098385, 0.5568120515289984)" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unpack_cov(S)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Cdf.from_seq(sample_sigma_x).plot(label=r'$\\sigma_x$')\n", "Cdf.from_seq(sample_sigma_y).plot(label=r'$\\sigma_y$')\n", "\n", "decorate(xlabel='Standard deviation',\n", " ylabel='CDF',\n", " title='Posterior distribution of standard deviation')" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Cdf.from_seq(sample_rho).plot()\n", "\n", "decorate(xlabel='Coefficient of correlation',\n", " ylabel='CDF',\n", " title='Posterior distribution of correlation')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluate the Inverse Wishart PDF" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [], "source": [ "num = 51\n", "sigma_xs = np.linspace(0.01, 10, num)" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "sigma_ys = np.linspace(0.01, 10, num)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "rhos = np.linspace(-0.3, 0.9, num)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "sigma_x sigma_y rho \n", "0.01 0.01 -0.300 0\n", " -0.276 0\n", " -0.252 0\n", " -0.228 0\n", " -0.204 0\n", "dtype: int64" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "index = pd.MultiIndex.from_product([sigma_xs, sigma_ys, rhos],\n", " names=['sigma_x', 'sigma_y', 'rho'])\n", "joint = Pmf(0, index)\n", "joint.head()" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.006027909520828536" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist_cov.pdf(S)" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.700704063935837" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for sigma_x, sigma_y, rho in joint.index:\n", " Sigma = pack_cov(sigma_x, sigma_y, rho)\n", " joint.loc[sigma_x, sigma_y, rho] = dist_cov.pdf(Sigma)\n", " \n", "joint.normalize()" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2.210173131819553, 3.236037871074533, 0.5138102647160548)" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from utils import pmf_marginal\n", "\n", "posterior_sigma_x = pmf_marginal(joint, 0)\n", "posterior_sigma_y = pmf_marginal(joint, 1)\n", "marginal_rho = pmf_marginal(joint, 2)\n", "\n", "posterior_sigma_x.mean(), posterior_sigma_y.mean(), marginal_rho.mean()" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2.4036770231837497, 3.519357348098385, 0.5568120515289984)" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unpack_cov(S)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "posterior_sigma_x.plot(label='$\\sigma_x$')\n", "posterior_sigma_y.plot(label='$\\sigma_y$')\n", "\n", "decorate(xlabel='Standard deviation',\n", " ylabel='PDF',\n", " title='Posterior distribution of standard deviation')" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "posterior_sigma_x.make_cdf().plot(color='gray')\n", "posterior_sigma_y.make_cdf().plot(color='gray')\n", "\n", "Cdf.from_seq(sample_sigma_x).plot(label=r'$\\sigma_x$')\n", "Cdf.from_seq(sample_sigma_y).plot(label=r'$\\sigma_y$')\n", "\n", "decorate(xlabel='Standard deviation',\n", " ylabel='CDF',\n", " title='Posterior distribution of standard deviation')" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "marginal_rho.make_cdf().plot(color='gray')\n", "\n", "Cdf.from_seq(sample_rho).plot()\n", "\n", "decorate(xlabel='Coefficient of correlation',\n", " ylabel='CDF',\n", " title='Posterior distribution of correlation')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posterior distribution of mu" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([10.40928644, 19.83915611])" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m_n" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [], "source": [ "sample_mu = [multivariate_normal(m_n, Sigma/kappa_n).rvs()\n", " for Sigma in sample_Sigma]" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10.417787293315167, 19.808873575990383)" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_mu0, sample_mu1 = np.transpose(sample_mu)\n", "\n", "sample_mu0.mean(), sample_mu1.mean()" ] }, { "cell_type": "code", "execution_count": 96, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([10.40928644, 19.83915611])" ] }, "execution_count": 96, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xbar" ] }, { "cell_type": "code", "execution_count": 97, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.5383503263225001, 0.7858092840001913)" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_mu0.std(), sample_mu1.std()" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.4472135954999579, 0.6708203932499369)" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2 / np.sqrt(n), 3 / np.sqrt(n)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "Cdf.from_seq(sample_mu0).plot(label=r'$\\mu_0$ sample')\n", "Cdf.from_seq(sample_mu1).plot(label=r'$\\mu_1$ sample')\n", "\n", "decorate(xlabel=r'$\\mu$',\n", " ylabel='CDF',\n", " title=r'Posterior distribution of $\\mu$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Multivariate student t\n", "\n", "Let's use [this implementation](http://gregorygundersen.com/blog/2020/01/20/multivariate-t/)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": {}, "outputs": [], "source": [ "from scipy.special import gammaln\n", "\n", "def multistudent_pdf(x, mean, shape, df):\n", " return np.exp(logpdf(x, mean, shape, df))\n", "\n", "def logpdf(x, mean, shape, df):\n", " p = len(mean)\n", " vals, vecs = np.linalg.eigh(shape)\n", " logdet = np.log(vals).sum()\n", " valsinv = np.array([1.0/v for v in vals])\n", " U = vecs * np.sqrt(valsinv)\n", " dev = x - mean\n", " maha = np.square(dev @ U).sum(axis=-1)\n", "\n", " t = 0.5 * (df + p)\n", " A = gammaln(t)\n", " B = gammaln(0.5 * df)\n", " C = p/2. * np.log(df * np.pi)\n", " D = 0.5 * logdet\n", " E = -t * np.log(1 + (1./df) * maha)\n", "\n", " return A - B - C - D + E\n" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.4530003997972322" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = len(m_n)\n", "x = m_n\n", "mean = m_n\n", "df = nu_n - d + 1\n", "shape = Lambda_n / kappa_n\n", "multistudent_pdf(x, mean, shape, df)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [], "source": [ "mu0s = np.linspace(8, 12, 91)\n", "mu1s = np.linspace(18, 22, 101)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(101, 91, 2)" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mu_mesh = np.dstack(np.meshgrid(mu0s, mu1s))\n", "mu_mesh.shape" ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [], "source": [ "ps = multistudent_pdf(mu_mesh, mean, shape, df)" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "549.2874162564223" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joint = pd.DataFrame(ps, columns=mu0s, index=mu1s)\n", "normalize(joint)" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.contour.QuadContourSet at 0x7fb168b42410>" ] }, "execution_count": 106, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_contour(joint)" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [], "source": [ "from utils import marginal\n", "\n", "posterior_mu0_student = marginal(joint, 0)\n", "posterior_mu1_student = marginal(joint, 1)" ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "posterior_mu0_student.make_cdf().plot(color='gray', label=r'$\\mu_0 multi t$')\n", "posterior_mu1_student.make_cdf().plot(color='gray', label=r'$\\mu_1 multi t$')\n", "\n", "Cdf.from_seq(sample_mu0).plot(label=r'$\\mu_0$ sample')\n", "Cdf.from_seq(sample_mu1).plot(label=r'$\\mu_1$ sample')\n", "\n", "decorate(xlabel=r'$\\mu$',\n", " ylabel='CDF',\n", " title=r'Posterior distribution of $\\mu$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compare to analytic univariate distributions" ] }, { "cell_type": "code", "execution_count": 109, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(20, 10.409286443363563, 5.777663231781492)" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prior = 0, 0, 0, 0\n", "summary = n, xbar[0], S[0][0]\n", "summary" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10.409286443363563, 20, 10.0, 57.77663231781492)" ] }, "execution_count": 110, "metadata": {}, "output_type": "execute_result" } ], "source": [ "params = update_normal(prior, summary)\n", "params" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10.409286443363563, 0.5665521076251745)" ] }, "execution_count": 111, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist_mu0 = make_posterior_mu(*params)\n", "dist_mu0.mean(), dist_mu0.std()" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "19.930298060446894" ] }, "execution_count": 112, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mu0s = np.linspace(7, 12, 101)\n", "ps = dist_mu0.pdf(mu0s)\n", "posterior_mu0 = Pmf(ps, index=mu0s)\n", "posterior_mu0.normalize()" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(20, 19.839156108992704, 12.385876143614098)" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prior = 0, 0, 0, 0\n", "summary = n, xbar[1], S[1][1]\n", "summary" ] }, { "cell_type": "code", "execution_count": 114, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(19.839156108992704, 20, 10.0, 123.85876143614098)" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "params = update_normal(prior, summary)\n", "params" ] }, { "cell_type": "code", "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(19.839156108992704, 0.8295204820863575)" ] }, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dist_mu1 = make_posterior_mu(*params)\n", "dist_mu1.mean(), dist_mu1.std()" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "16.64813735166017" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mu1s = np.linspace(17, 23, 101)\n", "ps = dist_mu1.pdf(mu1s)\n", "posterior_mu1 = Pmf(ps, index=mu1s)\n", "posterior_mu1.normalize()" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "posterior_mu0.make_cdf().plot(label=r'$\\mu_0$ uni t', color='gray')\n", "posterior_mu1.make_cdf().plot(label=r'$\\mu_1$ uni t', color='gray')\n", "\n", "Cdf.from_seq(sample_mu0).plot(label=r'$\\mu_0$ sample')\n", "Cdf.from_seq(sample_mu1).plot(label=r'$\\mu_1$ sample')\n", "\n", "decorate(xlabel=r'$\\mu$',\n", " ylabel='CDF',\n", " title=r'Posterior distribution of $\\mu$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling from posterior predictive" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [], "source": [ "sample_pred = [multivariate_normal(mu, Sigma).rvs()\n", " for mu, Sigma in zip(sample_mu, sample_Sigma)]" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10.343334031620982, 19.79428758822268)" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_x0, sample_x1 = np.transpose(sample_pred)\n", "\n", "sample_x0.mean(), sample_x1.mean()" ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2.4491840957223703, 3.5023208219780724)" ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_x0.std(), sample_x1.std()" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10.409286443363563, 2.5962679183291297)" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prior = 0, 0, 0, 0\n", "summary = n, xbar[0], S[0][0]\n", "params = update_normal(prior, summary)\n", "dist_x0 = make_posterior_pred(*params)\n", "dist_x0.mean(), dist_x0.std()" ] }, { "cell_type": "code", "execution_count": 122, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6.224974183920316" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x0s = np.linspace(2, 18, 101)\n", "ps = dist_x0.pdf(x0s)\n", "pred_x0 = Pmf(ps, index=x0s)\n", "pred_x0.normalize()" ] }, { "cell_type": "code", "execution_count": 123, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(19.839156108992704, 3.8013403996769934)" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prior = 0, 0, 0, 0\n", "summary = n, xbar[1], S[1][1]\n", "params = update_normal(prior, summary)\n", "dist_x1 = make_posterior_pred(*params)\n", "dist_x1.mean(), dist_x1.std()" ] }, { "cell_type": "code", "execution_count": 124, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.944574125732073" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x1s = np.linspace(10, 30, 101)\n", "ps = dist_x1.pdf(x1s)\n", "pred_x1 = Pmf(ps, index=x1s)\n", "pred_x1.normalize()" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pred_x0.make_cdf().plot(label=r'$x_0$ student t', color='gray')\n", "pred_x1.make_cdf().plot(label=r'$x_1$ student t', color='gray')\n", "\n", "Cdf.from_seq(sample_x0).plot(label=r'$x_0$ sample')\n", "Cdf.from_seq(sample_x1).plot(label=r'$x_1$ sample')\n", "\n", "decorate(xlabel='Quantity',\n", " ylabel='CDF',\n", " title='Posterior predictive distributions')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparing to the multivariate student t" ] }, { "cell_type": "code", "execution_count": 126, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.02157144760939201" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = len(m_n)\n", "x = m_n\n", "mean = m_n\n", "df = nu_n - d + 1\n", "shape = Lambda_n * (kappa_n+1) / kappa_n\n", "multistudent_pdf(x, mean, shape, df)" ] }, { "cell_type": "code", "execution_count": 127, "metadata": {}, "outputs": [], "source": [ "x0s = np.linspace(0, 20, 91)\n", "x1s = np.linspace(10, 30, 101)" ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(101, 91, 2)" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x_mesh = np.dstack(np.meshgrid(x0s, x1s))\n", "x_mesh.shape" ] }, { "cell_type": "code", "execution_count": 129, "metadata": {}, "outputs": [], "source": [ "ps = multistudent_pdf(x_mesh, mean, shape, df)" ] }, { "cell_type": "code", "execution_count": 130, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "22.23349484051357" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joint = pd.DataFrame(ps, columns=x0s, index=x1s)\n", "normalize(joint)" ] }, { "cell_type": "code", "execution_count": 131, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.contour.QuadContourSet at 0x7fb16888dc50>" ] }, "execution_count": 131, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_contour(joint)" ] }, { "cell_type": "code", "execution_count": 132, "metadata": {}, "outputs": [], "source": [ "from utils import marginal\n", "\n", "posterior_x0_student = marginal(joint, 0)\n", "posterior_x1_student = marginal(joint, 1)" ] }, { "cell_type": "code", "execution_count": 133, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "posterior_x0_student.make_cdf().plot(color='gray', label=r'$x_0$ multi t')\n", "posterior_x1_student.make_cdf().plot(color='gray', label=r'$x_1$ multi t')\n", "\n", "Cdf.from_seq(sample_x0).plot(label=r'$x_0$ sample')\n", "Cdf.from_seq(sample_x1).plot(label=r'$x_1$ sample')\n", "\n", "decorate(xlabel='Quantity',\n", " ylabel='CDF',\n", " title='Posterior predictive distributions')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bayesian linear regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "inter, slope = 5, 2\n", "sigma = 3\n", "n = 20" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xs = norm(0, 3).rvs(n)\n", "xs = np.sort(xs)\n", "ys = inter + slope * xs + norm(0, sigma).rvs(20)\n", "\n", "plt.plot(xs, ys, 'o');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import statsmodels.api as sm\n", "\n", "X = sm.add_constant(xs)\n", "X" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model = sm.OLS(ys, X)\n", "results = model.fit()\n", "results.summary()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beta_hat = results.params\n", "beta_hat" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# k = results.df_model\n", "k = 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s2 = results.resid @ results.resid / (n - k)\n", "s2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s2 = results.ssr / (n - k)\n", "s2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.sqrt(s2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grid algorithm" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beta0s = np.linspace(2, 8, 71)\n", "prior_inter = Pmf(1, beta0s, name='inter')\n", "prior_inter.index.name = 'Intercept'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beta1s = np.linspace(1, 3, 61)\n", "prior_slope = Pmf(1, beta1s, name='slope')\n", "prior_slope.index.name = 'Slope'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sigmas = np.linspace(1, 6, 51)\n", "ps = sigmas**-2\n", "prior_sigma = Pmf(ps, sigmas, name='sigma')\n", "prior_sigma.index.name = 'Sigma'\n", "prior_sigma.normalize()\n", "\n", "prior_sigma.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from utils import make_joint\n", "\n", "def make_joint3(pmf1, pmf2, pmf3):\n", " \"\"\"Make a joint distribution with three parameters.\n", " \n", " pmf1: Pmf object\n", " pmf2: Pmf object\n", " pmf3: Pmf object\n", " \n", " returns: Pmf representing a joint distribution\n", " \"\"\"\n", " joint2 = make_joint(pmf2, pmf1).stack()\n", " joint3 = make_joint(pmf3, joint2).stack()\n", " return Pmf(joint3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "prior3 = make_joint3(prior_slope, prior_inter, prior_sigma)\n", "prior3.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from utils import normalize\n", "\n", "def update_optimized(prior, data):\n", " \"\"\"Posterior distribution of regression parameters\n", " `slope`, `inter`, and `sigma`.\n", " \n", " prior: Pmf representing the joint prior\n", " data: DataFrame with columns `x` and `y`\n", " \n", " returns: Pmf representing the joint posterior\n", " \"\"\"\n", " xs = data['x']\n", " ys = data['y']\n", " sigmas = prior.columns\n", " likelihood = prior.copy()\n", "\n", " for slope, inter in prior.index:\n", " expected = slope * xs + inter\n", " resid = ys - expected\n", " resid_mesh, sigma_mesh = np.meshgrid(resid, sigmas)\n", " densities = norm.pdf(resid_mesh, 0, sigma_mesh)\n", " likelihood.loc[slope, inter] = densities.prod(axis=1)\n", " \n", " posterior = prior * likelihood\n", " normalize(posterior)\n", " return posterior" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = pd.DataFrame(dict(x=xs, y=ys))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from utils import normalize\n", "\n", "posterior = update_optimized(prior3.unstack(), data)\n", "normalize(posterior)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from utils import marginal\n", "\n", "posterior_sigma_grid = marginal(posterior, 0)\n", "posterior_sigma_grid.plot(label='grid')\n", "\n", "decorate(title='Posterior distribution of sigma')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "joint_posterior = marginal(posterior, 1).unstack()\n", "plot_contour(joint_posterior)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "posterior_beta0_grid = marginal(joint_posterior, 0)\n", "posterior_beta1_grid = marginal(joint_posterior, 1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "posterior_beta0_grid.make_cdf().plot(label=r'$\\beta_0$')\n", "posterior_beta1_grid.make_cdf().plot(label=r'$\\beta_1$')\n", "\n", "decorate(title='Posterior distributions of parameters')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posterior distribution of sigma" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to Gelman et al, the posterior distribution of $\\sigma^2$ is scaled inverse chi2 with $\\nu=n-k$ and scale $s^2$.\n", "\n", "According to [Wikipedia](https://en.wikipedia.org/wiki/Scaled_inverse_chi-squared_distribution), that's equivalent to inverse gamma with parameters $\\nu/2$ and $\\nu s^2 / 2$." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "nu = n-k\n", "nu/2, nu*s2/2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import invgamma\n", "\n", "dist_sigma2 = invgamma(nu/2, scale=nu*s2/2)\n", "dist_sigma2.mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sigma2s = np.linspace(0.01, 30, 101)\n", "ps = dist_sigma2.pdf(sigma2s)\n", "posterior_sigma2_invgamma = Pmf(ps, sigma2s)\n", "posterior_sigma2_invgamma.normalize()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "posterior_sigma2_invgamma.plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sigmas = np.sqrt(sigma2s)\n", "posterior_sigma_invgamma = Pmf(ps, sigmas)\n", "posterior_sigma_invgamma.normalize()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "posterior_sigma_invgamma.mean(), posterior_sigma_grid.mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "posterior_sigma_grid.make_cdf().plot(color='gray', label='grid')\n", "posterior_sigma_invgamma.make_cdf().plot(label='invgamma')\n", "\n", "decorate(title='Posterior distribution of sigma')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posterior distribution of sigma, updatable version\n", "\n", "Per the Wikipedia page: https://en.wikipedia.org/wiki/Bayesian_linear_regression" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Lambda_0 = np.zeros((k, k))\n", "Lambda_n = Lambda_0 + X.T @ X\n", "Lambda_n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.linalg import inv\n", "\n", "mu_0 = np.zeros(k)\n", "mu_n = inv(Lambda_n) @ (Lambda_0 @ mu_0 + X.T @ X @ beta_hat)\n", "mu_n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a_0 = 0\n", "a_n = a_0 + n / 2\n", "a_n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "b_0 = 0\n", "b_n = b_0 + (ys.T @ ys + \n", " mu_0.T @ Lambda_0 @ mu_0 - \n", " mu_n.T @ Lambda_n @ mu_n) / 2\n", "b_n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a_n, nu/2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "b_n, nu * s2 / 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling the posterior of the parameters" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sample_sigma2 = dist_sigma2.rvs(1000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sample_sigma = np.sqrt(sample_sigma2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.linalg import inv\n", "\n", "V_beta = inv(X.T @ X)\n", "V_beta" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sample_beta = [multivariate_normal(beta_hat, V_beta * sigma2).rvs()\n", " for sigma2 in sample_sigma2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.mean(sample_beta, axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beta_hat" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.std(sample_beta, axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "results.bse" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sample_beta0, sample_beta1 = np.transpose(sample_beta)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Cdf.from_seq(sample_beta0).plot(label=r'$\\beta_0$')\n", "Cdf.from_seq(sample_beta1).plot(label=r'$\\beta_1$')\n", "\n", "decorate(title='Posterior distributions of the parameters')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posterior using multivariate Student t" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = beta_hat\n", "mean = beta_hat\n", "df = (n - k)\n", "shape = (V_beta * s2)\n", "multistudent_pdf(x, mean, shape, df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "low, high = sample_beta0.min(), sample_beta0.max()\n", "low, high" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beta0s = np.linspace(0.9*low, 1.1*high, 101)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "low, high = sample_beta1.min(), sample_beta1.max()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beta1s = np.linspace(0.9*low, 1.1*high, 91)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beta0_mesh, beta1_mesh = np.meshgrid(beta0s, beta1s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "beta_mesh = np.dstack(np.meshgrid(beta0s, beta1s))\n", "beta_mesh.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ps = multistudent_pdf(beta_mesh, mean, shape, df)\n", "ps.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "joint = pd.DataFrame(ps, columns=beta0s, index=beta1s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from utils import normalize\n", "\n", "normalize(joint)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from utils import plot_contour\n", "\n", "plot_contour(joint)\n", "decorate(xlabel=r'$\\beta_0$',\n", " ylabel=r'$\\beta_1$')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "marginal_beta0_student = marginal(joint, 0)\n", "marginal_beta1_student = marginal(joint, 1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from utils import marginal\n", "\n", "posterior_beta0_grid.make_cdf().plot(color='gray', label=r'grid $\\beta_0$')\n", "posterior_beta1_grid.make_cdf().plot(color='gray', label=r'grid $\\beta_1$')\n", "\n", "marginal_beta0_student.make_cdf().plot(label=r'student $\\beta_0$', color='gray')\n", "marginal_beta1_student.make_cdf().plot(label=r'student $\\beta_0$', color='gray')\n", "\n", "Cdf.from_seq(sample_beta0).plot(label=r'sample $\\beta_0$')\n", "Cdf.from_seq(sample_beta1).plot(label=r'sample $\\beta_1$')\n", "\n", "decorate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling the predictive distribution" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t = [X @ beta + norm(0, sigma).rvs(n)\n", " for beta, sigma in zip(sample_beta, sample_sigma)]\n", "predictions = np.array(t)\n", "predictions.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "low, median, high = np.percentile(predictions, [5, 50, 95], axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plt.plot(xs, ys, 'o')\n", "plt.plot(xs, median)\n", "plt.fill_between(xs, low, high, color='C1', alpha=0.3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modeling the predictive distribution" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xnew = [1, 2, 3]\n", "Xnew = sm.add_constant(xnew)\n", "Xnew" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "t = [Xnew @ beta + norm(0, sigma).rvs(len(xnew))\n", " for beta, sigma in zip(sample_beta, sample_sigma)]\n", "predictions = np.array(t)\n", "predictions.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x0, x1, x2 = predictions.T\n", "\n", "Cdf.from_seq(x0).plot()\n", "Cdf.from_seq(x1).plot()\n", "Cdf.from_seq(x2).plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mu_new = Xnew @ beta_hat\n", "mu_new" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cov_new = s2 * (np.eye(len(xnew)) + Xnew @ V_beta @ Xnew.T)\n", "cov_new" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x = mu_new\n", "mean = mu_new\n", "df = (n - k)\n", "shape = cov_new\n", "multistudent_pdf(x, mean, shape, df)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "y1s = np.linspace(0, 20, 51)\n", "y0s = np.linspace(0, 20, 61)\n", "y2s = np.linspace(0, 20, 71)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mesh = np.stack(np.meshgrid(y0s, y1s, y2s), axis=-1)\n", "mesh.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ps = multistudent_pdf(mesh, mean, shape, df)\n", "ps.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ps /= ps.sum()\n", "ps.sum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p1s = ps.sum(axis=1).sum(axis=1)\n", "p1s.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p0s = ps.sum(axis=0).sum(axis=1)\n", "p0s.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "p2s = ps.sum(axis=0).sum(axis=0)\n", "p2s.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pmf_y0 = Pmf(p0s, y0s)\n", "pmf_y1 = Pmf(p1s, y1s)\n", "pmf_y2 = Pmf(p2s, y2s)\n", "\n", "pmf_y0.mean(), pmf_y1.mean(), pmf_y2.mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pmf_y0.make_cdf().plot(color='gray')\n", "pmf_y1.make_cdf().plot(color='gray')\n", "pmf_y2.make_cdf().plot(color='gray')\n", "\n", "Cdf.from_seq(x0).plot()\n", "Cdf.from_seq(x1).plot()\n", "Cdf.from_seq(x2).plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "stop" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Leftovers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Related discussion saved for the future\n", "\n", "https://stats.stackexchange.com/questions/78177/posterior-covariance-of-normal-inverse-wishart-not-converging-properly" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import chi2\n", "\n", "\n", "class NormalInverseWishartDistribution(object):\n", " def __init__(self, mu, lmbda, nu, psi):\n", " self.mu = mu\n", " self.lmbda = float(lmbda)\n", " self.nu = nu\n", " self.psi = psi\n", " self.inv_psi = np.linalg.inv(psi)\n", "\n", " def sample(self):\n", " sigma = np.linalg.inv(self.wishartrand())\n", " return (np.random.multivariate_normal(self.mu, sigma / self.lmbda), sigma)\n", "\n", " def wishartrand(self):\n", " dim = self.inv_psi.shape[0]\n", " chol = np.linalg.cholesky(self.inv_psi)\n", " foo = np.zeros((dim,dim))\n", "\n", " for i in range(dim):\n", " for j in range(i+1):\n", " if i == j:\n", " foo[i,j] = np.sqrt(chi2.rvs(self.nu-(i+1)+1))\n", " else:\n", " foo[i,j] = np.random.normal(0,1)\n", " return np.dot(chol, np.dot(foo, np.dot(foo.T, chol.T)))\n", "\n", " def posterior(self, data):\n", " n = len(data)\n", " mean_data = np.mean(data, axis=0)\n", " sum_squares = np.sum([np.array(np.matrix(x - mean_data).T * np.matrix(x - mean_data)) for x in data], axis=0)\n", " mu_n = (self.lmbda * self.mu + n * mean_data) / (self.lmbda + n)\n", " lmbda_n = self.lmbda + n\n", " nu_n = self.nu + n\n", " dev = mean_data - self.mu\n", " psi_n = (self.psi + sum_squares + \n", " self.lmbda * n / (self.lmbda + n) * np.array(dev.T @ dev))\n", " return NormalInverseWishartDistribution(mu_n, lmbda_n, nu_n, psi_n)\n", "\n", " \n", "\n", "x = NormalInverseWishartDistribution(np.array([0,0])-3,1,3,np.eye(2))\n", "samples = [x.sample() for _ in range(100)]\n", "data = [np.random.multivariate_normal(mu,cov) for mu,cov in samples]\n", "y = NormalInverseWishartDistribution(np.array([0,0]),1,3,np.eye(2))\n", "z = y.posterior(data)\n", "\n", "print('mu_n: {0}'.format(z.mu))\n", "\n", "print('psi_n: {0}'.format(z.psi))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from scipy.linalg import inv\n", "from scipy.linalg import cholesky\n", "\n", "def wishartrand(nu, Lambda):\n", " d, _ = Lambda.shape\n", " chol = cholesky(Lambda)\n", " foo = np.empty((d, d))\n", "\n", " for i in range(d):\n", " for j in range(i+1):\n", " if i == j:\n", " foo[i,j] = np.sqrt(chi2.rvs(nu-(i+1)+1))\n", " else:\n", " foo[i,j] = np.random.normal(0, 1)\n", " \n", " return np.dot(chol, np.dot(foo, np.dot(foo.T, chol.T)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sample = [wishartrand(nu_n, Lambda_n) for i in range(1000)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "np.mean(sample, axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Lambda_n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
daniestevez/jupyter_notebooks
dslwp/DSLWP-B deorbit.ipynb
1
667987
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.dates as mdates\n", "\n", "from astropy.time import Time\n", "\n", "import subprocess\n", "\n", "# Larger figure size\n", "fig_size = [10, 6]\n", "plt.rcParams['figure.figsize'] = fig_size" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mjd_unixtimestamp_offset = 10587.5\n", "seconds_in_day = 3600 * 24\n", "\n", "def mjd2unixtimestamp(m):\n", " return (m - mjd_unixtimestamp_offset) * seconds_in_day\n", "\n", "def unixtimestamp2mjd(u):\n", " return u / seconds_in_day + mjd_unixtimestamp_offset" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def load_orbit_file(path):\n", " ncols = 8\n", " data = np.fromfile(path, sep=' ')\n", " return data.reshape((data.size // ncols, ncols))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Keys for each of the columns in the orbit (Keplerian state) report." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "utc = 0\n", "sma = 1\n", "ecc = 2\n", "inc = 3\n", "raan = 4\n", "aop = 5\n", "ma = 6\n", "ta = 7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot the orbital parameters which are vary significantly between different tracking files." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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mIj6zmDHBnmw+lU4vT3tsLEyZF9ODeTE90Ol02FuacjGjbbOzQgghRFtr8Uyh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\n4Pb6UE9xY+WN7eVMuf9dXtxYChCxljcuq0mmj0qSJEWQbEkhScNYfWD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OlK2krG4fzglL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\n5yB+Fe66dBHnL86NyT0AdDqFjCTzaN/wyQr2lox2RnYqSFAYJ03dQyzIs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1cAACAASURB\nVA2Vn+x9eeiZyaWFqXFbW7xIUCgOsuXCgrWQ6IAFl2rPHfd57fliaUEhhBBCiOknNzGbZoMO/3Nf\nA687Jveobteqm5bmnTDByPHlppXzToKVt2rWg3P84jg6nUJeqnXcc26qqtLoGhw9EzeRV9/4bx5y\nfcgXX/syO01GCjHgVRR2vf8Avn/dDu7J9Xssz0qmf8Qbcq+/1/e2A3DtqmK+fkZ8i+OM5b4rlnDn\nJVof7o8auz/x+eqOAVYU27n/c0u5+sTiOK8u9iQoFEd2/r3w1S0SDAohhBBiWsvPOQ6votC+48/j\nt9eKQHV3NToVilIjq8J+QsVaAL6SncnO2tcnHJ9vHz8o7B70MOD2UZAWWqZwf5cWiLp04NYpXFWi\ntRD5+c7/4/iGp3ntnTtDmudw5YHqofvbQttCuqOph6WFqfzogvkUT3E7iqCyzGSuWFnI7KwkdjV/\nfMuuz69S0znAkoJUzluUO+OKzIAEhWIs5mRwTI93boQQQgghxlKQq529W1OYx86a2ASFNcNO8vQW\nzPrICoysLV/Lfy35OgAftW+bcHy+3Upj19jN7oMBY6iZwlp3F1n+g38+Y9mXKPJ42GM2MaLT8ULr\nxpDmOdzsrGQAtjd0s+MIWbZD+fwqu5p6WJSXMql7xVpFto19bR+vpNrcPYTb66d0igvixJIEhUII\nIYQQ4qi1PHs5l1dcDsALnVFuYu/34X33QbYaVCoSciOeTq/Tc+XC67H6VeoHWyccn29PoHPAzZD7\nk4VPABpcg4FxIQSF3hHqVDdzE3K5LeMkvlNyEZlJOcwzO0aHVI2E32sQIC3RhCPJzD2v7ueC+99h\nc03XmGNrnP0MuH0szI/OuTyf/8jfm8mqyEqi0TVE/4iXrgE3I17faOuM0mmS1YwFCQqFEEIIIcRR\ny6gz8v0Tvs8ixcp+z/jtG8LV/vYvOHnvA7QbDJxQ+umozKnodOSqCi0hBGDBYK+p+8jZwoauYFA4\n8fZRX1c19QYDJbZCPnvOQ1xzyk8A+PyaX3NF6YVcYcqlHjcevyfUL+VjvrG6DEeSlkldt61pzHE7\nGrWf0aL8yDOF69/9BSsfX8aG2n/x4svf5KanVuMcaI9ozmDW8+1KJyf//HW+9PgHVHdoZy0lUyiE\nEEIIIcQ0NsuczgE8Ua1CuqnmZfp1Oq4sX8tFi66L2rwOxUSnb+xtoUHBoLDhsHOFB9r7uOPFPXzU\n1IM9wUiK1TjhXK2t23DrFArtsz/2/Nzspdx68k9ZkDILr6LQ1LkvjK/koKtOLGbL98/klNkZbKn9\nZMDb2jPM3z5o5IM6F1ajPiq9CV/f8xfc+Pn7lvu4p/Fl3h5p59ktv4pozopsLSi86+W9DLh9vLGv\ngy11XSRbDDiSpr7abqxIUCiEEEIIIY56JckFdOl19HTXRmdCv4+9fY2Y0fGdE74f8XnCQ6UbEnD6\nJ66UGswAVrX384e3a2gPVPe848W9/Patap7f0UJhemhbGuuduwAoylp8xM8XZywAoLbpvZDmG8uK\nIjv72/voGfx4xvFLT3zAd/66nT9tqmdxQUrExVpUzzAfqFpg/dpALW0GAwC7OyY+qzmeAnsCVqN+\nNDsI8OJHrZRlJoXdC/JoIkGhEEIIIYQ46hUFMmD1ze9HZ0JXLXsMUJGQhUFniM6cAQ6TjU7FP+G4\njCQzJr2O+16r5MfP7+a//7ETVVX5sP5gJm5eji2ke9a7qgAoyFh4xM8X5x0PQG3HzpDmG8vy4jRU\nFV7a2cK5v9rA/a9X0tozzLaGgwVozp6fHdE9ABoOvEy7Qc9pvoNZ0sUelX0hnNUcj06nUBI4O/gf\nJxSSbNF+9rMzkyOad7qL7t9wIYQQQgghpkBx1hLY/yh1HTtZyGURz+dv38Mes4lz7HOisLqPS7ek\nMzRcz+BQNwnWsQuu6HQKuakWaju1jNjmmk5aeoZxDXr41pmzGfb6uHZV8bj3Ghnp47ZXvsiL/TtJ\nUiEzMeuI41IyF5Lm81HbUz3prwtgSUEqBp3CLc98BMCu5l78gR296795Ml6fOtoofjKcgx38347f\nYm3VMp9f+dRt9L75X5TbZ5MxPMj9/nb63f0kmSa/PfXMeVnsbe1l7bJ8ugc9PL+jhZUlaZOe72gQ\ns0xhQ0MDp59+OnPnzmX+/Pncd999AHR1dbFmzRrKy8tZs2YNLpf2Toeqqnz961+nrKyMRYsW8eGH\nH47O9eijj1JeXk55eTmPPvporJYshBBCCCGOUvm5K9GpKnXdVVGZr6n1A/p1OubmrIjKfIdyJGqZ\nMmfn3gnHrijWgpGKrGR6h728vEvLhJ1Uns5/fXoOWTbLuNcf+OBhXuzSArR5Rjs6ZYyX/wYTRaqe\nms49+B+/BEb6jjxuAlaTngWBdhPHBwKpe17dT26KhYqsZBbkpUS0DfOP67/Cn/f9hd/37CRNVaiY\nfSGPXr2Z71/6HPMd8wF4at1VPP38DaiTPF/6rTPL2Xn72SwttPOTCxfwx2tXcPHSvEmv+WgQs6DQ\nYDBw9913s2fPHt577z0eeOABdu/ezZ133snq1auprKxk9erV3Hmn1iTzpZdeorKyksrKSh5++GFu\nuukmQAsib7/9djZt2sTmzZu5/fbbRwNJIYQQQgghAEwWGzl+qBtojsp8ewLbKOdmL4vKfIdypBQC\n0NFVOeHYW8+dy2+vOo471mrbPv/yfgOKAnOyQ8u2NTZtBuBED/zXiv8cd2yJNYsPLRaW+fazbevv\nQpr/SG47fx5XnVDEQ/9xHCeXay0vTq3IiMqZvJ2ug4VwVllztTktKaAozC89C4D7Bg/wk873eGff\n3yd1D0VRSDBpGyrtiSZOn5OJbgY2rD9UzILCnJwcli3T/idKTk5m7ty5NDU1sW7dOq655hoArrnm\nGp599lkA1q1bx9VXX42iKJxwwgl0d3fT0tLCyy+/zJo1a0hLS8Nut7NmzRrWr18fq2ULIYQQQoij\nVLHOSm2U2lLs6avDAJSnlkdlvkNl2csAaO+pmXBsaoKJs+dnMz/XhkmvY29rHyXpiSSaQzsF1hQ4\nY3fP1e8ye/Z54449dcXXAfApCi83bQhp/iNZVmjnJxctIC3RxM1rZnPWvCxuOrVs0vMF+T1D7NV5\nucxj5B5PMt8/7X8/9nl76WoWew5mBz+qfzPiex4r4lJopra2lq1bt3L88cfT1tZGTk4OoAWO7e1a\nL5GmpiYKCgpGr8nPz6epqWnM5w/38MMPs3z5cpYvX05HR0eMvyIhhBBCCDHdFFkzqVdHUO+eC70R\nZAy9I+z0dFNmsGHSR78NQUa6dk6xvW/sfn6HMxv0LMjTsoPzwjiT1+TuJgV9SGfsTi87n7+d/zcW\n+hT2DUYn47q00M7DVy+nMH3iXooTaWrYyKBOx5xZZ7PmCxtJzFrw8QFGK/eufY6/rX6YPI+Xmt7a\niO95rIh5UNjf38/atWu59957sdnG/gt8pD2/iqKM+fzhbrzxRrZs2cKWLVvIyMiIbNFCCCGEEOKo\nU1R8GgM6HY8pfXh2/GVSc/hq3+YfDy7gPauZxenzo7xCTXJKIVa/SvtgW1jXnbNQS6xcuCTE822q\nSpN/mFxDaEVXFEWhIq2CIn0Sjb6hiS+Is5pAu4xZ45zzdNhLqcg/kRK/jtohZ7yWdtSLaVDo8XhY\nu3YtV155JZdccgkAWVlZtLS0ANDS0kJmZiagZQAbGhpGr21sbCQ3N3fM54UQQgghhDhUedFpAPxv\nup0/1708qTle3Hgnt6VoxVs+s/gL0Vraxyg6HRmqQvtwV1jXfeHkUrb/8CzWzDtyBdFPGHLRpFPI\nt6SHdZ8cSxrt+PD6vWFdF2s1gV6LpQUnTTi22Gij1jcw6WIzx5qYBYWqqnL99dczd+5cbr755tHn\nL7jggtEKoo8++igXXnjh6POPPfYYqqry3nvvkZKSQk5ODmeffTavvPIKLpcLl8vFK6+8wtlnnx2r\nZQshhBBCiKPU8qzl/Oykn2EA3hkKfWvmoTb115KMng2Xb+C4nJXRXeAhMg2JtA+0wtYnwrouxWqc\neFCAv7ueZoOBvKTwEiq5iTn4FIWO3oaJB8dRTV89dhVSEzMnHFucmM2QotI+2B6HlR39YhYUvvPO\nOzz++OO8/vrrLFmyhCVLlvDiiy9yyy238Oqrr1JeXs6rr77KLbfcAsA555xDaWkpZWVl3HDDDTz4\n4IMApKWl8YMf/IAVK1awYsUKbrvtNtLSZnafECGEEEIIET5FUTh/1vlclFzOTp0fdSjMojN9bexS\nPCxJyCPVMnb/wGjIS59DrdGAuu4rMNQ98QWT4Ozci1unkJdSEtZ1uYHxze0fxWJZYXP73Dy++3Fe\n8HdTYQjtPGWRXSsQVBfILorxxax5/UknnTRmuva11177xHOKovDAAw8ccfx1113HddddF9X1CSGE\nEEKImWmuYyF/66ukuf5t8irODfm6wYZ3qTYaWZOxKIar0ywpP491zg84oyCPdc1bsc06Per3aOrU\n2jfkpc8N67qc9NlQDU3OPRw3+4Korytcz27+JXftfwIUheNTKkK6pjhzCTS/RG3LB6wsOiPGKzz6\nxaX6qBBCCCGEEPEyJ187c7av6Z2wrttT9wZ+RWF+4GxiLJ1bei6nZi7HadDzQeNbMblHY28dAHmO\neWFdl5e1FEVVaeyuisWywranWmtH94WePv7fypsnGK3JzF2O1e+ntn07eN2xXN6MIEGhEEIIIYSY\nUcryV6GoKvvC3Dq4s2M7APOzj4vFsj7GarByx8l3AlDdO3G/wsloHNCKO+Ymh1itNMBkLyHL56eh\nrz4WywpbzYiLJVj4xjVvf7INxRh0aaUUev087trOfY+swu/3xXiVRzcJCoUQQgghxIySYEqkCCN7\n++ph30vg949/gdcNDZvZOdBCts6Mw+qIyzqTk7Kw+f20DMWmx3b9SBfZqh6LwRLehTodBYqJxuHO\nmKwrLH4fNbgptWZCSn7o1+kNnJJ9PAC/M47wUe3rMVrgzCBBoRBCCCGEmHEqzA72qcOoT14BW34/\n7tjWZ2/kJ89exvoEE4tTyuK0Qk2Gqsc5EmZBnBDVevspCrFH4eHyLWnU+wZQX/gOTGGWrad9J116\nPSUppWFf+9Xz/sC9828CYG+YW4mPNRIUCiGEEEKIGWdOwadoMhpYXFzAul3jtH3wDPNw6waetiUD\ncPbCa+OzwIAMnZkO32BU5xx2D3LFk6ex06hjvq14UnPML1pNl17PJc3/pL1yfVTXF46axncBKMlY\nGPa1OkXHGbMvIcHvp8Z1INpLm1EkKBRCCCGEEDPOglmfBkBVFP7gbQP3GIFX42betRg5JXUuz130\nHGtKPh3HVUKGMRGn6onqnDXbH2WXu5M0n4/LFt04qTnOXPYl8hKyOWAy8dKBZ6O6vnDUdGhtMUry\nT5jU9UpSJrleHy1D0q9wPBIUCiGEEEKIGef47OP52/l/4ysFn6baZKSt6pUjjmusfJFGo5FVJWdT\nEmY/v2hwmFLpUNQxW7lNRkvTJgAePOt35BWfMqk50q3prP/sq+T4/OzqrY3a2sJV01ONUVXJDbOC\n6iidjizFSJu7N7oLm2EkKBRCCCGEEDOOoihUpFVw6rzLAdi880/w5Odgzz+1Af+6HZ6+hk2BAiQn\nFka/T2AoMqwOPIpCb19z1OZsCVQdzU4PraffeIow0ejujnieyaoa6qAYEwbd5NurZxsSafUNRXFV\nM0/MmtcLIYQQQggx1SqylmFTFTa1baHW66Pslc18xjGbX+36Pc8lJWLWqWTqkqckSwjgSMyGTujo\n2k+KLbzWEWNpHXZhMiikWdIinivPZOMNT1cUVjUJqkqlf5AlCZF9X7LMdjrdA7h9bkx6U5QWN7NI\nplAIIYQQQsxYOkXHcmsO65KTeNiewn+mWql78lKesCXTZjBQbzTyqbxPoSjKlKzPkVIIgLO7Ompz\ntnj7ydFZovI15Vuz6NLBgLs/CisLXdtAK2f/5TRaDHoWp82JaK7shCwA2vtbo7G0GUmCQiGEEEII\nMaOtDGwhtRqsANyQ6GVIp+O/j/9vzig4gy8sv3nK1paRqmUoO3obojOhz0MLXrKNtqhMl2/Tgtbm\n9p1RmS9Uz7/9U5pHuvjU4BDnLfpCRHNlBb7HLe07orG0GUmCQiGEEEIIMaOdX3EpX1v6Nf5+/t85\nOamEFoOBQmsmV1RcwX1n3EdhIPCZCo407dyfsz9KZwr722gx6Mm1OqIyXa59NgDNgSqg8XKgbRs5\nXi+/OfUeUnIWRzRXgWM+AI0d8Q1sjyYSFAohhBBCiBnNZrJx46IbKbAV8PlP/YBUcyrfPP6WKdsy\neqjE5Dysfj/OIWdU5nN319NhMJCTFJ3ziblZWn/Apq7KqMwXqjpvL4WmVJh3QcRz5eQch0FVqXfF\n92s4mkihGSGEEEIIccxYkb2CDVdsmOpljFL0ehyqQsdIdCp8tnXuBSA7tTgq86VnLMDi99PUVx+V\n+UKhet3U4uMca1ZU5jOkFpLn9VHX3xiV+WYiyRQKIYQQQggxhRw6M87hLhiJvJhLa6BgTY498nYU\nAIolmVw//P/27jw+yvLe+/jnnpkEkrBl30MgUWRHFkWUIktBEZCoh6JtUalQFzytlUd9PNWHelTQ\nY1sQfLRqCyqy9EGFCkhZjnAQOCJLZC1CGrKRQEJYQ8gycz9/oDmoIAEmuS9mvu/Xy9crzNxz+cv1\nnTuZX+7lOnCq8RZ/P3poByfcLtJapvtnQJeb1p5mbKwo4KuPH/LPmAFGTaGIiIiIiINiwuMo9Z6G\nN24En++yxir++oY1idFX+6M0AJKsJhTVNN7i73kHNgKQ/vW1gP4w4JpRHHO7+cnhtRw5Uey3cdnz\nCfyhI5Rd2aemqikUEREREXFQRtsfsz80hDfscuzD+y5rrAOVZ47oJTRL9EdpACQ3jeKAtxIKvvDb\nmD8kr2wnAGlJ1/ltzDuun8i/Jg+i1rLIzlnit3E/O7CBrOa15Ff75/Rfp6gpFBERERFx0OCM4QC8\nFtmKnbkrLmusA1VHiLFdNHE38UdpALRJuo5jbhezFtyB71jDX5eXd2w/btsmObaD38a0LIvR19wD\nwP6yXX4bt7iimH2hoYRGxPltTCeoKRQRERERcdBVkVexbOg8AHYc3nlZYxV4K0h1h/ujrDoDej6K\nBfw+qhVrts3y69jnsr/yICl4CHGF+HXc5tFX0cLrpehkkd/GPHL6CACRzRL8NqYT1BSKiIiIiDgs\nKbo9YT6b/MtoWOya0+ThJaVptB8rg8RmiXx610oANh7a4texz2W/t4LWIS39P3DTliR7bQorS/02\n5OHqY0TY+PXIrBPUFIqIiIiIOMxyuYi3LUqqjlzS609VnWD4/H6Uetx0ifbfDVq+ER0RT/tayD11\n0O9jn23V3kXs9bi4pnma/we3LJKtUIr9eNOc0pqTxNluv43nFDWFIiIiIiIGSHA15WDtpS1L8fnn\nfyTPe4qBlVWM6DHBz5WdkR7SnLxLrK8+tuav4dfrfwvAgPRbGuT/kRDSgmJfFbZt+2W8Um8lcVf4\nUUJQUygiIiIiYoSEkOaU2DWX9NqvDm4FYPL9mwhvmerPsuqkNY3lgOWlpra6QcZf88WrWLbN4to4\nOnYc1SD/j8SwGCotOF7tn6OFpdQQE9LcL2M5SU2hiIiIiIgB4ptGU2bZ1F5C01VUWUaMbRHWpEUD\nVHZGWsvW+CyLosO7G2T83BMFpOOh9S9Wgdu/N5n5RlKzZAAOHNt/2WMdOl5EkdtF67D4yx7LaWoK\nRUREREQMEBeRiM+yKCu/+LUKC2tPkOxq2gBV/Y/UqHYAFBQ3zM1m9nsraBPaqkHG/kZiqzYAFF/m\nshRllWUM/OjMKa4dW2Vedl1OU1MoIiIiImKAhK9P+zx4CUfiiuwakhu4oUqNvxaAggY4UlhbUUq+\n2yI9IsXvY58t4evGtrh872WNs6HgvwB4+MhRbmp762XX5TQ1hSIiIiIiBohrlQHAoSM5F/U676ly\nDrotksIbdgH16PiuhPl8FBzf7/exiwo/p9ayaPN109ZQomKuoYnPR/HxvMsaJ2fvYjy2zfgqN67E\nrn6qzjlqCkVEREREDBAf0x6Ag8fzL+p15aU78VoW8c0a9iib1SSCVB8UNMCyFPtLNgOQntjd72Of\nzWqZQmKtl+KKy/seSo/mEuOzcT+2C0LC/FSdc9QUioiIiIgYoFVkJqG2zaGLbLoOlZ05nTMusm1D\nlPUtaa5wCvy4zt839pfvAaBNUm+/j/0tIU1JsDyUVBRD1aUvr1FafZxYdxiEhvuxOOeoKRQRERER\nMYDlCSHOByWnD1/U6w4e/ScA8dHXNERZ35IaFkOhXYNv/Wt+HTf3ZCFRPmgZHu3Xcc+ldbMUtlnV\nzJw96JLHKPVVE+uJ8GNVzlJTKCIiIiJiiHgrlEPHC2D3x/V+zaHjBQDENfD1eABtM4ZQ7bK4d/s0\njhdt9tu4udVHSG+kJuvOm58H4A+eCvJKvrz4AWybUstHbGhLP1fmHDWFIiIiIiKGiA+P56DHTdVf\nfwbHi+v1mkOnDuG2ISo8poGrgx93G0e3qPZkN23Cku0z/TJmybE8tnjgqvBEv4x3Ie0TerKox28B\n+DJ32UW/vvpECcfcLmIb+MY+jUlNoYiIiIiIIdIzBlMYEkLv1qkU7Ftar9ccrDpCrOXB7XI3cHUQ\nERLBe8Pmk+j1sfXIPy57vNyyXQxeOAyAG5P6XPZ49ZWa1AuPbZN7CUtTlB4+833HRjROE9sY1BSK\niIiIiBji9mt+QpsW6dRaFv+Z/2m9XrPfe4o0T/MGruwslkU7VwR7qsoue6iP1/4ObJuXTtrcfO2D\nfiiufkJappFSW8v+isKLfm3p10uGxLZs7e+yHGPZtm07XYS/9WzenE09ejhdhoiIiIjIJcku+YIW\nVght47uddxvbtjl0LJf804eJ9USQHtOh0eorLN1BibeS7vE9cFmXfpzpq5LNVAOdEhr/s/tXxV9Q\n43LTMf7ilsEoP5pLzukyOra6ivCmrRqousvX8+RJNm3aVK9tdaRQRERERMQwYbiotGt/cJtjR880\nhAAtmzRucxLmCcMGTldf+rIO2DaVto9wd4jf6roYTS0Pp20fF3uErMZbDUBISGAsRwHgcbqABtGu\nHaxe7XQVIiIiIiKX5MO5Q1h4uoj/vu9TLMs65zYLZvVlmtWS11rfQasf/R9wNd7xnsM75jJ284u8\n3O4ebu098ZLGOJa/jp99+iC/Sf4xbQf9wc8VXtja+SOZcjqHT0ctICas/jfpmffX4cw6lcvmez+D\nyzhK2uB69qz3pgZ/FyIiIiIiwSm9WRKnXBZlJw+cd5t9VYdJsJrwo5t/h9WIDSFAempf3LbN3tId\nlzzGVwXrAbgqsZe/yrooqS1SASj8ep3H+jpUdZQY23VZp82aJnC+ExERERGRANG6VSYA+4vPc01Y\n5RH2WV4yw2Ibsar/EdoimTSvj5wT+Zc8xj/LzjSUmak3+ausi5IaeTUABYe2XdTrSmsriHM1aYiS\nHKOmUERERETEMOlxXQDIO0/DUntwN7khIVz1dfPY6CyLTHcEOdVHLnmInON5RNgQ3zzFj4XVX3Jc\nJyzbpuDwnot6XYmvmtiQiAaqyhlqCkVEREREDJOQ1ItQn03ekXOvo1dQvJFql0VGXNdGrux/ZIQl\nkmfVsu3P/aHq4m84s6+6nAxX+HmvmWxooVEZJHi9FByv/9HO1Xn/Sa7Hok3TwFm4HtQUioiIiIgY\nx9U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MGTNwuVwcP36cgoICoqOjHa3XsnURhVzA4cOHGTRoEH/5y18ICQlhxYoV7Nmzh/79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\n", "text/plain": [ "<Figure size 1080x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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24u9t6zFtfSb/N2/nZf+jIyIiInI9Zs+eTb9+/Thw4AAZGRlkZmZSt25dVq1a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\nr6w6kmrTZeuFELZx4Hwuoz/fj9mqsPrRHoyJDlQ7khDVUuWcwujoaH7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blsLXXnuNyMhI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\nzRoUFRWhf//+AIC+ffuisrISa9asweDBg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"text/plain": [ "<Figure size 1080x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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mNJgpcK/xSLh3iOvYZncPij3VBXQI1TXKGtH6IsFiObu98Tf8FH9u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\nmVMCoDPSS+/MWcWxY1/95KcAZI2gqMnpbq2ATSjYdz/Cshw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kznNA1hw48oKk/7zE+CHB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dLslDhGDWiZVdxUsYlPyz7l8f89jk9RGOtp6fGbkD+NsR4veX4/o9366wmqyoxGFxWOqk6vd8XU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"text/plain": [ "<Figure size 1080x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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rW7d6x/S19aQMPalFKa2YmUFq6l4q1Wp6ufRvdozePoa9jwkZB0yVVpOkF6cC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QLwwuHn/bqWpo4q0p1OtNdvGrZQTHWbmjVVmBLl07XiI0O5dVURe+q7+M7ze3ya61StFjWwTI2V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\nT9w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"text/plain": [ "<Figure size 1080x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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056EqFPxERERERK5wv7/Vc8uWLYwZM4Y9e/bwww8/8MMPP9C+fXsA8vPzOXToEDExMefV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"text/plain": [ "<Figure size 1080x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Q04OcGd6xaR1FKoTmPdrJ3z1m6LQhLS2N3bt3ExERgUqlori4GJVKxezZsytd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4ibmF5Ty9rMb2i0Nnbp2eqGahIbKxnfdxCMXmi3mERERM62DpO/22+/nSlTpvD000/z9NNPc9ll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\nP2WEJnypyDZs2GDMnj275PvMzExj8ODBhmEYRmJiovHAAw8Yjz76qNG+f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"text/plain": [ "<Figure size 1080x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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G0tKyTrGqotLcjnXCegoICLht8yuEEEIIIcT9KT4+ng4dOjR2GneMpKQkBgwYQFxcXINe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\nEhrwREREREREvIQGPBERERERES+hAU9ERERERMRLa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"text/plain": [ "<Figure size 1080x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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pJa/dGsVtbepSP8CDVbuOMn1tBgu3HCSnoIz6Ae40ru3BEz2a8PHgdkzsG8NH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"text/plain": [ "<Figure size 1080x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#fig1 = plt.figure(figsize = [15,8], facecolor='w')\n", "fig_peri = plt.figure(figsize = [15,8], facecolor='w')\n", "fig_peri_deorbit = plt.figure(figsize = [15,8], facecolor='w')\n", "fig_apo = plt.figure(figsize = [15,8], facecolor='w')\n", "fig3 = plt.figure(figsize = [15,8], facecolor='w')\n", "fig4 = plt.figure(figsize = [15,8], facecolor='w')\n", "fig4_rap = plt.figure(figsize = [15,8], facecolor='w')\n", "fig5 = plt.figure(figsize = [15,8], facecolor='w')\n", "fig6 = plt.figure(figsize = [15,8], facecolor='w')\n", "#sub1 = fig1.add_subplot(111)\n", "sub_peri = fig_peri.add_subplot(111)\n", "sub_peri_deorbit = fig_peri_deorbit.add_subplot(111)\n", "sub_apo = fig_apo.add_subplot(111)\n", "sub3 = fig3.add_subplot(111)\n", "sub4 = fig4.add_subplot(111)\n", "sub4_rap = fig4_rap.add_subplot(111)\n", "sub5 = fig5.add_subplot(111)\n", "sub6 = fig6.add_subplot(111)\n", "\n", "subs = [sub_peri, sub_peri_deorbit, sub_apo, sub3, sub4, sub4_rap, sub5, sub6]\n", "\n", "for file in ['orbit_deorbit.txt', 'orbit_deorbit2.txt', 'orbit_deorbit3.txt']:\n", " orbit = load_orbit_file(file)\n", "\n", " t = Time(mjd2unixtimestamp(orbit[:,utc]), format='unix')\n", "\n", " #sub1.plot(t.datetime, orbit[:,sma])\n", " sub_peri.plot(t.datetime, orbit[:,sma]*(1-orbit[:,ecc]))\n", " \n", " deorbit_sel = (mjd2unixtimestamp(orbit[:,utc]) >= 1564012800) & (mjd2unixtimestamp(orbit[:,utc]) <= 1564963200)\n", " if np.any(deorbit_sel):\n", " sub_peri_deorbit.plot(t[deorbit_sel].datetime, orbit[deorbit_sel,sma]*(1-orbit[deorbit_sel,ecc]))\n", " \n", " sub_apo.plot(t.datetime, orbit[:,sma]*(1+orbit[:,ecc]))\n", " sub3.plot(t.datetime, orbit[:,ecc])\n", " sub4.plot(t.datetime, orbit[:,aop])\n", " sub4_rap.plot(t.datetime, np.fmod(orbit[:,aop] + orbit[:,raan],360))\n", " sub5.plot(t.datetime, orbit[:,inc])\n", " sub6.plot(t.datetime, orbit[:,raan])\n", "\n", "sub_peri.axhline(y = 1737, color='red')\n", "sub_peri_deorbit.axhline(y = 1737, color='red')\n", "\n", "month_locator = mdates.MonthLocator()\n", "day_locator = mdates.DayLocator()\n", "\n", "for sub in subs:\n", " sub.set_xlabel('Time')\n", " sub.xaxis.set_major_locator(month_locator)\n", " sub.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n", " sub.xaxis.set_tick_params(rotation=45)\n", "sub_peri_deorbit.xaxis.set_major_locator(day_locator)\n", "sub_peri_deorbit.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n", "\n", "#sub1.set_ylabel('SMA (km)')\n", "sub_peri.set_ylabel('Periapsis radius (km)')\n", "sub_peri_deorbit.set_ylabel('Periapsis radius (km)')\n", "sub_apo.set_ylabel('Apoapsis radius (km)')\n", "sub3.set_ylabel('ECC')\n", "sub4.set_ylabel('AOP (deg)')\n", "sub4_rap.set_ylabel('RAOP (deg)')\n", "sub5.set_ylabel('INC (deg)')\n", "sub6.set_ylabel('RAAN (deg)')\n", "\n", "#sub1.set_title('Semi-major axis')\n", "sub_peri.set_title('Periapsis radius')\n", "sub_peri_deorbit.set_title('Periapsis radius')\n", "sub_apo.set_title('Apoapsis radius')\n", "sub3.set_title('Eccentricity')\n", "sub4.set_title('Argument of periapsis')\n", "sub4_rap.set_title('Right ascension of periapsis')\n", "sub5.set_title('Inclination')\n", "sub6.set_title('Right ascension of ascending node')\n", "\n", "for sub in subs:\n", " sub.legend(['Before periapsis lowering', 'After periapsis lowering', 'Latest ephemeris'])\n", " \n", "sub_peri.legend(['Before periapsis lowering', 'After periapsis lowering', 'Latest ephemeris', 'Lunar radius']);\n", "sub_peri_deorbit.legend(['Before periapsis lowering', 'After periapsis lowering', 'Latest ephemeris', 'Lunar radius']);" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
georgetown-analytics/yelp-classification
machine_learning/User_Sample_test_draft_ed.ipynb
1
14619
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "import pandas as pd\n", "import re\n", "import random\n", "from scipy import sparse\n", "import numpy as np\n", "from pymongo import MongoClient\n", "from nltk.corpus import stopwords\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from sklearn import svm\n", "from sklearn.decomposition import LatentDirichletAllocation\n", "from sklearn.base import BaseEstimator, TransformerMixin\n", "from sklearn.feature_extraction.text import CountVectorizer\n", "from sklearn.pipeline import Pipeline, FeatureUnion\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn import svm\n", "from sklearn.metrics import accuracy_score\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.metrics import precision_score\n", "from sklearn.metrics import log_loss\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "\n", "from gensim import corpora, models, similarities, matutils\n", "import tqdm\n", "import sys\n", "sys.path.append('/Users/ed/yelp-classification/machine_learning')\n", "import yelp_ml as yml\n", "#reload(yml)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lh_neg = open('../input/negative-words.txt', 'r', encoding = \"ISO-8859-1\").read()\n", "lh_neg = lh_neg.split('\\n')\n", "lh_pos = open('../input/positive-words.txt', 'r', encoding = \"ISO-8859-1\").read()\n", "lh_pos = lh_pos.split('\\n')\n", "users = json.load(open('../input/many_reviews_dictionary.json'))\n", "\n", "word_list = list(set(lh_pos + lh_neg))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Fix users JSON\n", "users_dict = {}\n", "user_ids = []\n", "\n", "users_dict = {}\n", "user_ids = []\n", "\n", "for list in users['reviews']:\n", " users_dict[list[0]['user_id']]= list\n", "\n", "\n", "\n", "\n", "for list_reviews in users['reviews']:\n", " user_ids.append(list_reviews[0]['user_id'])\n", " \n", "#We have 228 users, creat a new dictionary where the user_ids are the keys and the entries are a list of reviews\n", "\n", " \n", "with open('cleaned_large_user_dictionary.json', 'w') as outfile:\n", " json.dump(users_dict, outfile)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try running a few tests on a subset of users, the keys are our unique user IDs. We proceed as follows for each user ID:\n", "\n", "1.Create a user dataframe with the following columns:•(review_text, review rating, business_id)\n", "\n", "2.Create a list of unique business IDs for that user\n", "\n", "3.Connect to the MongoDB server and pull all of the reviews for the restaurants that the user has reviewed\n", "\n", "4.Create a restaurant dataframe with the following columns:•(review_text, biz rating, business_id)\n", "\n", "5.Do a 80/20 training/test split, randomizing over the set of user' reviewed restaurants\n", "\n", "6.Train the LSI model on the set of training reviews, get the number of topics used in fitting\n", "\n", "7.Set up the FeatureUnion with the desired features, then fit according to the train reviews and transform the train reviews \n", "\n", "8.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#####Test Machine Learning Algorithms\n", "ip = 'Insert IP here'\n", "conn = MongoClient(ip, 27017)\n", "conn.database_names()\n", "db = conn.get_database('cleaned_data')\n", "reviews = db.get_collection('restaurant_reviews')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1.Create a user dataframe with the following columns:•(review_text, review rating, business_id)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "useridlist =[]\n", "\n", "for user in users_dict.keys():\n", " useridlist.append(user)\n", "print(useridlist[1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def make_user_df(user_specific_reviews):\n", " #Input:\n", " #user_specific_reviews: A list of reviews for a specific user\n", " #Output: A dataframe with the columns (user_reviews, user_ratings, biz_ids)\n", " user_reviews = []\n", " user_ratings = []\n", " business_ids = []\n", "\n", " for review in user_specific_reviews:\n", " user_reviews.append(review['text'])\n", " user_ratings.append(review['stars'])\n", " business_ids.append(review['business_id'])\n", "\n", " ###WE SHOULD MAKE THE OUR OWN PUNCTUATION RULES\n", " #https://www.tutorialspoint.com/python/string_translate.htm\n", " #I'm gonna have to go and figure out what this does -ed\n", " #user_reviews = [review.encode('utf-8').translate(None, string.punctuation) for review in user_reviews]\n", "\n", " user_df = pd.DataFrame({'review_text': user_reviews, 'rating': user_ratings, 'biz_id': business_ids})\n", " return user_df\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#test to make users_dict,make_user_df works \n", "user_specific_reviews = users_dict[useridlist[0]]\n", "x= make_user_df(user_specific_reviews)\n", "x.head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2.Create a list of unique business IDs for that user" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "business_ids = list(set(user['biz_id']))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3.Connect to the MongoDB server and pull all of the reviews for the restaurants that the user has reviewed" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "restreview = {}\n", "\n", "for i in range(0, len(business_ids)):\n", " rlist = []\n", " for obj in reviews.find({'business_id':business_ids[i]}):\n", " rlist.append(obj)\n", " restreview[business_ids[i]] = rlist\n", " \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4.Create a restaurant dataframe with the following columns:•(review_text, biz rating, business_id)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "restaurant_df = yml.make_biz_df(user, restreview)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "5.Do a 80/20 training/test split, randomizing over the set of user' reviewed restaurants" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ " #Create a training and test sample from the user reviewed restaurants\n", "split_samp = .30\n", "random_int = random.randint(1, len(business_ids)-1)\n", "len_random = int(len(business_ids) * split_samp)\n", "test_set = business_ids[random_int:random_int+len_random]\n", "training_set = business_ids[0:random_int]+business_ids[random_int+len_random:len(business_ids)]\n", "train_reviews, train_ratings = [], []\n", "\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Create a list of training reviews and training ratings\n", "for rest_id in training_set:\n", " train_reviews.extend(list(user_df[user_df['biz_id'] == rest_id]['review_text']))\n", " train_ratings.extend(list(user_df[user_df['biz_id'] == rest_id]['rating']))\n", "\n", " \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Transform the star labels into a binary class problem, 0 if rating is < 4 else 1\n", "train_labels = [1 if x >=4 else 0 for x in train_ratings]\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6.Train the LSI model on the set of training reviews, get the number of topics used in fitting" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#this is just for my understand of how the model is working under the hood\n", "def fit_lsi(train_reviews):\n", " #Input: train_reviews is a list of reviews that will be used to train the LSI feature transformer\n", " #Output: A trained LSI model and the transformed training reviews\n", "\n", " texts = [[word for word in review.lower().split() if (word not in stop_words)]\n", " for review in train_reviews]\n", " \n", " dictionary = corpora.Dictionary(texts)\n", "\n", " corpus = [dictionary.doc2bow(text) for text in texts]\n", "\n", " numpy_matrix = matutils.corpus2dense(corpus, num_terms=10000)\n", " singular_values = np.linalg.svd(numpy_matrix, full_matrices=False, compute_uv=False)\n", " mean_sv = sum(list(singular_values))/len(singular_values)\n", " topics = int(mean_sv)\n", "\n", " tfidf = models.TfidfModel(corpus)\n", " corpus_tfidf = tfidf[corpus]\n", "\n", " lsi = models.LsiModel(corpus_tfidf, id2word=dictionary, num_topics=topics)\n", "\n", " return lsi, topics, dictionary" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Fit LSI model and return number of LSI topics\n", "lsi, topics, dictionary = yml.fit_lsi(train_reviews)\n", " \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "7.Set up the FeatureUnion with the desired features, then fit according to the train reviews and transform the train reviews " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Make a FeatureUnion object with the desired features then fit to train reviews\n", "comb_features = yml.make_featureunion()\n", "comb_features.fit(train_reviews)\n", " \n", "train_features = comb_features.transform(train_reviews)\n", "train_lsi = yml.get_lsi_features(train_reviews, lsi, topics, dictionary)\n", "train_features = sparse.hstack((train_features, train_lsi))\n", "train_features = train_features.todense()\n", " \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#fit each model in turn \n", "model_runs = [(True, False, False, False, False), (False, True, False, False, False), \n", " (False, False, True, False, False), (False, False, False, True, False),\n", " (False, False, False, False, True)]\n", "\n", "test_results = {}\n", "\n", "for i in tqdm.tqdm(range(0, len(model_runs))):\n", " clf = yml.fit_model(train_features, train_labels, svm_clf = model_runs[i][0], \n", " RandomForest = model_runs[i][1], nb = model_runs[i][2])\n", " threshold = 0.7\n", " error = yml.test_user_set(test_set, clf, restaurant_df, user_df, comb_features, threshold, lsi, topics, dictionary)\n", " test_results[clf] = error\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\n", "#Get top predictions\n", "\n", "for key in test_results.keys():\n", " results = test_results[test_results.keys()[0]]\n", " log_loss = yml.get_log_loss()\n", " print log_loss\n", " \n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
timcera/tsgettoolbox
notebooks/tsgettoolbox-nwis-cli.ipynb
1
25891
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# tsgettoolbox and tstoolbox - Command Line Interface\n", "## 'tsgettoolbox nwis ...': Download data from the National Water Information System (NWIS)\n", "This notebook is to illustrate the command line usage for 'tsgettoolbox' and 'tstoolbox' to download and work with data from the National Water Information System (NWIS). There is a different notebook to do the same thing from within a Python program called tsgettoolbox-nwis-api.\n", "\n", "First off, always nice to remind myself about the options. Each sub-command has their own options kept consistent with the options available from the source service. The way that NWIS works is you have one major filter and one or more minor filters to define what sites you want." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tsgettoolbox nwis_dv --help" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's say that I want flow (parameterCd=00060) for site '02325000'. I first make sure that I am getting what I want by allowing the output to be printed to the screen. Note the pipe ('|') that directs output to the 'head' command to display the top 10 lines of the time-series." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Datetime,USGS-02325000-00060\r\n", "2000-01-01,82\r\n", "2000-01-02,81\r\n", "2000-01-03,80\r\n", "2000-01-04,79\r\n", "2000-01-05,75\r\n", "2000-01-06,75\r\n", "2000-01-07,74\r\n", "2000-01-08,73\r\n", "2000-01-09,75\r\n" ] } ], "source": [ "tsgettoolbox nwis_dv --sites 02325000 --startDT '2000-01-01' --parameterCd 00060 | head" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then I redirect to a file with \"> filename.csv\" so that I don't have to wait for the USGS NWIS services for the remaining work or analysis." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "tsgettoolbox nwis_dv --sites 02325000 --startDT '2000-01-01' --parameterCd 00060 > 02325000_flow.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 'tstoolbox ...': Process data using 'tstoolbox'\n", "Now lets use \"tstoolbox\" to plot the time-series. Note the redirection again, this time for input as \"< filename.csv\". Default plot filename is \"plot.png\"." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "tstoolbox plot < 02325000_flow.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![title](plot.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "'tstoolbox plot' has many options that can be used to modify the plot." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "usage: tstoolbox plot [-h] [--ofilename <str>] [--type <str>] [--xtitle <str>]\r\n", " [--ytitle <str>] [--title <str>] [--figsize <str>] [--legend LEGEND]\r\n", " [--legend_names <str>] [--subplots] [--sharex] [--sharey] [--style <str>]\r\n", " [--logx] [--logy] [--xaxis <str>] [--yaxis <str>] [--xlim XLIM] [--ylim\r\n", " YLIM] [--secondary_y] [--mark_right] [--scatter_matrix_diagonal <str>]\r\n", " [--bootstrap_size BOOTSTRAP_SIZE] [--bootstrap_samples BOOTSTRAP_SAMPLES]\r\n", " [--norm_xaxis] [--norm_yaxis] [--lognorm_xaxis] [--lognorm_yaxis]\r\n", " [--xy_match_line <str>] [--grid GRID] [-i <str>] [-s <str>] [-e <str>]\r\n", " [--label_rotation <int>] [--label_skip <int>] [--force_freq FORCE_FREQ]\r\n", " [--drawstyle <str>] [--por] [--columns COLUMNS] [--invert_xaxis]\r\n", " [--invert_yaxis] [--plotting_position <str>]\r\n", "\r\n", "Plot data.\r\n", "\r\n", "optional arguments:\r\n", " -h | --help\r\n", " show this help message and exit\r\n", " --ofilename <str>\r\n", " Output filename for the plot. Extension defines the type, ('.png').\r\n", " Defaults to 'plot.png'.\r\n", " --type <str>\r\n", " The plot type. Defaults to 'time'.\r\n", " Can be one of the following:\r\n", " time\r\n", " standard time series plot\r\n", "\r\n", " xy\r\n", " (x,y) plot, also know as a scatter plot\r\n", "\r\n", " double_mass\r\n", " (x,y) plot of the cumulative sum of x and y\r\n", "\r\n", " boxplot\r\n", " box extends from lower to upper quartile, with line at the median.\r\n", " Depending on the statistics, the wiskers represent the range of\r\n", " the data or 1.5 times the inter-quartile range (Q3 - Q1)\r\n", "\r\n", " scatter_matrix\r\n", " plots all columns against each other\r\n", "\r\n", " lag_plot\r\n", " indicates structure in the data\r\n", "\r\n", " autocorrelation\r\n", " plot autocorrelation\r\n", "\r\n", " bootstrap\r\n", " visually asses aspects of a data set by plotting random selections of\r\n", " values\r\n", "\r\n", " probability_density\r\n", " sometime called kernel density estimation (KDE)\r\n", "\r\n", " bar\r\n", " sometimes called a column plot\r\n", "\r\n", " barh\r\n", " a horizontal bar plot\r\n", "\r\n", " bar_stacked\r\n", " sometimes called a stacked column\r\n", "\r\n", " barh_stacked\r\n", " a horizontal stacked bar plot\r\n", "\r\n", " histogram\r\n", " calculate and create a histogram plot\r\n", "\r\n", " norm_xaxis\r\n", " sort, calculate probabilities, and plot data against an x axis normal\r\n", " distribution\r\n", "\r\n", " norm_yaxis\r\n", " sort, calculate probabilities, and plot data against an y axis normal\r\n", " distribution\r\n", "\r\n", " lognorm_xaxis\r\n", " sort, calculate probabilities, and plot data against an x axis lognormal\r\n", " distribution\r\n", "\r\n", " lognorm_yaxis\r\n", " sort, calculate probabilities, and plot data against an y axis lognormal\r\n", " distribution\r\n", "\r\n", " weibull_xaxis\r\n", " sort, calculate and plot data against an x axis weibull distribution\r\n", "\r\n", " weibull_yaxis\r\n", " sort, calculate and plot data against an y axis weibull distribution\r\n", "\r\n", " --xtitle <str>\r\n", " Title of x-axis, default depend on type.\r\n", " --ytitle <str>\r\n", " Title of y-axis, default depend on type.\r\n", " --title <str>\r\n", " Title of chart, defaults to ''.\r\n", " --figsize <str>\r\n", " The 'width,height' of plot as inches. Defaults to '10,6.5'.\r\n", " --legend LEGEND\r\n", " Whether to display the legend. Defaults to True.\r\n", " --legend_names <str>\r\n", " Legend would normally use the time-series names associated with the input\r\n", " data. The 'legend_names' option allows you to override the names in\r\n", " the data set. You must supply a comma separated list of strings for\r\n", " each time-series in the data set. Defaults to None.\r\n", " --subplots\r\n", " boolean, default False. Make separate subplots for each time series\r\n", " --sharex\r\n", " boolean, default True In case subplots=True, share x axis\r\n", " --sharey\r\n", " boolean, default False In case subplots=True, share y axis\r\n", " --style <str>\r\n", " Comma separated matplotlib style strings matplotlib line style per\r\n", " time-series. Just combine codes in 'ColorLineMarker' order, for\r\n", " example r--* is a red dashed line with star marker.\r\n", " ┌──────┬─────────┐\r\n", " │ Code │ Color │\r\n", " ├──────┼─────────┤\r\n", " │ b │ blue │\r\n", " ├──────┼─────────┤\r\n", " │ g │ green │\r\n", " ├──────┼─────────┤\r\n", " │ r │ red │\r\n", " ├──────┼─────────┤\r\n", " │ c │ cyan │\r\n", " ├──────┼─────────┤\r\n", " │ m │ magenta │\r\n", " ├──────┼─────────┤\r\n", " │ y │ yellow │\r\n", " ├──────┼─────────┤\r\n", " │ k │ black │\r\n", " ├──────┼─────────┤\r\n", " │ w │ white │\r\n", " ╘══════╧═════════╛\r\n", "\r\n", " ┌─────────┬───────────┐\r\n", " │ Number │ Color │\r\n", " ├─────────┼───────────┤\r\n", " │ 0.75 │ 0.75 gray │\r\n", " ├─────────┼───────────┤\r\n", " │ ...etc. │ │\r\n", " ╘═════════╧═══════════╛\r\n", "\r\n", " ┌──────────────────┐\r\n", " │ HTML Color Names │\r\n", " ├──────────────────┤\r\n", " │ red │\r\n", " ├──────────────────┤\r\n", " │ burlywood │\r\n", " ├──────────────────┤\r\n", " │ chartreuse │\r\n", " ├──────────────────┤\r\n", " │ ...etc. │\r\n", " ╘══════════════════╛\r\n", "\r\n", " Color reference: <\u001b[4;36mhttp://matplotlib.org/api/colors_api.html\u001b[0m>\r\n", " ┌──────┬──────────────┐\r\n", " │ Code │ Lines │\r\n", " ├──────┼──────────────┤\r\n", " │ • │ solid │\r\n", " ├──────┼──────────────┤\r\n", " │ -- │ dashed │\r\n", " ├──────┼──────────────┤\r\n", " │ -. │ dash_dot │\r\n", " ├──────┼──────────────┤\r\n", " │ : │ dotted │\r\n", " ├──────┼──────────────┤\r\n", " │ None │ draw nothing │\r\n", " ├──────┼──────────────┤\r\n", " │ ' ' │ draw nothing │\r\n", " ├──────┼──────────────┤\r\n", " │ '' │ draw nothing │\r\n", " ╘══════╧══════════════╛\r\n", "\r\n", " Line reference: <\u001b[4;36mhttp://matplotlib.org/api/artist_api.html\u001b[0m>\r\n", " ┌──────┬────────────────┐\r\n", " │ Code │ Markers │\r\n", " ├──────┼────────────────┤\r\n", " │ . │ point │\r\n", " ├──────┼────────────────┤\r\n", " │ o │ circle │\r\n", " ├──────┼────────────────┤\r\n", " │ v │ triangle down │\r\n", " ├──────┼────────────────┤\r\n", " │ ^ │ triangle up │\r\n", " ├──────┼────────────────┤\r\n", " │ < │ triangle left │\r\n", " ├──────┼────────────────┤\r\n", " │ > │ triangle right │\r\n", " ├──────┼────────────────┤\r\n", " │ 1 │ tri_down │\r\n", " ├──────┼────────────────┤\r\n", " │ 2 │ tri_up │\r\n", " ├──────┼────────────────┤\r\n", " │ 3 │ tri_left │\r\n", " ├──────┼────────────────┤\r\n", " │ 4 │ tri_right │\r\n", " ├──────┼────────────────┤\r\n", " │ 8 │ octagon │\r\n", " ├──────┼────────────────┤\r\n", " │ s │ square │\r\n", " ├──────┼────────────────┤\r\n", " │ p │ pentagon │\r\n", " ├──────┼────────────────┤\r\n", " │ • │ star │\r\n", " ├──────┼────────────────┤\r\n", " │ h │ hexagon1 │\r\n", " ├──────┼────────────────┤\r\n", " │ H │ hexagon2 │\r\n", " ├──────┼────────────────┤\r\n", " │ • │ plus │\r\n", " ├──────┼────────────────┤\r\n", " │ x │ x │\r\n", " ├──────┼────────────────┤\r\n", " │ D │ diamond │\r\n", " ├──────┼────────────────┤\r\n", " │ d │ thin diamond │\r\n", " ├──────┼────────────────┤\r\n", " │ _ │ hline │\r\n", " ├──────┼────────────────┤\r\n", " │ None │ nothing │\r\n", " ├──────┼────────────────┤\r\n", " │ ' ' │ nothing │\r\n", " ├──────┼────────────────┤\r\n", " │ '' │ nothing │\r\n", " ╘══════╧════════════════╛\r\n", "\r\n", " Marker reference: <\u001b[4;36mhttp://matplotlib.org/api/markers_api.html\u001b[0m>\r\n", " --logx\r\n", " DEPRECATED: use '--xaxis=\"log\"' instead.\r\n", " --logy\r\n", " DEPRECATED: use '--yaxis=\"log\"' instead.\r\n", " --xaxis <str>\r\n", " Defines the type of the xaxis. One of 'arithmetic', 'log'. Default is\r\n", " 'arithmetic'.\r\n", " --yaxis <str>\r\n", " Defines the type of the yaxis. One of 'arithmetic', 'log'. Default is\r\n", " 'arithmetic'.\r\n", " --xlim XLIM\r\n", " Comma separated lower and upper limits (--xlim 1,1000) Limits for the\r\n", " x-axis. Default is based on range of x values.\r\n", " --ylim YLIM\r\n", " Comma separated lower and upper limits (--ylim 1,1000) Limits for the\r\n", " y-axis. Default is based on range of y values.\r\n", " --secondary_y\r\n", " Boolean or sequence, default False Whether to plot on the secondary y-axis\r\n", " If a list/tuple, which time-series to plot on secondary y-axis\r\n", " --mark_right\r\n", " Boolean, default True : When using a secondary_y axis, should the legend\r\n", " label the axis of the various time-series automatically\r\n", " --scatter_matrix_diagonal <str>\r\n", " If plot type is 'scatter_matrix', this specifies the plot along the\r\n", " diagonal. Defaults to 'probability_density'.\r\n", " --bootstrap_size BOOTSTRAP_SIZE\r\n", " The size of the random subset for 'bootstrap' plot. Defaults to 50.\r\n", " --bootstrap_samples BOOTSTRAP_SAMPLES\r\n", " The number of random subsets of 'bootstrap_size'. Defaults to 500.\r\n", " --norm_xaxis\r\n", " DEPRECATED: use '--type=\"norm_xaxis\"' instead.\r\n", " --norm_yaxis\r\n", " DEPRECATED: use '--type=\"norm_yaxis\"' instead.\r\n", " --lognorm_xaxis\r\n", " DEPRECATED: use '--type=\"lognorm_xaxis\"' instead.\r\n", " --lognorm_yaxis\r\n", " DEPRECATED: use '--type=\"lognorm_yaxis\"' instead.\r\n", " --xy_match_line <str>\r\n", " Will add a match line where x == y. Default is ''. Set to a line style\r\n", " code.\r\n", " --grid GRID\r\n", " Boolean, default True Whether to plot grid lines on the major ticks.\r\n", " -i <str> | --input_ts <str>\r\n", " Filename with data in 'ISOdate,value' format or '-' for stdin.\r\n", " -s <str> | --start_date <str>\r\n", " The start_date of the series in ISOdatetime format, or 'None' for\r\n", " beginning.\r\n", " -e <str> | --end_date <str>\r\n", " The end_date of the series in ISOdatetime format, or 'None' for end.\r\n", " --label_rotation <int>\r\n", " Rotation for major labels for bar plots.\r\n", " --label_skip <int>\r\n", " Skip for major labels for bar plots.\r\n", " --force_freq FORCE_FREQ\r\n", " Force this frequency for the plot. WARNING: you may lose data if not\r\n", " careful with this option. In general, letting the algorithm\r\n", " determine the frequency should always work, but this option will\r\n", " override. Use PANDAS offset codes,\r\n", " --drawstyle <str>\r\n", " 'default' connects the points with lines. The steps variants produce\r\n", " step-plots. 'steps' is equivalent to 'steps-pre' and is maintained\r\n", " for backward-compatibility. ACCEPTS:\r\n", " ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']\r\n", "\r\n", " --por\r\n", " Plot from first good value to last good value. Strip NANs from beginning\r\n", " and end.\r\n", " --columns COLUMNS\r\n", " Columns to pick out of input. Can use column names or column numbers. If\r\n", " using numbers, column number 1 is the first data column. To pick\r\n", " multiple columns; separate by commas with no spaces. As used in\r\n", " 'pick' command.\r\n", " --invert_xaxis\r\n", " Invert the x-axis.\r\n", " --invert_yaxis\r\n", " Invert the y-axis.\r\n", " --plotting_position <str>\r\n", " 'weibull', 'benard', 'tukey', 'gumbel', 'hazen', 'cunnane', or\r\n", " 'california'. The default is 'weibull'.\r\n", " ┌────────────┬─────────────────┬───────────────────────┐\r\n", " │ weibull │ i/(n+1) │ mean of sampling │\r\n", " │ │ │ distribution │\r\n", " ├────────────┼─────────────────┼───────────────────────┤\r\n", " │ benard │ (i-0.3)/(n+0.4) │ approx. median of │\r\n", " │ │ │ sampling distribution │\r\n", " ├────────────┼─────────────────┼───────────────────────┤\r\n", " │ tukey │ (i-1/3)/(n+1/3) │ approx. median of │\r\n", " │ │ │ sampling distribution │\r\n", " ├────────────┼─────────────────┼───────────────────────┤\r\n", " │ gumbel │ (i-1)/(n-1) │ mode of sampling │\r\n", " │ │ │ distribution │\r\n", " ├────────────┼─────────────────┼───────────────────────┤\r\n", " │ hazen │ (i-1/2)/n │ midpoints of n equal │\r\n", " │ │ │ intervals │\r\n", " ├────────────┼─────────────────┼───────────────────────┤\r\n", " │ cunnane │ (i-2/5)/(n+1/5) │ subjective │\r\n", " ├────────────┼─────────────────┼───────────────────────┤\r\n", " │ california │ i/n │ │\r\n", " ╘════════════╧═════════════════╧═══════════════════════╛\r\n", "\r\n", " Where 'i' is the sorted rank of the y value, and 'n' is the total number\r\n", " of values to be plotted.\r\n", " Only used for norm_xaxis, norm_yaxis, lognorm_xaxis, lognorm_yaxis,\r\n", " weibull_xaxis, and weibull_yaxis.\r\n" ] } ], "source": [ "tstoolbox plot --help" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "tstoolbox plot --ofilename flow.png --ytitle 'Flow (cfs)' --title '02325000: FENHOLLOWAY RIVER NEAR PERRY, FLA' --legend False < 02325000_flow.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![title](flow.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Monthly Average Flow\n", "You can also use tstoolbox to make calculations on the time-series, for example to aggregate to monthly average flow:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Datetime,USGS-02325000-00060_mean\r\n", "2000-01-31,80\r\n", "2000-02-29,89.7931\r\n", "2000-03-31,80.0323\r\n", "2000-04-30,81.7667\r\n", "2000-05-31,90.8387\r\n", "2000-06-30,94.4\r\n", "2000-07-31,85.9032\r\n", "2000-08-31,83.0323\r\n", "2000-09-30,128.067\r\n" ] } ], "source": [ "tstoolbox aggregate --groupby M --statistic mean < 02325000_flow.csv | head" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [] } ], "source": [ "tstoolbox aggregate --groupby M --statistic mean < 02325000_flow.csv | tstoolbox plot --ofilename plot_monthly.png --drawstyle steps-pre" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![title](plot_monthly.png)" ] } ], "metadata": { "kernelspec": { "display_name": "Bash", "language": "bash", "name": "bash" }, "language_info": { "codemirror_mode": "shell", "file_extension": ".sh", "mimetype": "text/x-sh", "name": "bash" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
albertfxwang/grizli
examples/WFC3IR_Reduction.ipynb
1
1397454
null
mit
emsi/ml-toolbox
random/Atmosfera/tfidf.ipynb
1
5853
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer, TfidfTransformer\n", "import numpy as np\n", "import pandas as pd\n", "#define vectorizer parameters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "vectorizer = TfidfVectorizer()#smooth_idf=True, sublinear_tf=False)\n", "# docs=[\"błąd danych na uprawnienia się administratorem problem acs\",\n", "# \"ewidencja czasu pracy problem tk dzień dobry jest problem czasu pracy\",\n", "# \"proszę przekierowanie faktur na panak do dnia data\",\n", "# \"pd sap sap nadal nie działa\"\n", "# ]\n", "docs = [\"This is about about Messi Messi Messi Messi\",\n", " \"This is is about tfidf\"]\n", "tfidf_matrix = vectorizer.fit_transform(docs)\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "feature_names = vectorizer.get_feature_names()\n", "dense = tfidf_matrix.todense()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>This is about about Messi Messi Messi Messi</th>\n", " <th>This is is about tfidf</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>about</th>\n", " <td>0.326141</td>\n", " <td>0.354100</td>\n", " </tr>\n", " <tr>\n", " <th>is</th>\n", " <td>0.163070</td>\n", " <td>0.708199</td>\n", " </tr>\n", " <tr>\n", " <th>messi</th>\n", " <td>0.916760</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>tfidf</th>\n", " <td>0.000000</td>\n", " <td>0.497675</td>\n", " </tr>\n", " <tr>\n", " <th>this</th>\n", " <td>0.163070</td>\n", " <td>0.354100</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " This is about about Messi Messi Messi Messi This is is about tfidf\n", "about 0.326141 0.354100\n", "is 0.163070 0.708199\n", "messi 0.916760 0.000000\n", "tfidf 0.000000 0.497675\n", "this 0.163070 0.354100" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(dense.transpose(), index=feature_names, columns=docs)\n", "df" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>about</th>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>is</th>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>messi</th>\n", " <td>0.405465</td>\n", " </tr>\n", " <tr>\n", " <th>tfidf</th>\n", " <td>0.405465</td>\n", " </tr>\n", " <tr>\n", " <th>this</th>\n", " <td>0.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0\n", "about 0.000000\n", "is 0.000000\n", "messi 0.405465\n", "tfidf 0.405465\n", "this 0.000000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.DataFrame(vectorizer.idf_-1,index=feature_names) # <- dodałem -1 do idf ;)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Wnioski\n", "Na tej podstawie można ocenić, które wyrazy mają niski tfidf, czyli nie różnicują i można próbować je usunąc aby zrobić miejsce dla rzadszych wyrazów, które bez tego nie ząłapałyby się do limitu tokenizera :)\n", "\n", "p.s. wartości tfidf nie zgadzają się z tymi z https://www.analyticsvidhya.com/blog/2017/06/word-embeddings-count-word2veec/ bo stosujemy inny wzór ale to nie wpływa na sposób wnioskowania." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
agpl-3.0
ga-students/DAT_SF_11_homework
andrewjtimmons/hw1_soln-andrewjtimmons.ipynb
1
142429
{ "metadata": { "name": "", "signature": "sha256:d778638f1fb1a3758e09db8088b9a70efdd4dc95fcb6b59ad2828925e0b8751c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Homework 1 - Data Analysis and Regression\n", "In this assignment your challenge is to do some basic analysis for Airbnb. Provided in hw/data/ there are 2 data files, <a href=../data/bookings.csv>bookings.csv</a> and <a href=../data/listings.csv>listings.csv</a>. The objective is to practice data munging and begin our exploration of regression." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import numpy\n", "import scipy\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Part 1 - Data exploration\n", "###First, create 2 data frames: `listings` and `bookings` from their respective data files" ] }, { "cell_type": "code", "collapsed": false, "input": [ "bookings = pd.read_csv(\"../data/bookings.csv\")\n", "listings = pd.read_csv('../data/listings.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###What is the mean, median and standard deviation of price, person capacity, picture count, description length and tenure of the properties?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "listings.describe()\n", "#or\n", "#listings.median()\n", "#listings.mean()\n", "#listings.std()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>prop_id</th>\n", " <th>price</th>\n", " <th>person_capacity</th>\n", " <th>picture_count</th>\n", " <th>description_length</th>\n", " <th>tenure_months</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td> 408.000000</td>\n", " <td> 408.000000</td>\n", " <td> 408.000000</td>\n", " <td> 408.000000</td>\n", " <td> 408.000000</td>\n", " <td> 408.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td> 204.500000</td>\n", " <td> 187.806373</td>\n", " <td> 2.997549</td>\n", " <td> 14.389706</td>\n", " <td> 309.159314</td>\n", " <td> 8.487745</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td> 117.923704</td>\n", " <td> 353.050858</td>\n", " <td> 1.594676</td>\n", " <td> 10.477428</td>\n", " <td> 228.021684</td>\n", " <td> 5.872088</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td> 1.000000</td>\n", " <td> 39.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 0.000000</td>\n", " <td> 1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td> 102.750000</td>\n", " <td> 90.000000</td>\n", " <td> 2.000000</td>\n", " <td> 6.000000</td>\n", " <td> 179.000000</td>\n", " <td> 4.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td> 204.500000</td>\n", " <td> 125.000000</td>\n", " <td> 2.000000</td>\n", " <td> 12.000000</td>\n", " <td> 250.000000</td>\n", " <td> 7.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td> 306.250000</td>\n", " <td> 199.000000</td>\n", " <td> 4.000000</td>\n", " <td> 20.000000</td>\n", " <td> 389.500000</td>\n", " <td> 13.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td> 408.000000</td>\n", " <td> 5000.000000</td>\n", " <td> 10.000000</td>\n", " <td> 71.000000</td>\n", " <td> 1969.000000</td>\n", " <td> 30.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ " prop_id price person_capacity picture_count \\\n", "count 408.000000 408.000000 408.000000 408.000000 \n", "mean 204.500000 187.806373 2.997549 14.389706 \n", "std 117.923704 353.050858 1.594676 10.477428 \n", "min 1.000000 39.000000 1.000000 1.000000 \n", "25% 102.750000 90.000000 2.000000 6.000000 \n", "50% 204.500000 125.000000 2.000000 12.000000 \n", "75% 306.250000 199.000000 4.000000 20.000000 \n", "max 408.000000 5000.000000 10.000000 71.000000 \n", "\n", " description_length tenure_months \n", "count 408.000000 408.000000 \n", "mean 309.159314 8.487745 \n", "std 228.021684 5.872088 \n", "min 0.000000 1.000000 \n", "25% 179.000000 4.000000 \n", "50% 250.000000 7.000000 \n", "75% 389.500000 13.000000 \n", "max 1969.000000 30.000000 " ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###What what are the mean price, person capacity, picture count, description length and tenure of the properties grouped by property type?" ] }, { "cell_type": "code", "collapsed": false, "input": [ "listings.groupby('prop_type').mean()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>prop_id</th>\n", " <th>price</th>\n", " <th>person_capacity</th>\n", " <th>picture_count</th>\n", " <th>description_length</th>\n", " <th>tenure_months</th>\n", " </tr>\n", " <tr>\n", " <th>prop_type</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Property type 1</th>\n", " <td> 204.754647</td>\n", " <td> 237.085502</td>\n", " <td> 3.516729</td>\n", " <td> 14.695167</td>\n", " <td> 313.171004</td>\n", " <td> 8.464684</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 206.392593</td>\n", " <td> 93.288889</td>\n", " <td> 2.000000</td>\n", " <td> 13.948148</td>\n", " <td> 304.851852</td>\n", " <td> 8.377778</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 3</th>\n", " <td> 123.500000</td>\n", " <td> 63.750000</td>\n", " <td> 1.750000</td>\n", " <td> 8.750000</td>\n", " <td> 184.750000</td>\n", " <td> 13.750000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " prop_id price person_capacity picture_count \\\n", "prop_type \n", "Property type 1 204.754647 237.085502 3.516729 14.695167 \n", "Property type 2 206.392593 93.288889 2.000000 13.948148 \n", "Property type 3 123.500000 63.750000 1.750000 8.750000 \n", "\n", " description_length tenure_months \n", "prop_type \n", "Property type 1 313.171004 8.464684 \n", "Property type 2 304.851852 8.377778 \n", "Property type 3 184.750000 13.750000 " ] } ], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Same, but by property type per neighborhood? " ] }, { "cell_type": "code", "collapsed": false, "input": [ "listings.groupby(['neighborhood', 'prop_type']).mean()" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th></th>\n", " <th>prop_id</th>\n", " <th>price</th>\n", " <th>person_capacity</th>\n", " <th>picture_count</th>\n", " <th>description_length</th>\n", " <th>tenure_months</th>\n", " </tr>\n", " <tr>\n", " <th>neighborhood</th>\n", " <th>prop_type</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Neighborhood 1</th>\n", " <th>Property type 1</th>\n", " <td> 235.000000</td>\n", " <td> 85.000000</td>\n", " <td> 2.000000</td>\n", " <td> 26.000000</td>\n", " <td> 209.000000</td>\n", " <td> 6.000000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 10</th>\n", " <th>Property type 1</th>\n", " <td> 307.500000</td>\n", " <td> 142.500000</td>\n", " <td> 3.500000</td>\n", " <td> 13.333333</td>\n", " <td> 391.000000</td>\n", " <td> 3.833333</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 327.000000</td>\n", " <td> 137.500000</td>\n", " <td> 2.000000</td>\n", " <td> 20.000000</td>\n", " <td> 126.000000</td>\n", " <td> 3.500000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">Neighborhood 11</th>\n", " <th>Property type 1</th>\n", " <td> 174.000000</td>\n", " <td> 159.428571</td>\n", " <td> 3.214286</td>\n", " <td> 9.928571</td>\n", " <td> 379.000000</td>\n", " <td> 9.642857</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 146.250000</td>\n", " <td> 78.750000</td>\n", " <td> 2.000000</td>\n", " <td> 16.750000</td>\n", " <td> 161.250000</td>\n", " <td> 11.250000</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 3</th>\n", " <td> 178.000000</td>\n", " <td> 75.000000</td>\n", " <td> 2.000000</td>\n", " <td> 15.000000</td>\n", " <td> 196.000000</td>\n", " <td> 8.000000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 12</th>\n", " <th>Property type 1</th>\n", " <td> 211.307692</td>\n", " <td> 365.615385</td>\n", " <td> 3.435897</td>\n", " <td> 10.820513</td>\n", " <td> 267.205128</td>\n", " <td> 7.897436</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 164.263158</td>\n", " <td> 96.894737</td>\n", " <td> 1.947368</td>\n", " <td> 10.473684</td>\n", " <td> 244.526316</td>\n", " <td> 9.842105</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 13</th>\n", " <th>Property type 1</th>\n", " <td> 190.142857</td>\n", " <td> 241.897959</td>\n", " <td> 4.061224</td>\n", " <td> 15.653061</td>\n", " <td> 290.408163</td>\n", " <td> 9.122449</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 199.000000</td>\n", " <td> 81.130435</td>\n", " <td> 1.826087</td>\n", " <td> 16.695652</td>\n", " <td> 418.565217</td>\n", " <td> 9.739130</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">Neighborhood 14</th>\n", " <th>Property type 1</th>\n", " <td> 220.764706</td>\n", " <td> 164.676471</td>\n", " <td> 3.205882</td>\n", " <td> 14.764706</td>\n", " <td> 317.205882</td>\n", " <td> 8.441176</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 195.047619</td>\n", " <td> 83.809524</td>\n", " <td> 1.857143</td>\n", " <td> 15.904762</td>\n", " <td> 348.619048</td>\n", " <td> 8.714286</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 3</th>\n", " <td> 286.000000</td>\n", " <td> 75.000000</td>\n", " <td> 1.000000</td>\n", " <td> 1.000000</td>\n", " <td> 113.000000</td>\n", " <td> 5.000000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 15</th>\n", " <th>Property type 1</th>\n", " <td> 191.560000</td>\n", " <td> 178.880000</td>\n", " <td> 3.720000</td>\n", " <td> 14.320000</td>\n", " <td> 321.760000</td>\n", " <td> 9.320000</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 194.666667</td>\n", " <td> 95.000000</td>\n", " <td> 2.266667</td>\n", " <td> 11.733333</td>\n", " <td> 301.733333</td>\n", " <td> 8.200000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 16</th>\n", " <th>Property type 1</th>\n", " <td> 233.000000</td>\n", " <td> 158.928571</td>\n", " <td> 2.928571</td>\n", " <td> 21.642857</td>\n", " <td> 310.714286</td>\n", " <td> 7.071429</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 251.562500</td>\n", " <td> 83.625000</td>\n", " <td> 2.062500</td>\n", " <td> 15.375000</td>\n", " <td> 246.250000</td>\n", " <td> 6.687500</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"3\" valign=\"top\">Neighborhood 17</th>\n", " <th>Property type 1</th>\n", " <td> 166.043478</td>\n", " <td> 189.869565</td>\n", " <td> 3.521739</td>\n", " <td> 16.086957</td>\n", " <td> 317.347826</td>\n", " <td> 9.869565</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 242.181818</td>\n", " <td> 102.454545</td>\n", " <td> 2.000000</td>\n", " <td> 15.454545</td>\n", " <td> 308.272727</td>\n", " <td> 7.181818</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 3</th>\n", " <td> 10.000000</td>\n", " <td> 65.000000</td>\n", " <td> 2.000000</td>\n", " <td> 15.000000</td>\n", " <td> 189.000000</td>\n", " <td> 23.000000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 18</th>\n", " <th>Property type 1</th>\n", " <td> 210.000000</td>\n", " <td> 173.590909</td>\n", " <td> 2.954545</td>\n", " <td> 16.090909</td>\n", " <td> 369.227273</td>\n", " <td> 8.227273</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 179.333333</td>\n", " <td> 120.666667</td>\n", " <td> 2.222222</td>\n", " <td> 12.333333</td>\n", " <td> 297.777778</td>\n", " <td> 9.222222</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 19</th>\n", " <th>Property type 1</th>\n", " <td> 253.250000</td>\n", " <td> 222.375000</td>\n", " <td> 3.625000</td>\n", " <td> 11.000000</td>\n", " <td> 254.500000</td>\n", " <td> 6.500000</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 256.750000</td>\n", " <td> 88.875000</td>\n", " <td> 2.000000</td>\n", " <td> 15.125000</td>\n", " <td> 383.375000</td>\n", " <td> 5.500000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 2</th>\n", " <th>Property type 1</th>\n", " <td> 244.000000</td>\n", " <td> 250.000000</td>\n", " <td> 6.000000</td>\n", " <td> 8.000000</td>\n", " <td> 423.000000</td>\n", " <td> 6.000000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 20</th>\n", " <th>Property type 1</th>\n", " <td> 174.111111</td>\n", " <td> 804.333333</td>\n", " <td> 2.777778</td>\n", " <td> 9.444444</td>\n", " <td> 223.555556</td>\n", " <td> 9.666667</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 230.000000</td>\n", " <td> 60.000000</td>\n", " <td> 1.000000</td>\n", " <td> 3.000000</td>\n", " <td> 101.000000</td>\n", " <td> 6.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 21</th>\n", " <th>Property type 1</th>\n", " <td> 79.250000</td>\n", " <td> 362.500000</td>\n", " <td> 4.250000</td>\n", " <td> 49.000000</td>\n", " <td> 306.250000</td>\n", " <td> 14.750000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 22</th>\n", " <th>Property type 1</th>\n", " <td> 162.000000</td>\n", " <td> 225.000000</td>\n", " <td> 3.000000</td>\n", " <td> 19.000000</td>\n", " <td> 500.000000</td>\n", " <td> 9.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 3</th>\n", " <th>Property type 2</th>\n", " <td> 166.000000</td>\n", " <td> 60.000000</td>\n", " <td> 2.000000</td>\n", " <td> 7.000000</td>\n", " <td> 264.000000</td>\n", " <td> 9.000000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 4</th>\n", " <th>Property type 2</th>\n", " <td> 118.000000</td>\n", " <td> 60.000000</td>\n", " <td> 2.000000</td>\n", " <td> 10.000000</td>\n", " <td> 95.000000</td>\n", " <td> 11.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 3</th>\n", " <td> 20.000000</td>\n", " <td> 40.000000</td>\n", " <td> 2.000000</td>\n", " <td> 4.000000</td>\n", " <td> 241.000000</td>\n", " <td> 19.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 5</th>\n", " <th>Property type 1</th>\n", " <td> 132.500000</td>\n", " <td> 194.500000</td>\n", " <td> 2.500000</td>\n", " <td> 8.500000</td>\n", " <td> 266.500000</td>\n", " <td> 11.500000</td>\n", " </tr>\n", " <tr>\n", " <th>Neighborhood 6</th>\n", " <th>Property type 1</th>\n", " <td> 291.333333</td>\n", " <td> 146.000000</td>\n", " <td> 3.333333</td>\n", " <td> 12.666667</td>\n", " <td> 290.666667</td>\n", " <td> 4.000000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 7</th>\n", " <th>Property type 1</th>\n", " <td> 273.333333</td>\n", " <td> 161.000000</td>\n", " <td> 3.666667</td>\n", " <td> 14.333333</td>\n", " <td> 343.000000</td>\n", " <td> 5.333333</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 365.000000</td>\n", " <td> 100.000000</td>\n", " <td> 2.000000</td>\n", " <td> 3.000000</td>\n", " <td> 148.000000</td>\n", " <td> 2.000000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 8</th>\n", " <th>Property type 1</th>\n", " <td> 218.250000</td>\n", " <td> 174.750000</td>\n", " <td> 5.000000</td>\n", " <td> 11.000000</td>\n", " <td> 300.000000</td>\n", " <td> 6.750000</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 343.000000</td>\n", " <td> 350.000000</td>\n", " <td> 4.000000</td>\n", " <td> 5.000000</td>\n", " <td> 223.000000</td>\n", " <td> 3.000000</td>\n", " </tr>\n", " <tr>\n", " <th rowspan=\"2\" valign=\"top\">Neighborhood 9</th>\n", " <th>Property type 1</th>\n", " <td> 265.857143</td>\n", " <td> 151.142857</td>\n", " <td> 4.285714</td>\n", " <td> 13.428571</td>\n", " <td> 471.428571</td>\n", " <td> 5.714286</td>\n", " </tr>\n", " <tr>\n", " <th>Property type 2</th>\n", " <td> 165.500000</td>\n", " <td> 110.000000</td>\n", " <td> 2.000000</td>\n", " <td> 3.500000</td>\n", " <td> 114.500000</td>\n", " <td> 9.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ " prop_id price person_capacity \\\n", "neighborhood prop_type \n", "Neighborhood 1 Property type 1 235.000000 85.000000 2.000000 \n", "Neighborhood 10 Property type 1 307.500000 142.500000 3.500000 \n", " Property type 2 327.000000 137.500000 2.000000 \n", "Neighborhood 11 Property type 1 174.000000 159.428571 3.214286 \n", " Property type 2 146.250000 78.750000 2.000000 \n", " Property type 3 178.000000 75.000000 2.000000 \n", "Neighborhood 12 Property type 1 211.307692 365.615385 3.435897 \n", " Property type 2 164.263158 96.894737 1.947368 \n", "Neighborhood 13 Property type 1 190.142857 241.897959 4.061224 \n", " Property type 2 199.000000 81.130435 1.826087 \n", "Neighborhood 14 Property type 1 220.764706 164.676471 3.205882 \n", " Property type 2 195.047619 83.809524 1.857143 \n", " Property type 3 286.000000 75.000000 1.000000 \n", "Neighborhood 15 Property type 1 191.560000 178.880000 3.720000 \n", " Property type 2 194.666667 95.000000 2.266667 \n", "Neighborhood 16 Property type 1 233.000000 158.928571 2.928571 \n", " Property type 2 251.562500 83.625000 2.062500 \n", "Neighborhood 17 Property type 1 166.043478 189.869565 3.521739 \n", " Property type 2 242.181818 102.454545 2.000000 \n", " Property type 3 10.000000 65.000000 2.000000 \n", "Neighborhood 18 Property type 1 210.000000 173.590909 2.954545 \n", " Property type 2 179.333333 120.666667 2.222222 \n", "Neighborhood 19 Property type 1 253.250000 222.375000 3.625000 \n", " Property type 2 256.750000 88.875000 2.000000 \n", "Neighborhood 2 Property type 1 244.000000 250.000000 6.000000 \n", "Neighborhood 20 Property type 1 174.111111 804.333333 2.777778 \n", " Property type 2 230.000000 60.000000 1.000000 \n", "Neighborhood 21 Property type 1 79.250000 362.500000 4.250000 \n", "Neighborhood 22 Property type 1 162.000000 225.000000 3.000000 \n", "Neighborhood 3 Property type 2 166.000000 60.000000 2.000000 \n", "Neighborhood 4 Property type 2 118.000000 60.000000 2.000000 \n", " Property type 3 20.000000 40.000000 2.000000 \n", "Neighborhood 5 Property type 1 132.500000 194.500000 2.500000 \n", "Neighborhood 6 Property type 1 291.333333 146.000000 3.333333 \n", "Neighborhood 7 Property type 1 273.333333 161.000000 3.666667 \n", " Property type 2 365.000000 100.000000 2.000000 \n", "Neighborhood 8 Property type 1 218.250000 174.750000 5.000000 \n", " Property type 2 343.000000 350.000000 4.000000 \n", "Neighborhood 9 Property type 1 265.857143 151.142857 4.285714 \n", " Property type 2 165.500000 110.000000 2.000000 \n", "\n", " picture_count description_length \\\n", "neighborhood prop_type \n", "Neighborhood 1 Property type 1 26.000000 209.000000 \n", "Neighborhood 10 Property type 1 13.333333 391.000000 \n", " Property type 2 20.000000 126.000000 \n", "Neighborhood 11 Property type 1 9.928571 379.000000 \n", " Property type 2 16.750000 161.250000 \n", " Property type 3 15.000000 196.000000 \n", "Neighborhood 12 Property type 1 10.820513 267.205128 \n", " Property type 2 10.473684 244.526316 \n", "Neighborhood 13 Property type 1 15.653061 290.408163 \n", " Property type 2 16.695652 418.565217 \n", "Neighborhood 14 Property type 1 14.764706 317.205882 \n", " Property type 2 15.904762 348.619048 \n", " Property type 3 1.000000 113.000000 \n", "Neighborhood 15 Property type 1 14.320000 321.760000 \n", " Property type 2 11.733333 301.733333 \n", "Neighborhood 16 Property type 1 21.642857 310.714286 \n", " Property type 2 15.375000 246.250000 \n", "Neighborhood 17 Property type 1 16.086957 317.347826 \n", " Property type 2 15.454545 308.272727 \n", " Property type 3 15.000000 189.000000 \n", "Neighborhood 18 Property type 1 16.090909 369.227273 \n", " Property type 2 12.333333 297.777778 \n", "Neighborhood 19 Property type 1 11.000000 254.500000 \n", " Property type 2 15.125000 383.375000 \n", "Neighborhood 2 Property type 1 8.000000 423.000000 \n", "Neighborhood 20 Property type 1 9.444444 223.555556 \n", " Property type 2 3.000000 101.000000 \n", "Neighborhood 21 Property type 1 49.000000 306.250000 \n", "Neighborhood 22 Property type 1 19.000000 500.000000 \n", "Neighborhood 3 Property type 2 7.000000 264.000000 \n", "Neighborhood 4 Property type 2 10.000000 95.000000 \n", " Property type 3 4.000000 241.000000 \n", "Neighborhood 5 Property type 1 8.500000 266.500000 \n", "Neighborhood 6 Property type 1 12.666667 290.666667 \n", "Neighborhood 7 Property type 1 14.333333 343.000000 \n", " Property type 2 3.000000 148.000000 \n", "Neighborhood 8 Property type 1 11.000000 300.000000 \n", " Property type 2 5.000000 223.000000 \n", "Neighborhood 9 Property type 1 13.428571 471.428571 \n", " Property type 2 3.500000 114.500000 \n", "\n", " tenure_months \n", "neighborhood prop_type \n", "Neighborhood 1 Property type 1 6.000000 \n", "Neighborhood 10 Property type 1 3.833333 \n", " Property type 2 3.500000 \n", "Neighborhood 11 Property type 1 9.642857 \n", " Property type 2 11.250000 \n", " Property type 3 8.000000 \n", "Neighborhood 12 Property type 1 7.897436 \n", " Property type 2 9.842105 \n", "Neighborhood 13 Property type 1 9.122449 \n", " Property type 2 9.739130 \n", "Neighborhood 14 Property type 1 8.441176 \n", " Property type 2 8.714286 \n", " Property type 3 5.000000 \n", "Neighborhood 15 Property type 1 9.320000 \n", " Property type 2 8.200000 \n", "Neighborhood 16 Property type 1 7.071429 \n", " Property type 2 6.687500 \n", "Neighborhood 17 Property type 1 9.869565 \n", " Property type 2 7.181818 \n", " Property type 3 23.000000 \n", "Neighborhood 18 Property type 1 8.227273 \n", " Property type 2 9.222222 \n", "Neighborhood 19 Property type 1 6.500000 \n", " Property type 2 5.500000 \n", "Neighborhood 2 Property type 1 6.000000 \n", "Neighborhood 20 Property type 1 9.666667 \n", " Property type 2 6.000000 \n", "Neighborhood 21 Property type 1 14.750000 \n", "Neighborhood 22 Property type 1 9.000000 \n", "Neighborhood 3 Property type 2 9.000000 \n", "Neighborhood 4 Property type 2 11.000000 \n", " Property type 3 19.000000 \n", "Neighborhood 5 Property type 1 11.500000 \n", "Neighborhood 6 Property type 1 4.000000 \n", "Neighborhood 7 Property type 1 5.333333 \n", " Property type 2 2.000000 \n", "Neighborhood 8 Property type 1 6.750000 \n", " Property type 2 3.000000 \n", "Neighborhood 9 Property type 1 5.714286 \n", " Property type 2 9.000000 " ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Plot daily bookings:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "with plt.style.context('fivethirtyeight'):\n", " #bookings.sort('booking_date', inplace = True)\n", " ax = bookings['booking_date'].value_counts().sort_index().plot(figsize = (12,8))\n", " ax.set_ylabel(\"Number of bookings\")\n", " ax.set_xlabel('Date')\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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aI7/GMwfz+OamUTRkgIGcW4pI6xyXZkXJVDCIKnsPUH9FBpPNdhVdzMd40cLq\nDaPYdLiAN5/QgmtPb0fWoLplUbiAyfhriwzG0FmSMmRpYj6c2a5CWD5CxXzEGHCe1vdE8wUOqyv9\n5UAe39o0guaGDD75mrno7myqonWEkClo+SBHDd/bPIr1BwvYPVrCvz49jPGI/VJs28bXnhnGS8NF\nvDhUFPdJq/AqrVSmxRXBXPmIMuZD43aV0gGUV5/juVaUMR+/3T2J+/dMYt94CT/qGcemwwWj46R7\nM3K70tb5MLpsICTLR8GyRcHcoXx43IbuHl3Kx5H/e1o+YlQ+Uut2pVtUCHGukm3jX58exraREjYP\nFnHrxtGq2kYImYHKBzlqeHBvbvrfeQt4pDcf6flHCjb2jXtPYymVXUVhLi1tNW1GtDEfwbbXmiRr\nLZj0gem1v6EIbN96zkyAkwR0s4Dz5GI+pPbo3Lsq+8tLcNf1veqamDWI+YgqyF5qk1e64Fqia1WY\n5vaOlXBgcmawTBVnQog/VD7IUUvUQdZmlZ/TOSmnOeajJpaPOlM+pGbVtM5HyH6aMPTXkSxQ1aTa\njcPtSlJoJjQCvyPVrsc3QveMq82fdrvy8GCLzu0qOatbtehT7QZvcFSpigkhbqh8kKMWA9fyQJgI\nv2mdr+Rq7Mm3Q8JUYYtW+dC5t6SkUxSSjNmJMuYj7HGSjuI3/iVbdnkCkgs4113HpMK512+6Oh+Z\nTEY7icdbZDCd74nW8hHiXJNiQoF03jch9QaVD3LUEnUdC5OVxJTOyXLMR0omUlOFbTa7XUlDFZcg\nFKflw7TNYrYrHzciLwUjjlVs6XqTGu3HGfPhEXBumu2q8jfNuaK65zQnq1CJMtvVeEGI6YkhdoiQ\n2QiVD3IUE636YZJtJyXyvIs0u12Z9llUbiSAXpFJq1CVpOuLUZ2PkMkKTNscps6Hl3ISh+VDcuWa\n0FzHxkwNIC93OdM6H1mDT1usAecpfU90zQrTFWPCYERpfSVkNkPlgxy11MLtKq2TcpoDzo1dcWZx\nkUHp1i07nqKWJuMR9rLGyodww77Kh8fvsSgfQhslV50ppn7ytHxo7kEdZ5N0xVGt0kvvRD1lhQPC\nPa9jghUrygUQQmYzVD7IUUvkblcGk7lpwbykEQWIlDTVOOA8QkG73tyu9IG0yV0r6D4SprEC0vPq\n50bkpWDEU+Hc3O0KqFA+PIsMeh87hcnEHWedj7S+J1rLR4jv8pjgdsUgdEKiITHlY8+ePbjkkkvw\nhje8AefwlJqnAAAgAElEQVSddx5uv/12AMBXv/pVnHnmmVixYgVWrFiBBx98MKkmERKIeo75kN2u\n0tFY4wrnEfpb61xf0lq/IEnlw8SlKux1TY+TBHS/988z5iOOOh9Ce7wsH1O7e/WBzrqjbjVxu4q1\nyGBKP3RRxnyMCn62dLsiJBoSq3De1NSEr3zlK1i+fDlGR0fx13/917jwwguRyWRw3XXX4frrr0+q\nKWSWwIDzGY4Kt6tILR9mgb1pQacolu8j2ifdpJvDKq6m/Rsu4Fz/WyypdiXLh6fyUR4rr+dYp/y6\nAs6NYj789zGhniwfulsOFXAumKEYcE5INCSmfCxatAiLFi0CAHR0dOC0005Db28vgHj8lgmJWvsw\nmXjS+ijLFc7TgXmdj+iuqXe7SucA6lpVb25Xpv0rCehVxXwkVGTQyPLh8RybVjhvMLF8RPRwSGNm\nWK4lcXSPV5jmMuaDkPhITPmoZOfOndiwYQPOPvtsPPHEE1izZg3uvfdevO51r8OXv/xlzJ8/vxbN\nIhX8fNs47u4Zx5L2BtzwV/OwpK2h1k0KTNSWD9M6H7ZtIxN1tHuVSAJPWuZRU51C1//bh4v4ytPD\nGMhZ+PAZ7fg/Tpoj7jdZtPHvzw5j3YE8BvI6S4JhY6rEtm3c3deKdS8dwPJjmvDp181DR5PeC1br\ndhWRQvbDF8fwi+0TePncBhzQVcozaI8fcQSc7xot4qb1w9gyWNSeL6k6H0YxH56pduXtqqUpY/B1\nG8hZuOHPg3jucAFvObEV1y/rCPVdElPtBnwAipaNrz0zgif6czi3qxn/9Jp5aDHRoAKijfkI5XYV\nXAEmhJiReMD56OgoPvjBD+Lmm29GR0cHPvShD2HDhg145JFHsHjxYnz2s59NuklE4eBkCbdtGsWh\nnIVNhwu4u2es1k3yRbKe1cLtCgi3yhY3OtkrDXEfpk3QWT7WvDCKnqEiDk5auGXDCIY1UaEP907i\nwb05reIBJKeQPdmfx0MDLRjO23ikL4+1uyY994+ycrPKrtEivr95DAM5C+sOFjz7Z+a64a5lqiwF\ncbv6/uYxT8UD8HbJCov0PdCl2gVmxtC7yKDG8qH8bRLzUbSBR/vyGMzb+Pm2CTxzqOB/kHTtCNyu\nfr93EvfvmcRw3sYDe3L4733ez3tYdF4UYb5zY8IHJ47YIUJmI4laPgqFAq6++mpcfvnluOSSSwAA\nCxcunP79qquuwqpVq7TH9/T0xN5GAvzXgRbYaJ3++1c7J/HOtv3a/dMwLuUJstOxrbevDz3j4SZc\niT0DTQDafPfb0rMVjSkxfEyNzc7xBgAdrt/T0NYDh1qAiudNR+/+fvQU8q7tj++fGffJEvCTZ3bj\nrxe49/vKC52ubSqDw8Po6dE/61Hx1RfnonLt53svjOC11l7t/qNjbQCaXNu3btuOw03VKSA/2DcH\nQHOgY3r7+tAzafJuOfvcgtn34uDhVgAtjm0HDg+gp6fXte8f9/mPa65k4cUXewKl3/Zr56Eh95iM\nThagW9Pbum0b5jfaKNn69vb2H0BP0f3sHh5w9sehgwfQY5X3O2feHPx52H/8bl1/EDecEnwhae9w\nI4B2x7Z9+/vRk3e3U8dXlXfvP58fxCmTewK3ZQrd2PQOyt/ofb296BnzVlBVDo+2QxWRduzZi86h\nYOeZbaRBHiB6khqf7u5uz9+NlI9vfvObeNOb3oTXvOY1eOqpp/D3f//3yGaz+M53voNzzz3XqCG2\nbeP666/HK1/5Slx33XXT2/v6+rB48WIAwK9//WuceeaZ2nP43QyJhvmlUeDguGObru97enpSMS75\nkg1sPuDY1rVoMbpP9BdqTdm4fRzoG/Xd7xWvOBXNMbgUBKVybEYP5oGdg659Xv6KU2NxfwjCAuF5\nk5h/7HHoPrXd/cML/Y4/J+ccg+7uub77SbR1zEV394m++1XLkNKWnJXxfI9aDwwAY25h/+SXnVK1\nS2T7yDAwFGwleuGiReg+WXZvqyS7ud+1cm7yvZg7MQIMTDi2tc3rRHf3Se6dDcbVRgannHoqmkxM\nBjD7rjUdHARGnQJ4KdMAne1z6ctOwbGtWdd3qpIFmme8U+mPRV0L0X1KWcj+v5cUcdPTw+gft3Ao\npzer5LLN6O4+3uuWRPbumwT2Dju2Hbtw5vpGKGNUamgKPW94jc3WXRNA74hre9fiJeg+IdhcUNx9\nCIDT1LFw8RJ0Hx/dnHK0kRZ5gMikaXyMlI9vf/vbuPrqqwEAX/jCF3Ddddeho6MDn/nMZ/D73//e\n6EJPPPEEfvrTn2LZsmVYsWIFAOBzn/scfvazn2Hjxo3IZDJYunQpVq9eHfJWSFSkwBMnMKJfcsQ3\nYmpyT6NbsK5NaWhrtW5XKrtGw/tG1CrV7vHt3gqENuC8RgNoetmGTLhnLEydDz/yJdtY+TA6n+h2\npd/fsv0LZeqecbU/Km0rJ3Y04tsrjgEAvO3X/dpzTISMEpdjPsyPl1yeFs2JJ4ZQd4ehigyyzgch\nsWGkfIyMjKCzsxPDw8N47rnn8F//9V9oaGgIFJ9x3nnnYWBgwLX9bW97m3lrSSKkQSANipjNKeL7\nMA02LBe0qr3loxJ9atnat9W89oPZjrsDuldUEqYYWVAkX/JFc7zD7+IsihimMKbpdRsyGRQiOn+1\nwb65EtDu9lyr4nzBUu2WbNs3XbS2wrnyty5wvCmb0faTV9u8kGM+zM/VN+5+3ltjsrZGucgyJmiK\nDDgnJBqMlI8TTjgBTzzxBDZv3ozzzz8fDQ0NGBoaQjbLAulHI/X4fZUtH9FewzTgPI39pw84T7Yd\nEqZyjOmqY9+4Bcu2kVUEtCz8M2slYfnYMeJeHvfrAl0fRaJ8hDiHaQBvWBlTLDKoudnWhnKsjx9R\np9sNmkHLgr/lQ5/tyvm3zoDjkTDNqI8kJOua331Usn3EvRggWRXiJGjAedGyxSQFhbQWOCGkzjBS\nPm688UZ88IMfRFNTE+666y4AwO9+9zucffbZsTaO1IYkVn+jJolUsqYFptLotpZkheygmD5vulXh\n1oaMa1W3b9xyuTJlDVyAkuiPbcNuYcxPkI0z21UYTN8taYG+ZNto8In8lu5L507U1pjFpIHWGHWh\nwaAuOJbtP146od5U+WjOZhB1vj2pSUG6crvwvI8G0V4CoHsug/aIVOMDoNsVIVFhpHxcdNFF2LJl\ni2PbZZddhssuuyyWRpHakkbh2Q/Z7SraGzF3u0ofutiANFg+qi0yKI399pGiS/loyAJFHwUyCWFe\nWgn2SwWrU9BqpTwaV6UXxixXstHmk2ItiNuV6ZhFXesjsOXDIOZD63alKh+a4/1iWsLUIKrW7Up6\n3qXq4VGgT7Ub7Dw6ywyLDBISDUbKx44dO8Ttzc3NWLx4Md2vjjLq8fsqyQFR38dR6XaVAiuXecC5\nvKMk0G0bLuKNi52pWrPwXxVOQpiXhDG/Vfk4iwyGuWXjYoFS4HgJaPOZeSQhXJfwwbQtUSsfQS0p\n5ZgP7310v7sCznWWD5847gkDxc99bfe2IIYLydIXl9uVrlmBlQ/NDZomvSCEeGOkfLzuda/T/pbN\nZnHxxRfj61//Orq6uiJrGKkd9fh9lYSc6GM+zPZLQ+E+lTRnuzJ93iR5oGTL4dKSgG+S6CgR5UNy\nu/IZiDjdrsKcwfQZ11k+/BCLDOosH4YPUNQuM0FjSEq2//OlU7DVrWo80xTNPg/54UkLbR3BFgul\nsTZ9TwqWLWafGy/aYlxWteiaFXTopermQPSue4TMVoy+Qrfeeiv+7u/+DuvXr0dfXx/WrVuHK664\nAv/xH/+Bxx57DMViEZ/85CfjbitJiDQIpEFJIuDc2O0qhf2nE1LTMJcau10J96BbgZUEfJPg57gV\nx4GchUGhgrifQK77tXYB5yb7yIqhiQVRGtc0uV3ZthyQ7IWJ25VO6FVvXSezewWcAxCfPT+kbvfL\n2jXFntGS+IzaiMf1StcsnTuWDl3bmO2KkGgwsnzcfPPNWLduHebMKReVevnLX46vf/3rOPvss3Ht\ntdfi9ttv97SOkPoijSv3fsgB57Vxs0hj7+mzXck//GTrOP7zxTF0zcniC2d34mVzjT4VoTAdJyng\nXyd47hotoWA56zoEsXz8bNs47tw8huNas/j82Z14+bxo7l9SigD/GjJpSxhgIoPpBO3/9dQQesdL\n+KvjmvHZv5qHdkFiDlLnw7QPovTXD2NFMQk4f7g3hw/+4RC+dE4nTu6YeebUpusUab/ipgMeRQh1\nVFPnQ7JATjFetNFhkPr4T705/Mezw2jMZvDp182DVz37qCy8UjpsgAHnhESFkeXDsizs2rXLsW3P\nnj0olcoz5pw5c6b/TeqfNArPfiRT58O0LdFeNwqCpNodyFm44/lRjBdt7Bgp4a4Xx2Jtm2l3BbF8\nlOzyqmslfhmWgLJQNZizcNumUYwVbewcLeEHW6K7/x0aYczf7aq6VX8vwsV8GFgvNPvsGCkhVwIe\n35/HH/bmNOd3b5NWnW3brknMRxhFxrJto1gJ6ZlzWT40x/oFnA+GUD5ktyuz+9c974DeyqNe+9aN\nIxjM2zg4aeF/b3JXLzchsNsVLR+ExIrRct7HP/5xvPOd78QHPvABnHDCCdi7dy9+/OMf42Mf+xgA\n4P7778c555wTa0NJctTj9zVNAedpNBwFUT72jpUck/XuKiqGm2CeOclc+QCAQWWZ0sTyYdk2/tTr\nFIgf7pUF5DAMe7jVFC0bjZpGxhpwHuJ5NTnEpG3/sWEEl75sjtGxUo0F3SUyAFYsaXGM3UiEQc5h\nfP9LNpBRBrKtMSO6+Dw/UHD8rSaG0MVK+LldDYRYuq/GpXUwp99RZ12oZKJYVjqm2DFS8vxeaFPt\nBrZ8MOaDkDgxUj7+4R/+AcuWLcMvfvELPPvss1i0aBFuu+02vPWtbwUAXHrppbj00ktjbShJjqNF\n+Yg6bappnY80uq0FiflQV/fiTi9ZTapdL99zVYBoMLDzxi1beK2c5koeyofmmFS7XVXRNlO3K0lJ\nOaG9AVe8og0DOcuhfIRZ9dcRNN4DkMequ7MR53Q14ycvjWO4Ih6jb9zCeNFCW2P5oQ1W50NPOMuH\ne5up0utVVV1XS6MS9Z234e36JEcZBX9PdDEfdLsiJBqMHZnf+ta3Tisb5OgmfaKzP1Idi5pVOI/2\nspGgExYkRUkVkGOqBzZNNal2vcZYTZeZ1TqrmJ0vCryVD6Bd4wOftmxXJv2kq1lRSYsmNawk+0nv\nn9qO1gbgx285FgDwyx0Tjt8OR6p8hHO7gu18BhsywPu72/H+7nZc9YdDDivjjpESzlxQVj7UYdY9\nyX7KR5iYj2oCzqtVPqTne9zS32NUAeejGqsM3a4IiQYj5SOXy+Huu+/Gxo0bMTZW9kWdKlZ0xx13\nxNpAkjxpXLn3I01uV2mcn4JU/lWVjbgnXFMBWky169G2MLUEdBmaosJLkfOK+9AJT/Vu+ZijCZCW\nxrVguYvkqc9OZVzPgmbnuSO1fISK+XC7T1Vauk6Z2+hQPrYNF3HmgrI2qo6zLq68yafORzjlI/zC\nzoTHQ2Dyfko6wISH8qGt8+F7JSc6xYjKByHRYBzz8dxzz+Htb387Fi5cOL09aKVUUh/Uoe6RiPJh\nGnCexvkpiNuVqmTFXVjLtLsk5c9LwFWDRk2UnFpaPrz8yXU/1Srmw2SBwksxnKJVU/BON64Fy1lI\nz0son9/i9LMLE++gI4zvv2V7Z616+bxGh5tYZaYoVSXWTb2+blcRxXyYWLUAH8uHwYdFus5EKbjl\nI7IK58yrQ0gkGCkfDz74IDZs2ID58+fH3R6SAuoxpk7OdhXtjRin2k1h/wUJOFdX500FjbCYB5xL\n2/QHjysChIkSFb/yof/Ny7Km+yUat6twgrQfJpaPVp3lQ6t82I50sqryVXm6BYryEaXlI4wQWi4y\nqFg+MpWWD6fZojItszvmQxdwnlCqXcNHplq3K2mXyRCWj8AB57R8EBIrRql2TzrpJORy0WV8Iemm\nHj+v4gQZ8TWMiwymsAeD5L9X7zNuy4d5wHkwy4ca82Hip16y4na78o750BFnnY9axnzM0Vk+NMeq\nC/cut6sK4dtl+fDIvBSUcG5XtktZaqxo4ilKLRlP5UNzjWYft6vhvB14McFkgULHpJfblYnyIVk+\nPJQP3cMc2O2KdT4IiRUjy8eVV16J97///fjoRz+Krq4ux28rV66MpWGkdtTj4o5cZDDaaxyNbldy\nwLn6d7w3ZHp2OeZDv79aR8BEYIrbq8JLePEKYq7LIoPVxHx4WD689qs8XUdjBk3Zmed5smRjomhr\nFZ4ghAk4L9luBbiyvce3NTjaO5C3MZCzsKAl63pHdAYOP8uHjbICckyreR9UU+djwtPtykT5kM6p\n318b8xHQ9KFTjOLO/EfIbMFI+VizZg0ymQy+9KUvuX7bsGFD5I0itaU+A84lt6tor1HPAefamAED\nf+6i7Q70jRLjCuei5cN8ZdVkxTeKGAovvC0fXsqH/FsUbldh7tnEumdyXp0ioOsK1fXRHXA+8+9M\nJoP5zVkcqKgTMZi3MKfRxzxgQLhsV+77qgw4b8xm8LK5jegZmrF47BgpYkFLs+s4fapd/3YM5i0c\n02rk9ABAk2o3Ercrg5gP4XAvy4cuMUNUMR90uyIkGoyUj40bN8bdDhIDYQXGWnxepyaNsAJu3HU+\nbNsObfmo5t4s24ZlQ1v/wfw85tvlehpAU0z5Jaqq8+ExJmrMh5Hlw0ZVQTt+71zYmA9tnY+QypJ9\nJKtXNpMxTpvqaI/BISbvny5AWhes7na7cv6tVrFf0KIoHzkLS9qqVz7CBZy7K5w3KjrAKYrysX24\niNcd1+wSqnUxH34B50DwuA/x22p4Cq9sVyYVziVh3zPgPOB2Ccu2tXU+TGs9EUK8Ma7zQeqHbcNF\n3PDnQfSNW/jAaW340OkdgY5P0pWjaNm4af0wHtqXwxkLGnHT6+cHWpWbIu5sV0HiHipXqfsnSrjh\nz0PoGSri7Se14n++dq5WcFC568Ux3LllDAtbs/ji6ztx+nxNEQgDdMKcSZ2PqW1+Lh1hMe3aKSH5\n0b4cvrJ+GCUb+OvjW7T7j1ZIeiXbNrpOWKvfi4MFfPapIRycsHDt6e34wGnt4n5+RQZ16JoV5l3N\nl2zcuG4Ij/Tl8epjmoyyDoW5romyF9SdzOV2pQacK58Od9xHNKatMKeR3K4alW+BGnS+7UjGK7Wf\ndG+iyTsatA9ky4eZ4uD1nOgE/Hu3jmHNC2M4piWLd71sjut3z1S7ARZZdEwU9XFfdLsiJBq0Ut7r\nX//66X8vW7ZM/O9Vr3pVIo0kwfhRzxh6x8uOEXe9OI59Y8GWa5L0ulp/MI+H9pWTGbwwUMQvd4yH\nOk/cRQaDTDqVe963fWJ6JfO3uyex4VDB6BwDOQt3bh6DZQP7JyyseX40SHNdBMl2JSsfVV3eE/Mi\ng+UV+28/N4qxoo3Jko3f7p7U7l/pOmEaIBv2mfn+5jH0T1iwAHx38xgOTcrvnFc7vDIoRVlk8JG+\nHB7pywMANh4uYNtI8OVcEyXNxKKiuy/dsf5uV4rlozkm5SOs25WPsqQGne84MjZqq7V1PgzWbYJb\nPiR3R//jvILNATmoeyhv4fbny9+9g5MWvrd5zLWPp9uVZnsQncErEJ5uV4REg9byceutt07/+/bb\nb0+kMSQa/rDXmZnswb2TuFqzEishfV/j8vlXJ5f/fHEcfx/QUgPoLB/RTRRBhO/Ktty91alM/bhn\nHK89rtn3HC8OFRwCx/qDharGIFDMh0EcSJQEOXXBAvYYKtOVQoRp+8MqH0/05x1/P9aXx6XCqm3B\n45n0yqAUZcD5bZuqU2QBw4BzIzc3TSyL5lj1PfQrvqdaPsLUuZAIswJu2W7htUl5n49TrL7jR27Y\nZfnQxXzotJIKdNW7dYiWD4P7V4PNs3AqUWodHgBYdyDv2uY+b7wVzr1c6ljng5Bo0Cof559//vS/\nTz31VCxZssS1D4PN64OggqMYvA2gek9pNwZzpRFxu10F8fH2mudMFSKp/kH/hIVFIf3V9dmu3NsK\nwr3GafkIoiQGWXEeL9qwbPtIXIN5eyT5Iqjip+tvzwrnXgHnmjVdEyEwyHVMMYv5CHce27Y9igyq\nlg/n7+prs6DFuaGWlo+SQcyH6pI5tbu7zod8DRO3K5MUt442GC5QqKjB5se0ZnGwIv5mrGCHWlAJ\nY/kIojN4Da2F8nxabQweIbMdI+f6yy67DIcPH3ZsW79+Pf72b/82lkaRaDF1OfHaP66Fb9XnOSxx\nZ7sKYm732tX0LJKwX1nxOCh6tytB0TB0xYqKIGfW+Yn77R9EAZeCSoOKrLrLea2Yeym4UVo+ovBb\nj6rOh7ho4LG/K+BcuYbqdjW/OZ5Cg2FWwC3bbflSlQVVeZq6PfU9zWqiPvzqfABmKW4rkb6tJuOv\nul11NmdRmdysZLvH02Q2CBPzEcQI7ndvcdc9ImQ2YKR8XHPNNXjPe96DkZERAMCTTz6JK6+8Erfd\ndlusjSPRENTyIflbx6Z8BI8tF6mmCq8JQbw1vNKQmvajJIhuG45B+RC2Sc9LnBNukHEKqnxMCVpB\nFHBJOA+aVUp3T2koMhjFWJrFfIQ7j1dfq2Pjsnwo3xO1ynltLR9uxVaN0VAX1KduV72a1u0qDsuH\nsM0k1ki1fMxpyKBdSZmnuoCZrEV51fmIItWu370x6JyQ6jES/T72sY/h4osvxuWXX477778fH/jA\nB7BmzRpcdNFFcbePREDAuSZRy4e6UhkWSWCJMtVuoIDzSCwf7j2rsXzo60S4t0mKVpwxH0GGyato\nmcSUoBXEciPtG1TW1F3OS/D3ivnQZ7uqjSBkFvPhv5Oo/Hrck+oSGDzmI5r+Clfh3N/yoX4Np95b\n9XL6gPNkYj5MlHn1XW1tBNqV1aagihAQf8C5v+WDygch1WKcavef//mfMTQ0hA996EO499578cY3\nvjHOdpEICfqx1AdvR+/nqq5UhiV2t6sA0qenz7Cp5UNyuxoOH+0YJNtV0paPIKcObvkonz2I8iC5\n1AR9/nVKgde7qHO7sj3SBKe5wnnYmA+v49x1PrzdrtJk+TCp86G2Xxfzobd8+Lcj6DskL+z4H6e6\nXbU2ZNChWD5UFzCTN2zSy+1Ks12fPNeNn5WTtT4IqR6t8rFs2TLXNtu2YVkWPvKRjwAoF03btGlT\nfK0jkRB0npSEz7hkT02B48Ckqc6H12XN08q6d9w5Wgwd7KibUOU6H2btiYogAedeRcskwlg+RLer\niCwfYQLOvS4dd0V2HSaXDVtR3uueggacu2I+8tZ0EoJqCBXzASHblfIu69yuXAHnmmsYBZwHjPmQ\n3EhNEh2obletDRm0KR/8MJaPop1BrmSjRTD/6D4ldLsiJF1olQ+m1z16CBxwHrMgX0lkblcxtznI\nhOO1a8lwBU6XcWrfeAkndwSvDRqswrl7Y9BnKAiB3K4CNmQ65iPANeJ1u/KwfGgVRP11aud2ZaBY\nmFhHJMHW49yuOh8+dTOaGzJob8xMC7mWDQznbcxvqVL5CPFxKVmS8uHcR6t8KP2kU55M6nxIKW69\nCBtPp1o+5jS6Yz7UWh+m3TpW0Cgfmv2jdLui8kFI9WilmBUrViTZDhIjQVetpRXLuGScqALOpTZH\nKZiFrXCuYtoknSC6fbgYSvnQ1lMw9OeOc8KN0+1qdDrblfkx0qq217MkBblK+9u27R3zoZF6vJUP\n/W9xElWdj6DxBO46H95uV0DZ9WqsODOog3nLFQsSlOhS7XpbPqYuoz5OOgOHSZ2PoBXt5VS7/vev\nBoa3NmTQ4RPzYdq0saKFYwT7j9byYXZaAMx2RUgSGH2B8/k8brrpJixfvhxdXV1Yvnw5brrpJuTz\n/gWBSO0JOk/KK13xSDnSVBkmuDmObFcTRXtacQtS58Or+bq6BmMFy3HfOmHfL+NV0bKxZ7SIPaNF\nDBdnerfaCudpKTIYNuYjSPulvg+qAEgCSsn2dqEK5XYV2CITzTgapdoNWeHcO+YjmNsV4A46jyLu\nI1TMB9ztV7NTqRaNKcVWvZxW+RB+aG1wTvR5wQLjhS4jWb5ke1Yxd7ldiZYPdTzN2qVz14om1a6P\n21WtNP6jkPGiFevcUrJtjBetSAsOk2gwWkL9/Oc/j/Xr12P16tU48cQTsWfPHnzta1/DyMgIbr75\n5rjbSAIgrcIGDjgPKHxVgzSHTJZsdASMa4g64Pxbm0bw020TOK41i5vO6QzmduXRJnXeKlo2Pv+X\nITzal8fL5jbga2+Yj645DdrVte0jemfzTYcL+JcnBzEyPaHPw0UTw/iX1831mJTNnpeAbuKBCDJO\nftmuGjPOZ2o8jOUjYKpdqUlSNiS/NugyKHkJQ0GLDAaNmdFhFHAe0vLh6XYVQvlYEEOtD6+0yDos\n2/0MuIsMuo8Bglg+3NvmNGTQmAVGK17isYK565ls+QDec/9BjBdtfPSMDlxxaptrHyngXL2P0JYP\nzQdJF1geRPj0DTin21XV2LaNL60fxh/25nBiewNuPrcTJ4aw6HsxmLPw6ScHsXmwiL86rglfOWc+\nWqMKMiVVY2T5uO+++3D33XfjzW9+M0477TS8+c1vxo9//GPcd999cbePBEQUhAKu1EjySVyfW+lD\nrq6YmaDP0BWcPaNF/HTbBADg4KSFO54fDeh2Vf6/1O+q8PdYXw6P9pUtiDtGSvj5kevqVtd2eqTb\nvevFsQrFo8z9eyaxZaiod7sStokB5zGu9uly80v4WT46FUFzdDrmI4DlI6DblfQMS+PnJ7Togpi9\nmh50WMIE+EqY1fnw30fq10BuVz5FBgG4hOyBIEV7NIRxQ7RsIeYjY+Z2pfa3ToSSAs5bG8txL5WM\nBdDGdc/YaMGGZQPffn4U48L5pDof7mxXzuNM31Od65ju6CjdriJ4fGY9zxwq4A97cwCAPWMl/OzI\nvENigisAACAASURBVBcl/7VzApsHy/Pl+oMFPLRvMvJrkPBE5HFP0oL0TQ5aG0Gal+KSPaX2epny\ndUirVWEXqP7Um3P8vf5gIWCdj/K+kjCpTsh3bx13/P2Tl8p/6wTVYQ8TRO+4LL1uGSxWnWo3IplV\nbkOAff1W7uc1y0JWEOUxaLYr6X2RVsb92qBTOKOM+Qia6UhHnDEfQRQ9vyKDANCmmBdyETzMQd3/\ngPJ9qc9Ak2KpUJs/E3Cu7KcJOJcWdlsaMuhQItGDPAcmz5iUBtxd5yPjW+fDVCeqpdsVLR/V8/Nt\nznnvvh3RKx/f3zzm+Ps7L4xp9iS1wEj5eNe73oVVq1bhwQcfxJYtW/DAAw/gfe97H971rnfF3T4S\nEGnlKKgwL50jLpdJSdANqiwBZi5Opkjzepg6H5LlQ1U+dFYp3eqa11jqBIrtw8GUj+RT7Zrv66d8\nqJaPqT4JErMUWPkQzi2Nq9+qrs7tykseCxqLFZXlI6qYDzHhgcdhqkJv4nalZoCqtgss2w6lfJTd\nrpzHNbosH2qdD3v6WOd+8jUywserISNZPszbH9aCLLldtfnEfJj6/+vary/GaXRao33DpFkmTuLM\nnqiDKmO6MHKy++IXv4h///d/xz/90z9h//79WLx4Md773vfiU5/6VNztIwGRXuqgbkyyFSGeVzcy\ny4fodhWiQQAkp4YwdT4kYXKyaMO27WkhQadk6IT9yZLz+Ep0rhTbR/RuV2KdD8PaH1ER5NEK6nY1\nU+fD/BpSETHv1Xj3NkmBUS0bLRkbOXtmHJPIdhU005GO6GI+BEUvUJ0Pf7crVSGpNsA1jOIBlMfK\nZfkwTbWrKh8BrtuQgW+gtxcm3SUpQy7LR0PGNRbqN8vY8qGN+ZAJZPnws1DS8lE1tbAeBXHvJfFj\npHy0tLTghhtuwA033BB3e0iVSBNrEGHe0lRTjs/tyt86YEKUGbqkRcUwdT4kYbKc8QZoOeJuoXO1\n0Va7Vo6fomjZmNSsyG0fLmJxmxCJCvNUu3FOFkGGO6zlI9ZsV8KPsuXD+fecBhu5YqXyIZ/fS/EP\nWmQwaI0HHWYxHybncW+LOuBcjYOotgvCuq6JMR9K29T2TwecK+cKko+jIYOqYj5MXh1pH6nOh3q/\nLstHtTEfOgtvkArndLuKnTjdeHUwVCddGKcXePjhh3Hvvfeir68PS5YsweWXX46VK1fG2TYSAmlO\nUfOtBz0eiDPg3L1NJ0R7EWWGLsntKozyoTPPTxRnCmTpzusV1Fh5/BReq7HDBRsNk/IJzVPt6ttT\nLboMNRJ+q866mI+qs115XFa0fIgxH4owlrUx6DhGtmpFWWRwPKqYD4N9jCqci8qHfn+X25VaZFB4\nd9WMUtUKj2Fd1ywp5kNpm/rtmbF82J77eZHNAO0xx3xI74xU4bxVGSBVGTY1zOmUaJ2SEWWRQdb5\nqJ6aKHDUGVOFkfX2hz/8Ia699losXrwYl1xyCRYtWoQPf/jD+MEPfhBz80hQdP7npiZH3cpTbKl2\npZiPGrtdiZaPAArRdMC5h+vUFLrVbq+Ps2QZ8hMmdLUN6i3mI7Db1ZF+kVzJdIhuVx6NFN85AwWu\nJWs7VrAtBM80FzzbVTSSk1nMh/8+ouWjGrcrwSSgFvKrtgtGQ0qfJdv9rLiKDCrH2Ch/T9R+ktzL\ndCQR8yF9HyS3K9X9S1WGTVNH674DuqYGCzj3/p11PqqnFjEf1BnThZHlY/Xq1fjFL36BV7/61dPb\n3vOe9+Cqq67CNddcE1fbSAikedFGWchtNRht3cQfV5FBk0nLhDiKDFYSRPiekl11PvyVyoNuIgus\nfIT8mosxH1Kdj1gtH+b4PRtuy0dZ8Q7iniS6XXnsL2e78u/Xxky5KJz6PKiuKV63HNjtKsFsVyaC\npPT8ebnehHG7UjNAVSv4hI35sGx34gq35SODLJzPmwV3fwepVpCtMubD5DtqErtnkvLXtFlBYz6C\npdql21Xc1CbmI/FLEg+MLB8DAwN45Stf6djW3d2NwcFBzRGkVug+nKZxFLp5Na4XN7qAc0lgjK7R\ngQLOj1xWq3xU3J87hWb5/16WFskyFNYVRG2ibduicJaWCucTPpLjnIaMQ6Ar2WXFO8hkF7TIoNQ3\nsvLh/Lsx447dkSxh3nU+go1LWMFZxWQ13OSTE3TRwFXnw0T5cMV8VNcHVcV8KIdKdTmkoHPdd8KE\nJGI+pEUUqc7HnMaMQ3GaLDnfH9PvzKim/brDoywyyDof1VOLmA+SLoyUj3PPPRc33HADxsbKeZJH\nR0fx2c9+Fuecc06sjSPB0QnJptYE3cc/Sber6IoMhmlRdDEfOpcqr7FoPiJVVOt2ZVrIVb1MyZZX\nD+O0fAQZJ794oMZsBh2CoBVksgua7Uo6tySguNOsumN3JHctz4DzoG5XdVDnw0sADZPtypVqt1q3\nq9CKvu1+BoQZWFQ+bO99vGjIZtwxHwm4XbmUj8YMspkM2jxcwOLKdlVNzIc6RMx2VT1xLmbp4LCl\nCyPl45ZbbsGmTZtw8skn49RTT8XSpUuxadMm3HLLLXG3LxUM5CyxgusUIwULQxEuh9i2jUOTpVAW\nAN1LbXoubT2IwC2ZIVeycXCyJMadSN0WSvmIsMigNNcGqRI/HXBuEPOh0jyVBcsn4FxFdad51TFN\n3o08gtpEnZJRjZk8Vyo/zzohJsoUiA1ZoE0RtF4cKgaa7IJme5POLa0Eq2PakAFaFClSOs67zofH\njwK6FeOglOxy7IPXdy9snQ/vgHNF+VD7VJjR1FoaVQech9TEy9munNuaDSwfkuKrKzIoIVo+DJTQ\nwZyFsYJlGHBe/i4dOrI6ULScwfUZAFPhWF5xH6ZWqcAxH0ZnLaP29xyl76TFiaQZLVgYrIjjmyja\nOKxJKpJGJMt+XOn8p6DukS6MYj6WLFmCtWvXYs+ePdN1Pk444YS425YKvrlxBD/fPoG5TRl86fWd\neO1xzY7ff793Ev/69DCKFvCxZR24/BVtVV3Ptm3cuG4YD+3L4diWLG5+Qye6O80ESUC/cmTsdhWx\n5WPnSBH/84lB7J+w8IZFzbjpnE7H6qQ02YQLOJdcZcI1WuqDIO4qUx9RE7crlSlhxGt1TRpLVTk+\nqaMBPYM5jJW81xfUD75u/MMKbHvHivjU44PYN27h9Qub8dVzO11uMFHO5Y1CcO2/PDlU9Xm9bl+S\nRcVUu4LlQxU+peOidLuKyvJxcNLCe+8/iHwJ+MgZ7VjV3e7ax8zyIby3XspHKLcr8/ObENZ1rWiZ\ntbesWOitAUFqfExdoyNgzMd3XxjFj3rG0daYMbrfDYcK+O4LoxjM27j4pFb8X6/qcPze2pCZzuKm\nvp9lhbi86mKqG+tilyKxfChtaG3IOKwztbZ8PNKbw5fXD2OyZOPaV7bjrIXN+MyfBzGUt/GOk1vx\nqdfOq2n7TJCeqbxhXGpYgmRVJPFj/B0bHBzEo48+Ov3fbIj32DtWxM+3TwAARgo2Vm8cce1z07ph\n5K3y6uS3nhsNtEIu0TNUxEP7cgCAQzkLP9s2Eeh43cqRqfKhr4Qd7r5+sGUM+yfKX/Mn9ufxl/68\n43c51W4Y5UPYFvgsZaS5NpDyceT/umByr/ubcsMJHnDu3NbRlMUJLf4zudpHuok1rI/uXS+OY994\nuR1PHcjjsb6ca58oF7was25BKwo8g74l1yHbrRiYxXwEE8aDBpxHFfMBlN0KbQB3vDAmLhiYKEZB\n3a7cAefOvyW3K1XZrdbyETZoX3Wpa8rKFclVy4cqkAdJswsAx7Y2uFydvCxghyct/KhnHID583L/\nnkkM5sv7rt09iecHCo7fWyuu75X213RsJktuFzZAP08Fc7ty7tyqWj5qrHx89enh6Tng+1vG8G/P\nDmPoSN//ZtckXhoq1rJ5vpRsW3Q9llxOo4QB5+nCSPn47//+byxfvhxr1qzB+vXrcccdd2D58uX4\n4x//GHPzasu6A84P6I4RtzirfsJ1KU1N2TfuvEbvWDARWjenmFoTdMeH/S5MKVJT/GbX5PS/S0IK\nSaD2MR/SpBamIrDuUfCO+Sj/3yvgXLKcqAJRe2MG8xv9n0X1VnXjH3bC/e3uScff9+1wK9NRzjmN\nmQzOWGBuKTTFu8K5/JuqSOiyXVUieTFFafmIKtuVSr9QTChszIfX6z+atx0CpsuSILldRRzzYRqs\nrcaaqM+DFGwOCMqHMsZ+8R7XLXNaHd53aptL4PdSKrYMFbS/mfL7vc7v/pwKJdsdk1XpdmV+Deke\ndIdXk2p3ToOqfJifKw7UhSZVLnlsv3uBJ03oag3FncKYuke6MDJyfepTn8Ktt96Kyy67bHrbfffd\nh0996lN46qmnYmtcrQnzsFa75qoKB0GzGGljPoyzXVW/cmSK1kUsqmxXYZUP0fJhPuMEyXal0mxg\n+ZCUF1U5am/KoMVgacEd8yFfN6oJV3o/ony2GrLAVd3tGC/a+MX2YFZDL7xjPuTt+RLQ1li5n7pK\nLwScizEf+ovXqs6HCeq35E1LWjBRtPHUgRnrpwW4Cit6uUtaAIbzNua3ZI7s6/xdtHwo25LKdtWc\nzTjeJ3VspWBzwL0i6HK78plk3v2yORjMWdg6XMQlJ7diUVsDRhSt1useorAbqt+RyuKCbR4uYEFi\ns8aKNuY5vaC1SkYpUIVz59+q5SPtdT7SHlitS9hQrdeIH7R8pAsjy0dfXx/e+c53Ora94x3vwP79\n+2NpVGrweVqlQNmgJnEVdTUnqLCgm1OqtnwEaoWeyolTJ+iGqvMhBpyH+9pI7YrD7UqaaKfm6KBu\nV6py1N6YQUvWxO3F2zVoiqiyk0iBslH64jZmysLCP7x6Lq6sMv6qEs9Uu5rnTHURcqVZzcClfIg1\nRjwtH/rfVGzbjizmQ0X67qnfklWntuHfzpvvmnTUe/C7p0rrstvtyr1/1NmuTBeElHqXrve2STNZ\nqO+Iy/Lhox40N2TwkTM78LU3zMebjm8F4Bb4x4u29vsYifKhGMIcblceaX+DjI0U+K87PJDlQ3nh\nVMtHLWM+TL7DcQduV4vW8hHzukj9hOPPDoyUjyuuuAJr1qxxbPve976HK664IpZGpQW/VziOwnbq\nBzWosKBbNTSP+dBZPqL5oFV+xqsNjq8kyrGQ2hXkwzgdcO6TeUxSsqa6ObDblbKtvSmL5ox/B6hN\n0AvSvqcyQlq1jdTtquIC6oplNXim2tX0jcvtSvm7IeMWUEXLh5fyEah+SXz59aWeVm9laqVfSiVb\niV8bB/OVyofzN0n5UK0h1SrSxpYPVWhVlQ8l3mcKv5iPIGl2p2jIZBzWBxseCyoRvDZelo8Oj5iP\nQMqH1H7NLVWTateV7aqGUqzJ3Jhu1UO/oBql5UOSV2w72syKpDq0blcXX3zx9L9t28add96Jb3zj\nG1iyZAl6e3tx4MABvP71r0+kkbXC7zGVPkJhMyxNoQqRUxWapcBE0zYBQbJdydujEhArb0Obijaq\nbFcRKh9BmKnz4W3Zke6zZJeFI68mmNT5aG/MoNVgaUHttvgtH+5t0cZ8zPy7VZJEQ+LVRl3fuJQP\nVRjP2Mi43K6CXTvIMx6X1UPXDrebWfles85kTu5aMz4PhLflw6DOR7ULRMoHojkrK+fuTGZKuzTf\ndPWxVe8xjPIBlBMxVH47xos2OoTwqCjeGvXbPsfT8lGhfAQQDqXnWffdrEb5UL8jtbR8mMzjqXe7\n0nyHolQ+pHnMPrK9WaP0k2TRKh8f+MAHHH9fddVVrn1MBeJ6xe87KH0oq7d8OE9g2eXCanMMU9Dp\nJlZjt6uYYz6StHyEj/mo7man63xorBdT9yfdp2X7T25ihXPla9velEGzkduV8++4Yz5E5SOaUwNw\nCp9zIpxkPGM+NL+pwqY71S7QaBLzEVGRwTjjPSS3NL3lw6l9lGNaMtrjVAZzHpYPMeA86jofzuPn\nNWdxUKixoCo96vuui/lQp9Vqs11N0d6YwcGKv0cLNrrmuPfzc+syQf1emMZ8BPnOSJaPZOp81FD5\nMJjH06586NwWo8x2pZtD85btskiS2qAVad///vcn2Y5U4vcqSHN5HDnkx4oW5jSaSVJVB5zHbfmo\n+HfcMR9AWXALUpALqN7yMdV6vdvVkf9rBE2/Cdgk1W57Y9Yo5sMvHewUkVk+BMEmSkt4pfCZlNuV\n7jl2xXyIqXb9V1W9uidItqt4LR/uc6tK/FTgt3tlXz3O+1oDFWYG9V01qvNRxftt27brXZvblMHB\nSfe+TT5jq812BW9lKaxOrRb30xZLjOC1Ue/V4XblEfMR7HkWYj4icBv2s3zU0u3KZG5Mue6BcU0H\nerkaB0UXb5krydY+kjxB6xWFZs+ePbjkkkvwhje8Aeeddx5uv/12AMDAwADe/e5346yzzsJll12W\nqvoh/sqHMOFWnUPe/WIGERqqdbvS7RbVB61SD9AJbVG5XQHhlKZqV0an2qL7AE4ckX4kC4ZlcH0j\n5cM025Xyd9RFBlVUPdC2oy39VJnZKCm3K50w65dqtyFju2I+pGcmaI0RHUEz5wVBOrWqGJjGfKjv\n8gLlQR4M6HblqnBehbabKzn7vCmrf85Utyv126xaRqbwU87Cehy0K1qY7nkImr5ZQv22mdf5ML+G\naPnQ7FtdkUHn3zV1uzJ4h6MYvzjRZbuKMouY7jmKUsEh1ZGY8tHU1ISvfOUreOKJJ/DAAw/gu9/9\nLrZs2YJbbrkFF154IdatW4eVK1di9erVSTXJF1+3qxgsH9IHNUhuft2Hx1Sg1x0f1QctU7Gspk1R\nagW/nq7fw4xHtWPon2r3yP91blc+H8hJpYaUbduuDCLtjRmjgHO325W8X1xuV1EvIjbGZvnQ/6YT\nZtXJVH3emzLuoGQp5sOzzkeV2YGiQorTULdN3apLuFaapfbTcUrw0oCX25WB5aMay6bqutbemBEV\nHsCdTMDdLsM6H0o/ho35cFk+dMpHBI+JOmdVZoxyVzif2TfI4p00L2qVD+OzSm5XzoGMOyuTF0YB\n5+nWPbSLqVG6XWlrL6XdJ20WkZjysWjRIixfvhwA0NHRgdNOOw29vb1Yu3YtVq1aBQBYtWoVfvOb\n3yTSnnzJxgsDBWw4lMfzAwVR6/a1fEiuBjFkUglSY0InX5i6MkXpdiWmIq74t9dqetDgM31l9kCn\nAVD9Kv9MwLn8+3DBws6RIobzsvLhd311LCdKzgD11oayYGOWanfq/zZ2jRYxpJlZi5aNvvESDk1W\nt3SkCk5RT5SVcoKaIrMavIKgtdmulO2S5cNV50NKtevRLgveLiVjBQu7R4soWm53oSgRLR/Ktilh\nW3WDVOuYqMLfsV7Kh+p2JcV8hMx2tXes6LCyAEIxz6asVhnw8y3XWT5cMR9Kc0MrH4rQ3zNYxIZD\neWw6XHDMMVEsQKsW/ErrkKoEVbrhBFEMJRdl3dBWV2TQ+XcSdT56x0quZw8ox3/6kfaUstqYD49+\nPTBREguZ6tAt4MVdS4SYo435eOtb34oHH3wQAHDzzTfj05/+dGQX3blzJzZs2ICzzjoL/f396Orq\nAgB0dXWhv78/suvoODBRwvWPDGD/xMxr2jUni9suWICuii+N3wdLzHYVg+UjiNCgjfkwrvMh7xdG\nSJTk2EqFzWshdqJoOwq0+eFtsUk25mPqcN0qy67REj740GH5WNt2mfVbGpyKjLr6pSqsbUckcBO3\nq5Jto2jZ+OTjg3j2kL6y8UDexpUPHkJjBvifr52Hi05q9T23pHz61Xioloa43K48fjPOdiXFfLgy\nIkkClXcnWbYskO4cKeKTjw/i4KSFVx/ThDcubnbvFBFytivn31Oyr6/blXLcQpfyMXNAmGxXJgag\nH/e24o8vHEZzFvjC2Z04f3ELACm2KiMqPOXr+ikfumxXqrLk/D3sU60K/fe+NI57XxoHUM6Etfr8\n+Ti1sykSK7f67XfW+dC7fwXLdmX+oa4m5sOdajdeAfabG0fw8+0TaGkAPndWJ9545NkDzBLHpN/y\noUu1K+///7w0jv/93CiyAK5/VQfe83L/+k1ayweVj9SgFU+2bt2KyclyFN1tt90W2QVHR0dx9dVX\n4+abb8bcuXMdv2UymUQyaP1+b86heABA/4SFB/Y4owZ9LR9SzEe1yofwYgZxu6o2g5Su/WHeWelF\nr7w9L9/roBmv9AHngU4DIMpsV8HPY8G9ajNXkZ7UCUiK9wBgnO3q8f15T8WjkqINfH3DsNG+svLp\n/DtOy0ekblceco7ev1hVPpx/lwPOncdI70yYRRAAuHPL2HQWpo2HC/jdbiEqOiIky5Ar4DxrFnCu\n/n2c4nQftM6H6t7kJ1zvHi3ij4NlgS9vAfdsHZ/+zeXe2JTRTqJ+blfagHMft6uwOnWHLr0WynPM\nfTsmjlwv3Pm98LJ8jIas8yHFDui+99W4XUkB53HVixjIWfjF9vI45ErAvRXPHnB0pNrVLaaK3ie2\njbt6xgCUx/Abm0aNlGNttivGfKQG7dry3/zN3+Css87CySefjImJCUfdj0rWrl1rfLFCoYCrr74a\nV1xxBS655BIAZWvH/v37sWjRIvT19WHhwoXa43t6eoyv5cVL+1sBtLi2b+s7jB7sm/77wKFmAM5c\nhJVt2D7RAKDD8fvuPXvROag45RtStIG81enavrO3Hz35vNE5Dg7I9zY0njPqv94B9z0DQG9fH3om\nZQFVd96BQgbAPOe24VH09JStW7tGGgG0i8e+uG0nxlvNp4xiaR6kNcGtL23D3MZgX+ORsXZ4vBq+\nDAwOoqenD+P5uQjq2ZjPF7F9125UPlctdgGVOW7GCyVHn7807nwOG4vlsW4xUORHRsdwz/NjAMxT\ngEyWzN7FsZJ7/Acrxh8AyrKx+5kPQwY2tm3dOv239PxJLG0tYuek93j3HzqEnp594m+HB+V3bm//\nAfSUZt7bwZE2VPZzY8bG8IE+VL4DvYPO/gGA3R7vCQBsePElHNPkfsb/uM/Zr9tH4pt5d+/dh2OG\nnd+9ouV8J7e9tBWNGaBUdL4X27bvwGjzzLuu9md+6CAa0IrSkXONF208t6UHzVlgbNz5rvbu3YOO\nAed9lmWdmb4olGzP5/d3ynd/4+HC9P5bh51jYU+OY9IGpPdnYnQEgN7aNDE24hprAMjnnPe0p7cP\nwMxqb7FQCDUXzhfmq0peOjiCnp792DvY5LheFAwc2I+efHn+UMdjvGhhy4s9yGaAguY7LnFweBw9\nPQcc28Yn5G93Pl807rNh5T3t7+tFBm2wj7TLBvBiz9bQ7m9e7JzIwsLMomzfyKSj3bsFmURlav6p\nFX79fHBYHqPeg4fQk3V+Y4s2MJx3fscee24bFrd4ywbbx+RnfceevegcCiefHS1EJUf70d3d7fm7\ndsb91re+hcceewy7d+/G008/jauuusql7QexUti2jeuvvx6vfOUrcd11101vv/jii3HPPffgE5/4\nBO655x684x3v0J7D72ZMaZsYAQ5PuLfP60R390nTfz+FMaB/TNuGiUN5YIczO9eiJceje7FbEDFh\nMGcBmw+6trfNPxbd3fpJo5IOzb1ZDc1G/bdh2zjQN+ra3rVoEbpPdn/0enp6tOfdM1oEtjrdi5rm\ntKG7+wQAwN59k8AeeRV94Qkno/sYc4HY2tIvmqqWnnIKjlXTlfjQ1HsYmAj/gZrXWX6OSj0HEDRP\nWLaxEV3HnwDsGpredlxHK/bmZhS/vJXBqaeeOv3+DfTngJ0z+x/TMQfd3cdj5IUZQVzHnLb28irR\nmJnlYwqTZ+nARAl48ZBjW0tb+/T4A0d8w7e4n/kwNGYzjnaNFCxgq3zuq09rw67REl53bBNGCja+\nu3lM3G+K+QuO0b6DbaPDwKDbqjB3wXHo7p4RVFsODQIjM8pIQwZYdsqJwJ6B6W35hjmO/gGA/r4c\nsGcIOhYcvxTd84V35YX4XVinWLTkeHQvmfnuWbYN+wWnYHh6d/mZbdl1CCjMKAgnL12KkzpmpqJ2\npT+PX9SF+QNjOFThA3/cSS/HorYGNPU539WlJ53k+m7Ytg1snmlLCc73R+VX4yMAnN/QqefqxZ0T\nwN6RmfteMLccJzXmXhw6bkEnMOT+Fk9xTOc8dHef6Nre1nfYkVXiuK5FwL6Za7a0mH3LVboBZBdM\n4I/7JpErlVfReyoEsalnr2fXBNA7oj9RCE5cshjdJ8y4arb09E+72djI4KSXvwJtjVkUN5s/s3ZT\nK7q7j3dsa+2Tv93ZxkbjPms9OOgYz5NOPB6N+4YcFsZTXnFqLPUiRg4oMkVDk6Pdj1tumURlav6p\nBV7ywBTWXnmMOjoXoLvb6Q0zXnTLRNYxJ6D7eG+334P7c445dIpjFy1xPIezDZPxSQrP5b7zzz8f\nAJDL5fC+972vqgs98cQT+OlPf4ply5ZhxYoVAIDPf/7z+Md//Edcc801uOuuu3DyySfjzjvvrOo6\nJuj8/lT3BX+3K/e2alwKpTS7gD41nYTOZajaOh9h7ktyu3G4XXksXgRNtxtlwHm1rnN+Fc69KNnu\nfmlrzKAxM9MuC+W+nXLZcVU3P+KmZeJ2VRJiTEwoWrY2W88Ustudc5uJ94KzJJ0e1VfeK+D84pPm\nYEl7uQPv6fGezAGfIoNaE7+321VTBpivppEVXhq/4RmoZfqdI6h9INXfyBjW+ZCKE85vyTqUj4G8\nhUVtDUYB55lMBg0Z53mLdrn/JXrH9RYi1WWkozGDUY3e7u92JW/3i4uqJkvMJUvn4JKl5UWk/okS\nLn9gZnFgKpA/Drcr9VvR3phFrmLwRgs2Whtsz2dd/Q5I86Wu6cECzt1xRA0ZoODYx/x8QVCfL3Uu\nMkkck/awBl2xUynmQ3J33T5Swkqfa+gKQTLmIz0Y+ZZcffXVePjhh3Hvvfeit7cXxx9/PC6//HKs\nXOn3CMxw3nnnYWBgQPztl7/8pfF5okD3AKpCmPTBsm17ehKVYhaqCdaTsncAydb50CkvoQLOJ/x2\n3QAAIABJREFUhWtW9rFX1pkghQZtWz9pxZHtak5DxrN9ll2+tzDfOdu23UJqNoPWxozDN3qyNJMp\nySUQTcV8ZPwFd8t2LEIbkzdQPvyUT8DMF7vVp7+nUF3aG7NOpa2SSiG1wcB/wuu91lY4VwVyV3yC\njQXN7kxOld8YwD9YdkCorp007kKBarzHzL/9As7VYxsyGSxocR40lQnIJOYDKAv6pYrnvGjphX9J\n+ZgqVqrG5LU1ZdAwqUm167MyrmbhmkLNBhZVql2Vzma34mvZdix1ItR3s70pg8O5mb/HCjbm++RD\nmNuccWQIDFLhPFjMh/PvhszUwoZdsY+NSKoxKqgKlTr2JvN42uXrIKl2JXlm+7C/V4JuPUZIIEZq\nhNEiyg9/+ENce+21WLx4MS699FIsWrQIH/7wh/GDH/wg5ubFg26lVxXCpN0qn11JK68uh3z1yocu\nJehk0TYKkvOqFB4U8WNS8WX0WrANEnDutWsY5cMvz70aMCldM2whqnKdD+exzUIhs0rLkCv955FA\n60zGXUE7qraaBO5Jyqc6mZpc2jRwXLpV3bGVwb4m3hNRWD7Ufm7MlNtXaaEp2e7x9OsjyVqSNGof\nuKwXmcr+9g4Ad1kzMsB8QUmTjtXV3HBnkNKvjO4TlI+pVVl3tiuPVLs+WoLOMuIKOFctHxElZWlp\nyDjS71o2MFKw47F8KE12Vzm3fRN9dCra4njBPafpF6GCzKHOv8vKh7JPTAK+ugCpjoVJtqtq0/3H\niW3rU36L84UwbttG/JUP3QJiEmmSiRlGlo/V/z977x21yVndCf6eetOXO6i71UGtVkv9SUhCEjJC\nMhgjFuOAxa5HPhibIBxwAIfx2maAtVh7vAev1xE8DCYYOB44O8CZteUxYGxjxgRJ2DIwWAGQutUt\ndavVub+c3lC1f9RX71t1n3ufUOELQvecPv29FZ+qesINv9+973437rnnHtxwww39bT/6oz+Ku+66\nCz/1Uz9VVdsqE0lxosoB5wEKo8FExHXwIl4jKauVFKbkRIp8UKiOJNICkGc94iJMaT3JNEn6wK7M\nFaD9v4etAvJYQ+G8IXFQGEVi2kCb9CLdKGvWlAYhShtntA7MSGphH66Zc8O71BXhxCV8zRmf9Nlc\nlILhGsDHTLPCeZKHaoodV2ndx8WTbHpFYp2Pnvm4+moRyG0thaXFwQ2m2iHGU5qpbeylU8+ul2jQ\nKQMcyjfyUQ+UVuV8KkfkI3sP/rjj8132Wy/3IgzXleYIGmsosf9IkZVEpMghfYaqIh9ADGdb6A46\n6vRKWIliTZ91RDM+QnRD8+I0VFeZtOMh4gg5vRYnZcCuMsdUZO/T/kXHgotTrsJaooVlqSejFLg1\nhZtbT873sNLTaySlRTIynoVdbRxxinxMTU3hmmuuyWybnJzE9PS0cMbGFpnzYYZJANmFki2sVSTy\nIcwaXnU+CqavLbPIIKeAp9+xkfPhFfmQj80z19i+IS3WRSVE/kmOMwYS2FVa0jAkXSEaDGtb1KCH\nCHMekbVEXCrF8otJdpvLnV3rdXCZRCXeRz0T+XCAXRn6hDTm9DofeuQDYHgfBBtgjXwwWIK19vDR\nKUozIFLv2FrngzEopHfEeak5ocqvZHAfm+Ut9cTjzNb5EI0Pc78SiwyS32XV+eCEg/1VArsijR4l\nD7/Q0eGm3DW0GiGOUcJCsKtAh2ZW8Y4AYJ587C5J6+vilKu6DkkRkaDlgLBecA5gxE4Ck0jB4LyI\nhGelfHEyPm677TbcfffdWFiIiZnz8/N4xzvegVtvvbXSxlUlkuJEOyyncKQnHc5zX2TNL4PzYVKc\nXSauMonbPOdj8LdpkvThfJQNu7KFrccsLs0oyj/JRYi0yFzDBrsS6nwAdsW93fOrI5OIU+SDNT6z\nv8uFXTGRD+HctA7jBruSGyoZ0XSeoccl95W8+olQaAk1fjnCeZXVzDmh70eL8nhEPrioifaO2gLs\nSlD4qWEqGZPHBEhH4gzhOB8SDMpe4dyN80HnyTKTLG0lXJqplbASrz591jECXV3o2Dly9UBpkFfa\nz6VL+EzHPOfDfExZQtf6iNzLZV2sAjZXlph0GY6PIT2L5CRIRC4yaDztWVlDcTI+3vWud+Hhhx/G\n5ZdfjkOHDuHAgQN4+OGH8a53vavq9lUiIuGcbJdgV4nw2a7yz0pSViufSq5GKJPLxCXBrvIYHxzs\nJtWG0mBXhteTj3Bu3m/jfPSi/JGPHhP5aAZKNz4ykY9sg9PKqU1xv2jCZBkkL+eDPpuT8VEg8iGd\nWybsSup/9Plpf2+sZiOT+Az965N7bx8yR0oAvzmjDKHvgD5r+n3XYPYic7AXanxMr0LNXGFXFJIn\nQSslMqsU+RirB+I9bdmuXGFX9BnLrMPLGXVVePVpFjJqQC90Q3vkI2DOo8p6CeuXE+yqosgH54BM\nf3+XNXwje/dNji4XjmAikpOgfy0RCrtx3813mjhxPvbs2YPPfvazeOqpp3D69Gns3r0bl12m5yff\nLCJBErxhV1xV30KwK8H48IJdyftMXpN2L8ITc11N8UkkF+HconwWgV0dn+9CAdg/VjcuBKFnnQ3A\nvrDYYFdRlL+SKkcAbwTAMLnn4Zku9o3WsH+sri1YaWiCKd0sAEy1803GeTkfGuzK4fb02SXhDuOM\nj7rK1ihygl0Z2ikpsrQPaJGP1f+tkQ9y3UtaAU7M98TjgfWPfOjQldT7plGIaHCNx2e6OLeUfZ66\nAsZEwjm5jwi7yv4WPaqGyMex2S6+TQrIjjZk2JWNcC6m2tU4H2R/icAr3airhvNBIx8658NOdK8r\nhVGisVA+pJhqd3UUrfQiHJ3tYv9YTYxg84RzajCb25pXOOW8Ew74DS5OuY1MODfxVzmjSdIRPn5k\nES/b18JVE3U28igSzjfwu/lOE68yzpdddtmmNjoSkUJvtKPzsKvB39w8UGRSkgZmO4wHkw1DDOSL\nJqz0IvzyvVOZglNU8hHO9W2dcJCu2OTpMhkfH3tsAR9eLQr3C9eO4vsuk4sG5fke1sgH52JPSYio\nIOcju40jnH/42/E7+NnnjOrZrjxgV3nFZRJnIx8UYuNgfbhHPvTjOMOFfj6uNgQVY6pdR9iVFA3Q\nFEBiENJXrUU+2np6Xh+oZhmipdrVyPWDvznYVRRFeOtXpvG183rRDJbzIcGuBEOSRj64eXKhE+LM\nEv8x3/PwHJ5gKsTHnI9yYVf0cjQaXTbhPC1TK6F1fssjWrYrhvNhMz4aAVAPzJwPMdVuFKex/fkv\nXsTTiyF2DAV4z4u3Yc+ITnLX+xRjMFdFOGdeQnrTZiec+3I+TPPuz31xCq/YP4S33Tyh7Xu2zsfG\nl/JnmU0gMueDRj7049IRAG6QFwnHmhQGV2UiTzThi08vGw0PICfhnDkpwsBoM7VVSikYRhE+cWSx\n//sD31owkmt9F4kosmOPbRnDwsiNkM3eH1yqXZ1wnsjHjyxqXu5MtivHqIGv5OV8VEk454wINvJB\ntDeXy5uzXbktdBQKUE9gV5bIB738SD0Lw+uEeqRjrSMfVGfianUkQj9TGEV46GKHNTyAOGrCQdOi\nKHIqMgi4Zbs6sSCHKznDA4gNfTnVrng5tk2J6GmBs/tLhV3RWh8r9pS3eUQrMshwN2z3rQeKgWsR\nQ104N4yA/3p4EU8vxkecXw7x+af4lIVc1M6WHros4db59Htx43xsXAXbyPlw4AhS+bsTy5hhMFbP\nwq42vnxHGh8i7ErjfOjHpLdxE1ARj4hJYXBNt2uEhwiX+NxTK/yOlJRFOI/bEW83LTbchBJfU39P\nx+dlpcEXLuais7VqCrftkitiRVGxSY4aiRzhPJGFbqRFtNIe16oiHy7EPbdUu9nflw4HGePpJ68e\ncSacS6l29eOyv10iisY6H8I+2gfEyIemAJoJ5wHkonuJrDnnw4twnm17GAFfOdMWr22qh+IOu7JH\nPlxqKKTl6i11jBjqfNj6lcT5oJs5T3xZohm+7apS7WZ/03fTDR1gV4FutGgwJaHtUQT815TTCgA+\nsho9p7KuhHOmD2YiH07ZrspsUblicsq5ZrtKSwRgljU+JAezuX3PytqJFXYVhiG+/OUv47u/+7vR\narXWok2VShRFhjRs2d9cwb70Jm6QF/EamRQG98iHCR7C73NRUMvifACxATFSN1fWlrgn3OR1xFDx\n1HeucbHxWjWFu79rAh97bAHdCLhmSx3/zzfm+vt7kY73f/m+Fp63o4kHL8Te3X8QvG6A/t6aNYUh\nQ7SFvpO0cu2quPtKXthVGMXKVOJJpJcZriv837duxV8eW8TekRp+4tAI/uJRXkmgwhLOmRmOKn22\negyADXYl9fPB9iiK9Arnq/9zCmDm3uS6wSoM6dTi4LiLKyEuGxscU1bk4yevHsHUSoSxhsK9p1dE\nQ5+OG/q+skUG6bHmKEHSV7h6KM6wKwfOhw8e/H89MITXT4563ZNKXs5HmSOa43xU4TmnjgGuD1gj\nH0qPfNAaR2Lkg9m2Z5SfVOm6v57ZroDB/NIz6C5psdWpWk8xcSG58eeyHnNqk5ztauO+m+80sRof\nQRDgta99LU6ePLkW7alcTINXLzKoH2MjnBfjfJgiH24XNg1WybZxgebkcRjYcm2bJkkugw/ATx5H\nTFwVz+/hsvC2agoTzQC/9NxxAMC/ns1GjiJEWl9q1hReeWAYrzwwjP95vm00PvTIhzJ+I6rkp5Uh\nG+E8r7jBrvhjuiFQW1336REKwFVb6njr8wY4XmfOB6MEcs9v88JyYox8ODgz6NhrBAP4DKcApoUO\nk0DZoyVlcT72jNTw088ZBgD8zwtydEIrMqhBVwZ/c5wP0zdIvtfWVtCHzQBJTQpynwLZrlyTRBya\nqOM3bhr0T6np9UAhgDx3iql2yW+qSJVV4RzgM62tReSDK9pn89jXA54rkhYp2xUn+yXjQ+tTSq/z\nUYGB1osiFlaVLP2uivNGTrVrco52wqxjijv+JXtaOL3Yw2OpNZ9N6S8Vkn7W+Ngw4gS7etGLXoQH\nHnig6rasiZgGcEKGToQ3PgYb2VS7FWS7AtxrMZi8R5L31sX4yONMsRVzNE2SM+2IbS83eRyZ4bHi\ngL8x6GLj0Sw2HIxEi16kzqHKJhUKaTLBrgCOSzD4uzrYlYvxwW9PLyg0osYpVu51PvRt3PM3yD1s\nWYkA87iWjOj0OzIV3ZtoqsxEPEcKrtH1WjEEbJq1jBYryyvp70FT5KZFh13J0TgOP2/qTqZ6KPTd\nFMl25VqcjUJ/ZIPHnMxAiozQMUDfTZmE84lmlrOy0I28Cry6CjW02D5gLTKotCxZND29T8tprZFB\nW7K/a8HaRD4WhTU+6ZeusMCNnNHJNsYoGoLOu/VAn6/5LFnPRj42ujhlu9q/fz9e9apX4Y477sC+\nffv625VSuPvuuytrXBVi63ydEGiuOkRskQ1O0S8EuzJMLjS8LInJeyRGPhwU1Dxj1gS7AsyTZAhg\nrh1pRbA4hTbtDdWu4/k9XBQQSjjnPLn02Vupd0y9jVTo4s/V+TBJ+tDqYFf2YyR8b7or00M4xaoI\n4ZwzrOlxTUsCAcAMu5IMk24KYsZFPvrtUQpbmipjQMy0Q+xYxdppCjbs0RJTVhkfSX8Pk9KrE86z\nv9NQN268mCCnideZPvOF5ew5gcqmUE4LxzGg4mx81N2Mj5qSyeiAbPRaU+2WOKQDFZP5L6b6D32v\nZYgtw1xc38h8jQbD+aD9xkfvloYIm+1qDYwPaf1P5hdXo3AjRz5s0cWVMMJQyslBx2RdKTTIfM31\nG0m3EAAVz8o6iJPxsbS0hDvuuAMA8PTTTwOAltpxs4jd+Ij6hF0b4ZwvMpivXb0oMioMrjAKk/dI\nMoxcFNSyigwC6ciH+aJTK6Hm4fXNIuX7PVwiVy2yErkZH4O/J5rm981FTXyMiLSit56RD5d6OvR1\nc611hY65Es6pIuoU+TA8rklpXelFGKnraaUp72RrK8BUO1u7Y2B8ZM9Vis/+lJayYFeuxkfRCudS\ngVVgoPjRZz5PlGRTN6H7WM6HI+yKKsASDKoeJDU5+GeThrRmfFSYaheIjbq08UHrrBQVBT3SwRXt\nsznuakFc1DEtdM306fXSuKVjva7WJtuVNGaTdi6bE1Jqx29EsbVNS9KhOTH0+dunPsizkY+NI07G\nx/ve976q27FmYvPYpjstpxxnYFdc5CPnwLd5Kk2Lc1pM3iPJI2JLCQn4YWkTkWFX2f8lmVoJcdDx\nmpL4fg4Xsh5VVqkuEELvZ2mDxYbZ1iIfNWA4yhf5qIrz4YKdlY3Pwd8cn4GKc5FBlnDOcD7IJhfO\nhzHVrmFfP7mCIfIBxArgsTm+cCAdJoECtgt1LxKxwTRH68qJR+ZufNDf1Hucgm8xXmSTsSTBrqiH\n3lQsUot8MOPcPfKRbYcc+TC/M5HzYYt8lEo5B7YSZ8j55ZwVUgXhxiV1FPQiu8e+ESiMWLJd+SxT\n0vrDpW+WCmOWKVJGy2SYukY+0rW0NprYIGF0XaH6VCNQmr7C1fSQs109a3xsFHFOtfvoo4/i93//\n9/GWt7wFAPDYY4/h4YcfrqxhVYlNaUp3ThvhnK/zka9dNk+lqyfT5D2SDCOX8ZhnOZIw/33CObkx\nVaho1p/0ua7iTzi3H0MjH1TpCSO9yKCLdz0RjnDuE/lYC9iVSwTKxvkByoVduRPOs9uKZLvqRZGx\njyXvgFtE06IV0UsbH8w7stUGsaXmpt57SbJGg4HzQWFXXql2zVHf5HvRZ6ZKsqmbUAXYB6pBRY98\n8MfF9SHk68iEc7OhVLZOSY26stORcuOSK9pnM/7qSoe8UQPaZ7qXnIuasQ+eIF+2SA6DpJ0uNT4S\n2agOflvfojqDFvlQXOSDuc+zhPMNL07Gx1//9V/jh3/4h3Hq1Cl84hOfAADMz89vOr4HYFea0p2T\nWw+t2a5yTkq2yIcr58N0mLTPZc3NE2W2eR9oe3YNm3HsgDs0IhHf8LhL5IpyBOjaGkX6s1ODxSQ+\ndT6oUNz7RiScZzkfBFLCeHWdCeeukQ+N85EfdmUblgND29wGmr1qamVwQzbbVUHOB1XiJEkf5Qe7\nIjCzdJFBCruCmSDfj3w0LZEPE7mbetodatBIonM++Bdji3zIhPPsb9p3yoZdUaOubOGMe9+kA0Bs\nhFKSeBHOB2eAcgkM1BrBrqQxm7TTpcYHPWejiRV2FVrmkUBp8zV3TZlwng/F8ayUL06wq9/93d/F\nPffcgxtvvBH33HMPAOCGG27AQw89VGnjqhAXwnkibBHBjPGhn593Upq3zBYu2a56UWT0/EhREZc2\nm+aMo7NdLPciXLu1nlF8Rc/3qlJKJ4idQzV8GwNgK1fro2rYlWuRwbToRcE43oZ7G7QK5zXlbPxR\nPXo9igw+vdDDueWemJ0l/d3pEZweVyTywRcZzBH5CIFvT3XQrClcOTGYNm1jJ+kHtK/TSJgW+Uhp\nwvQeAZgUqZ6wqzhlqd2Sdyacp253arGHr53PpuW1cT5Mkd3ke+mRD3fYlRb5YG7nnu0qezEx8qGS\nNvHXlSIfupedfP+KIx9lC01TC/DQO9v7bwQ65E2LfHisv9z9pNTNJsL5ifkuHp/tIoqA8WaAG7Y3\nvJxNiUhJF5J7+UQ+OmGEYQd43kovwremOtg7WsOuYYfMGwXFijzRYFfZ/XG2K3KOR+a6CNmkQhtd\nvj3dQTPIrjnPFHF6ovPnz+O5z32utj0INl+BdFtl5gzsivWMpCMj9snLVegkSpcsF3y2zQsr2Tcu\nThJJyfrHi0188lsXAQD/24Fh/PpN4/190kQjeYO1yAczq1RufLhku6Kpdsn+CHo/o4tRqyb3RQq5\nbljgG2mhirUrX8JXpG/7z2dW8H/+64wl61pqjDnArpwJ51zkw6HOhwsk7shsF2/68hQA4JeuH8OP\nXTUCwO5hTL6xCYYE8GlkE9ENNIWJpsrMEbPtCN0w6kOUbDDN4boyUKEHUnM0PpK58qvn2vg//mVa\ney/p63DKtZHzsfpq6Duic6IP7KoI4XzMMdtVPTBnu3IuMkjHiKV9vmLLvldU+MhH9rcL56OmFFo1\nIECEcFWx7oTxXJR4w32mex42TfuUEtsLAJ8/uYx3fm02c9/rttXxn75nm1jBXhKJ1zkgnBczrKh0\nwwi/cu8UHpvpohkAf/TCrbjxkqbzPfKIdb4k7dazXTGwK486H8k9XKLd6y3vfXgO/+3oEgDgTdeN\n4ScOjaxzi8oVp1nnpptu6sOtEvmrv/orPP/5z6+kUVWKzfLORj70/bbIR940dzTHN+U/mFJRDu5t\nfjYpE5YT7IrbFkX45Jnh/u+/eXIpAw+z1XnQIh/E88JGPrw5H37Hu4SrXbJd0X5Gz7lxu/sk3wzc\njQgKPakq8iFB6j7wzXnrO0yvoS6Ec1fYFXcY9970mgN+Ct17H5nv/20bc8l7ou9L43zQooEpw5vj\nfNQDpWVNSzIWScXK0tIKlLUCd3Kv/t8GT2riiPnvx5bY72/ijvRgdq4kXTgxuGzHcUKN8iKpdinp\nuWzCOQ3g0LaWWWQQqD7ywY1LrQ+EkXUsxYU5FUZq2eNm0mPFo10usOlkPpWKDP7NE0va2vjNqa6x\n8K0kkgGeLKk+9Vdc9JCHLnb6xfraIfCH/zbnfP28QscYXZ+o0aBlHguU5izygV3F99j4sKvFbtg3\nPADg/d+cNxy9OcUp8vEHf/AHuPPOO/Gxj30MS0tLuPPOO3HkyJE+BGsziU15zXpl9WOzhHMu8pGv\nY1PlZFsrwIWU8u0S+bApfZJz0WXN5Y65yBgHM+0II6u9SnrXbcEbvNOJ8+H3fv2LDNpPoCFbjkBL\n+wb1/r35+jH86xcuOrWpGSiMNwJcu7WOb02bF7W1g13x7ymdsUmSdF93SbXrXufDEXZFvoVazR1v\ni4pyYo82RqvHmfsDbWc6g4tmfKz+v3e0hpn2oD88MdfFruGaU42PVi1Wfm0KtzJwNdKSNPfLp1fY\n/SbYVTeUFasAg/GV1EOZbvPHmrNdkXsWgV0RbVpMtavMEUvJ+KPPQdtaNuG8qjkiES4CwGWPsnXb\n5DqXNELM9wYXeHK+13dc+WW7Kg67mhOIQrM5SBfSuE3WJF/YlU0eOJuFRp6YLzfLGSfUuBhrqMzY\np+sKlyXQrc6Hexs2onCO12eaOLk8rr76ajzwwAP4uZ/7Odx99914/etfj/vvvx+HDh2qun2liw/m\nkFMs0mOau1Te2l70WluIV9Ml25XN8JE8S7bKsgBvfByb1Udx2zCRJCJGPoZk0u3gmtamZsTb+HAY\n87ZUuxG4STN71JUTdfzRC7fizoPDsEly7u/dthWvsYReqYd3qCKoaB5FPRET4ZxTIGlRR0mcYVfM\nPXyykQEDbLnNWJXSSlOFzJTKk94hOfXgePbjJuPRZa5o1twiH66wK5vX2kQ4nzMWGMz+NnnpzdXE\n7ZEPZ9gVsWSk+9YCc5TCNdUunZ/LthVc+kGh67ORj+zvGHZl60Px//ta2f5ybHZggK817CpvIgpO\nJN5nP/JRMuF894g+sbomtskr1Mk6TqKIep0P/Xu41PkwZa7zRU+sh2zUhAFlirNqMjo6ittuuw0H\nDhzAnj17MD4+bj9pA4qd8zH4myWcp6Y31nOSs2PTa1FyZRmRD2lecVlzWeNjTvfCJ96ZKIrE9rQF\nhYwS3jjrv/pUu+YTakpXZDjCOa0Xwikat+xs4padTXztXBvHDV6nxNOztRXgF64bw6PTHXz9fIdv\n3xrBropM4BnCuYNXN1AKQzWdC0OFM1xcyOTxcS4MiIG0w9goyh35sBRdSw93jXAuGB9HV8eji/HR\nqiXwBfOx6W5r6kr2TEX8NQFgTohkAHr/oPVQ0mKGXWV/s5yPvJEP4biysl1pkQ9z87zF1/D2FTby\nwWSPco187Gtlv396HfKJfLCwKzHyobeXO166jotIa3zSTi/YlcuLYI55cq6Ha7dVZ41SPYcmvtCz\nXWXPbwRAj9S8onU+uqFb+vONLBu5UGRZ4tTLTpw4gVe84hW48cYb8eM//uO44YYb8IpXvALHjx+v\nun2lizXVbmq/LfLBcj5y9hl6rXGiNS12o9yRjf5+4Xw32JV+EGd8JN4ZY9gzUcjINbe1gowCsdSL\nNG+PP+Hc73ibMsllMaFraxTZCcam89MyyJiT3iafQJWssvHhiRTBzWYiH2Sf9JpcjCjOw8oV2uLG\ngauRksigfof5uK5gaFPogCmVp86LiY+lGVASD7Ctxgfgwflg7stJ0h0oVy2RLOE8ex0TRIXOp6a0\nsOZsV0RhKVBkkHI+uPeSwMWkbhsw4zp9brZd+rllSuWRD0fCubXOx+p19hoiHz46G7dGyZwPctzq\nfaQ1NU+xYZHzsbrZx/hw8ZxzyB5uTS9T6DunqZNtRQbrTJFBek17EpCNr9hzUVhfXWaji9O086Y3\nvQk33XQTjh8/jiNHjuDJJ5/E8573PLz5zW+uun2li5VwntpvKzLo4jlxFXqtVk1pWX6k1KX9a1ju\nXaTOB3dqetJPJJkgTQO8E0ZsZKQRMPULyMxSfapd8wmc7qMTzjnOh6wx+FRmjo+X22e6VplSZAL3\nLTIIuJHOXZUobhz4Zj/pZ2yzwq6izP+JaJEPpuhaIvQdJWfSyMeT8130osgpStqqufWVwACXSksS\n8aUk+ETSyj991abIBxVa6yMtPvwKLpOha52PEZrtilOuV7dJBpvJ2KXn6Kl2yx3jprmpqutz/d1W\nIyuB7umRj15fMfOZlfhslY6wqzD7v35tj4asijRuk7nDtv5z55iE04WOMmt6mULbRZ2sVOnWnHhK\nn6tpxNL27JuB88Hxe55pwRAn2NWDDz6Ie+65B81mnKFnbGwMv/M7v4Mrr7yy0sZVIYXrfKQjI8yl\n8hLOtUqeQVxJN90JFzoRxhqGa9giH6LxYW8zvXQYRSz8wcn46OnPG0MUFLY2g0z+/unKWYl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YYdYBcUqmPLTCJJctxsO8xAAeoKuExIE+tEOC+wRpaV8Ucy+DXOBzlOI5xnUn5n9/k0VYKTucLM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"text": [ "<matplotlib.figure.Figure at 0x107769490>" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Plot the daily bookings per neighborhood (provide a legend)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "#i need to copy all of the booking data to a new df\n", "#I need to get the neighborhood id from the listings data and add that column by matching the ID\n", "#now i need to add the count per neighborhood per day\n", "#then I need to plot that\n", "bookingsNeighborhood = bookings.merge(listings[['prop_id', 'neighborhood']], on='prop_id')\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Part 2 - Develop a data set" ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Add the columns `number_of_bookings` and `booking_rate` (number_of_bookings/tenure_months) to your `listings` data frame" ] }, { "cell_type": "code", "collapsed": false, "input": [ "listings2 = listings\n", "listings2[\"number_of_bookings\"] = bookings.groupby('prop_id')[['prop_id']].count()\n", "listings2[\"booking_rate\"] = listings2['number_of_bookings']/listings2['tenure_months']\n", "\n", "#[['col_name']] returns a data frame, just [] would return a series\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###We only want to analyze well established properties, so let's filter out any properties that have a tenure less than 10 months " ] }, { "cell_type": "code", "collapsed": false, "input": [ "listings3 = listings2[listings2['tenure_months'] >=10]\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###`prop_type` and `neighborhood` are categorical variables, use `get_dummies()`http://pandas.pydata.org/pandas-docs/stable/generated/pandas.core.reshape.get_dummies.html) to transform this column of categorical data to many columns of boolean values (after applying this function correctly there should be 1 column for every prop_type and 1 column for every neighborhood category." ] }, { "cell_type": "code", "collapsed": false, "input": [ "listings4 = listings3\n", "\n", "for column in ['neighborhood','prop_type']:\n", " dummies = pd.get_dummies(listings4[column])\n", " listings4[dummies.columns] = dummies\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 10 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###create test and training sets for your regressors and predictors\n", "predictor (y) is `booking_rate`, regressors (X) are everything else, except `prop_id`,`booking_rate`,`prop_type`,`neighborhood` and `number_of_bookings`<br>\n", "http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html<br>\n", "http://pandas.pydata.org/pandas-docs/stable/basics.html#dropping-labels-from-an-axis" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.cross_validation import train_test_split" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "#remove garbage\n", "listings5 = listings4\n", "#listings5.replace([numpy.inf, -numpy.inf], numpy.nan)\n", "#istings5.dropna(axis=0)\n", "#listings5.dropna(subset=['number_of_bookings'],how='any', inplace = True)\n", "listings5 = listings5.dropna()\n", "#listings5.info()\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "x_axis = listings5[['price', 'person_capacity',\n", " 'picture_count', 'description_length', 'tenure_months', 'Property type 2', 'Property type 3',\n", " 'Neighborhood 18', 'Neighborhood 19', 'Neighborhood 20',\n", " 'Neighborhood 21', 'Neighborhood 4', 'Neighborhood 5',\n", " 'Neighborhood 7', 'Neighborhood 8', 'Neighborhood 9',]]\n", "\n", "y_axis = listings5['booking_rate']\n", "\n", "x_train, x_test, y_train, y_test = train_test_split(x_axis, y_axis)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 132 }, { "cell_type": "code", "collapsed": false, "input": [ "listings5['Neighborhood 18'].value_counts()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 53, "text": [ "0 79\n", "1 34\n", "dtype: int64" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Part 3 - Model `booking_rate`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Create a linear regression model of your listings" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.linear_model import LinearRegression\n", "\n", "lr = LinearRegression()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 95 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###fit your model with your test sets" ] }, { "cell_type": "code", "collapsed": false, "input": [ "model = lr.fit(x_test, y_test, n_jobs = 10)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 133 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###report the score\n", "http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression.score" ] }, { "cell_type": "code", "collapsed": false, "input": [ "model.score(x_test, y_test)\n" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 134, "text": [ "0.62463395370380381" ] } ], "prompt_number": 134 }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Interpret the results of the above model:\n", "* What does the `score` method do?\n", "* What does this tell us about our model?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It returns the R^2 valued which measure of how close the data fits the regression. It is the coefficient of determination. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Optional - Iterate\n", "Create an alternative predictor (e.g. monthly revenue) and use the same modeling pattern in Part 3 to " ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
mauriciogtec/PropedeuticoDataScience2017
Alumnos/Victor Quintero/Tarea 2.ipynb
1
911657
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parte I" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " <li>¿Por qué una matriz equivale a una transformación lineal entre espacios vectoriales?\n", " \n", "R= Por que una matriz A al multiplicar a un vector X lo transforma en el vector b (Ax = b). Convierte vectores en vectores.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " <li>¿Cuál es el efecto de transformación lineal de una matriz diagonal y el de una matriz ortogonal?</li>\n", " \n", "R= El efecto de transformación lineal de una matriz diagonal equivale a multiplicar la matriz X por un escar. Las matrices ortonormales preservan la norma y el volumen de los vectores.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li>¿Qué es la descomposición en valores singulares de una matriz?</li>\n", "\n", "R= Es representar una matriz como producto de tres matices, las cuales se pueden interpretar como transformaciones geométricas: una rotción, un escalamiento o redimensión y otra rotación. En otras palabras, nos dice que toda transformación lineal es una rotación, redimensión de ejes canónicos y otra rotación.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li>¿Qué es diagonalizar una matriz y que representan los eigenvectores?</li>\n", " \n", " R= Diagonalizar una matriz es representarla como una multiplición de 3 matirces ($W, D, W^{t}$) donde W es ortogonal y D diagonal. Esto nos sirve para encontrar la base de eigenvectores. Los eigenvectores representan dirección (ejes) dentro de una transformación lineal, la cual es un reescalamiento o rotación. Por lo tanto al no repsentar un cambio de sentido, representan un reescalamiento.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li>¿Intuitivamente qué son los eigenvectores?</li>\n", "\n", " R = Son valores que reescalan dentro de una transformación lineal. Un eigenvector de valor de valor 1, por ejemplo, va a mantener el tamaño en la transformación. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li>¿Cómo interpretas la descomposición en valores singulares como una composición de tres tipos de transformaciones\n", " lineales simples?</li>\n", " \n", "R = Toda transformación lineal es una rotación, redimensión de ejes canónicos y otra rotación. Por lo que la primera matriz se encarga de hacer una rotación, la segunda matriz (diagonal) contiene a los eigenvectores por lo que se encarga de hacer una redimensión de los ejes canónicos, y la tercera matriz se encarda de dar una última rotación.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li>¿Qué relación hay entre la descomposición en valores singulares y la diagonalización?</li>\n", "\n", " R= que ambos sirven para representar una matriz con el producto de 3 matrices. Donde la segunda matriz es diagonal y contiene eigenvectores." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li>¿Cómo se usa la descomposición en valores singulares para dar un aproximación de rango menor a una matriz?</li>\n", " \n", " R= Al hacer la descomposición, tenemos la matriz diagonal con los eigenvectores acomodados en columnas de mayor a menor importancia, por lo que basta tomar solo un número menor de esas columnas y renglones de las otras matrices para tener una aproximación a la matriz original. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li>Describe el método de minimización por descenso gradiente</li>\n", "\n", "R= En este método sirve para encontrar el valor mínimo de un función (un valor x que minimice la función F(x)). El método lo que nos dice es que tomemos un punto cualquiera $x_{0}$ de la función, luego para iniciar la busqueda de un valor que minimice la función ($x_{1}$) vamos a restarle a $x_{0}$ alpha veces el gradiente de F(x) (recordemos que el gradiente apunta al máximo ascenso por lo que el gradiente negativo apunta al máximo descenso), alpha nos indica la magnitud del siguiente paso, por lo que si es muy chica el proceso se puede volver muy tardado, pero si es muy grande el algoritmo diverge. Conforme nos vamos acercando al valor mínimo, el gradiente se va volviendo más pequeño y nuestros valores $ x_{t}$ y $x_{t+1}$ se van acercando más y más, por lo que el proceso termina cuando $x_{t} = x_{t+1}$ o la diferencia entre ambos es un valor muy pequeño previamente fijado. La formula de este proceso es: $x_{t+1}=x_{t} - \\alpha \\nabla F(x_{t})$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " <li>Menciona 4 ejemplo de problemas de optimización (dos con restricciones y dos sin restricciones) que te parecan interesantes como Científico de Datos</li>\n", " \n", "1) Asignción de tripulación en la industria aérea (Crew Scheduling Problem).- El objetivo es asignar la tripulación a todos los vuelos sin incurrir en diferentes restricciones que se tienen a nivel persona (máximo en horas de trabajo, número de días fuera de base, etc.) al menor costo posible. Por lo regular este costo es el primero o segundo mayor para una aerolínea.\n", "\n", "2) En el sector agricultor, maximizar las ganancias determinando cuanto sembrar de cada cultivo para satisfacer cierto pronóstico de demanda.\n", "\n", "3) Determinar cuanto dinero poner en cada cajero de cierta ciudad y cada cuanto resurtirlo para dar un nivel correcto de servicio sin tener que tener parado mucho dinero.\n", "\n", "4) En la industria de acero, reducir el desperdicio generado por cortar placas grandes en piezas más pequeñas, determinando patrones de corte.\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Parte II" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ejercicio 1: Script para aproximar una imagen\n", "\n", "Vamos a aproximar una imagen blanco y negro utilizando la descomposición SVD." ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Introducir el path del archivo: ImagenTarea2.png\n", "Grado de aproximación k: 10\n" ] }, { "data": { "image/png": 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3CVZdKBQwMTEhUAXjajplsdPpoFgsSvxoZ2cH6XQaxWIRdrsdt2/fxuOPPy5w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62tLayvr0usgQQEQnQ0YMrKygS+YQYx95c+Y9wvkUjkmr15vXFLCXoeGh2g0K672VLlJtOv8Vr6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\nMg88c2trq6ampt52E70XlEql9MILL9g96K3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"text/plain": [ "<matplotlib.figure.Figure at 0x21f0aaa2a58>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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jeJEdxoq+xXtBgZfx8nFu3dnzLB8jR8tc504VA+vIC6V2ZBZ370oqZZIgj1UA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WhzFG+unT5x+2i/38m5ENM9RAK1jbAdsZwtAxZ1lOXPfX6d17vvcW9D3zTNIMk/SfKX27Y9c8Pa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\nkABAHscituDGutFoZNqOd7PdL/fQ36wgx/MoY1XMW9A4AVnGBA8REMO1F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"text/plain": [ "<matplotlib.figure.Figure at 0x21f0aaa2ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from PIL import Image\n", "\n", "#Pedir el path del archivo\n", "IM=input(\"Introducir el path del archivo: \")\n", "\n", "#Pedir el grado de aproximación k\n", "k=input(\"Grado de aproximación k: \")\n", "k=int(k)\n", "\n", "#Ejemplo de la imagen que usé\n", "#IM = \"ImagenTarea2.png\"\n", "\n", "img = Image.open(IM) \n", "imgmat = np.array(list(img.getdata(band=0)), float) #Hago un array con la informacion de los pixeles\n", "imgmat.shape = (img.size[1], img.size[0]) #Redimenciono el array en base al numero de pixeles\n", "imgmat = np.matrix(imgmat) #Convierto el array en matriz\n", "plt.imshow(imgmat, cmap='gray'); #Para visualizar la imagen usando matplotlib\n", "title = \"Imagen original:\"\n", "plt.title(title)\n", "plt.show()\n", "\n", "U, S, V = np.linalg.svd(imgmat) #Descomposicion svd\n", "\n", "matreconstruida = np.matrix(U[:, :k]) * np.diag(S[:k]) * np.matrix(V[:k, :])\n", "plt.imshow(matreconstruida, cmap='gray')\n", "title = \"Aproximación de grado k = %s\" % k\n", "plt.title(title)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ahora vamos a elegir diferentes grados de aproximación a la imagen original usando un ciclo \"For\":" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "image/png": 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ArUrBMVM3Bgw/aYdQDU61/igs19uo1G0IqtdRgb5OuKFAhL2ttTH8tVdTV/WY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\n9FYmgddTHVfKTEpA8JisOpEvCHO43PhA5qtpPS+8l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"text/plain": [ "<matplotlib.figure.Figure at 0x21f0b07f390>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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6uQodxu7Gm34wz/S/WF3FnEF7TCYTbW5uqt/vBwcNf47841zoKxVgw+Ew1gXQ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"text/plain": [ "<matplotlib.figure.Figure at 0x21f0c232dd8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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4OHqemTHhnmRzGGEMAFE1jCHGHAflDbzPWjzuLK0L+ygubKFWq6Xd3V0dHR2p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\nzzgQvMlMERxcX1+Hw/ambVAU0Ic4usvLy2g7gDSSPjKoqLwoDy3UbDY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m5dRNvl4B+pAU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\nTPI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"text/plain": [ "<matplotlib.figure.Figure at 0x21f0bb9ea90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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v9XoLqqnz83NdXFwYmpdmAKxYLJoRJirzSWjfd9BQvV5vQXaJs/QgqlKpqNFo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g8LzHDu9DBpBpb9A3NjYW9AmAhnlJakPRY2NjYfgBONC0IHfOH8FwYkQofeS6Wq1GdhOevl9p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\nFFQkayS1mrcdZTxQ0ef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"text/plain": [ "<matplotlib.figure.Figure at 0x21f0bab1be0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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hkPr9vhlovGmv11M6nbZMODwqKg/UM3D6/C66V/69tLRkfBryNjhzuEyfo5Nk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Mhh0aGjKNOu/xOQAYcgJW9AHN/fDwsBk5ZGhevQM6xCDCZfpEmJ2dHQv8Yii9TBNaiu9EL+zR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\n3kd+6YOX7HXvTfK5rF+8n9HRUW1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"text/plain": [ "<matplotlib.figure.Figure at 0x21f09ce4048>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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pgUlzuZzE8QCLUFEoFPChD30Iw+EQmUxGAuiGYaDdbqPZbCIajSIajWJ/fx8L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\nHAjOWBcKB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IJkjAxIakmFVqnLperKqsyqrEzmwp2MIIOxkpFcIhgxD5G/EyfYrSoKkIHU\nBQrFJGO597+c853vfOf8z860vb2tRqPxE8lnPiedTtv5te122wqm6LmEwwiHw9re3tbKyood0kJ7\nCxwQkTBtRE5PT63mwAMkWAhqeLLZ7EA0+2XXS2PoQZKUWaNIICkSCvVPw8FAE5ZiYPCsvmJW6hv+\nIOi3diW8Aw2BGjzvKvUpIN6LnA7U5qVqLG7ao+K5PafrWxn78DMcDiuTyZhKAPUPzon7YmGA0HzY\nfP5cSpyRpxgw2FBXbCDPqfuMf6fTGTjqDYPok5s4AGgWql+9c+W7yT1QPIPTAsFeuXJFjUZD09PT\nmpyctLA/HA6r0WhYJEcYn81mlc1m7aBtLkJd39M9Ho8rEomYKsI/L/QZztlXwnpqjTXD30Cg9Xrd\nxlmSCoWCNjc3VS6XdXBwYMcEvv7660ZhQOlx8Mt5ygUjBq8r9Q8NYQ+wPlmLvhaApDfrBXoTI8rP\nNPeCgiRCZE5Zf3zP8+fPBzhkqJRyuWzabnr0oGp7/vy5yuWynQUL/eojck+9QkdyMU90jgXMeNqW\nPc79MiadTsdQeTKZtPMNhoaGLBcBmo7H4/Z8XNCpPncXi8VMFr6/v6+dnR2dnJzYcxcKBetMms1m\nrYsl68/vN+wPBYP+PNpIJGL0omcbWA8XvV4KQ9/pdLS7u2ubDCSAMaQCUhrkmDGQGCxoDJ9g4/Kb\nBSTCIqB4ASNI2I8DwagfHx/bIkPjDYKiGIeCC4wLzggUhTyLz/bl3hhC+nH4qIKmVD7ZLPWrcUnS\nseh9QRNFLSBHnBOble9iM2MwyPKTlPRjSoLM0wQ+eYnzw/n6BDGRDNQBUdDR0ZFWV1dVKBRUqVRU\nrVYVjUZtjObn582JeGRORSqGiUgEhyv1kHwqlTIjzzier+71CT8vL8WBsn54pvNrrF6v6969eybf\nTaVSunXrlsbHx41qgCP2lbTz8/MaHR1VtVrV8fGxNZyjhTQ1EUSVPq/gDRBAx88Nc34+gc7ax4hs\nbGzo0aNHajQaunv3roGIarVq+5OjAxOJhKmm2u22Ll++bBz1/v6+5ufnrTCNHBC0xPb2tiYnJwcS\n2eQMMHQgdIqeuDCmkqxjKqjbc/ugdVokoM5qtVra3d1VvV43SenKyoo1vUskEpL6p8J52TAsAQfX\ngPDZe2tra7pz544ymYwBNQr7SqWSOToqcRnfcDhsjpX58HOMbSBKZb45DvEi10th6Pf39/V7v/d7\nJseanp7W0tKSPv30U0mDPDuDAJLCqEPTRKNRC28wLpQVgwo8teBfg6dEH8v3eSkXRpmwC4qDyaZ3\nO8YEaSTPMDQ0ZMe0sZi9E4DThNPtdDpW9EQYfHZ2pnK5PKBO8OoF0DoIAQkj98D3+rwGkQKcI46P\nz6X1KwbEG0KMIA4IB/j8+XMLVzF8JOe8CgnK5ujoSPv7+2o2m3r8+LGePHliRpjvZ45AhBMTE5qc\nnDSHgZOEApF64MBLOH2/EtYXm4pw3OchWAfnxwwDGolE7EB6WgVks1krREokEub84doBBtls1g6p\nOD09VTab1eXLl3V0dGQ8uCTrfAl14dvccl/Mr6c9AQ84ACgPDCS5nlgspkuXLmlubk5HR0cqFAq2\nTil+4iwE7yQAOnwnhujg4MBOQqM4KJlMGg0DPQjtwfz6/kLcI4CE797Z2bE2vkEQWNO/6elpzczM\n2Hsx0Dx7PB43Ll2SUa3JZFKff/65VdM+ffrUqmSnp6ftUHaAio9sJNnJaGNjYxofHzfAFwr1iuPG\nx8c1MzOjdrut7e1tzc7ODuR+fJKcNUoVradHcZQ4M5/7+7LrpTD04XBYN2/e1OzsrMrlsm7cuKHL\nly9rf39fe3t7lgChnJlBAhmTuMPYgiQ8VSH1nYRXQRAuo1+v1Wrm1UEcJLxYpNwHxo4eLK1WS6lU\nSl/5yldMXUFDKlrp+gSvvze4V9qVMsksdp9EhMIhdMaZgNK9bJGkm0+yQg0dHByYweRZ4M2JqFh8\nbGQchW8dAFUGN4uxp3iEseMZcHLME+O8tbVlZ3Iyr+Fw2NAXmm+ph3IODg60vr5uShOSbe1223rC\n53I5k5mS9OYZGDMcuJe6sYl8PsdHL0R6rLNms2l98uPxuGZmZnTlyhVDkX7epD7A4LlisZj1j6En\nCtEPuuxut6u9vT1L1vmkLIltioOYU++gMNrnab9Op2Pnsl6/ft0chJfufvDBB9ra2hrgz8vlsklv\nnz17pjfffFMTExMaGuod/AJlQhM/QAxjC11D3svTiAAqGsQx/hylCbXKPjg8PLSqWgy7JIui/F7n\n9c1m0+aNfEmxWDQ5qKe+2ANEYT7yq1QqJgwoFovWvgPQASVbrVa1v7+vTCZjlBf7A2cFlUY0jE3D\nyRHpEdld9HopDP3Q0JBu375tBS6JRELDw72Dh+EoScpxVBlIj0IENi0Zezr7+Unh7FJed54PZVIp\nfOHyoTtoEodDJEE4z6Yh+SL1aINLly7ZyfDQFhgWH6YScmJc+JvX4o+NjVml5ObmpsnBPG1F9EJO\nwYe/w8PDSqfT5hjYuDgUjCyUCsiXe+X1XITn0Gz826tXPN3EGO/v75uSgTmu1+sD6iHoOamf2JVk\nfCVOG4fIBgIpeR6beQXBN5tNK2aSBpG9R/X+Oq/i4vmhxjAoHLaBg6tWq0qn02o2mwP8L8VJRD2S\nrKKStUvjLrh0P5Y4LdYJxTTcK3QW88/9eP7b05U4Vw4J6Xa71mQrl8splUopkUiY00ylUhadcNYr\n8whlg3oKxZlP3ns064ED+wqhAXSkj6a8ZJrIBWcHn4+Dn5iYMBpxa2tLGxsbZkgx9uSjAJEcMsJ8\nARQBB1BDtHcmIT8xMaFIpHdsZiKRUKPRUDjcayw3MjKi1dXVn8ifSTKZ8fDwsKrVqp2J66M1qV8r\n44HJl10vhaGPxWK6fPmynj59qnq9ru9///uKRqO6cuWKqtWqnaspyZKWLGJvTKR+pSkIgKx4u902\nj4lRYoB9TxouEIM3al6qBefoDQMUyt27dy0ngMff2toaULRgRLwRBYHzO59wxvjBVyMfa7fbA4VD\nPnnmx4QeJoTPGKbt7e2fkBDy3qOjI01MTOizzz6z5JhHiBjDVqula9euKZ/Pq1gs6vPPPx+oDiZP\ngCyW973zzjuG/AuFgnG73/72t7W3t6c/+qM/GkD43W6vVJ6umD4aIaxGtYThZH6Qtnn1kZe4nkfs\njB3GicjDj7GXqpI0o7I3CALV6/WB05tAhQ8ePDD6Zna21woKo1IsFs24ra6umjwP2gtunwgBg+kd\nMnkubwT9+vJ8ORFnEAS6dOmSgqBXrIMOXOqh5tnZWZMRE72yVog+6DgJJYf4YGRkxBRRoVDIKDa/\n1olIfHsSKCap77QWFxeVTCYNcCFBTiaTRp+EQiHj9lutlslcibjj8bjVeKyvr2tnZ0fT09MmAtjd\n3bXoHRBCPuj09NTm2YsPKpWKJiYmrC8VZyPs7u5abQd5CeySzxuiFEJpRETuBQ37+/sG0EZHR7W1\ntXVhG/tSGHq0uXjTRqNh4TChTLfbb/SEphZU4dsGe64ZY02I6jesR74gJ885Y6DYSFwYFThp33YA\nY4tCg0QifWXg9EDz/r48940z8dp3TlvyXDyJYQzO+VBTkkVJLCr4d4wWiwkVBsaRU4a83NDnSZCD\nwoX75mUsXt+ICirFd/CcmZnR1NSUabUJbZ88eaJ6vW4FUPF43JKwjBPoE846FAqpVCpZ3UU+n7e/\n89zkaqACMaBsZO90fbsB1onUR9A++QvQIDl4enqqp0+fqlqtqlwua2ZmxqKPcrmsUqlkBVaU/p+c\nnKhQKNgzHBwcWKRGrqZarVqyHEPP3HKPqI1Y4zgzj4a90ujs7EyHh4eqVCp6/PixvWZzc9PWJxRp\nvV7X9va2dnd3NTc3Z++PRqO6fv26JFk0+9Zbb9lYUwjHXExOTmpxcdHECqihWIvsJaIQwBFtDubn\n5+152F9BEBg1JMlAgJ9TP14opYaHhwdO1OI97EucGmCL/U09jiSLTB88eKCFhQU7cQ56zx+8g+H2\nwNKrjiTZoejYC8AJa4S+Rf/de938fV/NZlMffPCBhaxIKguFghl8Sdb8h4ZQhFfhcNiO+/LJCwyt\n1NeBYyy9t/QG/jwtwaLAiGOAJRmv65PCbHbfBMvL9TySBGF5VQyculd0sBAkWX4A5+OdyvmKQdQI\nkgY2j5edElXgPEAwPlnq9cjcD/eGMYD2AVl7AwgNwzhGIhE1Gg39wR/8wcBG5H7effddSbL5YxND\nv/BaqgP5PnILJPtwOhT2ML++EthTGPydjY5R5Jkl/QRQ8JENlCEaa6qO0fFHo1HrRZNOp60lAJva\n6/0xkhhlX6TjOWgMDusbKpI1wXr2yWnWK3NGkU+5XNbP//zPGzXy9OlTu++JiQlrBra1taWFhQXL\nfXiHCyc/NTU1cCwohhzDDiqen5+3Jna++hUqhv8DZvgdUQ3rg2iaeYHqgfv2DetQvSSTSSuIKhaL\nVjzIffJMfLcHg+zR0dFRLS0t6ebNm1pZWdGtW7cMrJIbk3pACQkmrVzYR8xPsVg0CpfIG+aBOfAt\nRP4uEsuXwtA/f/5cDx8+HPhdEAQWbvlCIW+AMFwYfRYe8j50q2wCPkOScbVsHgwD2mWvamBxnXcA\n3IM3gv7v/J5Fh+Fj4Xte0VMmvjjEF4uAgDqdjm0in+Bl3LzmGGqDggsKz05OTuy+QCwsaAwmhtD/\nnWf2/DWOtdFomLzUJ4v9xuR54GlxTh59SrJ5YNNg6L3agDHjvkD2bFQ2Kzwon0+k58NyqU8PsLnY\nhF7v7OcZR83mBHWtr6+rVqspnU4rGu2d+zsxMWHOHQTI+p6cnNTBwYGdEYoh2Nzc1Pb2tnHORL5e\nXMD3+83vk/CsIamfX2HtY/wYC+YNcQLUz/j4uNbX141OQO4Jx91qtVQoFKwQrNPpmPQPh0UrAKlP\no4VCvcZ0h4eH9jcS7kRfzJ9H56xvXotjZl5ZW8w3tBJ0Ef3dAW/kVEDQp6enlsRFY+/3sO8DRSSK\niIM9sLu7a2Bifn5ek5OTdk7zlStX7L7g+2u1mmKxmNFpABnukXmDMuP5L3q9FIa+1WrZAQRcnpsC\nvXj+3SdvPMLxHDVGENTsE5IsBjavdwytVv+kKr4HBY+nadhohHlwq+d/z/1x/4SY3L+nh+AWcQIY\nIm/Q2AS0F2Az+7bOfsxA9pRPd7tdo7ukftM1zz2fp6RYXOfpLP7uv48xoGsndBGbnnHn/Xw/n+M7\navKZzAHvwXEwph6h4kzZFD4S86onDKHnu5kTaBkfajMnrDkcA05UkhV+kT9izAm3C4WCUW6oRFCA\nEAXS4wln12q1DIVi1H1LAE+rYaR9MtfnHnzuCVkx/0ky+pT7Zk6Y/1wup1wuZ4aGMfj44491+/bt\nAe6eSIwoZ2lpSZIG5MV+3Xm6hefxFBT7yCfbubwT81XyAA1kjxy6jpy1VqtpZ2fH5hlbdHx8bMVj\n3J+vj2GdUYuRz+c1OTmpVqulnZ0dFYtFO8Hq4OBAhULBpJOos1A8tVotVSoV63jZarUGqsIZB+wD\ndQT/4FQ3kizxwgLBqPiEJwaey/8eI8iE4wVZxBgE0BoGHJ6PifNUBggcJOnDd7/IMK5sKjYBaNR/\nNpvGJ0pJWHmqg83JZgcNMlbc33kpmkc7GCaQD6Gk30iMrzdojMn53AHPfJ7z9fw+CW/eJ/USjec3\nMBQJnLa/MGRsAgwAF3wpkj3ksD455vMC3ugSGbFuvGrFr8XzF4bDG1T/HKw32jkcHh7aYTGSTGKX\nSCSUTqd18+ZNe0bGLJlMmuOjtzpjDFI+j9bPJ+89RcVrPEWJMfQUJtGfJFvrHD143lGihPHjx5qr\nVqtWvcyZyVBDnhIsl8vm7KLRqKly2EOgbdYMY8L+YQxw6CD282oknh3ARVKUg0va7bZ2d3ftZ9YO\nOQO/B/nPOyDGr1wuW60AYwiYGxsbUy6XM6d+cHCgXC5nQAGpbCqVslYe0M7n1TXkXsgt+e/8suul\nMPTeIDOxGI/zhp3FDm+MAgbDwkb2xtmjbH7GuGMcfLLEO4HzEibe4y82kk+w+pDL3zvPh4H390u4\nRvsAEsVe4SH1DwLx3D08qeeZWfi+nzsbiA3Pf94IeFrGPzPf78fA0wegZGmw/THjQpiNU+cZ+K7z\nFZ2e82cc+TwMPa9n/tB++7bKkUjEnI0HC97p+JyIz3+wptiY3Bfv87keIkPGE36atchxcjTWYi4x\nhD4a4ZDt847QAyDmyv8evpfn8M/EnPj8AwiYPMHu7q6CoNf/xhs/JJMUGcXjcZMPn56e6uTkROVy\nWfV63TpUclIb+5qkJE3aUJH5YkbG2kdUfj35CFnSQD6IOfDjAPXCs/q1ep7So5gOZ+EFGR50eq6e\nSJMDwDudjs0t3VqDILD6FuYUR+wpYOaJfc0e8XlBvxfP26Evul4KQ49sanJyckDLy0Zut9sDh188\nf/7cNgebCa/vkyl8jufnoWwwFkEQ2HFtcGIjIyMmn/Ie3F+E0ef5fzYgfCYUAygbj+z5ZX7vC0b4\nTE4ZymQyRqmw0HkfC4LkEIacAg/CXZKUFKiQUPKUCigbGST36dUa5x0bfCPI2987P3N/3D91Dzgx\nnyQ+OjrSjRs3rPshzury5csaGhrS5OSkQqGQisWi/viP/1jhcK+EHES5v78/sBlxnKwVHN9Pc6Js\naDbgeePvnQNABAMiyU4lYoOiq87n89Zg7etf/7oODg5Ur9dNygeiOzg40L1793T//n09f/7cjk5k\n/KF1kBH6e/LgACoLJ0j+wOcgMBqMx+7urjWLW11dtXFIJpO6fv26rly5YsIHKCL2Afup2+2a+uTs\n7MyEFNPT07aW0um0wuGwUXr1et16QrGWoKSIQFmvUB6AAZwac0EeylNczMPZWa+4sFQq6fHjx8av\nc7ZwrVbTxMSEgS1sE5QL+nZsBbTa5OSkGXcOKZ+cnDQAS0vx3d1d68wKtSj1e+Vjq1iXOC6cD+OL\nmOAfXDK23W6bnlXqF+BI/Y0Hl+k5Vo9C6dAn9SWQ+Xz+J1QzGFwORJB6pdmUYHvqQuqjZ79YMA5M\npE9Ocm/QLRgxvyH4HoqnMPYs9PMenAObh4eHlUwmlUwm9fjxY0NE8OHw/xjhfD5vqA+1Ad/JvXhF\nBM/Ld5G4xbj4aIvXMjeMP5v3V3/1VzUxMWFhOY6P/iicwnR6eqrf+q3fci8f/wAAIABJREFUUrFY\ntHl6/fXX9Wu/9mvqdDrmLPheooZ4PG7tEtrttpXco3RhM3LfgAV6pOPgpH5RFqDCo3cun9SXZEoe\nb+g9lXDnzh1dunRJY2NjymQy1k3x7t271mOF+x4fH9fo6Ki2t7f1wQcf6NGjR3bK0fHx8YDED7oH\nw4OB9dw4ahsSt4gL/OtJIBP58LmAA1DnyEjvmMAHDx7o2bNnSqVS1oSNQiCMFtFNo9HQ06dPDXAN\nDQ1pZ2fHWhHv7OyY8aINA+uR8WRe2B++nQUOzUd5rHOvmMGAsueGhoaUTqeVSCSMStnd3dXGxoZC\noZAePXqkW7du6ZNPPlGj0dCtW7cUiUTM+e3t7RnVhkLq7OxMq6urKhaL+trXvmZ975HIHh8f68aN\nG1paWtLS0pI2NzeVyWTMuQIgoatA/oVCwSSsACIcL3vsH5y80iMoH3ae54ZZfGxcXw4PCmDC8bhS\nP+xjo+NIWFQUVPkwGEUCryE7L/VVJtwTF7p/ECwGH4WLl37xfR6RS30n51Ha+VBV6nG+TDiKA5wF\nBoGEEqEjjqDd7h/k4Q0bi+k85YQR8Iie/3AStVrNDn3O5/O6fv26bTyQvk96kldhoaOwQOOO0WEM\nut2unTtbKpWsORZNojAEExMT1hwLQ4/UsVqt2pxBeZ1Hw94wMjZSn5M/v0489316eqqFhQW9+uqr\nevvtt60AZnh42DqxPnv2zDYuHRCnpqa0vLys999/Xx9++KEhNx9FYOC88qjb7Q4YQI9qWa/QWD7v\nwjmyPOfp6amSyaQ15hoaGtLa2ppRMiDebrer27dv64033rD1RHRRKpW0tLSkdDpteYrh4WGNj4/r\n9PTUGpmdnZ1ZOxPmATRLBOcrZz26hb9nj3iwx9wAxLyqDkotCALrQw8w8sqjs7MzFQoFLS8vW+KZ\nc2sjkYgePnxoopFaraZGo2FRx+joqJ4+faqzs14RIt0xs9msKpWK3nrrLXM+OAMYCXJIqKuguki6\nApLId3D5w36+7HopDL3Up0Y8quX3nqeD3vCG03PXTCjUBgqVnyYx85lzUDUTcJ6i8BvJTwYLzuvm\nQZP+tTyDfx6pX3nJZHsP7zXDVAP6g695NsYBR0Rox3fzHUgUCe8ZSxJZUDWS7HnoZ46T/WlUhv8e\n0CRVlBgjKCQOaa7X62YwCUOhUNrtth4/fmwUHUavWCzaoRLZbFaffPKJJa48XYdxPTo6Urlc1snJ\niVWoAip4Dd9HFMDfzq8tz4tLfa7UGxbePz8/b5EE63Z/f98qpIeGhuxQ6eHhYX3yySe6e/euHj58\nOLBmaMnL+mDO+H7QHtQlfDt7CKPIs0LbsV79ujs5OVEsFtPk5KQymYwqlYo+/vhjdTodTUxM6LXX\nXlOn09G1a9c0OztrexB6FMP+/PlzK3hkTUFHYMD5PvZVLpczeo794cGGnwt6zXiw4AUFRBiME3vK\n05HIO1HAUDENYGJdIk9m/dIKwduf4+NjJRIJZbNZvfrqqyZLffLkiSYnJ6331scff2zV5dvb2+aU\nGB/kuJ6y5iwC1hF5PUDk3ytHHwTBvKT/KGlSvaMDv9vtdv9DEAQZSf+vpCX1jhP8Z91ut/riPb8m\n6ZclnUn61W63+ydf9j0+acog+2QMB0V4g+0TniRngyCwjD4DCxpCInl0dGR/8wdvUMnp2yL7RCCy\nNb7LUxhw4hR9eMQLSsQYsBHPo2kMuO/l3u127YQp7xT8/THxJB094gYR+eMLQem+aMM3ayMSYfFj\n5KW+fNWrnLycDtUCY03hCE6Ek4c4lDkU6lW0ksQMhfrKEV/1y3xCXx0cHKhWqxmdQogPRw6tAzVB\n/sXz2j7RRW7Cq6H8c/Ienzz2OY2fFoZjUKFJarWaSqWSGbZaraZKpaIf//jHWl5eVqVSUT6f18TE\nhEqlkp4/f24V49AczIFP4vn8E0BF6kdCXqRAZOspSmiebrerR48eWT6BcYDuQlX1+PFjhUIhkw8C\nBJCBFotFq0xmDwRBYOcI0EyPaJTeVkScOA/2iq8HGB4eVrlctjXb6XQGFDkUOGEboG6g5XhOjCod\nQwEVfh/WajWNjIxYF05f6BUOh4023N/fVxAE1nyPe8ZRTExMWHuJTqd3shgAC+ck9ZkJ5tufeUGk\nRiLcswAXuS6C6NuS/nW3270bBMG4pI+CIPgzSf+TpD/vdrv/PgiCfyvp30r6N0EQ3JL0zyXdVu+A\n8B8EQXCt+wVnx4KaPeLwSJhN7yV2bC68u9Sv4ATlYoz950g9yRNHznk6BsMCr4wTAZHwWSQhPeon\nPMTo8f3nIxWpz4MHQTDQ1ndsbMxOgud+aXQFXwvC73T6rYfPzs6UyWQMTbNAaG2LU/NoBRQKAiGx\nJMm66vmcBCiDxcr983lw+rS4/Yu/+AtzFt6BgfZY4DgVDDqqmR/84AdGGTDmzWZTiURCuVxOIyMj\ndnRfIpGwe6rX69rb2zM1C2sLI+GdK+E7z8UaYp14w8+Y+Z95nR9TwMbW1pa1rc5ms8rlcqrX61pa\nWjKenF7oz549U7vd1ttvv618Pq+VlRVJsvHzqjJ/QpNHe9wDlB335ivDuUevToHCOTg4sMOsb9y4\noXw+b8cvRqNRffOb37QaAQ7r4EjNdrttNSs4Gv5GDog9if6f+0fuyDonEsLQ88wkO/nZCwEajYY9\nRygUUjqdNpCTTqcl9Yzl7u7uAADZ2NgYSIivr68rEolYW3DGkfqabDZryVSMOOsE27G5uanp6Wnr\nvQM1tbGxYZEMUQfUF+sOypF9iP0gx8X7sBm87iLXRc6MLUgqvPj5IAiCh5JmJf1T9Q4Nl6T/R9K7\nkv7Ni9//frfbPZG0GgTBU0n/SNL7f9t3YGTZVMjwJJlhA7GyUH04CpqgXzcojs9GWYADIYykj/b4\n+Ljx90EQWEc+qY+KoHYwiBhrHBPJOTTEkUivR3m5XLa2sT9NTuXVHxhTFiNoxYeq8XjcFj0NmxiT\nIAismIXq1lQqZQ4LbhSVDXpzNqQv604mk+YY2HhuTRhiYrzhWTlRiS6jHFQhyRAUyCUej9t9wAfD\n64LecYbQQMlk0rpA8n0oOzKZzIDqAtUTz4ZxoU0F64HnOy+f5FlZQ15x4/8OZQgf/oMf/EAzMzPW\nn4g2HsvLy5qZmdHx8bH29vaM581kMhodHdXCwoKOj4/tUJVCoWDJwMPDw4GDZ5gDHwmHw2FDifyd\nOeQ+vLQSo8v+gqMulUqG1FFIoWbK5/PWKpdkLqd4gaZ9l03QdjgcNiUKawJH69eTfyb2EMgZh0yF\nK1QGxX84Cd+mnAaI5XJZV69eVTqdtiMTiVxoj01kAljjXFfWBPkQT+/5fA8FU+Pj4zo7O1O9Xjem\n4NKlS2q321pdXdXly5dt/WEPAG0Y9f39fS0uLtr6grbDEUApXfT6O3H0QRAsSXpT0geSJl84AUkq\nqkftSD0n8N/c27Ze/O6LPneABmFh+DCa3/tNyKIlLPP0jdTnrPG4eFEMst/whIf+nnwSzPO30BWE\nUygjMOi5XE6zs7PKZDJ68OCB9vf3bVGCPvx9o98FpXiZmS8kY8On02lLXIKI2dS+SAZUTvhNwtb3\n4CApCE/Pexl7xsFHAzhmKAQck9TXa+M0QR50FuQQDtq67u/vG/KBa45Go9YIDGfO/cbjccViMWv5\nKsmiAZwvqqLzh5Wcn1efp/CqG78euXx0d34tEr2R3C6XywNcMZw864ZKWCglVB0+UsLpezoIoIDT\n555w9J6q80l11hoOjftn/eDcT097xzZubGwom82aoiwcDptjYj16GjWRSGhtbc3K/H2rBvby7u6u\nLl++bOsDo8xznKe/GGfffsNHVIw7e9bn8Tw4I2FJARMOA9CIqsgXVYG6yTnxWp88Pb8+UAh5lYwX\nIvC8/A5g6efG54Mk2d8ZZ+wX0bgHHF92XdjQB0EQl/R9Sf+q2+02zqGabhAEF88M9D7vO5K+I2lA\nZuiP9PODSe8JDBAbUuorDuCX2ShU55FchQKimIOTYXASIHHKwMPhXktkwlGf4MTLguToAJjJZMwg\nxONxpVIpzc/P6/XXXzfqgNYA5ANAfPCxUB3Dw8NWRUe5Nm1POYiYRUVfeYyL51DZxC/G3RaVL93n\n+zACzWbTkDFGiygHdI8Rh+emEnR4eFjXrl1TKpVSq9XS3/zN36hUKmliYkI3btzQ9PS0pJ7xmp2d\n1cOHDwfK4jFyFOpQtk7HycPDQ127dk17e3sDbR9w9DhvIjs2Bo6Z7/DJSy6fcPebya9Fb+hZaziW\nSKQnawVZHh0dKRqN6uHDhwYupB4Vl06nLUEIv82aYI1IMtkohpO582jTF/3gNDD2OGNQutTv0xIE\nvToSKA6pZ7jffvttffTRRwYELl26pEajoZmZGavwlGTHOrZaLetQiSHHIdClc3Z2Vq1WSw8ePBhw\nmqijcEo+t4DjxzGTN2JfQ3fg0H3CnciXvYgw4Pi4d5D8s2fPFI1Gtby8bGucBmvlclmZTMbWDIaa\nvlS+QRxtj6F2Tk9Ptb6+ruvXr1uCem9vz1oabGxsmMOBViZ/Qc4wCAK9++679jqeoVgs2ilansr+\nsutChj4IgiH1jPx/6na7f/Di17tBEEx3u91CEATTkvZe/H5b0rx7+9yL3w1c3W73u5K+K0mRSKS7\nvr4uST+RgPCIBMRyXj0hDZaz8z5+B9pdX19XEASqVqt2RiQO5qcZs2QyaQlaPpPvAlnRo4TDoZ8/\nf66xsTFdvnxZ4+Pj+vGPf6xGo2EFJOFw2CgLDiIOgkCNRsMO+mi1WnbCDQ6n1ep1t0O6JckOVABt\nIMkCXYRCfV2uR4x8B0bOVwiyyHK5nD0bCTxJA/PAvPgI6uzszJpz1Wo1o6wODg6sO1+xWFS5XNby\n8rKF/ahLWq2W9vb29OmnnxoXjRO6cuWK1RHQslfqS1JRclSrVaOqqL/AaGAocPhsXDacVziAnvgO\n1gVIEvqAhm68LhqNKp1OG3UxMtI7uGN7e1uJREJbW1s2Zp1Ox6gajPXS0pJGR0f15MkTM9aABObW\n87QexftokaQsCJY14qMFnB3gZm9vT6urqxY94jw46KZYLKpSqVhkSb6h0+md+9xoNGxukBHG43FF\no1E9evRInU6vrzo5MtoSQF16VRNGlugVZ+4b7lWrVSUSCUvMYkOIkAFU3MvGxoaGhoZ0+fJlLSws\naHt726JyPgs6tFarDajuoH2QO/ocwfPnz7W9vW0JZl+pLkn5fF7ZbNYS5kSW2Ki9vT0TGgAibt++\nrSAIBvIaq6urmp6e1tDQkLWiuMh1EdVNIOn3JD3sdru/4/70XyT9j5L+/Yv//5H7/X8OguB31EvG\nXpX0N1/0HWwaXxFHKMrGgpfyXeVANaBKNiaeG/QEIicUajQaRgeAvOgzQqZd6lNDbJwX42GLDCfC\nfyw0jBeSLgw4STYcBYlGqnxJRvJeaI5sNqt2u62lpSXj9IeHe2dYVioV4zK9pA9tNKG2N2jcP+jx\nfEgvyZQNbAL/7Bh3r1zBUIVCPVnY9773PRWLRc3Ozpo2ul6va2JiQs+ePdPBwYEdi8chEPDLU1NT\n+pVf+RWlUikzaNw3qot33313QH/t5bIgZO/scKi+GpFIDgNK4tpLe32PGZ+Q5v+sD74LjjqVSukb\n3/iGTk9P7dCZZrOpnZ0dhcNhzc3NWQKbc2aHhob0yiuvKBKJ6PPPPzfe2iuCQH2eJya/wTN72o0z\nG8LhsK0nEDcqMpwR30G9QSaTsTzC6OioFfCk02kdHR0pnU6rVqtZdejQ0JBSqZRGRkaUz+cl9ZOr\nUCcnJyeamJhQMpm0oqtisahMJqPJycmB+gWSxvShB9CgpOEZMKjkGcjNSDIncnx8rO3tbbM3oOhu\nt6v79+8bnz4zM2OUbiwW0507d7S5uTlgqyjWw6YQ2W9sbKharZoUen193aJ/nIOkgeS6L97D4bLG\noR4BcIAJKOK/V0Mv6RuS/oWkT4Mg+OTF735dPQP/vSAIflnSuqR/9mIgHgRB8D1Jn6un2PmXX6S4\nkfqhJxtZ0gAPyf/ZwPzdJylRfLCxycLjFOAjaaFAWXk2mzVkUi6XB/g1vhuPjhEBqbG5cEigl6Gh\noYFqW7jghw8fqlQq2YJko/ukqE8YUz3XarXsGDcMDnp0Pof7AMn4nAS5CsaWFg8YMk+TgaChe4ge\nvPoE4+bDZS9tPTw81NTUlN566y195zvf0f379/Xuu+/qxo0b+va3v60///M/13vvvWcGRuqfH8o4\nMHYYLzYBfwuFQkomk2o2mwNUCvRONNo7qYge4LFYzCIML9v0yg7mijAZFOjRu8+NeJ4chB6J9Kp/\nL126ZOj76tWr5kBnZ2d1+/Ztcw4ki0dHR7W3t6dCoaDNzU3du3fPKjF9DoTx8pWo3AvAxEeenrvH\nYRMdsJcikciAbI+GbFI/39FsNrW1taXJyUnjl1GmBEFgEbIkM6IgcipQOTCEfcP94ugxZKw1kvN+\nrScSCUUikYG1DfXp1T9Ewcg5aXw3MjKiZrNph8L84R/+oZ49e6aJiQlrf3B6emr04v7+/gCd7IUX\nqODQ0V++fFkPHjzQ7Oysrl+/bi27iY7C4d75xziAVqtl/XE8aMKBzM3NqdvtFQrifP3pYH+v1E23\n231P0t/G+v+Tv+U9vy3pty96EyQhML6+GAlDwqL1dA2L1Yc20CkYDaIB3/IATWwkEtHU1JRu375t\n7QH29vaMD2XTwMmhwZUGJZIkZ9hAJLfg54IgsFamOBEME8aOxCuJLP+ssVjMFAD+ZCTGyBcrwb+D\nkHGi9Dhn4/A9SDpJGnpOGl7cKxG8IcG58XeSWtBekUjEnChGJZ/P65133tH+/r62t7eVyWTs76AZ\nxjKZTBoiJdHJ+LdaLSs62t/ft+pTEmLcE4YAxZFPiKbTaUN+cKEYZNYKiB70zFr08kTm/fDwULdv\n39Yv/dIvaWFhQcvLy7p//771an/11Vf1jW98wyK5s7MzNRoN7e7u6qOPPtLa2prJS6Uej1+v1wd6\nEWH0/TwAalhXpVLJ1hKV2ewzqB3/HHwf0WcsFjPjRETIwShUe+ZyOR0fHyufz2t4eFibm5uanZ21\nvUAyvtls2vnDtEDg3lg/rDsctuf4O52ORaZEjERyRGKdTscO5Tg4OLAePDgSqnhDod65Fa+++qpC\noZBWVla0s7OjW7du6erVq/Y8Ozs7Gh8f1+7urp0dAJhCVQedmEqlBuYMtRqHIxHhLS4umgwakYRP\nCuPMaAi3vr5u6hz2N/bHq+wuer0UlbFeL+on2288r19Hfgmvh2RQ6htg7y19EoxjuqanpzU/P68g\nCLS2tqazszPduXNH1WpVP/rRjwwhc8CHb2PgJZEYKSrgzs7O9OzZM/3+7/++oWqKZeDr4vG4JQqJ\nGDCoRA+oY1KplMrlshWgkJTFQIN2JQ103mPMfNjLgvF5AdQ2Pokp9UPUbrerGzdu6PPPP/+pqiQi\nGxxQt9urB7h27Zpee+01cz7MIxtlcXFRqVRKb7zxhjY3N82R+ggBhy31jEMulzNjBDqC5pJkqGlo\naMhkl+id2XQkp6Hx4KFxDqwnNiDGgmc+P0bIdL1aZGNjQ3t7e3r27JnOzs70zjvvKBqNqtFo6N13\n37W1SZLu4OBA29vbA7QZ667b7ZqB9dHreeqIPMHo6Kg5bZ7LFxZ5JQq5F7/uRkZGjB7lc4aGhrSw\nsGB0CcbWJ+tnZ2cH6E9qPYharly5olwup+HhYW1tbVlEiLOh/xRN7Hg2KEd6w5MLgypDqUVuDBAI\n/QJwJCmPgujTTz/Vn/xJr44zEolYG41Op2OJ02azqaWlJXNKiURiIM+B8T06OtLm5qZyuZxRdRTG\npdNpjYyM6NGjR6pUKhZl4Ah4VtaW31vr6+s2h+QodnZ2NDMzY/LYi14vhaEnJDqvqvEonqw8CMMr\nEPgdoT7cI94Poz0yMmIoJQh6J83UajXj4G7fvq10Oq0PP/xwQKp5XjMLzykNJungvuFNeTY4fpAq\nCVbvlLhP0DzfD5LzslPOH5X6RwuiV/ZJV68f90aKz+XzUFbgJGjE1W63VavVND4+ro2NDZsjnocx\n8NI3DP/Q0JBmZmYGpGmEsnwO3+UT4GwinoUwHQTXaDT04MEDbW9v6+zszDY3lAqGjHWBs/aN3DwS\n8hI9aVAj71ETXDnv8YadOcRxlEolRSIRM96cHuRpNXj1er2uRqNhTg5EDIqH4vEFbx7NM4esKa+2\n8XUbgCLyFLyfMfI5DRAqYMOvRyhUvz6hElAOlctlxWIxo2jGx8cHit8KhYIlPgF03Ctji8OGJsOx\nYCMYF5Aw+TkfaR0dHSmRSFh04YsAh4eHtb+/r1gspunpaesB5Z0H9gMHyBh5xRD3OT09rdnZWU1M\nTKher+vhw4eWr4hEImaUET9gU5hP7ALzPjzc72mPowaEXLt2zZzERa+XwtAzwX5j+eQng87CgXfk\ndfBxXjniKxxBiCDlbrfXVoAzMEnOQufgZLwunSQYP0v9A08w4hip80VVXmqZSCTMaNTrdU1OTlp4\ny2sxCiARJHNsWp4PLt8n6vj70NCQ8ay+IIbxYHNXKhUL/XE+FPTwmalUyt7veV+cD5sOeSXOKhaL\nGXIBdTEvlLpz/B/hKPdFm4pwOGwUVLvd1s7OjtbX1xWLxTQ1NWVUEf3QfQvr80oijASbE4PguXZp\n8DAW5tsrcLzD5N+8b3l5Wbu7u5YEZC7ZyKwFHCI87/T0tN2XJBUKBUNyrG3WGI7Jq6j8/GCgSBKy\nPngfn8F+86AImrJYLFrhUafTseMQKZI6Pj7W9PS0OVRfj9DpdJTL5axZIOMDf/7gwQOrq/B5MC9q\nYP9T4+FzZow9aywIAlNWobgBVEHtMpdBEFgO5/j4WD/7sz+rmZkZsw8c6bi/v6+FhQWrcGX//bRC\nJYw2FbTpdNqeD8fDe1FgkW8DwPCso6OjdsYt6jEMPVHu0tKSOZoL29gLv/K/4+WlVEyyN5w+rEda\nxYRdv35dU1NTA2hna2vLUC4LEeQEOjw6OtLOzo5mZ2f1cz/3c5b4fPz4sX1np9NrSVoulw0NZrNZ\npVIp2xQgd0JMn1jxmthms6mVlRVblNlsVqurq8rlcioUCgNG2ku4kH2mUilDyoSyvB6FAKgPBAiP\niATPV/6BWmg1e/XqVTWbTZ2e9joN3r1716iM+fn5gWPN+F6p33eFqlAS6q1WS1tbW8rn86ao4NB3\n1FXj4+MDJ0n5dQAnnM1mjaLZ3t7Ww4cPdXJyokuXLqlUKmlqampg3OhFAuLh9CYMABESlBbj5NVT\noDof0fmLzQs9AiWEM2JNQLXBWaOzxrlyT5Ks0hd5LD2DqCPw6jIfAcMfSzKHxz7A+GLI+RkwwvNX\nKhV1u13V63VrRpfL5czJEjF0Oh37O9ETuRfosOHhYZtrUCi6++4Lqeb4+LgikYhRfiTioUa8scdx\nsC48IITPBhTxev4OV8+4kTeSpPfff9+M7qNHjwy0QVdBibKmKADzvaxwFs+fP9eDBw+MkltYWBjY\nW9Fo1GSc2ASoWxwHSVtv0El0s0dQ6LVaLTUajX94HL0Pi8kmsxDYwJIGkF232yuHn5+f16VLlyT1\nFQLJZFKfffaZFUP5Tnls/rW1Ne3u7urq1ataXFy09zx79mzAY9frdT19+tQMtv8eT1nABcbjcTNk\noVDIaISRkRFT+BweHiqXy1mxBUYdxQqSR77TV9yR9Dvfoc8ngkHdvo0BaJlNHw6Hra0wixcuEuWB\nP7DaN0Hz6Ah0yLOCzCqVip4+fWq0DbQUBgFVjKeNcPRST+0Af849jY2N6ebNm4pEItZLpVwu29jT\nAgF0ipSPn9G2N5tNZbNZQ4MeGVEfQP8f7oeLsWQuWFOSLBdD+f+NGzcGKoT39vZsDZE/QBESifS6\nXkq9SJFiHxKXGFzGnnsBrbMGQZE4HDhlqU/zVKvVgTEdHh42eW6tVtPBwYElcHEIaN/5znq9bs+G\n2or2HKFQSE+ePJHUF0zs7OxYB8xarabDw0OLCHBIOA9JBh5QWHlVGeIL1rwfJ/YArz876/cKyufz\npvE/PDzUN7/5TX31q1/Vzs6Oceu+kA0JKLU0tF/GqXiJ69DQkF577TWVSiUtLi5qbGxM9+/fV6fT\nUSqV0ptvvqloNKr79+/r+vXr9rwwB9CxJI8fPXqka9euDajZfOI6FAqZIu4i10th6KV+ky2qWSVZ\nBSsLHaUC6JrwjJCNjZdMJo3PkvrnYMbjce3u7lryFKXHp59+auj/9u3b2t7e1vr6uqEunxTmPr1y\ngVCx0+lVR77++uva39/X6emp5ubmtLq6armDnZ0dJRIJFYtFyxl4jezU1JQZeYwvBwdTO3B2dmYb\nbWpqyjYF4SKhYrlcNoVAvV43CapHhqA5jJGnvKhb8M/PhfE4T2nVajVlMhldvXpV3/rWtxQEgf76\nr/9ah4eHOjw81J/+6Z+qXq/beZcgNe4B6Vq9Xlen0ysmIhQmwuh2u9bf3Rc7cQwe9zsyMmJGN5lM\nWptj1gZzzmaFguIzpb4ijNf5tePVR+Qg0Jd7hYlH2owv3w0YQQqKk+Ks4Xg8PtA2QxpsAsgY+jwO\n0QbriOQ7Do2ojsiu1WoN9GUBFKH6icfj+trXvqaTkxNduXJFkizKkHp5locPH2p8fFyxWExHR0cD\nebLR0VHFYjHNz8/bszE27F/oWR8p+YI9HFwymVQQBAPHLBJp40SgIjmhq9lsWnFiq9VSuVxWuVzW\nO++8Yy2Zu92uJiYmrMp2cnJSiURC9XrdeuqTpAaMtttty5URPWCYae1BTrBQKCiTydj3sZ+9mILa\nl3q9bjUKntpi3GdnZwf60l/keikMPSEZiPDo6MjCQJ84RFZI0oLNDm3B67zWFcqChBILCkXK6Oio\nNjc3LXxksEGBvh8Ixo1wtdvt2nf4RN/e3p4++OADDQ8Pa2lpSZLpuk63AAAgAElEQVQsacShB74y\nFsSEATs8PDTnhvxQkt0TWnNCQCR0hNTcm9RTouD4aLLF+JCoxQAQpcAlEi5K/cSk59K9Q+bZ2XTx\neNzoD5J8OAs4/1KppGq1OtDJLxKJWGVpJBJRLpezuVtYWDBnAE8s9TlzngMDQkKOMeNZMXBoz6W+\nvJLN6+ka6Byv/OLyCUrWwPk8AWDBJ/uk/gldx8fH1sGS8B2j4WW3fL6nI7gXngPjBur0h+EQbbKe\n/HPznKzn9fV1G0/fmI7DXthTksxYNxqNgYjGCykqlYqJHnZ2dlQulzU5OanT01M77g8HAfXFMzPO\n7D0+F6Ms9R0PTh8ETBSK8o5CJgz0w4cPtbq6avw8Y7i8vGy996kKRpHk6bFEIjFwNiyqu0qlonq9\nbusFBRn7jtbGfp+h2gPU+nuHBuN0Lub5otdLYei73a4ymYzm5+dtwAmHWMhsPjgrjNTW1pa1H2WB\n1Go14/9AOXxPEATmHOLxuObm5lQoFHR6eqp8Pm883tOnTyX1k71Sv/zdJ3zh8wgXJSmbzer69esa\nGhqyZCMFIvSkwTCSBIK39OXThJycVASKAdXThQ+HCO8syU7rCYfDFk6DCkFUPvkKLcD3g0Y8FeWR\nFwgXvheUFY1GlclkJEn37t1TuVxWsVg0B/bJJ5/Ys5TLZUNTvjAmHA7ryZMnCofDdsTg6OiotYXw\nzp6fGU/CcjY5ji8S6R3O7YuGvHIBFEvtQCqVsvXm58k7OCIMaBLm2SM7HylBJ1A52el0jKtlLWFE\nQHyMMTpyQn0uoi+/NplTHJOPUHAi3lBifOidFA6Htba2prGxMcsL0fKaewyHwwMnaDEOnU5Hjx8/\nNjoOEPLKK6/Yc9RqNU1OTlqylugGvhp1EH/L5XIDCWavnAIgwvVDIcKPEwVvbm7qk08+0dbWlkZH\nR3Xnzh3rZYMskuiSJDEdLu/du6dQKGTrmjmmuCkcDmt6elrRaFSzs7NG0XhJpwd5JK+5WL+0QGbP\nAUxhOahsJor3n/Fl10th6ElCUXzQaDTMOJHk5ALl+cIR9OCeekgmk9abm4IZ/o9nPj4+NgQFv16p\nVLSwsGBOxm8sr2TwyVg2VDgcNiN/7do1S55QnHT37l1JMp6x2WwaQmBhlUolJZNJVSoVU62Mjo5q\naWnJjBKGDwMUBIFl8UGpICM4Ri8/hLsdHR1VKpXS1taWJWylfssIrxeX+pEMKIVwutFoaGdnx5Qa\nrVZLP/7xj20zhMO9FrUkIh8+fKjh4WFzsvQAQT4ZCoX0l3/5l+boKBSBiolGo5qcnNT6+romJyft\nuT2Xzf2y2flcqe8E4eBBgkHQ64PUbDatvN5rs0HfbECkgCBunIqfI+YbKiUcDlvBGklXciAkZ31e\niePk6vW6gQXEAp4+w3iT0PTafuYJyon587I/ZKEYpUKhoPn5+QEaj6Q/zpVajnA4bMcTAlBOTnqd\nWqFaoCNYm56jxmGzX4iuGRMcntQ/6B1DD/VENEgUnkwmbQ6IAMvlsra2ttRut/ULv/AL2t7etuZx\nsAXYhKdPnxoYxMCy5r2BZa4ajYY2Nzc1Njam27dv680339THH39sSrDl5WWjH4n2cOo8G8AkGo1a\nm2/Wp4+QxsbGlMvlfoJO/aLrpTD0bGI2EhIlekmDqkGg2WzWQle4MlQkhGcYZDYl3CtoAckWahKK\nJqReX2neh6HySZfzCN8bwGazqcePH5uyhuQYXC1Iyut5+Rv8JAacDeU5dW88fQKTniOoMPgc8hBs\nDhAYtALvgUbiymazFllx3xgIr0The1CzcDgIPcwxENBBs7OzhsrowsczQilQcAV1gfzVy+9I1GHk\nmC8oHaIOIjqf8GPDMhbM5XnpHvPscxigfNYj4AKZHBp5qX9spM8/SLIktL83z5v7IiI+n3Hy+SHQ\nIpEFc+ETdshaqYr2CX5og0ajYWiYtV6pVDQ3N6dOp1eHsLOzY6gWOoi1xRrCCVQqFYtg6LZaqVSU\ny+VMU+/71RAJ4jyYP6+2OS/F9IoTfs96hCrDcbDfcrmc0Uc++bu7u6tut2sdVhmL/f39gbwJ80J0\nxBhCwUxNTalQKJjjwDGOjo7a8ZIwE9gjDyK8I2k0GpqcnLR1hGMG7NHz/qLXS2Ho4bbK5bL194jF\nYoYACIFBKSQw4Fk9oiGhxELgvT4H4MP1aDSqVCplVXlQQ2w8Nj0LznPB54s6MKaJRMK8LioOvg8D\nQ3KYyKHT6Rh6HB8fN8qnXC6b42NDQ2+hjuBUe8JDVBQYR08fQOXQ0x35IUiJSl5kYL5Sks/xElh0\n8xgjEswcy9ZqtSwUHR0d1SuvvKLj42PNz89ramrKDA3312g01Gg09OjRI3sGpG1EXYwDY4GiZ2Sk\nd+qUf40kex6SxhgzbzCJaOi57h2jv86jZHhzUB5rotvtt3D2aBuQgZFJp9NGUwAYcDBSL98A4MER\n4BBxCvwbA+7zIdBkfo1K/f72tMeFImSd8h/PBY2TyWSUy+XsvSQwG42Gbt++bUYQY51IJHR2dqb3\n3nvPDmNZXl42Zw8dBijD6eFI+H6oLhKjOGaKroiwaaDHWkekkUwmlc1mlUgkdOvWLX3rW9+yhCkU\nJ7bn+PhYsVhMGxsbunfvngkkGGMkuRhf2ARAHVEOEWMqldLSC+070RbSVtaYlz4PDw/bofFQWl6d\n4xWKF71eCkN/enpqYU+n0zEDGA6Hf0I3j1OAi5N6YSeDhTf3ht7L0UjS4qErlYoZXr6LyxennOdl\nvQdmkbFRKR6ikRbtdqFwKBX3aB6pXbvdtiQOF4aQJBjhKoaKhQFvzwKisRqOAaPEGDMGfBfRTqlU\n0v7+vqLRqIXcjAMbE2NCYgijShKJsfGor9vt2mHdtVrNohu+G76fugdeg8NKp9NGZaDiocQegxCN\nRo3ThIZhk4TDvarE/f19MyqcTiT1qSmoHZ6Jy+d6vOoFpMzF5uSZQaOsE8JwaAUiOww+Khycked6\nMdg+8Yg+m8/nHqRedOpltVKP6mHPYMwRNOBIvIzTV3MTQTKnUK5eucRz8G+SzD73hCMggufe+Ttz\nxbMQ3fhnOe/QJJn2nLHiZwzzyMiISSrr9boKhYJ2dnYGbNHx8bF+8Rd/Ub/7u7+rlZWVgWcBkcMY\nUFh4fHxsFc6c/xsEPa1/vV7X3bt3lUgkLJeII2Cvsg5wxuwB9hyOCxrQS5Evcr0Uhj4IAkvGxmIx\n5XI5K4RhMRPS+QXopY5ejuV7lZCUY1CIBDDsLBCSmZ575vvZcFzwp3B6GPuRkRE7OYnkKsklDt2Q\nZEYZNEyI6dU2PG8kEjGuk5OLeDZJVnbOCUYUqvgIA2PHBgENETnBF0r9MBjkIsnCU38h7/JJQIzO\n5OSk8vm8taA9Pj62HuY//OEP7bsopGGuS6WSjS9hLVHF0dGR4vH4AGfrUR98biwWU6VSUa1W0/7+\nviWef1oPG6gqojbCcoyfNyQgc15P1MQc4PCgSPyaGBkZMedNWO+lmnwXuZt0Om3qH8ACOnXGB9DD\nnPmcEREsDoE95qMZDDNRk1/PPLdXqVHTUKlUdHR0ZLSez40QEVGlygHcrKVSqWQOmCZkgA/GjYQ4\nqinGGGfvKUwMLnkc32AOIMUBIqiOxsfHNTU1ZSACvXw43CsO4/Xz8/MmIKhWq6avT6fTKhaLJhll\n/0SjUetHFYvF9OzZM83OzqpUKikWi+nGjRsmImE9ettDDoWD72EtMPIAk42NDQMBPnf5ZddLYehH\nRkY0MzNjG6BUKqler2t2dtZCOTas1DcE3vuxOEHJkiyxRTKGwURXK/WkXnSuJKFJ9MAAe27Yozoc\nAqX6VHT+8Ic/tEVIYuvZs2f66KOP7F65eJ2nEcgxkKiSpFdffdXUL1LfMcH3YgilvmyvVqsNJHxB\nhURDIDiMijdqII7zyIgx8IgejpKxYqEiM0RDDddOeMvB0CAfeHq6ixYKBUPy2WxW8/Pz9p5ms6mn\nT5+aagQNP1JVTyVhEP3YwQfDtTMn3uhhHPk/hhLKAcRO5Tb0A69PJBK2HqFFABqgVEmGxvl3o9Gw\nf3veHyDAZ/hnYl6IhJErQsURoZDPIgfgKQmMNg3BuDqdjrLZrCWnw+GwnbEACPK0QyjUO3yb7yQB\nOz4+bsY6mUxqZGTETlYjeicZyjNw+hUomL1ChAYqJiIh59btdk0qHQSBNjY29KMf/UjXr19XKpXS\n/fv39dlnnymbzapYLEqS1tfXdfXqVVubrCEcL3kwSVYxC7qWekV+AK1wOGyOQpJFsjhYXxjKPOJ4\nsHEAT0AUa6xer//UiPOLrpfC0DMplUrFlDHwvxw+QeLoPGWBEYZXQ/LEggZxSxrgIqEJON4MBxMK\nhTQ5OWmGcXx83FoE40g8uueeznP5eORYLGYKIJ9wQ/UDkgTZ1Ot123SMC8galOOlmWxO+HkMDQoE\nELDPE/B9cN6Elz4xlE6nLVwuFAoDiUXv+PgZhQzJr9XVVeMugyAwY0jxFr1sOAR5bGxMMzMzmp+f\nN/S2t7dnC91TUdPT0/rggw8GklHIacvlss7O+tWQjAfcLc5eks07gME7B8J1T9nBlTJOzCW8K68j\n90P+gWIbIhgiEVA5jgMpIsYTdI12/OzszFoJMDde+krEQtSCY2af0LuG+/OSUdr7EhVLslxCKBTS\n2tqaut1e73iqyzn0xjtS0Dy1CwcHBxbpAM7Y69BwvsgPUYanTKnApWKcSIQonrWOmAJqiLmlYCkc\nDmtiYsLyEpcvX1ahULCuryRyw+GwFfTFYjFlMhlzzhTE1et1K5Ji7TWbTd2/f1/5fF4nJyd6/Pix\nDg4OzOEQuRINQeNAF+Gk6TWF/BO7wvri3/74xy+7XgpDz41TCYuqBkoCr0doBgqBomCTeJTjF7Pn\np0mecsHRjY2N6Y033rDiDZ/dhoYBqYEuQMQsbowvCat4PK6trS3b0BMTE4ZaWdh8HkiKCj9CUQoo\nSICRzQe9sykwToTJGEhoBjYnYabUS+aApL///e/be9Hjv/766zY+5yWuXBikUqlk6DMWi2lhYWFA\ndUGy98qVK+aUFhYWlM1mB4qY2ODdbtcUOhi0aDSqbDarDz/8cKBzHxEQFFU+n7eDMUh2YiDhViWZ\nrNMrW8hxeP5Y6hdNSbJIDYPNfCCBBNHCa1ORDOWHg8Q5sP4x4DhMIgi01xhmcjTeCJDo82v19PTU\nzkXG6WNUoBT9/iEyYm0jvYzH43r77bfthLTPPvtMxWLRKjcx+iSfo9GodbDkyEByCozxvXv3ND4+\nrn/8j/+xgQ+elyjdFyJ5ysM76/O1EUQyXqlSLpf1ox/9yKjVaDSq3d1dq36/fPmyRc9UNB8fHyuX\ny6lWq1nV8Mcff6xMJmM0Tq1Ws0NTJicndePGDVMClstlm4vd3V29+uqrNsaXL1822hiQyP5lfa2v\nr2tpackYAPYmtoJo4Dd/8zcvZGNfCkN/cnKinZ0d5XK5gQTV5uamITRC1k6nV+CA941EIlZdSZMi\nDDKomM1FLxlJxuneuHFDpVLJEsFra2taXFy0lqoYW89RM0Fe7uZ547feekuzs7M6PDxUsVhUKBSy\n6lQilSAILAIhNKOalNAReoDXExX4xKiv1vS8H9ywDw1BwFQj0lCNJC8a5iDoFZUVCgVVKhV1Oh1d\nunTJjB2bUeqh2q2tLRuH4+NjO1uz3W7r2rVr6nT6VcZ7e3tmrJ48eaLh4WFls1lLTtK7BjrIq6Te\neOMNraysaGhoSJcuXTI0RJW0Hx+oLDYPUQKGl+Sn1FdS4EQxwH6+vdrFGxGcvdTvgUTY7SNTOHrm\nB9SOk+Zz4aoxWrTAgJfFWfB+6AwSsjs7O/ZcoE2vbAmFQmbMSHwSBVQqFUtsM1Y4R4DH5uamHj16\npO3t7YEoV5LllkCb8PTUBzAeRDkffvih3nrrLeu1RNSCUABQR5SBGqrT6TUb3N/fl9Rz2JlMRul0\n2pKgXrhRqVRM6bW1taWPP/5YGxsbSiQS1pKEaCkSiej111/X+++/r2azqVu3btnpWblcTvl83iia\nx48fW+6EpnvYI3oGBUGgcrmsp0+fmj3x6yQU6rcur9VqmpqasmhtbW3NIjPmkEjlH6S8MhwOG2cH\nciMpA59Flr/ZbFrvD15Hd8PZ2VnrkMjleXW/yaBaKB4CYXGEnHcW3COfR4IERCX1k5gcJkGBRrFY\nNK6aDQMa4/V4ekmWuDyvE+b7yA34kn6iFhwG6gBPc2GEMUwoQTwPfd4Ywp8SKmMAfIKv3e41FkPq\nim6Y+QFFMm6+9QRjjrQMw4XDwZiOjIzY+kDiB0rEGMDt+6IyDFs4HLYe8cyZr0dA4oYzxGn5XAr0\nCsbZJ6uhpeCaeZ1vfeArU0Fp1DOQ2xgdHVWlUrFGZ8w7xYN+TXrdO04RdRH0kpcv4sCIAHzFMA4S\n2aCPXKGnaL08PDysmZkZ6xCKI15bWxvok+OVVicnJwMgAhBAMpq1h0ILSoRusxz92Ww2VavVTApZ\nLBbtnoaHhw1BA4SIXguFguXmisWiKWng/qlHicfjA9Wv1WrVIkGa4iUSCaOYAUck9svlsmngUduh\nviPaisfjFnXQJI35W1tbG4hsqPb1807vIebsotdFDgefl/QfJU1K6kr6brfb/Q9BEPympP9FUunF\nS3+92+3+1xfv+TVJvyzpTNKvdrvdP/mi7+h0Ola1NjIyooWFBc3MzCgWiymVSpkxefTokRl9Emgk\nmM7Oen3ToSww/r7YxMvIcAArKyvG0T558sSq+ugo6XXwbH7CWowmqKHT6TXW+uEPf6hkMqm9vT17\nxsnJyQHUiyGHrmCxt9ttCwdJsp6cnCibzZq2HSQFZXBeHUK4j9OEH8XYop1n0zFuGDkW/97enqrV\nqtLp9IASCWSGDBbqhAVL3xGQOwis2Wwa0mIhE/VgOCmQkmR0wxtvvKFXXnnFEBSRDrUANImiYZlv\nfQD9kM/n7fvI70j9MzgxbDgQnsWDA8bMK7G8kWJNoskfHx83JExo7hNyzCeOwSclQaNscuaP93r+\nmXwL90hrAhyM16jD1fPMJOxJ7vM953snUaA2MzOj0dFRPXz4UJFIxJKds7Ozard7Z7bSithTMMPD\nwwZGQMCXL1+2s1xf2A2b3+fPn2tlZUWZTMZqM0qlkmZmZuw4vv39fasx8Ef+eYru5OREy8vL1uDv\nwYMHtmdbrZYdFvLo0SND/Tx7u93W+++/r3w+r3a7rYmJCQOa7LmTk16H1Fwup4WFBQNJ+XxeY2Nj\nOjo60sbGhtLptObn521PIhLwJ2HRjdczGKjbmNtGo6FcLmeJ4N/4jd/4MhMu6WKIvi3pX3e73btB\nEIxL+igIgj978bf/s9vt/u/+xUEQ3JL0zyXdljQj6QdBEFzrfsEB4dFoVFeuXLHDFs5XnLGQ6/X6\ngJEk6QMnDy+YzWYNDXoEBCpF5oRhy2azRoGQ1WYBYtzRsfpNBzLnZ6nfU+XSpUvWpbLV6h3w8eTJ\nE9N2813wjxgNFiGom5A/Go0aJ+rRZKvV61EyMTGh4eFhu2/Pr+7t7ZnUMBaL2ZF7UEqgfy6eCYMY\niUSs2pVeMsfHx4agfHSEI6tUKgNUCtzo5uam7t+/b2EsfCrrgG6NqE7i8bgV5fzZn/2Z4vG41SEg\npaVAyhb1izkIhUIDenFyNd1uT89P9a+nibgP32KA9cb8M3YY7uPjY+Oi+SzGBIdNzxgShxgE1pjP\nU0HRYIzYE6w91g/GnWeCv+WeATmsc37Hc8Ll09GUZ4QzZn0DmHZ2djQ/P69sNmt9bIi2cFBwzxSp\nQS35iJy5uHz5so6Ojqxjo69r2dra0tOnT3Xt2jWNjIxoeXlZpVLJ6FsK21hjHBvq245wD5yD22g0\nLJlK7U4ikdDnn3+uWq2mbDZrQDIUCimVSmljY0PXr1/X8fGxXnnlFTt7GUfd6XS0v7+v4+Nji2yJ\nEPP5vA4ODtRoNHTv3j3rveWTycz92NiYyuWy5ZOgI5lTgBRAiIPuL3pd5HDwgqTCi58PgiB4KGn2\nC97yTyX9frfbPZG0GgTBU0n/SNL7f9sbQqGQ9YWn1021Wh1ASrOzs7pz544tRr/5giDQ0tKS9afA\nQ1OxCcLCaOXzeaMIksmkksmkJA1UD05PT1u2nIkFfXkKgDJ/FrfUk1mVSiV1Oh3T0oJkhoaGrB94\noVCwDYvqg+PPQOyoX0BwbELOjfXFMj6kh6Yh/IaiQobIa46Pj7W5uWlFWXDVJEfJe/B7L6+TZKGm\nJItwUqmUlb2TnBsfHzeDAEcraUAi1m63tbGxYbTO8fGxksmkFhYWVCwWFYlE9M1vftOont3dXZXL\nZUky+SGfw2ERoKrd3V1D1SiZoFCgw6AtMPLcG/QFF3PjE7h8DwhckmnPke4yV7QBoIK70+nYWbrc\nTzabNfqCPiygfOYACg96Ecnki71qvLuXP7KGcGI4Z4wGSikaDJ6dnSmXy+mNN97Q6uqqGTruBa0/\ndB2ImiiG/APOotlsmhacYi4ov5WVFYVCIaM/19fXTSlTKpVUKBQkSXNzczo6OlKlUtGNGzcskiDn\nhTMdHR3Ve++9p4WFBeuHheS00+nX5jx58sTaK29sbJjEEn4eEQPttXHYtVpN1WrVJJFvvfWW7fto\nNKpEImFr+Gd+5me0sLBg63R6etryDlBtnKZFzUwmk7H1hO0gl4DDu+j1d+LogyBYkvSmpA8kfUPS\n/xYEwS9J+rF6qL+qnhP4b+5tW/pix2ALwHO4ICNQ+JtvvmlqFIyZ1E+Mzc3NaXt7WyMjI3rrrbdU\nLpctbGVAj46OrMwZQwYiYsH5tgRDQ0N2KAQoDmMK/UApOeGWNxwYI19Acnp6qqmpKZ2e9rpldjod\nU4lIGog+QH7b29vmsDya5CAPDD9dEXFKLPxSqaSdnR2TlVWrVVWrVTv4YWFhwZKy0EsgVxqfgd6o\nRvZl4CBmIg3m6bxBkWTvx+kSKeG4qQ1gw6bTaU1OTqpUKml+fl5f+cpXjH5ZXl42JIcqxTfFAm17\nZ0ckgXMGKTOHXIwxawxDC6ryNQV8Pp+JU6XtAwVPh4eHJvOkc6M0GIGQuIXm8NQO9CORrJchQimC\nor1zIKplz5yPVGhv4HM39FGnjgJqFIMIZUTkyGeTWOV+2GPQFDhb+Onx8XE7kIV8FRQb80i0MDTU\nO1wGuo11BSDDeSNwIE8WBIFee+01bW9vG8fP/a6trSmVSllzvHq9rnw+P5DvwGDPzMxYvgIQSrQU\nCoV08+ZNyx9Q79FoNMzBQ+dx1gJRJJw9BV04irm5OVuP8XjcIizAAvN4kevChj4Igrik70v6V91u\ntxEEwf8l6d+px9v/O0n/h6T/+e/wed+R9B2pZzQ2Nzc1OTk5oKf1xTBw6z5p5hNUGF5QDYkt/o6y\nIBQKWWgm9Qwrx3LBZx8eHmp8fNwUQO12/8Bp3gd36Qto2PTokT3/DoefSCS0uLioarWqra0tbWxs\nGH8p9ZUQHB2Id4/H41pZWbFwjgSud0K+URLjRs+cZrNp6ATKAR4f/tbry9nc8XhcuVzO6AjQPQuN\n0J7QMhqNDvTJoTiH/5NX4X1QBDw7rXKhhjAAu7u7Ojg40M7Oju7evatoNKqNjQ1TSvhunhgYqh8x\nqDRuAwllMhkzWKxDn6zEWbs1O7BumH+oLK8Hx4iiCqpUKhY54rDJMeA8Tk97pxyxTqHskFdi+Oj9\ngzPknlifXkJKL3W+11fUSjLHg1NOp9MDzpHr0aNHGh0dVb1et8SoryynESCG1xdoEUV41VAul7Px\nGB4eNro0l8tpf3/fjgj9yle+onA4rBs3bmhzc1MzMzOWB0CxBdVWrVZNUgz9g9G8c+eO5ubmtLu7\na+uC7pRf//rXlc/nNTQ0pL/6q7/SlStXLCpYXFy0NslEONAzrFfED7u7uzo6OtKzZ88UCvVO2SKS\n+PTTT+3goXq9rsePH5utgUVgXzOuvAfbRSM4bBxVwxe5LmTogyAYUs/I/6dut/sHLxbVrvv7/y3p\n/3vxz21J8+7tcy9+N3B1u93vSvquJI2MjHSbzabx0aA9v5hTqZR2d3cVCvXPbfSJqg8++MCSeLSq\n9RlsFrinfDDyhOkMOijG88EeXXkEi1GAzkEl5JUxTAqfSY+Vdrutvb091et11et1a1IUj8fNWNIF\nEmPsed1EImEOkeiFQz0YO8YLqonIAyrh9PRUxWJxIHHNMxJmemqKfAKLDP0xn89440xIsJKL8AVn\nJGnZnFRKokBhDOE5FxcXFYvF9Nprr6lYLFrCHe2+17z7g5WlfqGYj0BwCowTCN0XlLk1/hOKF0+3\nkXgmJwEfDhoFfeFg2dhorqF9UMGA4jlgwkcNjCGo00e45Gx8wp7noyiIOSKyYc9Jvarcg4MDo+34\nfMr/ufeFhQVbo8fHxxZ9MnesO8Zgb2/Pol8iL6SYOFtfQAYNiHyxXC6bcxkZGdHW1pYODg6UyWQU\ni8XUbDY1Pj5ua/To6EiFQkFf/epX9cknn1htR6PR0NOnT7WysmKO//79+zo8PNT169fNOTx69Eir\nq6sDkTVRUi6Xs4aCFP4RQefzeaVSKV29etWEBtFo1Nq6kLhmnbKHgiBQsVhUOp0eKMAEvCBPpqCM\nA3kuel1EdRNI+j1JD7vd7u+430+/4O8l6X+Q9NmLn/+LpP8cBMHvqJeMvSrpby50M27BsagJTzEG\nJAZ9kypf/cq/eR8b2xsXqBX4Y9QofDeD+eI5LbTyVASIiZAMAxiNRu30+MPDQ62urlqzMjzyycmJ\nbSCQMwdegEY5bQfZHjIzCkhYfFxwrVACJKMwECwmT1mwiEgsnZt3M4gYPgyvpAFDQwTm6Q42rNRb\nyL5SFdks94Ix5jvPn5xzenqqarWqubk5k+JK/QpIjLCvVOT3/rk9vYYRB5l7VIy6xdMiUFr++Xg9\nESOhvEfoJKm9M+GziciIJOjbAwjhmbgHH3mwR7zElzXNs/kSX/4AACAASURBVDP+fk/xzD7qIJlN\n+2A07FAFCCNwZERQGGheCy3mlWGMBXJbjCMCBkBAOBy2BnPQlOyzVqt3/F+1WlWj0dDJyYnV13j6\nC9qHNUC+C6M5Njamubk5nZycWO0M0aNf4+wd5oMx53k9CGW98H/yEkTcniYCROJ8WSv+AjDy2axt\nxhRgB2V20esiiP4bkv6FpE+DIPjkxe9+XdK3gyC4ox51sybpf5Wkbrf7IAiC70n6XD3Fzr/8IsWN\n1G9cVq1WB1AGD07b2nfffdeKWthUGDh0sEEQ6NmzZ6ZuYbP6cyx9xahX00AVeBkcfLxHrF5xA83h\nw9xUKqXFxUXt7OzoyZMnxuuhRgF9P3/+3JATi52NwmaUekYlm81qa2vLxoZkHd9HhOArJpFAElLz\nenhPvjcej6tarQ4YPb6HCAVDDV0G/YI6gZ9BaITG3JNXx0xPT1vflPNKIxY6m4TIgdcj08S5svEw\n6lIfJICESCR3u11TYfDcAAapn2Tlmc/nYkBYfnNiKIjeCKtB697o8t7zuSHaOL/YP4beiOgAOgAE\n6CAoNNYrjgHtNzQZUQaOyoMhVDdHR0dmRDFMgItyuay1tTWdnp5qYWHBaiOYV8bYK4uIyNgbvpKY\nQ0Ew8tBRk5OT2tvbU6PR0O7urvb29pTL5VSv17W6uqpms6m9vT3F43GjX8gj1Wo11Wo1i4CgTGZm\nZgZyEtlsVul0Wp999pntk8PDQ5NsYyOgfyVZAaBf8+Vy2dYU6/H+/fs6OztTsVi0iITneffdd22s\nAFA4Eu6jVqtpenrawBR7mLxGLBZTuVzW8PCwSqWSVcdf5LqI6uY9ST8tRvivX/Ce35b02xe9CRAR\nqMvTK55OiMViFr77JlKjo6Pa3t7W/HyPMUJyRUHV2VmvBwfGdn5+XqlUSqOjoxZCjoyMWFk3py6B\nDrg8IiMSgF/DQEQiEa2trWljY8N4XtACPTXW1taUyWTsmDyMCK/P5XKGrKLR3tF8LAqMMPIq0CGL\nlNdQpl0oFOxgcELHVCplaAZJGKgMR0YEQ8TC+LdaLWs6hsO6efPmADpks6DMYaH6ghyelc0P6uJ1\no6OjOjw8HJCRUWyCQR8eHh5QTNFXhKgA5IRxYw5p6EaBHAaQKAUjiZH3yWLuERCAZC4ej1t+BurQ\nvx8jTedStNggRqgbECjzSpU0kSwJUB9RSP0DwuGu+Tv9X0Dco6OjVqUN0KlUKgM5J9YVxi2dTmtx\ncVHNZtMSkslk0pwZY4YR85JnIpvT01NzJCTcQfjUQBweHhp1VCwWtb29rVwuZ7kMVEgHBwfa399X\nvV7X4uKi5XFA8EdHR1pZWdH8/Lyazabm5ua0vr6ufD6veDyuVCql27dvK51O68mTJ3bSFOs2CAI9\nfPjQ7Munn36qdDqt6elptVotpdPpgeZpOPIrV65odXVVr7zyim7evGnRyNjYmO7cuaPr16/bmrl9\n+7YxCSSnsWvtdttyFp6WA2zhuFk7F7leispYFrynVM4jJ3/IM3JDvCZcMhsLDh9jRRgYi8U0PT2t\nO3fu6NKlS4aWWJT1el3Ly8uWvPScuEe7fB5GHHQDciBaIFz3fCj8ajweN9UNvyOhR5IItINap1Qq\nmSHjXsjUg/KITmio5g8n2NraGjiQBCRVrVbN2TD+FGtQjYihIjGOfPPg4EArKysDSUEcAU4RyRzO\nN5PJDKBWDJsP80E1OMJ2u22nG/mkN+PPdzMGkuy1Uv8AGk8NcLKR59SZP8/jsw65R/9aohHu1yfI\nvVMC6SJfZG0h++U76QrpG9QhvYSKY+64N8bQjzfRC/eOagXjzTPgqKADUPQMDw/b31CwIGkk4uF1\nwQtFDYIF1gL3SQTh95HX9SPR3NraMuABveOdJ0VTGMB6vW5dIZFA0nPo4OBAV69eNX78888/N6kq\nifHj42PrBMleWlxctIpqqlnJqU1NTRkYwrlBbRLlzM7OqlAoWOsTDPfm5qbu3r1r+7tWq1kOib3A\nOpRkii0fnXJP7FXf0ffLrpfC0Hse3oeu/sCOeDyur371q1ZxRpiKQb1586Zl3+fm5rS5uWnhPJvd\nS+g4uAL0Tji4ubmp+fl5M9zolNl0vlBJ0kBHPY/CUDiAnkDNPjlI47Px8XFTj3gVCu2U/Qn2Ozv/\nf3vvGhtpet35/R9WFYvXYlWRVbxfmn2ZmZ6enmtrZM1C0MWSpR17nQTwQh+SGLAT+4PhTZDAXjsL\n7G6wWGQdJHI+eLGG4Cgwkji27F17B7bX9koa3eeiHs30jLqnu6fZzW7eb8Vi8dJksarefCj+Tp2i\nxjMU1tJ0N/gCDbLJYtX7PO/znPM///M/55m3Bd3d3a3BwUHTp6NfJ6QH0U5MTGhlZUXLy8tGfUnN\nXRo9ovbcL8laFjmoiUhqaWlJ165dM4SIwyFJK6lJDdPe3q5cLmeHe3iJIv8nUuju7lY6nTZEPDs7\nq9nZWVNESbLnAxXD+H2hkacr4EmlRm8ZNpznxvn94f/z1ecocNQUs2AwcT5evQPiRj3lOWGv9CHB\nn8/n7eg+D3xY13wG7wENyr4hgcg4cN5Ep1BLGBxQI3OOw8vn85IatBhtpDs6OpoOFYcqAsH72gDu\nB0oVjX+pVFJ3d7eWlpa0v7+v9vZ2nT59WhcuXNCJEyeUz+eVy+W0sLCgkZERZbNZTU5O6tvf/rY9\nQ2o1aF89NDSkXC6nnZ0d9fb2an19XQsLCzpz5oy+9rWvaXV1VbVavfAKUMJZvkQelUrFJKWsY9YS\nURWRBoa3q6tLi4uL5gSgCguFgq5du6Ynn3xSDz30kC5evGiRJE7LS8yRg/PenAXc2dmp5eVl6yF1\n1OueMPQgehY9PCbhUSwW0+XLl3Xp0iVNTEzYgmdDV6tV3bx506ICQmM/kaCEra0tvfPOO5bM2djY\nMH2zVC92Wl1dtV4VlG8TgrPB2OSS7OETcZCghI7K5XK6efOmSQZpK1AoFMzYspklWcUoGxR0RbUc\nc4bCplwua3Bw0JJNkqywxWuwvcSUClmPROElMdQUgnjk55NyHPtHjgRaAU5WqhsgUKTnhOG24T09\nhUPewhdudXR06IUXXtCtW7fMGG5sbBjaSiaTymaz6u/v18TEhEkSZ2dnNT8/r5mZGXPYdOykfF9q\nUCDSDyaSocb871paWizS4d4xUmxYqEOfXOzv77e2DVEUWWUyayuRSKinp8cix2KxqKGhoSZe3ldS\n8/mSzGlBU4UQrOrUI1cAQHt7uzX0oq0Fz2Jvb099fX2WuET2yZxA5ZG36unpsV4yfX199jl0foQm\npe4hhGDculQ/F2J2dtYiot7eXg0PD2tkZMR6GOXzeZ0+fdqACC0vvNiATqrPPfecUXSo+W7cuKHx\n8XEDR9evX7cWKwCXcrmsa9euGThBFcbawTGyrlECAjBaWlo0ODhowAmkjtM4deqUPvKRj9ihLLAY\n9OJhPL7VB46SZO+tW7d07tw5xeNx/fZv//aRbOw9Yei9EgPEjQGUGnTJzs6OnULE4vZIGgR3WLbF\nV3TAbH4SsSRkMN4Y+Hg8rpmZGUO9JC+9VA9DyyKBnqBYgzYG3DNO4ObNm8aTewUIiw5jK8mSS56r\n89RJKpXSxsaGJiYmbE7ZyF//+tcNXcFvSrJzMOPxuMbHx63RGLwq6Jv5RLInyfpsgHAIW7k3fo6x\nq1QqhprYOB7l+zCY9/CKCnqdcNYtRpc+RRcuXNDw8LCGhob06KOPmuHc3t5WOp3W8PCwtaSYnZ3V\n1772NUNLrC+Pjr26wkt9pYbaiLwP6ifWnufXUd0QMVUqFVN7IKHE0e7u7hrKX1lZMWMkSUtLS9Zx\n0lf1So0T0DCeOMmWlvq5C88995wKhYJeeeUVAxOsXzqJ4uB9XUGlUtFnP/tZ7e3t6Utf+pK+853v\nKJ1O65FHHpEk5XI5LS0tqa+vz+aIuWSueF4hBE1PTzfJoqmJYA2+8cYbJjNl3Hfu3NHubv2ciRs3\nbphzaW1t1fLysqrVqmZnZ3X+/HljA773ve9pcnJS2WzWEr7ZbFZPPPGELl++rK9+9avK5XJaXV3V\nwMCAJZmr1WpTrQmKPqlxNCiOFCUctBtSS1R2iUTCcogo6UIIeuihh5TL5TQzM2MOztdj0NMK28Lz\nRVYJkPJV5ke97glDj6Fls2H82DjxeNxQMBuIDSfVjT1l5X7z0P6WcHZlZcUy84SloCMmNpVKaXNz\nU+Pj48rlcsrn83bgBSgZegFelDAPJ0LvmMnJySYqgU0JZ41B9Coi2puyGfmcyclJk4+RsIWeQXu9\ntrbWJOlra2vTQw89ZDQImmNe09nZqQ9/+MNNLV0J+zHAVGp6Lt0n+zBYoBqcHJ+Dk+H0J84OBRWh\nhZdkPLxPxkp1Izg+Pq6FhQVduHBBr776qubm5rS7u6uPfexj+vjHP2595znopVAo6Pbt27p48aIK\nhYJaW1v18Y9/3PTcRJGsL0/PoPzxKiLPn6KKgp7yiBrAQsKe17EGoenIQ3hJLEaN4h0K+xKJRFP0\nhwGTGnJiagl8J1SKeDDePoFKlTM0y927d7W+vm5dQkulkpXpb21t2RkAqGFKpZJJfVOplLLZbFNb\naA7FYE52d3eNI6cymPwDLThoBkb+wkeCFy5cMCrQR+lTU1OmVYffP3PmjHp7e62LbGdnp5599lml\nUim9+OKLevrpp9XW1qaVlRVrr0EeJJPJaGlpyfJoJNlB3HTXRLNPC2QiMVoN0+YDANnV1aWPfexj\nymQymp6ebrIdklQoFJTNZq3IEyUVdCjrlMhwa2urqSHc+133hKFPJBIaHx+3kAwuG80zVANl4iTn\nuNh4PChOIcIxEF729/ebggXKwSsfCK+gRThAhFwByBsDQEjmFRXw7vB6VKLSUVBqeG88PqEnHfl4\njZdBguhwEHDUGBpKwH1fHFQ7SMf6+voMVeMQ+btarWbhu/97+FyfXMRRSI2zL327VtC5p2q8agXa\ngsIwkB7Swba2NquShprgEGqS6Y8++qgWFxf1oQ99SKOjo0Z30XtoeXlZxWLRzjft6+vT2tqaOXsS\nXaBHxoiDg8/nmXklGHkk0ClrwVM+UgOt8Z68L4ot5t2fkco9UNfg6wFQYFCJ7DXoPgGNtJHeLeVy\n2dYNjtrrwXE40FEoU6AKiMbYm6jc+vv7bY0SKdJzBsoTQMbfSg2FEEn94eFhQ6peMw4IwuAzNyRh\nASWlUkmFQkE7OzsmheT5DA4OqlQqGd1ULjcarLH+6R1DZELkDQAlf8I8EOmBsImA+/r6NDs7a/x9\nsVi0SIF2LuRDcrmcrS1JZq98MzsiQQATzoG8ZF9f35Ft7D1h6Ht6evTZz37W0BOGDJUD4cvZs2ct\nAcHveIA8HJQtGDkuULWXkXkVBe/DAvCKG5+sxLCxob2GGaNGmAXtAffGxudhgdZY+Dg61DKe0uD+\nfHTARiUXQEm1JOPKK5WK0TQsmv7+/qYQHdqBDS/J0DxGFh0+G55/3BO8NO/Fwtzf39fQ0JA5aE97\nsXBxpDg9FBe8lgOpQcjIATFohMG+GGdmZkazs7OWc2EOM5mM6fnJLaC6YuNhXBgfQMEn85krnh3R\nGolF9NwYYt/XX2ocY0gExPMDqGDgoaG6urqMT/f5BF8QSHTAHonH41pdXW0q0EOpxt/SOoAkOb1Z\n9vfrp4Nls1n19PRodXVVbW1tKpVKpv4iZ3D37l07FW5/f9/WO3PE/EItoey5fv26XnjhBZOcsndj\nsZjlE5g/9htKGXrUVyoV0613dXXp3LlzNm/5fN4Sp5ubm9rc3NTk5KQZ/sXFRdtrABm4e/Z6rVYz\ntL21tWU2pbe312TLHHrOGcctLS3W4pvx0j2WtY4jZx9wz0Rxm5ubGhgYsMgSZoBIr1wua2nJmhO8\n73VPGHq8HAgJ7rO/v98ePkiV0AX04+WAoC64ciaJxYJmt62trSlaALlBzfB/HIBHItyvRzjou70U\nzys7fIgMVcTnYywkWdKJBcV9gwZBdKBKevPgHJHIeYOVzWab+meDoigOw+BtbW3ZImRDYhBoE4FR\n4x4wHiA/5gV+nnsHvZGU9OflSo1Q3FciovZhPWxubuozn/mM9vb29BM/8RPa29vT7du3NTMzo2vX\nrtnf0zKWs3CTyaQGBgYsWbu3t2eIj7WEkff1EIzLO2ciL/hg39qBZ4giiflJJpOW1D5caSk1mrvh\nAGq1euEg7Qrg9tnkqHFYRx4Vssa9Qgz02d3dba19vSNIJpMaHByUJFt3GOKLFy8a/80YpqenLTHv\nK4cP34uXgUI1AX6oXSHa4jANlG+scyInPj8ej5taDgO5vb2tiYkJPfbYY3aOBTkAqZHfosX5hz/8\nYc3NzenKlStaWFiwdULEd/78eatvQdnX0tJitBfAamBgQDMzM5ZTAIg8/PDDRhvSRJDcAo4xiiIt\nLy83rTEiFCK1qakpM/S+qIp2EAMDA7aGjnLdE4Yer4/BZwERMqErh8LxemcvVySE9gdBe6oHbpKN\njVHnvdfW1n6guyKvwbhCqbCZpIbhJ0Ql+cKmIq+AkwG9S40NIckUIVJzko1ujrVazZqUEZmgA8dR\n+crdKKr3SuFgEYwAqIkCFCIdnCnziSFAQspYcY5eWYBTYpO2tLQYbVWpVIxGIRpB4868QSFIMnkt\nVBIG9Zvf/KY1CIM229jYsGdNIdnAwIDpldPptGmWBwYGrGYAtM78s4ExMH5t8ln8jVdZMW5ex1yB\nCBkDm5yEKOsCA0HinnVJhIUBIEHt5aGeYmMefdWqX59o8H0ym7/BQYDwWSMf/vCHNTk5qTt37ujN\nN980x8jnUBGMA4Ki6+7utj0E7cD6hpdmr2PIbt68aYolf8AOVcxtbW0aHR1VW1ubBgcHLX83Pz+v\ns2fP6tFHH1V/f792d3eN5qOyt6Ojw+SUPP9MJqPnn39eS0tLeuihh6xpIiqfJ598UpVKRRcvXjTq\nlY64xWLRIhb+sb+Y287OTi0sLJgd4dnWajXlcjkDAkTss7Oz2t/f18LCgh1w8uyzz9rvPbU0PT2t\nM2fONNHX73fdE4aekmlJpgFncVAksbe3p7GxsaYJ46svmkGRAArHMHm1AZdH2ujQkTF6tCc1+Faf\nDPaKCkJTrz8mRMNwkN334/YVijgH0CKyQq8awogjF+3p6TFUgowM4+t1vGxynAEIeHZ21qRvGF+a\nWlH0wcb0DpZw9+7du5qenm5CF319ffrlX/5lo49KpZL+6I/+SJVKRZ/4xCc0MDBgY9zf39df/uVf\n2ulTJDOfe+45S7ATgWxsbGh5eVkvvfSSKpWKent79fzzz2t7e9sS7HNzc6awyWazWl1d1ezsrEZH\nR61/PYdWe+dyOGpjTUGtca/JZFKrq6u2AaEruFC8JJNJ66aIxBF0ilNAUw6fDajAaEKLYRzZ6DS5\n43O80oXnE0KwBllEdB/96Ed1/vx5S+ojYe3q6tKVK1f0yiuvmLQQNdXW1pamD84uxWl6JwL4oWq6\np6fHlGfsm42NDaPzULXFYjFNTEzo+eefVzqdtsQiNA95m5mZGf3xH/+xPvnJTyqfzxv4KxQKunPn\njk6cOKFaraYrV67o+vXrlheIxWJNrYuRVq6vr+vZZ5/V/Py8ent79Td/8zf61Kc+pWw2a1EmfeDf\nfvttffe737UKcqK5d955xyJHnitKIF63vr5uCiFJWl9f16VLl2yPSzJD39bWZscivv766zp9+rR2\ndnZ0+/Zto/d8oztJunLlSlMb4/e1sUd+5Y/wIiTBaHrpHseOraysWCEEqBr6BmMuyQwnRpfLG1Cf\nCPNhZRRF1jsEJOIdhtdTQ2VIsqZqOBqfCyBhAlIk0YKx9VV2fA4IDDoGaobTkqSGQ6zValpeXjaK\nAEQPB1ir1U/A2d/ft4QVVcaUksMNgrh8EQ2aZjTJzC/UTDwetwXP1dfXZw45k8mYGgGpGQVpXV1d\nVnm7vr5uEUc+n9eZM2dsjqBgFhcXNTIyooWFBZVKJVPhENWRrJUaB11gVDnxioI2OGZJluPwSTgi\nGhy6T5gTkhMFoIbxhUm+NgOpnZeVAj5YeyDkVCplRpG14Y1yS0uL5X2q1WrTcYQgfF4H1cE+Ghwc\ntPVJ0RJdEMfGxtTZ2alUKmWH2nz/+99XsVjUzZs3zckgSyWiIdnI/mFvkdMhF+HzG+VyWRsbG7pz\n545VrPJ3/v3Ya7du3dLrr79uCWKQeqlUUiaTsQQ89EgUNdoDsOY6OzvthLRUKqWXXnpJmUxGw8PD\nxsOz7hlfV1eXHnroIb3xxhva39+3cxkymYzS6bQ2Nzctv4ICZ21tzc7UpZ0Fidhnn31WIQTrR0/e\nLhaLaXp6Wqurq/Ys5+fnNXEglx4ZGbG1iQyWPllHve4JQw8yYWPAR0mNcnoQkm8ZSoIM9MECwPh4\nNM8G9Coc7zB8EzPPy3N/npoBiUuNo9d4EJRN+854IGI2ted7oax8mI3R5O9wgMgnK5WK5ubmLPQn\nAUmozIYjeUfva0+FSLKk4dxcvYu0D7GJClAhEfWQW8ApML/0zwHpU72K1BRDkc/nlUqlrHy7t7fX\n7oM5oOIXlQNUx9jYmNrb2/VXf/VXunr1qnZ2dnT16lU98sgjxosPDQ1pZmZGjz32mDo7OzU3N2dK\njZaW+vFuUAsYK6IRr3f3z96vUagX1gTGifnnuEaoRNYNMj1yLUSSIFCiMyg+7pFoAUfNHoGz9zJL\nXocqB6EAiq+pqSk7BIX3qtVqdpZpKpXSRz7yEaMVXn31Vd25c8eiF9A8+5LEOeo21jvNCbkvkD70\nEGf8Li8v6+zZs3a2MmfUcu8tLS3Wq6a3t1cdHR26du2a0um0GT+cm9TIH2AIWcfs51u3bqmtrU0n\nT560TrDPPfecgTFOi2JuK5WKRkZGdOXKFTPmKJgYI2CitbXeqmRsbEx9fX2amZkxRxuLxaxID1sG\nOGSPk1egeR9N2nD8AwMDTfQh9NlRr3vG0JMEkhrtVJEtoSZYX183Hj2VSjWhYjhaz19KjVAc2gMj\n6A2sP0MVLp/3YDF5ByE1uFupUSnI34Be/EMhzGfhwQejWtjZ2VGxWFShULBw0NNHtVpN8/PzxgcW\ni0VbaLVaTdls1pCcz+QToeBcoKJAPiB37p15Yz6TyaTxnfzfz4d3ljhMPsMbIjoWEqHweSicoNF8\ngtyrWdDYl8tlnTp1SlNTU0ZLtbS02EEoGAvWBSEvUc3w8LDVShCZeBUVRo3vD2voARqE0YeVWzQe\n48QgxurXjKcG+eqpDvquewqFfSDJgA5r278/gEOqO1HUY6DH3d1dU6BAz9Fqoq2tTbdv31ZHR4fu\n3LmjxcVFq9AEPYKO0bFDseDkcVrMoV93nBFQLBabDh559NFHm+hBQFmtVpf3futb3zJKq6OjQ+fO\nndPw8LC6urq0ubmpTCZjBUysR0/nQU/yLG/cuKHr169b5Dk7O2u1A4CXlZUVFQoFTU9P28E/2BAi\nLp+T29/ft6aBSFLJBRH5kK+iI6jP4eGgoY1o74IzpoocJyw1JL9Hue4JQy/J5HVSw8ASrnLB9/mD\nNNhwqDnYhF5Li6Fj01cqFesdIzX4WSRw0A5sLBYf93DY8GPQSaAxHvh6byRwAh6NowPe2NiwjD8O\nhjGgsef4P/jfWq1+vi0bnDlAbhaLxYx/pOS/ra1Nk5OT2tzctAPZpYYh4vMSiURT2bxX+IBW4/G4\nenp6LAHKfL7yyitmeOPxuM6fP2+VzQsLC6bySSQSevjhh3Xq1CnjQkulkl599VVls9mm6tRqtV6N\nevr0aX3zm99UR0eH9fDp7+9XsVi0SIICLWjBYrGol156SY8//rgymYzJ39g43CtjwFHwHJBOku/w\na89TP/wMFRmfDYWBY/dUEfMgyXTfUJnLy8tG04C6WYeoNEgE8vzz+bxaWlr0uc99Tul0WqVSSdls\nVi+88II5ItpS7+/v25nG7DcqZScnJ606Op1Om3HGwa2vrzdFkDRk84ltr5SjyGhkZES5XM5QvgcL\nRCE4POiiUqmkvr4+XbhwwepBarWa0YK07qD4CyeXSqW0urqq0dFRM6x0sIVCHBsbUwhBQ0NDdkrU\nyMiI5ufndeXKFaNIMdo9PT1mD7a2tkxG+b3vfa/JYVUqFYt8hoeH7Zlls1mlUikr3oNui8Vi1lZj\nfHzcqm5nZ2e1uLioWq1mVPbf+cEjP45rf3/fNK0ewa2vr5uxhY4haVUoFNTd3W0ThIICRA41AKpE\nwsT3XplAkpbzJOnPQSgvNXqhgN5oQEXFH3pt+F94UNr84rCoUpWkxcVFra+v2/uzmTs6OqwZGtEH\nJ0319vbamDAyHR0dpg/HSaBgYOOCqBYXF5XL5UzitrKyopWVlSbFDRu0r69PmUxGvb29Vh1M9SWO\nOJGon6vL2bpQQ6BZEOUnP/lJRVFkCJxEp4+K0BkTma2srNj8wZl2dnZqaGhIv/qrv6ovfvGLunHj\nhlZXVzU+Pq4zZ85YheylS5eUyWRsc42Pj0uSqSzGx8f1F3/xFyYLpRUwRon15O+NyI+mZawHgADq\nKK/mgcoi0gDleY2+n9dKpaL+/n5J0ubmpr0OhEzbYq/YIM/U2tpq4AGevFAoaHBw0MrmOXuA3EK1\nWtWdO3esA+nS0pKGhob09NNPm8OiloHE+NbWlsbGxpRIJLS0tKTR0VGTRHu1FhFCX1+fcrmctra2\ndPfuXQ0NDWl5eVlf//rXlUqltLW1ZZW0Ozs7KhQKKhQK2tzcNC6aiIw9KsmKmDh1jA6dITTqWcrl\nsk6fPm09bebm5vT222+rv79f6XTajuqkVqNQKFjdR2dnp86cOaNXX33V9lWlUjGbQ4SRTCY1MTGh\n5557zlpXoHdfXl7W7u6upqenNTc3p8HBQauxePnll/Xmm28aoOD5Y+vW1tZM8XPhwgVrw+Aj66Ne\n94Shb21tVX9/fxOl4UMVkk5IC2kERQYbxQ4UBzwqzlvOOwAAIABJREFU0ieQC4Uo9E2RZIUTUqMV\nw97enhWISA0ZGg6iXC439bKgiOTu3btKp9M6efKkBgYGVK1WrSXCyZMnjb+HdyPUBT1yryhrBgcH\nrTJVkqHr/v5+PfXUU5Ia8kwSlhhhUCa0F5sf1E6FMEbd0zotLfXKYKnRayORSFgjJrpEovU+3GK1\nVCoZAllbW7OND/2Ty+XsjFFJVkgFHUdv7tOnT2t7e9s2OQos1C+f/vSn1d3drWvXrmlubk5bW1u6\nc+eOJco7OztNmjsxMWE9z6FFiBBZc8wfKIx78aXsh1GUl9n6HuJ+7ezv71spPgYaWor5Yr1SLUwU\n4FU3RBfQTvyfdU+kAY0G1w+qJ5dC11bvSBYXFzU/P28S2AsXLlj0ABCTGhTTyMiI7RHkkf7s3HPn\nzmlgYEBTU1Pa2toytHz79m299tprpsby0SbFcCT3SUwmk0ltbGxYnyCoRnrAEKl66pduj1BTMzMz\n6unpUTqdtmQ/UcAzzzyj1dVVXb9+3SLykydPamZmRpKsEeHu7q7p8uHbNzc31dPTo0ceeUStra0G\n3KBMh4eHVSqV1NPTo2vXrlnTw4GBAa2srFjlMlFUrVazNgyAPSSl7F+i2/tOXum5Rn9oAwgB+gEe\nDWON/I9FPTc3p8XFRfX29uqtt94yxQGUQ61Wb0UKCgJNgbDwtJlMxvTnPDDyCFKd+8zn8yZlpKAD\niSa8XSKRsGpeUBi0B4uREM9z07FYzM6DxZkVi0WNjY1JkiXGkKVWq1XlcjkbD5QAxnViYqKpepZQ\nGxlcR0dHU/8ZXwADfbS7u2v35/vpeJkr1AQKh1gsptHR0aZ2AYwZhyY1KjRxNBTTXLlyReVy2Zpb\nFQoFa2FA9FMsFo3nLBQKWl5eNtS/uLhoyeS9vT29+mr9REvaWiBZRdmzu1s/UNxz3awNIjmv8GK8\nOGuStTwjaCEMLo24crmczcHY2JitgZWVFXPsJGpJ4kFfkfRlr0gN44sB8Il/jN/bb79tPV22t7eN\nOpFkICWZTOqpp57Sk08+qcnJSQMmrKHd3V0r3S+Xy7p165aKxaIef/xxTUxMqLW1VWtra1aYhWGj\n183c3Jz1u8Ggrq6umoFcXFw0tO6LAamclWT9a1jP29vb1veJE8hYQzdu3FB7e7umpqaUSCS0urpq\nyeN8Pm/RPdQYkRT5suXlZS0tLZlqhn3R3d1tDpl1yxqntxOy1EcffVTz8/PK5XI6e/aszpw5Y/vl\n7Nmzeu211ywKkmSglBoQ8m3w/lQg0yH0qNc9Y+ihXThAhMkGgUZRZCdAgfBphIZMjIUyOjpqFIKX\naVHKzSKhYRMbtaenRz09PaYzhhIi8cUD8tWHOI1qtWp6dBIqhOpQArVazbhp5FqSjHfzbQhAaiDt\noaEhK69HjncY7XvlCnNIFSqhJhf3T1tXf0AJyVH624MskRviqDhjlAZO3As8qafhUEGANnnuvDdJ\nJ5pHxeNx66XS2dlp1BRHKmLgn376aTPaJMiYA/T+1Wq9BD2VSlkvIpQS/vW+PQZOCNSNY2KteLUN\nhpXjKr0aDN7+8ccf19e+9jUrQCMZjiHp6urSyMiIqtWq5ufnm6gxkvXeQVNNS4TJ71Cjwe+zJqg+\nBSQVi0WNj49b7QIRzMmTJw1QQB/cvHlTJ06cULVaVTqdNoojk8nYWBYXF/Xoo48a2Ono6NDNmzct\nuqHfUi6XUyaT0aVLl9TV1WWdJIkOSfQS7WJQAXNERT5h2dvbq9bWVuO+fYvfXC6nW7duWbFVsVi0\nA39orUzU1tPTo9nZWcVi9d49qHBY055+ow0D+8zXyECRwUAkEgldunTJKFvySF5yC7XE2KDiAFWl\nUsm6ZsbjcS0sLJhzOMp1lMPB2yR9Q1Ly4PV/EkXRPwshZCX9kaQJ1c+M/YdRFK0f/M1vSvpFSVVJ\n/yiKor9+r89YW1vT7/zO76itra2pJSfotq2tTRMTE9ZXm03Noi6Xy9atTpLJq+j0iAFChw7XJsmq\n3HAkGDFJpu7xkkKpgaS8waP/RzqdNtTnm389/vjjKpVKpm8nJE+lUnbSPDy5V+rQlwPJHklrGjjd\nvn1b7e3t1ogKPpdNgKMBpXoqAeSMQ8O4IZNbWVlRpVKxTpIktVFvwIUyTs/PsvEIqUHgnZ2dljwE\nUaNiIAGMY11bW7P3IoLp7OzUyMiIxsbGmiIL5GYUKIGEPBJ8+eWXdeHCBeOLv/rVrzYZUJ4VyVkM\ntkfwUkONI6mJP6fTpz9flK90d0RDjoOBA4d7plUDRXSsWf6PwyZqxDB6A9Tf36/Ozk5duXLFDFsi\nkdCZM2e0vb2tN954w6JMfwZvIpHQV7/6VX372982w8izxhi3tLRYIRqVv+VyWQMDA4rH41ZMtrq6\nqrfeesvkzowlm83q6aefNu6aKtW+vj4DSbdv3zZHSf4BI8kaI0m/v79vssudnR2NjIzorbfeMmnu\nrVu3JNWT3OwL5L+VSkXd3d2am5tTb2+vent7NT4+rldeecXOBPDO2zeGI0/mRRM7OzsWASWTSeXz\neb399tsGkgAaAFeeJZw84gn2JhE1NDNrCQHE3zV1syfpE1EUbYUQEpK+FUL4D5L+C0lfiaLoX4UQ\nfkPSb0j6xyGEs5I+J+lRSUOSvhxCOBO9xwHhSIu8d2Rh0wOEkJAN54sNQG70b4EXxoAQYkkyvTyd\n5PwkYtRoJctCX1lZsfuAksFoeo4aw4Xh2dra0vnz5zU7O2u6YZQliURC6+vrpg4KoV7JuLKyYkcH\nel324OCg5ubmDKlgQEHehItc2Wy2aaGwKLj3zs5OraysqLW11UJ0FADcPwYPY4AjwOB76opNiXIF\n/jKRSJhaw1M9SGf39vZMAYITAqVRkCI1Dolvb283vpR8BxJJ5p+WBzxrgMHk5KR2dnY0NTVljg8U\nj0PwdJWXNfp/PHcMP9WxHR0dymazisViVo0aQjAjSMTW0tJiIAKaDg48Ho8bQAGtY+DRV5M4JlLi\nnlkLn/rUp9Td3a27d+/q1q1bqlTqveeHh4eN9mhvb1ehUNDq6qqWlpasxQDRz/T0tE6fPq2VlRXj\npuPxuNGZe3t75rC+8Y1vqKOjw1Q4GPAQgn3m2tqapHo7Xk50Gxwc1DvvvKNisaiFhQVDtdCkvtYF\n6oqCManhZLe2tnT58mXLDZHAJT9VqVQ0Pz9veTX6xbS0tCiXyxlF8+1vf1s7Ozu6ceOGTp06ZUae\nKMF3luU+2FfValVXr15VNpttWuPlcllDQ0MWjdLDnzwJ6iCcLhXbFLmB7skF8KxxJke9jnI4eCQJ\nC5I4+BdJ+llJHzv4+e9L+pqkf3zw8z+MomhP0q0Qwg1JH5L00nveyAG/iPSQTQRNQwUloTSGVqrL\nIkmQtLS06Pvf/77ROoSkPhTv6uoyXhPPTkjO5ROM/Bw1hdfr+zAdDpvjx1hM0B/cg+eioWjghFHJ\nwAlK9YpPdLzQH4zVGyMy99AFoEYKg7xqhKhAkjkTDDhOBhUJyhHG658P8wNFhtoIHhnDRZWtD01R\nGWBkQbcUpxBZYFihl3AuLHieK31aeHaExtxTrVbT0tKSOUhJTeol1ovPsTCP0CMACx8B8dyZb/Ij\n0IL5fN4cORvZVxZ7Y81aY40wPmgjngFhPq9lnYH2QLv9/f1GRVy9etVaIsfjcTtvFbUK6w7jRm0G\n5yMsLi7q7t27djgHdJw/DIhnm0wm7bxVEpgY5evXr6tcrp+KhkILMObpWJ9gJTryrQAAAXNzcxbN\noGCSmnMX2AhyBl5R5yMOnBpom4I/WplQU+BpTEQUExMT5uQBNSh/iNiIEoiGsGOciUtOCGUPEYOP\nHqTGGdpHvY5E8oQQYpJek3RK0r+OouiVEEJ/FEULBy9ZlNR/8P2wpJfdn88e/Oy93t+QAL2bpQbK\npiLtxo0bTUaGxQ66HBoaUiqVMh4LfptkIsbGh9o8BIwtSouFhQWjRHitr3bkPSqVioXjaH7htqvV\nqm7cuGHIkgTr+Ph4k5IHNMti4hQmqZ749SoS0K9U5w0XFhbswGS6DyaTSRWLRWvyxAHFREieX0fC\nev36dUmNc3Xb29s1Ojpqhy14CgyKAcPokZ4k24TpdNoMqtQ4lo95pmEd/Xx87oV1Qc8PkN3y8rIZ\nMU7jIYymnYM3zt3d3SZNBfEzdxhG5h+jw5oCqft6Cr7HuLMeiSaRP0oyddLAwIDu3r2r4eFhkxED\nGnBKUA04K4yJl3x6NQm5LIyVd6A4i83NTVMZZbNZvfPOO6b4KBQKWlhYUCxW7wnT19dn1KlUT5LO\nzMyor6/PWgucPHnS6lhISnKc38LCgtWmMEePPPJI016mBoGzi69du6aTJ0/qzJkz+u53v9vUBA/n\n3NfXZ+OmFQccPtE649zb29Pg4KC15QZIMdfsl1OnTqmrq8sc2XPPPWdFiDMzM8Yq4HhZC77YEOoY\nZ3Tr1i1TcRGFsdZgHjKZjBKJhN5++22L2tfX1w0k4nBjsXqFN5Hy3bt3NTs7q6mpKZMCJ5NJi5KO\nch3J0B/QLk+EENKS/jSEcO7Q76MQQvTuf/3uVwjhlyT9klRfBPByJFAo34e/AkniDUG+oEj4W18V\n55E8hRskNdCmo4bI5XIWirERQF0ewaM9ZpMjsQSVccI7HRgJZ2/fvm06fklNUikcAQiFi3sm/Cdc\n9h0G0a+DMKBcWlpatLS0ZMkzr3RBzbG6umpHKg4NDdl74FgJdQkrmYuD52fPoLe3V5lMxug00GGl\nUjFagA1Kv23azcLze5R9uL6B/5M0k+pRCJXCGEu0xSS6QXQexXqU51sne9ksURbrB2OBwklq9E6C\nnsGYQJngCPhMPqtQKFiyE8cLmoOe6uvrs2Qfc4nRwbhgiDA0oEWpjnDb2trs1CZQN85udXVVyWRS\nY2NjFrmh+e/o6FAulzMHl0qlLLJqb29XT0+PGSzmp7u7W0NDQ9rZ2dHly5etY+Tm5qbm5uaaEp8A\nOvrOtLS0aGJiQu+884518cTw5fN59ff36/LlyyoUCpLqic6JiQkNDAwolUppbW1NuVxOb775pkII\n+pmf+RnNz8+rWCzq6tWrmpubM9VLMpm0A2mgTQcHBw2sME6/z3Z2dqxACyUeFeAHts+kza2trRod\nHbXP8qCpXC7bXDJvMzMzZr/8AeQrKyvWDqGlpV6lS3Uu+avNzc0mAcX7XT+U6iaKomII4UVJn5G0\nFEIYjKJoIYQwKGn54GVzkkbdn40c/Ozwe31B0hckqa2tLQLVeJQFz8mAaD+KHJD2wjTgJwF68J62\n2djIno+FRz579qxtiImJCQt3UedgsCQ1oVr+T/ECYfjY2Jiefvpp9fb2qlQq6dKlS7pz547pxkG0\nPsTFAHPCDMaK8J/KRJCdp00kWbjN0YKtra2mROns7DS+m7nByIGgJemJJ54wY1IoFHT58mVr7UrL\nAO80+Uo/GeRtGBOcC8lZ0AkyyWq1qlOnTllYv7q6qkqlYmffssAJsXFU8XjcFBA+N4BxYG551rdv\n35bUOL9AktUC+D5EJP1xZG6dGpWAc/fFTvyjRS9OheQzhj+KIqXTaQ0MDGh7e1uZTMYQIoiR4hjo\nAtAw9A1REIlnf2+s65aWFo2MjFghHYnswcFBTUxMWFMu3yuHOT0MrPweYo7IH3R3d5uxB8FOT09b\n5Fqp1IuTWPMUMAJscIYrKyt6/fXXlUgk9NRTT2lhYUEzMzNKJpN65JFHFI/HNTU1ZRHa4OCgarWa\nKXrYp1Q50wnyscce08DAgNra2nTixAnbp2fOnFEIQbdv39bs7KyJHLq6uqx4UKqDjdOnT5uUksaK\nnOsLVUikAjhl7fn2Bqz77e1tS7hWKvXuq88884zK5bL1z29tbTUgwD5DaUSk6qugj3odRXWTk7R/\nYOTbJX1K0m9JekHSz0v6Vwdf//3Bn7wg6Q9CCJ9XPRl7WtKr7/MZpptFcraysmLGMZPJmKZckm0A\nPCQHf7Dw4NcwYixmkn+SbFN7tQhobXNz00royQ/AobIwMNLI9Piszs5Oa7wEgvGqCDi5arVqKiHf\n+gFeGbRPchLEhjMrFot28DXJQ7hr+DvoDhQlLMTt7W0VCgWjdDwHiQSU8JXNQ+LNo9wQ6n1LvIPd\n3NzU2tpaE92RSqWUz+dVrVbtYIlEIqGf+qmf0sTEhC5evKhXX31VN2/eVFtbm37hF35B1WpVn//8\n583o8JxolZtMJptOjvJUWgjB6AH+T2MvciggTgw1tJykproLb9AlNSFnIiecACClvb3dqqsHBgZ0\n9uxZhVDvO/Tiiy82oX7AjdQ4/9U39WJ/HAYZRLFEtVCKlUrFKBOMmz9+kntEWutzA7TpBoUSLZCH\n4Z8kU7+BXOm8CM9cLBatyMwryRBAsLY5z5fDO9gjGxsbunr1qlGj7Blo2e9+97vGjff39zfp6ru7\nu+15cAYujAD7wkdQcP441Gq1fhJZpVKx09hou4KggvXA+3IiFM6V8eEYd3d3lclkjHojTzY7O2sV\nvkh/AaIUfOIwSAZT+JVMJvXiiy++l2m16yiIflDS7x/w9C2SvhRF0Z+HEF6S9KUQwi9Kui3pHx4M\n/HII4UuSrkiqSPqV91LcSI3sOaXDDML3lPDl5Wx8NgSLjYXOw2Tzw9viOGghiqoFCqe3t9eUPRh/\nWhzwM9QA0AXIz7a2tsy7V6v1ftQ0toLzn52dNWTn9fnobdmcJBoxYPB9qVTKkCiI2St0SHwRMtZq\n9QIxuD8cFuF6uVy2Y9B87oOFRcVyd3e3VaTye+YGR+tbS/hSeEm2+DGMIKQvfvGL5ohY0F1dXfry\nl79sShPGH4vF9JM/+ZOKx+NGa/T399s8QHEwh6wBEuMYOfjNrq4uXb582SoZyePgoKBafASGMWV+\ncMjIX3GM8/Pz1oukv79fp0+fNgplfHxcV65cMd4bx1ep1PuibGxsWFKePvW0XKCgzyNwnDXgAzVT\nMpm0roz8vTdO8Xjc+G+ollqtZhQhCXjWBM4Ix8mZvtzT2NiYnnjiCUlqAjesO+SzqNF2d3e1vLxs\nhtMrhIjWMfIogrLZrAYGBnTq1Cl9/OMfNxkqCWNolpmZGTt7IBaLmdPndTgCohsQd0tLveqY066u\nXLlioIVI1K8NnGY6nbaqcRSAqVTKomySqDxbX7+BDJr3Wl1d1fb2tj0HKsIHBwd1+vRpe9ZQlUe9\nAgv7g7xGR0ejX/u1XzPFAtw3XzEE8N4gVjYdm53wWfrBU5+y2awee+yxpsozSdZKFyQGFw1tRCk6\nRo5NA0pGC03yjIpZuDsSXCMjIyaBQx6H0YHeAC1DCYEm2dBeRUL4TvKa3ibVatW4XFQpOB9PASST\nSaNzkCD6RCMoxuuEDzs3qb6pZ2dnLWewsrLSVMkMOoSj55nQ28WjV8aMcYBagOOWGqcWYQBBcnxl\nExIl+oQa7w+a+93f/V3NzMzY8yQsJjoB7TJnfMUpYuyhkPy62N+v9zT66Ec/qlOnTmlgYEBdXV36\nsz/7M128eNHWDY4siiIz2tAfOB6iUJC4JFv//tnBa//6r/+6dR3l/b1cEToBtY5/DW1C+H2tVmsq\n/OOZMB/kcKD5fAdVn19hP2NgD98TRx0SAcBLLy0t2ZzQBgXE72sR2Gt9fX1N/aK44NBJ2vr58SCE\nPJ6kpmI1H9kkEglroIcaDkoHLT3z6h2WVwuxxjkHgufq17yPnqDKvJIuhKCf+7mfey2Komfez8be\nE5WxFERh1A5nuz0aQDFCWMTvSe6BJFCFEI5T8t3Z2WnZdoy7R7B8z+KnIx8b2SehCElRakhqSqqw\nKDAO8J6He+UzBjYP6I+5QG/tER3adxQ6RDOgBRQmhO5ewy7JHCg0FwaDTYo6gr8l4ewNjzeCLFSi\nHnqJMD7QISif10IVSTIjw7Mg6qKSkEjHI1JvqFB9hBAMYWNgiDAABhgwDxiYf54N9+rBkOfF+Z5n\n5bltnAqI2Z8YReTImsFIMX74esJ/EnZESj434FVM5H2kZt4fSg8n5GWgaNahcrxBog6CKBKjzWsP\nGy6/Znkm3BuGnXEQsW5sbDTlIvhbIjGK7liT9InCBnhdeSKRsP2BYaeASZLZhM3NTXsGAA5fGIcs\nlNoX8kTQT+QPiRCJPLe3t815sPagBZlXul9iOwCCVN1yj4wBRRfPifWKbTnqdU8YeqmBlDF+W1tb\nTafel8tl4zO9Cge0T7c7Dhtmo/kKRpCwD8mpegPdgJqRY3JAL/fB5PoNz4YGUZKFxyhhOAjJfec5\nCpa4oFY8uoceIVFXqdSLKThPEwfk5YugEXTx8O6SmnTl0CKehy2Xy1pdXTUVyt7enkqlkjY3N5vq\nAaRG62U2KwnPjY0NizZisZh6e3ttDngWGA8MTyKR0IkTJ4yiIiqDz4ZewIiQL2Hcvrufd65Sg44i\n6c3m9QndwzUc5XLZDCvPkt9xz1BEqHKobGxrqx9APzw8rPPnzxtdB63Ecwc9o4aCYmBeiKaIEj3o\n4L4wGqBe344b4478lzGx1jC4npphXcLB43hxIn5OWU/cO7kekDvrEZmyf4YYSByUdzIYaWSnvM5L\neFlvRA4YReptsAGcZIY4gUQ5hUiom7zCDhEIuUHmm/F0dHQY9YpNYd9CNzPfUEN+rbAPkIF6u+CB\nE88aVsFTubzmKNc9YegrlYr1RecBwXuzuDHeVLT6ghzCN5I66Nkx6Bi2ubk5dXV1aWhoyOR3lOpL\n9cVDKEZ0AKoCNXje0t8/2mhvkKrVepUhG5aHi+ECwfgH6ENEEBlJVZQvoDH4dToG+sSrv2+4c79Y\nqR7c2dmxkBeJYbFY1K1bt8x4JxIJmw+fGGUT5PN5ywOgxyayAOEjayR5SAELm5ccTT6fb+LaQYVe\n3gjPT/TGfYGwcJA+igHxEaFwHz6RKTWUTIcpTaIk77gBJaxB7s0bwkQiYUqkkZER69aIA8Sw8EzK\n5XKTfA8k5xOhJOe9M2OOuA9JNmc8K9aDT1yzP+DJGT9tJLxj4H2ZJ/YgF4aU9YmT8Mlk9oVft8wr\nB8vwWV7N5pvqER0hjoCKJKqCSuXzcKqAslgsZiCJdikkUNmb1EOgmSfKZl9xbzwb7wDg9EmuE13T\nDZT/V6tVc2ReWeaVTgA/IiIv6/5hrnvC0MfjcfPUGBe8JZdv/+rVJCwSqJhYLKaRkRGtra2Z7poD\nL5aWluxQX4qaSqWSUqmUta7t6OiwcA/H4+/H91fh/uj3Isk0s6CpUqlk4dby8rLdD7QQoSAb2/fy\nIUEbQtDo6Kh17qtWqxoYGNDGxoZ6e3ttfuD9pcY5ttQGeGkkiMarDEC7Ut2Q0LaAxczm8osPHTRG\nme/ZHDwPr/MG8ff392t0dNToFIxEW1ubcauoKnzSyXPpbHCUCsw70Q1j42+IIkBUPT09Jr/0mwtH\nKzUMv6dLpEavIu7zMIUCwovFYlYUtre3p5MnT1o+RZK9js2Os0Wm6d/X//PJVS4iUx91MX5Pc/B+\nJLC9kcI4Qj2w54iuMWDcQxRFhi4ZA0bIVzL7fxhukDBInvvywMQr5TwdSjRKlSvvDQoHTbMWGRfR\nB99HUb1NAg6Buevp6bE+Q4fBGPOOIccJ068KysbnQQAahy8veeaeeGZE4Kx/n6u5Lw09F5PC4Hnw\noFRvjDC4IBseFN/39/crl8spiiI7e3FiYkJLS0tWxLGzs2PGn9ARhIt0r1gsNiGIw7pj/lGxOTw8\nbJl2OOBSqaRcLqelpSUbCxuCUByPjgaeRI4kM9inTp1qkuKRczgcire01A8NYcGwmanawwChOoDn\nB11ms1kNDQ1ZOM7G5Pl4uRyI3RvJ7u5ua+LFvDFf8MFQMiB/wvUQQlN/G7h0nBObCHUR78N7YIzY\njMwzAEFqOBCkbocT+Kwx5tUbcE/dHM5NeJ4cuoLxQ794I8/pU547Z44O5x9wAjxTH1WC8FDu8KwY\nB5RTLFbvq4NT5mIvof4iF4QwgWSnf/a+AylOlHlhT3q1j1csoZQDPHHPUvPJXj5awbH7XIvUoA5Z\nF+wPUDdjARDiXNjT2AZUMex/2jn73lU+MmceuB+iWOguH+F5A++jTByUB7REeeVy2ZQ82AsoPy9U\nObJtPfIrf4QXxsEbdTaF5wExKDw07zmTyaSVE0vNSImukYSnGxsbWltbMz4XJIxh7u/vb0rS8eAw\njjwkv8CRU8EBQid5fpPDPqh4gz5ikSCLkxpHEbLx4CnR1mIACFlJiPm8ATQMdBZoLpPJGK3AwicJ\nJMkMAfMDh8mi9FQBr8PR0g4ChAxvjWHkvvg8jDLUBfdERMDnkowmUc/hMqApUBjjga4gYe/pAzan\nH4OkJmfB5zBnHglLjc6lrF+fqMcwQeNAD/lnj/qHZ8bcQ8FJdYeEBBIqzxsr3pv384nhWCzW1E5h\nd3fXju7zY4I+gN8HYYMmiXqZM563R+jvhlRRx/F79hMyYJAr9JykpspwnpdXMvlxY+y5X6khiaYG\nxlOUjJ28E/uHRDlAwCfEARh+bRC9wKeDrnFKJFSJRH0ukedLApsxAEBYd1KzwIHnKTX69APWjnrd\nM4beh1iHeVMMgg+3kOuxGTG8eFdCev6WXs69vb1WdOP7ahPajY+P26lEGBdfrPVuIXS1WrXKNh9+\n42jYCJ57ReLpjYhP4vhoBeNHszCoiiiKrJMjCxtkzu85qxJUSd4DJEUjMRyWV99gLDC6nqP1HPFh\n7jSTySiXy5nCCGfHAif8Bs37vi7VatUUVsw3VB0nT4F4WC8+oerRLjpu3pe1ggHwiIhxgMalZnTJ\na3h//zNvZP37Qdv4MJ5x+/sElHhnRCSFwSG/4+cZw+ujRMZATQVyXCg15oVnwD2QOyLvs7GxYdEi\n+RMcNPPnDwmXZM+TMfm1DZBhT0GzSWpa90RG7CMvB4UaZK69HWCN8FpAHfk5ABOSZOYNMMB4sDfb\n29vWtgERCAbXF0yx93gWAD14+I2NDXO+JMlEXyQ5AAAePUlEQVR9rg+HQfIXsOTXCWgeaor1cTiP\n9F7XPWHoJTUlJHjQhGSxWL3wxSsU8GZMkiTj5tBn+2IHKjapCpUahVpk5jmrkt+xcL2UibDNh8Ug\nGx8eUjhEKE7vERZcCMEKOggNQeAYLtAsmmpQPaiKg05YAB6xgZ7W19ft8zOZjLq7u623CQ4FvtrT\nHSxEnCeLyhuGw8kifo5RwOn4fICPjKAaQJRQZhg7T8Uxj0R23jmgnCK8J0rwDjqEYBsSKdzh3x92\n5oc5cP86xsGFIcCo7u/XezZ56S0G2a8f1i3l9HRTZT6hdjCGRLuHZZYYF5+E5vMkNSUqQdcYUp4T\n/3CgPGsOr+GUNBArDpn1SD0K98LYPFDwdSA+B4PRk9TksElkM9/04+GiJxVRCwWCHqwgTyYHBCpn\nHgEN3kADEGjtQYTHs4ZGYR59ZIzxZ9xQl+w19gmXX28++vS5CQ/8iGI8sHi/654x9KAUDB6D9UiT\ni8n1CxLPzcJBCijVFw5NvGhqxiKAt8bIen7Wy9O8YeN7LgwHCRc2EosZJDo3N2chcgjBumb6hA73\nhhEETbW01PX0njpJJpPW94coBNQEEonHG+0aMJxescRiIdnNGEG4/N4b+sNGn9fy3jgIEKNXE0Fr\nYJwJw/33h8NXjI93nKwVTyWwNvhMEmiSmjYxa8I/S8aJgeT9/dj97xkv88UYMOK1Ws0EBiTYUZZw\neXpRkiXzaKmBs/SG0+cJ+L+/dnd3NT8/b/fkIw2MF60fvPQVLnp6etreq7e3194DNAq94fMG5K34\nPaCEtcyz91JYT5l5ChCqD4cA4OM+SBL7Yjj2gKczMbQYR5/L82PwUZRnExBksD48yGIc/AOg+nXm\nVXSAHV/LsL+/b9F1LBZrOvCdiIs9xh7w80SC96jXPWPoD1MiLFDCVhJuIdTLrjmhhg1ZKpWMb2MC\n2HCgD09d9Pb2mgFdW1uz915cXNTa2pqmpqaaQmE2IAofNjp0ycrKyg9sZBa17z/NAyPyAEWB0Ehi\ngYwx5l1dXYa6MSigcw478GX7GDW/WdDVe0mf1Mj8s9BB4If5eI962Rw+Z8K/jY0NSfXWE75jnyQz\n8r7pmedo2YSsCZCiPwlpamrK5HP+efuKTv6eOfDcOvO/urpqGxuj4/lW3sOPF+rA86lsdigKqREt\nXr161RxVCMHOi+VzyL8gt4NqZN59JOe12hg5j9p5lq+//rqhZt5fktGZPH/W3+3bt9XV1dUkZ6QO\nA8Pr54L+Rn6vss4xROVy2doHg2x5ZiTkffEgAIk5xTgSPfjIlwNosBHQhdB4IHXaX5O/wxlwlCJ5\nBGoc6EHjE8WssSiKLBrCSfgoS6oDp0KhoHg8bvYI50Skz7GIUKk+wcy6p2r2sC4fEAug/TvvR/+j\nvljgGHPCMAp9arV69WShULBFRO8VvCoVrGyKwcFBC/V96A9vl0qllE6ntbS0ZKfPnDt3zsr4FxcX\nbdH4QhaQINwpi3x5eVlRFGlsbMzOJKWnzu7urh1/R+jI5sNjg4ZQl5BcBMmQdyCZR7SCSscnMSmo\nYHNQmOORvFRvQAZ36FUtGBkS1PCYnqOOosgWMJWKoHW+8lz29/ctCeXbAEM5cD4ohou1IMn6otRq\nNTMwOHjeg3mUZG1/ffWlVDeAaKZx3h5VsqE8141T9bQcdIN/LYjWJ+8lNY1ZqhvahYUFGyeG1PPF\nRIc4aOSx3oBB/8F3e308Z5WSFPfIEifsczLxeFy5XE6Dg4M2D349FotFO5geDhqHz3OhCSGOloRn\nFEXWboLq2lgsZt1iKW7CBlAdiiPDIUGHIA5gTUKLsd4AYxRX+ujcG3XWEIWNXrboI6F4PG4tiiVZ\n7QY1Lfwtz213d9eK9srlsp3H4KlNQBnR77sJTkKoq8oWFxeNjaAtA4WMVAYf9bonDP329rad37m/\nv6/FxUUrMSaExbD6EBsj7nlPvDubj6ZibW1tdpINKh8MKxuBSV1YWLB+1eiIKQWXGshRkqEMSqWT\nyaRWVlYM5XM4Rj6f1/Xr182wgpp8MRFtGkATIKgQGueiwrOyGVjkhKHcI1QQRt33zmCs/sAPnILn\nhzFoLFxPIRBt7ezsWHUhhhtpWiaTMeQBAsbh8Dx98tPTdTguHG0sFtPw8LA2NzeVTqct98B7ca8g\nPPh3citsYhwg4/XUHGE06wHj6NE9BoD7Zu2BVlta6v2RnnnmGat45PP9GKMoMu4bugu+WGoc0M46\nxiGRyMTwQV/69fTQQw81RcREaBhxck++0pW8EMaLecXZ006D9+F5rq+vW82Kp/nYWxxmD4jj73Ca\nxWKxCekzRxQHcm9eU46BI2LD4XO0ZKlUshOd/L0Ui0WrZUmlUioUCtZDBuPOZy8sLDSBMeSOPsL3\nSWLmk9YK8Xj9jNhMJmM0pVRH6xyLylqBOgY4AWIAufv79b5JRIWswx/muicM/c7Ojr73ve+Z4fbH\nlHljzmTBBx7WAxM+sxF990uQIZsaD8lJSm1tbSoUCtZ1klDKJ32lRkUgm5pFyIZsa2vTyZMndeLE\nCa2trWlubk7ValUjIyMW/rFYarW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"text/plain": [ "<matplotlib.figure.Figure at 0x21f0aa23518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(5, 85, 15):\n", " matreconstruida = np.matrix(U[:, :i]) * np.diag(S[:i]) * np.matrix(V[:i, :])\n", " plt.imshow(matreconstruida, cmap='gray')\n", " title = \"Aproximación de grado k = %s\" % i\n", " plt.title(title)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ¿Qué tiene que ver este proyecto con compresión de imágenes?\n", "\n", "R= Al descomponer la imagen nos estamos quedando únicamente con información relevante de la misma, por lo que podemos reconstruirla posteriormente utilizando únicamente un porcentaje de los vectores de U, Sigma y V, dependiendo de la fidelidad de la imagen que queramos. Como se puede observar en el ejercicio, con un porcentaje bajo de vectores podemos reconstruir la imagen sin alterar mucho su fidelidad, por lo que en lugar de guardar toda la matriz, basta con guardar ese porcentaje de vectores y así ahorrar memoria.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ejercicio 2: Cálculo de pseudoinversa y resoluver sistemas de ecuaciones\n", "\n", "Programar una función que dada cualquier matriz devuelva la pseudoinversa usando la descomposición SVD. Hacer otra función que resuelva un sistema de ecuaciones de la forma Ax=b usando la pseudoinversa." ] }, { "cell_type": "code", "execution_count": 669, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from copy import copy, deepcopy\n", "\n", "def pseudoinversa(A):\n", " U, S, V = np.linalg.svd(A)\n", " \n", " m, n = A.shape\n", "\n", " D = np.empty([m,n])\n", " \n", " D = D * 0\n", " \n", " for k in range (n):\n", " D[k,k] = 1\n", " \n", " S = D * S # Vuelvo a S una matriz diagonal mXn\n", " \n", " pseudo = deepcopy(S)\n", " \n", " for i in range (n): #Calculo pseudo inversa de sigma\n", " if pseudo[i,i] != 0:\n", " pseudo[i,i] = 1/pseudo[i,i]\n", " \n", " pseudo = pseudo.transpose()\n", " VT = V.transpose()\n", " UT = U.transpose()\n", " \n", " w = np.dot(VT,pseudo)\n", " pseudo = np.dot(w,UT)\n", " \n", " return pseudo\n", " \n", " \n", "def resuelve(A,b):\n", " y= pseudoinversa(A)\n", " x = np.dot(y,b)\n", " return x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ejemplo para ver que la función resuelve de manera correcta el sistema de ecuaciones:" ] }, { "cell_type": "code", "execution_count": 670, "metadata": { "collapsed": true }, "outputs": [], "source": [ "A = np.array([[2, 1, 3], [4, -1, 3], [-2, 5, 5]])\n", "b = np.array([[17],[31],[-5]])" ] }, { "cell_type": "code", "execution_count": 671, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 5.],\n", " [-2.],\n", " [ 3.]])" ] }, "execution_count": 671, "metadata": {}, "output_type": "execute_result" } ], "source": [ "resuelve(A,b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Jugar con la función donde b puede tomar distintos valores y A=[[1,1],[0,0]]:" ] }, { "cell_type": "code", "execution_count": 672, "metadata": { "collapsed": true }, "outputs": [], "source": [ "A = np.array([[1,1],[0,0]])\n", "b= np.array([[5],[0]])" ] }, { "cell_type": "code", "execution_count": 674, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 2.5],\n", " [ 2.5]])" ] }, "execution_count": 674, "metadata": {}, "output_type": "execute_result" } ], "source": [ "resuelve(A,b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "a) Si b esta en la imagen de A (La imagen es [x,0]) devuelve la solución al sistema de manera correcta. Si b no esta en la imagen (ej. b= [1,1]) devuelve la solución al sistema considerando la imagen, que es la solución más cercana, en el ejemplo b=[1,1] devuelve la solución al sistema considerando b=[1,0].\n", "\n", "b) ¿La solución resultante es única? No, ya que para diferentes valore de b, existe el mismo valor de x. Esto sucede porque la matriz es singular.\n", "\n", "c) Cambiar a: A=[[1,1],[0,1e-32]]. ¿La solución es única? Sí, para cada diferente valor de b1 y b2, devuelve un valor único de x1 y x2. ¿Cambia el valor devuelto de x en cada posible valor de b del punto anterior? sí, debido a que esta matriz si es invertible con el metodo de la pseudoinversa, aunque prácticamente sea la misma matriz que en el punto anterior." ] }, { "cell_type": "code", "execution_count": 675, "metadata": { "collapsed": true }, "outputs": [], "source": [ "A = np.array([[1,1],[0,1e-32]])\n", "b= np.array([[5],[0]])" ] }, { "cell_type": "code", "execution_count": 676, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 5.00000000e+00],\n", " [ -5.55111512e-16]])" ] }, "execution_count": 676, "metadata": {}, "output_type": "execute_result" } ], "source": [ "resuelve(A,b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ejercicio 3: Ajuste de mínimos cuadrados" ] }, { "cell_type": "code", "execution_count": 303, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "z = pd.read_csv(\"https://raw.githubusercontent.com/mauriciogtec/PropedeuticoDataScience2017/master/Tarea/study_vs_sat.csv\",index_col = False)\n" ] }, { "cell_type": "code", "execution_count": 328, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " study_hours sat_score\n", "0 4 390\n", "1 9 580\n", "2 10 650\n", "3 14 730\n", "4 4 410\n", "5 7 530\n", "6 12 600\n", "7 22 790\n", "8 1 350\n", "9 3 400\n", "10 8 590\n", "11 11 640\n", "12 5 450\n", "13 6 520\n", "14 10 690\n", "15 11 690\n", "16 16 770\n", "17 13 700\n", "18 13 730\n", "19 10 640 \n", " \n", " Sat_score ~ 353.164879499 + 25.3264677779Study_hours\n" ] } ], "source": [ "m, n = z.shape\n", "\n", "SX= z.iloc[0][0]\n", "SY = z.iloc[0][1]\n", "SXX = z.iloc[0][0] **2\n", "SYY = z.iloc[0][1] **2\n", "SXY = z.iloc[0][0] * z.iloc[0][1]\n", "\n", "\n", "for i in range (1,m):\n", " SX += z.iloc[i][0]\n", " SY += z.iloc[i][1]\n", " SXX += z.iloc[i][0] **2\n", " SYY += z.iloc[i][1] **2\n", " SXY += z.iloc[i][0] * z.iloc[i][1]\n", "\n", "Beta = (m*SXY - SX*SY) / (m*SXX- SX**2)\n", "Alpha = (1/m)*SY - Beta*(1/m)*SX\n", "\n", "funcion= \"Sat_score ~ \" + str(Alpha) + \" + \" + str(Beta) + \"Study_hours\"\n", "print(z,\"\\n \",\"\\n \",funcion) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li> ¿Cuál es el gradiente de la función que se quiere optimizar? R= El Vector [1, Study_hours]\n", "\n", "Programar una función que reciba los valores alpha, beta y el vector Study_hours y devuelva un vector array de numpy de predicciones alpha + beta * Study_hours_i, con un vaor por cada individuo." ] }, { "cell_type": "code", "execution_count": 537, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sat_score(Alpha,Beta,Study_hours):\n", " m, = Study_hours.shape\n", " \n", " Satscore= [0]\n", " for i in range (m-1):\n", " Satscore += [0]\n", " Satscore = np.array([Satscore])\n", " Satscore= Satscore.transpose()\n", " \n", " for j in range (m):\n", " Satscore[j,0]= Alpha + Beta * Study_hours[j]\n", " \n", " return Satscore\n", " " ] }, { "cell_type": "code", "execution_count": 604, "metadata": {}, "outputs": [ { "data": { "image/png": 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dqGrS+yJiSiNew72VrgCO7VE2F1gaEROBpWm8WV3Bm7cPwFfSfjQlIn4yxDHV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\nsD/aaScAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x21f0e10c1d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "array([[454],\n", " [581],\n", " [606],\n", " [707],\n", " [454],\n", " [530],\n", " [657],\n", " [910],\n", " [378],\n", " [429],\n", " [555],\n", " [631],\n", " [479],\n", " [505],\n", " [606],\n", " [631],\n", " [758],\n", " [682],\n", " [682],\n", " [606]])" ] }, "execution_count": 604, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SH= z.iloc[:,0]\n", "sat_s = sat_score(353.164879499,25.3264677779,SH)\n", "\n", "plt.scatter(SH, sat_s)\n", "plt.title('Scatter: Study hours vs Sat Score')\n", "plt.xlabel('Study_hours')\n", "plt.ylabel('Sat_score')\n", "plt.show()\n", "\n", "sat_s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li><strong>(Avanzado)</strong> Usen la libreria <code>matplotlib</code> par visualizar las predicciones con alpha y beta solución contra los valores reales de sat_score." ] }, { "cell_type": "code", "execution_count": 635, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x21f0e1a97b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "SS= z.iloc[:,1]\n", "g1 = (SH,SS)\n", "g2 = (SH,sat_s)\n", "\n", " \n", "data = (g1, g2)\n", "colors = (\"green\", \"red\")\n", "groups = (\"Real\", \"Forecast: Alpha + Beta * Study_Hours\") \n", "\n", "fig, ax = plt.subplots()\n", "for data, color, group in zip(data, colors, groups):\n", " x, y = data\n", " ax.scatter(x, y, alpha=0.8, c=color, edgecolors='none', s=30, label=group)\n", " \n", "plt.title('Real vs Forecast')\n", "plt.legend(loc=0)\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li> Definan un numpy array X de dos columnas, la primera con unos en todas sus entradas y la segunda y la segunda con la variable Study_hours. Observen que <code>X*[alpha,beta]</code> nos devuelve <code>alpha + beta*study_hours_i</code> en cada entrada y que entonces el problema se vuelve <code>sat_score ~ X*[alpha,beta]</code>\n" ] }, { "cell_type": "code", "execution_count": 643, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 454.47075061],\n", " [ 581.1030895 ],\n", " [ 606.42955728],\n", " [ 707.73542839],\n", " [ 454.47075061],\n", " [ 530.45015394],\n", " [ 657.08249283],\n", " [ 910.34717061],\n", " [ 378.49134728],\n", " [ 429.14428283],\n", " [ 555.77662172],\n", " [ 631.75602506],\n", " [ 479.79721839],\n", " [ 505.12368617],\n", " [ 606.42955728],\n", " [ 631.75602506],\n", " [ 758.38836395],\n", " [ 682.40896061],\n", " [ 682.40896061],\n", " [ 606.42955728]])" ] }, "execution_count": 643, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x=[1.]\n", "y= [z.iloc[0,0]]\n", "for i in range (19):\n", " x += [1]\n", " y += [z.iloc[i+1,0]]\n", "\n", "X = np.array([x,y])\n", "X = X.transpose()\n", "\n", "alpha = 353.164879499\n", "beta = 25.3264677779\n", "ab=np.array([[alpha],[beta]])\n", "R = np.dot(X,ab)\n", "R\n", "\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li>Calculen la pseudoinversa X^+ de X y computen <code>(X^+)*sat_score</code> para obtener alpha y beta soluciones.</li> \n" ] }, { "cell_type": "code", "execution_count": 677, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 353.1648795 , 25.32646778])" ] }, "execution_count": 677, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Xpseudo= pseudoinversa(X)\n", "Sscore= z.iloc[:,1]\n", "\n", "ab=np.dot(Xpseudo,Sscore)\n", "ab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<li>Comparen la solución anterior con la de la fórmula directa de solución exacta <code>(alpha,beta)=(X^t*X)^(-1)*X^t*study_hours</code>.</li>" ] }, { "cell_type": "code", "execution_count": 597, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 353.1648795 , 25.32646778])" ] }, "execution_count": 597, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SH= z.iloc[:,0]\n", "Sscore= z.iloc[:,1]\n", "XT = X.transpose()\n", "XT2 = np.dot(XT,X)\n", "XTI = np.linalg.inv(XT2)\n", "\n", "w= np.dot(XTI,XT)\n", "ab = np.dot(w,Sscore)\n", "ab\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La solución es la misma con ambos métodos" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
ES-DOC/esdoc-jupyterhub
notebooks/cmcc/cmip6/models/sandbox-2/atmos.ipynb
1
208997
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Atmos \n", "**MIP Era**: CMIP6 \n", "**Institute**: CMCC \n", "**Source ID**: SANDBOX-2 \n", "**Topic**: Atmos \n", "**Sub-Topics**: Dynamical Core, Radiation, Turbulence Convection, Microphysics Precipitation, Cloud Scheme, Observation Simulation, Gravity Waves, Solar, Volcanos. \n", "**Properties**: 156 (127 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/atmos?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-15 16:53:50" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'cmcc', 'sandbox-2', 'atmos')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties --&gt; Overview](#1.-Key-Properties---&gt;-Overview) \n", "[2. Key Properties --&gt; Resolution](#2.-Key-Properties---&gt;-Resolution) \n", "[3. Key Properties --&gt; Timestepping](#3.-Key-Properties---&gt;-Timestepping) \n", "[4. Key Properties --&gt; Orography](#4.-Key-Properties---&gt;-Orography) \n", "[5. Grid --&gt; Discretisation](#5.-Grid---&gt;-Discretisation) \n", "[6. Grid --&gt; Discretisation --&gt; Horizontal](#6.-Grid---&gt;-Discretisation---&gt;-Horizontal) \n", "[7. Grid --&gt; Discretisation --&gt; Vertical](#7.-Grid---&gt;-Discretisation---&gt;-Vertical) \n", "[8. Dynamical Core](#8.-Dynamical-Core) \n", "[9. Dynamical Core --&gt; Top Boundary](#9.-Dynamical-Core---&gt;-Top-Boundary) \n", "[10. Dynamical Core --&gt; Lateral Boundary](#10.-Dynamical-Core---&gt;-Lateral-Boundary) \n", "[11. Dynamical Core --&gt; Diffusion Horizontal](#11.-Dynamical-Core---&gt;-Diffusion-Horizontal) \n", "[12. Dynamical Core --&gt; Advection Tracers](#12.-Dynamical-Core---&gt;-Advection-Tracers) \n", "[13. Dynamical Core --&gt; Advection Momentum](#13.-Dynamical-Core---&gt;-Advection-Momentum) \n", "[14. Radiation](#14.-Radiation) \n", "[15. Radiation --&gt; Shortwave Radiation](#15.-Radiation---&gt;-Shortwave-Radiation) \n", "[16. Radiation --&gt; Shortwave GHG](#16.-Radiation---&gt;-Shortwave-GHG) \n", "[17. Radiation --&gt; Shortwave Cloud Ice](#17.-Radiation---&gt;-Shortwave-Cloud-Ice) \n", "[18. Radiation --&gt; Shortwave Cloud Liquid](#18.-Radiation---&gt;-Shortwave-Cloud-Liquid) \n", "[19. Radiation --&gt; Shortwave Cloud Inhomogeneity](#19.-Radiation---&gt;-Shortwave-Cloud-Inhomogeneity) \n", "[20. Radiation --&gt; Shortwave Aerosols](#20.-Radiation---&gt;-Shortwave-Aerosols) \n", "[21. Radiation --&gt; Shortwave Gases](#21.-Radiation---&gt;-Shortwave-Gases) \n", "[22. Radiation --&gt; Longwave Radiation](#22.-Radiation---&gt;-Longwave-Radiation) \n", "[23. Radiation --&gt; Longwave GHG](#23.-Radiation---&gt;-Longwave-GHG) \n", "[24. Radiation --&gt; Longwave Cloud Ice](#24.-Radiation---&gt;-Longwave-Cloud-Ice) \n", "[25. Radiation --&gt; Longwave Cloud Liquid](#25.-Radiation---&gt;-Longwave-Cloud-Liquid) \n", "[26. Radiation --&gt; Longwave Cloud Inhomogeneity](#26.-Radiation---&gt;-Longwave-Cloud-Inhomogeneity) \n", "[27. Radiation --&gt; Longwave Aerosols](#27.-Radiation---&gt;-Longwave-Aerosols) \n", "[28. Radiation --&gt; Longwave Gases](#28.-Radiation---&gt;-Longwave-Gases) \n", "[29. Turbulence Convection](#29.-Turbulence-Convection) \n", "[30. Turbulence Convection --&gt; Boundary Layer Turbulence](#30.-Turbulence-Convection---&gt;-Boundary-Layer-Turbulence) \n", "[31. Turbulence Convection --&gt; Deep Convection](#31.-Turbulence-Convection---&gt;-Deep-Convection) \n", "[32. Turbulence Convection --&gt; Shallow Convection](#32.-Turbulence-Convection---&gt;-Shallow-Convection) \n", "[33. Microphysics Precipitation](#33.-Microphysics-Precipitation) \n", "[34. Microphysics Precipitation --&gt; Large Scale Precipitation](#34.-Microphysics-Precipitation---&gt;-Large-Scale-Precipitation) \n", "[35. Microphysics Precipitation --&gt; Large Scale Cloud Microphysics](#35.-Microphysics-Precipitation---&gt;-Large-Scale-Cloud-Microphysics) \n", "[36. Cloud Scheme](#36.-Cloud-Scheme) \n", "[37. Cloud Scheme --&gt; Optical Cloud Properties](#37.-Cloud-Scheme---&gt;-Optical-Cloud-Properties) \n", "[38. Cloud Scheme --&gt; Sub Grid Scale Water Distribution](#38.-Cloud-Scheme---&gt;-Sub-Grid-Scale-Water-Distribution) \n", "[39. Cloud Scheme --&gt; Sub Grid Scale Ice Distribution](#39.-Cloud-Scheme---&gt;-Sub-Grid-Scale-Ice-Distribution) \n", "[40. Observation Simulation](#40.-Observation-Simulation) \n", "[41. Observation Simulation --&gt; Isscp Attributes](#41.-Observation-Simulation---&gt;-Isscp-Attributes) \n", "[42. Observation Simulation --&gt; Cosp Attributes](#42.-Observation-Simulation---&gt;-Cosp-Attributes) \n", "[43. Observation Simulation --&gt; Radar Inputs](#43.-Observation-Simulation---&gt;-Radar-Inputs) \n", "[44. Observation Simulation --&gt; Lidar Inputs](#44.-Observation-Simulation---&gt;-Lidar-Inputs) \n", "[45. Gravity Waves](#45.-Gravity-Waves) \n", "[46. Gravity Waves --&gt; Orographic Gravity Waves](#46.-Gravity-Waves---&gt;-Orographic-Gravity-Waves) \n", "[47. Gravity Waves --&gt; Non Orographic Gravity Waves](#47.-Gravity-Waves---&gt;-Non-Orographic-Gravity-Waves) \n", "[48. Solar](#48.-Solar) \n", "[49. Solar --&gt; Solar Pathways](#49.-Solar---&gt;-Solar-Pathways) \n", "[50. Solar --&gt; Solar Constant](#50.-Solar---&gt;-Solar-Constant) \n", "[51. Solar --&gt; Orbital Parameters](#51.-Solar---&gt;-Orbital-Parameters) \n", "[52. Solar --&gt; Insolation Ozone](#52.-Solar---&gt;-Insolation-Ozone) \n", "[53. Volcanos](#53.-Volcanos) \n", "[54. Volcanos --&gt; Volcanoes Treatment](#54.-Volcanos---&gt;-Volcanoes-Treatment) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties --&gt; Overview \n", "*Top level key properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.overview.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of atmosphere model code (CAM 4.0, ARPEGE 3.2,...)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.overview.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Model Family\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Type of atmospheric model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.overview.model_family') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"AGCM\" \n", "# \"ARCM\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Basic approximations made in the atmosphere.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.overview.basic_approximations') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"primitive equations\" \n", "# \"non-hydrostatic\" \n", "# \"anelastic\" \n", "# \"Boussinesq\" \n", "# \"hydrostatic\" \n", "# \"quasi-hydrostatic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Resolution \n", "*Characteristics of the model resolution*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Horizontal Resolution Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of the model grid, e.g. T42, N48.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.resolution.horizontal_resolution_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Canonical Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Expression quoted for gross comparisons of resolution, e.g. 2.5 x 3.75 degrees lat-lon.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Range Horizontal Resolution\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Range of horizontal resolution with spatial details, eg. 1 deg (Equator) - 0.5 deg*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.resolution.range_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.4. Number Of Vertical Levels\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of vertical levels resolved on the computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.5. High Top\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the atmosphere have a high-top? High-Top atmospheres have a fully resolved stratosphere with a model top above the stratopause.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.resolution.high_top') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Timestepping \n", "*Characteristics of the atmosphere model time stepping*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Timestep Dynamics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Timestep for the dynamics, e.g. 30 min.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_dynamics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Timestep Shortwave Radiative Transfer\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for the shortwave radiative transfer, e.g. 1.5 hours.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_shortwave_radiative_transfer') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Timestep Longwave Radiative Transfer\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for the longwave radiative transfer, e.g. 3 hours.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.timestepping.timestep_longwave_radiative_transfer') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Orography \n", "*Characteristics of the model orography*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time adaptation of the orography.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.orography.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"present day\" \n", "# \"modified\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Changes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *If the orography type is modified describe the time adaptation changes.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.key_properties.orography.changes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"related to ice sheets\" \n", "# \"related to tectonics\" \n", "# \"modified mean\" \n", "# \"modified variance if taken into account in model (cf gravity waves)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Grid --&gt; Discretisation \n", "*Atmosphere grid discretisation*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of grid discretisation in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Grid --&gt; Discretisation --&gt; Horizontal \n", "*Atmosphere discretisation in the horizontal*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal discretisation type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"spectral\" \n", "# \"fixed grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Scheme Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal discretisation method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"finite elements\" \n", "# \"finite volumes\" \n", "# \"finite difference\" \n", "# \"centered finite difference\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. Scheme Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal discretisation function order*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.scheme_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"second\" \n", "# \"third\" \n", "# \"fourth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. Horizontal Pole\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Horizontal discretisation pole singularity treatment*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.horizontal_pole') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"filter\" \n", "# \"pole rotation\" \n", "# \"artificial island\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.5. Grid Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal grid type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.horizontal.grid_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Gaussian\" \n", "# \"Latitude-Longitude\" \n", "# \"Cubed-Sphere\" \n", "# \"Icosahedral\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Grid --&gt; Discretisation --&gt; Vertical \n", "*Atmosphere discretisation in the vertical*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Coordinate Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Type of vertical coordinate system*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.grid.discretisation.vertical.coordinate_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"isobaric\" \n", "# \"sigma\" \n", "# \"hybrid sigma-pressure\" \n", "# \"hybrid pressure\" \n", "# \"vertically lagrangian\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Dynamical Core \n", "*Characteristics of the dynamical core*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of atmosphere dynamical core*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the dynamical core of the model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Timestepping Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Timestepping framework type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.timestepping_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Adams-Bashforth\" \n", "# \"explicit\" \n", "# \"implicit\" \n", "# \"semi-implicit\" \n", "# \"leap frog\" \n", "# \"multi-step\" \n", "# \"Runge Kutta fifth order\" \n", "# \"Runge Kutta second order\" \n", "# \"Runge Kutta third order\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prognostic Variables\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of the model prognostic variables*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"surface pressure\" \n", "# \"wind components\" \n", "# \"divergence/curl\" \n", "# \"temperature\" \n", "# \"potential temperature\" \n", "# \"total water\" \n", "# \"water vapour\" \n", "# \"water liquid\" \n", "# \"water ice\" \n", "# \"total water moments\" \n", "# \"clouds\" \n", "# \"radiation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Dynamical Core --&gt; Top Boundary \n", "*Type of boundary layer at the top of the model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Top Boundary Condition\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Top boundary condition*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_boundary_condition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"sponge layer\" \n", "# \"radiation boundary condition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Top Heat\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Top boundary heat treatment*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_heat') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Top Wind\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Top boundary wind treatment*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.top_boundary.top_wind') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Dynamical Core --&gt; Lateral Boundary \n", "*Type of lateral boundary condition (if the model is a regional model)*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Condition\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Type of lateral boundary condition*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.lateral_boundary.condition') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"sponge layer\" \n", "# \"radiation boundary condition\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Dynamical Core --&gt; Diffusion Horizontal \n", "*Horizontal diffusion scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Horizontal diffusion scheme name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Scheme Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Horizontal diffusion scheme method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.diffusion_horizontal.scheme_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"iterated Laplacian\" \n", "# \"bi-harmonic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Dynamical Core --&gt; Advection Tracers \n", "*Tracer advection scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Tracer advection scheme name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Heun\" \n", "# \"Roe and VanLeer\" \n", "# \"Roe and Superbee\" \n", "# \"Prather\" \n", "# \"UTOPIA\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Scheme Characteristics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Tracer advection scheme characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.scheme_characteristics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Eulerian\" \n", "# \"modified Euler\" \n", "# \"Lagrangian\" \n", "# \"semi-Lagrangian\" \n", "# \"cubic semi-Lagrangian\" \n", "# \"quintic semi-Lagrangian\" \n", "# \"mass-conserving\" \n", "# \"finite volume\" \n", "# \"flux-corrected\" \n", "# \"linear\" \n", "# \"quadratic\" \n", "# \"quartic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Conserved Quantities\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Tracer advection scheme conserved quantities*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conserved_quantities') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"dry mass\" \n", "# \"tracer mass\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.4. Conservation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Tracer advection scheme conservation method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_tracers.conservation_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"conservation fixer\" \n", "# \"Priestley algorithm\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Dynamical Core --&gt; Advection Momentum \n", "*Momentum advection scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Momentum advection schemes name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"VanLeer\" \n", "# \"Janjic\" \n", "# \"SUPG (Streamline Upwind Petrov-Galerkin)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Scheme Characteristics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Momentum advection scheme characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_characteristics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"2nd order\" \n", "# \"4th order\" \n", "# \"cell-centred\" \n", "# \"staggered grid\" \n", "# \"semi-staggered grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. Scheme Staggering Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Momentum advection scheme staggering type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.scheme_staggering_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Arakawa B-grid\" \n", "# \"Arakawa C-grid\" \n", "# \"Arakawa D-grid\" \n", "# \"Arakawa E-grid\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.4. Conserved Quantities\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Momentum advection scheme conserved quantities*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conserved_quantities') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Angular momentum\" \n", "# \"Horizontal momentum\" \n", "# \"Enstrophy\" \n", "# \"Mass\" \n", "# \"Total energy\" \n", "# \"Vorticity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.5. Conservation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Momentum advection scheme conservation method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.dynamical_core.advection_momentum.conservation_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"conservation fixer\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Radiation \n", "*Characteristics of the atmosphere radiation process*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Aerosols\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Aerosols whose radiative effect is taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.aerosols') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"sulphate\" \n", "# \"nitrate\" \n", "# \"sea salt\" \n", "# \"dust\" \n", "# \"ice\" \n", "# \"organic\" \n", "# \"BC (black carbon / soot)\" \n", "# \"SOA (secondary organic aerosols)\" \n", "# \"POM (particulate organic matter)\" \n", "# \"polar stratospheric ice\" \n", "# \"NAT (nitric acid trihydrate)\" \n", "# \"NAD (nitric acid dihydrate)\" \n", "# \"STS (supercooled ternary solution aerosol particle)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Radiation --&gt; Shortwave Radiation \n", "*Properties of the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of shortwave radiation in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Spectral Integration\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Shortwave radiation scheme spectral integration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_integration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"wide-band model\" \n", "# \"correlated-k\" \n", "# \"exponential sum fitting\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Transport Calculation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Shortwave radiation transport calculation methods*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.transport_calculation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"two-stream\" \n", "# \"layer interaction\" \n", "# \"bulk\" \n", "# \"adaptive\" \n", "# \"multi-stream\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Spectral Intervals\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Shortwave radiation scheme number of spectral intervals*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_radiation.spectral_intervals') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Radiation --&gt; Shortwave GHG \n", "*Representation of greenhouse gases in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Greenhouse Gas Complexity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Complexity of greenhouse gases whose shortwave radiative effects are taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.greenhouse_gas_complexity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"CO2\" \n", "# \"CH4\" \n", "# \"N2O\" \n", "# \"CFC-11 eq\" \n", "# \"CFC-12 eq\" \n", "# \"HFC-134a eq\" \n", "# \"Explicit ODSs\" \n", "# \"Explicit other fluorinated gases\" \n", "# \"O3\" \n", "# \"H2O\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. ODS\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Ozone depleting substances whose shortwave radiative effects are explicitly taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.ODS') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"CFC-12\" \n", "# \"CFC-11\" \n", "# \"CFC-113\" \n", "# \"CFC-114\" \n", "# \"CFC-115\" \n", "# \"HCFC-22\" \n", "# \"HCFC-141b\" \n", "# \"HCFC-142b\" \n", "# \"Halon-1211\" \n", "# \"Halon-1301\" \n", "# \"Halon-2402\" \n", "# \"methyl chloroform\" \n", "# \"carbon tetrachloride\" \n", "# \"methyl chloride\" \n", "# \"methylene chloride\" \n", "# \"chloroform\" \n", "# \"methyl bromide\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.3. Other Flourinated Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Other flourinated gases whose shortwave radiative effects are explicitly taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_GHG.other_flourinated_gases') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"HFC-134a\" \n", "# \"HFC-23\" \n", "# \"HFC-32\" \n", "# \"HFC-125\" \n", "# \"HFC-143a\" \n", "# \"HFC-152a\" \n", "# \"HFC-227ea\" \n", "# \"HFC-236fa\" \n", "# \"HFC-245fa\" \n", "# \"HFC-365mfc\" \n", "# \"HFC-43-10mee\" \n", "# \"CF4\" \n", "# \"C2F6\" \n", "# \"C3F8\" \n", "# \"C4F10\" \n", "# \"C5F12\" \n", "# \"C6F14\" \n", "# \"C7F16\" \n", "# \"C8F18\" \n", "# \"c-C4F8\" \n", "# \"NF3\" \n", "# \"SF6\" \n", "# \"SO2F2\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 17. Radiation --&gt; Shortwave Cloud Ice \n", "*Shortwave radiative properties of ice crystals in clouds*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 17.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General shortwave radiative interactions with cloud ice crystals*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.2. Physical Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of cloud ice crystals in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.physical_representation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bi-modal size distribution\" \n", "# \"ensemble of ice crystals\" \n", "# \"mean projected area\" \n", "# \"ice water path\" \n", "# \"crystal asymmetry\" \n", "# \"crystal aspect ratio\" \n", "# \"effective crystal radius\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 17.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to cloud ice crystals in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_ice.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"T-matrix\" \n", "# \"geometric optics\" \n", "# \"finite difference time domain (FDTD)\" \n", "# \"Mie theory\" \n", "# \"anomalous diffraction approximation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 18. Radiation --&gt; Shortwave Cloud Liquid \n", "*Shortwave radiative properties of liquid droplets in clouds*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 18.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General shortwave radiative interactions with cloud liquid droplets*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.2. Physical Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of cloud liquid droplets in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.physical_representation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"cloud droplet number concentration\" \n", "# \"effective cloud droplet radii\" \n", "# \"droplet size distribution\" \n", "# \"liquid water path\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 18.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to cloud liquid droplets in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_liquid.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"geometric optics\" \n", "# \"Mie theory\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 19. Radiation --&gt; Shortwave Cloud Inhomogeneity \n", "*Cloud inhomogeneity in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 19.1. Cloud Inhomogeneity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method for taking into account horizontal cloud inhomogeneity*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_cloud_inhomogeneity.cloud_inhomogeneity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Monte Carlo Independent Column Approximation\" \n", "# \"Triplecloud\" \n", "# \"analytic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 20. Radiation --&gt; Shortwave Aerosols \n", "*Shortwave radiative properties of aerosols*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 20.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General shortwave radiative interactions with aerosols*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.2. Physical Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of aerosols in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.physical_representation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"number concentration\" \n", "# \"effective radii\" \n", "# \"size distribution\" \n", "# \"asymmetry\" \n", "# \"aspect ratio\" \n", "# \"mixing state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 20.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to aerosols in the shortwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_aerosols.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"T-matrix\" \n", "# \"geometric optics\" \n", "# \"finite difference time domain (FDTD)\" \n", "# \"Mie theory\" \n", "# \"anomalous diffraction approximation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 21. Radiation --&gt; Shortwave Gases \n", "*Shortwave radiative properties of gases*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 21.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General shortwave radiative interactions with gases*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.shortwave_gases.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 22. Radiation --&gt; Longwave Radiation \n", "*Properties of the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 22.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of longwave radiation in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_radiation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.2. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the longwave radiation scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_radiation.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.3. Spectral Integration\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Longwave radiation scheme spectral integration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_integration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"wide-band model\" \n", "# \"correlated-k\" \n", "# \"exponential sum fitting\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.4. Transport Calculation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Longwave radiation transport calculation methods*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_radiation.transport_calculation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"two-stream\" \n", "# \"layer interaction\" \n", "# \"bulk\" \n", "# \"adaptive\" \n", "# \"multi-stream\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 22.5. Spectral Intervals\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Longwave radiation scheme number of spectral intervals*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_radiation.spectral_intervals') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 23. Radiation --&gt; Longwave GHG \n", "*Representation of greenhouse gases in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 23.1. Greenhouse Gas Complexity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Complexity of greenhouse gases whose longwave radiative effects are taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_GHG.greenhouse_gas_complexity') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"CO2\" \n", "# \"CH4\" \n", "# \"N2O\" \n", "# \"CFC-11 eq\" \n", "# \"CFC-12 eq\" \n", "# \"HFC-134a eq\" \n", "# \"Explicit ODSs\" \n", "# \"Explicit other fluorinated gases\" \n", "# \"O3\" \n", "# \"H2O\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.2. ODS\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Ozone depleting substances whose longwave radiative effects are explicitly taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_GHG.ODS') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"CFC-12\" \n", "# \"CFC-11\" \n", "# \"CFC-113\" \n", "# \"CFC-114\" \n", "# \"CFC-115\" \n", "# \"HCFC-22\" \n", "# \"HCFC-141b\" \n", "# \"HCFC-142b\" \n", "# \"Halon-1211\" \n", "# \"Halon-1301\" \n", "# \"Halon-2402\" \n", "# \"methyl chloroform\" \n", "# \"carbon tetrachloride\" \n", "# \"methyl chloride\" \n", "# \"methylene chloride\" \n", "# \"chloroform\" \n", "# \"methyl bromide\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 23.3. Other Flourinated Gases\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Other flourinated gases whose longwave radiative effects are explicitly taken into account in the atmosphere model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_GHG.other_flourinated_gases') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"HFC-134a\" \n", "# \"HFC-23\" \n", "# \"HFC-32\" \n", "# \"HFC-125\" \n", "# \"HFC-143a\" \n", "# \"HFC-152a\" \n", "# \"HFC-227ea\" \n", "# \"HFC-236fa\" \n", "# \"HFC-245fa\" \n", "# \"HFC-365mfc\" \n", "# \"HFC-43-10mee\" \n", "# \"CF4\" \n", "# \"C2F6\" \n", "# \"C3F8\" \n", "# \"C4F10\" \n", "# \"C5F12\" \n", "# \"C6F14\" \n", "# \"C7F16\" \n", "# \"C8F18\" \n", "# \"c-C4F8\" \n", "# \"NF3\" \n", "# \"SF6\" \n", "# \"SO2F2\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 24. Radiation --&gt; Longwave Cloud Ice \n", "*Longwave radiative properties of ice crystals in clouds*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 24.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General longwave radiative interactions with cloud ice crystals*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 24.2. Physical Reprenstation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of cloud ice crystals in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.physical_reprenstation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"bi-modal size distribution\" \n", "# \"ensemble of ice crystals\" \n", "# \"mean projected area\" \n", "# \"ice water path\" \n", "# \"crystal asymmetry\" \n", "# \"crystal aspect ratio\" \n", "# \"effective crystal radius\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 24.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to cloud ice crystals in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_ice.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"T-matrix\" \n", "# \"geometric optics\" \n", "# \"finite difference time domain (FDTD)\" \n", "# \"Mie theory\" \n", "# \"anomalous diffraction approximation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 25. Radiation --&gt; Longwave Cloud Liquid \n", "*Longwave radiative properties of liquid droplets in clouds*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 25.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General longwave radiative interactions with cloud liquid droplets*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.2. Physical Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of cloud liquid droplets in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.physical_representation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"cloud droplet number concentration\" \n", "# \"effective cloud droplet radii\" \n", "# \"droplet size distribution\" \n", "# \"liquid water path\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 25.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to cloud liquid droplets in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_liquid.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"geometric optics\" \n", "# \"Mie theory\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 26. Radiation --&gt; Longwave Cloud Inhomogeneity \n", "*Cloud inhomogeneity in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 26.1. Cloud Inhomogeneity\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method for taking into account horizontal cloud inhomogeneity*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_cloud_inhomogeneity.cloud_inhomogeneity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Monte Carlo Independent Column Approximation\" \n", "# \"Triplecloud\" \n", "# \"analytic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 27. Radiation --&gt; Longwave Aerosols \n", "*Longwave radiative properties of aerosols*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 27.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General longwave radiative interactions with aerosols*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.2. Physical Representation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical representation of aerosols in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.physical_representation') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"number concentration\" \n", "# \"effective radii\" \n", "# \"size distribution\" \n", "# \"asymmetry\" \n", "# \"aspect ratio\" \n", "# \"mixing state\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 27.3. Optical Methods\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Optical methods applicable to aerosols in the longwave radiation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_aerosols.optical_methods') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"T-matrix\" \n", "# \"geometric optics\" \n", "# \"finite difference time domain (FDTD)\" \n", "# \"Mie theory\" \n", "# \"anomalous diffraction approximation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 28. Radiation --&gt; Longwave Gases \n", "*Longwave radiative properties of gases*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 28.1. General Interactions\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *General longwave radiative interactions with gases*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.radiation.longwave_gases.general_interactions') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"scattering\" \n", "# \"emission/absorption\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 29. Turbulence Convection \n", "*Atmosphere Convective Turbulence and Clouds*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 29.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of atmosphere convection and turbulence*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 30. Turbulence Convection --&gt; Boundary Layer Turbulence \n", "*Properties of the boundary layer turbulence scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 30.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Boundary layer turbulence scheme name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Mellor-Yamada\" \n", "# \"Holtslag-Boville\" \n", "# \"EDMF\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.2. Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Boundary layer turbulence scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"TKE prognostic\" \n", "# \"TKE diagnostic\" \n", "# \"TKE coupled with water\" \n", "# \"vertical profile of Kz\" \n", "# \"non-local diffusion\" \n", "# \"Monin-Obukhov similarity\" \n", "# \"Coastal Buddy Scheme\" \n", "# \"Coupled with convection\" \n", "# \"Coupled with gravity waves\" \n", "# \"Depth capped at cloud base\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.3. Closure Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Boundary layer turbulence scheme closure order*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.closure_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 30.4. Counter Gradient\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Uses boundary layer turbulence scheme counter gradient*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.boundary_layer_turbulence.counter_gradient') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 31. Turbulence Convection --&gt; Deep Convection \n", "*Properties of the deep convection scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 31.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Deep convection scheme name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.2. Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Deep convection scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"mass-flux\" \n", "# \"adjustment\" \n", "# \"plume ensemble\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.3. Scheme Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Deep convection scheme method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.scheme_method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"CAPE\" \n", "# \"bulk\" \n", "# \"ensemble\" \n", "# \"CAPE/WFN based\" \n", "# \"TKE/CIN based\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.4. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical processes taken into account in the parameterisation of deep convection*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"vertical momentum transport\" \n", "# \"convective momentum transport\" \n", "# \"entrainment\" \n", "# \"detrainment\" \n", "# \"penetrative convection\" \n", "# \"updrafts\" \n", "# \"downdrafts\" \n", "# \"radiative effect of anvils\" \n", "# \"re-evaporation of convective precipitation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 31.5. Microphysics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Microphysics scheme for deep convection. Microphysical processes directly control the amount of detrainment of cloud hydrometeor and water vapor from updrafts*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.deep_convection.microphysics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"tuning parameter based\" \n", "# \"single moment\" \n", "# \"two moment\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 32. Turbulence Convection --&gt; Shallow Convection \n", "*Properties of the shallow convection scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 32.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Shallow convection scheme name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.2. Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *shallow convection scheme type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"mass-flux\" \n", "# \"cumulus-capped boundary layer\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.3. Scheme Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *shallow convection scheme method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.scheme_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"same as deep (unified)\" \n", "# \"included in boundary layer turbulence\" \n", "# \"separate diagnosis\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.4. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Physical processes taken into account in the parameterisation of shallow convection*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"convective momentum transport\" \n", "# \"entrainment\" \n", "# \"detrainment\" \n", "# \"penetrative convection\" \n", "# \"re-evaporation of convective precipitation\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 32.5. Microphysics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Microphysics scheme for shallow convection*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.turbulence_convection.shallow_convection.microphysics') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"tuning parameter based\" \n", "# \"single moment\" \n", "# \"two moment\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 33. Microphysics Precipitation \n", "*Large Scale Cloud Microphysics and Precipitation*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 33.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of large scale cloud microphysics and precipitation*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.microphysics_precipitation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 34. Microphysics Precipitation --&gt; Large Scale Precipitation \n", "*Properties of the large scale precipitation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 34.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name of the large scale precipitation parameterisation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 34.2. Hydrometeors\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Precipitating hydrometeors taken into account in the large scale precipitation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_precipitation.hydrometeors') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"liquid rain\" \n", "# \"snow\" \n", "# \"hail\" \n", "# \"graupel\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 35. Microphysics Precipitation --&gt; Large Scale Cloud Microphysics \n", "*Properties of the large scale cloud microphysics scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 35.1. Scheme Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name of the microphysics parameterisation scheme used for large scale clouds.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.scheme_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 35.2. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Large scale cloud microphysics processes*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.microphysics_precipitation.large_scale_cloud_microphysics.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"mixed phase\" \n", "# \"cloud droplets\" \n", "# \"cloud ice\" \n", "# \"ice nucleation\" \n", "# \"water vapour deposition\" \n", "# \"effect of raindrops\" \n", "# \"effect of snow\" \n", "# \"effect of graupel\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 36. Cloud Scheme \n", "*Characteristics of the cloud scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 36.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of the atmosphere cloud scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.2. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the cloud scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.3. Atmos Coupling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Atmosphere components that are linked to the cloud scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.atmos_coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"atmosphere_radiation\" \n", "# \"atmosphere_microphysics_precipitation\" \n", "# \"atmosphere_turbulence_convection\" \n", "# \"atmosphere_gravity_waves\" \n", "# \"atmosphere_solar\" \n", "# \"atmosphere_volcano\" \n", "# \"atmosphere_cloud_simulator\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.4. Uses Separate Treatment\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Different cloud schemes for the different types of clouds (convective, stratiform and boundary layer)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.uses_separate_treatment') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.5. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Processes included in the cloud scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"entrainment\" \n", "# \"detrainment\" \n", "# \"bulk cloud\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.6. Prognostic Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the cloud scheme a prognostic scheme?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.7. Diagnostic Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the cloud scheme a diagnostic scheme?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.diagnostic_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 36.8. Prognostic Variables\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List the prognostic variables used by the cloud scheme, if applicable.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.prognostic_variables') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"cloud amount\" \n", "# \"liquid\" \n", "# \"ice\" \n", "# \"rain\" \n", "# \"snow\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 37. Cloud Scheme --&gt; Optical Cloud Properties \n", "*Optical cloud properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 37.1. Cloud Overlap Method\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Method for taking into account overlapping of cloud layers*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_overlap_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"random\" \n", "# \"maximum\" \n", "# \"maximum-random\" \n", "# \"exponential\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 37.2. Cloud Inhomogeneity\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Method for taking into account cloud inhomogeneity*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.optical_cloud_properties.cloud_inhomogeneity') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 38. Cloud Scheme --&gt; Sub Grid Scale Water Distribution \n", "*Sub-grid scale water distribution*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 38.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale water distribution type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 38.2. Function Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale water distribution function name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 38.3. Function Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale water distribution function type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.function_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 38.4. Convection Coupling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Sub-grid scale water distribution coupling with convection*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_water_distribution.convection_coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"coupled with deep\" \n", "# \"coupled with shallow\" \n", "# \"not coupled with convection\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 39. Cloud Scheme --&gt; Sub Grid Scale Ice Distribution \n", "*Sub-grid scale ice distribution*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 39.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale ice distribution type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"prognostic\" \n", "# \"diagnostic\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 39.2. Function Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale ice distribution function name*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 39.3. Function Order\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sub-grid scale ice distribution function type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.function_order') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 39.4. Convection Coupling\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Sub-grid scale ice distribution coupling with convection*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.cloud_scheme.sub_grid_scale_ice_distribution.convection_coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"coupled with deep\" \n", "# \"coupled with shallow\" \n", "# \"not coupled with convection\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 40. Observation Simulation \n", "*Characteristics of observation simulation*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 40.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of observation simulator characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 41. Observation Simulation --&gt; Isscp Attributes \n", "*ISSCP Characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 41.1. Top Height Estimation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Cloud simulator ISSCP top height estimation methodUo*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_estimation_method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"no adjustment\" \n", "# \"IR brightness\" \n", "# \"visible optical depth\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 41.2. Top Height Direction\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator ISSCP top height direction*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.isscp_attributes.top_height_direction') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"lowest altitude level\" \n", "# \"highest altitude level\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 42. Observation Simulation --&gt; Cosp Attributes \n", "*CFMIP Observational Simulator Package attributes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 42.1. Run Configuration\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator COSP run configuration*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.run_configuration') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Inline\" \n", "# \"Offline\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 42.2. Number Of Grid Points\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator COSP number of grid points*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_grid_points') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 42.3. Number Of Sub Columns\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator COSP number of sub-cloumns used to simulate sub-grid variability*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_sub_columns') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 42.4. Number Of Levels\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator COSP number of levels*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.cosp_attributes.number_of_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 43. Observation Simulation --&gt; Radar Inputs \n", "*Characteristics of the cloud radar simulator*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 43.1. Frequency\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator radar frequency (Hz)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.frequency') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 43.2. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator radar type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"surface\" \n", "# \"space borne\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 43.3. Gas Absorption\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator radar uses gas absorption*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.gas_absorption') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 43.4. Effective Radius\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator radar uses effective radius*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.radar_inputs.effective_radius') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 44. Observation Simulation --&gt; Lidar Inputs \n", "*Characteristics of the cloud lidar simulator*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 44.1. Ice Types\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Cloud simulator lidar ice type*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.ice_types') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"ice spheres\" \n", "# \"ice non-spherical\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 44.2. Overlap\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Cloud simulator lidar overlap*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.observation_simulation.lidar_inputs.overlap') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"max\" \n", "# \"random\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 45. Gravity Waves \n", "*Characteristics of the parameterised gravity waves in the atmosphere, whether from orography or other sources.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 45.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of gravity wave parameterisation in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 45.2. Sponge Layer\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Sponge layer in the upper levels in order to avoid gravity wave reflection at the top.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.sponge_layer') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Rayleigh friction\" \n", "# \"Diffusive sponge layer\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 45.3. Background\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Background wave distribution*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.background') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"continuous spectrum\" \n", "# \"discrete spectrum\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 45.4. Subgrid Scale Orography\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Subgrid scale orography effects taken into account.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.subgrid_scale_orography') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"effect on drag\" \n", "# \"effect on lifting\" \n", "# \"enhanced topography\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 46. Gravity Waves --&gt; Orographic Gravity Waves \n", "*Gravity waves generated due to the presence of orography*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 46.1. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the orographic gravity wave scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 46.2. Source Mechanisms\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Orographic gravity wave source mechanisms*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.source_mechanisms') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"linear mountain waves\" \n", "# \"hydraulic jump\" \n", "# \"envelope orography\" \n", "# \"low level flow blocking\" \n", "# \"statistical sub-grid scale variance\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 46.3. Calculation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Orographic gravity wave calculation method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.calculation_method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"non-linear calculation\" \n", "# \"more than two cardinal directions\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 46.4. Propagation Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Orographic gravity wave propogation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.propagation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"linear theory\" \n", "# \"non-linear theory\" \n", "# \"includes boundary layer ducting\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 46.5. Dissipation Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Orographic gravity wave dissipation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.orographic_gravity_waves.dissipation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"total wave\" \n", "# \"single wave\" \n", "# \"spectral\" \n", "# \"linear\" \n", "# \"wave saturation vs Richardson number\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 47. Gravity Waves --&gt; Non Orographic Gravity Waves \n", "*Gravity waves generated by non-orographic processes.*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 47.1. Name\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Commonly used name for the non-orographic gravity wave scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 47.2. Source Mechanisms\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Non-orographic gravity wave source mechanisms*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.source_mechanisms') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"convection\" \n", "# \"precipitation\" \n", "# \"background spectrum\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 47.3. Calculation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Non-orographic gravity wave calculation method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.calculation_method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"spatially dependent\" \n", "# \"temporally dependent\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 47.4. Propagation Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Non-orographic gravity wave propogation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.propagation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"linear theory\" \n", "# \"non-linear theory\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 47.5. Dissipation Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Non-orographic gravity wave dissipation scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.gravity_waves.non_orographic_gravity_waves.dissipation_scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"total wave\" \n", "# \"single wave\" \n", "# \"spectral\" \n", "# \"linear\" \n", "# \"wave saturation vs Richardson number\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 48. Solar \n", "*Top of atmosphere solar insolation characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 48.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of solar insolation of the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 49. Solar --&gt; Solar Pathways \n", "*Pathways for solar forcing of the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 49.1. Pathways\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Pathways for the solar forcing of the atmosphere model domain*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.solar_pathways.pathways') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"SW radiation\" \n", "# \"precipitating energetic particles\" \n", "# \"cosmic rays\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 50. Solar --&gt; Solar Constant \n", "*Solar constant and top of atmosphere insolation characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 50.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time adaptation of the solar constant.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.solar_constant.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"transient\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 50.2. Fixed Value\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If the solar constant is fixed, enter the value of the solar constant (W m-2).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.solar_constant.fixed_value') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 50.3. Transient Characteristics\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *solar constant transient characteristics (W m-2)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.solar_constant.transient_characteristics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 51. Solar --&gt; Orbital Parameters \n", "*Orbital parameters and top of atmosphere insolation characteristics*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 51.1. Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Time adaptation of orbital parameters*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.orbital_parameters.type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"fixed\" \n", "# \"transient\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 51.2. Fixed Reference Date\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Reference date for fixed orbital parameters (yyyy)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.orbital_parameters.fixed_reference_date') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 51.3. Transient Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Description of transient orbital parameters*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.orbital_parameters.transient_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 51.4. Computation Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method used for computing orbital parameters.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.orbital_parameters.computation_method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Berger 1978\" \n", "# \"Laskar 2004\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 52. Solar --&gt; Insolation Ozone \n", "*Impact of solar insolation on stratospheric ozone*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 52.1. Solar Ozone Impact\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does top of atmosphere insolation impact on stratospheric ozone?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.solar.insolation_ozone.solar_ozone_impact') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 53. Volcanos \n", "*Characteristics of the implementation of volcanoes*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 53.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview description of the implementation of volcanic effects in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.volcanos.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 54. Volcanos --&gt; Volcanoes Treatment \n", "*Treatment of volcanoes in the atmosphere*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 54.1. Volcanoes Implementation\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *How volcanic effects are modeled in the atmosphere.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.atmos.volcanos.volcanoes_treatment.volcanoes_implementation') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"high frequency solar constant anomaly\" \n", "# \"stratospheric aerosols optical thickness\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2", "language": "python" }, "language_info": { "mimetype": "text/x-python", "nbconvert_exporter": "python", "name": "python", "file_extension": ".py", "version": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
jwist/chemometrics
6.5_Chemometrics_data_manipulation.ipynb
1
69893
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chemometrics\n", "<br>\n", "**Julien Wist / 2017 / Universidad del Valle**\n", "<br>\n", "**Andrés Bernal / 2017 / ???**\n", "\n", "An up-to-date version of this notebook can be found here: https://github.com/jwist/chemometrics/" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "options(repr.plot.width=4, repr.plot.height=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# manipulating data with R" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "R is a language tought from the statistical standpoint. It is at first a little counter intuitive for people used to matlab like programming. Matlab and the likes, scilab, octave, python, are build from the linear algebra standpoint.\n", "\n", "Thus R is optimal for statistical data manipulation since it provide a large number of built-in functions. However most of these functions are ignored because they have no equivalent in matlab. \n", "\n", "In this section, I will list some very important functions that are usually discovered too late... and that can greatly simplify the code and more importantly make it more readeable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First load some data to play with." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "load(url('https://github.com/jwist/chemometrics/raw/master/datasets/coffeeMulti.rda'))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "'coffeeMulti'" ], "text/latex": [ "'coffeeMulti'" ], "text/markdown": [ "'coffeeMulti'" ], "text/plain": [ "[1] \"coffeeMulti\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# use ls to list the variables and discover that whas saved into this file\n", "ls()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>'nmrBin'</li><li>'nmrParam'</li><li>'irms'</li><li>'gc'</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'nmrBin'\n", "\\item 'nmrParam'\n", "\\item 'irms'\n", "\\item 'gc'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'nmrBin'\n", "2. 'nmrParam'\n", "3. 'irms'\n", "4. 'gc'\n", "\n", "\n" ], "text/plain": [ "[1] \"nmrBin\" \"nmrParam\" \"irms\" \"gc\" " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# use the following command to explore the variable.\n", "names(coffeeMulti)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A AsIs: 6 × 6</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>code</th><th scope=col>country</th><th scope=col>department</th><th scope=col>caffeine1</th><th scope=col>caffeine2</th><th scope=col>mean</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>5</th><td>1172</td><td>Colombia</td><td>Tolima</td><td>-28.285</td><td>-27.936</td><td>-28.111</td></tr>\n", "\t<tr><th scope=row>6</th><td>1173</td><td>Colombia</td><td>Tolima</td><td>-28.931</td><td>-28.800</td><td>-28.866</td></tr>\n", "\t<tr><th scope=row>7</th><td>1198</td><td>Brasil </td><td>Otro </td><td>-28.056</td><td>-28.274</td><td>-28.165</td></tr>\n", "\t<tr><th scope=row>8</th><td>1199</td><td>Colombia</td><td>Huila </td><td>-28.154</td><td>-28.022</td><td>-28.088</td></tr>\n", "\t<tr><th scope=row>10</th><td>1201</td><td>Colombia</td><td>Huila </td><td>-28.198</td><td>-28.372</td><td>-28.285</td></tr>\n", "\t<tr><th scope=row>11</th><td>1204</td><td>Brasil </td><td>Otro </td><td>-27.949</td><td>-27.978</td><td>-27.964</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A AsIs: 6 × 6\n", "\\begin{tabular}{r|llllll}\n", " & code & country & department & caffeine1 & caffeine2 & mean\\\\\n", " & <int> & <fct> & <fct> & <dbl> & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t5 & 1172 & Colombia & Tolima & -28.285 & -27.936 & -28.111\\\\\n", "\t6 & 1173 & Colombia & Tolima & -28.931 & -28.800 & -28.866\\\\\n", "\t7 & 1198 & Brasil & Otro & -28.056 & -28.274 & -28.165\\\\\n", "\t8 & 1199 & Colombia & Huila & -28.154 & -28.022 & -28.088\\\\\n", "\t10 & 1201 & Colombia & Huila & -28.198 & -28.372 & -28.285\\\\\n", "\t11 & 1204 & Brasil & Otro & -27.949 & -27.978 & -27.964\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A AsIs: 6 × 6\n", "\n", "| <!--/--> | code &lt;int&gt; | country &lt;fct&gt; | department &lt;fct&gt; | caffeine1 &lt;dbl&gt; | caffeine2 &lt;dbl&gt; | mean &lt;dbl&gt; |\n", "|---|---|---|---|---|---|---|\n", "| 5 | 1172 | Colombia | Tolima | -28.285 | -27.936 | -28.111 |\n", "| 6 | 1173 | Colombia | Tolima | -28.931 | -28.800 | -28.866 |\n", "| 7 | 1198 | Brasil | Otro | -28.056 | -28.274 | -28.165 |\n", "| 8 | 1199 | Colombia | Huila | -28.154 | -28.022 | -28.088 |\n", "| 10 | 1201 | Colombia | Huila | -28.198 | -28.372 | -28.285 |\n", "| 11 | 1204 | Brasil | Otro | -27.949 | -27.978 | -27.964 |\n", "\n" ], "text/plain": [ " code country department caffeine1 caffeine2 mean \n", "5 1172 Colombia Tolima -28.285 -27.936 -28.111\n", "6 1173 Colombia Tolima -28.931 -28.800 -28.866\n", "7 1198 Brasil Otro -28.056 -28.274 -28.165\n", "8 1199 Colombia Huila -28.154 -28.022 -28.088\n", "10 1201 Colombia Huila -28.198 -28.372 -28.285\n", "11 1204 Brasil Otro -27.949 -27.978 -27.964" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# use head to visualize the data\n", "head( coffeeMulti$irms )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's play with the Isotope Ratio Mass Spectrometry (IRMS) data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", ".list-inline {list-style: none; margin:0; padding: 0}\n", ".list-inline>li {display: inline-block}\n", ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n", "</style>\n", "<ol class=list-inline><li>'AsIs'</li><li>'oldClass'</li></ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item 'AsIs'\n", "\\item 'oldClass'\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. 'AsIs'\n", "2. 'oldClass'\n", "\n", "\n" ], "text/plain": [ "[1] \"AsIs\" \"oldClass\"" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "d <- coffeeMulti$irms\n", "is(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's compute the mean colomn from the two columns \"caffeine1\" and \"caffeine2\". This is done easily using the apply function to manipulate arrays. The first argument is the array, in this case the two columns, the second argument is the \"MARGIN\", 1 for rows and 2 for columns, and the last arguments is the function to be applied. The MARGIN tells how to apply the mean function. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 1</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>mean</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>5</th><td>-28.1105</td></tr>\n", "\t<tr><th scope=row>6</th><td>-28.8655</td></tr>\n", "\t<tr><th scope=row>7</th><td>-28.1650</td></tr>\n", "\t<tr><th scope=row>8</th><td>-28.0880</td></tr>\n", "\t<tr><th scope=row>10</th><td>-28.2850</td></tr>\n", "\t<tr><th scope=row>11</th><td>-27.9635</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 1\n", "\\begin{tabular}{r|l}\n", " & mean\\\\\n", " & <dbl>\\\\\n", "\\hline\n", "\t5 & -28.1105\\\\\n", "\t6 & -28.8655\\\\\n", "\t7 & -28.1650\\\\\n", "\t8 & -28.0880\\\\\n", "\t10 & -28.2850\\\\\n", "\t11 & -27.9635\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 1\n", "\n", "| <!--/--> | mean &lt;dbl&gt; |\n", "|---|---|\n", "| 5 | -28.1105 |\n", "| 6 | -28.8655 |\n", "| 7 | -28.1650 |\n", "| 8 | -28.0880 |\n", "| 10 | -28.2850 |\n", "| 11 | -27.9635 |\n", "\n" ], "text/plain": [ " mean \n", "5 -28.1105\n", "6 -28.8655\n", "7 -28.1650\n", "8 -28.0880\n", "10 -28.2850\n", "11 -27.9635" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m <- apply(d[,c('caffeine1','caffeine2')], 1, mean)\n", "head( data.frame(mean = m) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "to make it clear we can use ```MARGIN = 2```" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 2 × 1</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>mean.by.columns</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>caffeine1</th><td>-29.00174</td></tr>\n", "\t<tr><th scope=row>caffeine2</th><td>-28.98335</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 2 × 1\n", "\\begin{tabular}{r|l}\n", " & mean.by.columns\\\\\n", " & <dbl>\\\\\n", "\\hline\n", "\tcaffeine1 & -29.00174\\\\\n", "\tcaffeine2 & -28.98335\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 2 × 1\n", "\n", "| <!--/--> | mean.by.columns &lt;dbl&gt; |\n", "|---|---|\n", "| caffeine1 | -29.00174 |\n", "| caffeine2 | -28.98335 |\n", "\n" ], "text/plain": [ " mean.by.columns\n", "caffeine1 -29.00174 \n", "caffeine2 -28.98335 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m <- apply(d[,c('caffeine1','caffeine2')], 2, mean)\n", "head( data.frame('mean by columns' = m) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of mean any function can be used, like max, min, quantile, etc." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 5 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>quantile.caffeine1</th><th scope=col>quantile.caffeine2</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>0%</th><td>-30.87700</td><td>-30.90900</td></tr>\n", "\t<tr><th scope=row>25%</th><td>-29.52950</td><td>-29.73650</td></tr>\n", "\t<tr><th scope=row>50%</th><td>-28.88300</td><td>-28.80000</td></tr>\n", "\t<tr><th scope=row>75%</th><td>-28.39175</td><td>-28.37375</td></tr>\n", "\t<tr><th scope=row>100%</th><td>-27.72200</td><td>-27.73500</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 5 × 2\n", "\\begin{tabular}{r|ll}\n", " & quantile.caffeine1 & quantile.caffeine2\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t0\\% & -30.87700 & -30.90900\\\\\n", "\t25\\% & -29.52950 & -29.73650\\\\\n", "\t50\\% & -28.88300 & -28.80000\\\\\n", "\t75\\% & -28.39175 & -28.37375\\\\\n", "\t100\\% & -27.72200 & -27.73500\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 5 × 2\n", "\n", "| <!--/--> | quantile.caffeine1 &lt;dbl&gt; | quantile.caffeine2 &lt;dbl&gt; |\n", "|---|---|---|\n", "| 0% | -30.87700 | -30.90900 |\n", "| 25% | -29.52950 | -29.73650 |\n", "| 50% | -28.88300 | -28.80000 |\n", "| 75% | -28.39175 | -28.37375 |\n", "| 100% | -27.72200 | -27.73500 |\n", "\n" ], "text/plain": [ " quantile.caffeine1 quantile.caffeine2\n", "0% -30.87700 -30.90900 \n", "25% -29.52950 -29.73650 \n", "50% -28.88300 -28.80000 \n", "75% -28.39175 -28.37375 \n", "100% -27.72200 -27.73500 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "m <- apply(d[,c('caffeine1','caffeine2')], 2, quantile)\n", "head( data.frame(quantile = m) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The message is that the loop should be avoided as much as possible in R and that there are many build-in function for that purpose." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another useful example is centering the data. Say you want to find the mean of a column and then substract this value to all the elements of that column. This way you will have centered your data. At first glance, this look fairly complex and implies several operations. R provide a simple framework for this, called ``` sweep()```" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 6 × 2</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>caffeine1</th><th scope=col>caffeine2</th></tr>\n", "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>5</th><td>0.71673529</td><td>1.0473529</td></tr>\n", "\t<tr><th scope=row>6</th><td>0.07073529</td><td>0.1833529</td></tr>\n", "\t<tr><th scope=row>7</th><td>0.94573529</td><td>0.7093529</td></tr>\n", "\t<tr><th scope=row>8</th><td>0.84773529</td><td>0.9613529</td></tr>\n", "\t<tr><th scope=row>10</th><td>0.80373529</td><td>0.6113529</td></tr>\n", "\t<tr><th scope=row>11</th><td>1.05273529</td><td>1.0053529</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 6 × 2\n", "\\begin{tabular}{r|ll}\n", " & caffeine1 & caffeine2\\\\\n", " & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t5 & 0.71673529 & 1.0473529\\\\\n", "\t6 & 0.07073529 & 0.1833529\\\\\n", "\t7 & 0.94573529 & 0.7093529\\\\\n", "\t8 & 0.84773529 & 0.9613529\\\\\n", "\t10 & 0.80373529 & 0.6113529\\\\\n", "\t11 & 1.05273529 & 1.0053529\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 6 × 2\n", "\n", "| <!--/--> | caffeine1 &lt;dbl&gt; | caffeine2 &lt;dbl&gt; |\n", "|---|---|---|\n", "| 5 | 0.71673529 | 1.0473529 |\n", "| 6 | 0.07073529 | 0.1833529 |\n", "| 7 | 0.94573529 | 0.7093529 |\n", "| 8 | 0.84773529 | 0.9613529 |\n", "| 10 | 0.80373529 | 0.6113529 |\n", "| 11 | 1.05273529 | 1.0053529 |\n", "\n" ], "text/plain": [ " caffeine1 caffeine2\n", "5 0.71673529 1.0473529\n", "6 0.07073529 0.1833529\n", "7 0.94573529 0.7093529\n", "8 0.84773529 0.9613529\n", "10 0.80373529 0.6113529\n", "11 1.05273529 1.0053529" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head( sweep( d[c('caffeine1', 'caffeine2')], 2, apply(d[c('caffeine1', 'caffeine2')], 2, mean), \"-\" ) )" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A matrix: 6 × 2 of type dbl</caption>\n", "<thead>\n", "\t<tr><th></th><th scope=col>caffeine1</th><th scope=col>caffeine2</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><th scope=row>5</th><td>0.71673529</td><td>1.0473529</td></tr>\n", "\t<tr><th scope=row>6</th><td>0.07073529</td><td>0.1833529</td></tr>\n", "\t<tr><th scope=row>7</th><td>0.94573529</td><td>0.7093529</td></tr>\n", "\t<tr><th scope=row>8</th><td>0.84773529</td><td>0.9613529</td></tr>\n", "\t<tr><th scope=row>10</th><td>0.80373529</td><td>0.6113529</td></tr>\n", "\t<tr><th scope=row>11</th><td>1.05273529</td><td>1.0053529</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A matrix: 6 × 2 of type dbl\n", "\\begin{tabular}{r|ll}\n", " & caffeine1 & caffeine2\\\\\n", "\\hline\n", "\t5 & 0.71673529 & 1.0473529\\\\\n", "\t6 & 0.07073529 & 0.1833529\\\\\n", "\t7 & 0.94573529 & 0.7093529\\\\\n", "\t8 & 0.84773529 & 0.9613529\\\\\n", "\t10 & 0.80373529 & 0.6113529\\\\\n", "\t11 & 1.05273529 & 1.0053529\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A matrix: 6 × 2 of type dbl\n", "\n", "| <!--/--> | caffeine1 | caffeine2 |\n", "|---|---|---|\n", "| 5 | 0.71673529 | 1.0473529 |\n", "| 6 | 0.07073529 | 0.1833529 |\n", "| 7 | 0.94573529 | 0.7093529 |\n", "| 8 | 0.84773529 | 0.9613529 |\n", "| 10 | 0.80373529 | 0.6113529 |\n", "| 11 | 1.05273529 | 1.0053529 |\n", "\n" ], "text/plain": [ " caffeine1 caffeine2\n", "5 0.71673529 1.0473529\n", "6 0.07073529 0.1833529\n", "7 0.94573529 0.7093529\n", "8 0.84773529 0.9613529\n", "10 0.80373529 0.6113529\n", "11 1.05273529 1.0053529" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "head( apply(d[c('caffeine1', 'caffeine2')], 2, function(x) x - mean(x)) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This second example is my favourite and shows how apply works. Instead of using a predifined function such as ```mean()``` we use a user defined function. In this case it is clear that x selects the column and the function performes: the column minus its means." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Althought ```sweep()``` achieves the same results, the second example is much more general." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another very useful example is the data aggregation. For example you want to find the mean value of your variable by country. In this case the isotope ratio mean by country. Again, if looked at from the matlab standpoint, this is not straighforward. However R provide a very handy solution. The first aggregation function you should know is ```table()```. Look how this works:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", " Brasil Colombia Peru \n", " 11 15 8 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "table( d$country )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, we know how many samples we have from each country. But now we want to compute the mean." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table class=\"dataframe\">\n", "<caption>A data.frame: 3 × 3</caption>\n", "<thead>\n", "\t<tr><th scope=col>Group.1</th><th scope=col>caffeine1</th><th scope=col>caffeine2</th></tr>\n", "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n", "</thead>\n", "<tbody>\n", "\t<tr><td>Brasil </td><td>-29.13264</td><td>-29.12582</td></tr>\n", "\t<tr><td>Colombia</td><td>-28.51793</td><td>-28.48793</td></tr>\n", "\t<tr><td>Peru </td><td>-29.72888</td><td>-29.71637</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "A data.frame: 3 × 3\n", "\\begin{tabular}{lll}\n", " Group.1 & caffeine1 & caffeine2\\\\\n", " <fct> & <dbl> & <dbl>\\\\\n", "\\hline\n", "\t Brasil & -29.13264 & -29.12582\\\\\n", "\t Colombia & -28.51793 & -28.48793\\\\\n", "\t Peru & -29.72888 & -29.71637\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "A data.frame: 3 × 3\n", "\n", "| Group.1 &lt;fct&gt; | caffeine1 &lt;dbl&gt; | caffeine2 &lt;dbl&gt; |\n", "|---|---|---|\n", "| Brasil | -29.13264 | -29.12582 |\n", "| Colombia | -28.51793 | -28.48793 |\n", "| Peru | -29.72888 | -29.71637 |\n", "\n" ], "text/plain": [ " Group.1 caffeine1 caffeine2\n", "1 Brasil -29.13264 -29.12582\n", "2 Colombia -28.51793 -28.48793\n", "3 Peru -29.72888 -29.71637" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aggregate(d[c('caffeine1', 'caffeine2')], by = list(unlist(d['country'], use.names = FALSE)), mean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ```by=``` argument must be a list object. Because our data are not perfectly stored, we have to first unlist our country column and create a new clean list. The ```unlist()``` function is very usefull to unformat any vector of data before reassigning it with a new type." ] }, { "cell_type": "code", "execution_count": 248, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "country: Brasil\n", "[1] -27.964\n", "------------------------------------------------------------ \n", "country: Colombia\n", "[1] -27.729\n", "------------------------------------------------------------ \n", "country: Peru\n", "[1] -28.567" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "by(d['mean'], d['country'], function(x) max(x))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another way to obtain the same results." ] }, { "cell_type": "code", "execution_count": 249, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "d$country: Brasil\n", "[1] -27.964\n", "------------------------------------------------------------ \n", "d$country: Colombia\n", "[1] -27.729\n", "------------------------------------------------------------ \n", "d$country: Peru\n", "[1] -28.567" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "by(d, d$country, function(x) max(x$mean))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For more control over the data, aggregation can be performed without applying any function. Functions can be applied later." ] }, { "cell_type": "code", "execution_count": 250, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th scope=col>Group.1</th><th scope=col>caffeine1</th><th scope=col>caffeine2</th></tr></thead>\n", "<tbody>\n", "\t<tr><td>Brasil </td><td>-28.056, -27.949, -29.317, -29.089, -28.933, -28.154, -30.877, -29.816, -28.411, -29.589, -30.268</td><td>-28.274, -27.978, -29.504, -28.934, -28.800, -27.906, -30.909, -29.877, -28.440, -29.814, -29.948</td></tr>\n", "\t<tr><td>Colombia </td><td>-28.285, -28.931, -28.154, -28.198, -28.406, -27.722, -28.605, -28.503, -28.835, -28.387, -28.226, -28.731, -29.029, -28.577, -29.180</td><td>-27.936, -28.800, -28.022, -28.372, -28.505, -27.735, -28.716, -28.163, -29.207, -28.379, -28.057, -28.893, -28.771, -28.658, -29.105</td></tr>\n", "\t<tr><td>Peru </td><td>-29.300, -30.027, -29.351, -30.261, -29.643, -30.517, -30.199, -28.533</td><td>-29.148, -30.038, -29.273, -30.089, -30.076, -30.247, -30.259, -28.601</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " Group.1 & caffeine1 & caffeine2\\\\\n", "\\hline\n", "\t Brasil & -28.056, -27.949, -29.317, -29.089, -28.933, -28.154, -30.877, -29.816, -28.411, -29.589, -30.268 & -28.274, -27.978, -29.504, -28.934, -28.800, -27.906, -30.909, -29.877, -28.440, -29.814, -29.948\\\\\n", "\t Colombia & -28.285, -28.931, -28.154, -28.198, -28.406, -27.722, -28.605, -28.503, -28.835, -28.387, -28.226, -28.731, -29.029, -28.577, -29.180 & -27.936, -28.800, -28.022, -28.372, -28.505, -27.735, -28.716, -28.163, -29.207, -28.379, -28.057, -28.893, -28.771, -28.658, -29.105\\\\\n", "\t Peru & -29.300, -30.027, -29.351, -30.261, -29.643, -30.517, -30.199, -28.533 & -29.148, -30.038, -29.273, -30.089, -30.076, -30.247, -30.259, -28.601\\\\\n", "\\end{tabular}\n" ], "text/markdown": [ "\n", "Group.1 | caffeine1 | caffeine2 | \n", "|---|---|---|\n", "| Brasil | -28.056, -27.949, -29.317, -29.089, -28.933, -28.154, -30.877, -29.816, -28.411, -29.589, -30.268 | -28.274, -27.978, -29.504, -28.934, -28.800, -27.906, -30.909, -29.877, -28.440, -29.814, -29.948 | \n", "| Colombia | -28.285, -28.931, -28.154, -28.198, -28.406, -27.722, -28.605, -28.503, -28.835, -28.387, -28.226, -28.731, -29.029, -28.577, -29.180 | -27.936, -28.800, -28.022, -28.372, -28.505, -27.735, -28.716, -28.163, -29.207, -28.379, -28.057, -28.893, -28.771, -28.658, -29.105 | \n", "| Peru | -29.300, -30.027, -29.351, -30.261, -29.643, -30.517, -30.199, -28.533 | -29.148, -30.038, -29.273, -30.089, -30.076, -30.247, -30.259, -28.601 | \n", "\n", "\n" ], "text/plain": [ " Group.1 \n", "1 Brasil \n", "2 Colombia\n", "3 Peru \n", " caffeine1 \n", "1 -28.056, -27.949, -29.317, -29.089, -28.933, -28.154, -30.877, -29.816, -28.411, -29.589, -30.268 \n", "2 -28.285, -28.931, -28.154, -28.198, -28.406, -27.722, -28.605, -28.503, -28.835, -28.387, -28.226, -28.731, -29.029, -28.577, -29.180\n", "3 -29.300, -30.027, -29.351, -30.261, -29.643, -30.517, -30.199, -28.533 \n", " caffeine2 \n", "1 -28.274, -27.978, -29.504, -28.934, -28.800, -27.906, -30.909, -29.877, -28.440, -29.814, -29.948 \n", "2 -27.936, -28.800, -28.022, -28.372, -28.505, -27.735, -28.716, -28.163, -29.207, -28.379, -28.057, -28.893, -28.771, -28.658, -29.105\n", "3 -29.148, -30.038, -29.273, -30.089, -30.076, -30.247, -30.259, -28.601 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "a <- aggregate(d[c('caffeine1', 'caffeine2')], list(d$country), function(x) x)\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For example to obtain boxplots" ] }, { "cell_type": "code", "execution_count": 241, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<dl>\n", "\t<dt>$caffeine1</dt>\n", "\t\t<dd><dl>\n", "\t<dt>$stats</dt>\n", "\t\t<dd><table>\n", "<tbody>\n", "\t<tr><td>-30.8770</td><td>-29.1800</td><td>-30.5170</td></tr>\n", "\t<tr><td>-29.7025</td><td>-28.7830</td><td>-30.2300</td></tr>\n", "\t<tr><td>-29.0890</td><td>-28.5030</td><td>-29.8350</td></tr>\n", "\t<tr><td>-28.2825</td><td>-28.2555</td><td>-29.3255</td></tr>\n", "\t<tr><td>-27.9490</td><td>-27.7220</td><td>-28.5330</td></tr>\n", "</tbody>\n", "</table>\n", "</dd>\n", "\t<dt>$n</dt>\n", "\t\t<dd><ol class=list-inline>\n", "\t<li>11</li>\n", "\t<li>15</li>\n", "\t<li>8</li>\n", "</ol>\n", "</dd>\n", "\t<dt>$conf</dt>\n", "\t\t<dd><table>\n", "<tbody>\n", "\t<tr><td>-29.76547</td><td>-28.7182 </td><td>-30.34027</td></tr>\n", "\t<tr><td>-28.41253</td><td>-28.2878 </td><td>-29.32973</td></tr>\n", "</tbody>\n", "</table>\n", "</dd>\n", "\t<dt>$out</dt>\n", "\t\t<dd></dd>\n", "\t<dt>$group</dt>\n", "\t\t<dd></dd>\n", "\t<dt>$names</dt>\n", "\t\t<dd><ol class=list-inline>\n", "\t<li>Brasil</li>\n", "\t<li>Colombia</li>\n", "\t<li>Peru</li>\n", "</ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<ol class=list-inline>\n", "\t\t<li>'Brasil'</li>\n", "\t\t<li>'Colombia'</li>\n", "\t\t<li>'Peru'</li>\n", "\t</ol>\n", "</details></dd>\n", "</dl>\n", "</dd>\n", "\t<dt>$caffeine2</dt>\n", "\t\t<dd><dl>\n", "\t<dt>$stats</dt>\n", "\t\t<dd><table>\n", "<tbody>\n", "\t<tr><td>-30.9090</td><td>-29.2070</td><td>-30.2590</td></tr>\n", "\t<tr><td>-29.8455</td><td>-28.7855</td><td>-30.1680</td></tr>\n", "\t<tr><td>-28.9340</td><td>-28.5050</td><td>-30.0570</td></tr>\n", "\t<tr><td>-28.3570</td><td>-28.1100</td><td>-29.2105</td></tr>\n", "\t<tr><td>-27.9060</td><td>-27.7350</td><td>-28.6010</td></tr>\n", "</tbody>\n", "</table>\n", "</dd>\n", "\t<dt>$n</dt>\n", "\t\t<dd><ol class=list-inline>\n", "\t<li>11</li>\n", "\t<li>15</li>\n", "\t<li>8</li>\n", "</ol>\n", "</dd>\n", "\t<dt>$conf</dt>\n", "\t\t<dd><table>\n", "<tbody>\n", "\t<tr><td>-29.6431 </td><td>-28.78057</td><td>-30.59187</td></tr>\n", "\t<tr><td>-28.2249 </td><td>-28.22943</td><td>-29.52213</td></tr>\n", "</tbody>\n", "</table>\n", "</dd>\n", "\t<dt>$out</dt>\n", "\t\t<dd></dd>\n", "\t<dt>$group</dt>\n", "\t\t<dd></dd>\n", "\t<dt>$names</dt>\n", "\t\t<dd><ol class=list-inline>\n", "\t<li>Brasil</li>\n", "\t<li>Colombia</li>\n", "\t<li>Peru</li>\n", "</ol>\n", "\n", "<details>\n", "\t<summary style=display:list-item;cursor:pointer>\n", "\t\t<strong>Levels</strong>:\n", "\t</summary>\n", "\t<ol class=list-inline>\n", "\t\t<li>'Brasil'</li>\n", "\t\t<li>'Colombia'</li>\n", "\t\t<li>'Peru'</li>\n", "\t</ol>\n", "</details></dd>\n", "</dl>\n", "</dd>\n", "</dl>\n" ], "text/latex": [ "\\begin{description}\n", "\\item[\\$caffeine1] \\begin{description}\n", "\\item[\\$stats] \\begin{tabular}{lll}\n", "\t -30.8770 & -29.1800 & -30.5170\\\\\n", "\t -29.7025 & -28.7830 & -30.2300\\\\\n", "\t -29.0890 & -28.5030 & -29.8350\\\\\n", "\t -28.2825 & -28.2555 & -29.3255\\\\\n", "\t -27.9490 & -27.7220 & -28.5330\\\\\n", "\\end{tabular}\n", "\n", "\\item[\\$n] \\begin{enumerate*}\n", "\\item 11\n", "\\item 15\n", "\\item 8\n", "\\end{enumerate*}\n", "\n", "\\item[\\$conf] \\begin{tabular}{lll}\n", "\t -29.76547 & -28.7182 & -30.34027\\\\\n", "\t -28.41253 & -28.2878 & -29.32973\\\\\n", "\\end{tabular}\n", "\n", "\\item[\\$out] \n", "\\item[\\$group] \n", "\\item[\\$names] \\begin{enumerate*}\n", "\\item Brasil\n", "\\item Colombia\n", "\\item Peru\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'Brasil'\n", "\\item 'Colombia'\n", "\\item 'Peru'\n", "\\end{enumerate*}\n", "\n", "\\end{description}\n", "\n", "\\item[\\$caffeine2] \\begin{description}\n", "\\item[\\$stats] \\begin{tabular}{lll}\n", "\t -30.9090 & -29.2070 & -30.2590\\\\\n", "\t -29.8455 & -28.7855 & -30.1680\\\\\n", "\t -28.9340 & -28.5050 & -30.0570\\\\\n", "\t -28.3570 & -28.1100 & -29.2105\\\\\n", "\t -27.9060 & -27.7350 & -28.6010\\\\\n", "\\end{tabular}\n", "\n", "\\item[\\$n] \\begin{enumerate*}\n", "\\item 11\n", "\\item 15\n", "\\item 8\n", "\\end{enumerate*}\n", "\n", "\\item[\\$conf] \\begin{tabular}{lll}\n", "\t -29.6431 & -28.78057 & -30.59187\\\\\n", "\t -28.2249 & -28.22943 & -29.52213\\\\\n", "\\end{tabular}\n", "\n", "\\item[\\$out] \n", "\\item[\\$group] \n", "\\item[\\$names] \\begin{enumerate*}\n", "\\item Brasil\n", "\\item Colombia\n", "\\item Peru\n", "\\end{enumerate*}\n", "\n", "\\emph{Levels}: \\begin{enumerate*}\n", "\\item 'Brasil'\n", "\\item 'Colombia'\n", "\\item 'Peru'\n", "\\end{enumerate*}\n", "\n", "\\end{description}\n", "\n", "\\end{description}\n" ], "text/markdown": [ "$caffeine1\n", ": $stats\n", ": \n", "| -30.8770 | -29.1800 | -30.5170 | \n", "| -29.7025 | -28.7830 | -30.2300 | \n", "| -29.0890 | -28.5030 | -29.8350 | \n", "| -28.2825 | -28.2555 | -29.3255 | \n", "| -27.9490 | -27.7220 | -28.5330 | \n", "\n", "\n", "\n", "$n\n", ": 1. 11\n", "2. 15\n", "3. 8\n", "\n", "\n", "\n", "$conf\n", ": \n", "| -29.76547 | -28.7182 | -30.34027 | \n", "| -28.41253 | -28.2878 | -29.32973 | \n", "\n", "\n", "\n", "$out\n", ": \n", "$group\n", ": \n", "$names\n", ": 1. Brasil\n", "2. Colombia\n", "3. Peru\n", "\n", "\n", "\n", "**Levels**: 1. 'Brasil'\n", "2. 'Colombia'\n", "3. 'Peru'\n", "\n", "\n", "\n", "\n", "\n", "\n", "$caffeine2\n", ": $stats\n", ": \n", "| -30.9090 | -29.2070 | -30.2590 | \n", "| -29.8455 | -28.7855 | -30.1680 | \n", "| -28.9340 | -28.5050 | -30.0570 | \n", "| -28.3570 | -28.1100 | -29.2105 | \n", "| -27.9060 | -27.7350 | -28.6010 | \n", "\n", "\n", "\n", "$n\n", ": 1. 11\n", "2. 15\n", "3. 8\n", "\n", "\n", "\n", "$conf\n", ": \n", "| -29.6431 | -28.78057 | -30.59187 | \n", "| -28.2249 | -28.22943 | -29.52213 | \n", "\n", "\n", "\n", "$out\n", ": \n", "$group\n", ": \n", "$names\n", ": 1. Brasil\n", "2. Colombia\n", "3. Peru\n", "\n", "\n", "\n", "**Levels**: 1. 'Brasil'\n", "2. 'Colombia'\n", "3. 'Peru'\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "$caffeine1\n", "$caffeine1$stats\n", " [,1] [,2] [,3]\n", "[1,] -30.8770 -29.1800 -30.5170\n", "[2,] -29.7025 -28.7830 -30.2300\n", "[3,] -29.0890 -28.5030 -29.8350\n", "[4,] -28.2825 -28.2555 -29.3255\n", "[5,] -27.9490 -27.7220 -28.5330\n", "\n", "$caffeine1$n\n", "[1] 11 15 8\n", "\n", "$caffeine1$conf\n", " [,1] [,2] [,3]\n", "[1,] -29.76547 -28.7182 -30.34027\n", "[2,] -28.41253 -28.2878 -29.32973\n", "\n", "$caffeine1$out\n", "numeric(0)\n", "\n", "$caffeine1$group\n", "numeric(0)\n", "\n", "$caffeine1$names\n", "[1] Brasil Colombia Peru \n", "Levels: Brasil Colombia Peru\n", "\n", "\n", "$caffeine2\n", "$caffeine2$stats\n", " [,1] [,2] [,3]\n", "[1,] -30.9090 -29.2070 -30.2590\n", "[2,] -29.8455 -28.7855 -30.1680\n", "[3,] -28.9340 -28.5050 -30.0570\n", "[4,] -28.3570 -28.1100 -29.2105\n", "[5,] -27.9060 -27.7350 -28.6010\n", "\n", "$caffeine2$n\n", "[1] 11 15 8\n", "\n", "$caffeine2$conf\n", " [,1] [,2] [,3]\n", "[1,] -29.6431 -28.78057 -30.59187\n", "[2,] -28.2249 -28.22943 -29.52213\n", "\n", "$caffeine2$out\n", "numeric(0)\n", "\n", "$caffeine2$group\n", "numeric(0)\n", "\n", "$caffeine2$names\n", "[1] Brasil Colombia Peru \n", "Levels: Brasil Colombia Peru\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "apply(a[c(2,3)], 2, function(x) { \n", " boxplot(x, main = names(x) , names = a[[1]], xlab = \"country\", ylab = \"IRMS\")\n", " }\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above example is, however, not a very good one since boxplot itself is a very powerful function to aggregate data. The same result is thus optained by the simple call:" ] }, { "cell_type": "code", "execution_count": 224, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "plot without title" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "boxplot(mean ~ country, d)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "4.0.3" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
Gelvazio/CODIGOS_ABERTOS
PROJETOS DE USUARIOS MEU GITHUB/SITE PYTHON/ApendiceF/ApendiceF.ipynb
2
14025
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Python para Desenvolvedores](http://ricardoduarte.github.io/python-para-desenvolvedores/#conteudo)\n", "===================================\n", "2&ordf; edi\u00e7\u00e3o, revisada e ampliada\n", "-----------------------------------\n", "\n", "Ap\u00eandice F: Integra\u00e7\u00e3o com outras linguagens\n", "=============================\n", "_____________________________\n", "Existe hoje muito c\u00f3digo legado desenvolvido em diversas linguagens que pode ser aproveitado pelo Python, atrav\u00e9s de v\u00e1rias formas de integra\u00e7\u00e3o.\n", "\n", "Uma forma gen\u00e9rica de fazer isso \u00e9 gerar uma biblioteca compartilhada (*shared library*) atrav\u00e9s do compilador da outra linguagem e fazer chamadas a fun\u00e7\u00f5es que est\u00e3o definidas na biblioteca.\n", "\n", "Como a implementa\u00e7\u00e3o original do Python \u00e9 usando Linguagem C, \u00e9 poss\u00edvel integrar Python e C nos dois sentidos:\n", "\n", "+ Python -> C (Python faz chamadas a um m\u00f3dulo compilado em C).\n", "+ C -> Python (C evoca o interpretador Python em modo *embedded*).\n", "\n", "Tamb\u00e9m \u00e9 poss\u00edvel integrar o Python com Fortran usando o utilit\u00e1rio f2py, que faz parte do projeto NumPy.\n", "\n", "Bibliotecas compartilhadas\n", "--------------------------\n", "A partir da vers\u00e3o 2.5, o Python incorporou o m\u00f3dulo *ctypes*, que implementa tipos compat\u00edveis com os tipos usados pela linguagem C e permite evocar fun\u00e7\u00f5es de bibliotecas compartilhadas.\n", "\n", "O m\u00f3dulo prov\u00ea v\u00e1rias formas de evocar fun\u00e7\u00f5es. Fun\u00e7\u00f5es que seguem a conven\u00e7\u00e3o de chamada *stdcall*, como a API do Windows, podem ser acessadas atrav\u00e9s da classe *windll*. *Dynamic-link library* (DLL) \u00e9 a implementa\u00e7\u00e3o de bibliotecas compartilhadas que s\u00e3o usadas no Windows.\n", "\n", "Exemplo com *windll*:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import ctypes\n", "\n", "# Evocando a caixa de mensagens do Windows\n", "# Os argumentos s\u00e3o: janela pai, mensagem,\n", "# t\u00edtulo da janela e o tipo da janela.\n", "# A fun\u00e7\u00e3o retorna um inteiro, que\n", "# corresponde a que bot\u00e3o foi pressionado\n", "i = ctypes.windll.user32.MessageBoxA(None,\n", " 'Teste de DLL!', 'Mensagem', 0)\n", "\n", "# O resultado indica qual bot\u00e3o foi clicado\n", "print i" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para fun\u00e7\u00f5es que seguem a conven\u00e7\u00e3o de chamada *cdecl*, usada pela maioria dos compiladores C, existe a classe *cdll*. Para as passagens de argumentos por refer\u00eancia \u00e9 preciso criar uma vari\u00e1vel que funciona como um *buffer* para receber os resultados. Isso \u00e9 necess\u00e1rio para receber *strings*, por exemplo.\n", "\n", "Exemplo com *cdll* e *buffer*:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import ctypes\n", "\n", "# msvcrt \u00e9 a biblioteca com a maioria das fun\u00e7\u00f5es\n", "# padr\u00f5es da linguagens C no Windows\n", "# O Windows coloca automaticamente\n", "# a extens\u00e3o do arquivo\n", "clib = ctypes.cdll.msvcrt\n", "\n", "# Cria um buffer para receber o resultado\n", "# a refer\u00eancia para o buffer ser\u00e1 passada para\n", "# a fun\u00e7\u00e3o, que preenche o buffer com o resultado\n", "s = ctypes.c_buffer('\\000', 40)\n", "\n", "# sscanf() \u00e9 uma fun\u00e7\u00e3o que extrai valores\n", "# de uma string conforme uma mascara\n", "clib.sscanf('Testando sscanf!\\n',\n", " 'Testando %s!\\n', s)\n", "\n", "# Mostra o resultado\n", "print s.value" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\u00c9 poss\u00edvel tamb\u00e9m evocar fun\u00e7\u00f5es de bibliotecas compartilhadas no Linux:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import ctypes\n", "\n", "# Carrega a biblioteca padr\u00e3o C no Linux\n", "# A extens\u00e3o do arquivo precisa passada\n", "# para a fun\u00e7\u00e3o LoadLibrary()\n", "clib = ctypes.cdll.LoadLibrary(\"libc.so.6\")\n", "\n", "# Cria um buffer para receber o resultado\n", "s = ctypes.c_buffer('\\000', 40)\n", "\n", "# Evoca a fun\u00e7\u00e3o sprintf\n", "clib.sprintf(s, 'Testando %s\\n', 'sprintf!')\n", "\n", "# Mostra o resultado\n", "print s.value" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Atrav\u00e9s de bibliotecas compartilhadas \u00e9 poss\u00edvel usar c\u00f3digo desenvolvido em outras linguagens de uma maneira simples.\n", "\n", "Python -> C\n", "-----------\n", "O m\u00f3dulo escrito em C deve utilizar as estruturas do Python (que est\u00e3o definidas na API de interface) para se comunicar com o interpretador Python.\n", "\n", "Exemplo:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "// Arquivo: mymodule.c\n", "\n", "// Python.h define as estruturas do Python em C\n", "#include <Python.h>\n", "\n", "// No Python, mesmo os erros sao objetos\n", "static PyObject *MyModuleError;\n", "\n", "// Chamando a funcao \"system\" em C\n", "static PyObject *\n", "mymodule_system(PyObject *self, PyObject *args)\n", "{\n", " const char *command;\n", " int sts;\n", "\n", " // \"PyArg_ParseTuple\" desempacota a tupla de parametros\n", " // \"s\" significa que ele deve identificar uma string\n", " if (!PyArg_ParseTuple(args, \"s\", &command))\n", " // retornando NULL gera uma excessao\n", " // caso falte parametros\n", " return NULL;\n", "\n", " // chamando \"system\":\n", " sts = system(command);\n", "\n", " // \"Py_BuildValue\" gera objetos que o Python conhece\n", " // \"i\" significa inteiro\n", " return Py_BuildValue(\"i\", sts);\n", "}\n", "\n", "// Tabela que o Python consulta para resolver\n", "// os metodos do modulo e pode ser usado\n", "// tambem para gerar a documentacao\n", "// por instrospeccao: dir(), help(),...\n", "static PyMethodDef MyModuleMethods[] = {\n", " {\"system\", mymodule_system, METH_VARARGS,\n", " \"Executa comandos externos.\"},\n", " // Fim da tabela:\n", " {NULL, NULL, 0, NULL}\n", "};\n", "\n", "// inicializacao do modulo:\n", "PyMODINIT_FUNC\n", "initmymodule(void)\n", "{\n", " // O modulo tambem e' um objeto\n", " PyObject *m;\n", "\n", " // \"Py_InitModule\" precisa do nome do modulo e da\n", " // tabela de metodos\n", " m = Py_InitModule(\"mymodule\", MyModuleMethods);\n", "\n", " // Erros...\n", " MyModuleError = PyErr_NewException(\"mymodule.error\",\n", " NULL, NULL);\n", "\n", " // \"Py_INCREF\" incrementa o numero de referencias do objeto\n", " Py_INCREF(MyModuleError);\n", "\n", " // \"PyModule_AddObject\" adiciona um objeto ao modulo\n", " PyModule_AddObject(m, \"error\", MyModuleError);\n", "}" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ao inv\u00e9s de compilar o m\u00f3dulo manualmente, use o Python para automatizar o processo. Primeiro, crie o *script*:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Arquivo: setup.py\n", "\n", "from distutils.core import setup, Extension\n", "\n", "mymodule = Extension('mymodule', sources = ['mymodule.c'])\n", "setup(name = 'MyPackage', version = '1.0',\n", " description = 'My Package',\n", " ext_modules = [mymodule])" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "E para compilar:\n", "\n", " python setup.py build\n", " \n", "O bin\u00e1rio compilado ser\u00e1 gerado dentro da pasta \u201cbuild\u201d. O m\u00f3dulo pode ser usado como qualquer outro m\u00f3dulo no Python (atrav\u00e9s de *import*).\n", "\n", "C -> Python\n", "-----------\n", "O inverso tamb\u00e9m \u00e9 poss\u00edvel. Um programa escrito em C pode evocar o interpretador Python seguindo tr\u00eas passos:\n", "\n", "+ Inicializar o interpretador.\n", "+ Interagir (que pode ser feito de diversas formas).\n", "+ Finalizar o interpretador.\n", "\n", "Exemplo:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "// Arquivo: py_call.c\n", "\n", "// Python.h com as definicoes para\n", "// interagir com o interpretador\n", "#include <Python.h>\n", "\n", "int main()\n", "{\n", " // Inicializa interpretador Python\n", " Py_Initialize();\n", "\n", " // Executando codigo Python\n", " PyRun_SimpleString(\"import os\\n\"\n", " \"for f in os.listdir('.'):\\n\"\n", " \" if os.path.isfile(f):\\n\"\n", " \" print f, ':', os.path.getsize(f)\\n\");\n", "\n", " // Finaliza interpretador Python\n", " Py_Finalize();\n", " return 0;\n", "}" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Para compilar, \u00e9 preciso passar a localiza\u00e7\u00e3o das headers e libraries do Python para o compilador C:\n", "\n", " gcc -I/usr/include/python2.5 \\\n", " -L/usr/lib/python2.5/config \\\n", " -lpython2.5 -opy_call py_call.c\n", "\n", "Observa\u00e7\u00f5es:\n", "\n", "+ Esta API faz parte do CPython (porte do Python escrito em C).\n", "+ Existem ferramentas para automatizar o processo para gerar interfaces para sistemas maiores: SWIG, Boost.Python e SIP." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " div.cell{\n", " width:800px;\n", " margin-left:16% !important;\n", " margin-right:auto;\n", " }\n", " h1 {\n", " font-family: Helvetica, serif;\n", " }\n", " h4{\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", " div.text_cell_render{\n", " font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 145%;\n", " font-size: 130%;\n", " width:800px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n", " }\n", " .note{\n", " border-bottom: 1px black dotted;\n", " }\n", " .prompt{\n", " display: None;\n", " }\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: #4057A1;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\n", " } \n", "</style>\n", "<script>\n", " MathJax.Hub.Config({\n", " TeX: {\n", " extensions: [\"AMSmath.js\"]\n", " },\n", " tex2jax: {\n", " inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n", " displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n", " },\n", " displayAlign: 'center', // Change this to 'center' to center equations.\n", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>" ], "output_type": "pyout", "prompt_number": 1, "text": [ "<IPython.core.display.HTML at 0x50f8f98>" ] } ], "prompt_number": 1 } ], "metadata": {} } ] }
gpl-2.0
JakeColtman/BayesianSurvivalAnalysis
Basic Presentation.ipynb
2
7794
{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import lifelines\n", "import pymc as pm\n", "from pyBMA.CoxPHFitter import CoxPHFitter\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from numpy import log\n", "from datetime import datetime\n", "import pandas as pd\n", "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first step in any data analysis is acquiring and munging the data\n", "\n", "Our starting data set can be found here:\n", " http://jakecoltman.com in the pyData post\n", "\n", "It is designed to be roughly similar to the output from DCM's path to conversion\n", "\n", "Download the file and transform it into something with the columns:\n", "\n", " id,lifetime,age,male,event,search,brand\n", " \n", "where lifetime is the total time that we observed someone not convert for and event should be 1 if we see a conversion and 0 if we don't. Note that all values should be converted into ints\n", "\n", "It is useful to note that end_date = datetime.datetime(2016, 5, 3, 20, 36, 8, 92165)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "####Data munging here" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "###Parametric Bayes\n", "#Shout out to Cam Davidson-Pilon" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [-----------------100%-----------------] 50000 of 50000 complete in 78.1 sec" ] } ], "source": [ "## Example fully worked model using toy data\n", "## Adapted from http://blog.yhat.com/posts/estimating-user-lifetimes-with-pymc.html\n", "## Note that we've made some corrections \n", "\n", "N = 2500\n", "\n", "##Generate some random data \n", "lifetime = pm.rweibull( 2, 5, size = N )\n", "birth = pm.runiform(0, 10, N)\n", "censor = ((birth + lifetime) >= 10)\n", "lifetime_ = lifetime.copy()\n", "lifetime_[censor] = 10 - birth[censor]\n", "\n", "\n", "alpha = pm.Uniform('alpha', 0, 20)\n", "beta = pm.Uniform('beta', 0, 20)\n", "\n", "@pm.observed\n", "def survival(value=lifetime_, alpha = alpha, beta = beta ):\n", " return sum( (1-censor)*(log( alpha/beta) + (alpha-1)*log(value/beta)) - (value/beta)**(alpha))\n", "\n", "mcmc = pm.MCMC([alpha, beta, survival ] )\n", "mcmc.sample(50000, 30000)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pm.Matplot.plot(mcmc)\n", "mcmc.trace(\"alpha\")[:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Problems: \n", "\n", " 1 - Try to fit your data from section 1 \n", " 2 - Use the results to plot the distribution of the median\n", " \n", "Note that the media of a Weibull distribution is:\n", "$$β(log 2)^{1/α}$$ " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#### Fit to your data here" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#### Plot the distribution of the median" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Problems:\n", " \n", " 4 - Try adjusting the number of samples for burning and thinnning\n", " 5 - Try adjusting the prior and see how it affects the estimate " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#### Adjust burn and thin, both paramters of the mcmc sample function" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#### Narrow and broaden prior" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Problems:\n", " \n", " 7 - Try testing whether the median is greater than a different values" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#### Hypothesis testing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to look at covariates, we need a new approach. \n", "\n", "We'll use Cox proprtional hazards, a very popular regression model.\n", "\n", "To fit in python we use the module lifelines:\n", "\n", "http://lifelines.readthedocs.io/en/latest/" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "### Fit a cox proprtional hazards model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we've fit the data, we need to do something useful with it. Try to do the following things:\n", "\n", " 1 - Plot the baseline survival function\n", "\n", " 2 - Predict the functions for a particular set of features\n", "\n", " 3 - Plot the survival function for two different set of features\n", "\n", " 4 - For your results in part 3 caculate how much more likely a death event is for one than the other for a given period of time" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#### Plot baseline hazard function" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#### Predict" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#### Plot survival functions for different covariates" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#### Plot some odds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Model selection\n", "\n", "Difficult to do with classic tools (here)\n", "\n", "Problem:\n", "\n", " 1 - Calculate the BMA coefficient values\n", " \n", " 2 - Try running with different priors" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#### BMA Coefficient values" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#### Different priors" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
pcmagic/stokes_flow
head_Force/do_calculate_table_loc/phase_map_ecoC01B05_T0.2.ipynb
1
10438990
null
mit
uwkejia/Clean-Energy-Outlook
examples/Extra/Jupyter Notebooks/data-cleaning.ipynb
1
4814
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import xlrd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def data_extract(df,state,param_list):\n", " datalist=[]\n", " dftemp=df[df['MSN'].isin([\"Year\"] + param_list) & (df.State==state)]\n", " del dftemp['Data_Status']\n", " del dftemp['State']\n", " del dftemp['MSN']\n", " dftemp=dftemp.T\n", " dftemp.columns=param_list\n", " datalist.append(dftemp)\n", " #datalist.to_csv('Data/Data_States/%s.csv'% (statelist[i]), encoding='utf-8', index=True)\n", " return datalist" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def data_extract_all(df,state_list,param_list):\n", " for i in state_list:\n", " data=data_extract(df,'CA',param_list)\n", " data[0].to_csv('Data/Data_States/%s.csv'%i, encoding='utf-8', index=True)\n", " return" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def add_clprb(state_list):\n", " for j in range(52):\n", " dftemp = pd.read_csv('Data/Data_States/%s.csv' % (statelist[j]))\n", " dftemp.rename(columns={'Unnamed: 0':'Year','Unnamed: 5':'GDP'}, inplace = True)\n", " dftemp['CLPRB']=0 \n", " if j==44: #for state US, missing data of GDP\n", " continue\n", " #else: dftemp.drop(dftemp.index[55],inplace=True) #delete last line of GDP\n", " data = xlrd.open_workbook('Data/Original Data/more MSN/CLPRB.xlsx') # open xlsx file \n", " table = data.sheets()[j] # open sheet j\n", " nrows = table.nrows # get how many lines\n", " for i in range(1,nrows): #cycle in the table\n", " dftemp['CLPRB'][i-1] = table.row_values(i)[3] # columns 4 \n", " dftemp.to_csv('Data/Data_States/%s.csv'% (statelist[j]), encoding='utf-8', index=False)\n", " return" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def add_msn(state_list,parameter):\n", " data=pd.read_excel(\"Data/Original Data/more MSN/%s.xlsx\" %parameter)\n", " for i in state_list:\n", " tempdf=data[data.StateCode=='%s'%i]\n", " del tempdf['MSN']\n", " del tempdf['StateCode']\n", " df = pd.read_csv('Data/Data_States/%s.csv' %i)\n", " df.rename(columns={'Unnamed: 0':'Year','Unnamed: 5':'GDP'}, inplace = True)\n", " df_r=pd.merge(df, tempdf,on='Year',how='outer')\n", " df_r.rename(columns={'Data':parameter}, inplace = True)\n", " df_r.to_csv('Data/Data_States/%s.csv' %i, encoding='utf-8', index=False)\n", " return" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def climate(data,param,statelist):\n", " for i in statelist:\n", " if i == 'US':\n", " continue\n", " dforigin = pd.read_csv('Data/Data_States/%s.csv' %i)\n", " dfstate=mydata_txt[mydata_txt.State==statesdic['%s' %i]]\n", " dftoadd=dfstate[['Year',param]]\n", " dfnew=pd.merge(dforigin, dftoadd,on='Year',how='outer')\n", " dfnew.to_csv('Data/Data_States/%s.csv'%i, encoding='utf-8', index=False)\n", " return" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def oil_price(oil_data,statelist):\n", " oiltoadd=oil_data[14:]\n", " for i in statelist:\n", " dforigin = pd.read_csv('Data/Data_States/%s.csv' %i)\n", " dfnew=pd.merge(dforigin, oiltoadd,on='Year',how='outer')\n", " dfnew.to_csv('Data/Data_States/%s.csv'%i, encoding='utf-8', index=False)\n", " return" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
tgenewein/LossyCompressionAndDecisionMaking
NotebooksAndCode/1-FreeEnergyForBoundedRationalDecisionMaking.ipynb
2
1510778
null
mit
anthonyng2/FX-Trading-with-Python-and-Oanda
Oanda v20 REST-oandapyV20/06.00 Position Management.ipynb
1
12308
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<!--NAVIGATION-->\n", "< [Trade Management](05.00 Trade Management.ipynb) | [Contents](Index.ipynb) | [Transaction History](07.00 Transaction History.ipynb) >\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Position Management" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[OANDA REST-V20 API Wrapper Doc on Position](http://oanda-api-v20.readthedocs.io/en/latest/endpoints/positions.html)\n", "\n", "[OANDA API Getting Started](http://developer.oanda.com/rest-live-v20/introduction/)\n", "\n", "[OANDA DOC on Position](http://developer.oanda.com/rest-live-v20/positions-ep/)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import oandapyV20\n", "import oandapyV20.endpoints.positions as positions\n", "import configparser" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "config = configparser.ConfigParser()\n", "config.read('../config/config_v20.ini')\n", "accountID = config['oanda']['account_id']\n", "access_token = config['oanda']['api_key']" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "client = oandapyV20.API(access_token=access_token)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List all Positions for an Account. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r = positions.PositionList(accountID=accountID)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "{'lastTransactionID': '77',\n", " 'positions': [{'instrument': 'NZD_USD',\n", " 'long': {'pl': '1.6491',\n", " 'resettablePL': '1.6491',\n", " 'units': '0',\n", " 'unrealizedPL': '0.0000'},\n", " 'pl': '1.6491',\n", " 'resettablePL': '1.6491',\n", " 'short': {'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'units': '0',\n", " 'unrealizedPL': '0.0000'},\n", " 'unrealizedPL': '0.0000'},\n", " {'instrument': 'AUD_USD',\n", " 'long': {'averagePrice': '0.75481',\n", " 'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'tradeIDs': ['31', '33'],\n", " 'units': '200',\n", " 'unrealizedPL': '0.2706'},\n", " 'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'short': {'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'units': '0',\n", " 'unrealizedPL': '0.0000'},\n", " 'unrealizedPL': '0.2706'},\n", " {'instrument': 'GBP_USD',\n", " 'long': {'averagePrice': '1.25892',\n", " 'pl': '0.2866',\n", " 'resettablePL': '0.2866',\n", " 'tradeIDs': ['74'],\n", " 'units': '100',\n", " 'unrealizedPL': '0.0555'},\n", " 'pl': '0.2866',\n", " 'resettablePL': '0.2866',\n", " 'short': {'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'units': '0',\n", " 'unrealizedPL': '0.0000'},\n", " 'unrealizedPL': '0.0555'},\n", " {'instrument': 'EUR_USD',\n", " 'long': {'pl': '-0.0086',\n", " 'resettablePL': '-0.0086',\n", " 'units': '0',\n", " 'unrealizedPL': '0.0000'},\n", " 'pl': '-0.0086',\n", " 'resettablePL': '-0.0086',\n", " 'short': {'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'units': '0',\n", " 'unrealizedPL': '0.0000'},\n", " 'unrealizedPL': '0.0000'}]}" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client.request(r)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'lastTransactionID': '77', 'positions': [{'resettablePL': '1.6491', 'instrument': 'NZD_USD', 'unrealizedPL': '0.0000', 'long': {'unrealizedPL': '0.0000', 'units': '0', 'pl': '1.6491', 'resettablePL': '1.6491'}, 'short': {'unrealizedPL': '0.0000', 'units': '0', 'pl': '0.0000', 'resettablePL': '0.0000'}, 'pl': '1.6491'}, {'resettablePL': '0.0000', 'instrument': 'AUD_USD', 'unrealizedPL': '0.2706', 'long': {'averagePrice': '0.75481', 'units': '200', 'pl': '0.0000', 'unrealizedPL': '0.2706', 'tradeIDs': ['31', '33'], 'resettablePL': '0.0000'}, 'short': {'unrealizedPL': '0.0000', 'units': '0', 'pl': '0.0000', 'resettablePL': '0.0000'}, 'pl': '0.0000'}, {'resettablePL': '0.2866', 'instrument': 'GBP_USD', 'unrealizedPL': '0.0555', 'long': {'averagePrice': '1.25892', 'units': '100', 'pl': '0.2866', 'unrealizedPL': '0.0555', 'tradeIDs': ['74'], 'resettablePL': '0.2866'}, 'short': {'unrealizedPL': '0.0000', 'units': '0', 'pl': '0.0000', 'resettablePL': '0.0000'}, 'pl': '0.2866'}, {'resettablePL': '-0.0086', 'instrument': 'EUR_USD', 'unrealizedPL': '0.0000', 'long': {'unrealizedPL': '0.0000', 'units': '0', 'pl': '-0.0086', 'resettablePL': '-0.0086'}, 'short': {'unrealizedPL': '0.0000', 'units': '0', 'pl': '0.0000', 'resettablePL': '0.0000'}, 'pl': '-0.0086'}]}\n" ] } ], "source": [ "print(r.response)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List all open Positions for an Account." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r = positions.OpenPositions(accountID=accountID)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'lastTransactionID': '77',\n", " 'positions': [{'instrument': 'AUD_USD',\n", " 'long': {'averagePrice': '0.75481',\n", " 'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'tradeIDs': ['31', '33'],\n", " 'units': '200',\n", " 'unrealizedPL': '0.2706'},\n", " 'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'short': {'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'units': '0',\n", " 'unrealizedPL': '0.0000'},\n", " 'unrealizedPL': '0.2706'},\n", " {'instrument': 'GBP_USD',\n", " 'long': {'averagePrice': '1.25892',\n", " 'pl': '0.2866',\n", " 'resettablePL': '0.2866',\n", " 'tradeIDs': ['74'],\n", " 'units': '100',\n", " 'unrealizedPL': '0.0555'},\n", " 'pl': '0.2866',\n", " 'resettablePL': '0.2866',\n", " 'short': {'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'units': '0',\n", " 'unrealizedPL': '0.0000'},\n", " 'unrealizedPL': '0.0555'}]}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client.request(r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get the details of a single instrument’s position in an Account" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "instrument = \"AUD_USD\"" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r = positions.PositionDetails(accountID=accountID, instrument=instrument)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'lastTransactionID': '77',\n", " 'position': {'instrument': 'AUD_USD',\n", " 'long': {'averagePrice': '0.75481',\n", " 'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'tradeIDs': ['31', '33'],\n", " 'units': '200',\n", " 'unrealizedPL': '0.2706'},\n", " 'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'short': {'pl': '0.0000',\n", " 'resettablePL': '0.0000',\n", " 'units': '0',\n", " 'unrealizedPL': '0.0000'},\n", " 'unrealizedPL': '0.2706'}}" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client.request(r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Closeout the open Position regarding instrument in an Account." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = {\n", " \"longUnits\": \"ALL\"\n", "}" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "r = positions.PositionClose(accountID=accountID,\n", " instrument=instrument,\n", " data=data)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'lastTransactionID': '79',\n", " 'longOrderCreateTransaction': {'accountID': '101-003-5120068-001',\n", " 'batchID': '78',\n", " 'id': '78',\n", " 'instrument': 'AUD_USD',\n", " 'longPositionCloseout': {'instrument': 'AUD_USD', 'units': 'ALL'},\n", " 'positionFill': 'REDUCE_ONLY',\n", " 'reason': 'POSITION_CLOSEOUT',\n", " 'time': '2017-01-30T01:58:38.167265656Z',\n", " 'timeInForce': 'FOK',\n", " 'type': 'MARKET_ORDER',\n", " 'units': '-200',\n", " 'userID': 5120068},\n", " 'longOrderFillTransaction': {'accountBalance': '100002.2293',\n", " 'accountID': '101-003-5120068-001',\n", " 'batchID': '78',\n", " 'financing': '0.0004',\n", " 'id': '79',\n", " 'instrument': 'AUD_USD',\n", " 'orderID': '78',\n", " 'pl': '0.2706',\n", " 'price': '0.75576',\n", " 'reason': 'MARKET_ORDER_POSITION_CLOSEOUT',\n", " 'time': '2017-01-30T01:58:38.167265656Z',\n", " 'tradesClosed': [{'financing': '0.0002',\n", " 'realizedPL': '0.1239',\n", " 'tradeID': '31',\n", " 'units': '-100'},\n", " {'financing': '0.0002',\n", " 'realizedPL': '0.1467',\n", " 'tradeID': '33',\n", " 'units': '-100'}],\n", " 'type': 'ORDER_FILL',\n", " 'units': '-200',\n", " 'userID': 5120068},\n", " 'relatedTransactionIDs': ['78', '79']}" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client.request(r)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!--NAVIGATION-->\n", "< [Trade Management](05.00 Trade Management.ipynb) | [Contents](Index.ipynb) | [Transaction History](07.00 Transaction History.ipynb) >\n" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
Parsl/parsl_demos
Bash-Tutorial.ipynb
1
9536
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Parsl Bash Tutorial\n", "\n", "This tutorial will show you how to run Bash scripts as Parsl apps. \n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Load parsl\n", "\n", "Import parsl, and check the module version. This tutorial requires version 0.2.0 or above." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.3.0-alpha\n" ] } ], "source": [ "# Import Parsl\n", "import parsl\n", "from parsl import *\n", "\n", "print(parsl.__version__) # The version should be v0.2.1+" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "### Define resources\n", "\n", "To execute parsl we need to first define a set of resources on which the apps can run. Here we use a pool of threads." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "workers = ThreadPoolExecutor(max_workers=4)\n", "# We pass the workers to the DataFlowKernel which will execute our Apps over the workers.\n", "dfk = DataFlowKernel(executors=[workers])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Defining Bash Apps\n", "\n", "To demonstrate how to run apps written as Bash scripts, we use two mock science applications: **simulate.sh** and **stats.sh**. The **simulation.sh** script serves as a trivial proxy for any more complex scientific simulation application. It generates and prints a set of one or more random integers in the range [0-2^62) as controlled by its command line arguments. The **stats.sh** script serves as a trivial model of an \"analysis\" program. It reads N files each containing M integers and prints the average of all those numbers to stdout. Like simulate.sh it logs environmental information to stderr.\n", "\n", "The following cell show how apps can be composed from arbitrary Bash scripts. The simulate signature shows how variables can be passed to the Bash script (e.g., \"sim_steps\") as well as how standard Parsl parameters are managed (e.g., \"stdout\")." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "@App('bash', dfk)\n", "def simulate(sim_steps=1, sim_range=100, sim_values=5, outputs=[], stdout=None, stderr=None):\n", " # The bash app function requires that the bash script is returned from the function as a \n", " # string. Positional and Keyword args to the fn() are formatted into the cmd_line string\n", " # All arguments to the app function are made available at the time of string formatting a\n", " # string assigned to cmd_line.\n", " \n", " # Here we compose the command-line call to simulate.sh with keyword arguments to simulate()\n", " # and redirect stdout to the first file listed in the outputs list.\n", " return '''echo \"sim_steps: {sim_steps}\\nsim_range: {sim_range}\\nsim_values: {sim_values}\"\n", " echo \"Starting run at $(date)\"\n", " $PWD/bin/simulate.sh --timesteps {sim_steps} --range {sim_range} --nvalues {sim_values} > {outputs[0]}\n", " echo \"Done at $(date)\"\n", " ls\n", " '''" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Running Bash Apps\n", "\n", "Now that we've defined an app, let's run 10 parallel instances of it using a for loop. Each run will write 100 random numbers, each between 0 and 99, to the output file.\n", "\n", "In order to track files created by Bash apps, a list of data futures (one for each file in the outputs[] list) is made available as an attribute of the AppFuture returned upon calling the decorated app fn. \n", "\n", "```\n", "<App_Future> = App_Function(... , outputs=['x.txt', 'y.txt'...])\n", "[<DataFuture> ... ] = <App_Future>.outputs\n", "```" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "simulated_results = []\n", "# Launch 10 parallel runs of simulate() and put the futures in a list\n", "for sim_index in range(10):\n", " sim_fut = simulate(sim_steps=1,\n", " sim_range=100,\n", " sim_values=100,\n", " outputs = ['stdout.{0}.txt'.format(sim_index)], \n", " stderr='stderr.{0}.txt'.format(sim_index)) \n", " simulated_results.extend([sim_fut])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Handling Futures\n", "\n", "The variable \"simulated_results\" contains a list of AppFutures, each corresponding to a running bash app.\n", "\n", "Now let's print the status of the 10 jobs by checking if the app futures are done.\n", "\n", "Note: you can re-run this step until all the jobs complete (all status are True) or go on, as a later step will block until all the jobs are complete." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[False, False, False, False, False, False, False, False, False, False]\n" ] } ], "source": [ "print ([i.done() for i in simulated_results])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Retrieving Results\n", "\n", "Each of the Apps return one DataFuture. Here we put all of these (data futures of file outputs) together into a list (`simulation_outputs`). This is done by iterating over each of the AppFutures and taking the first and only DataFuture in it's outputs list." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Grab just the data futures for the output files from each simulation\n", "simulation_outputs = [i.outputs[0] for i in simulated_results]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Defining a Second Bash App\n", "\n", "We now explore how Parsl can be used to block on results. Let's define another app, `analyze()`, that calls **stats.sh** to find the average of the numbers in a set of files." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "@App('bash', dfk)\n", "def analyze(inputs=[], stdout=None, stderr=None):\n", " # Here we compose the commandline for stats.sh that take a list of filenames as arguments\n", " # Since we want a space separated list, rather than a python list (e.g: ['x.txt', 'y.txt'])\n", " # we create a string by joining the filenames of each item in the inputs list and using\n", " # that string to format the cmd_line explicitly\n", " input_files = ' '.join([i for i in inputs])\n", " return '$PWD/bin/stats.sh {0}'.format(input_files)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Blocking on Results\n", "\n", "We call analyze with the list of data futures as inputs. This will block until all the simulate runs have completed and the data futures have 'resolved'. Finally, we print the result when it is ready.\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "49\n", "\n" ] } ], "source": [ "results = analyze(inputs=simulation_outputs, \n", " stdout='analyze.out', \n", " stderr='analyze.err')\n", "results.result()\n", "with open('analyze.out', 'r') as f:\n", " print(f.read())" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
vivekec/datascience
tutorials/python/Ipython files/py basics/OOPS basics.ipynb
1
7139
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Aggregation (HAS-A)\n", "Passing an object of Class 1 as an argument of class 2 constructer" ] }, { "cell_type": "code", "execution_count": 95, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "22" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class A():\n", " def __init__(self, a, b, c):\n", " self.a = a\n", " self.b = b\n", " self.c = c\n", "\n", " def addNums():\n", " self.b + self.c\n", "\n", "class B():\n", " def __init__(self, d, e, A):\n", " self.d = d\n", " self.e = e\n", " self.A = A\n", "\n", " def addAllNums(self):\n", " x = self.d + self.e + self.A.b + self.A.c\n", " return x\n", "\n", "objA = A(\"hi\", 2, 6)\n", "objB = B(5, 9, objA)\n", "objB.addAllNums()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Association (USES-A)\n", "Passing object of class 1 as an argument of class 2 methods " ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "22" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class A():\n", " def __init__(self, a, b, c):\n", " self.a = a\n", " self.b = b\n", " self.c = c\n", "\n", " def addNums():\n", " self.b + self.c\n", "\n", "class B():\n", " def __init__(self, d, e):\n", " self.d = d\n", " self.e = e \n", "\n", " def addAllNums(self, arg):\n", " x = self.d + self.e + arg.b + arg.c\n", " return x\n", "\n", "objA = A(\"hi\", 2, 6)\n", "objB = B(5, 9)\n", "objB.addAllNums(objA)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Composition (PART-OF)\n", "Object of class 1 is defined inside the constructor of class 2" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "22" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class A():\n", " def __init__(self, a, b, c):\n", " self.a = a\n", " self.b = b\n", " self.c = c\n", "\n", " def addNums():\n", " self.b + self.c\n", "\n", "class B():\n", " def __init__(self, d, e):\n", " self.d = d\n", " self.e = e\n", " self.objA = A(\"hi\", 2, 6)\n", "\n", " def addAllNums(self):\n", " x = self.d + self.e + self.objA.b + self.objA.c\n", " return x\n", "\n", "\n", "objB = B(5, 9)\n", "objB.addAllNums()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inheritance (IS-A)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20" ] }, "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class A():\n", " def __init__(self, a, b, c):\n", " self.a = a\n", " self.b = b\n", " self.c = c\n", "\n", " def addNums():\n", " self.b + self.c\n", "\n", "class B(A):\n", " def __init__(self, a, b, c, d, e):\n", "# A.__init__(self, a, b, c)\n", " super().__init__(a, b, c)\n", " self.d = d\n", " self.e = e\n", " \n", "\n", " def addAllNums(self):\n", " x = self.a + self.b + self.c + self.d + self.e\n", " return x\n", "\n", "\n", "objB = B(1, 2, 3, 5, 9)\n", "objB.addAllNums()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function overriding" ] }, { "cell_type": "code", "execution_count": 111, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Class B func: 14\n", "Class B func: 6\n" ] } ], "source": [ "class A():\n", " def __init__(self, a, b, c):\n", " self.a = a\n", " self.b = b\n", " self.c = c\n", "\n", " def addNums(self):\n", " return self.b * self.c\n", "\n", "class B(A):\n", " def __init__(self, a, b, c, d, e):\n", " super().__init__(a, b, c)\n", " self.d = d\n", " self.e = e\n", " \n", " def addNums(self):\n", " return self.d + self.e\n", " \n", " def check(self):\n", " print(\"Class B func:\", self.addNums())\n", " print(\"Class B func:\", super().addNums())\n", "\n", "\n", "objB = B(1,2,3,5, 9)\n", "objB.check()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## There is no function overloading \n", "Gives no error but only last defined function is executed" ] }, { "cell_type": "code", "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(8, 5)" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class A():\n", " def f1(self, x):\n", " return x\n", " def f1(self, x, y):\n", " return x, y\n", "\n", "\n", "objA = A()\n", "objA.f1(8,5)\n", "# objA.f1(8) # Gives error" ] }, { "cell_type": "code", "execution_count": 121, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] }, { "data": { "text/plain": [ "8" ] }, "execution_count": 121, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# How to work with function overloading\n", "class A():\n", " def f1(self,name = None):\n", " if name is None:\n", " return 5\n", " else:\n", " return name\n", "\n", "objA = A()\n", "print(objA.f1())\n", "objA.f1(8)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
DANA-Laboratory/CoolProp
doc/notebooks/fitting/Setting up residuals.ipynb
7
2259
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from CoolProp.CoolProp import Props\n", "from math import sqrt\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.optimize\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Residual Terms" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Pressure" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Presssure is given by\n", "$$ \\frac{p}{\\rho R T} = 1+\\delta\\sum_{i=1}^I n_i\\left(\\frac{\\partial A_i(\\tau,\\delta)}{\\partial\\delta} \\right)_{\\tau} $$\n", "Set up as a residual\n", "$$ \\zeta = \\frac{p}{\\rho R T} - 1-\\delta\\sum_{i=1}^I n_i\\left(\\frac{\\partial A_i(\\tau,\\delta)}{\\partial\\delta} \\right)_{\\tau} $$\n", "combine \n", "$$ \\zeta = \\frac{p-\\rho R T}{\\rho R T} -\\delta\\sum_{i=1}^I n_i\\left(\\frac{\\partial A_i(\\tau,\\delta)}{\\partial\\delta} \\right)_{\\tau} $$\n", "$$ \\zeta = \\frac{p-\\rho R T}{\\rho R T} -\\frac{\\rho}{\\rho_r}\\sum_{i=1}^I n_i\\left(\\frac{\\partial A_i(\\tau,\\delta)}{\\partial\\delta} \\right)_{\\tau} $$\n", "We can divide through by $\\rho$ since we know $\\rho$ is non-zero\n", "$$ \\zeta = \\frac{p-\\rho R T}{\\rho^2 R T} -\\sum_{i=1}^I n_i\\left(\\frac{1}{\\rho_r}\\frac{\\partial A_i(\\tau,\\delta)}{\\partial\\delta} \\right)_{\\tau} $$\n", "to yield the solution from Span, 2000" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Specific heat at constant volume" ] }, { "cell_type": "code", "collapsed": false, "input": [ "Specific heat at constant volume\n", "$$ \\frac{c_v}{R} = " ], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
hetaodie/hetaodie.github.io
assets/media/uda-ml/qinghua/dongtaiguihua/迷你项目：动态规划（第 2 部分）/Dynamic_Programming-zh.ipynb
2
22073
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 迷你项目：动态规划\n", "\n", "在此 notebook 中，你将自己编写很多经典动态规划算法的实现。\n", "\n", "虽然我们提供了一些起始代码，但是你可以删掉这些提示并从头编写代码。\n", "\n", "### 第 0 部分：探索 FrozenLakeEnv\n", "\n", "请使用以下代码单元格创建 [FrozenLake](https://github.com/openai/gym/blob/master/gym/envs/toy_text/frozen_lake.py) 环境的实例。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from frozenlake import FrozenLakeEnv\n", "\n", "env = FrozenLakeEnv()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "智能体将会在 $4 \\times 4$ 网格世界中移动，状态编号如下所示：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[[ 0 1 2 3]\n", " [ 4 5 6 7]\n", " [ 8 9 10 11]\n", " [12 13 14 15]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "智能体可以执行 4 个潜在动作：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "LEFT = 0\n", "DOWN = 1\n", "RIGHT = 2\n", "UP = 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "因此，$\\mathcal{S}^+ = \\{0, 1, \\ldots, 15\\}$ 以及 $\\mathcal{A} = \\{0, 1, 2, 3\\}$。请通过运行以下代码单元格验证这一点。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# print the state space and action space\n", "print(env.observation_space)\n", "print(env.action_space)\n", "\n", "# print the total number of states and actions\n", "print(env.nS)\n", "print(env.nA)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "动态规划假设智能体完全了解 MDP。我们已经修改了 `frozenlake.py` 文件以使智能体能够访问一步动态特性。 \n", "\n", "请执行以下代码单元格以返回特定状态和动作对应的一步动态特性。具体而言，当智能体在网格世界中以状态 1 向左移动时，`env.P[1][0]` 会返回每个潜在奖励的概率和下一个状态。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "env.P[1][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "每个条目的格式如下所示" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "prob, next_state, reward, done" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "其中：\n", "- `prob` 详细说明了相应的 (`next_state`, `reward`) 对的条件概率，以及\n", "- 如果 `next_state` 是终止状态，则 `done` 是 `True` ，否则是 `False`。\n", "\n", "因此，我们可以按照以下方式解析 `env.P[1][0]`：\n", "$$\n", "\\mathbb{P}(S_{t+1}=s',R_{t+1}=r|S_t=1,A_t=0) = \\begin{cases}\n", " \\frac{1}{3} \\text{ if } s'=1, r=0\\\\\n", " \\frac{1}{3} \\text{ if } s'=0, r=0\\\\\n", " \\frac{1}{3} \\text{ if } s'=5, r=0\\\\\n", " 0 \\text{ else}\n", " \\end{cases}\n", "$$\n", "\n", "你可以随意更改上述代码单元格，以探索在其他（状态、动作）对下环境的行为是怎样的。\n", "\n", "### 第 1 部分：迭代策略评估\n", "\n", "在此部分，你将自己编写迭代策略评估的实现。\n", "\n", "你的算法应该有四个**输入**参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "- `theta`：这是一个非常小的正数，用于判断估算值是否足够地收敛于真值函数 (默认值为：`1e-8`）。\n", "\n", "该算法会返回以下**输出结果**：\n", "- `V`：这是一个字典，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 在输入策略下的估算值。\n", "\n", "请完成以下代码单元格中的函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "def policy_evaluation(env, policy, gamma=1, theta=1e-8):\n", " V = np.zeros(env.nS)\n", " \n", " ## TODO: complete the function\n", " \n", " return V" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "我们将评估等概率随机策略 $\\pi$，其中对于所有 $s\\in\\mathcal{S}$ 和 $a\\in\\mathcal{A}(s)$ ，$\\pi(a|s) = \\frac{1}{|\\mathcal{A}(s)|}$。 \n", "\n", "请使用以下代码单元格在变量 `random_policy`中指定该策略。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "random_policy = np.ones([env.nS, env.nA]) / env.nA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行下个代码单元格以评估等概率随机策略并可视化输出结果。状态值函数已调整形状，以匹配网格世界的形状。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from plot_utils import plot_values\n", "\n", "# evaluate the policy \n", "V = policy_evaluation(env, random_policy)\n", "\n", "plot_values(V)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保你的 `policy_evaluation` 函数满足上文列出的要求（具有四个输入、一个输出，并且没有更改输入参数的默认值）。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import check_test\n", "\n", "check_test.run_check('policy_evaluation_check', policy_evaluation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 第 2 部分：通过 $v_\\pi$ 获取 $q_\\pi$\n", "\n", "在此部分，你将编写一个函数，该函数的输入是状态值函数估值以及一些状态 $s\\in\\mathcal{S}$。它会返回输入状态 $s\\in\\mathcal{S}$ 对应的**动作值函数中的行**。即你的函数应同时接受输入 $v_\\pi$ 和 $s$，并针对所有 $a\\in\\mathcal{A}(s)$ 返回 $q_\\pi(s,a)$。\n", "\n", "你的算法应该有四个**输入**参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "- `s`：这是环境中的状态对应的整数。它应该是在 `0` 到 `(env.nS)-1`（含）之间的值。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "\n", "该算法会返回以下**输出结果**：\n", "\n", "- `q`：这是一个一维 numpy 数组，其中 `q.shape[0]` 等于动作数量 (`env.nA`)。`q[a]` 包含状态 `s` 和动作 `a` 的（估算）值。\n", "\n", "请完成以下代码单元格中的函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def q_from_v(env, V, s, gamma=1):\n", " q = np.zeros(env.nA)\n", " \n", " ## TODO: complete the function\n", " \n", " return q" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "请运行以下代码单元格以输出上述状态值函数对应的动作值函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "Q = np.zeros([env.nS, env.nA])\n", "for s in range(env.nS):\n", " Q[s] = q_from_v(env, V, s)\n", "print(\"Action-Value Function:\")\n", "print(Q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保 `q_from_v` 函数满足上文列出的要求（具有四个输入、一个输出，并且没有更改输入参数的默认值）。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "check_test.run_check('q_from_v_check', q_from_v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 第 3 部分：策略改进\n", "\n", "在此部分，你将自己编写策略改进实现。 \n", "\n", "你的算法应该有三个**输入**参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "\n", "该算法会返回以下**输出结果**：\n", "\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "\n", "请完成以下代码单元格中的函数。建议你使用你在上文实现的 `q_from_v` 函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def policy_improvement(env, V, gamma=1):\n", " policy = np.zeros([env.nS, env.nA]) / env.nA\n", " \n", " ## TODO: complete the function\n", "\n", " return policy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保 `policy_improvement` 函数满足上文列出的要求（具有三个输入、一个输出，并且没有更改输入参数的默认值）。\n", "\n", "在继续转到该 notebook 的下个部分之前，强烈建议你参阅 **Dynamic_Programming_Solution.ipynb** 中的解决方案。该函数有很多正确的实现方式！" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "check_test.run_check('policy_improvement_check', policy_improvement)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 第 4 部分：策略迭代\n", "\n", "在此部分，你将自己编写策略迭代的实现。该算法会返回最优策略，以及相应的状态值函数。\n", "\n", "你的算法应该有三个**输入**参数：\n", "\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "- `theta`：这是一个非常小的正数，用于判断策略评估步骤是否足够地收敛于真值函数 (默认值为：`1e-8`）。\n", "\n", "该算法会返回以下**输出结果**：\n", "\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "\n", "请完成以下代码单元格中的函数。强烈建议你使用你在上文实现的 `policy_evaluation` 和 `policy_improvement` 函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import copy\n", "\n", "def policy_iteration(env, gamma=1, theta=1e-8):\n", " policy = np.ones([env.nS, env.nA]) / env.nA\n", " \n", " ## TODO: complete the function\n", "\n", " return policy, V" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行下个代码单元格以解决该 MDP 并可视化输出结果。最优状态值函数已调整形状，以匹配网格世界的形状。\n", "\n", "**将该最优状态值函数与此 notebook 第 1 部分的状态值函数进行比较**。_最优状态值函数一直都大于或等于等概率随机策略的状态值函数吗？_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# obtain the optimal policy and optimal state-value function\n", "policy_pi, V_pi = policy_iteration(env)\n", "\n", "# print the optimal policy\n", "print(\"\\nOptimal Policy (LEFT = 0, DOWN = 1, RIGHT = 2, UP = 3):\")\n", "print(policy_pi,\"\\n\")\n", "\n", "plot_values(V_pi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保 `policy_iteratio` 函数满足上文列出的要求（具有三个输入、两个输出，并且没有更改输入参数的默认值）。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "check_test.run_check('policy_iteration_check', policy_iteration)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 第 5 部分：截断策略迭代\n", "\n", "在此部分，你将自己编写截断策略迭代的实现。 \n", "\n", "首先，你将实现截断策略评估。你的算法应该有五个**输入**参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "- `max_it`：这是一个正整数，对应的是经历状态空间的次数（默认值为：`1`）。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "\n", "该算法会返回以下**输出结果**：\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "\n", "请完成以下代码单元格中的函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def truncated_policy_evaluation(env, policy, V, max_it=1, gamma=1):\n", " \n", " ## TODO: complete the function\n", " \n", " return V" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "接着，你将实现截断策略迭代。你的算法应该接受五个**输入**参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `max_it`：这是一个正整数，对应的是经历状态空间的次数（默认值为：`1`）。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。\n", "- `theta`：这是一个非常小的正整数，用作停止条件（默认值为：`1e-8`）。\n", "\n", "该算法会返回以下**输出结果**：\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。\n", "\n", "请完成以下代码单元格中的函数。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def truncated_policy_iteration(env, max_it=1, gamma=1, theta=1e-8):\n", " V = np.zeros(env.nS)\n", " policy = np.zeros([env.nS, env.nA]) / env.nA\n", " \n", " ## TODO: complete the function\n", " \n", " return policy, V" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行下个代码单元格以解决该 MDP 并可视化输出结果。状态值函数已调整形状，以匹配网格世界的形状。\n", "\n", "请实验不同的 `max_it` 参数值。始终都能获得最优状态值函数吗？" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "policy_tpi, V_tpi = truncated_policy_iteration(env, max_it=2)\n", "\n", "# print the optimal policy\n", "print(\"\\nOptimal Policy (LEFT = 0, DOWN = 1, RIGHT = 2, UP = 3):\")\n", "print(policy_tpi,\"\\n\")\n", "\n", "# plot the optimal state-value function\n", "plot_values(V_tpi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保 `truncated_policy_iteration` 函数满足上文列出的要求（具有四个输入、两个输出，并且没有更改输入参数的默认值）。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "check_test.run_check('truncated_policy_iteration_check', truncated_policy_iteration)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 第 6 部分：值迭代\n", "\n", "在此部分，你将自己编写值迭代的实现。\n", "\n", "你的算法应该接受三个输入参数：\n", "- `env`：这是 OpenAI Gym 环境的实例，其中 `env.P` 会返回一步动态特性。\n", "- `gamma`：这是折扣率。它必须是在 0 到 1（含）之间的值，默认值为：`1`。 \n", "- `theta`：这是一个非常小的正整数，用作停止条件（默认值为：`1e-8`）。\n", "\n", "该算法会返回以下**输出结果**：\n", "- `policy`：这是一个二维 numpy 数组，其中 `policy.shape[0]` 等于状态数量 (`env.nS`) ， `policy.shape[1]` 等于动作数量 (`env.nA`) 。`policy[s][a]` 返回智能体在状态 `s` 时根据该策略选择动作 `a` 的概率。\n", "- `V`：这是一个一维 numpy 数组，其中 `V.shape[0]` 等于状态数量 (`env.nS`)。`V[s]` 包含状态 `s` 的估值。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def value_iteration(env, gamma=1, theta=1e-8):\n", " V = np.zeros(env.nS)\n", " \n", " ## TODO: complete the function\n", " \n", " return policy, V" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行下个代码单元格以解决该 MDP 并可视化输出结果。状态值函数已调整形状，以匹配网格世界的形状。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "policy_vi, V_vi = value_iteration(env)\n", "\n", "# print the optimal policy\n", "print(\"\\nOptimal Policy (LEFT = 0, DOWN = 1, RIGHT = 2, UP = 3):\")\n", "print(policy_vi,\"\\n\")\n", "\n", "# plot the optimal state-value function\n", "plot_values(V_vi)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "运行以下代码单元格以测试你的函数。如果代码单元格返回 **PASSED**，则表明你正确地实现了该函数！ \n", "\n", "**注意：**为了确保结果准确，确保 `truncated_policy_iteration` 函数满足上文列出的要求（具有三个输入、两个输出，并且没有更改输入参数的默认值）。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "check_test.run_check('value_iteration_check', value_iteration)" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 2 }
mit
shareactorIO/pipeline
oreilly.ml/high-performance-tensorflow/notebooks/02_Feed_Queue_HDFS.ipynb
1
2076
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Feed Dataset through Queue to Tensorflow from HDFS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Populate HDFS with Sample Dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "\n", "hadoop fs -copyFromLocal /root/datasets/csv/ /csv" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "\n", "hadoop fs -ls /csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Open a Terminal through Jupyter Notebook \n", "### (Menu Bar -> Terminal -> New Terminal)\n", "![Jupyter Terminal](https://s3.amazonaws.com/fluxcapacitor.com/img/jupyter-terminal.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Queue and Feed Tensorflow Graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the Next Cell to Display the Code" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "\n", "cat /root/src/main/python/queue/tensorflow_hdfs.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the following in the Terminal\n", "```\n", "queue_hdfs\n", "```" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
linhbngo/cpsc-4770_6770
06-pleasantly-parallel.ipynb
1
27586
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## <center>Pleasantly Parallel</center>\n", "### <center> Linh B. Ngo </center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- Embarrassingly parallel/naturally parallel/pleasantly parallel\n", "- “A computation that can obviously be divided into a number of completely different parts, each of which can be executed by a separate process.”\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- No communication or very little communication among the processes.\n", "- Each process can do its tasks without any interaction with the other processes.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### <center> Example: Trapezoid Calculation" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<center> \n", " <img src=\"pictures/06/trapezoid01.png\" width=\"500\"/>\n", "</center>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "N = 8; a = 0; b = 2; h = (b - a)/N;" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.5 1.0\n" ] } ], "source": [ "# With 4 processors (cores)\n", "size = 4; rank = 1\n", "local_N = N / size\n", "local_a = a + rank * h * local_N\n", "local_b = local_a + h * local_N\n", "print (local_a, local_b)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- Which workload goes to which process?\n", "```\n", "if (rank == i) {\n", "\tdo great things\n", "}\n", "```\n", "- Start with small number of processes\n", "- Calculation workload assignment manually for each count of processes\n", "- Generalize assignment for process i based on sample calculations\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting codes/openmpi/static.c\n" ] } ], "source": [ "%%writefile codes/openmpi/static.c\n", "/*\n", " * MPI implementation of the trapezoid approach to integral calculation following a static\n", " * workload distribution and standard send()/recv() calls. \n", " * We assume that the number of trapezoids is divisible by the number of MPI process. \n", " */\n", "\n", "#include <stdio.h>\n", "#include <stdlib.h>\n", "#include \"mpi.h\"\n", "\n", "double Trap(double a, double b, int n);\n", "double f(double x);\n", "\n", "int main(int argc, char * argv[] ) {\n", " int rank; /* rank of each MPI process */\n", " int size; /* total number of MPI processes */\n", " double a, b; /* default left and right endpoints of the interval */\n", " int n; /* total number of trapezoids */\n", " double h; /* height of the trapezoids */\n", " double local_a, local_b; /* left and right endpoints on each MPI process */\n", " int local_n; /* number of trapezoids assigned to each individual MPI process */\n", " double result; /* final integration result */\n", " double local_result; /* partial integration result at each process */\n", " int p; /* counter */\n", " MPI_Status status;\n", "\n", " MPI_Init(&argc,&argv);\n", " MPI_Comm_rank(MPI_COMM_WORLD,&rank);\n", " MPI_Comm_size(MPI_COMM_WORLD,&size);\n", "\n", " a = atof(argv[1]);\n", " b = atof(argv[2]);\n", " n = atoi(argv[3]);\n", "\n", " // calculate work interval for each process\n", " h = (b - a) / n;\n", " local_n = n / size;\n", " local_a = a + rank * local_n * h;\n", " local_b = local_a + local_n * h;\n", " local_result = Trap(local_a,local_b,local_n);\n", "\n", " // sending the results back to the master\n", " if (rank == 0){\n", " result = local_result;\n", " for (p = 1; p < size; p++){\n", " MPI_Recv(&local_result,1,MPI_DOUBLE,p,MPI_ANY_TAG,MPI_COMM_WORLD,&status);\n", " result += local_result;\n", " }\n", " } \n", " else{\n", " MPI_Send(&local_result,1,MPI_DOUBLE,0,0,MPI_COMM_WORLD); \n", " }\n", "\n", " // displaying output at the master node\n", " if (rank == 0){\n", " printf(\"Calculating the integral of f(x) from %lf to %lf\\n\", a, b);\n", " printf(\"The integral is %lf\\n\", result); \n", " }\n", " MPI_Finalize();\n", "}\n", "\n", "double Trap(double a, double b, int n) {\n", " double len, area;\n", " double x;\n", " int i;\n", " len = (b - a) / n;\n", " area = 0.5 * (f(a) + f(b));\n", " x = a + len;\n", " for (i=1; i<n; i++) {\n", " area = area + f(x);\n", " x = x + len;\n", " }\n", " area = area * len;\n", " return area;\n", "}\n", "\n", "double f(double x) {\n", " return ( x*x );\n", "}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculating the integral of f(x) from 0.000000 to 100.000000\r\n", "The integral is 333333.500000\r\n" ] } ], "source": [ "!mpicc codes/openmpi/static.c -o ~/static\n", "!mpirun -np 8 --map-by core:OVERSUBSCRIBE ~/static 0 100 1000" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#### <center> Does each process receive the same amount of work?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting codes/openmpi/static_wtiming.c\n" ] } ], "source": [ "%%writefile codes/openmpi/static_wtiming.c\n", "/*\n", " * MPI implementation of the trapezoid approach to integral calculation following a static\n", " * workload distribution and standard send()/recv() calls. \n", " * We assume that the number of trapezoids is divisible by the number of MPI process. \n", " */\n", "\n", "#include <stdio.h>\n", "#include <stdlib.h>\n", "#include \"mpi.h\"\n", "\n", "double Trap(double a, double b, int n);\n", "double f(double x);\n", "\n", "int main(int argc, char * argv[] ) {\n", " int rank; /* rank of each MPI process */\n", " int size; /* total number of MPI processes */\n", " double a, b; /* default left and right endpoints of the interval */\n", " int n; /* total number of trapezoids */\n", " double h; /* height of the trapezoids */\n", " double local_a, local_b; /* left and right endpoints on each MPI process */\n", " int local_n; /* number of trapezoids assigned to each individual MPI process */\n", " double result; /* final integration result */\n", " double local_result; /* partial integration result at each process */\n", " double start, stop, tpar, tcomm; /* timing variables */\n", " int p; /* counter */\n", " MPI_Status status;\n", "\n", " MPI_Init(&argc,&argv);\n", " MPI_Comm_rank(MPI_COMM_WORLD,&rank);\n", " MPI_Comm_size(MPI_COMM_WORLD,&size);\n", "\n", " a = atof(argv[1]);\n", " b = atof(argv[2]);\n", " n = atoi(argv[3]);\n", "\n", " // calculate work interval for each process\n", " start = MPI_Wtime();\n", " h = (b - a) / n;\n", " local_n = n / size;\n", " local_a = a + rank * local_n * h;\n", " local_b = local_a + local_n * h;\n", " local_result = Trap(local_a,local_b,local_n);\n", " stop = MPI_Wtime();\n", " tpar = stop - start;\n", "\n", " printf(\"Process %d uses %lfs to calculate partial result %lf\\n\", rank, tpar, local_result);\n", "\n", " // sending the results back to the master\n", " start = MPI_Wtime();\n", " if (rank == 0){\n", " result = local_result;\n", " for (p = 1; p < size; p++){\n", " MPI_Recv(&local_result,1,MPI_DOUBLE,p,MPI_ANY_TAG,MPI_COMM_WORLD,&status);\n", " result += local_result;\n", " }\n", " } \n", " else{\n", " MPI_Send(&local_result,1,MPI_DOUBLE,0,0,MPI_COMM_WORLD); \n", " }\n", " stop = MPI_Wtime();\n", " tcomm = stop - start;\n", "\n", " // displaying output at the master node\n", " if (rank == 0){\n", " printf(\"Calculating the integral of f(x) from %lf to %lf\\n\", a, b);\n", " printf(\"The integral is %lf\\n\", result); \n", " printf(\"Communication time: %.5fs\\n\",tcomm);\n", " }\n", " MPI_Finalize();\n", "}\n", "\n", "double Trap(double a, double b, int n) {\n", " double len, area;\n", " double x;\n", " int i;\n", " len = (b - a) / n;\n", " area = 0.5 * (f(a) + f(b));\n", " x = a + len;\n", " for (i=1; i<n; i++) {\n", " area = area + f(x);\n", " x = x + len;\n", " }\n", " area = area * len;\n", " return area;\n", "}\n", "\n", "double f(double x) {\n", " return ( x*x );\n", "}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process 1 uses 0.000001s to calculate partial result 4557.312500\r\n", "Process 6 uses 0.000001s to calculate partial result 82682.312500\r\n", "Process 0 uses 0.000001s to calculate partial result 651.062500\r\n", "Process 5 uses 0.000001s to calculate partial result 59244.812500\r\n", "Process 4 uses 0.000001s to calculate partial result 39713.562500\r\n", "Process 7 uses 0.000001s to calculate partial result 110026.062500\r\n", "Process 2 uses 0.000001s to calculate partial result 12369.812500\r\n", "Process 3 uses 0.000001s to calculate partial result 24088.562500\r\n", "Calculating the integral of f(x) from 0.000000 to 100.000000\r\n", "The integral is 333333.500000\r\n", "Communication time: 0.00032s\r\n" ] } ], "source": [ "!mpicc codes/openmpi/static_wtiming.c -o ~/static_wtiming\n", "!mpirun -np 8 --map-by core:OVERSUBSCRIBE ~/static_wtiming 0 100 1000" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<center> \n", " <img src=\"pictures/06/static-wa.png\" width=\"400\"/>\n", "</center>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<center> \n", " <img src=\"pictures/06/cyclic-wl.png\" width=\"400\"/>\n", "</center>" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting codes/openmpi/cyclic.c\n" ] } ], "source": [ "%%writefile codes/openmpi/cyclic.c\n", "#include <stdio.h>\n", "#include <stdlib.h>\n", "#include <mpi.h>\n", "\n", "float f(float x);\n", "\n", "main(int argc, char** argv) {\n", " int rank; /* rank of each MPI process */\n", " int size; /* total number of MPI processes */\n", " double a, b; /* default left and right endpoints of the interval */\n", " int n; /* total number of trapezoids */\n", " double h; /* height of the trapezoids */\n", " double local_a, local_b; /* left and right endpoints on each MPI process */\n", " int local_n; /* number of trapezoids assigned to each individual MPI process */\n", " double result; /* final integration result */\n", " double local_result; /* partial integration result at each process */\n", " double start, stop, tpar, tcomm; /* timing variables */\n", " int i; /* counter */\n", " MPI_Status status;\n", "\n", " MPI_Init(&argc, &argv);\n", " MPI_Comm_rank(MPI_COMM_WORLD, &rank);\n", " MPI_Comm_size(MPI_COMM_WORLD, &size);\n", "\n", " a = atof(argv[1]); \n", " b = atof(argv[2]); \n", " n = atoi(argv[3]); \n", " \n", " // calculate work interval for each process\n", " start = MPI_Wtime(); \n", " h = (b-a)/n; /* height is the same for all processes */\n", " local_n = n/size; /* so is the number of trapezoids */\n", "\n", " /* Each process' interval starts at: */\n", " local_a = a + rank * h;\n", " local_b = local_a + h;\n", " local_result = 0;\n", "\n", " for (i = 0; i < n/size; i++){\n", " local_result = local_result + h * (f(local_a) + f(local_b)) / 2;\n", " local_a += h * size;\n", " local_b = local_a + h;\n", " }\n", " stop = MPI_Wtime();\n", " tpar = stop - start;\n", "\n", " printf(\"Process %d uses %lfs to calculate partial result %lf\\n\", rank, tpar, local_result);\n", " \n", " // sending the results back to the master using reduce \n", " start = MPI_Wtime();\n", " MPI_Reduce(&local_result, &result, 1, MPI_DOUBLE, MPI_SUM, 0, MPI_COMM_WORLD);\n", " stop = MPI_Wtime();\n", " tcomm = stop - start;\n", "\n", " /* Print the result */\n", " if (rank == 0){\n", " printf(\"Calculating the integral of f(x) from %lf to %lf\\n\", a, b);\n", " printf(\"The integral is %lf\\n\", result); \n", " printf(\"Communication time: %.5fs\\n\",tcomm);\n", " }\n", " MPI_Finalize();\n", "}\n", "\n", "float f(float x) {\n", " return ( x*x );\n", "} " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Process 2 uses 0.000003s to calculate partial result 41478.812410\r\n", "Process 4 uses 0.000003s to calculate partial result 41728.562518\r\n", "Process 7 uses 0.000003s to calculate partial result 42105.062441\r\n", "Process 1 uses 0.000003s to calculate partial result 41354.312473\r\n", "Process 3 uses 0.000003s to calculate partial result 41603.562379\r\n", "Process 0 uses 0.000003s to calculate partial result 41230.062246\r\n", "Process 6 uses 0.000003s to calculate partial result 41979.312502\r\n", "Process 5 uses 0.000003s to calculate partial result 41853.812428\r\n", "Calculating the integral of f(x) from 0.000000 to 100.000000\r\n", "The integral is 333333.499397\r\n", "Communication time: 0.00050s\r\n" ] } ], "source": [ "!mpicc codes/openmpi/cyclic.c -o ~/cyclic\n", "!mpirun -np 8 --map-by core:OVERSUBSCRIBE ~/cyclic 0 100 1000" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<center> \n", " <img src=\"pictures/06/dynamic-wl.png\" width=\"800\"/>\n", "</center>" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting codes/openmpi/dynamic.c\n" ] } ], "source": [ "%%writefile codes/openmpi/dynamic.c\n", "#include <stdio.h>\n", "#include <stdlib.h>\n", "#include <mpi.h>\n", "\n", "#define SEND 1\n", "#define STOP 0\n", "\n", "float f(float x);\n", "\n", "main(int argc, char** argv) {\n", " int rank; /* rank of each MPI process */\n", " int size; /* total number of MPI processes */\n", " double a, b; /* default left and right endpoints of the interval */\n", " int n; /* total number of trapezoids */\n", " double h; /* height of the trapezoids */\n", " double param[3]; /* array containing end points and height for each individual trapezoid\n", " for communication purpose */\n", " double local_result = 0.0; /* area of each individual trapezoid */\n", " double partial_result = 0.0; /* amount of area calculated by each process */\n", " double result = 0.0; /* Total integral */\n", " int source; /* Process sending the partial integral */\n", " int dest = 0; /* All messages go to 0 */\n", " int tag = 0;\n", " double start, stop, tpar, tcomm;\n", " int i,count, partial_count;\n", " MPI_Status status;\n", "\n", " MPI_Init(&argc, &argv);\n", " MPI_Comm_rank(MPI_COMM_WORLD, &rank);\n", " MPI_Comm_size(MPI_COMM_WORLD, &size);\n", " start = MPI_Wtime();\n", "\n", " /* initial job distribution is handled only by process 0 */\n", " if (rank == 0){\n", " a = atof(argv[1]); \n", " b = atof(argv[2]); \n", " n = atoi(argv[3]); \n", " h = (b-a)/n; \n", " count = 0;\n", " /* send out the first round of work assignment, incrementing count as needed */\n", " for (i = 1; i < size; i++){\n", " param[0] = a + count * h;\n", " param[1] = param[0] + h;\n", " param[2] = h;\n", " MPI_Send(param,3,MPI_DOUBLE,i,SEND,MPI_COMM_WORLD);\n", " count = count + 1;\n", " }\n", " }\n", " else {\n", " MPI_Recv(param,3,MPI_DOUBLE,0,MPI_ANY_TAG,MPI_COMM_WORLD,&status);\n", " }\n", " \n", " tpar = 0.0;\n", " tcomm = 0.0;\n", " partial_count = 0; \n", " /* Each process that is not process 0 works on its portion, send the partial result back to 0, \n", " * and wait for new workload unless the TAG of the message is 0 \n", " */\n", " if (rank != 0){\n", " do {\n", " start = MPI_Wtime();\n", " local_result = param[2] * (f(param[1]) + f(param[0])) / 2;\n", " partial_result += local_result;\n", " stop = MPI_Wtime(); \n", " tpar += stop - start;\n", " partial_count += 1;\n", " start = MPI_Wtime();\n", " MPI_Send(&local_result,1,MPI_DOUBLE,0,SEND,MPI_COMM_WORLD); \n", " MPI_Recv(param,3,MPI_DOUBLE,0,MPI_ANY_TAG,MPI_COMM_WORLD,&status);\n", " stop = MPI_Wtime();\n", " tcomm += stop - start;\n", " } while(status.MPI_TAG != 0);\n", " printf(\"Process %d uses %lfs to calculate partial result %lf of %d portions and %lfs for communication \\n\", rank, tpar, partial_result, partial_count, tcomm);\n", " }\n", " \n", "\n", " /* Process 0 receives results and sends out work while there is still work left to be sent\n", " * (count < n) */\n", " if (rank == 0) {\n", " do {\n", " MPI_Recv(&local_result,1,MPI_DOUBLE,MPI_ANY_SOURCE,MPI_ANY_TAG,MPI_COMM_WORLD,&status);\n", " result = result + local_result; \n", " param[0] = a + count * h;\n", " param[1] = param[0] + h;\n", " param[2] = h;\n", " MPI_Send(param,3,MPI_DOUBLE,status.MPI_SOURCE,SEND,MPI_COMM_WORLD); \n", " count = count + 1; \n", " } \n", " while (count < n); \n", "\n", " /* Make sure that we receive everything */\n", " for (i = 0; i < (size - 1); i++){\n", " MPI_Recv(&local_result,1,MPI_DOUBLE,MPI_ANY_SOURCE,MPI_ANY_TAG,MPI_COMM_WORLD,&status);\n", " result = result + local_result; \n", " } \n", " }\n", "\n", " /* All the work has been sent, */\n", " if (rank == 0){\n", " for (i = 1; i < size; i++){\n", " MPI_Send(param,3,MPI_DOUBLE,i,STOP,MPI_COMM_WORLD);\n", " }\n", " }\n", "\n", " /* Print the result */\n", " if (rank == 0) {\n", " printf(\"With n = %d trapezoids, our estimate\\n\",\n", " n);\n", " printf(\"of the integral from %f to %f = %f\\n\",\n", " a, b, result);\n", " }\n", "\n", " /* Shut down MPI */\n", " MPI_Finalize();\n", "} /* main */\n", "\n", "float f(float x) {\n", " return ( x*x );\n", "} " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "With n = 1000 trapezoids, our estimate\r\n", "of the integral from 0.000000 to 100.000000 = 333333.499397\r\n", "Process 3 uses 0.000011s to calculate partial result 48070.134273 of 145 portions and 0.005426s for communication \r\n", "Process 4 uses 0.000011s to calculate partial result 47221.898243 of 144 portions and 0.005598s for communication \r\n", "Process 6 uses 0.000013s to calculate partial result 46641.744142 of 145 portions and 0.005714s for communication \r\n", "Process 7 uses 0.000010s to calculate partial result 48331.990086 of 132 portions and 0.004679s for communication \r\n", "Process 2 uses 0.000012s to calculate partial result 50906.612141 of 153 portions and 0.005935s for communication \r\n", "Process 5 uses 0.000012s to calculate partial result 45724.445379 of 143 portions and 0.005760s for communication \r\n", "Process 1 uses 0.000012s to calculate partial result 46436.675133 of 138 portions and 0.006154s for communication \r\n" ] } ], "source": [ "!mpicc codes/openmpi/dynamic.c -o ~/dynamic\n", "!mpirun -np 8 --map-by core:OVERSUBSCRIBE ~/dynamic 0 100 1000" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<center> \n", " <img src=\"pictures/06/pi_montecarlo.png\" width=\"700\"/>\n", "</center>" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting codes/openmpi/pi_mc.c\n" ] } ], "source": [ "%%writefile codes/openmpi/pi_mc.c\n", "\n", "#include <stdio.h>\n", "#include <stdlib.h>\n", "#include <mpi.h>\n", "#include <math.h>\n", "\n", "int main(int argc, char* argv[]) {\n", "\n", " int nPointsInCircle = 0;\n", " int i = 0;\n", " int nPointsTotal = 0;\n", " int nPointsPerRegion = 0;\n", " int pointsReceived = 0;\n", " double piEstimate;\n", " double x_start, y_start;\n", " double x_rand, y_rand, rand_radius; \n", " int rank, size, squareWidth;\n", " MPI_Status status;\n", "\n", " nPointsTotal = atoi(argv[1]);\n", "\n", " MPI_Init(&argc, &argv);\n", " MPI_Comm_rank(MPI_COMM_WORLD, &rank);\n", " MPI_Comm_size(MPI_COMM_WORLD, &size);\n", "\n", " // Seed RNG and make calculations for constants\n", " nPointsPerRegion = nPointsTotal / size;\n", " srand( (unsigned)time(NULL) + rank ); // seed differently per node\n", " squareWidth = (int) sqrt(size);\n", "\n", " // Place and record points in the circle\n", " x_start = (double)(rank % squareWidth) / squareWidth;\n", " y_start = (double)((rank / squareWidth)) / squareWidth;\n", "\n", " //printf(\"Rank %d out of %d has starting x %f and starting y %f on a square of size %d \\n\", \n", " // rank, size, x_start, y_start, squareWidth);\n", " \n", " for (i = 0; i < nPointsPerRegion; i++) {\n", " x_rand = (double)rand() / ((double)RAND_MAX * squareWidth) + x_start;\n", " y_rand = (double)rand() / ((double)RAND_MAX * squareWidth) + y_start;\n", " rand_radius = (x_rand - 0.5) * (x_rand - 0.5) + (y_rand - 0.5) * (y_rand - 0.5);\n", " if (rand_radius <= 0.25) {\n", " nPointsInCircle += 1;\n", " }\n", " }\n", " \n", " MPI_Reduce(&nPointsInCircle, &pointsReceived, 1, MPI_INT, MPI_SUM, 0, MPI_COMM_WORLD);\n", " if (rank == 0) {\n", " piEstimate = (double)(pointsReceived * 4) / nPointsTotal;\n", " printf(\"%f\\n\", piEstimate);\n", " } \n", "\n", " MPI_Finalize();\n", " return 0;\n", "}" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[01m\u001b[Kcodes/openmpi/pi_mc.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmain\u001b[m\u001b[K’:\n", "\u001b[01m\u001b[Kcodes/openmpi/pi_mc.c:36:51:\u001b[m\u001b[K \u001b[01;31m\u001b[Kerror: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[K;\u001b[m\u001b[K’ before ‘\u001b[01m\u001b[K)\u001b[m\u001b[K’ token\n", " rank, size, x_start, y_start, squareWidth);\n", "\u001b[01;32m\u001b[K ^\u001b[m\u001b[K\n", "\u001b[01m\u001b[Kcodes/openmpi/pi_mc.c:36:51:\u001b[m\u001b[K \u001b[01;31m\u001b[Kerror: \u001b[m\u001b[Kexpected statement before ‘\u001b[01m\u001b[K)\u001b[m\u001b[K’ token\n", "Rank 0 out of 4 has starting x 0.000000 and starting y 0.000000 on a square of size 2 \n", "Rank 3 out of 4 has starting x 0.500000 and starting y 0.500000 on a square of size 2 \n", "Rank 1 out of 4 has starting x 0.500000 and starting y 0.000000 on a square of size 2 \n", "Rank 2 out of 4 has starting x 0.000000 and starting y 0.500000 on a square of size 2 \n", "3.135760\n" ] } ], "source": [ "!mpicc -lm codes/openmpi/pi_mc.c -o ~/pi_mc\n", "!mpirun -np 4 --map-by core:OVERSUBSCRIBE ~/pi_mc 100000" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
wasat/JupyTEPIDE
notebooks/deprecated/listdirs.ipynb
1
14351
{ "cells": [ { "cell_type": "markdown", "metadata": { "extensions": { "jupyter_dashboards": { "version": 1.0, "views": { "grid_default": {}, "report_default": { "hidden": false } } } } }, "source": [ "# Select product" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1.0, "views": { "grid_default": { "col": 0.0, "height": 16.0, "hidden": false, "row": 0.0, "width": 4.0 }, "report_default": { "hidden": false } } } }, "scrolled": true }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" } ], "source": [ "import ipywidgets as widgets\n", "from ipywidgets import DatePicker\n", "from IPython.display import display\n", "from os.path import join, isdir, isfile\n", "from os import listdir,walk\n", "\n", "path=['/eodata','','','','','','','','','','','','']\n", "\n", "def getN1Corners(path):\n", " import subprocess \n", " return [float(x) for x in str(subprocess.Popen(['/opt/scripts/forN1.py',path],stdout=subprocess.PIPE).communicate()[0]).split(\";\")[1:-1]]\n", "\n", "def saveN1image(path):\n", " import subprocess \n", " subprocess.Popen(['/opt/scripts/saveN1.py',path],stdout=subprocess.PIPE).communicate()\n", " \n", "\n", "\n", "label=widgets.Label(value=path[0])\n", "plotButton=widgets.Button(description=\"Show preview\")\n", "x=widgets.Dropdown(\n", " options=['None','Envisat','Landsat-5','Landsat-7','Landsat-8','Sentinel-1','Sentinel-2','Sentinel-3'],\n", " description='Mission:',\n", " value='None',\n", " disabled=False,\n", ")\n", "y=widgets.Dropdown(\n", " options=['None'],\n", " description='Instrument:',\n", " disabled=False,\n", ")\n", "processingMode=widgets.Dropdown(\n", " options=['None'],\n", " description='Processing Mode:',\n", " disabled=False,\n", ")\n", "\n", "year=widgets.Dropdown(\n", " options=['None'],\n", " description='Year:',\n", " disabled=False,\n", ")\n", "\n", "month=widgets.Dropdown(\n", " options=['None'],\n", " description='Month:',\n", " disabled=False,\n", ")\n", "\n", "day=widgets.Dropdown(\n", " options=['None'],\n", " description='Days:',\n", " disabled=False,\n", ")\n", "\n", "productName=widgets.Dropdown(\n", " options=['None'],\n", " description='Product name:',\n", " disabled=False,\n", ")\n", "\n", "files=widgets.Dropdown(\n", " options=['None'],\n", " description='Files:',\n", " disabled=False,\n", ")\n", "\n", "pp=widgets.Image(\n", " width=300,\n", " height=300,\n", ")\n", "\n", "def joinPath(i):\n", " global path\n", " return join(*path[:i+1])\n", " \n", "def getMetadata(filename):\n", " with open(filename,'r') as f:\n", " data=f.readlines()\n", " f.close()\n", " \n", " \n", "def fieldChange(*args): \n", " global path\n", " if args[0]['owner'].description=='Mission:':\n", " path[1]=x.value\n", " inpath=joinPath(1)\n", " dirs=sorted(listdir(inpath))\n", " dirs=[x for x in dirs if isdir(join(inpath,x))] \n", " y.options=['']\n", " y.options=dirs\n", " elif args[0]['owner'].description=='Instrument:':\n", " path[2]=y.value\n", " inpath=joinPath(2)\n", " dirs=sorted(listdir(inpath))\n", " dirs=[x for x in dirs if isdir(join(inpath,x))] \n", " processingMode.options=['']\n", " processingMode.options=dirs \n", " elif args[0]['owner'].description=='Processing Mode:':\n", " path[3]=processingMode.value\n", " inpath=joinPath(3)\n", " dirs=sorted(listdir(inpath))\n", " dirs=[x for x in dirs if isdir(join(inpath,x))] \n", " year.options=['']\n", " year.options=dirs\n", " elif args[0]['owner'].description=='Year:':\n", " path[4]=year.value\n", " inpath=joinPath(4)\n", " dirs=sorted(listdir(inpath))\n", " dirs=[x for x in dirs if isdir(join(inpath,x))] \n", " month.options=['']\n", " month.options=dirs\n", " elif args[0]['owner'].description=='Month:':\n", " path[5]=month.value\n", " inpath=joinPath(5)\n", " dirs=sorted(listdir(inpath))\n", " dirs=[x for x in dirs if isdir(join(inpath,x))] \n", " day.options=['']\n", " day.options=dirs\n", " elif args[0]['owner'].description=='Days:':\n", " path[6]=day.value\n", " inpath=joinPath(6)\n", " dirs=sorted(listdir(inpath))\n", " dirs1=[x for x in dirs if isdir(join(inpath,x))] \n", " if len(dirs1)==0:\n", " f=[x for x in dirs if isfile(join(inpath,x))] \n", " files.options=['']\n", " files.options=f\n", " productName.options=['']\n", " else:\n", " productName.options=['']\n", " files.options=['']\n", " productName.options=['']\n", " productName.options=dirs1\n", " elif args[0]['owner'].description=='Product name:': \n", " path[7]=productName.value \n", " inpath=joinPath(7) \n", " dirs=sorted(listdir(inpath)) \n", " f=[x for x in dirs if join(inpath,x)] \n", " files.options=['']\n", " files.options=f\n", " label.value=join(*path)\n", " \n", " \n", "def get_path(*args):\n", " global path\n", " \n", " label.value=join(*path)\n", " \n", " \n", "\n", " \n", "\n", "def drawPicture(*args): \n", " pass\n", " \n", " \n", "\n", "def on_button_clicked(b):\n", " global path\n", " global plotButton\n", " plotButton.disabled=True\n", " if len(files.value)!=0 and files.value.lower().endswith('jpg'): \n", " with open(join(*path,files.value),'rb') as f:\n", " image = f.read()\n", " pp.value=image \n", " pp.format='jpg' \n", " elif len(files.value)!=0 and files.value.lower().endswith('n1'): \n", " sciezka=(join(join(*path),files.value))\n", " saveN1image(sciezka)\n", " with open('/opt/tmp/'+sciezka.replace('/','_')+'.jpg','rb') as f:\n", " image = f.read()\n", " pp.value=image \n", " pp.format='jpg'\n", " plotButton.disabled=False\n", " \n", " \n", "\n", "\n", "\n", "with open('Globe.png','rb') as f:\n", " image = f.read()\n", " pp.value=image \n", " pp.format='png'\n", " \n", "x.observe(fieldChange,names='value')\n", "y.observe(fieldChange,names='value')\n", "processingMode.observe(fieldChange,names='value')\n", "year.observe(fieldChange,names='value')\n", "month.observe(fieldChange,names='value')\n", "day.observe(fieldChange,names='value')\n", "productName.observe(fieldChange,names='value')\n", "plotButton.on_click(on_button_clicked)\n", " \n", " \n", "widgets.VBox([widgets.HBox([x,y,processingMode]),widgets.HBox([year,month,day]),widgets.HBox([productName,files,plotButton]),pp])\n" ] }, { "cell_type": "markdown", "metadata": { "extensions": { "jupyter_dashboards": { "version": 1.0, "views": { "grid_default": {}, "report_default": { "hidden": false } } } } }, "source": [ "# Show on map\n", "Run the cell below to show selected product boundary on map." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1.0, "views": { "grid_default": { "hidden": true }, "report_default": { "hidden": false } } } } }, "outputs": [ { "data": {}, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "sciezka=(join(join(*path),files.value))\n", "\n", "from ipyleaflet import (\n", " Map,\n", " Marker,\n", " TileLayer, ImageOverlay,\n", " Polyline, Polygon, Rectangle, Circle, CircleMarker,\n", " GeoJSON,\n", " DrawControl\n", ")\n", "\n", "\n", "import json\n", "znalazlem=False\n", "if files.value.lower().endswith(\"n1\"):\n", " znalazlem=True \n", " ul_lon, ul_lat, ur_lon, ur_lat, ll_lon, ll_lat, lr_lon, lr_lat =getN1Corners(sciezka)\n", "else: \n", " for dirname,subdirlist,filelist in walk(label.value):\n", " pliki=[x for x in filelist if x.lower().endswith('mtl.txt')]\n", " if pliki:\n", " plik=pliki[0]\n", " plik=join(dirname,plik)\n", " znalazlem=True\n", " with open(plik,'r') as f:\n", " for line in f:\n", " if 'CORNER_UL_LAT_PRODUCT' in line:\n", " ul_lat=float(line.split('=')[1].strip())\n", " if 'CORNER_UL_LON_PRODUCT' in line:\n", " ul_lon=float(line.split('=')[1].strip())\n", " if 'CORNER_UR_LAT_PRODUCT' in line:\n", " ur_lat=float(line.split('=')[1].strip())\n", " if 'CORNER_UR_LON_PRODUCT' in line:\n", " ur_lon=float(line.split('=')[1].strip())\n", " if 'CORNER_LL_LAT_PRODUCT' in line:\n", " ll_lat=float(line.split('=')[1].strip())\n", " if 'CORNER_LL_LON_PRODUCT' in line:\n", " ll_lon=float(line.split('=')[1].strip())\n", " if 'CORNER_LR_LAT_PRODUCT' in line:\n", " lr_lat=float(line.split('=')[1].strip())\n", " if 'CORNER_LR_LON_PRODUCT' in line:\n", " lr_lon=float(line.split('=')[1].strip())\n", " f.close()\n", "if znalazlem:\n", " sson={ \"type\": \"FeatureCollection\",\n", " \"features\": [ \n", " { \"type\": \"Feature\",\n", " \"geometry\": {\n", " \"type\": \"Polygon\",\n", " \"coordinates\": [\n", " [ [ul_lon, ul_lat], [ur_lon, ur_lat], [lr_lon, lr_lat],\n", " [ll_lon, ll_lat], [ul_lon, ul_lat] ]\n", " ]\n", " },\n", " \"properties\": { }\n", " }\n", " ]\n", " }\n", "\n", "\n", " sson=json.loads(json.dumps(sson))\n", " for feature in sson['features']:\n", " feature['properties']['style'] = {\n", " 'color': 'green',\n", " 'weight': 4,\n", " 'fillColor': 'red',\n", " 'fillOpacity': 0.3\n", " }\n", " g=GeoJSON(data=sson,hover_style={'fillColor': 'red'})\n", "\n", "\n", " center = [(ul_lat+ur_lat+lr_lat+ll_lat)/4,(ul_lon+ur_lon+lr_lon+ll_lon)/4]\n", " zoom=5\n", "\n", " m = Map(center=center,zoom=zoom)\n", " m.add_layer(g)\n", "else:\n", " m=Map()\n", "m" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1.0, "views": { "grid_default": {}, "report_default": { "hidden": true } } } } }, "outputs": [ { "data": { "text/plain": [ "'3.6.1 |Anaconda 4.4.0 (64-bit)| (default, May 11 2017, 13:09:58) \\n[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sys\n", "sys.version" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "extensions": { "jupyter_dashboards": { "version": 1.0, "views": { "grid_default": {}, "report_default": { "hidden": true } } } } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.7.12 (default, Nov 19 2016, 06:48:10) \n", "[GCC 5.4.0 20160609]\n" ] } ], "source": [ "%%python2\n", "import sys\n", "print sys.version" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "extensions": { "jupyter_dashboards": { "activeView": "report_default", "version": 1.0, "views": { "grid_default": { "cellMargin": 10.0, "defaultCellHeight": 20.0, "maxColumns": 12.0, "name": "grid", "type": "grid" }, "report_default": { "name": "report", "type": "report" } } } }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
Ccaccia73/NLAAS-Labs
lab04.ipynb
1
846366
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "import shutil\n", "import matplotlib.pyplot as plt\n", "import json\n", "from matplotlib import rcParams\n", "rcParams['font.family'] = 'serif'\n", "rcParams['font.size'] = 14\n", "from IPython.display import Image\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#filename = '04_Hinged_roof_disp.dat'" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def readData(filename):\n", " \n", " file=open(filename,'r') \n", " row = file.readlines()\n", " \n", " disp = []\n", " force = []\n", " \n", " state = 0\n", "\n", " for line in row:\n", " strlist = line.split()\n", " if 'U2' in strlist:\n", " state = 1\n", " elif 'RF2' in strlist:\n", " state = 2\n", " elif '289' in strlist:\n", " if state == 1:\n", " disp.append(float(strlist[2]))\n", " state = 0\n", " if state == 2:\n", " force.append(float(strlist[2]))\n", " state = 0\n", " da = -np.array(disp)\n", " fa = -np.array(force)\n", " \n", " return da, fa\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "d1, f1 = readData('./L04data/disp40_1000.dat')\n", "d2, f2 = readData('./L04data/disp50_10.dat')\n", "d3, f3 = readData('./L04data/disp60_10.dat')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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M/PkoN4vKzBsls0TEjdrQwb9xw4ePT6U8DHgIcA5l97pNKTt7/ifdd4xdYZ+I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\nOaLd85I8sLu/NKIOAAAAwE5nz63uAAAAAAAsk8n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jUHDx5k+PDh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ZkHn0ViZw+uQh3LJkCrNPyOx3n8WFEEIIIYQQQgghhBBCCNG/9fVq9VFStV4IISTlIgae5u5Q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\nHcJut5/wtWM/9z1w4IDPNY81cuTIMz4mPT2dsLCwphD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ODUhrwz1WczsF7SMD4L/KVn+3XlQ3b9PA/YsGf7wLlWtZGkjk164E+S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\nnCsoplFm3gJ8vGH3nhFRF0RXrd8awF4Nu4/Mz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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe93de34a58>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "maxD = 60.0\n", "maxF = 20000.\n", "plt.figure(figsize=(16,10), dpi=300)\n", "\n", "plt.plot(d1,f1*4, 'o-', label='fine')\n", "plt.plot(d2,f2*4, '^-', ms=12, label='coarse')\n", "plt.plot(d3,f3*4, 'h-', ms=12, label='coarse 2')\n", "\n", "plt.xlim([0,maxD])\n", "plt.ylim([-500,maxF])\n", "plt.xticks(np.arange(0.0,maxD+1,5))\n", "plt.yticks(np.arange(-500.0,maxF+.5,1000))\n", "plt.title('Hinged Roof - Force displacement relation',fontsize=18, fontweight='bold')\n", "plt.xlabel('Node $y$ displacement $[mm]$', fontsize=16)\n", "plt.ylabel('Force $y$ $[N]$', fontsize=16)\n", "plt.legend(loc='lower right', shadow=True)\n", "plt.grid()\n", "plt.savefig('Lab04.jpg')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GhhAR21CuVav6pM2AW1C+r2tx/T3Vn7l+TuTHlDmR3y1Em1tFxB0o8287UebfVs2jrAQu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\n6SS/ON4wF+2CDH9+3N0c3C/J9/VUTdUcrKrbkjw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ZciSZIkSZIkSZJUp9lgL0mSJEmSJEmSJDVg/Vr346aBN/GL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\nrq3uWZIkSZIkSZIkSbVN00ZNeXDog3zzlW+yq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KnmgBJp/BxMIlXU4H2Tpq+x/jEz76hYRi9Nx9fU/V2pq1Hd14yIPYGjgKC4F/PUzDyryTqGzHuo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\ny+z2ebdmlULm0d/mnAGO7dYmK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"text/plain": [ "<matplotlib.figure.Figure at 0x7fe93fbda2e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "maxD = 40.0\n", "maxF = 2500.\n", "plt.figure(figsize=(16,10), dpi=300)\n", "\n", "plt.plot(d1,f1*4, 'o-', label='fine')\n", "plt.plot(d2,f2*4, '^-', ms=12, label='coarse')\n", "plt.plot(d3,f3*4, 'h-', ms=12, label='coarse 2')\n", "\n", "plt.xlim([0,maxD])\n", "plt.ylim([-500,maxF])\n", "plt.xticks(np.arange(0.0,maxD+1,2.5))\n", "plt.yticks(np.arange(-400.0,maxF+.5,200))\n", "plt.title('Hinged Roof - Force displacement relation',fontsize=18, fontweight='bold')\n", "plt.xlabel('Node $y$ displacement $[mm]$', fontsize=16)\n", "plt.ylabel('Force $y$ $[N]$', fontsize=16)\n", "plt.legend(loc='lower right', shadow=True)\n", "plt.grid()\n", "plt.savefig('Lab04_det.jpg')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:py36]", "language": "python", "name": "conda-env-py36-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
ttlekich/ds_practice
nlp/yelp/yelp_rating_predictions.ipynb
1
76494
{ "cells": [ { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yelp = pd.read_csv(\"yelp.csv\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>business_id</th>\n", " <th>date</th>\n", " <th>review_id</th>\n", " <th>stars</th>\n", " <th>text</th>\n", " <th>type</th>\n", " <th>user_id</th>\n", " <th>cool</th>\n", " <th>useful</th>\n", " <th>funny</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>9yKzy9PApeiPPOUJEtnvkg</td>\n", " <td>2011-01-26</td>\n", " <td>fWKvX83p0-ka4JS3dc6E5A</td>\n", " <td>5</td>\n", " <td>My wife took me here on my birthday for breakf...</td>\n", " <td>review</td>\n", " <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ZRJwVLyzEJq1VAihDhYiow</td>\n", " <td>2011-07-27</td>\n", " <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n", " <td>5</td>\n", " <td>I have no idea why some people give bad review...</td>\n", " <td>review</td>\n", " <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n", " <td>2012-06-14</td>\n", " <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n", " <td>4</td>\n", " <td>love the gyro plate. Rice is so good and I als...</td>\n", " <td>review</td>\n", " <td>0hT2KtfLiobPvh6cDC8JQg</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n", " <td>2010-05-27</td>\n", " <td>G-WvGaISbqqaMHlNnByodA</td>\n", " <td>5</td>\n", " <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n", " <td>review</td>\n", " <td>uZetl9T0NcROGOyFfughhg</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>6ozycU1RpktNG2-1BroVtw</td>\n", " <td>2012-01-05</td>\n", " <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n", " <td>5</td>\n", " <td>General Manager Scott Petello is a good egg!!!...</td>\n", " <td>review</td>\n", " <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " business_id date review_id stars \\\n", "0 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 fWKvX83p0-ka4JS3dc6E5A 5 \n", "1 ZRJwVLyzEJq1VAihDhYiow 2011-07-27 IjZ33sJrzXqU-0X6U8NwyA 5 \n", "2 6oRAC4uyJCsJl1X0WZpVSA 2012-06-14 IESLBzqUCLdSzSqm0eCSxQ 4 \n", "3 _1QQZuf4zZOyFCvXc0o6Vg 2010-05-27 G-WvGaISbqqaMHlNnByodA 5 \n", "4 6ozycU1RpktNG2-1BroVtw 2012-01-05 1uJFq2r5QfJG_6ExMRCaGw 5 \n", "\n", " text type \\\n", "0 My wife took me here on my birthday for breakf... review \n", "1 I have no idea why some people give bad review... review \n", "2 love the gyro plate. Rice is so good and I als... review \n", "3 Rosie, Dakota, and I LOVE Chaparral Dog Park!!... review \n", "4 General Manager Scott Petello is a good egg!!!... review \n", "\n", " user_id cool useful funny \n", "0 rLtl8ZkDX5vH5nAx9C3q5Q 2 5 0 \n", "1 0a2KyEL0d3Yb1V6aivbIuQ 0 0 0 \n", "2 0hT2KtfLiobPvh6cDC8JQg 0 1 0 \n", "3 uZetl9T0NcROGOyFfughhg 1 2 0 \n", "4 vYmM4KTsC8ZfQBg-j5MWkw 0 0 0 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yelp.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>stars</th>\n", " <th>cool</th>\n", " <th>useful</th>\n", " <th>funny</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " <td>10000.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>3.777500</td>\n", " <td>0.876800</td>\n", " <td>1.409300</td>\n", " <td>0.701300</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.214636</td>\n", " <td>2.067861</td>\n", " <td>2.336647</td>\n", " <td>1.907942</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>3.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>4.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>5.000000</td>\n", " <td>1.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>5.000000</td>\n", " <td>77.000000</td>\n", " <td>76.000000</td>\n", " <td>57.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " stars cool useful funny\n", "count 10000.000000 10000.000000 10000.000000 10000.000000\n", "mean 3.777500 0.876800 1.409300 0.701300\n", "std 1.214636 2.067861 2.336647 1.907942\n", "min 1.000000 0.000000 0.000000 0.000000\n", "25% 3.000000 0.000000 0.000000 0.000000\n", "50% 4.000000 0.000000 1.000000 0.000000\n", "75% 5.000000 1.000000 2.000000 1.000000\n", "max 5.000000 77.000000 76.000000 57.000000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yelp.describe()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 10000 entries, 0 to 9999\n", "Data columns (total 10 columns):\n", "business_id 10000 non-null object\n", "date 10000 non-null object\n", "review_id 10000 non-null object\n", "stars 10000 non-null int64\n", "text 10000 non-null object\n", "type 10000 non-null object\n", "user_id 10000 non-null object\n", "cool 10000 non-null int64\n", "useful 10000 non-null int64\n", "funny 10000 non-null int64\n", "dtypes: int64(4), object(6)\n", "memory usage: 781.3+ KB\n" ] } ], "source": [ "yelp.info()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "yelp[\"text length\"] = yelp[\"text\"].apply(len)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>business_id</th>\n", " <th>date</th>\n", " <th>review_id</th>\n", " <th>stars</th>\n", " <th>text</th>\n", " <th>type</th>\n", " <th>user_id</th>\n", " <th>cool</th>\n", " <th>useful</th>\n", " <th>funny</th>\n", " <th>text length</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>9yKzy9PApeiPPOUJEtnvkg</td>\n", " <td>2011-01-26</td>\n", " <td>fWKvX83p0-ka4JS3dc6E5A</td>\n", " <td>5</td>\n", " <td>My wife took me here on my birthday for breakf...</td>\n", " <td>review</td>\n", " <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>0</td>\n", " <td>889</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ZRJwVLyzEJq1VAihDhYiow</td>\n", " <td>2011-07-27</td>\n", " <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n", " <td>5</td>\n", " <td>I have no idea why some people give bad review...</td>\n", " <td>review</td>\n", " <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>1345</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>6oRAC4uyJCsJl1X0WZpVSA</td>\n", " <td>2012-06-14</td>\n", " <td>IESLBzqUCLdSzSqm0eCSxQ</td>\n", " <td>4</td>\n", " <td>love the gyro plate. Rice is so good and I als...</td>\n", " <td>review</td>\n", " <td>0hT2KtfLiobPvh6cDC8JQg</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>76</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>_1QQZuf4zZOyFCvXc0o6Vg</td>\n", " <td>2010-05-27</td>\n", " <td>G-WvGaISbqqaMHlNnByodA</td>\n", " <td>5</td>\n", " <td>Rosie, Dakota, and I LOVE Chaparral Dog Park!!...</td>\n", " <td>review</td>\n", " <td>uZetl9T0NcROGOyFfughhg</td>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>419</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>6ozycU1RpktNG2-1BroVtw</td>\n", " <td>2012-01-05</td>\n", " <td>1uJFq2r5QfJG_6ExMRCaGw</td>\n", " <td>5</td>\n", " <td>General Manager Scott Petello is a good egg!!!...</td>\n", " <td>review</td>\n", " <td>vYmM4KTsC8ZfQBg-j5MWkw</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>469</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " business_id date review_id stars \\\n", "0 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 fWKvX83p0-ka4JS3dc6E5A 5 \n", "1 ZRJwVLyzEJq1VAihDhYiow 2011-07-27 IjZ33sJrzXqU-0X6U8NwyA 5 \n", "2 6oRAC4uyJCsJl1X0WZpVSA 2012-06-14 IESLBzqUCLdSzSqm0eCSxQ 4 \n", "3 _1QQZuf4zZOyFCvXc0o6Vg 2010-05-27 G-WvGaISbqqaMHlNnByodA 5 \n", "4 6ozycU1RpktNG2-1BroVtw 2012-01-05 1uJFq2r5QfJG_6ExMRCaGw 5 \n", "\n", " text type \\\n", "0 My wife took me here on my birthday for breakf... review \n", "1 I have no idea why some people give bad review... review \n", "2 love the gyro plate. Rice is so good and I als... review \n", "3 Rosie, Dakota, and I LOVE Chaparral Dog Park!!... review \n", "4 General Manager Scott Petello is a good egg!!!... review \n", "\n", " user_id cool useful funny text length \n", "0 rLtl8ZkDX5vH5nAx9C3q5Q 2 5 0 889 \n", "1 0a2KyEL0d3Yb1V6aivbIuQ 0 0 0 1345 \n", "2 0hT2KtfLiobPvh6cDC8JQg 0 1 0 76 \n", "3 uZetl9T0NcROGOyFfughhg 1 2 0 419 \n", "4 vYmM4KTsC8ZfQBg-j5MWkw 0 0 0 469 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yelp.head()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x23aa145fd30>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x23a9e3b6b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.FacetGrid(yelp,col='stars')\n", "g.map(plt.hist,'text length')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x23aa16029b0>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x23aa11e3cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x = \"stars\", y = \"text length\", data = yelp)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x23aa1736eb8>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x23aa1086470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(\"stars\", data=yelp)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cool</th>\n", " <th>useful</th>\n", " <th>funny</th>\n", " <th>text length</th>\n", " </tr>\n", " <tr>\n", " <th>stars</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>0.576769</td>\n", " <td>1.604806</td>\n", " <td>1.056075</td>\n", " <td>826.515354</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.719525</td>\n", " <td>1.563107</td>\n", " <td>0.875944</td>\n", " <td>842.256742</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.788501</td>\n", " <td>1.306639</td>\n", " <td>0.694730</td>\n", " <td>758.498289</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.954623</td>\n", " <td>1.395916</td>\n", " <td>0.670448</td>\n", " <td>712.923142</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0.944261</td>\n", " <td>1.381780</td>\n", " <td>0.608631</td>\n", " <td>624.999101</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cool useful funny text length\n", "stars \n", "1 0.576769 1.604806 1.056075 826.515354\n", "2 0.719525 1.563107 0.875944 842.256742\n", "3 0.788501 1.306639 0.694730 758.498289\n", "4 0.954623 1.395916 0.670448 712.923142\n", "5 0.944261 1.381780 0.608631 624.999101" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n", "df = yelp.groupby(\"stars\").mean()\n", "df" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>cool</th>\n", " <th>useful</th>\n", " <th>funny</th>\n", " <th>text length</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>cool</th>\n", " <td>1.000000</td>\n", " <td>-0.743329</td>\n", " <td>-0.944939</td>\n", " <td>-0.857664</td>\n", " </tr>\n", " <tr>\n", " <th>useful</th>\n", " <td>-0.743329</td>\n", " <td>1.000000</td>\n", " <td>0.894506</td>\n", " <td>0.699881</td>\n", " </tr>\n", " <tr>\n", " <th>funny</th>\n", " <td>-0.944939</td>\n", " <td>0.894506</td>\n", " <td>1.000000</td>\n", " <td>0.843461</td>\n", " </tr>\n", " <tr>\n", " <th>text length</th>\n", " <td>-0.857664</td>\n", " <td>0.699881</td>\n", " <td>0.843461</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " cool useful funny text length\n", "cool 1.000000 -0.743329 -0.944939 -0.857664\n", "useful -0.743329 1.000000 0.894506 0.699881\n", "funny -0.944939 0.894506 1.000000 0.843461\n", "text length -0.857664 0.699881 0.843461 1.000000" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stars_corr = df.corr()\n", "stars_corr" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x23aa1c120f0>" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x23aa1bf7908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.heatmap(stars_corr, annot=True)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yelp_class = yelp.loc[yelp[\"stars\"].isin([1, 5])]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>stars</th>\n", " <th>cool</th>\n", " <th>useful</th>\n", " <th>funny</th>\n", " <th>text length</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>4086.000000</td>\n", " <td>4086.000000</td>\n", " <td>4086.000000</td>\n", " <td>4086.000000</td>\n", " <td>4086.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>4.266765</td>\n", " <td>0.876897</td>\n", " <td>1.422663</td>\n", " <td>0.690651</td>\n", " <td>661.938815</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.547868</td>\n", " <td>2.336611</td>\n", " <td>2.598515</td>\n", " <td>1.961751</td>\n", " <td>601.621371</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>6.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>5.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>256.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>5.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>489.500000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>5.000000</td>\n", " <td>1.000000</td>\n", " <td>2.000000</td>\n", " <td>1.000000</td>\n", " <td>878.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>5.000000</td>\n", " <td>77.000000</td>\n", " <td>76.000000</td>\n", " <td>39.000000</td>\n", " <td>4986.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " stars cool useful funny text length\n", "count 4086.000000 4086.000000 4086.000000 4086.000000 4086.000000\n", "mean 4.266765 0.876897 1.422663 0.690651 661.938815\n", "std 1.547868 2.336611 2.598515 1.961751 601.621371\n", "min 1.000000 0.000000 0.000000 0.000000 6.000000\n", "25% 5.000000 0.000000 0.000000 0.000000 256.000000\n", "50% 5.000000 0.000000 1.000000 0.000000 489.500000\n", "75% 5.000000 1.000000 2.000000 1.000000 878.000000\n", "max 5.000000 77.000000 76.000000 39.000000 4986.000000" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "yelp_class.describe()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = yelp_class[\"text\"]\n", "y = yelp_class[\"stars\"]" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.feature_extraction.text import CountVectorizer\n", "cv = CountVectorizer()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = cv.fit_transform(X)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.3)" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.naive_bayes import MultinomialNB" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "outputs": [], "source": [ "nb = MultinomialNB()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True)" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nb.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pred = nb.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import confusion_matrix, classification_report" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[144 28]\n", " [ 81 973]]\n", "\n", "\n", " precision recall f1-score support\n", "\n", " 1 0.64 0.84 0.73 172\n", " 5 0.97 0.92 0.95 1054\n", "\n", "avg / total 0.93 0.91 0.92 1226\n", "\n" ] } ], "source": [ "print(confusion_matrix(pred, y_test))\n", "print(\"\\n\")\n", "print(classification_report(pred, y_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Not bad!" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.feature_extraction.text import TfidfTransformer " ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.pipeline import Pipeline" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pipeline = Pipeline([\n", " (\"cv\", CountVectorizer()),\n", " (\"Tfidf\", TfidfTransformer()),\n", " (\"mnb\", MultinomialNB())\n", "])" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = yelp_class[\"text\"]\n", "y = yelp_class[\"stars\"]\n", "X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.3)" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Pipeline(steps=[('cv', CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n", " dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n", " lowercase=True, max_df=1.0, max_features=None, min_df=1,\n", " ngram_range=(1, 1), preprocessor=None, stop_words=None,\n", " strip_a...inear_tf=False, use_idf=True)), ('mnb', MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))])" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pipeline.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ppred = pipeline.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1 0]\n", " [ 202 1023]]\n", "\n", "\n", " precision recall f1-score support\n", "\n", " 1 0.00 1.00 0.01 1\n", " 5 1.00 0.84 0.91 1225\n", "\n", "avg / total 1.00 0.84 0.91 1226\n", "\n" ] } ], "source": [ "print(confusion_matrix(ppred, y_test))\n", "print(\"\\n\")\n", "print(classification_report(ppred, y_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Slightly Worse!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
shankari/folium
examples/GeoJSON_and_choropleth.ipynb
1
1841651
null
mit
catch22/moment_polytopes
examples/qmp.ipynb
1
71645
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quantum Marginal Problems\n", "\n", "The following are solutions to a number of pure-state quantum marginal problem. See, e.g., [Klyachko (2004)](https://arxiv.org/abs/quant-ph/0409113) or [Walter (2014)](https://arxiv.org/abs/1410.6820)) for an introduction to the subject. Mathematically, these are given by the Kronecker polytopes $C(d_1,d_2,d_3,\\dots)$, i.e., the moment polytopes for the action of some $\\times_i GL(d_i)$ on $\\bigotimes_i \\mathbb C^{d_i}$." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#import logging\n", "#reload(logging)\n", "#logging.basicConfig(format='%(asctime)s %(levelname)s:%(message)s', level=logging.DEBUG, datefmt='%I:%M:%S')\n", "\n", "from moment_polytopes import *" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $C(4,4,4)$" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "C(4,4,4)\n", "========\n", "\n", "Facets\n", "------\n", "\n", " # H_A H_B H_C z Remarks\n", "--- --------------- --------------- --------------- --- ---------\n", " 1 (-5, -1, 3, 3) (-5, 3, 3, -1) (5, 1, -3, -3) 5 *\n", " 2 (-5, -1, 3, 3) (1, -3, -3, 5) (3, 3, -1, -5) 5\n", " 3 (-5, 3, -1, 3) (-5, 3, -1, 3) (5, 1, -3, -3) 5 *\n", " 4 (-5, 3, -1, 3) (-5, 3, 3, -1) (5, -3, 1, -3) 5 *\n", " 5 (-5, 3, -1, 3) (-3, 1, -3, 5) (3, 3, -1, -5) 5 *\n", " 6 (-5, 3, -1, 3) (-3, 5, 1, -3) (3, -5, 3, -1) 5 *\n", " 7 (-5, 3, -1, 3) (1, -3, -3, 5) (3, -1, 3, -5) 5\n", " 8 (-5, 3, -1, 3) (1, -3, 5, -3) (3, -1, -5, 3) 5\n", " 9 (-5, 3, 3, -1) (-5, 3, 3, -1) (5, -3, -3, 1) 5 *\n", " 10 (-5, 3, 3, -1) (-3, -3, 1, 5) (3, 3, -1, -5) 5 *\n", " 11 (-5, 3, 3, -1) (-3, -3, 5, 1) (3, 3, -5, -1) 5 *\n", " 12 (-5, 3, 3, -1) (-3, 1, -3, 5) (3, -1, 3, -5) 5 *\n", " 13 (-5, 3, 3, -1) (-3, 1, 5, -3) (3, -1, -5, 3) 5 *\n", " 14 (-5, 3, 3, -1) (-3, 5, -3, 1) (3, -5, 3, -1) 5 *\n", " 15 (-5, 3, 3, -1) (-3, 5, 1, -3) (3, -5, -1, 3) 5 *\n", " 16 (-5, 3, 3, -1) (-1, -5, 3, 3) (1, 5, -3, -3) 5 *\n", " 17 (-5, 3, 3, -1) (-1, 3, -5, 3) (1, -3, 5, -3) 5 *\n", " 18 (-5, 3, 3, -1) (-1, 3, 3, -5) (1, -3, -3, 5) 5 *\n", " 19 (-3, -1, 3, 1) (-3, 3, 1, -1) (3, 1, -1, -3) 3 *\n", " 20 (-3, -1, 3, 1) (1, -1, -3, 3) (3, 1, -1, -3) 3\n", " 21 (-3, 1, 1, 1) (-3, 1, 1, 1) (3, -1, -1, -1) 3 *\n", " 22 (-3, 1, 1, 1) (-2, -2, 2, 2) (2, 2, -2, -2) 3 *\n", " 23 (-3, 1, 1, 1) (-2, 2, -2, 2) (2, -2, 2, -2) 3 *\n", " 24 (-3, 1, 1, 1) (-2, 2, 2, -2) (2, -2, -2, 2) 3 *\n", " 25 (-3, 1, 1, 1) (-1, -1, -1, 3) (1, 1, 1, -3) 3 *\n", " 26 (-3, 1, 1, 1) (-1, -1, 3, -1) (1, 1, -3, 1) 3 *\n", " 27 (-3, 1, 1, 1) (-1, 3, -1, -1) (1, -3, 1, 1) 3 *\n", " 28 (-3, 3, 1, -1) (-3, 3, 1, -1) (3, -1, -3, 1) 3 *\n", " 29 (-3, 3, 1, -1) (-1, -3, 1, 3) (3, 1, -1, -3) 3\n", " 30 (-3, 3, 1, -1) (-1, -3, 3, 1) (1, 3, -1, -3) 3 *\n", " 31 (-3, 3, 1, -1) (-1, -3, 3, 1) (3, 1, -3, -1) 3\n", " 32 (-3, 3, 1, -1) (-1, 3, 1, -3) (1, -1, -3, 3) 3 *\n", " 33 (-2, -2, 2, 2) (-2, 2, 2, -2) (1, 1, -3, 1) 3 *\n", " 34 (-2, 2, -2, 2) (-2, 2, 2, -2) (1, -3, 1, 1) 3 *\n", " 35 (-1, -1, -1, 3) (0, 0, 0, 0) (0, 0, 0, 0) 1 *\n", " 36 (-1, 0, 0, 1) (-1, 1, 0, 0) (1, 0, 0, -1) 1 *\n", " 37 (-1, 0, 0, 1) (0, 0, -1, 1) (1, 0, 0, -1) 1\n", " 38 (-1, 0, 1, 0) (-1, 0, 1, 0) (1, 0, 0, -1) 1 *\n", " 39 (-1, 0, 1, 0) (-1, 1, 0, 0) (1, 0, -1, 0) 1 *\n", " 40 (-1, 0, 1, 0) (0, -1, 0, 1) (1, 0, 0, -1) 1\n", " 41 (-1, 0, 1, 0) (0, -1, 1, 0) (0, 1, 0, -1) 1 *\n", " 42 (-1, 0, 1, 0) (0, -1, 1, 0) (1, 0, -1, 0) 1\n", " 43 (-1, 0, 1, 0) (0, 0, -1, 1) (0, 1, 0, -1) 1 *\n", " 44 (-1, 1, 0, 0) (-1, 1, 0, 0) (1, -1, 0, 0) 1 *\n", " 45 (-1, 1, 0, 0) (0, -1, 0, 1) (0, 1, 0, -1) 1 *\n", " 46 (-1, 1, 0, 0) (0, -1, 1, 0) (0, 1, -1, 0) 1 *\n", " 47 (-1, 1, 0, 0) (0, 0, -1, 1) (0, 0, 1, -1) 1 *\n", " 48 (0, 0, 0, 0) (0, 0, 0, 0) (0, 0, 1, -1) 0 o, *\n", " 49 (0, 0, 0, 0) (0, 0, 0, 0) (0, 1, -1, 0) 0 o, *\n", " 50 (0, 0, 0, 0) (0, 0, 0, 0) (1, -1, 0, 0) 0 o\n", "\n", "Facet format is (H_A,lambda_A) + ... + z >= 0. The last column states\n", "whether the facet includes the origin (o) or the highest weight (*).\n", "\n", "Vertices\n", "--------\n", "\n", " # V_A V_B V_C\n", "--- ------------------------ ------------------------ -------------------------\n", " 1 (1/4, 1/4, 1/4, 1/4) (1/4, 1/4, 1/4, 1/4) (1/4, 1/4, 1/4, 1/4)\n", " 2 (1/4, 1/4, 1/4, 1/4) (1/4, 1/4, 1/4, 1/4) (1/3, 1/3, 1/3, 0)\n", " 3 (1/4, 1/4, 1/4, 1/4) (1/4, 1/4, 1/4, 1/4) (1/2, 1/2, 0, 0)\n", " 4 (1/4, 1/4, 1/4, 1/4) (1/4, 1/4, 1/4, 1/4) (1, 0, 0, 0)\n", " 5 (1/4, 1/4, 1/4, 1/4) (1/3, 1/3, 1/3, 0) (1/3, 1/3, 1/3, 0)\n", " 6 (1/4, 1/4, 1/4, 1/4) (1/3, 1/3, 1/3, 0) (1/2, 1/2, 0, 0)\n", " 7 (1/4, 1/4, 1/4, 1/4) (1/3, 1/3, 1/3, 0) (2/3, 1/6, 1/6, 0)\n", " 8 (1/4, 1/4, 1/4, 1/4) (1/3, 1/3, 1/3, 0) (2/3, 1/4, 1/12, 0)\n", " 9 (1/4, 1/4, 1/4, 1/4) (1/3, 1/3, 1/3, 0) (3/4, 1/12, 1/12, 1/12)\n", " 10 (1/4, 1/4, 1/4, 1/4) (3/8, 3/8, 1/4, 0) (5/8, 3/8, 0, 0)\n", " 11 (1/4, 1/4, 1/4, 1/4) (3/8, 3/8, 1/4, 0) (3/4, 1/8, 1/8, 0)\n", " 12 (1/4, 1/4, 1/4, 1/4) (2/5, 3/10, 3/10, 0) (7/10, 3/20, 3/20, 0)\n", " 13 (1/4, 1/4, 1/4, 1/4) (5/12, 5/12, 1/6, 0) (2/3, 1/6, 1/12, 1/12)\n", " 14 (1/4, 1/4, 1/4, 1/4) (1/2, 1/6, 1/6, 1/6) (1/2, 1/2, 0, 0)\n", " 15 (1/4, 1/4, 1/4, 1/4) (1/2, 1/4, 1/8, 1/8) (5/8, 3/8, 0, 0)\n", " 16 (1/4, 1/4, 1/4, 1/4) (1/2, 1/4, 1/4, 0) (1/2, 1/2, 0, 0)\n", " 17 (1/4, 1/4, 1/4, 1/4) (1/2, 1/4, 1/4, 0) (2/3, 1/6, 1/6, 0)\n", " 18 (1/4, 1/4, 1/4, 1/4) (1/2, 1/4, 1/4, 0) (3/4, 1/4, 0, 0)\n", " 19 (1/4, 1/4, 1/4, 1/4) (1/2, 3/8, 1/8, 0) (5/8, 1/8, 1/8, 1/8)\n", " 20 (1/4, 1/4, 1/4, 1/4) (1/2, 1/2, 0, 0) (1/2, 1/2, 0, 0)\n", " 21 (2/7, 2/7, 2/7, 1/7) (4/7, 1/7, 1/7, 1/7) (4/7, 3/7, 0, 0)\n", " 22 (7/24, 7/24, 5/24, 5/24) (1/3, 1/3, 1/3, 0) (3/4, 1/8, 1/8, 0)\n", " 23 (3/10, 3/10, 1/5, 1/5) (2/5, 3/10, 3/10, 0) (4/5, 1/10, 1/10, 0)\n", " 24 (3/10, 3/10, 3/10, 1/10) (1/2, 1/2, 0, 0) (11/20, 3/20, 3/20, 3/20)\n", " 25 (3/10, 3/10, 3/10, 1/10) (1/2, 1/2, 0, 0) (3/5, 1/5, 1/10, 1/10)\n", " 26 (1/3, 2/9, 2/9, 2/9) (1/3, 1/3, 1/3, 0) (2/3, 1/3, 0, 0)\n", " 27 (1/3, 2/9, 2/9, 2/9) (1/3, 1/3, 1/3, 0) (7/9, 1/9, 1/9, 0)\n", " 28 (1/3, 2/9, 2/9, 2/9) (4/9, 4/9, 1/9, 0) (2/3, 1/9, 1/9, 1/9)\n", " 29 (1/3, 1/3, 1/6, 1/6) (1/3, 1/3, 1/3, 0) (7/9, 1/9, 1/9, 0)\n", " 30 (1/3, 1/3, 1/6, 1/6) (1/3, 1/3, 1/3, 0) (5/6, 1/6, 0, 0)\n", " 31 (1/3, 1/3, 1/6, 1/6) (1/2, 1/4, 1/4, 0) (3/4, 1/12, 1/12, 1/12)\n", " 32 (1/3, 1/3, 1/6, 1/6) (2/3, 1/6, 1/6, 0) (2/3, 1/6, 1/6, 0)\n", " 33 (1/3, 1/3, 1/3, 0) (1/3, 1/3, 1/3, 0) (1/3, 1/3, 1/3, 0)\n", " 34 (1/3, 1/3, 1/3, 0) (1/3, 1/3, 1/3, 0) (1/2, 1/2, 0, 0)\n", " 35 (1/3, 1/3, 1/3, 0) (1/3, 1/3, 1/3, 0) (1, 0, 0, 0)\n", " 36 (1/3, 1/3, 1/3, 0) (2/5, 1/5, 1/5, 1/5) (11/15, 2/15, 1/15, 1/15)\n", " 37 (1/3, 1/3, 1/3, 0) (5/12, 1/4, 1/6, 1/6) (3/4, 1/12, 1/12, 1/12)\n", " 38 (1/3, 1/3, 1/3, 0) (5/12, 5/12, 1/12, 1/12) (3/4, 1/12, 1/12, 1/12)\n", " 39 (1/3, 1/3, 1/3, 0) (4/9, 1/3, 1/9, 1/9) (7/9, 1/9, 1/9, 0)\n", " 40 (1/3, 1/3, 1/3, 0) (1/2, 1/6, 1/6, 1/6) (1/2, 1/2, 0, 0)\n", " 41 (1/3, 1/3, 1/3, 0) (1/2, 1/6, 1/6, 1/6) (2/3, 1/9, 1/9, 1/9)\n", " 42 (1/3, 1/3, 1/3, 0) (1/2, 1/6, 1/6, 1/6) (2/3, 1/3, 0, 0)\n", " 43 (1/3, 1/3, 1/3, 0) (1/2, 1/2, 0, 0) (1/2, 1/2, 0, 0)\n", " 44 (1/3, 1/3, 1/3, 0) (1/2, 1/2, 0, 0) (7/12, 1/4, 1/12, 1/12)\n", " 45 (1/3, 1/3, 1/3, 0) (1/2, 1/2, 0, 0) (2/3, 1/6, 1/6, 0)\n", " 46 (1/3, 1/3, 1/3, 0) (5/9, 2/9, 1/9, 1/9) (2/3, 1/9, 1/9, 1/9)\n", " 47 (1/3, 1/3, 1/3, 0) (2/3, 1/3, 0, 0) (2/3, 1/3, 0, 0)\n", " 48 (5/14, 5/14, 1/7, 1/7) (3/7, 2/7, 2/7, 0) (11/14, 1/14, 1/14, 1/14)\n", " 49 (4/11, 4/11, 3/11, 0) (5/11, 2/11, 2/11, 2/11) (8/11, 1/11, 1/11, 1/11)\n", " 50 (3/8, 1/4, 1/4, 1/8) (1/2, 1/2, 0, 0) (5/8, 1/8, 1/8, 1/8)\n", " 51 (3/8, 3/8, 1/4, 0) (5/8, 1/8, 1/8, 1/8) (5/8, 1/8, 1/8, 1/8)\n", " 52 (2/5, 1/5, 1/5, 1/5) (2/5, 2/5, 1/5, 0) (4/5, 1/5, 0, 0)\n", " 53 (2/5, 1/5, 1/5, 1/5) (1/2, 1/2, 0, 0) (3/5, 1/5, 1/10, 1/10)\n", " 54 (2/5, 3/10, 3/10, 0) (2/5, 2/5, 1/10, 1/10) (4/5, 1/10, 1/10, 0)\n", " 55 (2/5, 2/5, 1/10, 1/10) (3/5, 1/5, 1/5, 0) (7/10, 1/10, 1/10, 1/10)\n", " 56 (5/12, 5/12, 1/12, 1/12) (1/2, 1/4, 1/4, 0) (3/4, 1/12, 1/12, 1/12)\n", " 57 (3/7, 3/7, 1/7, 0) (4/7, 1/7, 1/7, 1/7) (5/7, 1/7, 1/7, 0)\n", " 58 (1/2, 1/6, 1/6, 1/6) (1/2, 1/2, 0, 0) (2/3, 1/6, 1/6, 0)\n", " 59 (1/2, 1/4, 1/4, 0) (1/2, 1/2, 0, 0) (5/8, 1/8, 1/8, 1/8)\n", " 60 (1/2, 1/4, 1/4, 0) (1/2, 1/2, 0, 0) (3/4, 1/4, 0, 0)\n", " 61 (1/2, 1/2, 0, 0) (1/2, 1/2, 0, 0) (1/2, 1/2, 0, 0)\n", " 62 (1/2, 1/2, 0, 0) (1/2, 1/2, 0, 0) (1, 0, 0, 0)\n", " 63 (1/2, 1/2, 0, 0) (5/8, 1/8, 1/8, 1/8) (5/8, 1/8, 1/8, 1/8)\n", " 64 (1/2, 1/2, 0, 0) (2/3, 1/6, 1/6, 0) (2/3, 1/6, 1/6, 0)\n", " 65 (1, 0, 0, 0) (1, 0, 0, 0) (1, 0, 0, 0)\n", "\n", "All data is up to permutations of subsystems." ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qmp.pretty((4, 4, 4), algorithm='mathematica')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $C(2,2,3,12)$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#qmp.pretty((2, 2, 3, 12), algorithm='mathematica', show_vrepr=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Further Examples" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "C(2,2,2)\n", "========\n", "\n", "Facets\n", "------\n", "\n", " # H_A H_B H_C z Remarks\n", "--- ------- ------- ------- --- ---------\n", " 1 (-1, 1) (-1, 1) (1, -1) 1 *\n", " 2 (0, 0) (0, 0) (1, -1) 0 o\n", "\n", "Facet format is (H_A,lambda_A) + ... + z >= 0. The last column states\n", "whether the facet includes the origin (o) or the highest weight (*).\n", "\n", "Vertices\n", "--------\n", "\n", " # V_A V_B V_C\n", "--- ---------- ---------- ----------\n", " 1 (1/2, 1/2) (1/2, 1/2) (1/2, 1/2)\n", " 2 (1/2, 1/2) (1/2, 1/2) (1, 0)\n", " 3 (1, 0) (1, 0) (1, 0)\n", "\n", "All data is up to permutations of subsystems." ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qmp.pretty((2, 2, 2))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "C(2,2,4)\n", "========\n", "\n", "Facets\n", "------\n", "\n", " # H_A H_B H_C z Remarks\n", "--- ------- ------- --------------- --- ---------\n", " 1 (-1, 1) (-1, 1) (2, 0, 0, -2) 0 o, *\n", " 2 (-1, 1) (0, 0) (1, 1, -1, -1) 0 o, *\n", " 3 (-1, 1) (1, -1) (0, 2, 0, -2) 0 o, *\n", " 4 (-1, 1) (1, -1) (2, 0, -2, 0) 0 o\n", " 5 (0, 0) (0, 0) (-1, -1, -1, 3) 1 *\n", " 6 (0, 0) (0, 0) (0, 0, 1, -1) 0 o, *\n", " 7 (0, 0) (0, 0) (0, 1, -1, 0) 0 o, *\n", " 8 (0, 0) (0, 0) (1, -1, 0, 0) 0 o\n", " 9 (0, 0) (1, -1) (0, 0, 0, 0) 0 o\n", "\n", "Facet format is (H_A,lambda_A) + ... + z >= 0. The last column states\n", "whether the facet includes the origin (o) or the highest weight (*).\n", "\n", "Vertices\n", "--------\n", "\n", " # V_A V_B V_C\n", "--- ---------- ---------- --------------------\n", " 1 (1/2, 1/2) (1/2, 1/2) (1/4, 1/4, 1/4, 1/4)\n", " 2 (1/2, 1/2) (1/2, 1/2) (1/3, 1/3, 1/3, 0)\n", " 3 (1/2, 1/2) (1/2, 1/2) (1/2, 1/2, 0, 0)\n", " 4 (1/2, 1/2) (1/2, 1/2) (1, 0, 0, 0)\n", " 5 (1/2, 1/2) (3/4, 1/4) (1/2, 1/4, 1/4, 0)\n", " 6 (1/2, 1/2) (1, 0) (1/2, 1/2, 0, 0)\n", " 7 (2/3, 1/3) (2/3, 1/3) (1/3, 1/3, 1/3, 0)\n", " 8 (1, 0) (1, 0) (1, 0, 0, 0)\n", "\n", "All data is up to permutations of subsystems." ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qmp.pretty((2, 2, 4))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "C(2,2,2,8)\n", "==========\n", "\n", "Facets\n", "------\n", "\n", " # H_A H_B H_C H_D z Remarks\n", "--- ------- ------- ------- ------------------------------- --- ---------\n", " 1 (-2, 2) (-1, 1) (-1, 1) (4, 2, 2, 0, 0, -2, -2, -4) 0 o, *\n", " 2 (-2, 2) (-1, 1) (1, -1) (2, 4, 2, 0, 0, -2, -2, -4) 0 o, *\n", " 3 (-2, 2) (-1, 1) (1, -1) (4, 2, 0, 2, 0, -2, -2, -4) 0 o\n", " 4 (-2, 2) (-1, 1) (1, -1) (4, 2, 2, 0, -2, 0, -2, -4) 0 o\n", " 5 (-2, 2) (-1, 1) (1, -1) (4, 2, 2, 0, 0, -2, -4, -2) 0 o\n", " 6 (-1, 1) (-1, 1) (-1, 1) (3, 1, 1, 1, -1, -1, -1, -3) 0 o, *\n", " 7 (-1, 1) (-1, 1) (0, 0) (2, 2, 0, 0, 0, 0, -2, -2) 0 o, *\n", " 8 (-1, 1) (-1, 1) (1, -1) (1, 3, 1, 1, -1, -1, -1, -3) 0 o, *\n", " 9 (-1, 1) (-1, 1) (1, -1) (3, 1, 1, 1, -1, -1, -3, -1) 0 o\n", " 10 (-1, 1) (0, 0) (0, 0) (1, 1, 1, 1, -1, -1, -1, -1) 0 o, *\n", " 11 (0, 0) (0, 0) (0, 0) (-1, -1, -1, -1, -1, -1, -1, 7) 1 *\n", " 12 (0, 0) (0, 0) (0, 0) (0, 0, 0, 0, 0, 0, 1, -1) 0 o, *\n", " 13 (0, 0) (0, 0) (0, 0) (0, 0, 0, 0, 0, 1, -1, 0) 0 o, *\n", " 14 (0, 0) (0, 0) (0, 0) (0, 0, 0, 0, 1, -1, 0, 0) 0 o, *\n", " 15 (0, 0) (0, 0) (0, 0) (0, 0, 0, 1, -1, 0, 0, 0) 0 o, *\n", " 16 (0, 0) (0, 0) (0, 0) (0, 0, 1, -1, 0, 0, 0, 0) 0 o, *\n", " 17 (0, 0) (0, 0) (0, 0) (0, 1, -1, 0, 0, 0, 0, 0) 0 o, *\n", " 18 (0, 0) (0, 0) (0, 0) (1, -1, 0, 0, 0, 0, 0, 0) 0 o\n", " 19 (0, 0) (0, 0) (1, -1) (0, 0, 0, 0, 0, 0, 0, 0) 0 o\n", "\n", "Facet format is (H_A,lambda_A) + ... + z >= 0. The last column states\n", "whether the facet includes the origin (o) or the highest weight (*).\n", "\n", "Vertices\n", "--------\n", "\n", " # V_A V_B V_C V_D\n", "--- ---------- ------------- ------------- ------------------------------------------\n", " 1 (1/2, 1/2) (1/2, 1/2) (1/2, 1/2) (1/8, 1/8, 1/8, 1/8, 1/8, 1/8, 1/8, 1/8)\n", " 2 (1/2, 1/2) (1/2, 1/2) (1/2, 1/2) (1/7, 1/7, 1/7, 1/7, 1/7, 1/7, 1/7, 0)\n", " 3 (1/2, 1/2) (1/2, 1/2) (1/2, 1/2) (1/6, 1/6, 1/6, 1/6, 1/6, 1/6, 0, 0)\n", " 4 (1/2, 1/2) (1/2, 1/2) (1/2, 1/2) (1/5, 1/5, 1/5, 1/5, 1/5, 0, 0, 0)\n", " 5 (1/2, 1/2) (1/2, 1/2) (1/2, 1/2) (1/4, 1/4, 1/4, 1/4, 0, 0, 0, 0)\n", " 6 (1/2, 1/2) (1/2, 1/2) (1/2, 1/2) (1/3, 1/3, 1/3, 0, 0, 0, 0, 0)\n", " 7 (1/2, 1/2) (1/2, 1/2) (1/2, 1/2) (1/2, 1/2, 0, 0, 0, 0, 0, 0)\n", " 8 (1/2, 1/2) (1/2, 1/2) (1/2, 1/2) (1, 0, 0, 0, 0, 0, 0, 0)\n", " 9 (1/2, 1/2) (1/2, 1/2) (4/7, 3/7) (1/7, 1/7, 1/7, 1/7, 1/7, 1/7, 1/7, 0)\n", " 10 (1/2, 1/2) (1/2, 1/2) (2/3, 1/3) (1/6, 1/6, 1/6, 1/6, 1/6, 1/6, 0, 0)\n", " 11 (1/2, 1/2) (1/2, 1/2) (4/5, 1/5) (1/5, 1/5, 1/5, 1/5, 1/5, 0, 0, 0)\n", " 12 (1/2, 1/2) (1/2, 1/2) (1, 0) (1/4, 1/4, 1/4, 1/4, 0, 0, 0, 0)\n", " 13 (1/2, 1/2) (1/2, 1/2) (1, 0) (1/3, 1/3, 1/3, 0, 0, 0, 0, 0)\n", " 14 (1/2, 1/2) (1/2, 1/2) (1, 0) (1/2, 1/2, 0, 0, 0, 0, 0, 0)\n", " 15 (1/2, 1/2) (1/2, 1/2) (1, 0) (1, 0, 0, 0, 0, 0, 0, 0)\n", " 16 (1/2, 1/2) (7/12, 5/12) (2/3, 1/3) (1/6, 1/6, 1/6, 1/6, 1/6, 1/12, 1/12, 0)\n", " 17 (1/2, 1/2) (3/5, 2/5) (7/10, 3/10) (1/5, 1/5, 1/5, 1/10, 1/10, 1/10, 1/10, 0)\n", " 18 (1/2, 1/2) (11/18, 7/18) (11/18, 7/18) (1/6, 1/6, 1/6, 1/6, 1/9, 1/9, 1/9, 0)\n", " 19 (1/2, 1/2) (5/8, 3/8) (5/8, 3/8) (1/4, 1/8, 1/8, 1/8, 1/8, 1/8, 1/8, 0)\n", " 20 (1/2, 1/2) (5/8, 3/8) (7/8, 1/8) (1/4, 1/4, 1/4, 1/8, 1/8, 0, 0, 0)\n", " 21 (1/2, 1/2) (2/3, 1/3) (2/3, 1/3) (1/6, 1/6, 1/6, 1/6, 1/6, 1/6, 0, 0)\n", " 22 (1/2, 1/2) (2/3, 1/3) (5/6, 1/6) (1/3, 1/6, 1/6, 1/6, 1/6, 0, 0, 0)\n", " 23 (1/2, 1/2) (7/10, 3/10) (7/10, 3/10) (1/5, 1/5, 1/5, 1/5, 1/5, 0, 0, 0)\n", " 24 (1/2, 1/2) (3/4, 1/4) (1, 0) (1/2, 1/4, 1/4, 0, 0, 0, 0, 0)\n", " 25 (1/2, 1/2) (5/6, 1/6) (5/6, 1/6) (1/3, 1/3, 1/3, 0, 0, 0, 0, 0)\n", " 26 (1/2, 1/2) (5/6, 1/6) (5/6, 1/6) (1/2, 1/6, 1/6, 1/6, 0, 0, 0, 0)\n", " 27 (1/2, 1/2) (1, 0) (1, 0) (1/2, 1/2, 0, 0, 0, 0, 0, 0)\n", " 28 (4/7, 3/7) (4/7, 3/7) (4/7, 3/7) (1/7, 1/7, 1/7, 1/7, 1/7, 1/7, 1/7, 0)\n", " 29 (3/5, 2/5) (3/5, 2/5) (4/5, 1/5) (1/5, 1/5, 1/5, 1/5, 1/5, 0, 0, 0)\n", " 30 (2/3, 1/3) (2/3, 1/3) (1, 0) (1/3, 1/3, 1/3, 0, 0, 0, 0, 0)\n", " 31 (3/4, 1/4) (3/4, 1/4) (3/4, 1/4) (1/4, 1/4, 1/4, 1/4, 0, 0, 0, 0)\n", " 32 (1, 0) (1, 0) (1, 0) (1, 0, 0, 0, 0, 0, 0, 0)\n", "\n", "All data is up to permutations of subsystems." ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qmp.pretty((2, 2, 2, 8))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#qmp.pretty((2, 2, 2, 2, 16), algorithm='mathematica', show_vrepr=False)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "C(3,3,9)\n", "========\n", "\n", "Facets\n", "------\n", "\n", " # H_A H_B H_C z Remarks\n", "--- ----------- ----------- ----------------------------------- --- ---------\n", " 1 (-5, 1, 4) (-3, 0, 3) (8, 5, 2, 2, -1, -1, -4, -4, -7) 0 o, *\n", " 2 (-5, 1, 4) (-3, 3, 0) (8, 2, 5, 2, -1, -1, -4, -4, -7) 0 o, *\n", " 3 (-5, 1, 4) (-3, 3, 0) (8, 5, 2, 2, -1, -4, -1, -4, -7) 0 o, *\n", " 4 (-5, 1, 4) (-3, 3, 0) (8, 5, 2, 2, -1, -1, -4, -7, -4) 0 o, *\n", " 5 (-5, 1, 4) (0, -3, 3) (5, 8, 2, 2, -1, -1, -4, -4, -7) 0 o, *\n", " 6 (-5, 1, 4) (0, -3, 3) (8, 5, 2, -1, 2, -1, -4, -4, -7) 0 o\n", " 7 (-5, 1, 4) (0, -3, 3) (8, 5, 2, 2, -1, -4, -1, -4, -7) 0 o\n", " 8 (-5, 1, 4) (0, 3, -3) (5, 2, 8, 2, -1, -1, -4, -4, -7) 0 o, *\n", " 9 (-5, 1, 4) (3, -3, 0) (2, 8, 5, 2, -1, -1, -4, -4, -7) 0 o, *\n", " 10 (-5, 1, 4) (3, 0, -3) (2, 5, 8, 2, -1, -1, -4, 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(-5, 4, 1) (-3, 3, 0) (8, 5, 2, -1, 2, -4, -1, -4, -7) 0 o, *\n", " 26 (-5, 4, 1) (-3, 3, 0) (8, 5, 2, -1, 2, -1, -4, -7, -4) 0 o, *\n", " 27 (-5, 4, 1) (0, -3, 3) (5, 8, 2, -1, 2, -1, -4, -4, -7) 0 o, *\n", " 28 (-5, 4, 1) (0, -3, 3) (5, 8, 2, 2, -1, -4, -1, -4, -7) 0 o, *\n", " 29 (-5, 4, 1) (0, -3, 3) (5, 8, 2, 2, -1, -1, -4, -7, -4) 0 o, *\n", " 30 (-5, 4, 1) (0, -3, 3) (8, 5, 2, -1, 2, -1, -4, -7, -4) 0 o\n", " 31 (-5, 4, 1) (0, -3, 3) (8, 5, 2, 2, -1, -4, -1, -7, -4) 0 o\n", " 32 (-5, 4, 1) (0, 3, -3) (5, 2, 8, -1, 2, -1, -4, -4, -7) 0 o, *\n", " 33 (-5, 4, 1) (0, 3, -3) (5, 2, 8, 2, -1, -4, -1, -4, -7) 0 o, *\n", " 34 (-5, 4, 1) (0, 3, -3) (5, 2, 8, 2, -1, -1, -4, -7, -4) 0 o, *\n", " 35 (-5, 4, 1) (0, 3, -3) (8, 5, 2, -4, 2, -1, -1, -4, -7) 0 o\n", " 36 (-5, 4, 1) (3, -3, 0) (2, 8, 5, -1, 2, -1, -4, -4, -7) 0 o, *\n", " 37 (-5, 4, 1) (3, -3, 0) (2, 8, 5, 2, -1, -4, -1, -4, -7) 0 o, *\n", " 38 (-5, 4, 1) (3, -3, 0) (2, 8, 5, 2, -1, -1, -4, -7, -4) 0 o, *\n", " 39 (-5, 4, 1) (3, -3, 0) (8, 5, 2, 2, -1, -4, -4, -7, -1) 0 o\n", " 40 (-5, 4, 1) (3, 0, -3) (2, 5, 8, -1, 2, -1, -4, -4, -7) 0 o, *\n", " 41 (-5, 4, 1) (3, 0, -3) (2, 5, 8, 2, -1, -4, -1, -4, -7) 0 o, *\n", " 42 (-5, 4, 1) (3, 0, -3) (2, 5, 8, 2, -1, -1, -4, -7, -4) 0 o, *\n", " 43 (-5, 4, 1) (3, 0, -3) (2, 8, 5, -1, 2, -1, -4, -7, -4) 0 o, *\n", " 44 (-5, 4, 1) (3, 0, -3) (2, 8, 5, 2, -1, -4, -1, -7, -4) 0 o, *\n", " 45 (-5, 4, 1) (3, 0, -3) (5, 2, 8, -1, 2, -4, -1, -4, -7) 0 o\n", " 46 (-5, 4, 1) (3, 0, -3) (5, 2, 8, -1, 2, -1, -4, -7, -4) 0 o\n", " 47 (-5, 4, 1) (3, 0, -3) (5, 8, 2, 2, -1, -4, -4, -7, -1) 0 o\n", " 48 (-5, 4, 1) (3, 0, -3) (8, 2, 5, -4, 2, -1, -1, -4, -7) 0 o\n", " 49 (-5, 4, 1) (3, 0, -3) (8, 5, 2, -4, -1, 2, -1, -4, -7) 0 o\n", " 50 (-5, 4, 1) (3, 0, -3) (8, 5, 2, 2, -1, -4, -7, -4, -1) 0 o\n", " 51 (-4, -1, 5) (-3, 0, 3) (7, 4, 4, 1, 1, -2, -2, -5, -8) 0 o, *\n", " 52 (-4, -1, 5) (-3, 3, 0) (7, 4, 1, 4, 1, -2, -2, -5, -8) 0 o, *\n", " 53 (-4, -1, 5) (-3, 3, 0) (7, 4, 4, 1, -2, 1, -2, -5, -8) 0 o, *\n", " 54 (-4, -1, 5) (-3, 3, 0) (7, 4, 4, 1, 1, -2, -2, -8, -5) 0 o, *\n", " 55 (-4, -1, 5) (0, -3, 3) (4, 7, 4, 1, 1, -2, -2, -5, -8) 0 o, *\n", " 56 (-4, -1, 5) (0, -3, 3) (7, 4, 1, 4, 1, -2, -2, -5, -8) 0 o\n", " 57 (-4, -1, 5) (0, -3, 3) (7, 4, 4, 1, 1, -2, -5, -2, -8) 0 o\n", " 58 (-4, -1, 5) (0, 3, -3) (7, 4, 4, 1, 1, -2, -5, -8, -2) 0 o\n", " 59 (-4, -1, 5) (3, -3, 0) (7, 4, 4, 1, 1, -2, -8, -2, -5) 0 o\n", " 60 (-4, -1, 5) (3, 0, -3) (1, 7, 4, 4, 1, -2, -2, -5, -8) 0 o, *\n", " 61 (-4, -1, 5) (3, 0, -3) (4, 1, 7, 4, 1, -2, -2, -5, -8) 0 o\n", " 62 (-4, -1, 5) (3, 0, -3) (4, 7, 4, 1, 1, -2, -8, -2, -5) 0 o\n", " 63 (-4, -1, 5) (3, 0, -3) (7, 4, 1, -2, 4, 1, -2, -5, -8) 0 o\n", " 64 (-4, -1, 5) (3, 0, -3) (7, 4, 1, 1, -2, 4, -2, -5, -8) 0 o\n", " 65 (-4, -1, 5) (3, 0, -3) (7, 4, 1, 4, 1, -2, -8, -2, -5) 0 o\n", " 66 (-4, -1, 5) (3, 0, -3) (7, 4, 1, 4, 1, -2, -5, -8, -2) 0 o\n", " 67 (-4, -1, 5) (3, 0, -3) (7, 4, 4, 1, -2, 1, -5, -8, -2) 0 o\n", " 68 (-4, -1, 5) (3, 0, -3) (7, 4, 4, 1, 1, -2, -8, -5, -2) 0 o\n", " 69 (-4, 5, -1) (0, -3, 3) (7, 4, 4, 1, 1, -2, -5, -8, -2) 0 o\n", " 70 (-3, 0, 3) (-2, 1, 1) (5, 2, 2, 2, -1, -1, -1, -4, -4) 0 o, *\n", " 71 (-3, 0, 3) (-1, -4, 5) (4, 7, 4, 1, 1, -2, -2, -5, -8) 0 o, *\n", " 72 (-3, 0, 3) (-1, -4, 5) (7, 4, 1, 4, 1, -2, -2, -5, -8) 0 o\n", " 73 (-3, 0, 3) (-1, -4, 5) (7, 4, 4, 1, -2, 1, -2, -5, -8) 0 o\n", " 74 (-3, 0, 3) (-1, -1, 2) (4, 4, 1, 1, 1, -2, -2, -2, -5) 0 o, *\n", " 75 (-3, 0, 3) (-1, 2, -1) (4, 1, 4, 1, 1, -2, -2, -2, -5) 0 o, *\n", " 76 (-3, 0, 3) (-1, 2, -1) (4, 4, 1, 1, -2, 1, -2, -2, -5) 0 o, *\n", " 77 (-3, 0, 3) (-1, 2, -1) (4, 4, 1, 1, 1, -2, -2, -5, -2) 0 o, *\n", " 78 (-3, 0, 3) (1, -2, 1) (2, 5, 2, 2, -1, -1, -1, -4, -4) 0 o, *\n", " 79 (-3, 0, 3) (1, -2, 1) (5, 2, 2, -1, 2, -1, -1, -4, -4) 0 o\n", " 80 (-3, 0, 3) (1, -2, 1) (5, 2, 2, 2, -1, -1, -4, -1, -4) 0 o\n", " 81 (-3, 0, 3) (1, 1, -2) (2, 2, 5, 2, -1, -1, -1, -4, -4) 0 o, *\n", " 82 (-3, 0, 3) (1, 1, -2) (2, 5, 2, -1, 2, -1, -1, -4, -4) 0 o, *\n", " 83 (-3, 0, 3) (1, 1, -2) (2, 5, 2, 2, -1, -1, -4, -1, -4) 0 o, *\n", " 84 (-3, 0, 3) (1, 1, -2) (5, 2, 2, -1, -1, 2, -1, -4, -4) 0 o\n", " 85 (-3, 0, 3) (1, 1, -2) (5, 2, 2, -1, 2, -1, -4, -1, -4) 0 o\n", " 86 (-3, 0, 3) (1, 1, -2) (5, 2, 2, 2, -1, -1, -4, -4, -1) 0 o\n", " 87 (-3, 0, 3) (2, -1, -1) (1, 4, 4, 1, 1, -2, -2, -2, -5) 0 o, *\n", " 88 (-3, 0, 3) (2, -1, -1) (4, 1, 4, 1, -2, 1, -2, -2, -5) 0 o\n", " 89 (-3, 0, 3) (2, -1, -1) (4, 1, 4, 1, 1, -2, -2, -5, -2) 0 o\n", " 90 (-3, 0, 3) (2, -1, -1) (4, 4, 1, -2, 1, 1, -2, -2, -5) 0 o\n", " 91 (-3, 0, 3) (2, -1, -1) (4, 4, 1, 1, -2, 1, -2, -5, -2) 0 o\n", " 92 (-3, 0, 3) (2, -1, -1) (4, 4, 1, 1, 1, -2, -5, -2, -2) 0 o\n", " 93 (-3, 3, 0) (-2, 1, 1) (5, 2, 2, -1, 2, -1, -1, -4, -4) 0 o, *\n", " 94 (-3, 3, 0) (-1, -4, 5) (4, 7, 1, 4, 1, -2, -2, -5, -8) 0 o, *\n", " 95 (-3, 3, 0) (-1, -4, 5) (4, 7, 4, 1, -2, 1, -2, -5, -8) 0 o, *\n", " 96 (-3, 3, 0) (-1, -4, 5) (4, 7, 4, 1, 1, -2, -2, -8, -5) 0 o, *\n", " 97 (-3, 3, 0) (-1, -4, 5) (7, 4, 1, 4, 1, -2, -2, -8, -5) 0 o\n", " 98 (-3, 3, 0) (-1, -4, 5) (7, 4, 4, 1, -2, 1, -2, -8, -5) 0 o\n", " 99 (-3, 3, 0) (-1, -1, 2) (4, 4, 1, 1, 1, -2, -2, -5, -2) 0 o, *\n", "100 (-3, 3, 0) (-1, 2, -1) (4, 1, 4, 1, 1, -2, -2, -5, -2) 0 o, *\n", "101 (-3, 3, 0) (-1, 2, -1) (4, 4, 1, -2, 1, 1, -2, -2, -5) 0 o, *\n", "102 (-3, 3, 0) (1, -5, 4) (2, 8, 5, 2, -1, -1, -4, -4, -7) 0 o, *\n", "103 (-3, 3, 0) (1, -2, 1) (2, 5, 2, -1, 2, -1, -1, -4, -4) 0 o, *\n", "104 (-3, 3, 0) (1, -2, 1) (5, 2, 2, 2, -1, -1, -4, -4, -1) 0 o\n", "105 (-3, 3, 0) (1, 1, -2) (2, 2, 5, -1, 2, -1, -1, -4, -4) 0 o, *\n", "106 (-3, 3, 0) (1, 1, -2) (2, 5, 2, 2, -1, -1, -4, -4, -1) 0 o, *\n", "107 (-3, 3, 0) (2, -1, -1) (1, 4, 4, 1, 1, -2, -2, -5, -2) 0 o, *\n", "108 (-3, 3, 0) (2, -1, -1) (4, 1, 4, -2, 1, 1, -2, -2, -5) 0 o\n", "109 (-2, 1, 1) (-2, 1, 1) (4, 1, 1, 1, 1, -2, -2, -2, -2) 0 o, *\n", "110 (-2, 1, 1) (-1, -1, 2) (3, 3, 0, 0, 0, 0, 0, -3, -3) 0 o, *\n", "111 (-2, 1, 1) (-1, 2, -1) (3, 0, 3, 0, 0, 0, 0, -3, -3) 0 o, *\n", "112 (-2, 1, 1) (0, -3, 3) (2, 5, 2, 2, -1, -1, -1, -4, -4) 0 o, *\n", "113 (-2, 1, 1) (0, 0, 0) (2, 2, 2, -1, -1, -1, -1, -1, -1) 0 o, *\n", "114 (-2, 1, 1) (1, -2, 1) (1, 4, 1, 1, 1, -2, -2, -2, -2) 0 o, *\n", "115 (-2, 1, 1) (1, 1, -2) (1, 1, 4, 1, 1, -2, -2, -2, -2) 0 o, *\n", "116 (-2, 1, 1) (1, 1, -2) (4, 1, 1, -2, 1, 1, -2, -2, -2) 0 o\n", "117 (-2, 1, 1) (2, -1, -1) (0, 3, 3, 0, 0, 0, 0, -3, -3) 0 o, *\n", "118 (-2, 1, 1) (2, -1, -1) (3, 3, 0, 0, 0, 0, -3, -3, 0) 0 o\n", "119 (-1, -4, 5) (0, -3, 3) (4, 7, 4, 1, -2, 1, -2, -5, -8) 0 o\n", "120 (-1, -4, 5) (0, -3, 3) (4, 7, 4, 1, 1, -2, -5, -2, -8) 0 o\n", "121 (-1, -4, 5) (0, -3, 3) (7, 4, 1, 4, -2, 1, -2, -5, -8) 0 o\n", "122 (-1, -4, 5) (0, -3, 3) (7, 4, 1, 4, 1, -2, -5, -2, -8) 0 o\n", "123 (-1, -4, 5) (0, -3, 3) (7, 4, 4, 1, -2, 1, -5, -2, -8) 0 o\n", "124 (-1, -4, 5) (0, 3, -3) (1, 7, 4, 4, 1, -2, -2, -5, -8) 0 o, *\n", "125 (-1, -4, 5) (0, 3, -3) (4, 7, 4, 1, 1, -2, -5, -8, -2) 0 o\n", "126 (-1, -4, 5) (0, 3, -3) (7, 4, 1, 4, 1, -2, -5, -8, -2) 0 o\n", "127 (-1, -4, 5) (0, 3, -3) (7, 4, 4, 1, -2, 1, -5, -8, -2) 0 o\n", "128 (-1, -4, 5) (3, -3, 0) (4, 7, 4, 1, 1, -2, -8, -2, -5) 0 o\n", "129 (-1, -4, 5) (3, -3, 0) (7, 4, 1, 1, -2, 4, -2, -5, -8) 0 o\n", "130 (-1, -4, 5) (3, -3, 0) (7, 4, 1, 4, 1, -2, -8, -2, -5) 0 o\n", "131 (-1, -4, 5) (3, -3, 0) (7, 4, 4, 1, -2, 1, -8, -2, -5) 0 o\n", "132 (-1, -4, 5) (3, 0, -3) (1, 4, 7, 4, 1, -2, -2, -5, -8) 0 o\n", "133 (-1, -4, 5) (3, 0, -3) (1, 7, 4, 4, 1, -2, -2, -8, -5) 0 o\n", "134 (-1, -4, 5) (3, 0, -3) (4, 7, 1, 4, 1, -2, -5, -8, -2) 0 o\n", "135 (-1, -4, 5) (3, 0, -3) (4, 7, 4, 1, -2, 1, -8, -2, -5) 0 o\n", "136 (-1, -4, 5) (3, 0, -3) (4, 7, 4, 1, -2, 1, -5, -8, -2) 0 o\n", "137 (-1, -4, 5) (3, 0, -3) (4, 7, 4, 1, 1, -2, -8, -5, -2) 0 o\n", "138 (-1, -4, 5) (3, 0, -3) (7, 4, 1, -2, 1, 4, -2, -5, -8) 0 o\n", "139 (-1, -4, 5) (3, 0, -3) (7, 4, 1, 1, -2, 4, -5, -2, -8) 0 o\n", "140 (-1, -4, 5) (3, 0, -3) (7, 4, 1, 4, -2, 1, -8, -2, -5) 0 o\n", "141 (-1, -4, 5) (3, 0, -3) (7, 4, 1, 4, 1, -2, -8, -5, -2) 0 o\n", "142 (-1, -4, 5) (3, 0, -3) (7, 4, 4, 1, -2, 1, -8, -5, -2) 0 o\n", "143 (-1, -1, 2) (-1, -1, 2) (2, 2, 2, 2, -1, -1, -1, -1, -4) 0 o, *\n", "144 (-1, -1, 2) (-1, 2, -1) (2, 2, 2, 2, -1, -1, -1, -4, -1) 0 o, *\n", "145 (-1, -1, 2) (0, -3, 3) (4, 4, 1, 1, -2, 1, -2, -2, -5) 0 o\n", "146 (-1, -1, 2) (0, 0, 0) (1, 1, 1, 1, 1, 1, -2, -2, -2) 0 o, *\n", "147 (-1, -1, 2) (1, -2, 1) (3, 3, 0, 0, 0, 0, -3, 0, -3) 0 o\n", "148 (-1, -1, 2) (1, 1, -2) (0, 3, 3, 0, 0, 0, 0, -3, -3) 0 o, *\n", "149 (-1, -1, 2) (1, 1, -2) (3, 3, 0, 0, 0, 0, -3, -3, 0) 0 o\n", "150 (-1, -1, 2) (2, -1, -1) (2, 2, 2, -1, -1, 2, -1, -1, -4) 0 o\n", "151 (-1, -1, 2) (2, -1, -1) (2, 2, 2, 2, -1, -1, -4, -1, -1) 0 o\n", "152 (-1, 0, 1) (-1, 0, 1) (2, 1, 1, 0, 0, 0, -1, -1, -2) 0 o, *\n", "153 (-1, 0, 1) (-1, 1, 0) (2, 1, 0, 1, 0, 0, -1, -1, -2) 0 o, *\n", "154 (-1, 0, 1) (-1, 1, 0) (2, 1, 1, 0, 0, -1, 0, -1, -2) 0 o, *\n", "155 (-1, 0, 1) (-1, 1, 0) (2, 1, 1, 0, 0, 0, -1, -2, -1) 0 o, *\n", "156 (-1, 0, 1) (0, -1, 1) (1, 2, 1, 0, 0, 0, -1, -1, -2) 0 o, *\n", "157 (-1, 0, 1) (0, -1, 1) (2, 1, 0, 1, 0, 0, -1, -1, -2) 0 o\n", "158 (-1, 0, 1) (0, -1, 1) (2, 1, 1, 0, 0, -1, 0, -1, -2) 0 o\n", "159 (-1, 0, 1) (1, 0, -1) (0, 2, 1, 1, 0, 0, -1, -1, -2) 0 o, *\n", "160 (-1, 0, 1) (1, 0, -1) (1, 0, 2, 1, 0, 0, -1, -1, -2) 0 o\n", "161 (-1, 0, 1) (1, 0, -1) (2, 1, 1, 0, 0, -1, -2, 0, -1) 0 o\n", "162 (-1, 0, 1) (1, 0, -1) (2, 1, 1, 0, 0, -1, -1, -2, 0) 0 o\n", "163 (-1, 1, 0) (-1, 1, 0) (2, 0, 1, 1, 0, 0, -1, -1, -2) 0 o, *\n", "164 (-1, 1, 0) (-1, 1, 0) (2, 1, 0, 0, 1, 0, -1, -1, -2) 0 o, *\n", "165 (-1, 1, 0) (0, -1, 1) (1, 2, 0, 1, 0, 0, -1, -1, -2) 0 o, *\n", "166 (-1, 1, 0) (0, -1, 1) (1, 2, 1, 0, 0, -1, 0, -1, -2) 0 o, *\n", "167 (-1, 1, 0) (0, -1, 1) (1, 2, 1, 0, 0, 0, -1, -2, -1) 0 o, *\n", "168 (-1, 1, 0) (0, -1, 1) (2, 1, 0, 1, 0, 0, -1, -2, -1) 0 o\n", "169 (-1, 1, 0) (0, -1, 1) (2, 1, 1, 0, 0, -1, 0, -2, -1) 0 o\n", "170 (-1, 1, 0) (0, 1, -1) (1, 0, 2, 1, 0, 0, -1, -1, -2) 0 o, *\n", "171 (-1, 1, 0) (1, -1, 0) (0, 2, 1, 1, 0, 0, -1, -1, -2) 0 o, *\n", "172 (-1, 1, 0) (1, -1, 0) (2, 1, 1, 0, 0, -1, -1, -2, 0) 0 o\n", "173 (-1, 1, 0) (1, 0, -1) (0, 1, 2, 1, 0, 0, -1, -1, -2) 0 o, *\n", "174 (-1, 1, 0) (1, 0, -1) (0, 2, 1, 1, 0, 0, -1, -2, -1) 0 o, *\n", "175 (-1, 1, 0) (1, 0, -1) (1, 0, 2, 0, 1, 0, -1, -1, -2) 0 o\n", "176 (-1, 1, 0) (1, 0, -1) (1, 2, 1, 0, 0, -1, -1, -2, 0) 0 o\n", "177 (-1, 1, 0) (1, 0, -1) (2, 1, 1, 0, 0, -1, -2, -1, 0) 0 o\n", "178 (-1, 2, -1) (0, -3, 3) (1, 4, 4, 1, 1, -2, -2, -2, -5) 0 o, *\n", "179 (-1, 2, -1) (0, -3, 3) (4, 4, 1, 1, -2, 1, -2, -5, -2) 0 o\n", "180 (-1, 2, -1) (1, -2, 1) (0, 3, 3, 0, 0, 0, 0, -3, -3) 0 o, *\n", "181 (-1, 2, -1) (1, -2, 1) (3, 3, 0, 0, 0, 0, -3, -3, 0) 0 o\n", "182 (0, -3, 3) (1, -2, 1) (2, 5, 2, 2, -1, -1, -4, -1, -4) 0 o\n", "183 (0, -3, 3) (1, -2, 1) (5, 2, 2, -1, -1, 2, -1, -4, -4) 0 o\n", "184 (0, -3, 3) (1, 1, -2) (2, 5, 2, 2, -1, -1, -4, -4, -1) 0 o\n", "185 (0, -3, 3) (1, 1, -2) (5, 2, 2, -1, -1, 2, -4, -1, -4) 0 o\n", "186 (0, -3, 3) (2, -1, -1) (1, 4, 4, 1, 1, -2, -2, -5, -2) 0 o\n", "187 (0, -3, 3) (2, -1, -1) (4, 4, 1, 1, -2, 1, -5, -2, -2) 0 o\n", "188 (0, -1, 1) (0, -1, 1) (2, 1, 1, 0, -1, 0, 0, -1, -2) 0 o\n", "189 (0, -1, 1) (0, -1, 1) (2, 1, 1, 0, 0, -1, -1, 0, -2) 0 o\n", "190 (0, -1, 1) (0, 1, -1) (0, 2, 1, 1, 0, 0, -1, -1, -2) 0 o, *\n", "191 (0, -1, 1) (0, 1, -1) (2, 1, 1, 0, 0, -1, -1, -2, 0) 0 o\n", "192 (0, -1, 1) (1, -1, 0) (2, 1, 1, 0, 0, -1, -2, 0, -1) 0 o\n", "193 (0, -1, 1) (1, 0, -1) (0, 1, 2, 1, 0, 0, -1, -1, -2) 0 o\n", "194 (0, -1, 1) (1, 0, -1) (0, 2, 1, 1, 0, 0, -1, -2, -1) 0 o\n", "195 (0, -1, 1) (1, 0, -1) (1, 2, 1, 0, 0, -1, -1, -2, 0) 0 o\n", "196 (0, -1, 1) (1, 0, -1) (2, 1, 1, 0, -1, 0, -2, 0, -1) 0 o\n", "197 (0, -1, 1) (1, 0, -1) (2, 1, 1, 0, 0, -1, -2, -1, 0) 0 o\n", "198 (0, 0, 0) (0, 0, 0) (-1, -1, -1, -1, -1, -1, -1, -1, 8) 1 *\n", "199 (0, 0, 0) (0, 0, 0) (0, 0, 0, 0, 0, 0, 0, 1, -1) 0 o, *\n", "200 (0, 0, 0) (0, 0, 0) (0, 0, 0, 0, 0, 0, 1, -1, 0) 0 o, *\n", "201 (0, 0, 0) (0, 0, 0) (0, 0, 0, 0, 0, 1, -1, 0, 0) 0 o, *\n", "202 (0, 0, 0) (0, 0, 0) (0, 0, 0, 0, 1, -1, 0, 0, 0) 0 o, *\n", "203 (0, 0, 0) (0, 0, 0) (0, 0, 0, 1, -1, 0, 0, 0, 0) 0 o, *\n", "204 (0, 0, 0) (0, 0, 0) (0, 0, 1, -1, 0, 0, 0, 0, 0) 0 o, *\n", "205 (0, 0, 0) (0, 0, 0) (0, 1, -1, 0, 0, 0, 0, 0, 0) 0 o, *\n", "206 (0, 0, 0) (0, 0, 0) (1, -1, 0, 0, 0, 0, 0, 0, 0) 0 o\n", "207 (0, 0, 0) (0, 1, -1) (0, 0, 0, 0, 0, 0, 0, 0, 0) 0 o, *\n", "208 (0, 0, 0) (1, -1, 0) (0, 0, 0, 0, 0, 0, 0, 0, 0) 0 o\n", "\n", "Facet format is (H_A,lambda_A) + ... + z >= 0. The last column states\n", "whether the facet includes the origin (o) or the highest weight (*).\n", "\n", "All data is up to permutations of subsystems." ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qmp.pretty((3, 3, 9), algorithm='mathematica', show_vrepr=False)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "C(2,3,6)\n", "========\n", "\n", "Facets\n", "------\n", "\n", " # H_A H_B H_C z Remarks\n", "--- ------- ----------- ----------------------- --- ---------\n", " 1 (-3, 3) (-4, 2, 2) (7, 1, 1, 1, -5, -5) 0 o, *\n", " 2 (-3, 3) (-2, -2, 4) (5, 5, -1, -1, -1, -7) 0 o, *\n", " 3 (-3, 3) (-2, 4, -2) (5, -1, 5, -1, -1, -7) 0 o, *\n", " 4 (-3, 3) (-2, 4, -2) (5, 5, -1, -1, -7, -1) 0 o, *\n", " 5 (-3, 3) (2, -4, 2) (1, 7, 1, 1, -5, -5) 0 o, *\n", " 6 (-3, 3) (2, -4, 2) (7, 1, 1, -5, 1, -5) 0 o\n", " 7 (-3, 3) (2, 2, -4) (1, 1, 7, 1, -5, -5) 0 o, *\n", " 8 (-3, 3) (2, 2, -4) (1, 7, 1, -5, 1, -5) 0 o, *\n", " 9 (-3, 3) (2, 2, -4) (7, 1, 1, -5, -5, 1) 0 o\n", " 10 (-3, 3) (4, -2, -2) (-1, 5, 5, -1, -1, -7) 0 o, *\n", " 11 (-3, 3) (4, -2, -2) (5, -1, 5, -1, -7, -1) 0 o\n", " 12 (-3, 3) (4, -2, -2) (5, 5, -1, -7, -1, -1) 0 o\n", " 13 (-1, 1) (-2, 0, 2) (3, 1, 1, -1, -1, -3) 0 o, *\n", " 14 (-1, 1) (-2, 2, 0) (3, 1, -1, 1, -1, -3) 0 o, *\n", " 15 (-1, 1) (-2, 2, 0) (3, 1, 1, -1, -3, -1) 0 o, *\n", " 16 (-1, 1) (0, -2, 2) (1, 3, 1, -1, -1, -3) 0 o, *\n", " 17 (-1, 1) (0, -2, 2) (3, 1, -1, 1, -1, -3) 0 o\n", " 18 (-1, 1) (0, 0, 0) (1, 1, 1, -1, -1, -1) 0 o, *\n", " 19 (-1, 1) (2, 0, -2) (-1, 3, 1, 1, -1, -3) 0 o, *\n", " 20 (-1, 1) (2, 0, -2) (1, -1, 3, 1, -1, -3) 0 o\n", " 21 (-1, 1) (2, 0, -2) (3, 1, -1, -3, 1, -1) 0 o\n", " 22 (-1, 1) (2, 0, -2) (3, 1, -1, -1, -3, 1) 0 o\n", " 23 (0, 0) (-2, 1, 1) (2, 2, -1, -1, -1, -1) 0 o, *\n", " 24 (0, 0) (-1, -1, 2) (1, 1, 1, 1, -2, -2) 0 o, *\n", " 25 (0, 0) (0, 0, 0) (-1, -1, -1, -1, -1, 5) 1 *\n", " 26 (0, 0) (0, 0, 0) (0, 0, 0, 0, 1, -1) 0 o, *\n", " 27 (0, 0) (0, 0, 0) (0, 0, 0, 1, -1, 0) 0 o, *\n", " 28 (0, 0) (0, 0, 0) (0, 0, 1, -1, 0, 0) 0 o, *\n", " 29 (0, 0) (0, 0, 0) (0, 1, -1, 0, 0, 0) 0 o, *\n", " 30 (0, 0) (0, 0, 0) (1, -1, 0, 0, 0, 0) 0 o\n", " 31 (0, 0) (0, 1, -1) (0, 0, 0, 0, 0, 0) 0 o, *\n", " 32 (0, 0) (1, -1, 0) (0, 0, 0, 0, 0, 0) 0 o\n", " 33 (0, 0) (1, 1, -2) (-1, 2, 2, -1, -1, -1) 0 o, *\n", " 34 (0, 0) (2, -1, -1) (1, 1, 1, -2, -2, 1) 0 o\n", " 35 (1, -1) (-2, 0, 2) (1, 3, 1, -1, -1, -3) 0 o, *\n", " 36 (1, -1) (-2, 0, 2) (3, 1, -1, 1, -1, -3) 0 o\n", " 37 (1, -1) (-2, 0, 2) (3, 1, 1, -1, -3, -1) 0 o\n", " 38 (1, -1) (-2, 2, 0) (1, 3, -1, 1, -1, -3) 0 o, *\n", " 39 (1, -1) (-2, 2, 0) (1, 3, 1, -1, -3, -1) 0 o, *\n", " 40 (1, -1) (0, -2, 2) (1, 3, 1, -1, -3, -1) 0 o\n", " 41 (1, -1) (0, -2, 2) (3, 1, -1, 1, -3, -1) 0 o\n", " 42 (1, -1) (0, 0, 0) (0, 0, 0, 0, 0, 0) 0 o\n", " 43 (1, -1) (0, 2, -2) (-1, 3, 1, 1, -1, -3) 0 o, *\n", " 44 (1, -1) (2, -2, 0) (3, 1, -1, -1, -3, 1) 0 o\n", " 45 (1, -1) (2, 0, -2) (-1, 1, 3, 1, -1, -3) 0 o\n", " 46 (1, -1) (2, 0, -2) (3, 1, -1, -3, -1, 1) 0 o\n", " 47 (3, -3) (-4, 2, 2) (1, 7, 1, 1, -5, -5) 0 o, *\n", " 48 (3, -3) (-2, -2, 4) (5, 5, -1, -1, -7, -1) 0 o\n", " 49 (3, -3) (-2, 4, -2) (-1, 5, 5, -1, -1, -7) 0 o, *\n", " 50 (3, -3) (2, -4, 2) (7, 1, 1, -5, -5, 1) 0 o\n", "\n", "Facet format is (H_A,lambda_A) + ... + z >= 0. The last column states\n", "whether the facet includes the origin (o) or the highest weight (*).\n", "\n", "Vertices\n", "--------\n", "\n", " # V_A V_B V_C\n", "--- ------------ ------------------ ---------------------------------\n", " 1 (1/2, 1/2) (1/3, 1/3, 1/3) (1/6, 1/6, 1/6, 1/6, 1/6, 1/6)\n", " 2 (1/2, 1/2) (1/3, 1/3, 1/3) (2/9, 2/9, 2/9, 1/6, 1/6, 0)\n", " 3 (1/2, 1/2) (1/3, 1/3, 1/3) (7/30, 7/30, 1/5, 1/5, 2/15, 0)\n", " 4 (1/2, 1/2) (1/3, 1/3, 1/3) (1/4, 5/24, 5/24, 5/24, 1/8, 0)\n", " 5 (1/2, 1/2) (1/3, 1/3, 1/3) (1/4, 1/4, 1/6, 1/6, 1/6, 0)\n", " 6 (1/2, 1/2) (1/3, 1/3, 1/3) (1/4, 1/4, 1/4, 1/4, 0, 0)\n", " 7 (1/2, 1/2) (1/3, 1/3, 1/3) (1/3, 1/6, 1/6, 1/6, 1/6, 0)\n", " 8 (1/2, 1/2) (1/3, 1/3, 1/3) (1/3, 1/3, 1/3, 0, 0, 0)\n", " 9 (1/2, 1/2) (1/3, 1/3, 1/3) (1/2, 1/6, 1/6, 1/6, 0, 0)\n", " 10 (1/2, 1/2) (1/3, 1/3, 1/3) (1/2, 1/4, 1/12, 1/12, 1/12, 0)\n", " 11 (1/2, 1/2) (1/3, 1/3, 1/3) (1/2, 1/2, 0, 0, 0, 0)\n", " 12 (1/2, 1/2) (1/3, 1/3, 1/3) (7/12, 1/4, 1/12, 1/12, 0, 0)\n", " 13 (1/2, 1/2) (1/3, 1/3, 1/3) (2/3, 1/6, 1/6, 0, 0, 0)\n", " 14 (1/2, 1/2) (5/14, 5/14, 2/7) (3/14, 3/14, 3/14, 3/14, 1/7, 0)\n", " 15 (1/2, 1/2) (4/11, 4/11, 3/11) (5/22, 5/22, 2/11, 2/11, 2/11, 0)\n", " 16 (1/2, 1/2) (3/8, 3/8, 1/4) (1/4, 3/16, 3/16, 3/16, 3/16, 0)\n", " 17 (1/2, 1/2) (7/18, 7/18, 2/9) (2/9, 2/9, 2/9, 1/6, 1/6, 0)\n", " 18 (1/2, 1/2) (2/5, 3/10, 3/10) (1/5, 1/5, 1/5, 1/5, 1/5, 0)\n", " 19 (1/2, 1/2) (5/12, 5/12, 1/6) (1/4, 1/4, 1/6, 1/6, 1/6, 0)\n", " 20 (1/2, 1/2) (3/7, 3/7, 1/7) (2/7, 3/14, 3/14, 1/7, 1/7, 0)\n", " 21 (1/2, 1/2) (1/2, 1/4, 1/4) (1/4, 1/4, 1/4, 1/4, 0, 0)\n", " 22 (1/2, 1/2) (1/2, 1/4, 1/4) (1/2, 1/8, 1/8, 1/8, 1/8, 0)\n", " 23 (1/2, 1/2) (1/2, 1/4, 1/4) (5/8, 1/8, 1/8, 1/8, 0, 0)\n", " 24 (1/2, 1/2) (1/2, 1/4, 1/4) (3/4, 1/4, 0, 0, 0, 0)\n", " 25 (1/2, 1/2) (1/2, 1/3, 1/6) (1/3, 1/6, 1/6, 1/6, 1/6, 0)\n", " 26 (1/2, 1/2) (1/2, 3/8, 1/8) (1/4, 1/4, 1/4, 1/8, 1/8, 0)\n", " 27 (1/2, 1/2) (1/2, 1/2, 0) (1/4, 1/4, 1/4, 1/4, 0, 0)\n", " 28 (1/2, 1/2) (1/2, 1/2, 0) (1/3, 1/3, 1/3, 0, 0, 0)\n", " 29 (1/2, 1/2) (1/2, 1/2, 0) (1/2, 1/2, 0, 0, 0, 0)\n", " 30 (1/2, 1/2) (1/2, 1/2, 0) (1, 0, 0, 0, 0, 0)\n", " 31 (1/2, 1/2) (3/5, 1/5, 1/5) (2/5, 1/5, 1/5, 1/10, 1/10, 0)\n", " 32 (1/2, 1/2) (2/3, 1/6, 1/6) (1/3, 1/3, 1/3, 0, 0, 0)\n", " 33 (1/2, 1/2) (2/3, 1/6, 1/6) (1/2, 1/6, 1/6, 1/6, 0, 0)\n", " 34 (1/2, 1/2) (2/3, 1/6, 1/6) (2/3, 1/6, 1/6, 0, 0, 0)\n", " 35 (1/2, 1/2) (3/4, 1/4, 0) (1/2, 1/4, 1/4, 0, 0, 0)\n", " 36 (1/2, 1/2) (1, 0, 0) (1/2, 1/2, 0, 0, 0, 0)\n", " 37 (5/9, 4/9) (1/3, 1/3, 1/3) (2/9, 2/9, 2/9, 2/9, 1/9, 0)\n", " 38 (7/12, 5/12) (1/3, 1/3, 1/3) (1/4, 1/4, 1/6, 1/6, 1/6, 0)\n", " 39 (3/5, 2/5) (2/5, 2/5, 1/5) (1/5, 1/5, 1/5, 1/5, 1/5, 0)\n", " 40 (5/8, 3/8) (3/8, 3/8, 1/4) (1/4, 1/4, 1/4, 1/4, 0, 0)\n", " 41 (5/8, 3/8) (1/2, 1/4, 1/4) (1/4, 1/4, 1/4, 1/8, 1/8, 0)\n", " 42 (2/3, 1/3) (1/3, 1/3, 1/3) (1/3, 1/6, 1/6, 1/6, 1/6, 0)\n", " 43 (2/3, 1/3) (1/3, 1/3, 1/3) (1/3, 2/9, 2/9, 2/9, 0, 0)\n", " 44 (2/3, 1/3) (1/3, 1/3, 1/3) (1/3, 1/3, 1/9, 1/9, 1/9, 0)\n", " 45 (2/3, 1/3) (1/3, 1/3, 1/3) (1/2, 1/6, 1/6, 1/6, 0, 0)\n", " 46 (2/3, 1/3) (1/3, 1/3, 1/3) (2/3, 1/3, 0, 0, 0, 0)\n", " 47 (2/3, 1/3) (2/3, 1/3, 0) (1/3, 1/3, 1/3, 0, 0, 0)\n", " 48 (5/7, 2/7) (3/7, 2/7, 2/7) (2/7, 2/7, 1/7, 1/7, 1/7, 0)\n", " 49 (3/4, 1/4) (1/2, 1/4, 1/4) (1/4, 1/4, 1/4, 1/4, 0, 0)\n", " 50 (3/4, 1/4) (1/2, 1/4, 1/4) (1/2, 1/2, 0, 0, 0, 0)\n", " 51 (3/4, 1/4) (1/2, 1/2, 0) (1/2, 1/4, 1/4, 0, 0, 0)\n", " 52 (4/5, 1/5) (2/5, 2/5, 1/5) (2/5, 1/5, 1/5, 1/5, 0, 0)\n", " 53 (5/6, 1/6) (1/3, 1/3, 1/3) (1/3, 1/3, 1/6, 1/6, 0, 0)\n", " 54 (1, 0) (1/3, 1/3, 1/3) (1/3, 1/3, 1/3, 0, 0, 0)\n", " 55 (1, 0) (1/2, 1/2, 0) (1/2, 1/2, 0, 0, 0, 0)\n", " 56 (1, 0) (1, 0, 0) (1, 0, 0, 0, 0, 0)\n", "\n", "All data is up to permutations of subsystems." ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qmp.pretty((2, 3, 6))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "C(2,4,8)\n", "========\n", "\n", "Facets\n", "------\n", "\n", " # H_A H_B H_C z Remarks\n", "--- ------- --------------- ------------------------------- --- ---------\n", " 1 (-2, 2) (-5, -1, 3, 3) (7, 3, 3, -1, -1, -1, -5, -5) 0 o, *\n", " 2 (-2, 2) (-5, 3, -1, 3) (7, 3, -1, 3, -1, -1, -5, -5) 0 o, *\n", " 3 (-2, 2) (-5, 3, -1, 3) (7, 3, 3, -1, -1, -5, -1, -5) 0 o, *\n", " 4 (-2, 2) (-5, 3, 3, -1) (7, 3, -1, -1, 3, -1, -5, -5) 0 o, *\n", " 5 (-2, 2) (-5, 3, 3, -1) (7, 3, -1, 3, -1, -5, -1, -5) 0 o, *\n", " 6 (-2, 2) (-5, 3, 3, -1) (7, 3, 3, -1, -1, -5, -5, -1) 0 o, *\n", " 7 (-2, 2) (-3, -3, 1, 5) (5, 5, 1, 1, 1, -3, -3, -7) 0 o, *\n", " 8 (-2, 2) (-3, -3, 5, 1) (5, 5, 1, 1, -3, 1, -3, -7) 0 o, *\n", " 9 (-2, 2) (-3, -3, 5, 1) (5, 5, 1, 1, 1, -3, -7, -3) 0 o, *\n", " 10 (-2, 2) (-3, 1, -3, 5) (5, 1, 5, 1, 1, -3, -3, -7) 0 o, *\n", " 11 (-2, 2) (-3, 1, -3, 5) (5, 5, 1, 1, -3, 1, -3, -7) 0 o, *\n", " 12 (-2, 2) (-3, 1, 1, 1) (5, 1, 1, 1, 1, -3, -3, -3) 0 o, *\n", " 13 (-2, 2) (-3, 5, 1, -3) (5, 5, 1, 1, -3, -7, 1, -3) 0 o, *\n", " 14 (-2, 2) (-3, 5, 1, -3) (5, 5, 1, 1, -3, -3, -7, 1) 0 o, *\n", " 15 (-2, 2) (-1, -5, 3, 3) (3, 7, 3, -1, -1, -1, -5, -5) 0 o, *\n", " 16 (-2, 2) (-1, -5, 3, 3) (7, 3, -1, 3, -1, -1, -5, -5) 0 o\n", " 17 (-2, 2) (-1, -1, -1, 3) (3, 3, 3, -1, -1, -1, -1, -5) 0 o, *\n", " 18 (-2, 2) (-1, -1, 3, -1) (3, 3, -1, 3, -1, -1, -1, -5) 0 o, *\n", " 19 (-2, 2) (-1, -1, 3, -1) (3, 3, 3, -1, -1, -1, -5, -1) 0 o, *\n", " 20 (-2, 2) (-1, 3, -1, -1) (3, -1, 3, 3, -1, -1, -1, -5) 0 o, *\n", " 21 (-2, 2) (-1, 3, -1, -1) (3, 3, -1, 3, -1, -1, -5, -1) 0 o, *\n", " 22 (-2, 2) (-1, 3, -1, -1) (3, 3, 3, -1, -1, -5, -1, -1) 0 o, *\n", " 23 (-2, 2) (1, -3, -3, 5) (1, 5, 5, 1, 1, -3, -3, -7) 0 o, *\n", " 24 (-2, 2) (1, -3, -3, 5) (5, 1, 5, 1, -3, 1, -3, -7) 0 o\n", " 25 (-2, 2) (1, -3, -3, 5) (5, 5, 1, -3, 1, 1, -3, -7) 0 o\n", " 26 (-2, 2) (1, -3, 1, 1) (1, 5, 1, 1, 1, -3, -3, -3) 0 o, *\n", " 27 (-2, 2) (1, -3, 1, 1) (5, 1, 1, 1, -3, 1, -3, -3) 0 o\n", " 28 (-2, 2) (1, 1, -3, 1) (1, 1, 5, 1, 1, -3, -3, -3) 0 o, *\n", " 29 (-2, 2) (1, 1, -3, 1) (1, 5, 1, 1, -3, 1, -3, -3) 0 o, *\n", " 30 (-2, 2) (1, 1, -3, 1) (5, 1, 1, 1, -3, -3, 1, -3) 0 o\n", " 31 (-2, 2) (1, 1, 1, -3) (1, 1, 1, 5, 1, -3, -3, -3) 0 o, *\n", " 32 (-2, 2) (1, 1, 1, -3) (1, 1, 5, 1, -3, 1, -3, -3) 0 o, *\n", " 33 (-2, 2) (1, 1, 1, -3) (1, 5, 1, 1, -3, -3, 1, -3) 0 o, *\n", " 34 (-2, 2) (1, 1, 1, -3) (5, 1, 1, 1, -3, -3, -3, 1) 0 o\n", " 35 (-2, 2) (3, -1, -5, 3) (-1, 7, 3, 3, -1, -1, -5, -5) 0 o, *\n", " 36 (-2, 2) (3, -1, -5, 3) (3, -1, 7, 3, -1, -1, -5, -5) 0 o\n", " 37 (-2, 2) (3, -1, -1, -1) (-1, 3, 3, 3, -1, -1, -1, -5) 0 o, *\n", " 38 (-2, 2) (3, -1, -1, -1) (3, -1, 3, 3, -1, -1, -5, -1) 0 o\n", " 39 (-2, 2) (3, -1, -1, -1) (3, 3, -1, 3, -1, -5, -1, -1) 0 o\n", " 40 (-2, 2) (3, -1, -1, -1) (3, 3, 3, -1, -5, -1, -1, -1) 0 o\n", " 41 (-2, 2) (3, 3, -1, -5) (3, -1, -1, 7, 3, -1, -5, -5) 0 o\n", " 42 (-2, 2) (3, 3, -1, -5) (7, 3, -1, -1, -1, -5, -5, 3) 0 o\n", " 43 (-2, 2) (5, 1, -3, -3) (-3, 5, 5, 1, 1, 1, -3, -7) 0 o, *\n", " 44 (-2, 2) (5, 1, -3, -3) (5, 5, 1, -3, -7, 1, 1, -3) 0 o\n", " 45 (-1, 1) (-3, -1, 1, 3) (4, 2, 2, 0, 0, -2, -2, -4) 0 o, *\n", " 46 (-1, 1) (-3, -1, 3, 1) (4, 2, 2, 0, -2, 0, -2, -4) 0 o, *\n", " 47 (-1, 1) (-3, -1, 3, 1) (4, 2, 2, 0, 0, -2, -4, -2) 0 o, *\n", " 48 (-1, 1) (-3, 1, -1, 3) (4, 2, 0, 2, 0, -2, -2, -4) 0 o, *\n", " 49 (-1, 1) (-3, 1, -1, 3) (4, 2, 2, 0, -2, 0, -2, -4) 0 o, *\n", " 50 (-1, 1) (-3, 3, 1, -1) (4, 2, -2, 2, 0, 0, -2, -4) 0 o, *\n", " 51 (-1, 1) (-3, 3, 1, -1) (4, 2, 0, -2, 2, 0, -2, -4) 0 o, *\n", " 52 (-1, 1) (-3, 3, 1, -1) (4, 2, 2, 0, -2, -4, 0, -2) 0 o, *\n", " 53 (-1, 1) (-3, 3, 1, -1) (4, 2, 2, 0, -2, -2, -4, 0) 0 o, *\n", " 54 (-1, 1) (-2, 0, 0, 2) (3, 1, 1, 1, -1, -1, -1, -3) 0 o, *\n", " 55 (-1, 1) (-2, 0, 2, 0) (3, 1, 1, -1, 1, -1, -1, -3) 0 o, *\n", " 56 (-1, 1) (-2, 0, 2, 0) (3, 1, 1, 1, -1, -1, -3, -1) 0 o, *\n", " 57 (-1, 1) (-2, 2, 0, 0) (3, 1, -1, 1, 1, -1, -1, -3) 0 o, *\n", " 58 (-1, 1) (-2, 2, 0, 0) (3, 1, 1, -1, 1, -1, -3, -1) 0 o, *\n", " 59 (-1, 1) (-2, 2, 0, 0) (3, 1, 1, 1, -1, -3, -1, -1) 0 o, *\n", " 60 (-1, 1) (-1, -3, 1, 3) (2, 4, 2, 0, 0, -2, -2, -4) 0 o, *\n", " 61 (-1, 1) (-1, -3, 1, 3) (4, 2, 0, 2, 0, -2, -2, -4) 0 o\n", " 62 (-1, 1) (-1, -3, 3, 1) (2, 4, 2, 0, -2, 0, -2, -4) 0 o, *\n", " 63 (-1, 1) (-1, -3, 3, 1) (2, 4, 2, 0, 0, -2, -4, -2) 0 o, *\n", " 64 (-1, 1) (-1, -3, 3, 1) (4, 2, 0, 2, -2, 0, -2, -4) 0 o\n", " 65 (-1, 1) (-1, -3, 3, 1) (4, 2, 0, 2, 0, -2, -4, -2) 0 o\n", " 66 (-1, 1) (-1, -1, 1, 1) (2, 2, 0, 0, 0, 0, -2, -2) 0 o, *\n", " 67 (-1, 1) (-1, 1, -1, 1) (2, 0, 2, 0, 0, 0, -2, -2) 0 o, *\n", " 68 (-1, 1) (-1, 1, -1, 1) (2, 2, 0, 0, 0, -2, 0, -2) 0 o, *\n", " 69 (-1, 1) (-1, 1, 1, -1) (2, 0, 0, 2, 0, 0, -2, -2) 0 o, *\n", " 70 (-1, 1) (-1, 1, 1, -1) (2, 0, 2, 0, 0, -2, 0, -2) 0 o, *\n", " 71 (-1, 1) (-1, 1, 1, -1) (2, 2, 0, 0, 0, -2, -2, 0) 0 o, *\n", " 72 (-1, 1) (0, -2, 0, 2) (1, 3, 1, 1, -1, -1, -1, -3) 0 o, *\n", " 73 (-1, 1) (0, -2, 0, 2) (3, 1, 1, -1, 1, -1, -1, -3) 0 o\n", " 74 (-1, 1) (0, -2, 2, 0) (1, 3, 1, -1, 1, -1, -1, -3) 0 o, *\n", " 75 (-1, 1) (0, -2, 2, 0) (1, 3, 1, 1, -1, -1, -3, -1) 0 o, *\n", " 76 (-1, 1) (0, -2, 2, 0) (3, 1, -1, 1, 1, -1, -1, -3) 0 o\n", " 77 (-1, 1) (0, -2, 2, 0) (3, 1, 1, -1, -1, 1, -1, -3) 0 o\n", " 78 (-1, 1) (0, -2, 2, 0) (3, 1, 1, -1, 1, -1, -3, -1) 0 o\n", " 79 (-1, 1) (0, 0, -2, 2) (1, 1, 3, 1, -1, -1, -1, -3) 0 o, *\n", " 80 (-1, 1) (0, 0, -2, 2) (1, 3, 1, -1, 1, -1, -1, -3) 0 o, *\n", " 81 (-1, 1) (0, 0, -2, 2) (3, 1, 1, -1, -1, 1, -1, -3) 0 o\n", " 82 (-1, 1) (0, 0, 0, 0) (1, 1, 1, 1, -1, -1, -1, -1) 0 o, *\n", " 83 (-1, 1) (0, 2, 0, -2) (1, -1, 3, 1, 1, -1, -1, -3) 0 o, *\n", " 84 (-1, 1) (0, 2, 0, -2) (1, 1, -1, 3, 1, -1, -1, -3) 0 o, *\n", " 85 (-1, 1) (0, 2, 0, -2) (3, 1, 1, -1, -1, -3, 1, -1) 0 o\n", " 86 (-1, 1) (0, 2, 0, -2) (3, 1, 1, -1, -1, -1, -3, 1) 0 o\n", " 87 (-1, 1) (1, -1, -3, 3) (0, 4, 2, 2, 0, -2, -2, -4) 0 o, *\n", " 88 (-1, 1) (1, -1, -3, 3) (2, 0, 4, 2, 0, -2, -2, -4) 0 o\n", " 89 (-1, 1) (1, -1, -3, 3) (4, 2, 0, -2, 2, 0, -2, -4) 0 o\n", " 90 (-1, 1) (1, -1, -3, 3) (4, 2, 0, 0, -2, 2, -2, -4) 0 o\n", " 91 (-1, 1) (1, -1, -1, 1) (0, 2, 2, 0, 0, 0, -2, -2) 0 o, *\n", " 92 (-1, 1) (1, -1, -1, 1) (2, 0, 2, 0, 0, -2, 0, -2) 0 o\n", " 93 (-1, 1) (1, -1, -1, 1) (2, 2, 0, 0, -2, 0, 0, -2) 0 o\n", " 94 (-1, 1) (1, -1, 1, -1) (0, 2, 0, 2, 0, 0, -2, -2) 0 o, *\n", " 95 (-1, 1) (1, -1, 1, -1) (0, 2, 2, 0, 0, -2, 0, -2) 0 o, *\n", " 96 (-1, 1) (1, -1, 1, -1) (2, 0, 0, 2, 0, -2, 0, -2) 0 o\n", " 97 (-1, 1) (1, -1, 1, -1) (2, 0, 2, 0, -2, 0, 0, -2) 0 o\n", " 98 (-1, 1) (1, -1, 1, -1) (2, 0, 2, 0, 0, -2, -2, 0) 0 o\n", " 99 (-1, 1) (1, -1, 1, -1) (2, 2, 0, 0, -2, 0, -2, 0) 0 o\n", "100 (-1, 1) (1, 1, -1, -1) (0, 0, 2, 2, 0, 0, -2, -2) 0 o, *\n", "101 (-1, 1) (1, 1, -1, -1) (0, 2, 0, 2, 0, -2, 0, -2) 0 o, *\n", "102 (-1, 1) (1, 1, -1, -1) (0, 2, 2, 0, -2, 0, 0, -2) 0 o, *\n", "103 (-1, 1) (1, 1, -1, -1) (2, 0, 0, 2, 0, -2, -2, 0) 0 o\n", "104 (-1, 1) (1, 1, -1, -1) (2, 0, 2, 0, -2, 0, -2, 0) 0 o\n", "105 (-1, 1) (1, 1, -1, -1) (2, 2, 0, 0, -2, -2, 0, 0) 0 o\n", "106 (-1, 1) (1, 3, -1, -3) (4, 2, 0, 0, -2, -2, -4, 2) 0 o\n", "107 (-1, 1) (2, 0, -2, 0) (-1, 3, 1, 1, 1, -1, -1, -3) 0 o, *\n", "108 (-1, 1) (2, 0, -2, 0) (1, -1, 3, 1, 1, -1, -1, -3) 0 o\n", "109 (-1, 1) (2, 0, -2, 0) (3, 1, 1, -1, -3, 1, -1, -1) 0 o\n", "110 (-1, 1) (2, 0, -2, 0) (3, 1, 1, -1, -1, -3, 1, -1) 0 o\n", "111 (-1, 1) (2, 0, 0, -2) (-1, 1, 3, 1, 1, -1, -1, -3) 0 o, *\n", "112 (-1, 1) (2, 0, 0, -2) (-1, 3, 1, 1, -1, 1, -1, -3) 0 o, *\n", "113 (-1, 1) (2, 0, 0, -2) (1, -1, 1, 3, 1, -1, -1, -3) 0 o\n", "114 (-1, 1) (2, 0, 0, -2) (1, -1, 3, 1, -1, 1, -1, -3) 0 o\n", "115 (-1, 1) (2, 0, 0, -2) (1, -1, 3, 1, 1, -1, -3, -1) 0 o\n", "116 (-1, 1) (2, 0, 0, -2) (1, 1, -1, 3, 1, -1, -3, -1) 0 o\n", "117 (-1, 1) (2, 0, 0, -2) (1, 3, 1, -1, -3, 1, -1, -1) 0 o\n", "118 (-1, 1) (2, 0, 0, -2) (1, 3, 1, -1, -1, -3, 1, -1) 0 o\n", "119 (-1, 1) (2, 0, 0, -2) (3, 1, -1, 1, -1, -3, 1, -1) 0 o\n", "120 (-1, 1) (2, 0, 0, -2) (3, 1, -1, 1, -1, -1, -3, 1) 0 o\n", "121 (-1, 1) (2, 0, 0, -2) (3, 1, 1, -1, -3, -1, 1, -1) 0 o\n", "122 (-1, 1) (2, 0, 0, -2) (3, 1, 1, -1, -1, -3, -1, 1) 0 o\n", "123 (-1, 1) (3, 1, -3, -1) (-2, 4, 2, 2, 0, 0, -2, -4) 0 o, *\n", "124 (-1, 1) (3, 1, -1, -3) (-2, 2, 4, 2, 0, 0, -2, -4) 0 o, *\n", "125 (-1, 1) (3, 1, -1, -3) (-2, 4, 2, 0, 2, 0, -2, -4) 0 o, *\n", "126 (-1, 1) (3, 1, -1, -3) (2, 0, -2, 4, 2, 0, -2, -4) 0 o\n", "127 (-1, 1) (3, 1, -1, -3) (4, 2, 0, -2, -4, 2, 0, -2) 0 o\n", "128 (-1, 1) (3, 1, -1, -3) (4, 2, 0, -2, 0, -2, -4, 2) 0 o\n", "129 (-1, 1) (3, 1, -1, -3) (4, 2, 0, 0, -2, -4, -2, 2) 0 o\n", "130 (0, 0) (-3, 1, 1, 1) (3, 3, -1, -1, -1, -1, -1, -1) 0 o, *\n", "131 (0, 0) (-1, -1, -1, 3) (1, 1, 1, 1, 1, 1, -3, -3) 0 o, *\n", "132 (0, 0) (-1, -1, 1, 1) (1, 1, 1, 1, -1, -1, -1, -1) 0 o, *\n", "133 (0, 0) (-1, 1, 1, -1) (1, 1, -1, 1, 1, -1, -1, -1) 0 o, *\n", "134 (0, 0) (-1, 3, -1, -1) (1, 1, 1, 1, 1, -3, -3, 1) 0 o, *\n", "135 (0, 0) (0, 0, 0, 0) (-1, -1, -1, -1, -1, -1, -1, 7) 1 *\n", "136 (0, 0) (0, 0, 0, 0) (0, 0, 0, 0, 0, 0, 1, -1) 0 o, *\n", "137 (0, 0) (0, 0, 0, 0) (0, 0, 0, 0, 0, 1, -1, 0) 0 o, *\n", "138 (0, 0) (0, 0, 0, 0) (0, 0, 0, 0, 1, -1, 0, 0) 0 o, *\n", "139 (0, 0) (0, 0, 0, 0) (0, 0, 0, 1, -1, 0, 0, 0) 0 o, *\n", "140 (0, 0) (0, 0, 0, 0) (0, 0, 1, -1, 0, 0, 0, 0) 0 o, *\n", "141 (0, 0) (0, 0, 0, 0) (0, 1, -1, 0, 0, 0, 0, 0) 0 o, *\n", "142 (0, 0) (0, 0, 0, 0) (1, -1, 0, 0, 0, 0, 0, 0) 0 o\n", "143 (0, 0) (0, 0, 1, -1) (0, 0, 0, 0, 0, 0, 0, 0) 0 o, *\n", "144 (0, 0) (0, 1, -1, 0) (0, 0, 0, 0, 0, 0, 0, 0) 0 o, *\n", "145 (0, 0) (1, -1, -1, 1) (1, 1, 1, -1, -1, 1, -1, -1) 0 o\n", "146 (0, 0) (1, -1, 0, 0) (0, 0, 0, 0, 0, 0, 0, 0) 0 o\n", "147 (0, 0) (1, 1, -3, 1) (-1, 3, 3, -1, -1, -1, -1, -1) 0 o, *\n", "148 (0, 0) (1, 1, -1, -1) (-1, 1, 1, 1, 1, -1, -1, -1) 0 o, *\n", "149 (0, 0) (1, 1, -1, -1) (1, 1, 1, -1, -1, -1, -1, 1) 0 o\n", "150 (1, -1) (-3, -1, 1, 3) (2, 4, 2, 0, 0, -2, -2, -4) 0 o, *\n", "151 (1, -1) (-3, -1, 1, 3) (4, 2, 0, 2, 0, -2, -2, -4) 0 o\n", "152 (1, -1) (-3, -1, 1, 3) (4, 2, 2, 0, -2, 0, -2, -4) 0 o\n", "153 (1, -1) (-3, -1, 1, 3) (4, 2, 2, 0, 0, -2, -4, -2) 0 o\n", "154 (1, -1) (-3, -1, 3, 1) (2, 4, 2, 0, -2, 0, -2, -4) 0 o, *\n", "155 (1, -1) (-3, -1, 3, 1) (2, 4, 2, 0, 0, -2, -4, -2) 0 o, *\n", "156 (1, -1) (-3, -1, 3, 1) (4, 2, 0, 2, -2, 0, -2, -4) 0 o\n", "157 (1, -1) (-3, -1, 3, 1) (4, 2, 0, 2, 0, -2, -4, -2) 0 o\n", "158 (1, -1) (-3, 1, -1, 3) (2, 4, 0, 2, 0, -2, -2, -4) 0 o, *\n", "159 (1, -1) (-3, 1, -1, 3) (2, 4, 2, 0, -2, 0, -2, -4) 0 o, *\n", "160 (1, -1) (-3, 1, -1, 3) (4, 2, 0, 2, 0, -2, -4, -2) 0 o\n", "161 (1, -1) (-3, 1, -1, 3) (4, 2, 2, 0, -2, 0, -4, -2) 0 o\n", "162 (1, -1) (-3, 1, 3, -1) (4, 2, -2, 2, 0, 0, -2, -4) 0 o\n", "163 (1, -1) (-3, 3, -1, 1) (4, 2, 2, 0, -2, -2, -4, 0) 0 o\n", "164 (1, -1) (-3, 3, 1, -1) (2, 4, -2, 2, 0, 0, -2, -4) 0 o, *\n", "165 (1, -1) (-3, 3, 1, -1) (2, 4, 0, -2, 2, 0, -2, -4) 0 o, *\n", "166 (1, -1) (-3, 3, 1, -1) (2, 4, 2, 0, -2, -4, 0, -2) 0 o, *\n", "167 (1, -1) (-3, 3, 1, -1) (2, 4, 2, 0, -2, -2, -4, 0) 0 o, *\n", "168 (1, -1) (-3, 3, 1, -1) (4, 2, -2, 0, 2, 0, -2, -4) 0 o\n", "169 (1, -1) (-3, 3, 1, -1) (4, 2, 2, 0, -2, -4, -2, 0) 0 o\n", "170 (1, -1) (-2, 0, 0, 2) (1, 3, 1, 1, -1, -1, -1, -3) 0 o, *\n", "171 (1, -1) (-2, 0, 0, 2) (3, 1, 1, 1, -1, -1, -3, -1) 0 o\n", "172 (1, -1) (-2, 0, 2, 0) (1, 3, 1, -1, 1, -1, -1, -3) 0 o, *\n", "173 (1, -1) (-2, 0, 2, 0) (1, 3, 1, 1, -1, -1, -3, -1) 0 o, *\n", "174 (1, -1) (-2, 0, 2, 0) (3, 1, -1, 1, 1, -1, -1, -3) 0 o\n", "175 (1, -1) (-2, 2, 0, 0) (1, 3, -1, 1, 1, -1, -1, -3) 0 o, *\n", "176 (1, -1) (-2, 2, 0, 0) (1, 3, 1, -1, 1, -1, -3, -1) 0 o, *\n", "177 (1, -1) (-2, 2, 0, 0) (1, 3, 1, 1, -1, -3, -1, -1) 0 o, *\n", "178 (1, -1) (-1, -3, 1, 3) (2, 4, 2, 0, -2, 0, -2, -4) 0 o\n", "179 (1, -1) (-1, -3, 1, 3) (2, 4, 2, 0, 0, -2, -4, -2) 0 o\n", "180 (1, -1) (-1, -3, 1, 3) (4, 2, 0, 2, -2, 0, -2, -4) 0 o\n", "181 (1, -1) (-1, -3, 1, 3) (4, 2, 0, 2, 0, -2, -4, -2) 0 o\n", "182 (1, -1) (-1, 1, -3, 3) (0, 4, 2, 2, 0, -2, -2, -4) 0 o, *\n", "183 (1, -1) (-1, 1, -1, 1) (0, 2, 2, 0, 0, 0, -2, -2) 0 o, *\n", "184 (1, -1) (-1, 1, -1, 1) (2, 2, 0, 0, 0, -2, -2, 0) 0 o\n", "185 (1, -1) (-1, 1, 1, -1) (0, 2, 0, 2, 0, 0, -2, -2) 0 o, *\n", "186 (1, -1) (-1, 1, 1, -1) (0, 2, 2, 0, 0, -2, 0, -2) 0 o, *\n", "187 (1, -1) (0, -2, 0, 2) (1, 3, 1, 1, -1, -1, -3, -1) 0 o\n", "188 (1, -1) (0, -2, 0, 2) (3, 1, 1, -1, -1, 1, -1, -3) 0 o\n", "189 (1, -1) (0, -2, 0, 2) (3, 1, 1, -1, 1, -1, -3, -1) 0 o\n", "190 (1, -1) (0, 0, -2, 2) (1, 1, 3, 1, -1, -1, -3, -1) 0 o\n", "191 (1, -1) (0, 0, -2, 2) (1, 3, 1, -1, 1, -1, -3, -1) 0 o\n", "192 (1, -1) (0, 0, -2, 2) (3, 1, 1, -1, -1, 1, -3, -1) 0 o\n", "193 (1, -1) (0, 0, 0, 0) (0, 0, 0, 0, 0, 0, 0, 0) 0 o\n", "194 (1, -1) (0, 2, -2, 0) (-1, 3, 1, 1, 1, -1, -1, -3) 0 o, *\n", "195 (1, -1) (0, 2, -2, 0) (3, 1, 1, -1, -1, -1, -3, 1) 0 o\n", "196 (1, -1) (0, 2, 0, -2) (-1, 1, 3, 1, 1, -1, -1, -3) 0 o, *\n", "197 (1, -1) (0, 2, 0, -2) (-1, 3, 1, 1, -1, 1, -1, -3) 0 o, *\n", "198 (1, -1) (0, 2, 0, -2) (3, 1, 1, -1, -1, -3, -1, 1) 0 o\n", "199 (1, -1) (1, -3, -1, 3) (4, 2, 0, 0, -2, 2, -2, -4) 0 o\n", "200 (1, -1) (1, -1, -3, 3) (0, 2, 4, 2, 0, -2, -2, -4) 0 o\n", "201 (1, -1) (1, -1, -3, 3) (0, 4, 2, 2, 0, -2, -4, -2) 0 o\n", "202 (1, -1) (1, -1, -3, 3) (2, 0, 4, 2, 0, -2, -4, -2) 0 o\n", "203 (1, -1) (1, -1, -3, 3) (4, 2, 0, -2, 0, 2, -2, -4) 0 o\n", "204 (1, -1) (1, -1, -3, 3) (4, 2, 0, -2, 2, 0, -4, -2) 0 o\n", "205 (1, -1) (1, -1, -3, 3) (4, 2, 0, 0, -2, 2, -4, -2) 0 o\n", "206 (1, -1) (1, -1, -1, 1) (2, 0, 2, 0, 0, -2, -2, 0) 0 o\n", "207 (1, -1) (1, -1, -1, 1) (2, 2, 0, 0, -2, 0, -2, 0) 0 o\n", "208 (1, -1) (1, 3, -3, -1) (-2, 4, 2, 2, 0, 0, -2, -4) 0 o, *\n", "209 (1, -1) (1, 3, -3, -1) (4, 2, 0, 0, -2, -2, -4, 2) 0 o\n", "210 (1, -1) (1, 3, -1, -3) (-2, 2, 4, 2, 0, 0, -2, -4) 0 o, *\n", "211 (1, -1) (1, 3, -1, -3) (-2, 4, 2, 0, 2, 0, -2, -4) 0 o, *\n", "212 (1, -1) (1, 3, -1, -3) (4, 2, 0, -2, 0, -2, -4, 2) 0 o\n", "213 (1, -1) (1, 3, -1, -3) (4, 2, 0, 0, -2, -4, -2, 2) 0 o\n", "214 (1, -1) (2, 0, -2, 0) (-1, 1, 3, 1, 1, -1, -1, -3) 0 o\n", "215 (1, -1) (2, 0, -2, 0) (3, 1, -1, 1, -1, -1, -3, 1) 0 o\n", "216 (1, -1) (2, 0, -2, 0) (3, 1, 1, -1, -1, -3, -1, 1) 0 o\n", "217 (1, -1) (3, 1, -3, -1) (-2, 2, 4, 2, 0, 0, -2, -4) 0 o\n", "218 (1, -1) (3, 1, -3, -1) (-2, 4, 2, 0, 2, 0, -2, -4) 0 o\n", "219 (1, -1) (3, 1, -3, -1) (4, 2, 0, -2, 0, -2, -4, 2) 0 o\n", "220 (1, -1) (3, 1, -3, -1) (4, 2, 0, 0, -2, -4, -2, 2) 0 o\n", "221 (2, -2) (-5, -1, 3, 3) (3, 7, 3, -1, -1, -1, -5, -5) 0 o, *\n", "222 (2, -2) (-5, -1, 3, 3) (7, 3, -1, 3, -1, -1, -5, -5) 0 o\n", "223 (2, -2) (-5, 3, -1, 3) (3, 7, -1, 3, -1, -1, -5, -5) 0 o, *\n", "224 (2, -2) (-5, 3, -1, 3) (3, 7, 3, -1, -1, -5, -1, -5) 0 o, *\n", "225 (2, -2) (-5, 3, -1, 3) (7, 3, 3, -1, -1, -5, -5, -1) 0 o\n", "226 (2, -2) (-5, 3, 3, -1) (3, 7, -1, -1, 3, -1, -5, -5) 0 o, *\n", "227 (2, -2) (-5, 3, 3, -1) (3, 7, -1, 3, -1, -5, -1, -5) 0 o, *\n", "228 (2, -2) (-5, 3, 3, -1) (3, 7, 3, -1, -1, -5, -5, -1) 0 o, *\n", "229 (2, -2) (-3, -3, 1, 5) (5, 5, 1, 1, -3, 1, -3, -7) 0 o\n", "230 (2, -2) (-3, -3, 1, 5) (5, 5, 1, 1, 1, -3, -7, -3) 0 o\n", "231 (2, -2) (-3, 1, -3, 5) (1, 5, 5, 1, 1, -3, -3, -7) 0 o, *\n", "232 (2, -2) (-3, 1, -3, 5) (5, 1, 5, 1, 1, -3, -7, -3) 0 o\n", "233 (2, -2) (-3, 1, -3, 5) (5, 5, 1, 1, -3, 1, -7, -3) 0 o\n", "234 (2, -2) (-3, 1, 1, 1) (1, 5, 1, 1, 1, -3, -3, -3) 0 o, *\n", "235 (2, -2) (-3, 5, -3, 1) (5, 5, 1, 1, -3, -3, -7, 1) 0 o\n", "236 (2, -2) (-3, 5, 1, -3) (5, 5, 1, 1, -3, -7, -3, 1) 0 o\n", "237 (2, -2) (-1, -1, -1, 3) (3, 3, 3, -1, -1, -1, -5, -1) 0 o\n", "238 (2, -2) (-1, 3, -5, 3) (-1, 7, 3, 3, -1, -1, -5, -5) 0 o, *\n", "239 (2, -2) (-1, 3, -1, -1) (-1, 3, 3, 3, -1, -1, -1, -5) 0 o, *\n", "240 (2, -2) (1, -3, -3, 5) (1, 5, 5, 1, 1, -3, -7, -3) 0 o\n", "241 (2, -2) (1, -3, -3, 5) (5, 1, 5, 1, -3, 1, -7, -3) 0 o\n", "242 (2, -2) (1, -3, -3, 5) (5, 5, 1, -3, 1, 1, -7, -3) 0 o\n", "243 (2, -2) (1, 1, -3, 1) (5, 1, 1, 1, -3, -3, -3, 1) 0 o\n", "244 (2, -2) (1, 5, -3, -3) (-3, 5, 5, 1, 1, 1, -3, -7) 0 o, *\n", "245 (2, -2) (3, -1, -5, 3) (-1, 3, 7, 3, -1, -1, -5, -5) 0 o\n", "246 (2, -2) (3, 3, -5, -1) (7, 3, -1, -1, -1, -5, -5, 3) 0 o\n", "\n", "Facet format is (H_A,lambda_A) + ... + z >= 0. The last column states\n", "whether the facet includes the origin (o) or the highest weight (*).\n", "\n", "All data is up to permutations of subsystems." ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "qmp.pretty((2, 4, 8), algorithm='mathematica', show_vrepr=False)" ] } ], "metadata": { "kernelspec": { "display_name": "SageMath 7.5.1", "language": "", "name": "sagemath" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
BerndSchwarzenbacher/numdiff
ueb4/ex17.ipynb
1
62771
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Links zu Dokumentationen/Tutorials für IPython/Python/numpy/matplotlib/git sowie die Sourcodes findet ihr im [GitHub Repo](https://github.com/BerndSchwarzenbacher/numdiff)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "adaptive = np.loadtxt('data/ex17.out')\n", "time = adaptive[:,0]\n", "h = adaptive[:,1]\n", "V1 = adaptive[:,2]\n", "V2 = adaptive[:,3]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEQCAYAAACX5IJuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJztvXm4JVV19/9Z9ARNzwM9QyMzKrZEEMUBEJQgQWP0hyiK\n", 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"U7CaMw1JlwL/DGwr6TZJ29UvLQM+IOlmqquxjptDaOcBj2ylOWWfFICXA/8ypT8SMTLZDyOiZSRt\n", "Duw3w+52SPos8E3by0cfWYy7nGFEtIztq4HlU3ogU/0oxSKakjOMiIjoSc4wIiKiJykYERHRkxSM\n", "iIjoSQpGRET0JAUjIiJ68v8BQu82nweVTx0AAAAASUVORK5CYII=\n" ], "text/plain": [ "<matplotlib.figure.Figure at 0x7febbf18a668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eps2 = np.loadtxt('data/ex17_tol_e2.out')\n", "eps3 = np.loadtxt('data/ex17_tol_e3.out')\n", "eps4 = np.loadtxt('data/ex17_tol_e4.out')\n", "eps5 = np.loadtxt('data/ex17_tol_e5.out')\n", "eps6 = np.loadtxt('data/ex17_tol_e6.out')\n", "tol = np.arange(2, 6);\n", "V1_eps2 = eps2[-1:,2]\n", "V1_eps3 = eps3[-1:,2]\n", "V1_eps4 = eps4[-1:,2]\n", "V1_eps5 = eps5[-1:,2]\n", "V1_eps6 = eps6[-1:,2]\n", "V1_err = np.array([V1_eps2 - V1_eps6, V1_eps3-V1_eps6, V1_eps4-V1_eps6, V1_eps5-V1_eps6])\n", "V1_err = np.abs(V1_err)\n", "plt.plot(tol, V1_err)\n", "plt.ylabel('err')\n", "plt.yscale('log')\n", "plt.xlabel('tol 10^(.)')\n", "plt.grid()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
hvillanua/deep-learning
tensorboard/Anna_KaRNNa_Summaries.ipynb
1
175598
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Anna KaRNNa\n", "\n", "In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.\n", "\n", "This network is based off of Andrej Karpathy's [post on RNNs](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) and [implementation in Torch](https://github.com/karpathy/char-rnn). Also, some information [here at r2rt](http://r2rt.com/recurrent-neural-networks-in-tensorflow-ii.html) and from [Sherjil Ozair](https://github.com/sherjilozair/char-rnn-tensorflow) on GitHub. Below is the general architecture of the character-wise RNN.\n", "\n", "<img src=\"assets/charseq.jpeg\" width=\"500\">" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import time\n", "from collections import namedtuple\n", "\n", "import numpy as np\n", "import tensorflow as tf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we'll load the text file and convert it into integers for our network to use." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "with open('anna.txt', 'r') as f:\n", " text=f.read()\n", "vocab = set(text)\n", "vocab_to_int = {c: i for i, c in enumerate(vocab)}\n", "int_to_vocab = dict(enumerate(vocab))\n", "chars = np.array([vocab_to_int[c] for c in text], dtype=np.int32)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Chapter 1\\n\\n\\nHappy families are all alike; every unhappy family is unhappy in its own\\nway.\\n\\nEverythin'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "text[:100]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 6, 55, 75, 1, 64, 12, 26, 8, 61, 73, 73, 73, 25, 75, 1, 1, 76,\n", " 8, 62, 75, 7, 18, 82, 18, 12, 5, 8, 75, 26, 12, 8, 75, 82, 82,\n", " 8, 75, 82, 18, 54, 12, 47, 8, 12, 32, 12, 26, 76, 8, 35, 63, 55,\n", " 75, 1, 1, 76, 8, 62, 75, 7, 18, 82, 76, 8, 18, 5, 8, 35, 63,\n", " 55, 75, 1, 1, 76, 8, 18, 63, 8, 18, 64, 5, 8, 29, 72, 63, 73,\n", " 72, 75, 76, 19, 73, 73, 23, 32, 12, 26, 76, 64, 55, 18, 63], dtype=int32)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chars[:100]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now I need to split up the data into batches, and into training and validation sets. I should be making a test set here, but I'm not going to worry about that. My test will be if the network can generate new text.\n", "\n", "Here I'll make both input and target arrays. The targets are the same as the inputs, except shifted one character over. I'll also drop the last bit of data so that I'll only have completely full batches.\n", "\n", "The idea here is to make a 2D matrix where the number of rows is equal to the number of batches. Each row will be one long concatenated string from the character data. We'll split this data into a training set and validation set using the `split_frac` keyword. This will keep 90% of the batches in the training set, the other 10% in the validation set." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def split_data(chars, batch_size, num_steps, split_frac=0.9):\n", " \"\"\" \n", " Split character data into training and validation sets, inputs and targets for each set.\n", " \n", " Arguments\n", " ---------\n", " chars: character array\n", " batch_size: Size of examples in each of batch\n", " num_steps: Number of sequence steps to keep in the input and pass to the network\n", " split_frac: Fraction of batches to keep in the training set\n", " \n", " \n", " Returns train_x, train_y, val_x, val_y\n", " \"\"\"\n", " \n", " slice_size = batch_size * num_steps\n", " n_batches = int(len(chars) / slice_size)\n", " \n", " # Drop the last few characters to make only full batches\n", " x = chars[: n_batches*slice_size]\n", " y = chars[1: n_batches*slice_size + 1]\n", " \n", " # Split the data into batch_size slices, then stack them into a 2D matrix \n", " x = np.stack(np.split(x, batch_size))\n", " y = np.stack(np.split(y, batch_size))\n", " \n", " # Now x and y are arrays with dimensions batch_size x n_batches*num_steps\n", " \n", " # Split into training and validation sets, keep the virst split_frac batches for training\n", " split_idx = int(n_batches*split_frac)\n", " train_x, train_y= x[:, :split_idx*num_steps], y[:, :split_idx*num_steps]\n", " val_x, val_y = x[:, split_idx*num_steps:], y[:, split_idx*num_steps:]\n", " \n", " return train_x, train_y, val_x, val_y" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "train_x, train_y, val_x, val_y = split_data(chars, 10, 200)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(10, 178400)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_x.shape" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 6, 55, 75, 1, 64, 12, 26, 8, 61, 73],\n", " [30, 63, 79, 8, 55, 12, 8, 7, 29, 32],\n", " [ 8, 53, 75, 64, 53, 55, 18, 63, 56, 8],\n", " [29, 64, 55, 12, 26, 8, 72, 29, 35, 82],\n", " [ 8, 64, 55, 12, 8, 82, 75, 63, 79, 46],\n", " [ 8, 33, 55, 26, 29, 35, 56, 55, 8, 82],\n", " [64, 8, 64, 29, 73, 79, 29, 19, 73, 73],\n", " [29, 8, 55, 12, 26, 5, 12, 82, 62, 48],\n", " [55, 75, 64, 8, 18, 5, 8, 64, 55, 12],\n", " [12, 26, 5, 12, 82, 62, 8, 75, 63, 79]], dtype=int32)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_x[:,:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'll write another function to grab batches out of the arrays made by split data. Here each batch will be a sliding window on these arrays with size `batch_size X num_steps`. For example, if we want our network to train on a sequence of 100 characters, `num_steps = 100`. For the next batch, we'll shift this window the next sequence of `num_steps` characters. In this way we can feed batches to the network and the cell states will continue through on each batch." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_batch(arrs, num_steps):\n", " batch_size, slice_size = arrs[0].shape\n", " \n", " n_batches = int(slice_size/num_steps)\n", " for b in range(n_batches):\n", " yield [x[:, b*num_steps: (b+1)*num_steps] for x in arrs]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def build_rnn(num_classes, batch_size=50, num_steps=50, lstm_size=128, num_layers=2,\n", " learning_rate=0.001, grad_clip=5, sampling=False):\n", " \n", " if sampling == True:\n", " batch_size, num_steps = 1, 1\n", "\n", " tf.reset_default_graph()\n", " \n", " # Declare placeholders we'll feed into the graph\n", " with tf.name_scope('inputs'):\n", " inputs = tf.placeholder(tf.int32, [batch_size, num_steps], name='inputs')\n", " x_one_hot = tf.one_hot(inputs, num_classes, name='x_one_hot')\n", " \n", " with tf.name_scope('targets'):\n", " targets = tf.placeholder(tf.int32, [batch_size, num_steps], name='targets')\n", " y_one_hot = tf.one_hot(targets, num_classes, name='y_one_hot')\n", " y_reshaped = tf.reshape(y_one_hot, [-1, num_classes])\n", " \n", " keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n", " \n", " # Build the RNN layers\n", " with tf.name_scope(\"RNN_cells\"):\n", " lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)\n", " drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)\n", " cell = tf.contrib.rnn.MultiRNNCell([drop] * num_layers)\n", " \n", " with tf.name_scope(\"RNN_init_state\"):\n", " initial_state = cell.zero_state(batch_size, tf.float32)\n", "\n", " # Run the data through the RNN layers\n", " with tf.name_scope(\"RNN_forward\"):\n", " outputs, state = tf.nn.dynamic_rnn(cell, x_one_hot, initial_state=initial_state)\n", " \n", " final_state = state\n", " \n", " # Reshape output so it's a bunch of rows, one row for each cell output\n", " with tf.name_scope('sequence_reshape'):\n", " seq_output = tf.concat(outputs, axis=1,name='seq_output')\n", " output = tf.reshape(seq_output, [-1, lstm_size], name='graph_output')\n", " \n", " # Now connect the RNN outputs to a softmax layer and calculate the cost\n", " with tf.name_scope('logits'):\n", " softmax_w = tf.Variable(tf.truncated_normal((lstm_size, num_classes), stddev=0.1),\n", " name='softmax_w')\n", " softmax_b = tf.Variable(tf.zeros(num_classes), name='softmax_b')\n", " logits = tf.matmul(output, softmax_w) + softmax_b\n", " tf.summary.histogram('softmax_w', softmax_w)\n", " tf.summary.histogram('softmax_b', softmax_b)\n", "\n", " with tf.name_scope('predictions'):\n", " preds = tf.nn.softmax(logits, name='predictions')\n", " tf.summary.histogram('predictions', preds)\n", " \n", " with tf.name_scope('cost'):\n", " loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_reshaped, name='loss')\n", " cost = tf.reduce_mean(loss, name='cost')\n", " tf.summary.scalar('cost', cost)\n", "\n", " # Optimizer for training, using gradient clipping to control exploding gradients\n", " with tf.name_scope('train'):\n", " tvars = tf.trainable_variables()\n", " grads, _ = tf.clip_by_global_norm(tf.gradients(cost, tvars), grad_clip)\n", " train_op = tf.train.AdamOptimizer(learning_rate)\n", " optimizer = train_op.apply_gradients(zip(grads, tvars))\n", " \n", " merged = tf.summary.merge_all()\n", " \n", " # Export the nodes \n", " export_nodes = ['inputs', 'targets', 'initial_state', 'final_state',\n", " 'keep_prob', 'cost', 'preds', 'optimizer', 'merged']\n", " Graph = namedtuple('Graph', export_nodes)\n", " local_dict = locals()\n", " graph = Graph(*[local_dict[each] for each in export_nodes])\n", " \n", " return graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hyperparameters\n", "\n", "Here I'm defining the hyperparameters for the network. The two you probably haven't seen before are `lstm_size` and `num_layers`. These set the number of hidden units in the LSTM layers and the number of LSTM layers, respectively. Of course, making these bigger will improve the network's performance but you'll have to watch out for overfitting. If your validation loss is much larger than the training loss, you're probably overfitting. Decrease the size of the network or decrease the dropout keep probability." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "batch_size = 100\n", "num_steps = 100\n", "lstm_size = 512\n", "num_layers = 2\n", "learning_rate = 0.001" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training\n", "\n", "Time for training which is is pretty straightforward. Here I pass in some data, and get an LSTM state back. Then I pass that state back in to the network so the next batch can continue the state from the previous batch. And every so often (set by `save_every_n`) I calculate the validation loss and save a checkpoint." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "!mkdir -p checkpoints/anna" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10 Iteration 1/1780 Training loss: 4.4188 1.2876 sec/batch\n", "Epoch 1/10 Iteration 2/1780 Training loss: 4.3775 0.1364 sec/batch\n", "Epoch 1/10 Iteration 3/1780 Training loss: 4.2100 0.1310 sec/batch\n", "Epoch 1/10 Iteration 4/1780 Training loss: 4.5256 0.1212 sec/batch\n", "Epoch 1/10 Iteration 5/1780 Training loss: 4.4524 0.1271 sec/batch\n", "Epoch 1/10 Iteration 6/1780 Training loss: 4.3496 0.1272 sec/batch\n", "Epoch 1/10 Iteration 7/1780 Training loss: 4.2637 0.1260 sec/batch\n", "Epoch 1/10 Iteration 8/1780 Training loss: 4.1856 0.1231 sec/batch\n", "Epoch 1/10 Iteration 9/1780 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sec/batch\n", "Epoch 10/10 Iteration 1779/1780 Training loss: 1.3625 0.1228 sec/batch\n", "Epoch 10/10 Iteration 1780/1780 Training loss: 1.3626 0.1239 sec/batch\n", "Validation loss: 1.24267 Saving checkpoint!\n" ] } ], "source": [ "epochs = 10\n", "save_every_n = 100\n", "train_x, train_y, val_x, val_y = split_data(chars, batch_size, num_steps)\n", "\n", "model = build_rnn(len(vocab), \n", " batch_size=batch_size,\n", " num_steps=num_steps,\n", " learning_rate=learning_rate,\n", " lstm_size=lstm_size,\n", " num_layers=num_layers)\n", "\n", "saver = tf.train.Saver(max_to_keep=100)\n", "\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " train_writer = tf.summary.FileWriter('./logs/2/train', sess.graph)\n", " test_writer = tf.summary.FileWriter('./logs/2/test')\n", " \n", " # Use the line below to load a checkpoint and resume training\n", " #saver.restore(sess, 'checkpoints/anna20.ckpt')\n", " \n", " n_batches = int(train_x.shape[1]/num_steps)\n", " iterations = n_batches * epochs\n", " for e in range(epochs):\n", " \n", " # Train network\n", " new_state = sess.run(model.initial_state)\n", " loss = 0\n", " for b, (x, y) in enumerate(get_batch([train_x, train_y], num_steps), 1):\n", " iteration = e*n_batches + b\n", " start = time.time()\n", " feed = {model.inputs: x,\n", " model.targets: y,\n", " model.keep_prob: 0.5,\n", " model.initial_state: new_state}\n", " summary, batch_loss, new_state, _ = sess.run([model.merged, model.cost, \n", " model.final_state, model.optimizer], \n", " feed_dict=feed)\n", " loss += batch_loss\n", " end = time.time()\n", " print('Epoch {}/{} '.format(e+1, epochs),\n", " 'Iteration {}/{}'.format(iteration, iterations),\n", " 'Training loss: {:.4f}'.format(loss/b),\n", " '{:.4f} sec/batch'.format((end-start)))\n", " \n", " train_writer.add_summary(summary, iteration)\n", " \n", " if (iteration%save_every_n == 0) or (iteration == iterations):\n", " # Check performance, notice dropout has been set to 1\n", " val_loss = []\n", " new_state = sess.run(model.initial_state)\n", " for x, y in get_batch([val_x, val_y], num_steps):\n", " feed = {model.inputs: x,\n", " model.targets: y,\n", " model.keep_prob: 1.,\n", " model.initial_state: new_state}\n", " summary, batch_loss, new_state = sess.run([model.merged, model.cost, \n", " model.final_state], feed_dict=feed)\n", " val_loss.append(batch_loss)\n", " \n", " test_writer.add_summary(summary, iteration)\n", "\n", " print('Validation loss:', np.mean(val_loss),\n", " 'Saving checkpoint!')\n", " #saver.save(sess, \"checkpoints/anna/i{}_l{}_{:.3f}.ckpt\".format(iteration, lstm_size, np.mean(val_loss)))" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "model_checkpoint_path: \"checkpoints/anna/i3560_l512_1.122.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i200_l512_2.432.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i400_l512_1.980.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i600_l512_1.750.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i800_l512_1.595.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i1000_l512_1.484.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i1200_l512_1.407.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i1400_l512_1.349.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i1600_l512_1.292.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i1800_l512_1.255.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i2000_l512_1.224.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i2200_l512_1.204.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i2400_l512_1.187.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i2600_l512_1.172.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i2800_l512_1.160.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i3000_l512_1.148.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i3200_l512_1.137.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i3400_l512_1.129.ckpt\"\n", "all_model_checkpoint_paths: \"checkpoints/anna/i3560_l512_1.122.ckpt\"" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.train.get_checkpoint_state('checkpoints/anna')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling\n", "\n", "Now that the network is trained, we'll can use it to generate new text. The idea is that we pass in a character, then the network will predict the next character. We can use the new one, to predict the next one. And we keep doing this to generate all new text. I also included some functionality to prime the network with some text by passing in a string and building up a state from that.\n", "\n", "The network gives us predictions for each character. To reduce noise and make things a little less random, I'm going to only choose a new character from the top N most likely characters.\n", "\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def pick_top_n(preds, vocab_size, top_n=5):\n", " p = np.squeeze(preds)\n", " p[np.argsort(p)[:-top_n]] = 0\n", " p = p / np.sum(p)\n", " c = np.random.choice(vocab_size, 1, p=p)[0]\n", " return c" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sample(checkpoint, n_samples, lstm_size, vocab_size, prime=\"The \"):\n", " prime = \"Far\"\n", " samples = [c for c in prime]\n", " model = build_rnn(vocab_size, lstm_size=lstm_size, sampling=True)\n", " saver = tf.train.Saver()\n", " with tf.Session() as sess:\n", " saver.restore(sess, checkpoint)\n", " new_state = sess.run(model.initial_state)\n", " for c in prime:\n", " x = np.zeros((1, 1))\n", " x[0,0] = vocab_to_int[c]\n", " feed = {model.inputs: x,\n", " model.keep_prob: 1.,\n", " model.initial_state: new_state}\n", " preds, new_state = sess.run([model.preds, model.final_state], \n", " feed_dict=feed)\n", "\n", " c = pick_top_n(preds, len(vocab))\n", " samples.append(int_to_vocab[c])\n", "\n", " for i in range(n_samples):\n", " x[0,0] = c\n", " feed = {model.inputs: x,\n", " model.keep_prob: 1.,\n", " model.initial_state: new_state}\n", " preds, new_state = sess.run([model.preds, model.final_state], \n", " feed_dict=feed)\n", "\n", " c = pick_top_n(preds, len(vocab))\n", " samples.append(int_to_vocab[c])\n", " \n", " return ''.join(samples)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Farlathit that if had so\n", "like it that it were. He could not trouble to his wife, and there was\n", "anything in them of the side of his weaky in the creature at his forteren\n", "to him.\n", "\n", "\"What is it? I can't bread to those,\" said Stepan Arkadyevitch. \"It's not\n", "my children, and there is an almost this arm, true it mays already,\n", "and tell you what I have say to you, and was not looking at the peasant,\n", "why is, I don't know him out, and she doesn't speak to me immediately, as\n", "you would say the countess and the more frest an angelembre, and time and\n", "things's silent, but I was not in my stand that is in my head. But if he\n", "say, and was so feeling with his soul. A child--in his soul of his\n", "soul of his soul. He should not see that any of that sense of. Here he\n", "had not been so composed and to speak for as in a whole picture, but\n", "all the setting and her excellent and society, who had been delighted\n", "and see to anywing had been being troed to thousand words on them,\n", "we liked him.\n", "\n", "That set in her money at the table, he came into the party. The capable\n", "of his she could not be as an old composure.\n", "\n", "\"That's all something there will be down becime by throe is\n", "such a silent, as in a countess, I should state it out and divorct.\n", "The discussion is not for me. I was that something was simply they are\n", "all three manshess of a sensitions of mind it all.\"\n", "\n", "\"No,\" he thought, shouted and lifting his soul. \"While it might see your\n", "honser and she, I could burst. And I had been a midelity. And I had a\n", "marnief are through the countess,\" he said, looking at him, a chosing\n", "which they had been carried out and still solied, and there was a sen that\n", "was to be completely, and that this matter of all the seconds of it, and\n", "a concipation were to her husband, who came up and conscaously, that he\n", "was not the station. All his fourse she was always at the country,,\n", "to speak oft, and though they were to hear the delightful throom and\n", "whether they came towards the morning, and his living and a coller and\n", "hold--the children. \n" ] } ], "source": [ "checkpoint = \"checkpoints/anna/i3560_l512_1.122.ckpt\"\n", "samp = sample(checkpoint, 2000, lstm_size, len(vocab), prime=\"Far\")\n", "print(samp)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Farnt him oste wha sorind thans tout thint asd an sesand an hires on thime sind thit aled, ban thand and out hore as the ter hos ton ho te that, was tis tart al the hand sostint him sore an tit an son thes, win he se ther san ther hher tas tarereng,.\n", "\n", "Anl at an ades in ond hesiln, ad hhe torers teans, wast tar arering tho this sos alten sorer has hhas an siton ther him he had sin he ard ate te anling the sosin her ans and\n", "arins asd and ther ale te tot an tand tanginge wath and ho ald, so sot th asend sat hare sother horesinnd, he hesense wing ante her so tith tir sherinn, anded and to the toul anderin he sorit he torsith she se atere an ting ot hand and thit hhe so the te wile har\n", "ens ont in the sersise, and we he seres tar aterer, to ato tat or has he he wan ton here won and sen heren he sosering, to to theer oo adent har herere the wosh oute, was serild ward tous hed astend..\n", "\n", "I's sint on alt in har tor tit her asd hade shithans ored he talereng an soredendere tim tot hees. Tise sor and \n" ] } ], "source": [ "checkpoint = \"checkpoints/anna/i200_l512_2.432.ckpt\"\n", "samp = sample(checkpoint, 1000, lstm_size, len(vocab), prime=\"Far\")\n", "print(samp)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fard as astice her said he celatice of to seress in the raice, and to be the some and sere allats to that said to that the sark and a cast a the wither ald the pacinesse of her had astition, he said to the sount as she west at hissele. Af the cond it he was a fact onthis astisarianing.\n", "\n", "\n", "\"Or a ton to to be that's a more at aspestale as the sont of anstiring as\n", "thours and trey.\n", "\n", "The same wo dangring the\n", "raterst, who sore and somethy had ast out an of his book. \"We had's beane were that, and a morted a thay he had to tere. Then to\n", "her homent andertersed his his ancouted to the pirsted, the soution for of the pirsice inthirgest and stenciol, with the hard and and\n", "a colrice of to be oneres,\n", "the song to this anderssad.\n", "The could ounterss the said to serom of\n", "soment a carsed of sheres of she\n", "torded\n", "har and want in their of hould, but\n", "her told in that in he tad a the same to her. Serghing an her has and with the seed, and the camt ont his about of the\n", "sail, the her then all houg ant or to hus to \n" ] } ], "source": [ "checkpoint = \"checkpoints/anna/i600_l512_1.750.ckpt\"\n", "samp = sample(checkpoint, 1000, lstm_size, len(vocab), prime=\"Far\")\n", "print(samp)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Farrat, his felt has at it.\n", "\n", "\"When the pose ther hor exceed\n", "to his sheant was,\" weat a sime of his sounsed. The coment and the facily that which had began terede a marilicaly whice whether the pose of his hand, at she was alligated herself the same on she had to\n", "taiking to his forthing and streath how to hand\n", "began in a lang at some at it, this he cholded not set all her. \"Wo love that is setthing. Him anstering as seen that.\"\n", "\n", "\"Yes in the man that say the mare a crances is it?\" said Sergazy Ivancatching. \"You doon think were somether is ifficult of a mone of\n", "though the most at the countes that the\n", "mean on the come to say the most, to\n", "his feesing of\n", "a man she, whilo he\n", "sained and well, that he would still at to said. He wind at his for the sore in the most\n", "of hoss and almoved to see him. They have betine the sumper into at he his stire, and what he was that at the so steate of the\n", "sound, and shin should have a geest of shall feet on the conderation to she had been at that imporsing the dre\n" ] } ], "source": [ "checkpoint = \"checkpoints/anna/i1000_l512_1.484.ckpt\"\n", "samp = sample(checkpoint, 1000, lstm_size, len(vocab), prime=\"Far\")\n", "print(samp)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
google-aai/tf-serving-k8s-tutorial
jupyter/resnet_model_understanding.ipynb
1
23588
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Understanding Resnet Model Features\n", "\n", "We know that the Resnet model works well, but why does it work? How can we have confidence that it is searching out the correct features? A recent paper, [Axiomatic Attribution for Deep Networks](https://arxiv.org/pdf/1703.01365.pdf), shows that averaging gradients taken along a path of images from a blank image (e.g. pure black or grey) to the actual image, can robustly predict sets of pixels that have a strong impact on the overall classification of the image. The below code shows how to modify the TF estimator code to analyze model behavior of different images." ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "import csv\n", "import io\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os\n", "import pickle\n", "import requests\n", "import tensorflow as tf\n", "\n", "from io import BytesIO\n", "from PIL import Image\n", "from subprocess import call" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Constants" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "_DEFAULT_IMAGE_SIZE = 224\n", "_NUM_CHANNELS = 3\n", "_LABEL_CLASSES = 1001\n", "\n", "RESNET_SIZE = 50 # We're loading a resnet-50 saved model.\n", "\n", "# Model directory\n", "MODEL_DIR='resnet_model_checkpoints'\n", "VIS_DIR='visualization'\n", "\n", "# RIEMANN STEPS is the number of steps in a Riemann Sum.\n", "# This is used to compute an approximate the integral of gradients by supplying\n", "# images on the path from a blank image to the original image.\n", "RIEMANN_STEPS = 30\n", "\n", "# Return the top k classes and probabilities, so we can also visualize model inference\n", "# against other contending classes besides the most likely class.\n", "TOP_K = 5\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Download model checkpoint\n", "\n", "The next step is to load the researcher's saved checkpoint into our estimator. We will download it from\n", "http://download.tensorflow.org/models/official/resnet50_2017_11_30.tar.gz using the following commands." ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "import urllib.request\n", "\n", "urllib.request.urlretrieve(\"http://download.tensorflow.org/models/official/resnet50_2017_11_30.tar.gz \", \"resnet.tar.gz\")" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "#unzip the file into a directory called resnet\n", "call([\"mkdir\", MODEL_DIR])\n", "call([\"tar\", \"-zxvf\", \"resnet.tar.gz\", \"-C\", MODEL_DIR])" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "# Make sure you see model checkpoint files in this directory\n", "os.listdir(MODEL_DIR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import the Model Architecture\n", " \n", "In order to reconstruct the Resnet neural network used to train the Imagenet model, we need to load the architecture pieces. During the setup step, we checked out https://github.com/tensorflow/models/tree/v1.4.0/official/resnet. We can now load functions and constants from resnet_model.py into the notebook." ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "%run ../models/official/resnet/resnet_model.py #TODO: modify directory based on where you git cloned the TF models." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Image preprocessing functions\n", "\n", "Note that preprocessing functions are called during training as well (see https://github.com/tensorflow/models/blob/master/official/resnet/imagenet_main.py and https://github.com/tensorflow/models/blob/master/official/resnet/vgg_preprocessing.py), so we will need to extract relevant logic from these functions. Below is a simplified preprocessing code that normalizes the image's pixel values.\n", "\n", "For simplicity, we assume the client provides properly-sized images 224 x 224 x 3 in batches. It will become clear later that sending images over ip in protobuf format can be more easily handled by storing a 4d tensor. The only preprocessing required here is to subtract the mean." ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "def preprocess_images(images):\n", " \"\"\"Preprocesses the image by subtracting out the mean from all channels.\n", " Args:\n", " image: A 4D `Tensor` representing a batch of images.\n", " Returns:\n", " image pixels normalized to be between -0.5 and 0.5\n", " \"\"\"\n", " return tf.to_float(images) / 255 - 0.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Resnet Model Functions\n", "\n", "We are going to create two estimators here since we need to run two model predictions. \n", "\n", "* The first prediction computes the top labels for the image by returning the argmax_k top logits. \n", "\n", "* The second prediction returns a sequence of gradients along the straightline path from a purely grey image (127.5, 127.5, 127.5) to the final image. We use grey here because the resnet model transforms this pixel value to all 0s.\n", "\n", "Below is the resnet model function." ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "def resnet_model_fn(features, labels, mode):\n", " \"\"\"Our model_fn for ResNet to be used with our Estimator.\"\"\"\n", "\n", " # Preprocess images as necessary for resnet\n", " features = preprocess_images(features['images'])\n", "\n", " # This network must be IDENTICAL to that used to train.\n", " network = imagenet_resnet_v2(RESNET_SIZE, _LABEL_CLASSES)\n", "\n", " # tf.estimator.ModeKeys.TRAIN will be false since we are predicting.\n", " logits = network(\n", " inputs=features, is_training=(mode == tf.estimator.ModeKeys.TRAIN))\n", "\n", " # Instead of the top 1 result, we can now return top k!\n", " top_k_logits, top_k_classes = tf.nn.top_k(logits, k=TOP_K)\n", " top_k_probs = tf.nn.softmax(top_k_logits)\n", " predictions = {\n", " 'classes': top_k_classes,\n", " 'probabilities': top_k_probs\n", " }\n", "\n", "\n", " return tf.estimator.EstimatorSpec(\n", " mode=mode,\n", " predictions=predictions, \n", " )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradients Model Function\n", "\n", "The Gradients model function takes as input a single image (a 4d tensor of dimension [1, 244, 244, 3]) and expands it to a series of images (tensor dimension [RIEMANN_STEPS + 1, 244, 244, 3]), where each image is simply a \"fractional\" image, with image 0 being pure gray to image RIEMANN_STEPS being the original image. The gradients are then computed for each of these images, and various outputs are returned.\n", "\n", "**Note:** Each step is a single inference that returns an entire gradient pixel map.\n", "The total gradient map evaluation can take a couple minutes!\n" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "def gradients_model_fn(features, labels, mode):\n", " \"\"\"Our model_fn for ResNet to be used with our Estimator.\"\"\"\n", " \n", " # Supply the most likely class from features dict to determine which logit function\n", " # to use gradients along the\n", " most_likely_class = features['most_likely_class']\n", " \n", " # Features here is a 4d tensor of ONE image. Normalize it as in training and serving.\n", " features = preprocess_images(features['images'])\n", "\n", " # This network must be IDENTICAL to that used to train.\n", " network = imagenet_resnet_v2(RESNET_SIZE, _LABEL_CLASSES)\n", "\n", " # path_features should have dim [RIEMANN_STEPS + 1, 224, 224, 3]\n", " path_features = tf.zeros([1, 224, 224, 3])\n", " for i in range(1, RIEMANN_STEPS + 1):\n", " path_features = tf.concat([path_features, features * i / RIEMANN_STEPS], axis=0)\n", " \n", " # Path logits should evaluate logits for each path feature and return a 2d array for all path images and classes\n", " path_logits = network(inputs=path_features, is_training=(mode == tf.estimator.ModeKeys.TRAIN))\n", "\n", " # The logit we care about is only that pertaining to the most likely class\n", " # The most likely class contains only a single integer, so retrieve it.\n", " target_logits = path_logits[:, most_likely_class[0]]\n", " \n", " # Compute gradients for each image with respect to each logit\n", " gradients = tf.gradients(target_logits, path_features)\n", " \n", " # Multiply elementwise to the original image to get weighted gradients for each pixel.\n", " gradients = tf.squeeze(tf.multiply(gradients, features))\n", " \n", " predictions = {\n", " 'path_features': path_features, # for debugging\n", " 'path_logits': path_logits, # for debugging\n", " 'target_logits': target_logits, # use this to verify that the riemann integral works out\n", " 'path_features': path_features, # for displaying path images\n", " 'gradients': gradients # for displaying gradient images and computing integrated gradient\n", " }\n", "\n", "\n", " return tf.estimator.EstimatorSpec(\n", " mode=mode,\n", " predictions=predictions, # This is the returned value\n", " )\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Estimators\n", "\n", "Load in the model_fn using the checkpoints from MODEL_DIR. This will initialize our weights which we will then use to run backpropagation to find integrated gradients." ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "# Load this model into our estimator\n", "resnet_estimator = tf.estimator.Estimator(\n", " model_fn=resnet_model_fn, # Call our generate_model_fn to create model function\n", " model_dir=MODEL_DIR, # Where to look for model checkpoints\n", " #config not needed\n", ")\n", "\n", "gradients_estimator = tf.estimator.Estimator(\n", " model_fn=gradients_model_fn, # Call our generate_model_fn to create model function\n", " model_dir=MODEL_DIR, # Where to look for model checkpoints\n", " #config not needed\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create properly sized image in numpy\n", "\n", "Load whatever image you would like (local or url), and resize to 224 x 224 x 3 using opencv2." ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "def resize_and_pad_image(img, output_image_dim):\n", " \"\"\"Resize the image to make it IMAGE_DIM x IMAGE_DIM pixels in size.\n", "\n", " If an image is not square, it will pad the top/bottom or left/right\n", " with black pixels to ensure the image is square.\n", "\n", " Args:\n", " img: the input 3-color image\n", " output_image_dim: resized and padded output length (and width)\n", "\n", " Returns:\n", " resized and padded image\n", " \"\"\"\n", "\n", " old_size = img.size # old_size[0] is in (width, height) format\n", "\n", " ratio = float(output_image_dim) / max(old_size)\n", " new_size = tuple([int(x * ratio) for x in old_size])\n", " # use thumbnail() or resize() method to resize the input image\n", "\n", " # thumbnail is a in-place operation\n", "\n", " # im.thumbnail(new_size, Image.ANTIALIAS)\n", "\n", " scaled_img = img.resize(new_size, Image.ANTIALIAS)\n", " # create a new image and paste the resized on it\n", "\n", " padded_img = Image.new(\"RGB\", (output_image_dim, output_image_dim))\n", " padded_img.paste(scaled_img, ((output_image_dim - new_size[0]) // 2,\n", " (output_image_dim - new_size[1]) // 2))\n", "\n", " return padded_img" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "IMAGE_PATH = 'https://www.popsci.com/sites/popsci.com/files/styles/1000_1x_/public/images/2017/09/depositphotos_33210141_original.jpg?itok=MLFznqbL&fc=50,50'\n", "IMAGE_NAME = os.path.splitext(os.path.basename(IMAGE_PATH))[0]\n", "print(IMAGE_NAME)" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "image = None\n", "if 'http' in IMAGE_PATH:\n", " resp = requests.get(IMAGE_PATH)\n", " image = Image.open(BytesIO(resp.content))\n", "else:\n", " image = Image.open(IMAGE_PATH) # Parse the image from your local disk.\n", "# Resize and pad the image\n", "image = resize_and_pad_image(image, _DEFAULT_IMAGE_SIZE)\n", "feature = np.asarray(image)\n", "feature = np.array([feature])" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "# Display the image to validate\n", "imgplot = plt.imshow(feature[0])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prediction Input Function\n", "\n", "Since we are analyzing the model using the estimator api, we need to provide an input function for prediction. Fortunately, there are built-in input functions that can read from numpy arrays, e.g. tf.estimator.inputs.numpy_input_fn." ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "label_predictions = resnet_estimator.predict(\n", " tf.estimator.inputs.numpy_input_fn(\n", " x={'images': feature},\n", " shuffle=False\n", " )\n", ")\n", "\n", "label_dict = next(label_predictions)\n" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "# Print out probabilities and class names\n", "classval = label_dict['classes']\n", "probsval = label_dict['probabilities']\n", "labels = []\n", "with open('client/imagenet1000_clsid_to_human.txt', 'r') as f:\n", " label_reader = csv.reader(f, delimiter=':', quotechar='\\'')\n", " for row in label_reader:\n", " labels.append(row[1][:-1])\n", "# The served model uses 0 as the miscellaneous class, and so starts indexing\n", "# the imagenet images from 1. Subtract 1 to reference the text correctly.\n", "classval = [labels[x - 1] for x in classval]\n", "class_and_probs = [str(p) + ' : ' + c for c, p in zip(classval, probsval)]\n", "for j in range(0, 5):\n", " print(class_and_probs[j])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Computing Gradients\n", "\n", "Run the gradients estimator to retrieve a generator of metrics and gradient pictures, and pickle the images. " ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "# make the visualization directory\n", "IMAGE_DIR = os.path.join(VIS_DIR, IMAGE_NAME)\n", "call(['mkdir', '-p', IMAGE_DIR])\n" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "# Get one of the top classes. 0 picks out the best, 1 picks out second best, etc...\n", "best_label = label_dict['classes'][0]\n", "\n", "# Compute gradients with respect to this class\n", "gradient_predictions = gradients_estimator.predict(\n", " tf.estimator.inputs.numpy_input_fn(\n", " x={'images': feature, 'most_likely_class': np.array([best_label])},\n", " shuffle=False\n", " )\n", ")\n", "\n", "# Start computing the sum of gradients (to be used for integrated gradients)\n", "int_gradients = np.zeros((224, 224, 3))\n", "gradients_and_logits = []\n", "\n", "# Print gradients along the path, and pickle them\n", "for i in range(0, RIEMANN_STEPS + 1):\n", " gradient_dict = next(gradient_predictions)\n", " gradient_map = gradient_dict['gradients']\n", " print('Path image %d: gradient: %f, logit: %f' % (i, np.sum(gradient_map), gradient_dict['target_logits']))\n", " # Gradient visualization output pickles\n", " pickle.dump(gradient_map, open(os.path.join(IMAGE_DIR, 'path_gradient_' + str(i) + '.pkl'), \"wb\" ))\n", " int_gradients = np.add(int_gradients, gradient_map)\n", " gradients_and_logits.append((np.sum(gradient_map), gradient_dict['target_logits']))\n", " \n", "pickle.dump(int_gradients, open(os.path.join(IMAGE_DIR, 'int_gradients.pkl'), \"wb\" ))\n", "pickle.dump(gradients_and_logits, open(os.path.join(IMAGE_DIR, 'gradients_and_logits.pkl'), \"wb\" ))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualization\n", "\n", "If you simply want to play around with visualization, unpickle the result from above so you do not have to rerun prediction again. The following visualizes the gradients with different amplification of pixels, and prints their derivatives and logits as well to view where the biggest differentiators lie. You can also modify the INTERPOLATION flag to increase the \"fatness\" of pixels.\n", "\n", "Below are two examples of visualization methods: one computing the gradient value normalized to between 0 and 1, and another visualizing absolute deviation from the median.\n", "\n", "## Plotting individual image gradients along path\n", "\n", "First, let us plot the individual gradient value for all gradient path images. Pay special attention to the images with a large positive gradient (i.e. in the direction of increasing logit for the most likely class). Do the pixel gradients resemble the image class you are trying to detect?" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "AMPLIFICATION = 2.0\n", "INTERPOLATION = 'none'\n", "\n", "gradients_and_logits = pickle.load(open(os.path.join(IMAGE_DIR, 'gradients_and_logits.pkl'), \"rb\" ))\n", "for i in range(0, RIEMANN_STEPS + 1):\n", " gradient_map = pickle.load(open(os.path.join(IMAGE_DIR, 'path_gradient_' + str(i) + '.pkl'), \"rb\" ))\n", " min_grad = np.ndarray.min(gradient_map)\n", " max_grad = np.ndarray.max(gradient_map)\n", " median_grad = np.median(gradient_map)\n", " gradient_and_logit = gradients_and_logits[i]\n", "\n", " plt.figure(figsize=(10,10))\n", " plt.subplot(121)\n", " plt.title('Image %d: grad: %.2f, logit: %.2f' % (i, gradient_and_logit[0], gradient_and_logit[1]))\n", " imgplot = plt.imshow((gradient_map - min_grad) / (max_grad - min_grad),\n", " interpolation=INTERPOLATION)\n", " plt.subplot(122)\n", " plt.title('Image %d: grad: %.2f, logit: %.2f' % (i, gradient_and_logit[0], gradient_and_logit[1]))\n", " imgplot = plt.imshow(np.abs(gradient_map - median_grad) * AMPLIFICATION / max(max_grad - median_grad, median_grad - min_grad),\n", " interpolation=INTERPOLATION)\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the Integrated Gradient\n", "\n", "When integrating over all gradients along the path, the result is an image that captures larger signals from pixels with the large gradients. Is the integrated gradient a clear representation of what it is trying to detect?" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "AMPLIFICATION = 2.0\n", "INTERPOLATION = 'none'\n", "\n", "# Plot the integrated gradients\n", "int_gradients = pickle.load(open(os.path.join(IMAGE_DIR, 'int_gradients.pkl'), \"rb\" ))\n", "min_grad = np.ndarray.min(int_gradients)\n", "max_grad = np.ndarray.max(int_gradients)\n", "median_grad = np.median(int_gradients)\n", "plt.figure(figsize=(15,15))\n", "plt.subplot(131)\n", "imgplot = plt.imshow((int_gradients - min_grad) / (max_grad - min_grad),\n", " interpolation=INTERPOLATION)\n", "plt.subplot(132)\n", "imgplot = plt.imshow(np.abs(int_gradients - median_grad) * AMPLIFICATION / max(max_grad - median_grad, median_grad - min_grad),\n", " interpolation=INTERPOLATION)\n", "plt.subplot(133)\n", "imgplot = plt.imshow(feature[0])\n", "plt.show()\n", "\n", "# Verify that the average of gradients is equal to the difference in logits\n", "print('total logit diff: %f' % (gradients_and_logits[RIEMANN_STEPS][1] - gradients_and_logits[0][1]))\n", "print('sum of integrated gradients: %f' % (np.sum(int_gradients) / RIEMANN_STEPS + 1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot the integrated gradients for each channel\n", "\n", "We can also visualize individual pixel contributions from different RGB channels.\n", "\n", "Can you think of any other visualization ideas to try out?" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [ "AMPLIFICATION = 2.0\n", "INTERPOLATION = 'none'\n", "\n", "# Show red-green-blue channels for integrated gradients\n", "for channel in range(0, 3):\n", " gradient_channel = int_gradients[:,:,channel]\n", " min_grad = np.ndarray.min(gradient_channel)\n", " max_grad = np.ndarray.max(gradient_channel)\n", " median_grad = np.median(gradient_channel)\n", " plt.figure(figsize=(10,10))\n", " plt.subplot(121)\n", " imgplot = plt.imshow((gradient_channel - min_grad) / (max_grad - min_grad),\n", " interpolation=INTERPOLATION,\n", " cmap='gray')\n", " plt.subplot(122)\n", " imgplot = plt.imshow(np.abs(gradient_channel - median_grad) * AMPLIFICATION / max(max_grad - median_grad, median_grad - min_grad),\n", " interpolation=INTERPOLATION,\n", " cmap='gray')\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
hardmaru/pytorch_notebooks
mnist_es/pytorch_mnist_mini_es_rank_pepg.ipynb
1
201831
{ "cells": [ { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import math\n", "import multiprocessing as mp\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.optim as optim\n", "from torchvision import datasets, transforms\n", "from torch.autograd import Variable\n", "from collections import namedtuple\n", "\n", "from PIL import Image\n", "import os\n", "import os.path\n", "import errno\n", "import codecs\n", "import copy\n", "\n", "import time" ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "starts\n" ] } ], "source": [ "print('starts')" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.cuda.device_count() 4\n", "torch.cuda.current_device() 2\n", "torch.cuda.current_device() 2\n" ] } ], "source": [ "torch.manual_seed(0)\n", "np.random.seed(0)\n", "print(\"torch.cuda.device_count()\", torch.cuda.device_count())\n", "print(\"torch.cuda.current_device()\", torch.cuda.current_device())\n", "torch.cuda.set_device(2)\n", "print(\"torch.cuda.current_device()\", torch.cuda.current_device())" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def compute_ranks(x):\n", " \"\"\"\n", " Returns ranks in [0, len(x))\n", " Note: This is different from scipy.stats.rankdata, which returns ranks in [1, len(x)].\n", " (https://github.com/openai/evolution-strategies-starter/blob/master/es_distributed/es.py)\n", " \"\"\"\n", " assert x.ndim == 1\n", " ranks = np.empty(len(x), dtype=int)\n", " ranks[x.argsort()] = np.arange(len(x))\n", " return ranks\n", "\n", "def compute_centered_ranks(x):\n", " \"\"\"\n", " https://github.com/openai/evolution-strategies-starter/blob/master/es_distributed/es.py\n", " \"\"\"\n", " y = compute_ranks(x.ravel()).reshape(x.shape).astype(np.float32)\n", " y /= (x.size - 1)\n", " y -= .5\n", " return y\n", "\n", "def compute_weight_decay(weight_decay, model_param_list):\n", " model_param_grid = np.array(model_param_list)\n", " return - weight_decay * np.mean(model_param_grid * model_param_grid, axis=1)\n", "\n", "class CMAES:\n", " '''CMA-ES wrapper.'''\n", " def __init__(self, num_params, # number of model parameters\n", " sigma_init=0.10, # initial standard deviation\n", " popsize=255, # population size\n", " done_threshold=1e-6, # threshold when we say we are done\n", " weight_decay=0.01): # weight decay coefficient\n", "\n", " self.num_params = num_params\n", " self.sigma_init = sigma_init\n", " self.done_threshold = done_threshold\n", " self.popsize = popsize\n", " self.weight_decay = weight_decay\n", " self.solutions = None\n", "\n", " import cma\n", " self.es = cma.CMAEvolutionStrategy( self.num_params * [0],\n", " self.sigma_init,\n", " {'popsize': self.popsize,\n", " 'ftarget': self.done_threshold,\n", " })\n", "\n", " def rms_stdev(self):\n", " sigma = self.es.result[6]\n", " return np.mean(np.sqrt(sigma*sigma))\n", "\n", " def ask(self):\n", " '''returns a list of parameters'''\n", " self.solutions = np.array(self.es.ask())\n", " return self.solutions\n", "\n", " def tell(self, reward_table_result):\n", " reward_table = -np.array(reward_table_result)\n", " if self.weight_decay > 0:\n", " l2_decay = compute_weight_decay(self.weight_decay, self.solutions)\n", " reward_table += l2_decay\n", " self.es.tell(self.solutions, (reward_table).tolist()) # convert minimizer to maximizer.\n", "\n", " def done(self):\n", " return self.es.stop()\n", "\n", " def current_param(self):\n", " return self.es.result[5] # mean solution, presumably better with noise\n", " \n", " def best_param(self):\n", " return self.es.result[0] # best evaluated solution\n", "\n", " def result(self): # return best params so far, along with historically best reward, curr reward, sigma\n", " r = self.es.result\n", " return (r[0], -r[1], -r[1], r[6])\n", "\n", "class SimpleES:\n", " '''Simple Evolution Strategies.'''\n", " def __init__(self, num_params, # number of model parameters\n", " sigma_init=0.10, # initial standard deviation\n", " sigma_alpha=0.20, # learning rate for standard deviation\n", " sigma_decay=0.999, # anneal standard deviation\n", " sigma_limit=0.01, # stop annealing if less than this\n", " popsize=255, # population size\n", " elite_ratio=0.1, # percentage of the elites\n", " done_threshold=1e-6, # threshold when we say we are done\n", " average_baseline=True, # set baseline to average of batch\n", " weight_decay=0.01, # weight decay coefficient\n", " rank_fitness=True, # use rank rather than fitness numbers\n", " forget_best=True): # don't keep the historical best solution\n", "\n", " self.num_params = num_params\n", " self.sigma_init = sigma_init\n", " self.sigma_alpha = sigma_alpha\n", " self.sigma_decay = sigma_decay\n", " self.sigma_limit = sigma_limit\n", " self.popsize = popsize\n", " self.average_baseline = average_baseline\n", " if self.average_baseline:\n", " assert (self.popsize % 2 == 0), \"Population size must be even\"\n", " self.batch_size = int(self.popsize / 2)\n", " else:\n", " assert (self.popsize & 1), \"Population size must be odd\"\n", " self.batch_size = int((self.popsize - 1) / 2)\n", " self.elite_ratio = elite_ratio\n", " self.elite_popsize = int(self.popsize * self.elite_ratio)\n", " self.forget_best = forget_best\n", " self.batch_reward = np.zeros(self.batch_size * 2)\n", " self.mu = np.zeros(self.num_params)\n", " self.sigma = np.ones(self.num_params) * self.sigma_init\n", " self.curr_best_mu = np.zeros(self.num_params)\n", " self.best_mu = np.zeros(self.num_params)\n", " self.best_reward = 0\n", " self.first_interation = True\n", " self.weight_decay = weight_decay\n", " self.rank_fitness = rank_fitness\n", " if self.rank_fitness:\n", " self.forget_best = True # always forget the best one if we rank\n", " self.done_threshold = done_threshold\n", "\n", " def rms_stdev(self):\n", " sigma = self.sigma\n", " return np.mean(np.sqrt(sigma*sigma))\n", "\n", " def ask(self):\n", " '''returns a list of parameters'''\n", " # antithetic sampling\n", " self.epsilon = np.random.randn(self.batch_size, self.num_params) * self.sigma.reshape(1, self.num_params)\n", " self.epsilon_full = np.concatenate([self.epsilon, - self.epsilon])\n", " if self.average_baseline:\n", " epsilon = self.epsilon_full\n", " else:\n", " # first population is mu, then positive epsilon, then negative epsilon\n", " epsilon = np.concatenate([np.zeros((1, self.num_params)), self.epsilon_full])\n", " solutions = self.mu.reshape(1, self.num_params) + epsilon\n", " self.solutions = solutions\n", " return solutions\n", "\n", " def tell(self, reward_table_result):\n", " # input must be a numpy float array\n", " assert(len(reward_table_result) == self.popsize), \"Inconsistent reward_table size reported.\"\n", "\n", " reward_table = np.array(reward_table_result)\n", " \n", " if self.rank_fitness:\n", " reward_table = compute_centered_ranks(reward_table)\n", " \n", " if self.weight_decay > 0:\n", " l2_decay = compute_weight_decay(self.weight_decay, self.solutions)\n", " reward_table += l2_decay\n", "\n", " reward_offset = 1\n", " if self.average_baseline:\n", " b = np.mean(reward_table)\n", " reward_offset = 0\n", " else:\n", " b = reward_table[0] # baseline\n", " \n", " reward = reward_table[reward_offset:]\n", " idx = np.argsort(reward)[::-1][0:self.elite_popsize]\n", "\n", " best_reward = reward[idx[0]]\n", " if (best_reward > b or self.average_baseline):\n", " best_mu = self.mu + self.epsilon_full[idx[0]]\n", " best_reward = reward[idx[0]]\n", " else:\n", " best_mu = self.mu\n", " best_reward = b\n", "\n", " self.curr_best_reward = best_reward\n", " self.curr_best_mu = best_mu\n", "\n", " if self.first_interation:\n", " self.first_interation = False\n", " self.best_reward = self.curr_best_reward\n", " self.best_mu = best_mu\n", " else:\n", " if self.forget_best or (self.curr_best_reward > self.best_reward):\n", " self.best_mu = best_mu\n", " self.best_reward = self.curr_best_reward\n", "\n", " # adaptive sigma\n", " # normalization\n", " stdev_reward = reward.std()\n", " epsilon = self.epsilon\n", " sigma = self.sigma\n", " S = ((epsilon * epsilon - (sigma * sigma).reshape(1, self.num_params)) / sigma.reshape(1, self.num_params))\n", " reward_avg = (reward[:self.batch_size] + reward[self.batch_size:]) / 2.0\n", " rS = reward_avg - b\n", " delta_sigma = (np.dot(rS, S)) / (2 * self.batch_size * stdev_reward)\n", "\n", " # move mean to the average of the best idx means\n", " self.mu += self.epsilon_full[idx].mean(axis=0)\n", "\n", " # adjust sigma according to the adaptive sigma calculation\n", " change_sigma = self.sigma_alpha * delta_sigma\n", " change_sigma = np.minimum(change_sigma, self.sigma)\n", " change_sigma = np.maximum(change_sigma, - 0.5 * self.sigma)\n", " self.sigma += change_sigma\n", " self.sigma[self.sigma > self.sigma_limit] *= self.sigma_decay\n", "\n", " def done(self):\n", " return (self.rms_stdev() < self.done_threshold)\n", "\n", " def current_param(self):\n", " return self.curr_best_mu\n", " \n", " def best_param(self):\n", " return self.best_mu\n", "\n", " def result(self): # return best params so far, along with historically best reward, curr reward, sigma\n", " return (self.best_mu, self.best_reward, self.curr_best_reward, self.sigma)\n", "\n", "class SimpleGA:\n", " '''Simple Genetic Algorithm.'''\n", " def __init__(self, num_params, # number of model parameters\n", " sigma_init=0.1, # initial standard deviation\n", " sigma_decay=0.999, # anneal standard deviation\n", " sigma_limit=0.01, # stop annealing if less than this\n", " popsize=255, # population size\n", " elite_ratio=0.1, # percentage of the elites\n", " forget_best=False, # forget the historical best elites\n", " weight_decay=0.01, # weight decay coefficient\n", " ):\n", "\n", " self.num_params = num_params\n", " self.sigma_init = sigma_init\n", " self.sigma_decay = sigma_decay\n", " self.sigma_limit = sigma_limit\n", " self.popsize = popsize\n", "\n", " self.elite_ratio = elite_ratio\n", " self.elite_popsize = int(self.popsize * self.elite_ratio)\n", "\n", " self.sigma = self.sigma_init\n", " self.elite_params = np.zeros((self.elite_popsize, self.num_params))\n", " self.elite_rewards = np.zeros(self.elite_popsize)\n", " self.best_param = np.zeros(self.num_params)\n", " self.best_reward = 0\n", " self.first_iteration = True\n", " self.forget_best = forget_best\n", " self.weight_decay = weight_decay\n", "\n", " def rms_stdev(self):\n", " return self.sigma # same sigma for all parameters.\n", "\n", " def ask(self):\n", " '''returns a list of parameters'''\n", " self.epsilon = np.random.randn(self.popsize, self.num_params) * self.sigma\n", " solutions = []\n", " \n", " def mate(a, b):\n", " c = np.copy(a)\n", " idx = np.where(np.random.rand((c.size)) > 0.5)\n", " c[idx] = b[idx]\n", " return c\n", " \n", " elite_range = range(self.elite_popsize)\n", " for i in range(self.popsize):\n", " idx_a = np.random.choice(elite_range)\n", " idx_b = np.random.choice(elite_range)\n", " child_params = mate(self.elite_params[idx_a], self.elite_params[idx_b])\n", " solutions.append(child_params + self.epsilon[i])\n", "\n", " solutions = np.array(solutions)\n", " self.solutions = solutions\n", "\n", " return solutions\n", "\n", " def tell(self, reward_table_result):\n", " # input must be a numpy float array\n", " assert(len(reward_table_result) == self.popsize), \"Inconsistent reward_table size reported.\"\n", "\n", " reward_table = np.array(reward_table_result)\n", " \n", " if self.weight_decay > 0:\n", " l2_decay = compute_weight_decay(self.weight_decay, self.solutions)\n", " reward_table += l2_decay\n", "\n", " if (not self.forget_best or self.first_iteration):\n", " reward = reward_table\n", " solution = self.solutions\n", " else:\n", " reward = np.concatenate([reward_table, self.elite_rewards])\n", " solution = np.concatenate([self.solutions, self.elite_params])\n", "\n", " idx = np.argsort(reward)[::-1][0:self.elite_popsize]\n", "\n", " self.elite_rewards = reward[idx]\n", " self.elite_params = solution[idx]\n", "\n", " self.curr_best_reward = self.elite_rewards[0]\n", " \n", " if self.first_iteration or (self.curr_best_reward > self.best_reward):\n", " self.first_iteration = False\n", " self.best_reward = self.elite_rewards[0]\n", " self.best_param = np.copy(self.elite_params[0])\n", "\n", " if (self.sigma > self.sigma_limit):\n", " self.sigma *= self.sigma_decay\n", "\n", " def done(self):\n", " return (self.rms_stdev() < self.done_threshold)\n", "\n", " def current_param(self):\n", " return self.elite_params[0]\n", "\n", " def best_param(self):\n", " return self.best_param\n", "\n", " def result(self): # return best params so far, along with historically best reward, curr reward, sigma\n", " return (self.best_param, self.best_reward, self.curr_best_reward, self.sigma)\n", "\n", "class OpenES:\n", " ''' Basic Version of OpenAI Evolution Strategies.'''\n", " def __init__(self, num_params, # number of model parameters\n", " sigma_init=0.1, # initial standard deviation\n", " sigma_decay=0.999, # anneal standard deviation\n", " sigma_limit=0.01, # stop annealing if less than this\n", " learning_rate=0.01, # learning rate for standard deviation\n", " learning_rate_decay = 0.9999, # annealing the learning rate\n", " learning_rate_limit = 0.001, # stop annealing learning rate\n", " popsize=255, # population size\n", " antithetic=False, # whether to use antithetic sampling\n", " weight_decay=0.01, # weight decay coefficient\n", " rank_fitness=True, # use rank rather than fitness numbers\n", " forget_best=True): # forget historical best\n", "\n", " self.num_params = num_params\n", " self.sigma_decay = sigma_decay\n", " self.sigma = sigma_init\n", " self.sigma_limit = sigma_limit\n", " self.learning_rate = learning_rate\n", " self.learning_rate_decay = learning_rate_decay\n", " self.learning_rate_limit = learning_rate_limit\n", " self.popsize = popsize\n", " self.antithetic = antithetic\n", " if self.antithetic:\n", " assert (self.popsize % 2 == 0), \"Population size must be even\"\n", " self.half_popsize = int(self.popsize / 2)\n", "\n", " self.reward = np.zeros(self.popsize)\n", " self.mu = np.zeros(self.num_params)\n", " self.best_mu = np.zeros(self.num_params)\n", " self.best_reward = 0\n", " self.first_interation = True\n", " self.forget_best = forget_best\n", " self.weight_decay = weight_decay\n", " self.rank_fitness = rank_fitness\n", " if self.rank_fitness:\n", " self.forget_best = True # always forget the best one if we rank\n", "\n", " def rms_stdev(self):\n", " sigma = self.sigma\n", " return np.mean(np.sqrt(sigma*sigma))\n", "\n", " def ask(self):\n", " '''returns a list of parameters'''\n", " # antithetic sampling\n", " if self.antithetic:\n", " self.epsilon_half = np.random.randn(self.half_popsize, self.num_params)\n", " self.epsilon = np.concatenate([self.epsilon_half, - self.epsilon_half])\n", " else:\n", " self.epsilon = np.random.randn(self.popsize, self.num_params)\n", "\n", " self.solutions = self.mu.reshape(1, self.num_params) + self.epsilon * self.sigma\n", "\n", " return self.solutions\n", "\n", " def tell(self, reward_table_result):\n", " # input must be a numpy float array\n", " assert(len(reward_table_result) == self.popsize), \"Inconsistent reward_table size reported.\"\n", " \n", " reward = np.array(reward_table_result)\n", " \n", " if self.rank_fitness:\n", " reward = compute_centered_ranks(reward)\n", " \n", " if self.weight_decay > 0:\n", " l2_decay = compute_weight_decay(self.weight_decay, self.solutions)\n", " reward += l2_decay\n", "\n", " idx = np.argsort(reward)[::-1]\n", "\n", " best_reward = reward[idx[0]]\n", " best_mu = self.solutions[idx[0]]\n", "\n", " self.curr_best_reward = best_reward\n", " self.curr_best_mu = best_mu\n", "\n", " if self.first_interation:\n", " self.first_interation = False\n", " self.best_reward = self.curr_best_reward\n", " self.best_mu = best_mu\n", " else:\n", " if self.forget_best or (self.curr_best_reward > self.best_reward):\n", " self.best_mu = best_mu\n", " self.best_reward = self.curr_best_reward\n", "\n", " # main bit:\n", " # standardize the rewards to have a gaussian distribution\n", " normalized_reward = (reward - np.mean(reward)) / np.std(reward)\n", " self.mu += self.learning_rate/(self.popsize*self.sigma)*np.dot(self.epsilon.T, normalized_reward)\n", "\n", " # adjust sigma according to the adaptive sigma calculation\n", " if (self.sigma > self.sigma_limit):\n", " self.sigma *= self.sigma_decay\n", "\n", " if (self.learning_rate > self.learning_rate_limit):\n", " self.learning_rate *= self.learning_rate_decay\n", "\n", " def done(self):\n", " return False\n", "\n", " def current_param(self):\n", " return self.curr_best_mu\n", "\n", " def best_param(self):\n", " return self.best_mu\n", "\n", " def result(self): # return best params so far, along with historically best reward, curr reward, sigma\n", " return (self.best_mu, self.best_reward, self.curr_best_reward, self.sigma)\n" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class PEPG:\n", " '''Extension of PEPG with bells and whistles.'''\n", " def __init__(self, num_params, # number of model parameters\n", " sigma_init=0.10, # initial standard deviation\n", " sigma_alpha=0.20, # learning rate for standard deviation\n", " sigma_decay=0.999, # anneal standard deviation\n", " sigma_limit=0.01, # stop annealing if less than this\n", " learning_rate=0.01, # learning rate for standard deviation\n", " learning_rate_decay = 0.9999, # annealing the learning rate\n", " learning_rate_limit = 0.001, # stop annealing learning rate\n", " popsize=255, # population size\n", " done_threshold=1e-6, # threshold when we say we are done\n", " average_baseline=True, # set baseline to average of batch\n", " weight_decay=0.01, # weight decay coefficient\n", " rank_fitness=True, # use rank rather than fitness numbers\n", " forget_best=True): # don't keep the historical best solution\n", "\n", " self.num_params = num_params\n", " self.sigma_init = sigma_init\n", " self.sigma_alpha = sigma_alpha\n", " self.sigma_decay = sigma_decay\n", " self.sigma_limit = sigma_limit\n", " self.learning_rate = learning_rate\n", " self.learning_rate_decay = learning_rate_decay\n", " self.learning_rate_limit = learning_rate_limit\n", " self.popsize = popsize\n", " self.average_baseline = average_baseline\n", " if self.average_baseline:\n", " assert (self.popsize % 2 == 0), \"Population size must be even\"\n", " self.batch_size = int(self.popsize / 2)\n", " else:\n", " assert (self.popsize & 1), \"Population size must be odd\"\n", " self.batch_size = int((self.popsize - 1) / 2)\n", " self.forget_best = forget_best\n", " self.batch_reward = np.zeros(self.batch_size * 2)\n", " self.mu = np.zeros(self.num_params)\n", " self.sigma = np.ones(self.num_params) * self.sigma_init\n", " self.curr_best_mu = np.zeros(self.num_params)\n", " self.best_mu = np.zeros(self.num_params)\n", " self.best_reward = 0\n", " self.first_interation = True\n", " self.weight_decay = weight_decay\n", " self.rank_fitness = rank_fitness\n", " if self.rank_fitness:\n", " self.forget_best = True # always forget the best one if we rank\n", " self.done_threshold = done_threshold\n", "\n", " def rms_stdev(self):\n", " sigma = self.sigma\n", " return np.mean(np.sqrt(sigma*sigma))\n", "\n", " def ask(self):\n", " '''returns a list of parameters'''\n", " # antithetic sampling\n", " self.epsilon = np.random.randn(self.batch_size, self.num_params) * self.sigma.reshape(1, self.num_params)\n", " self.epsilon_full = np.concatenate([self.epsilon, - self.epsilon])\n", " if self.average_baseline:\n", " epsilon = self.epsilon_full\n", " else:\n", " # first population is mu, then positive epsilon, then negative epsilon\n", " epsilon = np.concatenate([np.zeros((1, self.num_params)), self.epsilon_full])\n", " solutions = self.mu.reshape(1, self.num_params) + epsilon\n", " self.solutions = solutions\n", " return solutions\n", "\n", " def tell(self, reward_table_result):\n", " # input must be a numpy float array\n", " assert(len(reward_table_result) == self.popsize), \"Inconsistent reward_table size reported.\"\n", "\n", " reward_table = np.array(reward_table_result)\n", " \n", " if self.rank_fitness:\n", " reward_table = compute_centered_ranks(reward_table)\n", " \n", " if self.weight_decay > 0:\n", " l2_decay = compute_weight_decay(self.weight_decay, self.solutions)\n", " reward_table += l2_decay\n", "\n", " reward_offset = 1\n", " if self.average_baseline:\n", " b = np.mean(reward_table)\n", " reward_offset = 0\n", " else:\n", " b = reward_table[0] # baseline\n", " \n", " reward = reward_table[reward_offset:]\n", " idx = np.argsort(reward)[::-1]\n", "\n", " best_reward = reward[idx[0]]\n", " if (best_reward > b or self.average_baseline):\n", " best_mu = self.mu + self.epsilon_full[idx[0]]\n", " best_reward = reward[idx[0]]\n", " else:\n", " best_mu = self.mu\n", " best_reward = b\n", "\n", " self.curr_best_reward = best_reward\n", " self.curr_best_mu = best_mu\n", "\n", " if self.first_interation:\n", " self.first_interation = False\n", " self.best_reward = self.curr_best_reward\n", " self.best_mu = best_mu\n", " else:\n", " if self.forget_best or (self.curr_best_reward > self.best_reward):\n", " self.best_mu = best_mu\n", " self.best_reward = self.curr_best_reward\n", "\n", " # adaptive sigma\n", " # normalization\n", " stdev_reward = reward.std()\n", " epsilon = self.epsilon\n", " sigma = self.sigma\n", " S = ((epsilon * epsilon - (sigma * sigma).reshape(1, self.num_params)) / sigma.reshape(1, self.num_params))\n", " reward_avg = (reward[:self.batch_size] + reward[self.batch_size:]) / 2.0\n", " rS = reward_avg - b\n", " delta_sigma = (np.dot(rS, S)) / (2 * self.batch_size * stdev_reward)\n", "\n", " # move mean to the average of the best idx means\n", " rT = (reward[:self.batch_size] - reward[self.batch_size:])\n", " change_mu = self.learning_rate * np.dot(rT, epsilon)\n", " self.mu += change_mu\n", "\n", " # adjust sigma according to the adaptive sigma calculation\n", " change_sigma = self.sigma_alpha * delta_sigma\n", " change_sigma = np.minimum(change_sigma, self.sigma)\n", " change_sigma = np.maximum(change_sigma, - 0.5 * self.sigma)\n", " self.sigma += change_sigma\n", " self.sigma[self.sigma > self.sigma_limit] *= self.sigma_decay\n", " \n", " if (self.learning_rate > self.learning_rate_limit):\n", " self.learning_rate *= self.learning_rate_decay\n", "\n", " def done(self):\n", " return (self.rms_stdev() < self.done_threshold)\n", "\n", " def current_param(self):\n", " return self.curr_best_mu\n", " \n", " def best_param(self):\n", " return self.best_mu\n", "\n", " def result(self): # return best params so far, along with historically best reward, curr reward, sigma\n", " return (self.best_mu, self.best_reward, self.curr_best_reward, self.sigma)" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Args = namedtuple('Args', ['batch_size', 'test_batch_size', 'epochs', 'lr', 'cuda', 'seed', 'log_interval'])" ] }, { "cell_type": "code", "execution_count": 159, "metadata": { "collapsed": true }, "outputs": [], "source": [ "args = Args(batch_size=1000, test_batch_size=1000, epochs=30, lr=0.001, cuda=True, seed=0, log_interval=10)" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": true }, "outputs": [], "source": [ "torch.manual_seed(args.seed)\n", "if args.cuda:\n", " torch.cuda.manual_seed(args.seed)" ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "collapsed": true }, "outputs": [], "source": [ "kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}\n", "\n", "train_loader = torch.utils.data.DataLoader(\n", " datasets.MNIST('MNIST_data', train=True, download=True, transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])),\n", " batch_size=args.batch_size, shuffle=True, **kwargs)\n", "\n", "valid_loader = train_loader\n", "\n", "test_loader = torch.utils.data.DataLoader(\n", " datasets.MNIST('MNIST_data', train=False, transform=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,), (0.3081,))])),\n", " batch_size=args.batch_size, shuffle=True, **kwargs)" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Net(nn.Module):\n", " def __init__(self):\n", " super(Net, self).__init__()\n", " self.num_filter1 = 8\n", " self.num_filter2 = 16\n", " self.num_padding = 2\n", " # input is 28x28\n", " # padding=2 for same padding\n", " self.conv1 = nn.Conv2d(1, self.num_filter1, 5, padding=self.num_padding)\n", " # feature map size is 14*14 by pooling\n", " # padding=2 for same padding\n", " self.conv2 = nn.Conv2d(self.num_filter1, self.num_filter2, 5, padding=self.num_padding)\n", " # feature map size is 7*7 by pooling\n", " self.fc = nn.Linear(self.num_filter2*7*7, 10)\n", "\n", " def forward(self, x):\n", " x = F.max_pool2d(F.relu(self.conv1(x)), 2)\n", " x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n", " x = x.view(-1, self.num_filter2*7*7) # reshape Variable\n", " x = self.fc(x)\n", " return F.log_softmax(x)" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": true }, "outputs": [], "source": [ "NPOPULATION = 101\n", "weight_decay_coef = 0.1" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": true }, "outputs": [], "source": [ "'''\n", "models = []\n", "for i in range(NPOPULATION):\n", " model = Net()\n", " if args.cuda:\n", " model.cuda()\n", " model.eval()\n", " models.append(model)\n", "'''\n", "\n", "model = Net()\n", "if args.cuda:\n", " model.cuda()\n", "\n", "orig_model = copy.deepcopy(model)" ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11274\n" ] } ], "source": [ "# get init params\n", "orig_params = []\n", "model_shapes = []\n", "for param in orig_model.parameters():\n", " p = param.data.cpu().numpy()\n", " model_shapes.append(p.shape)\n", " orig_params.append(p.flatten())\n", "orig_params_flat = np.concatenate(orig_params)\n", "NPARAMS = len(orig_params_flat)\n", "print(NPARAMS)" ] }, { "cell_type": "code", "execution_count": 166, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# NPARAMS = 11274" ] }, { "cell_type": "code", "execution_count": 167, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def update_model(flat_param, model, model_shapes):\n", " idx = 0\n", " i = 0\n", " for param in model.parameters():\n", " delta = np.product(model_shapes[i])\n", " block = flat_param[idx:idx+delta]\n", " block = np.reshape(block, model_shapes[i])\n", " i += 1\n", " idx += delta\n", " block_data = torch.from_numpy(block).float()\n", " if args.cuda:\n", " block_data = block_data.cuda()\n", " param.data = block_data" ] }, { "cell_type": "code", "execution_count": 168, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def evaluate(model, test_loader, print_mode=True, return_loss=False):\n", " model.eval()\n", " test_loss = 0\n", " correct = 0\n", " for data, target in test_loader:\n", " if args.cuda:\n", " data, target = data.cuda(), target.cuda()\n", " data, target = Variable(data, volatile=True), Variable(target)\n", " output = model(data)\n", " test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss\n", " pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n", " correct += pred.eq(target.data.view_as(pred)).cpu().sum()\n", "\n", " test_loss /= len(test_loader.dataset)\n", " acc = correct / len(test_loader.dataset)\n", " \n", " if print_mode:\n", " print('\\nAverage loss: {:.4f}, Accuracy: {}/{} ({:.4f}%)\\n'.format(\n", " test_loss, correct, len(test_loader.dataset),\n", " 100. * acc))\n", " \n", " if return_loss:\n", " return test_loss\n", " return acc" ] }, { "cell_type": "code", "execution_count": 169, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "es = SimpleES(NPARAMS,\n", " popsize=NPOPULATION,\n", " sigma_init=0.01,\n", " sigma_decay=0.999,\n", " sigma_alpha=0.2,\n", " sigma_limit=0.001,\n", " elite_ratio=0.1,\n", " average_baseline=False,\n", " forget_best=True\n", " )\n", "es = OpenES( NPARAMS, # number of model parameters\n", " popsize=NPOPULATION,\n", " sigma_init=0.01, # initial standard deviation\n", " sigma_decay=0.999, # anneal standard deviation\n", " sigma_limit=0.01,\n", " antithetic=True,\n", " )\n", "es = SimpleGA(NPARAMS,\n", " popsize=NPOPULATION,\n", " sigma_init=0.01,\n", " sigma_decay=0.999,\n", " sigma_limit=0.001\n", " )\n", "\"\"\"\n", "start_time = time.time()\n", "\n", "es = PEPG( NPARAMS,\n", " popsize=NPOPULATION,\n", " sigma_init=0.01,\n", " sigma_decay=0.999,\n", " sigma_alpha=0.2,\n", " sigma_limit=0.01,\n", " learning_rate=0.1, # learning rate for standard deviation\n", " learning_rate_decay = 0.9999, # annealing the learning rate\n", " learning_rate_limit = 0.01, # stop annealing learning rate\n", " average_baseline=False,\n", " )\n", "\n", "end_time = time.time()" ] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "init time 0.0007948875427246094\n" ] } ], "source": [ "print('init time', end_time-start_time)" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def worker(procnum, model, solution, data, target, send_end):\n", " update_model(solution, model, model_shapes)\n", " output = model(data)\n", " loss = F.nll_loss(output, target)\n", " reward = - loss.data[0]\n", " send_end.send(reward)\n", "\n", "def batch_simulation(model_list, solutions, data, target, process_count):\n", " jobs = []\n", " pipe_list = []\n", "\n", " for i in range(process_count):\n", " recv_end, send_end = mp.Pipe(False)\n", " p = mp.Process(target=worker, args=(i, model_list[i], solutions[i], data, target, send_end))\n", " jobs.append(p)\n", " pipe_list.append(recv_end)\n", "\n", " for p in jobs:\n", " p.start()\n", "\n", " for p in jobs:\n", " p.join()\n", "\n", " result_list = [x.recv() for x in pipe_list]\n", " return np.array(result_list)\n", "\n", "\n", "def batch_simulation_sequential(model_list, solutions, data, target, process_count):\n", " result_list = []\n", " for i in range(process_count):\n", " update_model(solutions[i], model_list[i], model_shapes)\n", " output = model_list[i](data)\n", " loss = F.nll_loss(output, target)\n", " reward = - loss.data[0]\n", " result_list.append(reward)\n", " return np.array(result_list)\n" ] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 0 -2.3015370369 -1.3625762887e-05 0.00999493357973\n", "1 10 -2.29273867607 6.49126070597e-05 0.00994640534138\n", "1 20 -2.20771574974 0.000668892864525 0.00990713349438\n", "1 30 -1.93725275993 0.00101594478195 0.00986611657684\n", "1 40 -1.55533969402 0.00124605839688 0.00983399961559\n", "1 50 -1.20898079872 0.00166836133973 0.00979191068696\n", "valid_acc 74.89166666666667\n", "best valid_acc 74.89166666666667\n", "2 0 -1.02994275093 0.00186914703878 0.00975176003435\n", "2 10 -0.945468187332 0.00202775603505 0.00969804607107\n", "2 20 -0.752176880836 0.00210628802819 0.00964647891847\n", "2 30 -0.725091934204 0.00219084125642 0.00960169752304\n", "2 40 -0.700149953365 0.00213861995048 0.0095581890539\n", "2 50 -0.579843878746 0.00228639487486 0.00951103984905\n", "valid_acc 83.84166666666667\n", "best valid_acc 83.84166666666667\n", "3 0 -0.534947991371 0.00239417188268 0.00948159932136\n", "3 10 -0.588443875313 0.00249070259302 0.00941749535365\n", "3 20 -0.556837201118 0.00261357393045 0.00936498248429\n", "3 30 -0.452707111835 0.00258899662774 0.00932860551505\n", "3 40 -0.48762050271 0.00259752851086 0.00927083251153\n", "3 50 -0.466498196125 0.00259642977417 0.00920004533465\n", "valid_acc 86.265\n", "best valid_acc 86.265\n", "4 0 -0.477015793324 0.00256985328985 0.00915108006735\n", "4 10 -0.437452077866 0.00249764138551 0.00910470903234\n", "4 20 -0.385276913643 0.00254663529785 0.00905435457787\n", "4 30 -0.423624098301 0.00246848767799 0.00898625109022\n", "4 40 -0.420540690422 0.00248314869051 0.00892808886835\n", "4 50 -0.402457535267 0.0022960201622 0.00887009107729\n", "valid_acc 88.03333333333333\n", "best valid_acc 88.03333333333333\n", "5 0 -0.388254404068 0.00229812033202 0.00883767569532\n", "5 10 -0.36739128828 0.00225515689787 0.00879538684326\n", "5 20 -0.428320139647 0.00232945911414 0.00875153921834\n", "5 30 -0.383684903383 0.00218269981679 0.00870113507805\n", "5 40 -0.432154774666 0.00211687321264 0.0086407168929\n", "5 50 -0.390559136868 0.00225944899588 0.00857692081228\n", "valid_acc 89.45666666666666\n", "best valid_acc 89.45666666666666\n", "6 0 -0.350162833929 0.00217606557115 0.00853646823665\n", "6 10 -0.376082777977 0.00216022693157 0.00849116771494\n", "6 20 -0.323433965445 0.00219216193793 0.00843914685768\n", "6 30 -0.339197009802 0.00218088411865 0.00838950164032\n", "6 40 -0.357838600874 0.00213946051877 0.00832941052172\n", "6 50 -0.307298094034 0.00231204224598 0.00828102141417\n", "valid_acc 90.27\n", "best valid_acc 90.27\n", "7 0 -0.317924231291 0.00223062203635 0.00822577723894\n", "7 10 -0.285778015852 0.00236541841039 0.00819597635313\n", "7 20 -0.284006983042 0.00198477741541 0.00814251824586\n", "7 30 -0.322694391012 0.00188935074482 0.00808718646583\n", "7 40 -0.27657687664 0.00197277407865 0.00804459896425\n", "7 50 -0.301543593407 0.00205474232989 0.00800435444149\n", "valid_acc 91.33666666666667\n", "best valid_acc 91.33666666666667\n", "8 0 -0.283228337765 0.00203809646956 0.00797065817268\n", "8 10 -0.283903241158 0.00194159524936 0.00793005655767\n", "8 20 -0.283037543297 0.00191677193605 0.00788335025407\n", "8 30 -0.290317207575 0.00193486343986 0.00784347825599\n", "8 40 -0.265616208315 0.00190065085014 0.00781388655411\n", "8 50 -0.287095457315 0.00186444017786 0.00776820602259\n", "valid_acc 91.97166666666666\n", "best valid_acc 91.97166666666666\n", "9 0 -0.279303252697 0.00181614868304 0.00773340583823\n", "9 10 -0.295207768679 0.00196544461349 0.00769751001031\n", "9 20 -0.229951828718 0.00181948822975 0.00766496465422\n", "9 30 -0.234405517578 0.00182119278696 0.00760823026436\n", "9 40 -0.250581771135 0.00166729777559 0.00757545657994\n", "9 50 -0.27878895402 0.00169749509708 0.0075298828501\n", "valid_acc 92.53833333333333\n", "best valid_acc 92.53833333333333\n", "10 0 -0.255490064621 0.00162704056657 0.0074867963852\n", "10 10 -0.217073246837 0.00160804824496 0.0074384021698\n", "10 20 -0.249490201473 0.00161058284927 0.00740082490856\n", "10 30 -0.231452286243 0.00162493850201 0.00735639284748\n", "10 40 -0.283614039421 0.0018873890804 0.00731254288374\n", "10 50 -0.202628836036 0.00179595386168 0.00725622318866\n", "valid_acc 92.98666666666666\n", "best valid_acc 92.98666666666666\n", "11 0 -0.228246167302 0.00185595304637 0.00721301971792\n", "11 10 -0.246928885579 0.00190782238076 0.00718078882934\n", "11 20 -0.250431805849 0.00173171655879 0.0071363476788\n", "11 30 -0.236849501729 0.00181300292912 0.00708508880735\n", "11 40 -0.211556985974 0.00187782002318 0.00706362938696\n", "11 50 -0.250708609819 0.00175434221584 0.00702612942197\n", "valid_acc 93.23166666666667\n", "best valid_acc 93.23166666666667\n", "12 0 -0.247138738632 0.00160160840657 0.00698477796163\n", "12 10 -0.201228529215 0.00172650950514 0.00692577066559\n", "12 20 -0.257842421532 0.00180919927104 0.00688569783683\n", "12 30 -0.269445091486 0.00173720811415 0.00684203242737\n", "12 40 -0.223444715142 0.00155992023688 0.00680660576905\n", "12 50 -0.22124402225 0.00166730077266 0.00677689358591\n", "valid_acc 93.44833333333334\n", "best valid_acc 93.44833333333334\n", "13 0 -0.214715898037 0.00179637737642 0.00673256274863\n", "13 10 -0.234049871564 0.00169248353386 0.00668281554243\n", "13 20 -0.222717136145 0.0018210551148 0.00664488739897\n", "13 30 -0.196537688375 0.00160163345886 0.00661051698536\n", "13 40 -0.192704156041 0.00168987255665 0.0065717295929\n", "13 50 -0.239279657602 0.00181448715169 0.00655409701373\n", "valid_acc 93.78\n", "best valid_acc 93.78\n", "14 0 -0.241502359509 0.00168230898669 0.00653021922311\n", "14 10 -0.202527299523 0.00154452262961 0.0064784422372\n", "14 20 -0.17320817709 0.00153281650973 0.00644997898052\n", "14 30 -0.226853609085 0.00143728849548 0.0064220583614\n", "14 40 -0.18000896275 0.00162157542925 0.00638457212854\n", "14 50 -0.189259782434 0.00173029026986 0.00634249501971\n", "valid_acc 94.10833333333333\n", "best valid_acc 94.10833333333333\n", "15 0 -0.203682333231 0.00171083056476 0.00631216941869\n", "15 10 -0.187640994787 0.00164813804619 0.00629653885912\n", "15 20 -0.164975613356 0.00177802857191 0.00626794273906\n", "15 30 -0.184161305428 0.00159335852653 0.00624137012062\n", "15 40 -0.198896929622 0.0014962170437 0.00623708513843\n", "15 50 -0.148714929819 0.00153750646892 0.00620636466898\n", "valid_acc 94.13\n", "best valid_acc 94.13\n", "16 0 -0.205399557948 0.0012951463989 0.00617187675095\n", "16 10 -0.173367708921 0.00128017314244 0.00613150017287\n", "16 20 -0.198582842946 0.00130505093479 0.00609943391596\n", "16 30 -0.165766522288 0.0013833225837 0.00607156006282\n", "16 40 -0.130758330226 0.00121558669369 0.00605021441079\n", "16 50 -0.177086427808 0.000984930712074 0.00600680909679\n", "valid_acc 94.63333333333334\n", "best valid_acc 94.63333333333334\n", "17 0 -0.177764698863 0.00134585874721 0.0059875821467\n", "17 10 -0.143626958132 0.00119224786603 0.00596928168881\n", "17 20 -0.141031444073 0.0012797151177 0.00594968314532\n", "17 30 -0.174031272531 0.00124694237002 0.00591305367993\n", "17 40 -0.167387768626 0.00118592691112 0.00587774804562\n", "17 50 -0.218059495091 0.00121502436347 0.00586143348668\n", "valid_acc 94.71666666666667\n", "best valid_acc 94.71666666666667\n", "18 0 -0.148777604103 0.00131193322655 0.00583270227887\n", "18 10 -0.166642814875 0.00114690493126 0.00581041180846\n", "18 20 -0.186952114105 0.00121549549845 0.0057782466257\n", "18 30 -0.15556588769 0.00122752032368 0.00574015300794\n", "18 40 -0.172016680241 0.0012493801262 0.00571581099189\n", "18 50 -0.137878060341 0.00131641035889 0.00568227689086\n", "valid_acc 94.95333333333333\n", "best valid_acc 94.95333333333333\n", "19 0 -0.146104156971 0.00120955651369 0.00565169476766\n", "19 10 -0.198827937245 0.00141291048931 0.00562262071709\n", "19 20 -0.144020780921 0.00154895079385 0.00560796990616\n", "19 30 -0.154505193233 0.00169510530994 0.00556795175492\n", "19 40 -0.14247302711 0.001748578763 0.00554667657647\n", "19 50 -0.141776800156 0.00180491148413 0.00552286419132\n", "valid_acc 95.205\n", "best valid_acc 95.205\n", "20 0 -0.146836578846 0.00167703830221 0.00549648485563\n", "20 10 -0.138202950358 0.00154670039383 0.00548004573553\n", "20 20 -0.184400141239 0.00152600296823 0.00545366490062\n", "20 30 -0.143896520138 0.00160661668659 0.00543165118697\n", "20 40 -0.175112649798 0.00172598462062 0.00539599507578\n", "20 50 -0.119840249419 0.00154576443407 0.0053683516701\n", "valid_acc 95.62333333333333\n", "best valid_acc 95.62333333333333\n", "21 0 -0.14806997776 0.00168400541571 0.0053501100737\n", "21 10 -0.140091404319 0.00160504614447 0.00531015889016\n", "21 20 -0.130147084594 0.00160213152406 0.00528674630605\n", "21 30 -0.154827266932 0.00149827744721 0.00525816371571\n", "21 40 -0.124978497624 0.00152841931547 0.00523408121851\n", "21 50 -0.156943887472 0.00155619766655 0.00520790130635\n", "valid_acc 95.32666666666667\n", "22 0 -0.173790097237 0.00172289916085 0.00519213474151\n", "22 10 -0.128586992621 0.0016886308845 0.00516810229852\n", "22 20 -0.150675520301 0.00173865741996 0.00514251304142\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "22 30 -0.18594917655 0.00171536419735 0.00510050577734\n", "22 40 -0.130576968193 0.00167346014895 0.00508697490512\n", "22 50 -0.176683276892 0.00169889200255 0.00506609005525\n", "valid_acc 95.67166666666667\n", "best valid_acc 95.67166666666667\n", "23 0 -0.123710893095 0.00175887741862 0.00504928099572\n", "23 10 -0.148195341229 0.00156132246357 0.00501304154251\n", "23 20 -0.152287974954 0.00153527711225 0.00497958675176\n", "23 30 -0.16320194304 0.00129272222605 0.00496548395933\n", "23 40 -0.133498221636 0.00147776282228 0.00494285211434\n", "23 50 -0.138774454594 0.00147879835235 0.00492893151602\n", "valid_acc 95.66666666666667\n", "24 0 -0.143809661269 0.00156957833378 0.00490763531684\n", "24 10 -0.124419309199 0.00144986556885 0.00488567931825\n", "24 20 -0.140805080533 0.00149847732212 0.00485429002956\n", "24 30 -0.106018573046 0.00141167055513 0.00483692717515\n", "24 40 -0.116842091084 0.00131821749433 0.00481588374114\n", "24 50 -0.110816329718 0.00133028239606 0.00478639111557\n", "valid_acc 96.02333333333334\n", "best valid_acc 96.02333333333334\n", "25 0 -0.159575849771 0.00137692057491 0.0047658879135\n", "25 10 -0.133688047528 0.0013833476154 0.00474327288848\n", "25 20 -0.108072891831 0.00126796734447 0.00471332464723\n", "25 30 -0.120328903198 0.00122464149458 0.00469300210782\n", "25 40 -0.125829622149 0.00126145593631 0.00467960737569\n", "25 50 -0.141977772117 0.00132713181665 0.00466025034871\n", "valid_acc 96.08166666666666\n", "best valid_acc 96.08166666666666\n", "26 0 -0.128144487739 0.00123311464139 0.0046514291662\n", "26 10 -0.125442802906 0.00111283540272 0.00463212334944\n", "26 20 -0.13814291358 0.000976153462259 0.00462397255841\n", "26 30 -0.134485304356 0.00119751321698 0.0045903791726\n", "26 40 -0.11636903137 0.0011160751882 0.00457235319571\n", "26 50 -0.122194960713 0.00114406087639 0.00454908884765\n", "valid_acc 96.00999999999999\n", "27 0 -0.124889291823 0.0011201247686 0.00452502091071\n", "27 10 -0.123839326203 0.00101655327753 0.00450688188045\n", "27 20 -0.111128486693 0.00109660191301 0.00448823224412\n", "27 30 -0.114916615188 0.00121730598323 0.00447517049183\n", "27 40 -0.128138899803 0.00110000243883 0.00445165795566\n", "27 50 -0.142946183681 0.00113777595335 0.00444716161661\n", "valid_acc 96.3\n", "best valid_acc 96.3\n", "28 0 -0.0771485492587 0.00106669481591 0.004420195888\n", "28 10 -0.115099839866 0.00110934501396 0.00439348348317\n", "28 20 -0.0974150672555 0.000970675311077 0.00436644196771\n", "28 30 -0.104851402342 0.00115356691446 0.0043557045474\n", "28 40 -0.0992491543293 0.00102187330095 0.00433801918758\n", "28 50 -0.154656141996 0.000922835777842 0.00431815452155\n", "valid_acc 96.465\n", "best valid_acc 96.465\n", "29 0 -0.110808476806 0.000935465203041 0.00429304041465\n", "29 10 -0.0713839754462 0.0010944629372 0.00428607181398\n", "29 20 -0.146833896637 0.00112079785318 0.00427141123857\n", "29 30 -0.107853420079 0.00123810584388 0.00425739700635\n", "29 40 -0.121415421367 0.00108632022071 0.00423585662346\n", "29 50 -0.0819023102522 0.00120421669081 0.00421383565347\n", "valid_acc 96.50666666666666\n", "best valid_acc 96.50666666666666\n", "30 0 -0.0928794592619 0.00114407587364 0.00419099853528\n", "30 10 -0.149121239781 0.00110120434331 0.0041713923168\n", "30 20 -0.0905337557197 0.00105364304058 0.00414727786489\n", "30 30 -0.100121870637 0.00113780513286 0.00414119506585\n", "30 40 -0.1223333776 0.00108560839877 0.00413111987588\n", "30 50 -0.139318734407 0.0010752668621 0.00412115387288\n", "valid_acc 96.38166666666666\n" ] } ], "source": [ "#'''\n", "best_valid_acc = 0\n", "training_log = []\n", "for epoch in range(1, 1*args.epochs + 1):\n", "\n", " # train loop\n", " model.eval()\n", " for batch_idx, (data, target) in enumerate(train_loader):\n", " if args.cuda:\n", " data, target = data.cuda(), target.cuda()\n", " data, target = Variable(data), Variable(target)\n", " \n", " solutions = es.ask()\n", " reward = np.zeros(es.popsize)\n", " \n", " for i in range(es.popsize):\n", " update_model(solutions[i], model, model_shapes)\n", " output = model(data)\n", " loss = F.nll_loss(output, target)\n", " reward[i] = - loss.data[0]\n", "\n", " best_raw_reward = reward.max()\n", "\n", " es.tell(reward)\n", "\n", " result = es.result()\n", " \n", " if (batch_idx % 10 == 0):\n", " print(epoch, batch_idx, best_raw_reward, result[0].mean(), es.rms_stdev())\n", "\n", " curr_solution = es.current_param()\n", " update_model(curr_solution, model, model_shapes)\n", "\n", " valid_acc = evaluate(model, valid_loader, print_mode=False)\n", " training_log.append([epoch, valid_acc])\n", " print('valid_acc', valid_acc * 100.)\n", " if valid_acc >= best_valid_acc:\n", " best_valid_acc = valid_acc\n", " best_model = copy.deepcopy(model)\n", " print('best valid_acc', best_valid_acc * 100.)\n", "#'''" ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 0 -0.133420318365 0.00100647542086 0.00410479424588\n", "1 10 -0.0963906720281 0.00101848202417 0.0040793648565\n", "1 20 -0.12268974632 0.00110729788891 0.00406207562388\n", "1 30 -0.0774867087603 0.00103202388317 0.00404572607528\n", "1 40 -0.110574513674 0.00109495313029 0.00403260798392\n", "1 50 -0.120058402419 0.000908460687557 0.00401968590472\n", "valid_acc 96.47666666666666\n", "2 0 -0.0982737392187 0.000913908051058 0.00400306431467\n", "2 10 -0.0826506316662 0.00095355464919 0.00399272188401\n", "2 20 -0.0968995243311 0.000939595503046 0.00397913546538\n", "2 30 -0.0916180536151 0.000905829544027 0.00396826501974\n", "2 40 -0.100446596742 0.000948154835541 0.00395949369288\n", "2 50 -0.11905310303 0.000888758552033 0.00394812686508\n", "valid_acc 96.66666666666667\n", "best valid_acc 96.66666666666667\n", "3 0 -0.121412277222 0.000843183125303 0.00393802627524\n", "3 10 -0.0769304484129 0.000904433437317 0.00391759855502\n", "3 20 -0.0717141255736 0.000935867888462 0.00390324359036\n", "3 30 -0.118696071208 0.000800283276926 0.00389216911056\n", "3 40 -0.0934861376882 0.000889597969688 0.00388495732114\n", "3 50 -0.0841078013182 0.000818508930957 0.00387083970762\n", "valid_acc 96.69\n", "best valid_acc 96.69\n", "4 0 -0.104738637805 0.000662866614839 0.00385961326847\n", "4 10 -0.0727778226137 0.00074135527529 0.00384495678846\n", "4 20 -0.101395905018 0.000776288844157 0.00383040370787\n", "4 30 -0.102986305952 0.000572479397929 0.00381530973364\n", "4 40 -0.122906856239 0.000731725395055 0.00380346058975\n", "4 50 -0.0769169777632 0.000783253671179 0.00379927951226\n", "valid_acc 96.67\n", "5 0 -0.0780416727066 0.000713250362582 0.00378323443046\n", "5 10 -0.0806866586208 0.000861353883914 0.00376864531953\n", "5 20 -0.0795269757509 0.000739684070131 0.0037594109615\n", "5 30 -0.134278312325 0.00083583152241 0.00375275161931\n", "5 40 -0.138093292713 0.000983117423222 0.00373513174461\n", "5 50 -0.107232280076 0.000910562194792 0.00371619993869\n", "valid_acc 96.75333333333333\n", "best valid_acc 96.75333333333333\n", "6 0 -0.105698496103 0.00096022615292 0.0037040463898\n", "6 10 -0.106836400926 0.00100941367848 0.00368848218059\n", "6 20 -0.0910117700696 0.00103888822197 0.00367493861566\n", "6 30 -0.112654075027 0.00104590881413 0.00366612125905\n", "6 40 -0.0967606157064 0.00102788524022 0.00364758022364\n", "6 50 -0.0981357097626 0.00101602336317 0.0036347299994\n", "valid_acc 96.87833333333333\n", "best valid_acc 96.87833333333333\n", "7 0 -0.106010444462 0.00111367571843 0.00362775965205\n", "7 10 -0.102175667882 0.00113681701888 0.00361116210933\n", "7 20 -0.0681428536773 0.00112282416519 0.00359762351432\n", "7 30 -0.0999428555369 0.00113082783383 0.00358199935772\n", "7 40 -0.0899093002081 0.00108036206728 0.00357126908187\n", "7 50 -0.10363214463 0.00113993451612 0.0035569434788\n", "valid_acc 96.89833333333333\n", "best valid_acc 96.89833333333333\n", "8 0 -0.0868514254689 0.00113612600623 0.00354999161785\n", "8 10 -0.111564956605 0.00117583188516 0.00353546500392\n", "8 20 -0.113716006279 0.00119112174132 0.00352937917261\n", "8 30 -0.0885119959712 0.00125341256 0.0035151487036\n", "8 40 -0.109584592283 0.0012628922389 0.00350538662091\n", "8 50 -0.0903675332665 0.00117750698483 0.00349280545627\n", "valid_acc 96.91666666666666\n", "best valid_acc 96.91666666666666\n", "9 0 -0.120133377612 0.00124462281184 0.00347943430786\n", "9 10 -0.174418032169 0.00127189523421 0.00346566902411\n", "9 20 -0.1269223243 0.00117058758585 0.00346423987129\n", "9 30 -0.0888032391667 0.00119877872278 0.00344825600223\n", "9 40 -0.0758956447244 0.00120006770934 0.00343812606766\n", "9 50 -0.098711438477 0.00115555717821 0.00341870805784\n", "valid_acc 96.96166666666667\n", "best valid_acc 96.96166666666667\n", "10 0 -0.0909817144275 0.00115131177764 0.00340807798618\n", "10 10 -0.114620372653 0.00112263442455 0.00339249543137\n", "10 20 -0.0920019671321 0.00109921764645 0.00337931469249\n", "10 30 -0.10684799403 0.0009468329089 0.00337695517202\n", "10 40 -0.121073998511 0.000898825070837 0.00336625013195\n", "10 50 -0.114142149687 0.0009582173953 0.0033630978411\n", "valid_acc 96.91166666666666\n", "11 0 -0.0873347967863 0.000902955442807 0.00335764969979\n", "11 10 -0.124757565558 0.000767529468536 0.0033457814097\n", "11 20 -0.105243451893 0.00071801944591 0.00333394019578\n", "11 30 -0.0848548561335 0.000714599585306 0.00332258074051\n", "11 40 -0.0751351267099 0.000647725632998 0.00330933239278\n", "11 50 -0.062558196485 0.000676834855723 0.00329714617438\n", "valid_acc 96.92666666666668\n", "12 0 -0.091876655817 0.000678327779487 0.00328361745151\n", "12 10 -0.0622350163758 0.00069562844212 0.0032776560951\n", "12 20 -0.107692480087 0.000780777486769 0.00326302334042\n", "12 30 -0.100586794317 0.000808636414056 0.00324683109376\n", "12 40 -0.0938239470124 0.000751731979001 0.00323460317442\n", "12 50 -0.0935280546546 0.000824563670148 0.00322407892103\n", "valid_acc 97.04833333333333\n", "best valid_acc 97.04833333333333\n", "13 0 -0.126834049821 0.000928358963003 0.00321070199538\n", "13 10 -0.0741639956832 0.000814800982735 0.00320132849411\n", "13 20 -0.109093934298 0.000841955900339 0.00319496063707\n", "13 30 -0.121440917253 0.000799832549388 0.00317674311603\n", "13 40 -0.07275108248 0.00083736628366 0.00317137162089\n", "13 50 -0.0720087066293 0.000590339543898 0.00316425540063\n", "valid_acc 96.90833333333333\n", "14 0 -0.068630002439 0.000647449010022 0.00315712611185\n", "14 10 -0.113144636154 0.000712758675454 0.00314915603671\n", "14 20 -0.0965237021446 0.000710908029592 0.00313837291398\n", "14 30 -0.0937501341105 0.000699625587473 0.00313332126441\n", "14 40 -0.087478287518 0.000889381731809 0.00312463809603\n", "14 50 -0.109475709498 0.000822526144248 0.00311558782996\n", "valid_acc 97.11\n", "best valid_acc 97.11\n", "15 0 -0.0921492651105 0.00080792763754 0.00310341768609\n", "15 10 -0.0793410167098 0.000937948016001 0.00309252396014\n", "15 20 -0.0774940922856 0.000874527546045 0.0030851333997\n", "15 30 -0.0809038430452 0.000816788785752 0.00307863165174\n", "15 40 -0.0932660475373 0.000835907809712 0.00306984374528\n", "15 50 -0.105781376362 0.000913642340341 0.00306768884014\n", "valid_acc 97.14500000000001\n", "best valid_acc 97.14500000000001\n", "16 0 -0.0815956145525 0.000830251654488 0.00306520546294\n", "16 10 -0.0860729664564 0.000766246401879 0.00305972031462\n", "16 20 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0.000796389126971 0.00294387229224\n", "18 50 -0.0686716809869 0.000799980343448 0.00293837737521\n", "valid_acc 97.195\n", "19 0 -0.0856649652123 0.000762679742039 0.00293103074105\n", "19 10 -0.101165525615 0.000766863462173 0.00292541837404\n", "19 20 -0.0697371661663 0.000855322252848 0.00292296218655\n", "19 30 -0.0662379711866 0.000779218714688 0.00291749457298\n", "19 40 -0.0742920935154 0.00083586279433 0.00291174261033\n", "19 50 -0.113724455237 0.00079082383892 0.00290501435986\n", "valid_acc 97.245\n", "best valid_acc 97.245\n", "20 0 -0.110401973128 0.000744738232473 0.00289348468036\n", "20 10 -0.111841656268 0.000744533842544 0.00288821112621\n", "20 20 -0.0661686733365 0.000751873776162 0.00288188946678\n", "20 30 -0.066940702498 0.000666129520325 0.00287758233138\n", "20 40 -0.0745821893215 0.00072557250156 0.00287355522569\n", "20 50 -0.0916078686714 0.000691483054633 0.00286862182801\n", "valid_acc 97.19666666666666\n", "21 0 -0.0759673789144 0.000591725454877 0.0028676485156\n", "21 10 -0.088543407619 0.000632907981836 0.00285969448278\n", "21 20 -0.0698254331946 0.000643858235298 0.00285509236396\n", "21 30 -0.0965352877975 0.000666586113059 0.00284856364684\n", "21 40 -0.0755655020475 0.000586839663368 0.00284008832513\n", "21 50 -0.0636272057891 0.00056963285479 0.00283591193074\n", "valid_acc 97.25333333333333\n", "best valid_acc 97.25333333333333\n", "22 0 -0.0863511487842 0.000615699047296 0.0028301248636\n", "22 10 -0.0910157412291 0.000566634121854 0.00282548563224\n", "22 20 -0.0751781463623 0.000688409779627 0.00282301892269\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "22 30 -0.0981971025467 0.000760815200301 0.00281621185794\n", "22 40 -0.0811848863959 0.000727178905861 0.00281083828747\n", "22 50 -0.114038132131 0.000734380403618 0.00280220744286\n", "valid_acc 97.31333333333333\n", "best valid_acc 97.31333333333333\n", "23 0 -0.0964765027165 0.000655040699541 0.00279404309991\n", "23 10 -0.0868964418769 0.000815731894 0.00278759889754\n", "23 20 -0.0750052630901 0.000772387613005 0.00278148350474\n", "23 30 -0.0984847769141 0.000740256872494 0.00277466742\n", "23 40 -0.0840069204569 0.000792859750229 0.00276823407172\n", "23 50 -0.0768686532974 0.000796182110385 0.00276240720782\n", "valid_acc 97.40666666666667\n", "best valid_acc 97.40666666666667\n", "24 0 -0.0898569822311 0.000665555343138 0.00275643284746\n", "24 10 -0.0746565312147 0.0007787089569 0.00275221930355\n", "24 20 -0.0942489132285 0.000822913810508 0.00274483697712\n", "24 30 -0.100115001202 0.000875561783549 0.0027386865403\n", "24 40 -0.0833004489541 0.000895611761626 0.00273319817884\n", "24 50 -0.049983587116 0.000828409837475 0.00272557113309\n", "valid_acc 97.44166666666668\n", "best valid_acc 97.44166666666668\n", "25 0 -0.0781069025397 0.00092110885121 0.00272639317862\n", "25 10 -0.0600539520383 0.0009750103088 0.00271816172613\n", "25 20 -0.0799731314182 0.000940920346499 0.00271447605412\n", "25 30 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"valid_acc 97.37666666666667\n", "28 0 -0.0585933998227 0.000819895745287 0.00262521690242\n", "28 10 -0.064607642591 0.000892668490011 0.00262285693074\n", "28 20 -0.0913728773594 0.000920521916696 0.00261663601835\n", "28 30 -0.08716596663 0.000845465898745 0.00261146071977\n", "28 40 -0.0709830448031 0.000858944773693 0.00260814786384\n", "28 50 -0.0834282115102 0.000828356007816 0.00260282191432\n", "valid_acc 97.435\n", "29 0 -0.0939118638635 0.000693606211115 0.00259914004608\n", "29 10 -0.100371494889 0.000792630399459 0.00259241093024\n", "29 20 -0.0572604425251 0.000680107837797 0.00258667990656\n", "29 30 -0.0875898525119 0.000783182386826 0.00258613706477\n", "29 40 -0.0706899240613 0.00077857477003 0.00258304675775\n", "29 50 -0.123943537474 0.000738485877819 0.00257810361391\n", "valid_acc 97.44666666666667\n", "best valid_acc 97.44666666666667\n", "30 0 -0.0625935047865 0.000720208367204 0.00256881129134\n", "30 10 -0.0537013038993 0.000696676362529 0.00256505226506\n", "30 20 -0.0539420470595 0.000739144239937 0.00256325650971\n", "30 30 -0.0672374516726 0.000710882674688 0.00256112642434\n", "30 40 -0.0539236776531 0.000693949993365 0.00255481542539\n", "30 50 -0.106714650989 0.000850658777249 0.00254824391973\n", "valid_acc 97.42166666666667\n", "31 0 -0.0618389286101 0.000741116906622 0.00254368536012\n", "31 10 -0.065222889185 0.000698541745111 0.00253907290806\n", "31 20 -0.0620870441198 0.000787148412144 0.0025314525653\n", "31 30 -0.0685837939382 0.000873634208639 0.00252903947848\n", "31 40 -0.0497282445431 0.000821183836304 0.00252200115766\n", "31 50 -0.0787934362888 0.000826065564397 0.00251784312884\n", "valid_acc 97.50666666666666\n", "best valid_acc 97.50666666666666\n", "32 0 -0.0684808194637 0.000760147708218 0.00251340921587\n", "32 10 -0.0882711410522 0.000714405530171 0.00251068341357\n", "32 20 -0.0733887702227 0.0007711657318 0.00251032612503\n", "32 30 -0.0677174702287 0.000780572970414 0.0025059947093\n", "32 40 -0.0732608437538 0.000745994545735 0.00250150063903\n", "32 50 -0.0599242709577 0.000822999098545 0.00250232411358\n", "valid_acc 97.43833333333333\n", "33 0 -0.0795421227813 0.000857622799219 0.00249536703723\n", "33 10 -0.053082883358 0.000855472408987 0.00249380030988\n", "33 20 -0.0733324736357 0.000813818529958 0.00248998328282\n", "33 30 -0.082282744348 0.000788717850934 0.00248537058032\n", "33 40 -0.0712718069553 0.000762714535954 0.00247771303934\n", "33 50 -0.0864481329918 0.000727643965297 0.00247765315545\n", "valid_acc 97.47166666666666\n", "34 0 -0.0802294239402 0.000806089622526 0.00247311893502\n", "34 10 -0.0806183665991 0.000786926922183 0.00246532127996\n", "34 20 -0.054039567709 0.000865365112911 0.00245876842543\n", "34 30 -0.093410231173 0.000902904830188 0.00245306034416\n", "34 40 -0.0570801943541 0.00086284793898 0.00244782977314\n", "34 50 -0.0608786195517 0.000860999657666 0.00244361640628\n", "valid_acc 97.49333333333333\n", "35 0 -0.0715939700603 0.000774853587566 0.00244144971938\n", "35 10 -0.0528771914542 0.000765654353147 0.0024335897789\n", "35 20 -0.0853509157896 0.000760809847663 0.00243066038424\n", "35 30 -0.0649507790804 0.000750688785208 0.00242552336423\n", "35 40 -0.0666754469275 0.0007591790659 0.0024195082454\n", "35 50 -0.0651993080974 0.000816804476804 0.00241756780786\n", "valid_acc 97.53\n", "best valid_acc 97.53\n", "36 0 -0.0954441726208 0.00079256064847 0.00241457628061\n", "36 10 -0.065805748105 0.000745725360203 0.00240866365663\n", "36 20 -0.0656291246414 0.000814805357718 0.00240708831571\n", "36 30 -0.0630030557513 0.000769361441197 0.00239911231569\n", "36 40 -0.0795306488872 0.000797620105092 0.00239474786519\n", "36 50 -0.0824849233031 0.000835353581745 0.00238931468511\n", "valid_acc 97.56666666666666\n", "best valid_acc 97.56666666666666\n", "37 0 -0.0607663877308 0.000839995468746 0.00238535784474\n", "37 10 -0.0527637600899 0.000790600122903 0.00238275474369\n", "37 20 -0.0946824848652 0.000787990790923 0.0023816717476\n", "37 30 -0.118307799101 0.000816411440093 0.00237701516921\n", "37 40 -0.101476542652 0.000680773269167 0.00237426792495\n", "37 50 -0.100584730506 0.000778281200486 0.00237337714939\n", "valid_acc 97.59833333333333\n", "best valid_acc 97.59833333333333\n", "38 0 -0.0621845684946 0.00079083240791 0.0023684082599\n", "38 10 -0.0752103850245 0.00083326647271 0.00236478463397\n", "38 20 -0.100619666278 0.000837856927427 0.00235893827826\n", "38 30 -0.107862584293 0.000942671877808 0.00235660672675\n", "38 40 -0.0638026669621 0.000896027608622 0.00235488907065\n", "38 50 -0.0858690291643 0.000883621298993 0.00234943652577\n", "valid_acc 97.58\n", "39 0 -0.0600058101118 0.000968522077767 0.00234539261305\n", "39 10 -0.0734886080027 0.000913649308739 0.0023390576001\n", "39 20 -0.0572930239141 0.000915319422961 0.00233624963926\n", "39 30 -0.0616451203823 0.000994602823963 0.00233112775134\n", "39 40 -0.0785417333245 0.000979794781211 0.00232821087425\n", "39 50 -0.0574326775968 0.00094064720122 0.00232605615333\n", "valid_acc 97.56333333333333\n", "40 0 -0.0695713609457 0.000932690897206 0.00232661560398\n", "40 10 -0.054268874228 0.000872722455717 0.00232344079548\n", "40 20 -0.0599458403885 0.000896832209411 0.00231882210138\n", "40 30 -0.0874785631895 0.000867126517523 0.00231603649142\n", "40 40 -0.0799371302128 0.000873482348772 0.00231032562637\n", "40 50 -0.0853391289711 0.000893601908588 0.00230452415148\n", "valid_acc 97.61833333333333\n", "best valid_acc 97.61833333333333\n", "41 0 -0.0643394291401 0.000836286535841 0.00230266624884\n", "41 10 -0.115371324122 0.0008470405761 0.00229952956417\n", "41 20 -0.0746751353145 0.000859359567072 0.00229524450477\n", "41 30 -0.086985707283 0.000865446901358 0.00229277676564\n", "41 40 -0.0628702491522 0.000837572485127 0.00228977345713\n", "41 50 -0.0752843692899 0.000875413309459 0.00228674837131\n", "valid_acc 97.67166666666667\n", "best valid_acc 97.67166666666667\n", "42 0 -0.0957732871175 0.000879192664508 0.00228505154564\n", "42 10 -0.0591648295522 0.0008706906624 0.00228084908391\n", "42 20 -0.0748505890369 0.000872591146288 0.0022777494194\n", "42 30 -0.0726604908705 0.000884416936264 0.00227478205856\n", "42 40 -0.071018435061 0.00083723190549 0.00226797754938\n", "42 50 -0.0601659715176 0.000901190131833 0.00226778546307\n", "valid_acc 97.66333333333334\n", "43 0 -0.0849346145988 0.000889606010607 0.0022669781507\n", "43 10 -0.073446765542 0.000929350923764 0.00226291293005\n", "43 20 -0.100777536631 0.000907866067774 0.00226104225317\n", "43 30 -0.0579813085496 0.00100139777685 0.00225538736785\n", "43 40 -0.0744867697358 0.000941130124493 0.00225079567424\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "43 50 -0.0792680904269 0.000914882776134 0.00224679863005\n", "valid_acc 97.70166666666667\n", "best valid_acc 97.70166666666667\n", "44 0 -0.075072363019 0.000836165406018 0.00224473281394\n", "44 10 -0.0701611191034 0.00091151686297 0.00224041683243\n", "44 20 -0.069411829114 0.000828415653719 0.00223795204295\n", "44 30 -0.0785206630826 0.000835618069931 0.0022337878104\n", "44 40 -0.0848891362548 0.000808167372237 0.00223141096723\n", "44 50 -0.077173307538 0.0007737650749 0.00223180421032\n", "valid_acc 97.72166666666666\n", "best valid_acc 97.72166666666666\n", "45 0 -0.0850303843617 0.000763602986558 0.00222883808472\n", "45 10 -0.0666282474995 0.000779541823704 0.00222408242543\n", "45 20 -0.0490468293428 0.000899632008915 0.00222098279078\n", "45 30 -0.072115086019 0.000929868887371 0.00222007735025\n", "45 40 -0.0645697340369 0.000895964765108 0.00221927892535\n", "45 50 -0.0955443605781 0.00100496419308 0.00221507411858\n", "valid_acc 97.695\n", "46 0 -0.0653354004025 0.000943062083234 0.00221520272895\n", "46 10 -0.0552923940122 0.000880100645763 0.0022111725581\n", "46 20 -0.0951291322708 0.000895699067234 0.00220752999717\n", "46 30 -0.0765769332647 0.000924007919901 0.00220449644678\n", "46 40 -0.06977660954 0.000928857717646 0.00220394815783\n", "46 50 -0.0982071906328 0.000911789165754 0.00219899457196\n", "valid_acc 97.77666666666667\n", "best valid_acc 97.77666666666667\n", "47 0 -0.0800124108791 0.000969500264666 0.00219539986003\n", "47 10 -0.0866805389524 0.000905857167835 0.00219087990901\n", "47 20 -0.0606841035187 0.000931297936222 0.00218856412894\n", "47 30 -0.0674100518227 0.000878207576974 0.00218653715917\n", "47 40 -0.0628547519445 0.000797368786835 0.00218384369264\n", "47 50 -0.0465587861836 0.000828437076876 0.00218072325432\n", "valid_acc 97.73333333333333\n", "48 0 -0.0629188269377 0.000838087230216 0.00217878337965\n", "48 10 -0.0431539155543 0.000917386129146 0.00217687312358\n", "48 20 -0.0773417055607 0.000884219942525 0.00217144160865\n", "48 30 -0.0824057906866 0.000919294959794 0.00216839241258\n", "48 40 -0.0780591592193 0.000866536659489 0.00216475782071\n", "48 50 -0.0648985132575 0.000792370070898 0.00216066843953\n", "valid_acc 97.75333333333333\n", "49 0 -0.101374305785 0.000830550479906 0.0021550572905\n", "49 10 -0.0865886956453 0.000857676468529 0.00215307864863\n", "49 20 -0.0490629784763 0.000823530640284 0.00214570330164\n", "49 30 -0.0759544149041 0.000798722616035 0.0021436612146\n", "49 40 -0.0677886605263 0.000868629544429 0.00214116034228\n", "49 50 -0.0632658824325 0.00079474760017 0.00213816905852\n", "valid_acc 97.68166666666667\n", "50 0 -0.0678626000881 0.000761145321945 0.00213668222638\n", "50 10 -0.0522820614278 0.000824255435962 0.00213396209998\n", "50 20 -0.0672715529799 0.00079615758859 0.00213209846776\n", "50 30 -0.0797168537974 0.00086563342714 0.00213312548203\n", "50 40 -0.0585340596735 0.000844790869395 0.00213084808861\n", "50 50 -0.067994877696 0.000781536370352 0.002127033416\n", "valid_acc 97.69333333333333\n", "51 0 -0.073095664382 0.000875295964367 0.00212746361601\n", "51 10 -0.0834045261145 0.000908657300881 0.00212424350032\n", "51 20 -0.0689187571406 0.000790334340972 0.00211952347752\n", "51 30 -0.0632844567299 0.000822686433632 0.00211875648986\n", "51 40 -0.0924103632569 0.000800868503599 0.00211637274647\n", "51 50 -0.0689345225692 0.000880870250429 0.00211102970259\n", "valid_acc 97.72166666666666\n", "52 0 -0.0947291105986 0.000846968184172 0.00210888943584\n", "52 10 -0.089002572 0.000890778415982 0.00210668880783\n", "52 20 -0.0698742046952 0.000908033753808 0.00210191811428\n", "52 30 -0.0667767226696 0.000901697816572 0.0020995990245\n", "52 40 -0.0879523679614 0.000900569543637 0.00209621051964\n", "52 50 -0.0943448618054 0.000880322568763 0.00209245412307\n", "valid_acc 97.77333333333334\n", "53 0 -0.0778740420938 0.000902357576433 0.00208594457303\n", "53 10 -0.067453019321 0.000829116519509 0.00208325169632\n", "53 20 -0.0919718593359 0.000907901069295 0.00208362865449\n", "53 30 -0.0871060118079 0.000878529326586 0.00208362322209\n", "53 40 -0.0753285959363 0.000891301602449 0.00207867797141\n", "53 50 -0.0840071886778 0.000838101542846 0.00207686512653\n", "valid_acc 97.735\n", "54 0 -0.0857009291649 0.000856540828409 0.00207364544964\n", "54 10 -0.0620195008814 0.000928458922279 0.00207330554396\n", "54 20 -0.0659448429942 0.000909405843993 0.00207215423726\n", "54 30 -0.0814234316349 0.000903825783721 0.00206812466182\n", "54 40 -0.0444434806705 0.000819721623361 0.0020650212519\n", "54 50 -0.0567604489625 0.000849128183432 0.00206225902154\n", "valid_acc 97.78833333333333\n", "best valid_acc 97.78833333333333\n", "55 0 -0.0987149253488 0.000856154825741 0.0020613320909\n", "55 10 -0.0786830112338 0.000815956091566 0.00205868809973\n", "55 20 -0.0703766494989 0.000779840919311 0.00205876572591\n", "55 30 -0.0728942602873 0.000749020169418 0.0020547745199\n", "55 40 -0.101957194507 0.000769530085517 0.00205076530821\n", "55 50 -0.0630824640393 0.000760654049899 0.0020477216454\n", "valid_acc 97.74666666666667\n", "56 0 -0.069021217525 0.000753309303124 0.00204565541595\n", "56 10 -0.0624925121665 0.000709239778572 0.00204413411096\n", "56 20 -0.0595703162253 0.000693382208807 0.00204564040481\n", "56 30 -0.0635353922844 0.000749134687351 0.00204123363125\n", "56 40 -0.0784621685743 0.000706445933329 0.00204026239134\n", "56 50 -0.06208634004 0.000709207274993 0.00203677206585\n", "valid_acc 97.715\n", "57 0 -0.0858890041709 0.000769726788553 0.00203175496427\n", "57 10 -0.0609682016075 0.000769269663244 0.00203058020893\n", "57 20 -0.0663688257337 0.000747668025769 0.00202697853082\n", "57 30 -0.0469357594848 0.000757533567352 0.0020271250007\n", "57 40 -0.0602676570415 0.000862774773423 0.00202782270449\n", "57 50 -0.0689224377275 0.000846492202034 0.00202782248639\n", "valid_acc 97.78999999999999\n", "best valid_acc 97.78999999999999\n", "58 0 -0.0611949935555 0.000885035218408 0.00202482058783\n", "58 10 -0.0849110111594 0.000898055465668 0.00202173624298\n", "58 20 -0.0478942878544 0.000874202478611 0.00201983792082\n", "58 30 -0.0571486763656 0.000896188156921 0.0020179430264\n", "58 40 -0.0852044001222 0.000951445551051 0.00201293921083\n", "58 50 -0.0748377889395 0.00102293218283 0.00201123761129\n", "valid_acc 97.75\n", "59 0 -0.0674690753222 0.0010037334318 0.00200776489855\n", "59 10 -0.087249211967 0.00102710396061 0.00200430310068\n", "59 20 -0.0669419541955 0.000951406743521 0.0020036836979\n", "59 30 -0.0667329728603 0.0010097565621 0.00200335310412\n", "59 40 -0.0710822492838 0.00102152032597 0.00200211659432\n", "59 50 -0.0790116414428 0.00102052195583 0.00199911200223\n", "valid_acc 97.82333333333332\n", "best valid_acc 97.82333333333332\n", "60 0 -0.0910212025046 0.00101500369249 0.00199448847521\n", "60 10 -0.0518956147134 0.00100542441358 0.00199319247087\n", "60 20 -0.072878330946 0.00110327888663 0.00199109674301\n", "60 30 -0.0510129742324 0.00101671460227 0.00198803325336\n", "60 40 -0.0758544504642 0.00102032695955 0.00198683802088\n", "60 50 -0.0688111558557 0.00104289662424 0.00198508503145\n", "valid_acc 97.82333333333332\n", "best valid_acc 97.82333333333332\n", "61 0 -0.0560882352293 0.000999009513045 0.00198380728796\n", "61 10 -0.0673373937607 0.00100933292546 0.00198094698713\n", "61 20 -0.0772756487131 0.000986103783757 0.00197804212473\n", "61 30 -0.0594056621194 0.00100358338816 0.00197331258198\n", "61 40 -0.0690764039755 0.000972293742727 0.0019727209229\n", "61 50 -0.054340865463 0.00100330288366 0.00197008675206\n", "valid_acc 97.84333333333333\n", "best valid_acc 97.84333333333333\n", "62 0 -0.0633680596948 0.000976205642001 0.00196804942402\n", "62 10 -0.0591195188463 0.000918664705975 0.00196496806246\n", "62 20 -0.0359679460526 0.000993846055784 0.00196407507932\n", "62 30 -0.0446812622249 0.000927292736976 0.00195936270654\n", "62 40 -0.05496410653 0.000933241754459 0.00195715008153\n", "62 50 -0.0724743083119 0.000908667814018 0.00195426035589\n", "valid_acc 97.85833333333333\n", "best valid_acc 97.85833333333333\n", "63 0 -0.0671240612864 0.000953063001933 0.00195204969351\n", "63 10 -0.0597227215767 0.000960898226489 0.00195220299821\n", "63 20 -0.0615901425481 0.000973407616041 0.00195049162166\n", "63 30 -0.0698002576828 0.000926711166239 0.00194397800675\n", "63 40 -0.0635195076466 0.000912999629477 0.00194174068335\n", "63 50 -0.0774204432964 0.000983576806572 0.00194043120702\n", "valid_acc 97.86166666666666\n", "best valid_acc 97.86166666666666\n", "64 0 -0.0495466180146 0.00100262786998 0.00194164018025\n", "64 10 -0.0676609799266 0.00102323744005 0.00193909963806\n", "64 20 -0.0621678121388 0.00100605081984 0.00193861436018\n", "64 30 -0.0625037252903 0.00101113861253 0.00193618670305\n", "64 40 -0.0771643295884 0.00106717846238 0.00193342329899\n", "64 50 -0.0650186985731 0.00107367770329 0.00192781252123\n", "valid_acc 97.84833333333334\n", "65 0 -0.0661346539855 0.00106718802888 0.00192707358482\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "65 10 -0.0551601871848 0.00101899955405 0.00192477046671\n", "65 20 -0.0618918389082 0.00103471800953 0.00192364485393\n", "65 30 -0.0420793183148 0.0010320759224 0.00192650656589\n", "65 40 -0.0882163867354 0.00110078139627 0.0019249035331\n", "65 50 -0.0591966845095 0.00108420629361 0.0019235407947\n", "valid_acc 97.89666666666666\n", "best valid_acc 97.89666666666666\n", "66 0 -0.0564950481057 0.00106584435556 0.00192256792892\n", "66 10 -0.0769885703921 0.00112152037154 0.00191855229838\n", "66 20 -0.0795011892915 0.00102180580491 0.00191808438065\n", "66 30 -0.0635740086436 0.00101875772466 0.0019152974685\n", "66 40 -0.0733740255237 0.00108151591709 0.0019149526173\n", "66 50 -0.076630756259 0.00105306325432 0.00191427066544\n", "valid_acc 97.84833333333334\n", "67 0 -0.0518630295992 0.0010034458747 0.00191294091986\n", "67 10 -0.0835065096617 0.00104453128774 0.00191106540714\n", "67 20 -0.0626259073615 0.00106560312265 0.00190890213556\n", "67 30 -0.060091484338 0.00106153885739 0.00190635538456\n", "67 40 -0.0508683472872 0.0010358376257 0.00190357294224\n", "67 50 -0.0604690462351 0.00101629898361 0.00190147783448\n", "valid_acc 97.88833333333334\n", "68 0 -0.0693193003535 0.00101660363342 0.00189670851617\n", "68 10 -0.0630562379956 0.00104230949721 0.00189372338917\n", "68 20 -0.0738985612988 0.00105517302021 0.00189158314398\n", "68 30 -0.0659157857299 0.000996692561452 0.00188911174099\n", "68 40 -0.0726225823164 0.000981353704603 0.00188713296189\n", "68 50 -0.0622058883309 0.00100055452957 0.00188333734798\n", "valid_acc 97.89333333333333\n", "69 0 -0.0741473734379 0.000968096960082 0.00188008595057\n", "69 10 -0.050063200295 0.000979291871519 0.00187964946478\n", "69 20 -0.0814615264535 0.00105375239017 0.00187554782369\n", "69 30 -0.108646273613 0.00102819561339 0.00187677630563\n", "69 40 -0.0606661364436 0.00101180597282 0.00187338241494\n", "69 50 -0.0642534643412 0.0010643685851 0.0018716999355\n", "valid_acc 97.92166666666667\n", "best valid_acc 97.92166666666667\n", "70 0 -0.0691145434976 0.00101517434421 0.00186827229871\n", "70 10 -0.0565928965807 0.000971208855856 0.00186557229432\n", "70 20 -0.0684570521116 0.000979591204969 0.00186365380903\n", "70 30 -0.0982895046473 0.00099924236221 0.0018619297986\n", "70 40 -0.0993212983012 0.000973685463751 0.0018606627636\n", "70 50 -0.0458309948444 0.000989820432115 0.00186053817241\n", "valid_acc 97.98166666666667\n", "best valid_acc 97.98166666666667\n", "71 0 -0.0626209303737 0.000999985934219 0.0018571738446\n", "71 10 -0.0564146377146 0.000956198808335 0.00185540444745\n", "71 20 -0.0550193637609 0.00100790560199 0.00185368857616\n", "71 30 -0.0553949251771 0.000917544114546 0.00185231370564\n", "71 40 -0.0620043724775 0.000940707697208 0.00185118348704\n", "71 50 -0.061731684953 0.000952225451051 0.00184922916319\n", "valid_acc 97.95666666666666\n", "72 0 -0.0688015818596 0.00102107433746 0.00184706637518\n", "72 10 -0.0749062523246 0.000995824703581 0.00184623260191\n", "72 20 -0.064060613513 0.000946743056381 0.00184450296503\n", "72 30 -0.0569655150175 0.000895229588879 0.00184098690569\n", "72 40 -0.0603754743934 0.000932145607884 0.0018379617612\n", "72 50 -0.0657326802611 0.000949158298337 0.0018362937226\n", "valid_acc 97.92833333333333\n", "73 0 -0.0471868515015 0.000877304173614 0.00183541641576\n", "73 10 -0.0671922191978 0.000901401452307 0.00183144980973\n", "73 20 -0.06249486655 0.000949970366394 0.00182961730268\n", "73 30 -0.0384009107947 0.000949133956041 0.00182977357851\n", "73 40 -0.050106190145 0.000924707158082 0.00182954514275\n", "73 50 -0.0801233127713 0.000926692095372 0.00182658010842\n", "valid_acc 97.98166666666667\n", "best valid_acc 97.98166666666667\n", "74 0 -0.059365209192 0.000903306472799 0.00182578584947\n", "74 10 -0.0657164901495 0.000889497767926 0.0018261823317\n", "74 20 -0.0532665736973 0.000899325844657 0.00182307273309\n", "74 30 -0.0509440638125 0.000978435608166 0.00182232033812\n", "74 40 -0.0826149061322 0.000931610307634 0.00182038734439\n", "74 50 -0.0431626848876 0.000977117352538 0.00181840909019\n", "valid_acc 97.95166666666667\n", "75 0 -0.0729602724314 0.000902438625089 0.00181338155329\n", "75 10 -0.0467497818172 0.000929394215198 0.0018147714233\n", "75 20 -0.0727706998587 0.000962801899447 0.00181333228156\n", "75 30 -0.0447062402964 0.000935278785154 0.00181249877618\n", "75 40 -0.0632866024971 0.00103295345002 0.00181044019696\n", "75 50 -0.072931535542 0.000965590969969 0.00180819764028\n", "valid_acc 97.96666666666667\n", "76 0 -0.0526820644736 0.0009516067016 0.00180644508957\n", "76 10 -0.082777634263 0.000959421601088 0.00180535047314\n", "76 20 -0.0805727988482 0.000942459155479 0.00180503977443\n", "76 30 -0.0653473436832 0.00100681448685 0.00180575271963\n", "76 40 -0.0559695847332 0.000973830634133 0.00180231051405\n", "76 50 -0.0683535784483 0.000909687247042 0.00179972254124\n", "valid_acc 97.975\n", "77 0 -0.0498190224171 0.000979965664505 0.00179777095544\n", "77 10 -0.0610929131508 0.00096301215366 0.00179613309592\n", "77 20 -0.0799521580338 0.00102335008127 0.00179631773997\n", "77 30 -0.0434830784798 0.000989922592751 0.00179470494954\n", "77 40 -0.055815923959 0.00100585994729 0.00179431511675\n", "77 50 -0.0402360931039 0.000967366944467 0.00179339993816\n", "valid_acc 97.965\n", "78 0 -0.0590526498854 0.000990530157938 0.00179362742356\n", "78 10 -0.0663658082485 0.000994462996298 0.00179189297628\n", "78 20 -0.0733836218715 0.000944985878873 0.00179003456417\n", "78 30 -0.0747561976314 0.00097404427858 0.0017862875363\n", "78 40 -0.0671813189983 0.00100929606954 0.00178515809564\n", "78 50 -0.0545134581625 0.000918487059186 0.00178395855694\n", "valid_acc 98.01833333333333\n", "best valid_acc 98.01833333333333\n", "79 0 -0.0619371570647 0.000972532308392 0.00177941633878\n", "79 10 -0.0534271709621 0.000952542720723 0.0017780212145\n", "79 20 -0.0538930594921 0.000945694619845 0.00177513815335\n", "79 30 -0.0443243049085 0.000947463568221 0.00177368075108\n", "79 40 -0.0575834736228 0.000961992397586 0.00177143531163\n", "79 50 -0.05028777197 0.0010078966998 0.00176927926577\n", "valid_acc 98.015\n", "80 0 -0.0776270180941 0.000960969906715 0.00177062601733\n", "80 10 -0.0744371339679 0.00102944867541 0.00177147518945\n", "80 20 -0.0584052801132 0.00101377393142 0.00176808535669\n", "80 30 -0.0462822690606 0.000981550479651 0.00176576324649\n", "80 40 -0.0613155551255 0.000937072908695 0.00176403585575\n", "80 50 -0.0720825791359 0.0009312935227 0.00176569940175\n", "valid_acc 98.0\n", "81 0 -0.0647581294179 0.000982442571786 0.00176412863357\n", "81 10 -0.068183593452 0.00101487987668 0.00176157136219\n", "81 20 -0.0478085875511 0.00100663779936 0.00176031765849\n", "81 30 -0.0649609491229 0.000937548088305 0.00176057327993\n", "81 40 -0.0642245039344 0.000995455546908 0.0017599811906\n", "81 50 -0.0527836754918 0.000922384927303 0.00175805851225\n", "valid_acc 98.0\n", "82 0 -0.0514001213014 0.00100736678909 0.00175499773809\n", "82 10 -0.0521895289421 0.000964643128355 0.00175268466193\n", "82 20 -0.058268725872 0.000910348971758 0.00174950106207\n", "82 30 -0.0521750971675 0.000926999008177 0.00175046228571\n", "82 40 -0.064679376781 0.000937883366396 0.00174891317338\n", "82 50 -0.0656731277704 0.000908316520225 0.00174727999948\n", "valid_acc 98.04833333333333\n", "best valid_acc 98.04833333333333\n", "83 0 -0.0838786885142 0.000914958729156 0.00174539845632\n", "83 10 -0.0664583295584 0.000918692186072 0.00174435641752\n", "83 20 -0.0600226633251 0.000894070666751 0.00174467914573\n", "83 30 -0.0797089189291 0.000922798609039 0.00174534798336\n", "83 40 -0.0969624891877 0.000933969500208 0.00174655419502\n", "83 50 -0.0451970174909 0.000964256620874 0.00174712489417\n", "valid_acc 98.03333333333333\n", "84 0 -0.0628429725766 0.000906593476775 0.00174676277581\n", "84 10 -0.0485133603215 0.000871292744007 0.00174742944489\n", "84 20 -0.0648955628276 0.000889383002405 0.00174664141772\n", "84 30 -0.0895208269358 0.000928599405486 0.00174550565728\n", "84 40 -0.0573744364083 0.000884608366498 0.00174423469309\n", "84 50 -0.0560118369758 0.000862420442187 0.00174160425924\n", "valid_acc 97.995\n", "85 0 -0.0701581016183 0.000895487954246 0.00174033662407\n", "85 10 -0.0646138563752 0.00087120557794 0.00173830295085\n", "85 20 -0.0678824037313 0.000996864791671 0.00173881112248\n", "85 30 -0.0431652404368 0.000931986451195 0.00173757501702\n", "85 40 -0.0843843147159 0.00094939948461 0.00173682030342\n", "85 50 -0.0404578112066 0.000912925803245 0.00173456899095\n", "valid_acc 98.02333333333333\n", "86 0 -0.0423459075391 0.000877713544337 0.00173333125426\n", "86 10 -0.046411767602 0.000946733993116 0.00173110937245\n", "86 20 -0.058477897197 0.000904848223163 0.00172704024784\n", "86 30 -0.10389482975 0.00089759276746 0.00172473377581\n", "86 40 -0.0549925491214 0.000903093836781 0.00172578691671\n", "86 50 -0.0627077296376 0.00091837987651 0.00172451113951\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "valid_acc 98.02\n", "87 0 -0.0777481198311 0.000940643831532 0.00172023912799\n", "87 10 -0.096484683454 0.000939182971627 0.00171984165381\n", "87 20 -0.0778428837657 0.000920028409411 0.00171694432099\n", "87 30 -0.0822711363435 0.000915325942824 0.00171623849054\n", "87 40 -0.0805753022432 0.000915709264529 0.00171382652637\n", "87 50 -0.0416690334678 0.000938822345066 0.00171345517599\n", "valid_acc 98.01833333333333\n", "88 0 -0.0676549896598 0.000937524115906 0.00171290754584\n", "88 10 -0.0408354438841 0.000911403998275 0.00171226837614\n", "88 20 -0.0545636191964 0.000942955804327 0.00170993460011\n", "88 30 -0.0477593317628 0.000894997811992 0.00170701387578\n", "88 40 -0.0569612495601 0.000878442887661 0.00170510410988\n", "88 50 -0.0681399330497 0.000874688110441 0.0017042411075\n", "valid_acc 98.00666666666666\n", "89 0 -0.0612490214407 0.000889218206416 0.00170261873047\n", "89 10 -0.0507856570184 0.00088574976998 0.00170065127694\n", "89 20 -0.0541860610247 0.000871785942116 0.00170021476813\n", "89 30 -0.0447176061571 0.000909938222834 0.00169707009964\n", "89 40 -0.0458347611129 0.00092301706496 0.00169299578221\n", "89 50 -0.0636148974299 0.000851430611088 0.00169206500439\n", "valid_acc 98.08666666666667\n", "best valid_acc 98.08666666666667\n", "90 0 -0.0660084709525 0.000921558046932 0.00169153325554\n", "90 10 -0.0618583112955 0.000881403593776 0.00168955713774\n", "90 20 -0.0545303337276 0.000901777538236 0.00168842587373\n", "90 30 -0.0639667436481 0.000951962607543 0.00168699224021\n", "90 40 -0.0348572395742 0.000936726115475 0.00168632142912\n", "90 50 -0.0405433252454 0.000944326895625 0.00168519326521\n", "valid_acc 98.10333333333332\n", "best valid_acc 98.10333333333332\n", "91 0 -0.0607943497598 0.000974765084934 0.00168442629289\n", "91 10 -0.0637477859855 0.000956135635508 0.00168370842464\n", "91 20 -0.0663855299354 0.00100561083925 0.00168058789526\n", "91 30 -0.073567956686 0.000930612258977 0.00167835291822\n", "91 40 -0.0495889149606 0.000975089496373 0.00167704236543\n", "91 50 -0.0634032264352 0.000897673296722 0.00167563139031\n", "valid_acc 98.10333333333332\n", "best valid_acc 98.10333333333332\n", "92 0 -0.0770059674978 0.00101898297007 0.00167440411647\n", "92 10 -0.0449941046536 0.000973585621933 0.00167455134229\n", "92 20 -0.0416094996035 0.00100810299373 0.00167437141626\n", "92 30 -0.055460549891 0.00091151423375 0.00167238394314\n", "92 40 -0.0666783228517 0.000925751750735 0.00166954095626\n", "92 50 -0.0648067891598 0.000887368792104 0.00166844608163\n", "valid_acc 98.03500000000001\n", "93 0 -0.059121824801 0.000943354535481 0.0016681261966\n", "93 10 -0.0486593022943 0.000963999354737 0.00166454801813\n", "93 20 -0.0615062490106 0.000995774691432 0.00166323815623\n", "93 30 -0.0719784721732 0.00088480622463 0.0016607843508\n", "93 40 -0.0633070170879 0.000880982762492 0.00166017678587\n", "93 50 -0.0473600663245 0.00087139217916 0.00165871362471\n", "valid_acc 98.05333333333334\n", "94 0 -0.0558058917522 0.000863882707377 0.00165706558582\n", "94 10 -0.0692510381341 0.000898215942551 0.00165462195116\n", "94 20 -0.0448820739985 0.000855823130037 0.00165289994665\n", "94 30 -0.0726057216525 0.000866396001084 0.0016508496918\n", "94 40 -0.034600533545 0.000787551957182 0.00164938112997\n", "94 50 -0.0538180284202 0.000839535921916 0.00164716691347\n", "valid_acc 98.07666666666667\n", "95 0 -0.0538425520062 0.0008741031245 0.00164508105912\n", "95 10 -0.0491554550827 0.00090409087299 0.00164350509018\n", "95 20 -0.0438895300031 0.000898422823661 0.00164419675659\n", "95 30 -0.0745923370123 0.000870729398874 0.00164356172832\n", "95 40 -0.0452623851597 0.000872808257803 0.00164022660364\n", "95 50 -0.059683624655 0.000939359003259 0.00164084432648\n", "valid_acc 98.06\n", "96 0 -0.0755808353424 0.000836045524779 0.00164039401686\n", "96 10 -0.06174659729 0.00095092937899 0.00163848364321\n", "96 20 -0.0836167931557 0.00090833066946 0.00163589226461\n", "96 30 -0.0566252246499 0.000957253876085 0.00163391742049\n", "96 40 -0.0417882986367 0.000955260300709 0.00163066287204\n", "96 50 -0.0791959315538 0.000935498456929 0.00162912949859\n", "valid_acc 98.05\n", "97 0 -0.0589312836528 0.000945140959759 0.00162887712248\n", "97 10 -0.074634462595 0.000936003254369 0.00162778222034\n", "97 20 -0.056673400104 0.000926761493505 0.00162711278229\n", "97 30 -0.0364587008953 0.000910534199966 0.00162648251611\n", "97 40 -0.0607174374163 0.00088501744524 0.00162524100888\n", "97 50 -0.0550161153078 0.0008762307121 0.00162371611231\n", "valid_acc 98.08666666666667\n", "98 0 -0.0632099062204 0.000914587891342 0.00162153061651\n", "98 10 -0.0742261111736 0.000872568649323 0.00161860264098\n", "98 20 -0.0768845528364 0.000935816042083 0.00161842091807\n", "98 30 -0.0535627640784 0.000939518982641 0.00161422251754\n", "98 40 -0.0572390146554 0.000879787660338 0.0016138235034\n", "98 50 -0.0428619682789 0.00092433182125 0.0016140151632\n", "valid_acc 98.065\n", "99 0 -0.0703194588423 0.00089938422331 0.00161146824315\n", "99 10 -0.0652059391141 0.000951177154447 0.00161021404659\n", "99 20 -0.065917827189 0.000957591202309 0.00160866589771\n", "99 30 -0.0578117743134 0.000926504581393 0.00160661566655\n", "99 40 -0.0532854795456 0.000915314053127 0.00160533523677\n", "99 50 -0.0824108272791 0.000899143510971 0.00160353384126\n", "valid_acc 98.04666666666667\n", "100 0 -0.0374194942415 0.000943643211349 0.00160421695355\n", "100 10 -0.0638034194708 0.000932166051768 0.00160419947393\n", "100 20 -0.0632742866874 0.000933659380102 0.00160287909453\n", "100 30 -0.0607291199267 0.000972646633426 0.00160233008572\n", "100 40 -0.0705016329885 0.000957030289252 0.00160158232027\n", "100 50 -0.0600859485567 0.000982164433307 0.00159907717252\n", "valid_acc 98.12166666666667\n", "best valid_acc 98.12166666666667\n", "101 0 -0.0443351753056 0.000974826506787 0.00160068225104\n", "101 10 -0.060005620122 0.000988642900007 0.00159945283342\n", "101 20 -0.0935455262661 0.00096610461533 0.00159926769796\n", "101 30 -0.0458681434393 0.000892023088298 0.00159881554352\n", "101 40 -0.04735988006 0.00088391478872 0.00159652967673\n", "101 50 -0.0473527312279 0.000945025692916 0.00159567677595\n", "valid_acc 98.07000000000001\n", "102 0 -0.0373088605702 0.000937668944207 0.00159374214847\n", "102 10 -0.045034237206 0.000962622990542 0.00159135224173\n", "102 20 -0.0685488954186 0.000930440870135 0.00159198303307\n", "102 30 -0.0571148730814 0.000913053730203 0.00158940855778\n", "102 40 -0.0495847724378 0.000930048419717 0.00158885812664\n", "102 50 -0.0779059156775 0.000921291757975 0.00158866409918\n", "valid_acc 98.13166666666666\n", "best valid_acc 98.13166666666666\n", "103 0 -0.0539617985487 0.000966096811555 0.00158847525873\n", "103 10 -0.0465403944254 0.000954626285512 0.0015873842874\n", "103 20 -0.033874232322 0.000936470495717 0.0015870634701\n", "103 30 -0.0509653612971 0.000979559967499 0.0015857430639\n", "103 40 -0.0502147376537 0.00102185128697 0.00158466424907\n", "103 50 -0.060563467443 0.000988537627142 0.00158408384195\n", "valid_acc 98.12833333333333\n", "104 0 -0.0689146891236 0.000967509655781 0.0015838555426\n", "104 10 -0.059033934027 0.0010133122094 0.00158289113454\n", "104 20 -0.0602011159062 0.000932561791046 0.00158191568539\n", "104 30 -0.0590440817177 0.00090179248662 0.0015830784619\n", "104 40 -0.0639892667532 0.000924105426336 0.00158245519614\n", "104 50 -0.0586908273399 0.000967169249816 0.00158189533619\n", "valid_acc 98.155\n", "best valid_acc 98.155\n", "105 0 -0.0634963288903 0.000948757123308 0.00158130930648\n", "105 10 -0.0621558055282 0.000947644350446 0.00157985781767\n", "105 20 -0.0477008372545 0.00100879558144 0.00157843691379\n", "105 30 -0.0576192960143 0.000946701190997 0.00157749414667\n", "105 40 -0.0578346736729 0.000939211965668 0.00157495112829\n", "105 50 -0.0593102499843 0.000983349076004 0.00157253976147\n", "valid_acc 98.12333333333333\n", "106 0 -0.0609729588032 0.000930499973499 0.00157029512142\n", "106 10 -0.0444496907294 0.000932096016439 0.0015680508966\n", "106 20 -0.0696717351675 0.0009448194553 0.00156717437296\n", "106 30 -0.071718968451 0.000975665925611 0.00156436688736\n", "106 40 -0.0647897273302 0.000906192750403 0.00156321049155\n", "106 50 -0.0437019057572 0.000890563637133 0.00156139103111\n", "valid_acc 98.14166666666667\n", "107 0 -0.0544052012265 0.000953106824507 0.0015606950638\n", "107 10 -0.0589485131204 0.00101440402095 0.0015590127657\n", "107 20 -0.0393206998706 0.000986785624154 0.00155917497449\n", "107 30 -0.061154525727 0.000985032126402 0.00155703625588\n", "107 40 -0.0663799494505 0.00101761661695 0.00155765997905\n", "107 50 -0.0469104535878 0.000988045347472 0.00155614231467\n", "valid_acc 98.14500000000001\n", "108 0 -0.0540928281844 0.00101316544175 0.00155530399704\n", "108 10 -0.0496025234461 0.00101411345008 0.00155320044711\n", "108 20 -0.0469730384648 0.000990506143448 0.00155120635155\n", "108 30 -0.0567871257663 0.000998663262567 0.00155036795633\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "108 40 -0.0556872114539 0.00106142434395 0.00155019819748\n", "108 50 -0.0595418028533 0.0010605068868 0.00154758505204\n", "valid_acc 98.11333333333333\n", "109 0 -0.0369131043553 0.00103928407782 0.00154645192774\n", "109 10 -0.0623419955373 0.00102584045853 0.00154575932014\n", "109 20 -0.0577274262905 0.00102343489173 0.00154530877328\n", "109 30 -0.0563415847719 0.00105209152399 0.00154457958783\n", "109 40 -0.0651528611779 0.00101600109503 0.00154522319947\n", "109 50 -0.0583098307252 0.0010535386947 0.00154579014627\n", "valid_acc 98.16\n", "best valid_acc 98.16\n", "110 0 -0.0586435981095 0.00106064693875 0.00154446707534\n", "110 10 -0.0646004751325 0.00103500394315 0.00154264059704\n", "110 20 -0.0640068277717 0.00107025638743 0.00153893721339\n", "110 30 -0.0749094486237 0.00105418578371 0.00153685666784\n", "110 40 -0.0879976674914 0.00106998675532 0.00153593208991\n", "110 50 -0.045017555356 0.00105410679326 0.00153495861022\n", "valid_acc 98.16833333333334\n", "best valid_acc 98.16833333333334\n", "111 0 -0.0624889619648 0.00112438855222 0.00153423246139\n", "111 10 -0.0582691058517 0.00114247367602 0.0015325372117\n", "111 20 -0.101775594056 0.00109065991406 0.00153182736173\n", "111 30 -0.0499654300511 0.00107194671937 0.001530847994\n", "111 40 -0.0624065697193 0.00103335267155 0.00153097306005\n", "111 50 -0.0604842863977 0.00103115630016 0.00152939298602\n", "valid_acc 98.12166666666667\n", "112 0 -0.0691769570112 0.00104067555391 0.00152796926324\n", "112 10 -0.0416245907545 0.00100892407211 0.00152800939396\n", "112 20 -0.0502525791526 0.00108781180832 0.00152688680058\n", "112 30 -0.0468201935291 0.00102004414813 0.00152779241106\n", "112 40 -0.0464553572237 0.0010245530608 0.00152704286817\n", "112 50 -0.0565340444446 0.00103038862792 0.00152615690073\n", "valid_acc 98.16333333333334\n", "113 0 -0.0543060451746 0.00104709395939 0.00152682126843\n", "113 10 -0.0525717064738 0.000969947399929 0.00152671996938\n", "113 20 -0.0530771054327 0.00101928580732 0.00152562260185\n", "113 30 -0.0506352484226 0.00106714241063 0.00152413941444\n", "113 40 -0.0595132783055 0.00105091350528 0.0015238644177\n", "113 50 -0.0632568672299 0.00102683558034 0.00152528381237\n", "valid_acc 98.12833333333333\n", "114 0 -0.0680893883109 0.000987880094961 0.00152467931743\n", "114 10 -0.0703449249268 0.00101972990157 0.00152476348923\n", "114 20 -0.0571644864976 0.00100874802456 0.00152455605715\n", "114 30 -0.0599075295031 0.00102509878929 0.00152324297486\n", "114 40 -0.0345547497272 0.00101697031351 0.00152316307194\n", "114 50 -0.0684372782707 0.00102776397715 0.00152243817323\n", "valid_acc 98.13\n", "115 0 -0.0690058842301 0.00102392767648 0.00152139690686\n", "115 10 -0.0474396906793 0.00101443165377 0.00152109257334\n", "115 20 -0.0628973916173 0.00107201615627 0.00152033184855\n", "115 30 -0.0639133676887 0.000964473288782 0.00151870686824\n", "115 40 -0.0572389923036 0.00102174093072 0.00151800608645\n", "115 50 -0.0614593848586 0.00101280861426 0.00151524827413\n", "valid_acc 98.2\n", "best valid_acc 98.2\n", "116 0 -0.0581579320133 0.00100104118444 0.00151340367881\n", "116 10 -0.0802026987076 0.00103891899294 0.00151097791263\n", "116 20 -0.0604534894228 0.00103886155826 0.00151045915794\n", "116 30 -0.066201262176 0.00100302479373 0.00151091744939\n", "116 40 -0.0815343409777 0.00101906283816 0.00150808330511\n", "116 50 -0.0539462044835 0.00102000386175 0.00150816803611\n", "valid_acc 98.16666666666667\n", "117 0 -0.0512875393033 0.000978362525419 0.00150777834753\n", "117 10 -0.0519607886672 0.000934103631102 0.00150736754972\n", "117 20 -0.0487578101456 0.000980051360828 0.00150543392035\n", "117 30 -0.0531307160854 0.00102321998587 0.00150507690794\n", "117 40 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0.000912436035408 0.00149583346317\n", "120 10 -0.0642639771104 0.000939297819805 0.00149447955232\n", "120 20 -0.0516867190599 0.000921019396291 0.00149332213625\n", "120 30 -0.0453649386764 0.000921559160355 0.00149190235522\n", "120 40 -0.065965436399 0.000979420528328 0.001490983616\n", "120 50 -0.0247657541186 0.000931640519686 0.00149067512034\n", "valid_acc 98.16833333333334\n", "121 0 -0.0467200241983 0.00090365305088 0.00148966742698\n", "121 10 -0.0571050420403 0.000897662767822 0.00148880856117\n", "121 20 -0.0447224751115 0.0009038937712 0.00148865258341\n", "121 30 -0.0576079562306 0.000873654707494 0.00148756924606\n", "121 40 -0.0404568761587 0.000890483940398 0.00148697836725\n", "121 50 -0.0507467463613 0.000893018997223 0.001485283747\n", "valid_acc 98.18333333333334\n", "122 0 -0.0531623065472 0.00085772484669 0.00148444917338\n", "122 10 -0.0677298232913 0.000885101314443 0.00148409181438\n", "122 20 -0.0521187409759 0.000856797356528 0.00148249163678\n", "122 30 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0.000863493355647 0.0014817118092\n", "valid_acc 98.17666666666666\n", "125 0 -0.0486226566136 0.000917722575315 0.00147962550954\n", "125 10 -0.083194501698 0.000937767461062 0.00147887654654\n", "125 20 -0.0488283261657 0.000891457154825 0.00147834477984\n", "125 30 -0.0579899437726 0.000931356779971 0.00147704122557\n", "125 40 -0.0519074313343 0.000949886201767 0.00147593307787\n", "125 50 -0.05057810992 0.000936999836526 0.00147476719417\n", "valid_acc 98.16\n", "126 0 -0.0639023706317 0.000853568559177 0.00147218947607\n", "126 10 -0.0797488465905 0.000904710356956 0.00147138662255\n", "126 20 -0.0500069111586 0.000909812819293 0.0014709935299\n", "126 30 -0.0672949329019 0.000941091416159 0.00147067521786\n", "126 40 -0.0468855239451 0.000938603350448 0.00146944357506\n", "126 50 -0.0746327862144 0.000897770209232 0.0014699785626\n", "valid_acc 98.17\n", "127 0 -0.0500261858106 0.000906795548719 0.0014694734605\n", "127 10 -0.0837894529104 0.000947332080587 0.00146997272959\n", "127 20 -0.0476412586868 0.000923283368855 0.00146885756829\n", "127 30 -0.0526360720396 0.000958632221237 0.00147031577205\n", "127 40 -0.0553583055735 0.000977993502289 0.00146955507167\n", "127 50 -0.0520910173655 0.000983383636599 0.00146752563848\n", "valid_acc 98.175\n", "128 0 -0.0468688122928 0.00100388116162 0.00146616943893\n", "128 10 -0.0380031429231 0.000970854597291 0.00146462580926\n", "128 20 -0.0601692013443 0.000951968420507 0.00146272797975\n", "128 30 -0.0720954462886 0.00093655415686 0.00146202287028\n", "128 40 -0.0468284785748 0.000923944769686 0.00146146453759\n", "128 50 -0.0559154488146 0.000906014217657 0.00146165689615\n", "valid_acc 98.15666666666667\n", "129 0 -0.0593315213919 0.000913395084767 0.00146170237342\n", "129 10 -0.0416588671505 0.000904416646664 0.00146046726726\n", "129 20 -0.0553086549044 0.000930107995267 0.00146086964891\n", "129 30 -0.0571566745639 0.000932546066348 0.00145990974352\n", "129 40 -0.0691854506731 0.000925317982284 0.0014582186128\n", "129 50 -0.0552246049047 0.000965077841309 0.00145767255141\n", "valid_acc 98.20833333333333\n", "130 0 -0.0432448498905 0.00092993367754 0.0014567601132\n", "130 10 -0.0491031520069 0.000953161130167 0.00145572346841\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "130 20 -0.0604798309505 0.00086817886067 0.00145388007167\n", "130 30 -0.0587204732001 0.000880146961633 0.00145530190804\n", "130 40 -0.0550262071192 0.000877560659967 0.00145611823889\n", "130 50 -0.0568213164806 0.000830259123293 0.00145446753167\n", "valid_acc 98.15333333333334\n", "131 0 -0.0379025191069 0.000865505400295 0.00145409521218\n", "131 10 -0.054735083133 0.000819740570391 0.00145452113704\n", "131 20 -0.0565762966871 0.000811922366819 0.00145383574788\n", "131 30 -0.0510967709124 0.000825840424633 0.00145234886141\n", "131 40 -0.0445847995579 0.000842794823152 0.00145111062573\n", "131 50 -0.0704174637794 0.000851216126938 0.00145039319535\n", "valid_acc 98.21833333333333\n", "132 0 -0.0531365945935 0.000811932867921 0.00144929997268\n", "132 10 -0.0517261847854 0.000864895013253 0.00144883069485\n", "132 20 -0.0414656028152 0.000834906499722 0.00144795880913\n", "132 30 -0.0430491343141 0.00082664917777 0.0014465132805\n", "132 40 -0.0662213861942 0.000841894039614 0.00144480124347\n", "132 50 -0.0427861809731 0.000824715165048 0.00144299235052\n", "valid_acc 98.2\n", "133 0 -0.0435525923967 0.000828925735707 0.00144177674567\n", "133 10 -0.0827163383365 0.000869581501765 0.00144103421105\n", "133 20 -0.0615793131292 0.00084856131495 0.00143856063147\n", "133 30 -0.0579777061939 0.000889173450718 0.00143671765872\n", "133 40 -0.050244640559 0.000854203557871 0.00143549613335\n", "133 50 -0.0476381406188 0.00084188853857 0.00143574650943\n", "valid_acc 98.18166666666667\n", "134 0 -0.0635540485382 0.000902019355222 0.00143590963733\n", "134 10 -0.042935859412 0.000836536823122 0.00143531893459\n", "134 20 -0.0439970381558 0.000875449225764 0.00143421467398\n", "134 30 -0.0370463840663 0.000896943980413 0.00143362921043\n", "134 40 -0.0681588724256 0.000833707639298 0.0014303532886\n", "134 50 -0.0608901679516 0.000815960033827 0.00143067617273\n", "valid_acc 98.225\n", "135 0 -0.0619049444795 0.000844187228049 0.00143035998358\n", "135 10 -0.0347783118486 0.000870215386563 0.00143090567156\n", "135 20 -0.0506092719734 0.000846158090151 0.00143075197625\n", "135 30 -0.0432473383844 0.000834863485171 0.00143086034358\n", "135 40 -0.0498476400971 0.00086003521492 0.00143070164275\n", "135 50 -0.0600943267345 0.000884568067986 0.00142920345835\n", "valid_acc 98.25\n", "best valid_acc 98.25\n", "136 0 -0.0592861659825 0.000848830296654 0.00142944088335\n", "136 10 -0.0629855245352 0.000871746343817 0.00142785700143\n", "136 20 -0.0453311614692 0.000902844538075 0.00142761046389\n", "136 30 -0.0401224680245 0.000845939884956 0.00142685512919\n", "136 40 -0.0696270316839 0.000858999923587 0.00142727544576\n", "136 50 -0.0461598746479 0.000820548697457 0.00142667425738\n", "valid_acc 98.225\n", "137 0 -0.0468771792948 0.000827802282619 0.00142794061327\n", "137 10 -0.0625063478947 0.00082026757996 0.00142816455926\n", "137 20 -0.0516846068203 0.000798345215958 0.00143092924488\n", "137 30 -0.0551550127566 0.000772385713143 0.00143083987484\n", "137 40 -0.0755837112665 0.000838864700971 0.0014302982986\n", "137 50 -0.0656704679132 0.000835050174528 0.00143007270263\n", "valid_acc 98.22\n", "138 0 -0.0477900877595 0.000823958669583 0.00142813546339\n", "138 10 -0.0850610285997 0.0008390113828 0.00142658205869\n", "138 20 -0.0516856350005 0.00081950768841 0.00142568724707\n", "138 30 -0.0677756592631 0.000820787670253 0.00142578596017\n", "138 40 -0.0521739162505 0.000822329719593 0.00142519281068\n", "138 50 -0.0523561351001 0.000849909564355 0.00142523889319\n", "valid_acc 98.22833333333332\n", "139 0 -0.0670092180371 0.000806155864701 0.00142347599214\n", "139 10 -0.0498820953071 0.000856380059083 0.00142295687321\n", "139 20 -0.0370899438858 0.000850806752693 0.00142122884292\n", "139 30 -0.0432335361838 0.000841016844231 0.00141976530958\n", "139 40 -0.050335124135 0.000856523477836 0.0014198875526\n", "139 50 -0.0524032227695 0.000855443957272 0.00141996996347\n", "valid_acc 98.22166666666666\n", "140 0 -0.0457323379815 0.000879064062711 0.0014204505704\n", "140 10 -0.0548699088395 0.00086326506696 0.00142053655508\n", "140 20 -0.0584314651787 0.000839993265545 0.00141921039014\n", "140 30 -0.0450072064996 0.000813552146804 0.00141964364045\n", "140 40 -0.0542894974351 0.000882583015955 0.00141795268398\n", "140 50 -0.0392369292676 0.000863211349973 0.00141761462622\n", "valid_acc 98.195\n", "141 0 -0.0632042363286 0.000816767576025 0.00141620792373\n", "141 10 -0.0385671928525 0.000846210549797 0.00141611053303\n", "141 20 -0.0566364377737 0.000871355232244 0.00141532096836\n", "141 30 -0.0614061057568 0.000868429062339 0.0014145333103\n", "141 40 -0.0446271412075 0.000848109073764 0.00141322250802\n", "141 50 -0.0577774122357 0.000875138238109 0.0014117346532\n", "valid_acc 98.25666666666667\n", "best valid_acc 98.25666666666667\n", "142 0 -0.0692539587617 0.000824007535429 0.00141030650029\n", "142 10 -0.0687352865934 0.000842052106924 0.0014096591242\n", "142 20 -0.0734281912446 0.000819485949909 0.00140832910439\n", "142 30 -0.085573412478 0.00088771806557 0.00140960913744\n", "142 40 -0.0529267564416 0.000836292518824 0.00140757731595\n", "142 50 -0.0460841581225 0.000839108365125 0.00140656727907\n", "valid_acc 98.235\n", "143 0 -0.0794718191028 0.000872661730089 0.00140379985286\n", "143 10 -0.0774687901139 0.000861326145566 0.0014022254064\n", "143 20 -0.0423369519413 0.000858452738659 0.00140153548548\n", "143 30 -0.0535514205694 0.000833127413838 0.00140070335357\n", "143 40 -0.0348535217345 0.000892067343616 0.00139967050614\n", "143 50 -0.0548965297639 0.000881019730641 0.00139728921689\n", "valid_acc 98.28\n", "best valid_acc 98.28\n", "144 0 -0.0531147681177 0.000876755434282 0.00139904739366\n", "144 10 -0.0727975517511 0.000814389153282 0.00139839598574\n", "144 20 -0.0325495377183 0.000837181074959 0.00139706007748\n", "144 30 -0.070158533752 0.000831035251174 0.00139590235339\n", "144 40 -0.0641935840249 0.000892859645767 0.00139604099643\n", "144 50 -0.0479542315006 0.000850402129422 0.00139479297641\n", "valid_acc 98.27666666666667\n", "145 0 -0.0551719516516 0.000856142978517 0.00139364702193\n", "145 10 -0.0478553511202 0.000840439606512 0.00139389464742\n", "145 20 -0.0568789541721 0.00083273893452 0.00139304641093\n", "145 30 -0.0771863684058 0.000839004639963 0.00139125174865\n", "145 40 -0.0653681308031 0.000785487155717 0.00139042519728\n", "145 50 -0.0506055988371 0.000821849469062 0.00139051132874\n", "valid_acc 98.26833333333333\n", "146 0 -0.0736427530646 0.000867281914047 0.00138942157473\n", "146 10 -0.0507362224162 0.000838371311173 0.00139035945877\n", "146 20 -0.0549088530242 0.000853826254938 0.00138798251611\n", "146 30 -0.0649789050221 0.000860351144674 0.00138575802785\n", "146 40 -0.0657670348883 0.000847286763336 0.00138482879024\n", "146 50 -0.0565243475139 0.000834146232448 0.00138487652701\n", "valid_acc 98.25333333333333\n", "147 0 -0.0281095672399 0.000862693990976 0.0013855980532\n", "147 10 -0.0437469370663 0.000860429688583 0.00138420068519\n", "147 20 -0.0515654869378 0.000896346548027 0.00138253921284\n", "147 30 -0.0571266375482 0.000897837465906 0.00138124340176\n", "147 40 -0.0439893826842 0.000881136901988 0.00138084021267\n", "147 50 -0.0725200921297 0.000880345542174 0.00137949753314\n", "valid_acc 98.23666666666668\n", "148 0 -0.0641972720623 0.000928272499988 0.00138003126037\n", "148 10 -0.0594927482307 0.000904794116332 0.00137993845394\n", "148 20 -0.0665240064263 0.000902261456189 0.00137874986295\n", "148 30 -0.0470177605748 0.000923904496712 0.00137843148915\n", "148 40 -0.0615574605763 0.000886185686557 0.00137679969952\n", "148 50 -0.0553658492863 0.000921820073382 0.00137608463809\n", "valid_acc 98.21833333333333\n", "149 0 -0.0625844597816 0.000927943966688 0.0013772601489\n", "149 10 -0.0467022620142 0.000956052768022 0.00137681936682\n", "149 20 -0.0387440361083 0.000959552292474 0.00137525509465\n", "149 30 -0.048654448241 0.000931684748844 0.00137476988468\n", "149 40 -0.0622861683369 0.000916770351304 0.00137257791929\n", "149 50 -0.0600502751768 0.000886746297916 0.00137010496765\n", "valid_acc 98.23666666666668\n", "150 0 -0.0416145995259 0.000880307137691 0.00136922968921\n", "150 10 -0.056270994246 0.00086441215287 0.0013678959798\n", "150 20 -0.0593546703458 0.000883316417742 0.00136725356552\n", "150 30 -0.0512640401721 0.00085595231601 0.00136633939732\n", "150 40 -0.0612505562603 0.000891243831737 0.00136451713142\n", "150 50 -0.0475879609585 0.000878113181719 0.00136318242325\n", "valid_acc 98.235\n", "151 0 -0.0678986683488 0.000900347620755 0.00136209641135\n", "151 10 -0.0689625665545 0.000873214592864 0.00135961897962\n", "151 20 -0.0607396773994 0.00088844028149 0.00135832783415\n", "151 30 -0.0511861853302 0.00093426043351 0.00135797574903\n", "151 40 -0.0575734190643 0.000898574763944 0.0013577122296\n", "151 50 -0.0402120500803 0.000886651090028 0.0013570071452\n", "valid_acc 98.24833333333333\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "152 0 -0.0514714568853 0.000876312943036 0.00135715858157\n", "152 10 -0.0459122695029 0.000915753740334 0.00135585178092\n", "152 20 -0.0612772218883 0.00089560732777 0.0013565657805\n", "152 30 -0.0727086514235 0.000908773148453 0.00135681761297\n", "152 40 -0.0557520873845 0.000904829769985 0.00135655875222\n", "152 50 -0.0371167771518 0.000897012961126 0.00135647267128\n", "valid_acc 98.25333333333333\n", "153 0 -0.0732221901417 0.000868551480532 0.00135555031315\n", "153 10 -0.030618423596 0.00091822367954 0.00135407110072\n", "153 20 -0.0453856512904 0.000883491310067 0.00135299255999\n", "153 30 -0.0422092378139 0.000946581091616 0.00135272564787\n", "153 40 -0.0665907040238 0.000906939815556 0.00135165521483\n", "153 50 -0.0795193836093 0.000863634493359 0.0013508647911\n", "valid_acc 98.26833333333333\n", "154 0 -0.0536656007171 0.000853769863079 0.00135006670751\n", "154 10 -0.0649991855025 0.000932211747687 0.00134901274442\n", "154 20 -0.067850984633 0.000871738017735 0.00134804485224\n", "154 30 -0.05040660128 0.000865354431528 0.00134984562278\n", "154 40 -0.0436394736171 0.00092723123316 0.00134952384904\n", "154 50 -0.0744359493256 0.000897592586469 0.00134970374975\n", "valid_acc 98.26166666666667\n", "155 0 -0.0365789495409 0.000910337176827 0.00134912370451\n", "155 10 -0.0527518466115 0.000907790045027 0.00134914503141\n", "155 20 -0.0372278876603 0.000886603125196 0.00134745260247\n", "155 30 -0.0500158518553 0.000868163820153 0.00134656465097\n", "155 40 -0.0382633209229 0.000905553953109 0.00134674148447\n", "155 50 -0.0470318645239 0.000922258896106 0.0013463317191\n", "valid_acc 98.26833333333333\n", "156 0 -0.0641775280237 0.00090339045036 0.00134551821942\n", "156 10 -0.053704995662 0.000948232686155 0.00134516252838\n", "156 20 -0.0443421825767 0.000934255596908 0.00134441184719\n", "156 30 -0.053166039288 0.000928733775567 0.00134402986879\n", "156 40 -0.0504281930625 0.000868137391038 0.00134296522659\n", "156 50 -0.0517241768539 0.00088311718092 0.00134261096966\n", "valid_acc 98.26166666666667\n", "157 0 -0.0519532077014 0.000887010550307 0.00134103903261\n", "157 10 -0.0535536259413 0.000908222846324 0.00133971033055\n", "157 20 -0.0474943034351 0.000875404315638 0.00133879164922\n", "157 30 -0.0398260131478 0.000865557207286 0.00133685755633\n", "157 40 -0.0640741884708 0.000899348107656 0.00133637832042\n", "157 50 -0.0393374450505 0.000848390117567 0.00133648691218\n", "valid_acc 98.26833333333333\n", "158 0 -0.0397514328361 0.000873805828496 0.00133627920861\n", "158 10 -0.0526900999248 0.000896431674344 0.00133560300346\n", "158 20 -0.0475940704346 0.00086449675319 0.00133510021049\n", "158 30 -0.0554804392159 0.000876088814169 0.0013341919297\n", "158 40 -0.0576372481883 0.000857406235863 0.00133290224833\n", "158 50 -0.0334681235254 0.000871129166894 0.00133124787254\n", "valid_acc 98.275\n", "159 0 -0.0589462071657 0.000887153241394 0.00132980278669\n", "159 10 -0.0611152611673 0.000868834629263 0.00132806472622\n", "159 20 -0.0461984761059 0.000830464674602 0.00132669653543\n", "159 30 -0.0592455528677 0.000833248258689 0.00132545907837\n", "159 40 -0.0544679276645 0.000854740529191 0.00132375113972\n", "159 50 -0.0405938923359 0.000852620309939 0.0013244279749\n", "valid_acc 98.26\n", "160 0 -0.0441596917808 0.000822745204225 0.00132366153764\n", "160 10 -0.0784973353148 0.000881899449709 0.00132451720025\n", "160 20 -0.0526842586696 0.000882834863872 0.00132328593784\n", "160 30 -0.0557131581008 0.000845126296274 0.00132223194149\n", "160 40 -0.0359999462962 0.000851800834704 0.00132174923064\n", "160 50 -0.0522544495761 0.000810212273329 0.00132040815475\n", "valid_acc 98.27166666666668\n", "161 0 -0.037908423692 0.000867349505996 0.00131955106923\n", "161 10 -0.06642575562 0.000894465388601 0.00131797899395\n", "161 20 -0.0649001449347 0.000880411927377 0.00131702814513\n", "161 30 -0.0566593967378 0.000911674641612 0.00131656246472\n", "161 40 -0.0564725287259 0.000871550434908 0.00131652759064\n", "161 50 -0.0548553168774 0.000900400879642 0.00131616169151\n", "valid_acc 98.26\n", "162 0 -0.0505974404514 0.000883012399051 0.00131536514972\n", "162 10 -0.0280693732202 0.000868342555668 0.00131476976124\n", "162 20 -0.0478326752782 0.000919059483559 0.00131406438888\n", "162 30 -0.0661531016231 0.00095081457021 0.00131449885982\n", "162 40 -0.0650525614619 0.000915297483331 0.00131469516441\n", "162 50 -0.0442657135427 0.000885867750001 0.00131281949088\n", "valid_acc 98.29666666666667\n", "best valid_acc 98.29666666666667\n", "163 0 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"165 30 -0.0451746061444 0.000840576705374 0.00130041631143\n", "165 40 -0.0391593426466 0.000900545580191 0.0012995986795\n", "165 50 -0.0628209412098 0.000912902293848 0.00129920662005\n", "valid_acc 98.31333333333333\n", "best valid_acc 98.31333333333333\n", "166 0 -0.0577647536993 0.000867701390987 0.00129853808693\n", "166 10 -0.0652845054865 0.000915932689281 0.00129869236841\n", "166 20 -0.0507251359522 0.000865666442996 0.00129898661773\n", "166 30 -0.041658077389 0.000887870127949 0.00129795655464\n", "166 40 -0.0542897433043 0.000879395183496 0.00129825174768\n", "166 50 -0.0594994351268 0.000871055862398 0.00129679403838\n", "valid_acc 98.29833333333333\n", "167 0 -0.0537619553506 0.000924807787477 0.00129492394927\n", "167 10 -0.0463777668774 0.000928754436705 0.00129443069898\n", "167 20 -0.0541921332479 0.000900418984858 0.0012926955385\n", "167 30 -0.0501507334411 0.00089165616987 0.00129322692043\n", "167 40 -0.0375060774386 0.000908186580145 0.00129239372592\n", "167 50 -0.0568967908621 0.000911996769728 0.0012918812641\n", "valid_acc 98.29\n", "168 0 -0.0591671653092 0.000902331913618 0.00129114941653\n", "168 10 -0.0533181093633 0.000905578724766 0.00129124360928\n", "168 20 -0.0416429303586 0.000936072507636 0.00129055540163\n", "168 30 -0.0373829826713 0.00090891598614 0.00128944106295\n", "168 40 -0.0517991632223 0.000889954417289 0.00128769890812\n", "168 50 -0.0323797278106 0.000889961543375 0.0012867857451\n", "valid_acc 98.28\n", "169 0 -0.0349053964019 0.000910341455035 0.00128758375306\n", "169 10 -0.0471982061863 0.000897183191061 0.00128571427912\n", "169 20 -0.0604808963835 0.000896866898732 0.00128586683231\n", "169 30 -0.0446158833802 0.000859833446329 0.00128416833473\n", "169 40 -0.0463829226792 0.000872932656478 0.00128361062895\n", "169 50 -0.0499342307448 0.000883723062109 0.00128296380646\n", "valid_acc 98.265\n", "170 0 -0.0636169016361 0.000893941412531 0.00128330709184\n", "170 10 -0.0479315333068 0.000888884490188 0.00128277072677\n", "170 20 -0.0394618064165 0.00086527797441 0.00128071477705\n", "170 30 -0.0309218876064 0.000883679481285 0.00128073015659\n", "170 40 -0.0685916915536 0.000891690559053 0.00127964795295\n", "170 50 -0.0658219456673 0.000881333893071 0.00127778291556\n", "valid_acc 98.295\n", "171 0 -0.031484592706 0.00087695115127 0.0012777559831\n", "171 10 -0.0569985471666 0.000861520778062 0.00127857923711\n", "171 20 -0.043642539531 0.000879488707673 0.00127750055964\n", "171 30 -0.0482472516596 0.000792150373691 0.00127696293559\n", "171 40 -0.0567648261786 0.000837342822301 0.00127690199509\n", "171 50 -0.0427005626261 0.000842949374484 0.00127673132082\n", "valid_acc 98.28666666666666\n", "172 0 -0.0487403199077 0.000851407103215 0.00127678322204\n", "172 10 -0.0539409480989 0.000851923648514 0.00127607002542\n", "172 20 -0.0494241937995 0.000816059387607 0.00127710555463\n", "172 30 -0.0430926606059 0.000830076013202 0.0012757505976\n", "172 40 -0.0506930761039 0.000874381398061 0.00127409383408\n", "172 50 -0.0538383573294 0.000835358027071 0.00127407448471\n", "valid_acc 98.235\n", "173 0 -0.0308667831123 0.000845975798649 0.00127204772959\n", "173 10 -0.050987072289 0.00088187029927 0.00127071560335\n", "173 20 -0.0621209293604 0.000849895824195 0.00127079784401\n", "173 30 -0.0708320885897 0.000817047829268 0.00127045035087\n", "173 40 -0.0783835202456 0.00083792769537 0.00126942474109\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "173 50 -0.0474831648171 0.00080795734194 0.00126842552536\n", "valid_acc 98.28833333333333\n", "174 0 -0.0477095209062 0.00083697961909 0.00126784047137\n", "174 10 -0.0536008737981 0.000836621086922 0.00126669675002\n", "174 20 -0.053429376334 0.000805206495312 0.00126616859063\n", "174 30 -0.0383417271078 0.000834426606725 0.00126455902387\n", "174 40 -0.056108020246 0.000821800097136 0.00126467802958\n", "174 50 -0.0518060848117 0.000790490813534 0.00126437058862\n", "valid_acc 98.31\n", "175 0 -0.0593904405832 0.000799294471051 0.00126401246379\n", "175 10 -0.045736502856 0.0008018735391 0.00126339634444\n", "175 20 -0.0324431620538 0.000846627189731 0.00126223262439\n", "175 30 -0.0604943707585 0.000826791312576 0.00126125241308\n", "175 40 -0.0627987980843 0.000804175525206 0.00126083844047\n", "175 50 -0.0545062199235 0.000787666965506 0.00126011890642\n", "valid_acc 98.31\n", "176 0 -0.0411993414164 0.000797386619866 0.00125907342602\n", "176 10 -0.0470187626779 0.000809215320605 0.00125841808311\n", "176 20 -0.0440991520882 0.000763487277486 0.00125854431006\n", "176 30 -0.0433922335505 0.000795839261837 0.00125606548858\n", "176 40 -0.0549840666354 0.000791263063247 0.00125391471312\n", "176 50 -0.0467537045479 0.000766123018675 0.00125327597107\n", "valid_acc 98.30166666666666\n", "177 0 -0.0539192669094 0.000748242950114 0.0012535012535\n", "177 10 -0.0339245423675 0.000764838982086 0.00125290359073\n", "177 20 -0.0433500520885 0.000742849204341 0.00125153311066\n", "177 30 -0.0601906441152 0.000750666912681 0.00125171597145\n", "177 40 -0.0552629828453 0.000773667942924 0.00125080823595\n", "177 50 -0.0412181280553 0.000749350337017 0.00125022653064\n", "valid_acc 98.30333333333333\n", "178 0 -0.0426410101354 0.000790674277884 0.00124958117796\n", "178 10 -0.0335296690464 0.000726389395708 0.001249103664\n", "178 20 -0.046478189528 0.000733613746928 0.00124879307283\n", "178 30 -0.0404225997627 0.00076674857297 0.00124663014222\n", "178 40 -0.0403753519058 0.000755426928675 0.0012456522883\n", "178 50 -0.0413160063326 0.000749211062828 0.00124501087661\n", "valid_acc 98.33666666666666\n", "best valid_acc 98.33666666666666\n", "179 0 -0.0578407496214 0.000761592377057 0.00124461139041\n", "179 10 -0.0582279525697 0.000748537635004 0.0012439152964\n", "179 20 -0.0620231740177 0.000766792133985 0.00124271053555\n", "179 30 -0.0670010820031 0.000771679991448 0.00124189246241\n", "179 40 -0.0513836406171 0.000778698890603 0.00124123275488\n", "179 50 -0.0589698031545 0.000746217502735 0.00124055294759\n", "valid_acc 98.31666666666666\n", "180 0 -0.0496127679944 0.000770090538822 0.00123957117986\n", "180 10 -0.0473784059286 0.000740213855823 0.0012389829117\n", "180 20 -0.0363952592015 0.000758513413111 0.00123913471099\n", "180 30 -0.0535427480936 0.000793446683927 0.0012387876089\n", "180 40 -0.0613001734018 0.000782981522764 0.00123882878871\n", "180 50 -0.0574913024902 0.000769046218073 0.00123825112401\n", "valid_acc 98.35666666666667\n", "best valid_acc 98.35666666666667\n", "181 0 -0.0463977009058 0.000786768212012 0.00123805737875\n", "181 10 -0.0451248213649 0.000798606569221 0.0012379665715\n", "181 20 -0.042706143111 0.000749390737626 0.00123668502579\n", "181 30 -0.043582059443 0.000783265886031 0.00123565264321\n", "181 40 -0.0613915100694 0.000798675500738 0.00123496694817\n", "181 50 -0.058913551271 0.000763463029781 0.00123494382985\n", "valid_acc 98.345\n", "182 0 -0.0740839838982 0.000784699808986 0.00123534328318\n", "182 10 -0.0519331134856 0.000736718479247 0.00123493136859\n", "182 20 -0.0699543356895 0.00075523476884 0.00123409350154\n", "182 30 -0.040231578052 0.000723356194869 0.00123269568957\n", "182 40 -0.0373398959637 0.000774818874261 0.00123271110177\n", "182 50 -0.0489723496139 0.000748692761742 0.00123213026033\n", "valid_acc 98.335\n", "183 0 -0.0708918124437 0.00072705210978 0.00123270176078\n", "183 10 -0.0459363982081 0.000746429957971 0.00123208986203\n", "183 20 -0.0477069318295 0.000722163111882 0.00123055450453\n", "183 30 -0.0572790019214 0.000690848009514 0.00122980746281\n", "183 40 -0.0329936742783 0.000735851007544 0.00122787203883\n", "183 50 -0.0672030597925 0.00070715076085 0.00122785614512\n", "valid_acc 98.31833333333333\n", "184 0 -0.044312722981 0.000739536311706 0.00122718793914\n", "184 10 -0.0811607912183 0.000778690225299 0.00122655389786\n", "184 20 -0.0747340992093 0.000761172398799 0.00122585271347\n", "184 30 -0.0431657321751 0.000708983343942 0.00122448722317\n", "184 40 -0.0389268510044 0.0007134473778 0.00122453549663\n", "184 50 -0.0381723493338 0.000723246834334 0.00122304895333\n", "valid_acc 98.34333333333333\n", "185 0 -0.0574692003429 0.000760293778809 0.00122292812228\n", "185 10 -0.0584839396179 0.000761790030468 0.00122224353108\n", "185 20 -0.0479243434966 0.000767104527897 0.00122187999868\n", "185 30 -0.0586817413568 0.000743933851219 0.00122202424011\n", "185 40 -0.052028875798 0.000699512054654 0.00122221241876\n", "185 50 -0.0832655206323 0.000711596803225 0.00122220339585\n", "valid_acc 98.315\n", "186 0 -0.0500752255321 0.000727990096874 0.00122115418609\n", "186 10 -0.0406265631318 0.000738485184541 0.00122031271342\n", "186 20 -0.0668438002467 0.000715922583266 0.0012193775461\n", "186 30 -0.058911472559 0.000727236260003 0.00121754958586\n", "186 40 -0.041939586401 0.00075713775805 0.00121686783162\n", "186 50 -0.0548929199576 0.000723994065687 0.00121751638311\n", "valid_acc 98.35166666666667\n", "187 0 -0.0720793381333 0.000779791563928 0.00121675555162\n", "187 10 -0.0487797707319 0.000748791576704 0.00121576895118\n", "187 20 -0.0406708829105 0.000751935153389 0.00121552698332\n", "187 30 -0.0526085831225 0.000773033591389 0.00121519904348\n", "187 40 -0.0452137254179 0.000796935702001 0.0012156824772\n", "187 50 -0.0654550492764 0.00073973616003 0.00121520342313\n", "valid_acc 98.33\n", "188 0 -0.0582276545465 0.000761173886869 0.00121285144603\n", "188 10 -0.0556368157268 0.000745903983354 0.00121288022479\n", "188 20 -0.0588191412389 0.000755748747222 0.00121286655752\n", "188 30 -0.0569982230663 0.000738458940251 0.00121244598938\n", "188 40 -0.0743990838528 0.000761987029647 0.00121149980951\n", "188 50 -0.065574683249 0.00077762337152 0.00121089602175\n", "valid_acc 98.32\n", "189 0 -0.0437116138637 0.00078888140893 0.00120973989964\n", "189 10 -0.0567984059453 0.000742566332919 0.00120933990181\n", "189 20 -0.0469182096422 0.000778409735886 0.00120938595415\n", "189 30 -0.048832770437 0.000789817315442 0.00120947805152\n", "189 40 -0.0427378043532 0.000767667973274 0.00120918809151\n", "189 50 -0.0581948123872 0.000772867735898 0.00120905143317\n", "valid_acc 98.33166666666666\n", "190 0 -0.0383418500423 0.000793265800657 0.00120896851233\n", "190 10 -0.0454430505633 0.000763204079821 0.0012084448247\n", "190 20 -0.0624247938395 0.00079942503312 0.00120916601642\n", "190 30 -0.0442630723119 0.000812917055005 0.00120844311422\n", "190 40 -0.053815510124 0.000788731942139 0.00120798623662\n", "190 50 -0.0360199660063 0.000777523205184 0.00120759671956\n", "valid_acc 98.33\n", "191 0 -0.0496319122612 0.000740282238082 0.00120777441159\n", "191 10 -0.0573468916118 0.000757482227782 0.00120723888259\n", "191 20 -0.0495000593364 0.000779897835829 0.00120708976275\n", "191 30 -0.0516080223024 0.000791567670107 0.00120675380849\n", "191 40 -0.0557386428118 0.000741442248809 0.00120640257765\n", "191 50 -0.0511899106205 0.000809706476314 0.0012051259229\n", "valid_acc 98.32333333333332\n", "192 0 -0.0283121727407 0.000759378756333 0.00120388411684\n", "192 10 -0.0581455677748 0.000782818178423 0.00120324874927\n", "192 20 -0.045495506376 0.000738374144138 0.00120177290832\n", "192 30 -0.0436939522624 0.000765754452316 0.00120163490544\n", "192 40 -0.0431192927063 0.000761414881196 0.00120027180094\n", "192 50 -0.0674074813724 0.00077669273952 0.00119984295545\n", "valid_acc 98.345\n", "193 0 -0.0526796653867 0.000800167663726 0.0011982708987\n", "193 10 -0.0411231964827 0.000796059404549 0.00119829470597\n", "193 20 -0.0506014712155 0.000777297813753 0.00119771526633\n", "193 30 -0.0628296211362 0.000790418153375 0.00119711385232\n", "193 40 -0.0550332702696 0.000802120933525 0.00119669723187\n", "193 50 -0.0570587478578 0.000751824378528 0.00119528125654\n", "valid_acc 98.32666666666667\n", "194 0 -0.0552328340709 0.000776879316239 0.00119476144852\n", "194 10 -0.0442096740007 0.000753933542581 0.00119390077769\n", "194 20 -0.0461954735219 0.000774908667451 0.00119362764369\n", "194 30 -0.0468613319099 0.000774508571173 0.00119321691757\n", "194 40 -0.0592838712037 0.000793845442876 0.00119315352961\n", "194 50 -0.0518176443875 0.000774420581761 0.00119362785456\n", "valid_acc 98.35166666666667\n", "195 0 -0.048651073128 0.000782783483412 0.00119254726322\n", "195 10 -0.0380353406072 0.000784610685455 0.00119199447323\n", "195 20 -0.055081486702 0.000788320284039 0.00119237526011\n", "195 30 -0.0866835042834 0.000822308614721 0.00119079615479\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "195 40 -0.0401849076152 0.000797278053776 0.00118940614611\n", "195 50 -0.05584590137 0.000819027831252 0.00118800309985\n", "valid_acc 98.34333333333333\n", "196 0 -0.0350663922727 0.000821504748481 0.00118651557139\n", "196 10 -0.0254001691937 0.000809798022409 0.00118535897098\n", "196 20 -0.0431939214468 0.000798445720637 0.00118538135469\n", "196 30 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0.00117585319776\n", "valid_acc 98.34\n", "199 0 -0.0521249175072 0.000784324109614 0.00117534302099\n", "199 10 -0.0471701622009 0.000775476115642 0.00117584615994\n", "199 20 -0.0420067720115 0.00081409471214 0.00117528136877\n", "199 30 -0.0533111654222 0.000763709664978 0.00117488191645\n", "199 40 -0.0439882874489 0.00077570937798 0.00117620255044\n", "199 50 -0.0455386191607 0.000759512579381 0.00117512599682\n", "valid_acc 98.34833333333334\n", "200 0 -0.0437854900956 0.000784017789794 0.00117520743798\n", "200 10 -0.0388743914664 0.000823873786535 0.00117422877014\n", "200 20 -0.0500965751708 0.000779603357464 0.00117436629002\n", "200 30 -0.055701635778 0.000808503155932 0.00117423857872\n", "200 40 -0.0418528914452 0.000800653407314 0.00117432954001\n", "200 50 -0.0625305101275 0.000810121155987 0.00117235051531\n", "valid_acc 98.34166666666667\n", "201 0 -0.0423362851143 0.000814069406257 0.00117257335066\n", "201 10 -0.0443955734372 0.000783577522822 0.00117163332761\n", "201 20 -0.046089373529 0.000805689167558 0.00117139704587\n", "201 30 -0.0346711017191 0.000818165594076 0.00117142301998\n", "201 40 -0.0791814252734 0.000839128557103 0.00116954690187\n", "201 50 -0.0531535334885 0.000841788397861 0.00117031151894\n", "valid_acc 98.345\n", "202 0 -0.0392623431981 0.000827604590503 0.00117035997425\n", "202 10 -0.0437484495342 0.000821741055265 0.00117047737492\n", "202 20 -0.0709912404418 0.000808460111558 0.00117004424735\n", "202 30 -0.063643693924 0.000833701161709 0.00116955288527\n", "202 40 -0.0291819907725 0.000853805869042 0.00116870324336\n", "202 50 -0.0508944652975 0.000762115017 0.00116877523498\n", "valid_acc 98.37833333333333\n", "best valid_acc 98.37833333333333\n", "203 0 -0.0450005717576 0.000834129794427 0.00116802989308\n", "203 10 -0.0702454671264 0.000809568363633 0.00116835986569\n", "203 20 -0.0435115173459 0.000816826992787 0.00116768085148\n", "203 30 -0.0500019788742 0.000811377579192 0.00116712551507\n", "203 40 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0.000791655096808 0.00116140667725\n", "206 10 -0.0815331190825 0.00079830429677 0.00116188686\n", "206 20 -0.0603085421026 0.000828866518975 0.00116198418757\n", "206 30 -0.0388705059886 0.000793179670502 0.00116107853578\n", "206 40 -0.0539342425764 0.00080115059367 0.00116132244912\n", "206 50 -0.0436648055911 0.000797518537968 0.00116127689127\n", "valid_acc 98.35000000000001\n", "207 0 -0.0413753502071 0.000818770319495 0.0011617312474\n", "207 10 -0.0684855878353 0.000751577705815 0.00116036460371\n", "207 20 -0.0524806827307 0.000811047432987 0.00115941767594\n", "207 30 -0.0546194203198 0.000771189756974 0.00116026934457\n", "207 40 -0.0449470840394 0.00074874087458 0.00116032628292\n", "207 50 -0.0481476299465 0.000810915405374 0.00115944983722\n", "valid_acc 98.36666666666667\n", "208 0 -0.047984752804 0.000797143471836 0.00115883222872\n", "208 10 -0.0360462777317 0.000776941039751 0.00115909079604\n", "208 20 -0.0469612330198 0.00081145653059 0.00115755962819\n", "208 30 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0.000791687266905 0.00115232404835\n", "valid_acc 98.37\n", "211 0 -0.0523530393839 0.00080022152817 0.0011524570128\n", "211 10 -0.0780738145113 0.000786408635968 0.00115299736374\n", "211 20 -0.0354016274214 0.000793536469953 0.00115168003344\n", "211 30 -0.061315998435 0.000787618809138 0.00115162157122\n", "211 40 -0.0420774295926 0.000798796652089 0.00114988764244\n", "211 50 -0.0429077595472 0.000772424363026 0.00114964187571\n", "valid_acc 98.37166666666667\n", "212 0 -0.0454244539142 0.000796814621661 0.00114901013984\n", "212 10 -0.0459855496883 0.000790409651757 0.00114804239666\n", "212 20 -0.0633552372456 0.000784101188865 0.00114695068478\n", "212 30 -0.0573824830353 0.0008095911074 0.00114599247277\n", "212 40 -0.0448735691607 0.000790783840166 0.00114615288001\n", "212 50 -0.0403268821537 0.000784063997109 0.00114655978257\n", "valid_acc 98.36833333333334\n", "213 0 -0.0460169278085 0.000769508908255 0.0011456313323\n", "213 10 -0.0446665249765 0.000807314854806 0.00114487215505\n", "213 20 -0.0387624725699 0.000805689680081 0.00114567986541\n", "213 30 -0.0626838281751 0.000851499160085 0.00114365400407\n", "213 40 -0.0460946857929 0.000790999619932 0.0011427046601\n", "213 50 -0.0593145154417 0.000795104430588 0.0011419557312\n", "valid_acc 98.38333333333334\n", "best valid_acc 98.38333333333334\n", "214 0 -0.0441919006407 0.00078657503844 0.00114194361045\n", "214 10 -0.049303907901 0.000783805401093 0.00114186656742\n", "214 20 -0.047202296555 0.000815244334139 0.00114158568444\n", "214 30 -0.0278968177736 0.000802615631984 0.00114118375238\n", "214 40 -0.0550539530814 0.000772442219024 0.00114068392017\n", "214 50 -0.0605138167739 0.000765021368836 0.00114004071951\n", "valid_acc 98.36\n", "215 0 -0.0563860796392 0.000761442412402 0.00114011007117\n", "215 10 -0.0342409797013 0.000776606277684 0.00113861580795\n", "215 20 -0.0459137707949 0.000769243245083 0.00113837010104\n", "215 30 -0.0668857097626 0.000768017282998 0.00113729630614\n", "215 40 -0.038609713316 0.000773880705152 0.00113663148917\n", "215 50 -0.0778477564454 0.000739849453309 0.00113626315363\n", "valid_acc 98.35333333333334\n", "216 0 -0.0479951240122 0.00078202932301 0.00113487001423\n", "216 10 -0.0570288449526 0.00074992567017 0.00113471010314\n", "216 20 -0.0884046927094 0.000751918915931 0.00113358222695\n", "216 30 -0.0457082055509 0.000722091029467 0.00113207194446\n", "216 40 -0.0656186938286 0.000766213213322 0.00113126998125\n", "216 50 -0.0430636592209 0.000768551402608 0.00113118854549\n", "valid_acc 98.39666666666666\n", "best valid_acc 98.39666666666666\n", "217 0 -0.0435220561922 0.000758143816386 0.00113099626712\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "217 10 -0.0331619977951 0.000745991584884 0.00113051080544\n", "217 20 -0.035424567759 0.000753384104668 0.00113144751067\n", "217 30 -0.0367377921939 0.00072147062138 0.00113148883086\n", "217 40 -0.0572004169226 0.00073390004988 0.00113123771878\n", "217 50 -0.0546262077987 0.000767081034778 0.00113069236172\n", "valid_acc 98.38333333333334\n", "218 0 -0.0520004332066 0.000746730055623 0.00113089491723\n", "218 10 -0.0301133580506 0.000735633103216 0.00113105254447\n", "218 20 -0.05264486745 0.000761892501132 0.00113064909396\n", "218 30 -0.0542731359601 0.00072701972617 0.00112978377118\n", "218 40 -0.0356832072139 0.000766315152333 0.00112935164282\n", "218 50 -0.0446718782187 0.00076455076966 0.00112989586504\n", "valid_acc 98.405\n", "best valid_acc 98.405\n", "219 0 -0.0325374901295 0.000738393857435 0.00112909371404\n", "219 10 -0.0382205843925 0.000751709017076 0.00112788758185\n", "219 20 -0.0401618108153 0.000711796707398 0.00112631300182\n", "219 30 -0.0359147936106 0.000765841167231 0.00112649059236\n", "219 40 -0.0439355559647 0.000741209477635 0.00112665628844\n", "219 50 -0.0567854419351 0.000734386713722 0.00112527268364\n", "valid_acc 98.38833333333334\n", "220 0 -0.0552187971771 0.000731648458631 0.00112492643228\n", "220 10 -0.0321407578886 0.000747947713903 0.00112384883014\n", "220 20 -0.0441067330539 0.000743277095197 0.00112360633405\n", "220 30 -0.0646108686924 0.000743514757586 0.00112131699461\n", "220 40 -0.0576377809048 0.000727739179617 0.00112054277475\n", "220 50 -0.0733422264457 0.000692103337809 0.00111969011658\n", "valid_acc 98.37666666666667\n", "221 0 -0.0492663122714 0.000743313777629 0.00111986833513\n", "221 10 -0.0467997342348 0.000718444490649 0.00111980494812\n", "221 20 -0.0509990379214 0.000732028757663 0.00111916950887\n", "221 30 -0.0360955148935 0.000740595031017 0.00111836874082\n", "221 40 -0.0427762381732 0.000741689671312 0.00111730107133\n", "221 50 -0.0653721913695 0.000724668389588 0.00111667497369\n", "valid_acc 98.39\n", "222 0 -0.0563725568354 0.000743966567476 0.00111610228024\n", "222 10 -0.0404825471342 0.00077739314895 0.00111530148186\n", "222 20 -0.0411101765931 0.000784955364489 0.00111369021008\n", "222 30 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"valid_acc 98.42166666666667\n", "best valid_acc 98.42166666666667\n", "225 0 -0.0774942114949 0.000756224432264 0.00110820672814\n", "225 10 -0.0791731700301 0.000734163171909 0.0011076397843\n", "225 20 -0.031609620899 0.000738931494359 0.00110725084836\n", "225 30 -0.0390453711152 0.000700404518441 0.00110718895989\n", "225 40 -0.0312963239849 0.00072675951639 0.0011076688074\n", "225 50 -0.0526623502374 0.000702915058836 0.00110796305266\n", "valid_acc 98.41333333333333\n", "226 0 -0.0371836163104 0.000700596600074 0.00110730017536\n", "226 10 -0.0382770225406 0.000728500125562 0.00110581073797\n", "226 20 -0.0527361854911 0.000729682847103 0.00110550672879\n", "226 30 -0.0501923076808 0.000734871811713 0.00110472349433\n", "226 40 -0.0614292770624 0.000711447753696 0.00110412143521\n", "226 50 -0.054987154901 0.000718385059675 0.00110353758583\n", "valid_acc 98.43499999999999\n", "best valid_acc 98.43499999999999\n", "227 0 -0.0503088831902 0.000788567139585 0.00110343618997\n", "227 10 -0.046155449003 0.000731731023445 0.00110239945652\n", "227 20 -0.0382932759821 0.000725537474937 0.00110236467731\n", "227 30 -0.0544111393392 0.000768589624618 0.00110273389027\n", "227 40 -0.0336452722549 0.00077212152642 0.0011023558843\n", "227 50 -0.0402236878872 0.000754474503196 0.00110209419053\n", "valid_acc 98.405\n", "228 0 -0.0621563605964 0.000742352441464 0.00110181341433\n", "228 10 -0.0591439083219 0.000745114801953 0.00110068496684\n", "228 20 -0.0476681664586 0.000794919021298 0.00109962923257\n", "228 30 -0.0416237637401 0.000789282926371 0.00110009800655\n", "228 40 -0.0598993450403 0.000747683001161 0.00110026140034\n", "228 50 -0.0327912755311 0.000792634680614 0.00109968497291\n", "valid_acc 98.41499999999999\n", "229 0 -0.068613640964 0.000734699417189 0.00109855061205\n", "229 10 -0.042048510164 0.000729529189296 0.00109824300407\n", "229 20 -0.064685113728 0.000759170296633 0.00109706333871\n", "229 30 -0.0605503171682 0.000771950827018 0.00109579286114\n", "229 40 -0.0433752834797 0.000738030686868 0.00109421540166\n", "229 50 -0.0521956533194 0.000746436851641 0.00109394419837\n", "valid_acc 98.42166666666667\n", "230 0 -0.0524156466126 0.000722532839382 0.00109411024017\n", "230 10 -0.0549267120659 0.000759859437563 0.00109328533145\n", "230 20 -0.0262710005045 0.000730141620181 0.00109198610462\n", "230 30 -0.0473004542291 0.000778429008184 0.00109215473307\n", "230 40 -0.0436242297292 0.000750649460041 0.00109156453637\n", "230 50 -0.0518412701786 0.000771279115978 0.00109201442071\n", "valid_acc 98.405\n", "231 0 -0.0497645922005 0.000748337727405 0.0010917807667\n", "231 10 -0.0730097740889 0.000751926472218 0.00109040619992\n", "231 20 -0.045311216265 0.000746210389763 0.00108999804182\n", "231 30 -0.0617454834282 0.00072664097748 0.00109035601425\n", "231 40 -0.0339466258883 0.000744057579874 0.00109031344185\n", "231 50 -0.0414500273764 0.00074397993566 0.00109005446129\n", "valid_acc 98.375\n", "232 0 -0.0634429678321 0.000741430087522 0.00108981760216\n", "232 10 -0.0480462945998 0.000777990660475 0.00108920100201\n", "232 20 -0.0593526512384 0.000728687532661 0.00108952883408\n", "232 30 -0.0439423508942 0.00072228395894 0.00108897338104\n", "232 40 -0.0464712455869 0.000734841919261 0.00108827859462\n", "232 50 -0.0636687576771 0.000742015150073 0.00108797947037\n", "valid_acc 98.41333333333333\n", "233 0 -0.0472339242697 0.000775006302574 0.00108822719424\n", "233 10 -0.0433186031878 0.000731393042916 0.00108732107863\n", "233 20 -0.0504835359752 0.000736398962871 0.00108665782035\n", "233 30 -0.0476487614214 0.000726851200264 0.00108651541166\n", "233 40 -0.0302079003304 0.000705067795819 0.00108571302187\n", "233 50 -0.0499394722283 0.000743030514676 0.00108565926226\n", "valid_acc 98.43333333333332\n", "234 0 -0.051200542599 0.000724121928285 0.00108568325676\n", "234 10 -0.0314697176218 0.000722256094316 0.00108494485058\n", "234 20 -0.0473480448127 0.000737493019708 0.00108461920566\n", "234 30 -0.0469308495522 0.000703508779496 0.00108396799164\n", "234 40 -0.0420580990613 0.000736372442616 0.00108363815093\n", "234 50 -0.0417136289179 0.000735925007131 0.00108293061901\n", "valid_acc 98.43166666666666\n", "235 0 -0.0384425111115 0.000721478239446 0.00108226551871\n", "235 10 -0.0573138184845 0.000748230343284 0.00108232557507\n", "235 20 -0.0448119007051 0.000748798570755 0.0010829671918\n", "235 30 -0.0518580675125 0.000737455728307 0.00108334372601\n", "235 40 -0.0613280497491 0.000719913093002 0.00108354964271\n", "235 50 -0.0770723894238 0.000730136965015 0.00108279899422\n", "valid_acc 98.42166666666667\n", "236 0 -0.0761702805758 0.000766462617226 0.00108236341961\n", "236 10 -0.0507903657854 0.000716282259395 0.00108107082446\n", "236 20 -0.0782979205251 0.000745782276349 0.0010807175259\n", "236 30 -0.0482357218862 0.000716956305553 0.00107931555144\n", "236 40 -0.0517282932997 0.000716673271895 0.00107914220001\n", "236 50 -0.0554807037115 0.000722955959016 0.00107917003124\n", "valid_acc 98.42833333333333\n", "237 0 -0.0732577815652 0.000725651559802 0.00107880876527\n", "237 10 -0.0635118037462 0.000692978284044 0.00107802101712\n", "237 20 -0.0598705485463 0.000714107323859 0.00107840743737\n", "237 30 -0.0732093304396 0.000714709587819 0.00107840208223\n", "237 40 -0.0727673172951 0.000715342836861 0.00107767036674\n", "237 50 -0.049179520458 0.000733916875118 0.0010773121905\n", "valid_acc 98.43499999999999\n", "best valid_acc 98.43499999999999\n", "238 0 -0.0419949628413 0.000720376271982 0.0010769924279\n", "238 10 -0.0649216398597 0.000756669420449 0.00107599586735\n", "238 20 -0.067502990365 0.000761538053166 0.00107599160531\n", "238 30 -0.0606059134007 0.000715783575881 0.00107650888161\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "238 40 -0.0404366776347 0.000736800007925 0.00107593857851\n", "238 50 -0.0422367453575 0.000730835292335 0.00107542783597\n", "valid_acc 98.43833333333333\n", "best valid_acc 98.43833333333333\n", "239 0 -0.0471026450396 0.000726711764279 0.00107467749244\n", "239 10 -0.0461262501776 0.000722845846587 0.00107478125676\n", "239 20 -0.0672420337796 0.000730854813352 0.0010740406916\n", "239 30 -0.0425000414252 0.000693283879092 0.0010737913064\n", "239 40 -0.0436086393893 0.000722382252289 0.00107330618744\n", "239 50 -0.0430294647813 0.000745815339338 0.00107279597928\n", "valid_acc 98.4\n", "240 0 -0.0435610041022 0.000714353293585 0.00107175311822\n", "240 10 -0.0484903417528 0.000717347666879 0.00107076304249\n", "240 20 -0.0864793881774 0.00076078621618 0.00107174503238\n", "240 30 -0.0588762648404 0.000744634732276 0.00107170051989\n", "240 40 -0.0667910948396 0.000723769275775 0.00107166405869\n", "240 50 -0.0651135742664 0.00070883444831 0.00107166265388\n", "valid_acc 98.42833333333333\n", "241 0 -0.0432455763221 0.00072040432116 0.00107121590565\n", "241 10 -0.0617080554366 0.000754924369604 0.0010701235304\n", "241 20 -0.0598648004234 0.000722720797357 0.00106965771985\n", "241 30 -0.0527330897748 0.000716930758872 0.0010698830567\n", "241 40 -0.0374614223838 0.00071812137621 0.00106939121849\n", "241 50 -0.0657548680902 0.000748373747633 0.00106906590328\n", "valid_acc 98.41666666666666\n", "242 0 -0.0596330091357 0.000733061562523 0.00106763887427\n", "242 10 -0.080892637372 0.00076671861515 0.00106610954209\n", "242 20 -0.0384303964674 0.000720898155961 0.00106519823225\n", "242 30 -0.0436999835074 0.000753167644134 0.00106471408636\n", "242 40 -0.0646029636264 0.000738639712506 0.00106562220533\n", "242 50 -0.0510055013001 0.000723912013696 0.00106499909909\n", "valid_acc 98.405\n", "243 0 -0.0329557545483 0.000709160626995 0.0010634673712\n", "243 10 -0.0428574383259 0.000717681830924 0.00106316136696\n", "243 20 -0.041273124516 0.00068794298239 0.00106253952988\n", "243 30 -0.0451250597835 0.000701398414509 0.00106171993779\n", "243 40 -0.061485465616 0.000714974842397 0.00106105599404\n", "243 50 -0.0357345603406 0.000712363495733 0.00106080936874\n", "valid_acc 98.42\n", "244 0 -0.0638526380062 0.000717753457461 0.00106016514511\n", "244 10 -0.0334291085601 0.000717333360953 0.00105979363092\n", "244 20 -0.0486078858376 0.00070971291474 0.00105937615129\n", "244 30 -0.0243111029267 0.000706877528742 0.00105905439332\n", "244 40 -0.0439167842269 0.00073055494369 0.00105866964175\n", "244 50 -0.0601619072258 0.000722827834199 0.001059120428\n", "valid_acc 98.44333333333334\n", "best valid_acc 98.44333333333334\n", "245 0 -0.0458662211895 0.000705350969344 0.00105883240168\n", "245 10 -0.0373425148427 0.000720959072123 0.00105867347465\n", "245 20 -0.0340591520071 0.000737122581751 0.00105898141322\n", "245 30 -0.0445691272616 0.000711412579133 0.00105780038323\n", "245 40 -0.0475545935333 0.000731005401337 0.00105820772921\n", "245 50 -0.057092115283 0.00072168136891 0.0010580798308\n", "valid_acc 98.41166666666666\n", "246 0 -0.0462033562362 0.000715661413725 0.00105781303683\n", "246 10 -0.0652857795358 0.000721435437222 0.00105719702138\n", "246 20 -0.0600479468703 0.000736152911782 0.00105696880714\n", "246 30 -0.0417059846222 0.000724558914183 0.00105674393551\n", "246 40 -0.0492502376437 0.000726659089428 0.00105593181599\n", "246 50 -0.0434093549848 0.000742407694904 0.00105570772059\n", "valid_acc 98.42\n", "247 0 -0.0448309071362 0.000711165570763 0.00105556785142\n", "247 10 -0.0525945052505 0.000698679116721 0.00105637698606\n", "247 20 -0.0551238022745 0.000756717030675 0.00105661733577\n", "247 30 -0.0492401272058 0.000702550451482 0.00105569264627\n", "247 40 -0.0480381920934 0.000709526881164 0.00105625501615\n", "247 50 -0.0633437857032 0.000725376859338 0.00105632475247\n", "valid_acc 98.43166666666666\n", "248 0 -0.0425701625645 0.000775873165822 0.00105567213269\n", "248 10 -0.0376820489764 0.000734253082245 0.00105469242711\n", "248 20 -0.0410680621862 0.000736758622467 0.00105494903214\n", "248 30 -0.0529600791633 0.000777371477681 0.00105463401367\n", "248 40 -0.0399911627173 0.000700406024477 0.00105475395912\n", "248 50 -0.0457464382052 0.000756861825473 0.00105521109567\n", "valid_acc 98.41833333333334\n", "249 0 -0.0308221317828 0.000749161519786 0.00105405009057\n", "249 10 -0.0379980579019 0.000729859288904 0.00105345527493\n", "249 20 -0.0629379674792 0.000733539721379 0.0010534642952\n", "249 30 -0.047852806747 0.000738022007294 0.00105336652323\n", "249 40 -0.0559568032622 0.000719882998888 0.00105343302697\n", "249 50 -0.0446618236601 0.000750466443607 0.00105274009637\n", "valid_acc 98.42666666666666\n", "250 0 -0.043635122478 0.000688052392783 0.00105270364604\n", "250 10 -0.0583987496793 0.000768506870693 0.0010532949718\n", "250 20 -0.0440122969449 0.000739617281327 0.00105305336477\n", "250 30 -0.0255456790328 0.00073848621776 0.00105291415311\n", "250 40 -0.0624394416809 0.000717543176398 0.00105292298477\n", "250 50 -0.0387649722397 0.000747195687887 0.00105298142569\n", "valid_acc 98.43666666666667\n", "251 0 -0.0495283342898 0.000732173778495 0.0010528802567\n", "251 10 -0.0430175252259 0.000710145716398 0.00105263923992\n", "251 20 -0.0413231253624 0.000737349560713 0.00105246859197\n", "251 30 -0.0631574168801 0.000695563796294 0.00105246723114\n", "251 40 -0.0396432876587 0.000740673093346 0.00105189349824\n", "251 50 -0.0473195426166 0.000747818229405 0.0010514453104\n", "valid_acc 98.43333333333332\n", "252 0 -0.0290349088609 0.000734789969899 0.00105148201871\n", "252 10 -0.0617532469332 0.000721879333443 0.00105129129147\n", "252 20 -0.0333962850273 0.000712557737312 0.001051642738\n", "252 30 -0.0364178270102 0.000732392708891 0.00105031277826\n", "252 40 -0.0567881576717 0.000738094358215 0.00104948767378\n", "252 50 -0.0616801194847 0.000773434733591 0.00104846290457\n", "valid_acc 98.44833333333334\n", "best valid_acc 98.44833333333334\n", "253 0 -0.0420758239925 0.000741304323107 0.00104892208523\n", "253 10 -0.0357096083462 0.000725529011696 0.00104813051791\n", "253 20 -0.0551472567022 0.000711364630582 0.00104732622927\n", "253 30 -0.0489547848701 0.000778014655452 0.00104743701453\n", "253 40 -0.0492023229599 0.000729094155727 0.00104654138275\n", "253 50 -0.049294680357 0.000769789741116 0.00104649096327\n", "valid_acc 98.45\n", "best valid_acc 98.45\n", "254 0 -0.0647092834115 0.000718393056729 0.00104552714607\n", "254 10 -0.0408861041069 0.000749010601428 0.00104555853572\n", "254 20 -0.0371011942625 0.000758833064467 0.00104519334315\n", "254 30 -0.0521018430591 0.000772214389252 0.00104516171614\n", "254 40 -0.0430004447699 0.000753617066521 0.00104485404515\n", "254 50 -0.0592818967998 0.000746663636642 0.00104569276113\n", "valid_acc 98.43166666666666\n", "255 0 -0.0391067042947 0.000768248735156 0.00104485326181\n", "255 10 -0.0854958891869 0.00075226804002 0.00104501101655\n", "255 20 -0.0572206713259 0.000776581252128 0.00104465571401\n", "255 30 -0.0456946119666 0.000760025545229 0.00104309509415\n", "255 40 -0.0380666516721 0.000760445531545 0.00104256827338\n", "255 50 -0.0414435341954 0.000789590741353 0.00104275709364\n", "valid_acc 98.455\n", "best valid_acc 98.455\n", "256 0 -0.0482233464718 0.000752545367925 0.00104307775365\n", "256 10 -0.0423187725246 0.000716101617876 0.00104227858356\n", "256 20 -0.0489676333964 0.000759810799313 0.0010420830767\n", "256 30 -0.057039860636 0.000758473846883 0.00104115029408\n", "256 40 -0.0423108302057 0.000749319008427 0.00104102400559\n", "256 50 -0.0295582655817 0.000787205955195 0.00104028199979\n", "valid_acc 98.44000000000001\n", "257 0 -0.046095173806 0.000748879219443 0.00103999026305\n", "257 10 -0.0498191639781 0.000733512168079 0.00104044736211\n", "257 20 -0.0483681559563 0.00076668019129 0.00103988016036\n", "257 30 -0.0573412589729 0.000729175996409 0.0010392016102\n", "257 40 -0.0392733477056 0.000762251593072 0.00103806755755\n", "257 50 -0.0651442557573 0.000740963199841 0.00103769713182\n", "valid_acc 98.455\n", "best valid_acc 98.455\n", "258 0 -0.0591080933809 0.000741526858144 0.00103791289458\n", "258 10 -0.0513682365417 0.000781270874515 0.00103808661836\n", "258 20 -0.042244117707 0.000739326697482 0.00103809160164\n", "258 30 -0.0270368736237 0.000775078112758 0.00103724725194\n", "258 40 -0.0471573881805 0.000718485583465 0.00103731552832\n", "258 50 -0.0381532981992 0.000736511189327 0.00103699878657\n", "valid_acc 98.45333333333333\n", "259 0 -0.0375639274716 0.000733278191883 0.00103711867798\n", "259 10 -0.0235104579479 0.00075054977443 0.00103736217175\n", "259 20 -0.0529659204185 0.000751072348978 0.00103775018528\n", "259 30 -0.0399264991283 0.000762219981729 0.0010382263189\n", "259 40 -0.0475068837404 0.000741019030454 0.00103810589801\n", "259 50 -0.0423272140324 0.000733342768716 0.00103809350552\n", "valid_acc 98.455\n", "best valid_acc 98.455\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "260 0 -0.0457518994808 0.000760560552252 0.00103728428732\n", "260 10 -0.0462750792503 0.000750703803292 0.00103778002138\n", "260 20 -0.0445556901395 0.000744839392877 0.00103724652193\n", "260 30 -0.0480827204883 0.000751531764181 0.0010379596667\n", "260 40 -0.0507237054408 0.000746387551102 0.00103770546624\n", "260 50 -0.0334367491305 0.000746515677197 0.00103708644417\n", "valid_acc 98.44000000000001\n", "261 0 -0.0489831827581 0.000757318603818 0.00103700114727\n", "261 10 -0.0682558938861 0.000762739962495 0.00103686947146\n", "261 20 -0.039802968502 0.000755727595952 0.00103702842389\n", "261 30 -0.0625465065241 0.000772176900046 0.00103617715986\n", "261 40 -0.0688991621137 0.000778079223615 0.00103584864121\n", "261 50 -0.0517372898757 0.000742311144476 0.0010353854101\n", "valid_acc 98.42833333333333\n", "262 0 -0.0418926812708 0.000740557005886 0.00103468096816\n", "262 10 -0.0539679601789 0.000741323992463 0.00103348365963\n", "262 20 -0.0190537795424 0.000757216486271 0.00103335562787\n", "262 30 -0.0557400844991 0.00076307458248 0.00103288808268\n", "262 40 -0.0673588961363 0.000768494583559 0.00103245989418\n", "262 50 -0.045354090631 0.000755762133672 0.00103212562746\n", "valid_acc 98.42333333333333\n", "263 0 -0.0449746847153 0.000738847778403 0.00103199456447\n", "263 10 -0.0497609041631 0.000753055328094 0.00103148125735\n", "263 20 -0.0533204525709 0.000772714782586 0.00103139856126\n", "263 30 -0.0455002449453 0.000709812372121 0.00103099037207\n", "263 40 -0.0395807176828 0.000782851390184 0.00103134143095\n", "263 50 -0.0474548116326 0.000756959808592 0.00103102447492\n", "valid_acc 98.43166666666666\n", "264 0 -0.0413037352264 0.000781172371747 0.00103076133972\n", "264 10 -0.0409549362957 0.000742803161942 0.00103052062528\n", "264 20 -0.0402264781296 0.000757274522269 0.00103030466393\n", "264 30 -0.0473902560771 0.000724563884922 0.00103065640556\n", "264 40 -0.0569106154144 0.000733121176166 0.00103053140401\n", "264 50 -0.0515726804733 0.000741469799505 0.00102984040358\n", "valid_acc 98.44166666666668\n", "265 0 -0.0346588939428 0.000730634172335 0.00102865239507\n", "265 10 -0.0710377246141 0.000727278470804 0.00102784570281\n", "265 20 -0.0469171665609 0.000779910133152 0.00102780232217\n", "265 30 -0.0507390089333 0.000737185262289 0.00102791289027\n", "265 40 -0.0364461354911 0.000739540258332 0.00102801201241\n", "265 50 -0.0634424462914 0.0007348034858 0.00102773526094\n", "valid_acc 98.43166666666666\n", "266 0 -0.0447925329208 0.000740485502906 0.00102747978509\n", "266 10 -0.0381395295262 0.000775252400479 0.00102675523462\n", "266 20 -0.062223855406 0.00071949070082 0.00102673825755\n", "266 30 -0.0531221851707 0.000773820061133 0.00102620317932\n", "266 40 -0.0409418083727 0.000719401203629 0.00102590886216\n", "266 50 -0.0422615520656 0.000747073717426 0.00102556552495\n", "valid_acc 98.455\n", "best valid_acc 98.455\n", "267 0 -0.0337676815689 0.000725668027623 0.00102501057105\n", "267 10 -0.0480612367392 0.000747566803986 0.00102575523638\n", "267 20 -0.0488763861358 0.000752164221573 0.00102547579921\n", "267 30 -0.0294189359993 0.0007672708289 0.00102539010122\n", "267 40 -0.0496172867715 0.000734983693533 0.00102563478076\n", "267 50 -0.0625252053142 0.000715969488612 0.00102599055275\n", "valid_acc 98.44833333333334\n", "268 0 -0.0579074621201 0.000751313132066 0.00102625670738\n", "268 10 -0.0697681382298 0.000744763113227 0.00102732929858\n", "268 20 -0.0403208881617 0.000733908320396 0.0010267763603\n", "268 30 -0.023074278608 0.000739211004097 0.00102643194436\n", "268 40 -0.0504035390913 0.000758045921453 0.00102658280692\n", "268 50 -0.0537093505263 0.000722600514513 0.00102662903017\n", "valid_acc 98.47666666666667\n", "best valid_acc 98.47666666666667\n", "269 0 -0.0374209322035 0.00072563369405 0.00102689556973\n", "269 10 -0.0396690182388 0.0007417834737 0.00102593458196\n", "269 20 -0.0333807170391 0.000765393369114 0.00102555230863\n", "269 30 -0.0343588963151 0.000755308269427 0.00102580604013\n", "269 40 -0.0584964230657 0.000761917696717 0.00102530222288\n", "269 50 -0.0592219606042 0.000743032888393 0.00102602294555\n", "valid_acc 98.455\n", "270 0 -0.0629172846675 0.000722028426727 0.00102574531325\n", "270 10 -0.0403426513076 0.000767606239183 0.00102590762472\n", "270 20 -0.0526979714632 0.000719502539949 0.00102506850485\n", "270 30 -0.0721899420023 0.000752800451613 0.00102516498364\n", "270 40 -0.028428081423 0.000766139253614 0.00102496631259\n", "270 50 -0.0584368929267 0.00071586023096 0.00102568366999\n", "valid_acc 98.465\n" ] } ], "source": [ "for epoch in range(1, 9*args.epochs + 1):\n", "\n", " # train loop\n", " model.eval()\n", " for batch_idx, (data, target) in enumerate(train_loader):\n", " if args.cuda:\n", " data, target = data.cuda(), target.cuda()\n", " data, target = Variable(data), Variable(target)\n", " \n", " solutions = es.ask()\n", " reward = np.zeros(es.popsize)\n", " \n", " for i in range(es.popsize):\n", " update_model(solutions[i], model, model_shapes)\n", " output = model(data)\n", " loss = F.nll_loss(output, target)\n", " reward[i] = - loss.data[0]\n", "\n", " best_raw_reward = reward.max()\n", "\n", " es.tell(reward)\n", "\n", " result = es.result()\n", " \n", " if (batch_idx % 10 == 0):\n", " print(epoch, batch_idx, best_raw_reward, result[0].mean(), es.rms_stdev())\n", "\n", " curr_solution = es.current_param()\n", " update_model(curr_solution, model, model_shapes)\n", "\n", " valid_acc = evaluate(model, valid_loader, print_mode=False)\n", " training_log.append([epoch, valid_acc])\n", " print('valid_acc', valid_acc * 100.)\n", " if valid_acc >= best_valid_acc:\n", " best_valid_acc = valid_acc\n", " best_model = copy.deepcopy(model)\n", " print('best valid_acc', best_valid_acc * 100.)" ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average loss: 0.0498, Accuracy: 59086/60000 (98.4767%)\n", "\n" ] }, { "data": { "text/plain": [ "0.9847666666666667" ] }, "execution_count": 193, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evaluate(best_model, valid_loader, print_mode=True)" ] }, { "cell_type": "code", "execution_count": 194, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average loss: 0.0612, Accuracy: 9798/10000 (97.9800%)\n", "\n" ] }, { "data": { "text/plain": [ "0.9798" ] }, "execution_count": 194, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evaluate(best_model, test_loader, print_mode=True)" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average loss: 0.0498, Accuracy: 59086/60000 (98.4767%)\n", "\n" ] }, { "data": { "text/plain": [ "0.9847666666666667" ] }, "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evaluate(best_model, train_loader, print_mode=True)" ] }, { "cell_type": "code", "execution_count": 196, "metadata": { "collapsed": true }, "outputs": [], "source": [ "update_model(es.best_param(), model, model_shapes)" ] }, { "cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average loss: 0.0498, Accuracy: 59079/60000 (98.4650%)\n", "\n" ] }, { "data": { "text/plain": [ "0.98465" ] }, "execution_count": 197, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evaluate(model, valid_loader, print_mode=True)" ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average loss: 0.0612, Accuracy: 9798/10000 (97.9800%)\n", "\n" ] }, { "data": { "text/plain": [ "0.9798" ] }, "execution_count": 198, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evaluate(model, test_loader, print_mode=True)" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average loss: 0.0498, Accuracy: 59079/60000 (98.4650%)\n", "\n" ] }, { "data": { "text/plain": [ "0.98465" ] }, "execution_count": 199, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evaluate(model, train_loader, print_mode=True)" ] }, { "cell_type": "code", "execution_count": 200, "metadata": { "collapsed": true }, "outputs": [], "source": [ "update_model(es.current_param(), model, model_shapes)" ] }, { "cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average loss: 0.0498, Accuracy: 59079/60000 (98.4650%)\n", "\n" ] }, { "data": { "text/plain": [ "0.98465" ] }, "execution_count": 201, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evaluate(model, valid_loader, print_mode=True)" ] }, { "cell_type": "code", "execution_count": 202, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average loss: 0.0612, Accuracy: 9798/10000 (97.9800%)\n", "\n" ] }, { "data": { "text/plain": [ "0.9798" ] }, "execution_count": 202, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evaluate(model, test_loader, print_mode=True)" ] }, { "cell_type": "code", "execution_count": 203, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average loss: 0.0498, Accuracy: 59079/60000 (98.4650%)\n", "\n" ] }, { "data": { "text/plain": [ "0.98465" ] }, "execution_count": 203, "metadata": {}, "output_type": "execute_result" } ], "source": [ "evaluate(model, train_loader, print_mode=True)" ] }, { "cell_type": "code", "execution_count": 204, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Average loss: 0.0612, Accuracy: 9798/10000 (97.9800%)\n", "\n", "final test acc 97.98\n" ] } ], "source": [ "eval_acc = evaluate(best_model, test_loader)\n", "print('final test acc', eval_acc * 100.)" ] }, { "cell_type": "code", "execution_count": 205, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([8, 1, 5, 5])\n", "torch.Size([8])\n", "torch.Size([16, 8, 5, 5])\n", "torch.Size([16])\n", "torch.Size([10, 784])\n", "torch.Size([10])\n", "11274\n" ] } ], "source": [ "param_count = 0\n", "for param in model.parameters():\n", " print(param.data.shape)\n", " param_count += np.product(param.data.shape)\n", "print(param_count)" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "collapsed": true }, "outputs": [], "source": [ "orig_params = []\n", "for param in orig_model.parameters():\n", " orig_params.append(param.data.cpu().numpy().flatten())" ] }, { "cell_type": "code", "execution_count": 207, "metadata": { "collapsed": true }, "outputs": [], "source": [ "orig_params_flat = np.concatenate(orig_params)" ] }, { "cell_type": "code", "execution_count": 208, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4e3b6104a8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = plt.hist(orig_params_flat, bins=200)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 210, "metadata": { "collapsed": true }, "outputs": [], "source": [ "final_params = []\n", "for param in best_model.parameters():\n", " final_params.append(param.data.cpu().numpy().flatten())\n", "final_params_flat = np.concatenate(final_params)" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD8CAYAAAB5Pm/hAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f4e3b2827f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = plt.hist(final_params_flat, bins=200)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
rohinkumar/galsurveystudy
old/astroML_try1.ipynb
1
246909
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Author: Jake VanderPlas\n", "# License: BSD\n", "# The figure produced by this code is published in the textbook\n", "# \"Statistics, Data Mining, and Machine Learning in Astronomy\" (2013)\n", "# For more information, see http://astroML.github.com\n", "# To report a bug or issue, use the following forum:\n", "# https://groups.google.com/forum/#!forum/astroml-general\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "\n", "from astroML.decorators import pickle_results\n", "from astroML.datasets import fetch_sdss_specgals\n", "from astroML.correlation import bootstrap_two_point_angular\n", "%matplotlib inline\n", "#----------------------------------------------------------------------\n", "# This function adjusts matplotlib settings for a uniform feel in the textbook.\n", "# Note that with usetex=True, fonts are rendered with LaTeX. This may\n", "# result in an error if LaTeX is not installed on your system. In that case,\n", "# you can set usetex to False.\n", "from astroML.plotting import setup_text_plots\n", "setup_text_plots(fontsize=8, usetex=True)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading http://www.astro.washington.edu/users/ivezic/DMbook/data/SDSSspecgalsDR8.fit\n", "[========================================] 114.85Mb / 114.85Mb \n" ] } ], "source": [ "#------------------------------------------------------------\n", "# Get data and do some quality cuts\n", "data = fetch_sdss_specgals()\n", "m_max = 17.7" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "data size:\n", " red gals: 38017\n", " blue gals: 16883\n" ] } ], "source": [ "# redshift and magnitude cuts\n", "data = data[data['z'] > 0.08]\n", "data = data[data['z'] < 0.12]\n", "data = data[data['petroMag_r'] < m_max]\n", "\n", "# RA/DEC cuts\n", "RAmin, RAmax = 140, 220\n", "DECmin, DECmax = 5, 45\n", "data = data[data['ra'] < RAmax]\n", "data = data[data['ra'] > RAmin]\n", "data = data[data['dec'] < DECmax]\n", "data = data[data['dec'] > DECmin]\n", "\n", "ur = data['modelMag_u'] - data['modelMag_r']\n", "flag_red = (ur > 2.22)\n", "flag_blue = ~flag_red\n", "\n", "data_red = data[flag_red]\n", "data_blue = data[flag_blue]\n", "\n", "print \"data size:\"\n", "print \" red gals: \", len(data_red)\n", "print \" blue gals:\", len(data_blue)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "@pickle_results: computing results and saving to 'correlation_functions.pkl'\n" ] } ], "source": [ "#------------------------------------------------------------\n", "# Set up correlation function computation\n", "# This calculation takes a long time with the bootstrap resampling,\n", "# so we'll save the results.\n", "@pickle_results(\"correlation_functions.pkl\")\n", "def compute_results(Nbins=16, Nbootstraps=10, method='landy-szalay', rseed=0):\n", " np.random.seed(rseed)\n", " bins = 10 ** np.linspace(np.log10(1. / 60.), np.log10(6), 16)\n", "\n", " results = [bins]\n", " for D in [data_red, data_blue]:\n", " results += bootstrap_two_point_angular(D['ra'],\n", " D['dec'],\n", " bins=bins,\n", " method=method,\n", " Nbootstraps=Nbootstraps)\n", "\n", " return results\n", "\n", "(bins, r_corr, r_corr_err, r_bootstraps,\n", " b_corr, b_corr_err, b_bootstraps) = compute_results()\n", "\n", "bin_centers = 0.5 * (bins[1:] + bins[:-1])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1153f6390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#------------------------------------------------------------\n", "# Plot the results\n", "corr = [r_corr, b_corr]\n", "corr_err = [r_corr_err, b_corr_err]\n", "bootstraps = [r_bootstraps, b_bootstraps]\n", "labels = ['$u-r > 2.22$\\n$N=%i$' % len(data_red),\n", " '$u-r < 2.22$\\n$N=%i$' % len(data_blue)]\n", "\n", "fig = plt.figure(figsize=(5, 2.5))\n", "fig.subplots_adjust(bottom=0.2, top=0.9,\n", " left=0.13, right=0.95)\n", "\n", "for i in range(2):\n", " ax = fig.add_subplot(121 + i, xscale='log', yscale='log')\n", "\n", " ax.errorbar(bin_centers, corr[i], corr_err[i],\n", " fmt='.k', ecolor='gray', lw=1)\n", "\n", " t = np.array([0.01, 10])\n", " ax.plot(t, 10 * (t / 0.01) ** -0.8, ':k', linewidth=1)\n", "\n", " ax.text(0.95, 0.95, labels[i],\n", " ha='right', va='top', transform=ax.transAxes)\n", " ax.set_xlabel(r'$\\theta\\ (deg)$')\n", " if i == 0:\n", " ax.set_ylabel(r'$\\hat{w}(\\theta)$')\n", "\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[array([ 5.80964513, 4.10388456, 3.07021431, 2.30839326, 1.61804519,\n", " 1.14809237, 0.79113056, 0.56845657, 0.42891209, 0.3213433 ,\n", " 0.23869431, 0.18770874, 0.13359044, 0.08228449, 0.03949176]),\n", " array([ 1.98647518, 1.2535259 , 1.043153 , 0.70981181, 0.56735277,\n", " 0.49987973, 0.36990619, 0.32173327, 0.2407058 , 0.21295028,\n", " 0.15638198, 0.10671161, 0.07297672, 0.04505819, 0.02687428])]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corr" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Author: Jake VanderPlas <[email protected]>\n", "# License: BSD\n", "# The figure is an example from astroML: see http://astroML.github.com\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "\n", "from astroML.datasets import fetch_sdss_specgals\n", "\n", "data = fetch_sdss_specgals()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hgQcecO65556pQqHwH6Ojo38ipRyYU2NVrkpViK4xQoi2xsbGvwyHw6++5557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\nXtJ+85vfTF+vqxUigtlsxsDAA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"text/plain": [ "<matplotlib.figure.Figure at 0x1105c5810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#------------------------------------------------------------\n", "# plot the RA/DEC in an area-preserving projection\n", "\n", "RA = data['ra']\n", "DEC = data['dec']\n", "\n", "# convert coordinates to degrees\n", "RA -= 180\n", "RA *= np.pi / 180\n", "DEC *= np.pi / 180\n", "\n", "ax = plt.axes(projection='mollweide')\n", "\n", "ax = plt.axes()\n", "ax.grid()\n", "plt.scatter(RA, DEC, s=1, lw=0, c=data['z'], cmap=plt.cm.copper,\n", " vmin=0, vmax=0.4)\n", "\n", "plt.title('SDSS DR8 Spectroscopic Galaxies')\n", "cb = plt.colorbar(cax=plt.axes([0.05, 0.1, 0.9, 0.05]),\n", " orientation='horizontal',\n", " ticks=np.linspace(0, 0.4, 9))\n", "cb.set_label('redshift')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x115264290>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GFEXs7O05dPwUp08cJzwslNXrN2bX2BzQtw/fdWrHgMHDuXHDl7GjhhMY8Bhj\nE1MEQcDv3l2srK3ZvvcQMxYsQaVScf7cWewdHGjdth3uHsXYvmc/HsWKUbBgQfT1Ddi6ex+mZuac\nP/cnASGxdOrRm/179lKremVmTJ3CTR9vVOlpGBkZ4mBrh06rJeDJE6Kiovjj5HEERBIS4vG9doVp\ns+bxwO8edapW4vrVy8TGRKNNS8Lvzh02rVtDRkYGl86dBaCw0z//dfIhSIhPyFWaYInPA60ud8en\nziflE+80aNxHvX7h8jU/6vVfJTMBVEdMTLJ824KAXC5n04Z13LrhS/kKFXFzL8rvU6dTr8G3APw2\nZiRFi3lSuVp11q9ZzbjfJ5MQH8/1694sXbmaVcuW4OTsTMPGTWjdpi0ZGRksmT+Hb5s0Y83yJQwZ\n9Qs/9f6e5OQU2rTvgJGRCT/1+QFDQyPS01KxsbHhwYMH2Nracv26N2NHDqV3359o3KQZ+/bs4uL5\nc0RGRmBqZoZOk4FWlYaRqRmqdBWxScmkpqsoV/EbwsLCcCrshL6+HgqFHpcvXaJ67XrY2TuwfusO\nJowZTply5Xhw5za16tajVt16JCcn8fjhA6rWrP3R7ombuztu7u7/PFDikyezsM+nb2Xnhk9KiUvk\npFLlKtmvzc3NadaiJX/+cQZra2saN20OwO6dO6lSrQZGRkZMmpZZVDvWKpbyFSsye8Y0nj8NxLGw\nE/XqN0CuJ6dBrWp0+e57bO3sWTBnFtHR0ahVKn748SfWzJnMrxMmExMTzdaN6ylTrjxqlQpBELB3\nyE+pMmXQ6nScPHEcMxNjhAIFeREcxKF9u0lPV6EwNEIuk2GgUGCTPz9lypTj4L5daDUa4mKiEGRy\noiIj8PT05MlDf/LZ2ePj40d6ciI3fLxxcffg0PHTlKtQCaXSiOjYuOzPb2JiSo8+P/23N0Dii+bL\nUOGSEv9gbFy/lq7de+Tpdu9zZ/9ArqfHvj27s5X48tX/z33y4w/fsWTFGkJeBKFUKqldpy5LF99j\nz64dVKpcmcuXLpKcnMR3PXshCAKFXVxQGBgy9ffxGBmbMGzCDGLj4pgycTzxMTEIMhl2dnbUqd+Q\nkqVK0aBWNVzdi9Khc1csLS2IiYriyePHINOjYKF83L5zF7lcD1Gn44HfPfwf3EdfLkej0RATl4BL\nERe++aYyFy9cYOaCxXRv24Lx02YREx7KlcuXsLYvgI/3NQoUdMTS0oLGzZqRlJSIqanZG9fjyePH\nuLq9XgshPsOiAAAgAElEQVRU4jMkLQb0jUHvzVk18x4R3RdiiX9SPvEvCX09/TyfMy0tlSMHD7Bq\n3cbstnt3b2f7zX+bMImg58+JiY6mRMlSlC3vhf8Df/oPGoqHZ3HKe1Vkz8FjnDh2lMTERCaM/YWq\n1atjbmmJnZ0dZhaWWFpZoVapuH//Lt5Xr6DT6YiLjWHUoJ84e9mbvYeOMWLUGLwqVKRdx840ad6S\n4OfPUGt0tG7fAQGRyIgIylesiKjTINdXIAKpKSmUKFWOdevWk5aazLL5s5HrG3D00CEqVq2Ji1sx\naterx8MHfgQFPSM8LIyrF8+zeN5srr9hM5JWq2XenJl5vsYSH4mkZ6CK+8dhecaXU51NssQ/FJ26\ndsvzOes1+JYyZctnn4uiyNYN69m2dTNdu3/PpOkzic2yoJcuWoCDfX527NlP3x+6U71GDQYMGoJa\nrWbQwH5sXr+W2w8eoVariYgI58Z1b04eP8rlCxdYuGwF+goF0yf/TkJiIhvWrKZEyRLs2r6NQwf2\no9Nque59jQmTppA/fwGKehZHJsD2zZuRywSUSgMCnzxBoTTGzc2Nu7dv4eTswsG9uzA1MUYmCBQs\nVJjrvjdwdirMqIE/UqFqDWQ6kYiwUIKeB7F0wRzqNWzEi6DnFHmDH3ruzOlMmSYp8S8G2/L/PCYP\nySufuCAIFkAfIBAIFEXxxivtXoBLVp/Pm8blBZIl/hmhp6eHvYMDkPkFbFi3FlNnz+NpaBSTps8k\nOTkZK2trxv8yGqVSiUwuoDBQUL5CRXZszQxJPHxgH2XLlKG8VwUgM5mUoVKJTBAoX6EiFy9e4OyZ\n0yxfvJCTR49QqXIVRv7yKwEBARzau4uwF0Eo9PWxs7OjU7furFm5FDMzM1q2bIlcJsPCygpBgNTk\nFHQZasp5eWFobMKzwABMzcwoX748ugwVB/buxMzYiN3btzBwxC8cObAXV49ipKWlY2dtRVxMNIFP\nHrN41XqsslLd+l46h/+dmwA8eeTPk0cPP8JdkPhS0Ipiro5/oA+wUhTF3cCrpaheZj30Acr9zbj3\nRlLinwjLlyzKLoeWGwRBoF79BqxcupibN3y4cukiPbp0YMWSRew/eoIBPw/B0cmZP06douI3Vchn\nZ8+k8b9y5dIlnJyLcPXyJYYPHohOp8PC0oqqNWqyYe1qOnTqxLPAAKpWq4GNrS3+fneZOmkijZu1\noEuPH9DpRCZOm0Hzlq0Y1Lc3cXHxPPDzY8XKVQCkpqYx+tcJGBgZIQoyLCyt0GnU6HQicXGxXL58\nici4RAwUCrQ6HRkZGbh6FGPTrv1837ENYydO5oeffqZi5cpcPHcW3+ve1KpUln07t5HPIT+WNvkA\nGDJyDAf2ZYZc6nQ67t6U4rclck+mJZ4n7pQKoii+TNjjkj2/KJ4m0+r+URTFmW8blxdISvw/pE3z\npqjV6jf21alXP0fB5Lcxb9ZMAgMyc5x07toNzxIlOX7kMCeOHWHqzDmUr1ARI2MTTM3MKFLElY5d\nulK/YSM6delGQmIivjd82bNrBx4lSjJ81Bga1apKCVdHvEp5MnTEaBwLO7N7xzZ0Oi1lvSowecYc\nYmNjOPfHGdYuX8rC5SvxLF6C0NAQgoKeY2BoRP9Bg5GLWmzy2WBpZsrVK5dITUlBrVIxc/o0VOoM\njJQKLMzMEQSBDHUGCUkphISEohVlrFmxDIWBEisLc04ePcQvQwdQuWotKteojVfFSjRt2Yak2GjS\n01XY5c9cI49inkyaPguA9LRULv95Oo/u0qfN1TPHOLh51ccW44vgHTb72AiC4PPK0efVebJcJwAW\nf5k/EBglCMKuvxv3vuSZEhcEwUIQhHqCIIz8S/vIV4T/qtm8Y9dbc2ErFAav+ei0Wm32TsuXtGzT\nFgOlARN//QU7ewdOHD1E2fJeWFhaExsbi6mZGQ1qVOb2znnMnDYVnU6HQqFAEARSk5IoXaoUIcFB\ntG3fAXuH/FSrVYf2nbsx6pdfadmkISuWLqJa7bocOXSQkGeBjP/1F8qUKUOn7j2o3aAhq5cvZefW\nzRw/dgwjIyOS4uPYvm0rxcuWR6vREBsbw73bt8nIUGNuboZdPhsQdWg1GpIS40lOScXcwhyFQkHH\nLt2YPHMWi1esxtDIkCeBT4lPTMKxcGH+OHGY8LBQrlw8z/Axv1K/WUsKuxR549oZGZvg6lk6Owd5\nXpOUlIRGo/kgc78rId7HadS268cW4/Pn3R5sRoui6PXKsfKVma4DLxPyZKfQFARhhiAILlnWt8vb\nxuUFeabEs4QNBKxftmUp7wr8X/ivGkNDw7f2nTl1IjuL4EsO7t/HxlfKpwE4u7jg4JAfURDYs3sn\nV69cpbxXRbRaDYgixw4fpHBhJ1ZvP4BcLs8OcZTJZCgNlcxbvIy7j5/xTeUqNKpbkyJu7rRo2Zrw\nsDDs7Ozp2etHosNCqF69OsN/GYcmQ83xY8dITU2hoKMTSUmJbN+yCQcHBxo2aU4xz+LoIXLujz+Q\n6ytQKA2xsbPDvagHpmZmxMXGoNGK6OvrkaETKVLEhbLlK+Dm5s6zx/4c3LmVkQP70b/X9xT1LIE2\nQ01qWhqdv/+RoSNG82P3jgQGPMahQCGWzJ3B86eBb1y/p4EBH0yJr1uziiuXLn6Qud+V1qPmoa98\n+/dIIvfoEHN1/AMrgbZZ1vk0QRBcsgzZHcDL16P+Oi4vP0eelmcTBMGFTB/QqKzzemQ69Xdn/Wnx\nVl6WZ/ucUKcmoTDKWcBXrVYjk8n+tnjBm1i3ZhXNW7TC2sbmtb6f+/3ITz8PwqOYJ7t37qBt+w5k\nZGTQpV0btu3Zh1wup33rFpgaG7Nm01ZKF3Nj575DFPXwADJj1suUKYeruzuL582mXIVKbNqwjgYN\nvqXLdz3wdC6IW7HitG7bnuSUZBRyGSVKlebhQ38mjR9HhUqVqVChPI8ePcbG1g5bO1usLS1p2bYD\nrZp8S0JCHJqMDGJjYylVqhT+Dx8hEyAxIQGVWo1cJsPBzpaYuDgyMjTI5bLsB6pqlYolq9ZzcP8e\nYqMiEBFp1rw1169dJSUpjlbtOuLu4cnla97Url2HB373WLlgJv0HDaVOk8wCEiEvXhAeFkr5ChX/\n5Z2U+Nx43/JspR2txRMjmuRqrMPPm77O8myCIJQj88ns343p89LPFPVK4YPPhft7l6JJT83Rtn3r\n5uwcJ3/lob8/A/r1eWNfEVdXDI2MAHhw/36OvoXLVqBWpdO9fWuCg55nu1kMDAxo2aQRKSkpeHp6\nUr5iJfr1/oHqNWsRHPSMx48eEh4WyrPAAEYNG8T82TPYvnUzq1csI/RFECIiD/0f0Kp9Jxo1bkps\ndASBt69x5sBODu7dTVxsHDv2HqBNu3bExMSyYOkKQoODCAoMYNmCebRsVJ+7d24RFhrKixcvMDM3\nx/f6dYwN9ImNiUGlyvyFVrtuPQoVLoxGo0GhL0dfLsfUxITomFgEUcumtcuJjgzn/t07lCpVGitr\na2YsWEKzFi2xtTQjJSWF0yeO4+/vT2EnZ9p3/R43z1LZ65OamkJcXOz73EqJrxApTvyfcSHTjVKB\nTB/Qyr8OyPItrYRMS/wDyvJBKNN11Gtt3Xv0fOv4oh4eLF6Wcxnu3rlNQnw8NbJyguh0Or7v1olj\np85i+Uru6/wFCmGgiiMtOQm/e3dZs2IZDRo1orCTMwqFgovnzmVWs9fpqFarFkbGJgzs2xtzEyMq\nVq2BSqWmes3alClXnoP79pKYEE9gYCB3795Bk6HmxOH95DM3xj8wiG+bNMXF1Y3o6Gh+HzuSg6fO\nsWT+HDq3aU5sTAw1a//ImTOniYyJwdjIGJmePpbmZmSo1egrFIRHZP5Clsnl6HQ6zp4+jVKpwEBP\nD61Oh75SD3VaCpYWFqSlpUGGGr/bt6hbrwF2BRw5c/okGh2snD2Z+cvXojUywdPNhVp16jLlt9FU\nq1GbQs5FUKvVdO/Ylu17D+LmXjQvbqnEV4T4hWy8z2slXg8ol+XQ353lE39d032lrF21ktMnjrF8\nzXqMTUyIi43FyMg4OxUsgEaj4UVwcA4FDmCTLx/uleszdMQo5HI5LVu35cmTxyQnJXLl0kU2bNtJ\nakoycXFxGBoaUrJ0GTIyMoiKS+Dcn2f5adBgRg0fSrMWLenZ50d+6NaFAoWe4+DggKHSiDoNG+Hi\nVJgCN27QsElzfh01gudBz7CxtmHFkkV06dGTth06sWzRAhwcHDA3NcHM1JS0tHRERExMzNCTywgN\nDiJdnYG1tTXJcdEgz3SZ6HQiokwGohatWo0ol6FQyBk0bASpSYmkqNQEPg/mxt1VNGveinGjhtKm\nQyfcvKrz67CBKGSZG4G6ft8bF7fMzT8KhYIlK1Znr9HGpXOpXKs+bp4l3/tenT97hhq16773PF87\nl8+fpUqNj5e07G2IgO7L0OF5604RRXGlKIr1X/q/RVGMzzp/zQr/GmnYuDFTZs7GzNycwCeP2bB6\nGUVcXSldpmz2GIVCQVB49BvfP2L0L8jlcgDsHRw4c/IYpcuWp1jx4hQoWBBzS0t2bN3M40cPGT1k\nIK5uboSHhaLT6Ti4dw+HT5zipu91tm/diqOTE4UKFsLn2lUqVanC44cPyV/YhTbtOmJsYkyZsuUo\nV96L6OhoFi+cz5mTJxjUvy+hwcFERkWiUBhgaGhMfEICsVGRxMdFExEeRnJaGvmzkl+ptSIajRoR\n0Go1qFUqnJ2d0NNXkJymJiUtjf27tnPp4gWs8tnS+6cBTJs5h7TkeH6dNBUjY2Pu37vDD/0GEhMT\nx/ZN63DzKJa9BgAblswlNPg5AC06f4+Le7HXF+5fcOP6tS8my93HQhRFbvlc+9hivBkpn7jEv6FA\ngYI4Z4XJRURE0G/Q8Bz9z589xf/BAx7c9+PyxQtvnadpw3oUK16C4qXK8PzZUwKfPOHSxQvY2tox\nZ+ESvL29qdeoCUtWrqVu/QaYm1sSGhbKskULSUpKom7desiAZ88CGTh0OI0aN8Hv7h1WLFnID907\nc2DPHqKiowgPC8OhYEEQRexsbbn05584FCiAhbk5d27fxsDQgHzW1iCCKl2FXC5Hk5FBSHAQly6c\nRavVIeoyfxC0Oh2WZiZEhYWg1WqwtDDHSGlIXEw0jx8/wkBfj+Dnz5gyYSxdv/+RBo2bcunSRcaN\nGUlGhoYfBgymU/dMV1VMTHT2+gydMJ38hQoDYG5hifwdHyi/jcEjf0EQhDyZ62tFEAR+Gjr6Y4vx\nVr4Un7ikxP9j/O/7odFoCHjyiLS0nA9Fo6KiCA8LxcBAifIv4YgajYYalcqzYe0qZs9fREpKCr7e\n3uh0YmYoIZkKJzw8jJNHD1OydBmWLJyPpYUl5mameJUvj7W1NZOmzaRs+fIcOH6aMb9N4Mrli/T+\nriuiKLJw2SpmzF1IenoaQ0eOJjI8nFp16mJsakZkRDharYYtGzdgYWVD1WrV8PW+TlJyIvoKBYWd\nnImIiMBAqcRQqUQUdRgbKbOS6gso9PQwMjTC3NIahYECTXoq6ao0UtNSMZBD6w6d2bphLTVr18Xc\n0hIBgb2HT7B64zbci3lSqlwFwkJD8fG+RlpqGlGRkf/RHZP4EhHJs233Hx0pAdZ/zKED++jxQ286\ndO6GUpkz7abX34TI6enp0aFrd3QizJkxlRlz5rP/6Aka1K5Jrz59iAgPw8nFhd9GD2fj9l0M/LE3\ntevVp2XrtqxZvoiGjRqTkaGleImSBDx6yPk/z1K3wbfcuX2XyIgwKlWsgEKhYNb0qSQmJHD75k3c\nPIpy9eIFBg0ZgqGRMQ/87qFvaMz82dOp3bAxOgTu3rhOQUcnHty/R0aGBpE0DOQydFot+gZ6mBor\nSUlNQ5WhISIqEktLK9LS0jExVKDQ6dDJ9FGrM/i5bx8y0lPw877E9OgYBLmc2QuWMHb4z/w8fDQm\nJqaYW1ig1WSQv0ABChZqA8D+HZtxKuJGGa9KH/S+SXx5fA6uktwgWeL/MSPG/Eo+Wzu6tm+NLqta\nvM917+z+2JgY1q5aAWTWdFy/OvNxglarpUbN2mxcu5r4uDiuXLpIi0b1KVumNO07deG7nr1JTkoi\nKOg5apWaB/f9UKWnc2jfLrr06E3Fb6oSERlBrcpejBk+mPVrVtG66bdUq16dvj8N5MqVTN+lq1sR\n1KpUGjVtSnhICLa2tmgy1IweNphUlZo27ToQFRXN2lUruXPjOpWqVic+IR7nwoWRy2XoNBrUGh1a\nnQ61Wk1iSjpyPX0UchlyQUZacgJOzs5k6ATUGh0pKWkUcHTE1MQElUbk3FUfHt6/S8jTxxw7uJdR\nY8ezdeUidm5Yxa51y3hw4yq+l/7MXq/KNergWtTzv7uBXxg6rZan/vc+thgfBcmdIvFeTJ05F5lM\nRmJCAjdv+GRHqKSrVKSlZrpZJvwyhsUL5pGamsrwIT9z5tRJ1mzcQlp6GgtmTKJK1epYWmduDnrx\nIpgjh/bTtHkrdm3bTMnSZQgODuL2XT8ioyI5e+Y061avpHjJMtg65MfeoQCtWrel/8+DqVWnDs2a\nN+NFcBBuRT2pUbseiYmJVK9VhzG/TeDWjZso9PVxdnZm25ZNhIWF8vPgoRgam/HHH2dJSU4mLCIC\nnU7k26bN0eq06MvlyPT00deTI6BDoxNRKg1IU+tIT1eh1WqRy/VQGigICnpOUnws+nIZhoYKMtRq\n2nbpwZFDh7DKZ0dwVDzHDx8kJj6R+i07UqF6HQD27NjGfb97mJiavnmRc0nIi2C2b97wXnN8rqSl\npuBz/uvIO5ODXD7U/Bysdcmd8h8SGBBAwUKFUCgUrF29gnG/T2HNiqXoRIiPi8PK2hqZTCB/wcwk\nT+MmTeHZs6cIgoAgQnx8LMuXLKZq1WrUrFWbQwf2UtSzBABPA59w5tRJjIyNqVOvAdHRUUydOYeQ\n4GCmTppAyIsXVK5cBTMzc2Jiorly6SJmZqbs2r6NFUsWoFQquXv3Hg0afouzSxGeBzzm8JFDpKWm\ncO3KZdSqdGbOW0RERATjfxnNH6dOEBUViVxPj7TUNGQykAlw7+5dZDIBLSCqVejEzC33FpbGJKak\nY2qkJDg4CAN9ffQNDSlY2JlHD/0xtbFHkRSHSqWicct23LhyAT25gImpKbMWLGbetCncvuVLYmIC\npmZmJMTHUatefRT6b85F8y6YmZvj7pE3US2fG8amZrTrM/hji/GfI4UYSrwTXTu2Y9eObezfu5sX\nwcEATJ89DyMjIwoWcqR+/QZ817EtTwMDsbd3oFWbdgDExcWxeP5cenRuj8JAge91b3yvX8X//n18\nfX1o1KwVRkolRw7uY96MaRQvWYp69RpQ6ZvKPHn0iLDQEFxcXZmzYDGbd+6mUuUqHDm4H/v8+XkR\nHETV6jWxsrZCT65HzVp1KFO6JPoKBSZGSs6ePIo2LZXTp04SHhbG9DkLmD9nJr16dGPJqrU8Dw5C\noa+HVqsjQ6dDrm+A0sgYM1NTdDodGq0OrU5ELpORodUSG59EhkZDclo6Mrk+Gq2WxIR4Ht6/i1aT\nQVR4CIJMTvU6DXjgd5eb165gaGhI8zpVeHTfj4CH9+jaoycFChZCq9Xy25B+WFvbYGr25tJt74Kp\nqRnlvF5/HqHTanly7+Z7zy/xaaITxVwdnzqSEv/ADB80kE5duuJSxJVGTZriUiRnJr5Bw0bSqFkL\nSpUuzYrF87PboyIjSEyIp3uPnhR2dsHRyYly5bzwu3MHhYGC+/fuERYWSnxiEmbmluw/fpqy5Suw\naP5cNq5djamZKYP79ebo4QP82PM7vuvYjp/79aF3v/40btKUYiVKERYSzKOHD7G0ycfly5cIDgpm\n+5ZNLFswm07f9aRWw8YkJyUzcPBQIiPCOXf6BKVLlWDuzKkE+D9AEATc3d3Ql8vQqNJJTUnG7/49\nbO3zZ4cWygSQiaDQl2Os0EOhp4dWq8FEqY+Bnh6pKg0ZGVr09fQo4upOiZIlKe7pQf2W7Xn2+DEx\nsXEM7fsdgiBw/OBexg4bSEpyEikqLY9vXQVg+eKFOTZM/RWVSkViQkKu71lSUhL79uwmMSGWdXMn\nv+Mdl/hckNwpErmiR6/elChZCh/va6+lMw0JeUF0VBSly5Tl2jVvqlSvnt03ZsRQ9PT1CX0RzKAh\nw6hd/1u2bNpAt+970a5jJ0YPH8L3vfowoE9Pps9fRFxMLOW9KnDm0lVkgkBKSgrh4aG0bNQQI2MT\nNmzbwaypU3Bxc+f506fc9vVh94HDfFu3Jk2aNuPA3t0UK1GSEqVLExUexqyZMzA2NMK9qDvz58yi\nerXqFHIszHVvb0yMjVBnZGCgMCA2OhoDpZKU5BQMlEoQxaxsgiIyQci0yEXQFwQytJkWurGhklR1\nZh4VuUyDQl8Peytz7vvd4enj+xiYWmJjaYG+Qg9BncKaHQdRGpmwd8dWzExNSU5OZs3WXRxZOR3X\n0pUoULBgdkz3qsXz6dV/UI4Y7yuXLvDk8WN6/dgv1/dNFEUsrPLx/cjf3+8LIPFJIrlTJN5IZEQE\nycnJOdpKlMxM1ORVsVL26+TEBCYM7kNCXDzhYWEAtO/UBZt8dtnvK1mqNL1696Nth05cPn+W/n1+\noEyZssyav5CK31SmY+cuxMbGIAIhwcEcP3aEmzd8eP70KW2bN+XS+XOMGTEMeztbfho4kNMnTpCS\nkoxLkSIsnDcbKzMjalYsi1qlxsBASXjIC86cPs31a1dp0qotzZq3QE+hz7dNmmFgaMjN2zcJfv4c\nhZ4chdIQkczSbwnx8cgFAUEQcHN3R63OICEuFqW+DAN9OebmFggIqDO0yAQBEwM9dDodgiCSrtZg\nYmRMxUqVSFNrEBBxdnUjIy2Z+KhwSpQuz5xVW8hfsBB3r18i5OljAh/dx8DAgH7dOxIQlULws0A2\nrl6erbRdXN1f26RTq069d1LgpqamtG6b6dJy9Sj+bl8Cic8GMZf/PnUkJZ6HHD92hFs3fN/Yp9Pp\naFI/M6rCxMyckVPmUtjZmdp16wFgZWPNgEGDSUiIJyMjg4iICFavXI5WJ2JmbYeenh4qtZpVy5Yw\nZeJ4wkJDSE5KwtbOnufPntOtR08ePnjAof17qFG7NrNnTMVAXx+H/IXYvnUzWzasIZ+tHYf37yVD\nk0GJchUp7OLCzr372bNzBzJ9A1KSk7h96xbzZs1AJtMDnYiv9zUsLcyxsrLC2j4/JmYWFHR0wsjY\nCHV6OqIgoNJoMVLo8dDfH32lAVqtFp2Y+UOSkpyMTCZibGSIThRJy9CRodGATsTWyhxRo+bpI39i\n4+MRdDr05HpoNFpsC7ty68wBgh/cYFDP7piZWTBv1SZqN2jE+VPHKVOuPD+PnYSjcxF2HjoBgCYj\ng7veb9/p+jY0fym8IfEV8AVVu5eUeB6RkZFB9x498apYiX27dwKZ+bRXLF0MZBZlOHr6LDu3beWh\n/wOMjE1oWrcas6ZPpe/33Xn86BGiKDLpt1+4cukiMkGGlZUl+nIZ3Xv0RKk04KavLzu2byMtLY3E\npCTS0tMo6OjI+T/PsHzpIjZvXE/Q8+fotFqGjhiDnZ09Pj7XEJBRoWJlfK97c+fWTcaMHY+Pz3Xi\nYmJo1bgBdva2qNUqLKyssbKw4PrFcwQEBDBm/O/4+/sTFR1D8RKlyO/ggCCAnlyGTBAwMzfD1NQU\nUaPGwNgER0dHrMzNEQRQyOVYWVkjl4EcAZVajSCTZdY21GWm2o+JS0AEUlNS0Ol0ZCAQFh6Oob6c\n3j8NIChRQ5JaICIyHEf3TIvYPn9B7j7w57u+A1g0N7Pa/UvLW09fn5ade7z1Hh06sJ+F8+fmaHvk\nd5u18/M0R7/EZ8KX4hOXlHgeMWX8r9y+eQNRFFGrMy07paEhxTxz/jlevGRJDuzdw+ED+zl79SY6\nrRZTMzNiYqK5fesm/QcPo0at2pQtV55N69eiEyHg8SMGDx9F7779aNS4MceOHCIkJJRuPX7Axtqa\nfLa2bN2wHqWeQDFPT5o2b8HRQwe4ceMGpmYW1KhVh7t3blKhUmWeBDyhW+f21KxZk6GjxyIKAmqV\nmoIFHNCTCQQEPKJ2w8a4uruzcM5MDJUGlCtXntj4eIyMlCQlJpKakkrVatUwMjQiPT0NPbkclTqD\nF8+fUqBQIRRyGekaDRFRMYjIUBookIkiOo0WUcz0kQPI5TJEIVOxW1tZYm1uRmRUNKoMDavmTuPn\nQUM5vH/3/9g77/ioqvSNf8+t09JDEgi9N6UJUkWKiordBXsXXXuvq7v+bNjWXlkVC3YUuwgiHemK\ndAihppE2Sabecn5/3BhFWQ2KdXk+n/uZmXPPvXMz5+adM+953uehfdvWJOIxurfJRzV8jDryKCa/\nMAFDU/k+WrfvxM3XXsnqlV83tM2dPYuCjRs46phjueyKq3bp37FbD8Zde+vevRn24Q8PicRxG7f9\n0bFXnX1+CX5vZx/XdRuszH5NRCIR/H4/d99+G9fccBOmaQJw7ZWXMXDQEBzHoahoB59/Np1YNILf\nNDjmxDFUVVVRVV7O8uXLaN26DR+8N4UrrrmO6upqhg0fyXVXXEzTpk3JbdqMRV98QXaTJkRjcWpr\nwihCoClwwIAhzJszC8W1aNuxM+VVYUKhIK1btWb29Knk5Ldg/do1mIbO0JGjmDdzBqFggKbNmxOL\nxggEA5QUF1O+sxQpBdF4EkNTcKREOg6apuLW3/QSSNgOWRkZ1NbWoiAxdRXHdVEVBUNTSFj1n7mA\noKESDAQ5/pTTmTd7JqOOGE1GZjbde/fl7OMPZ+yZ5/LJO29y0nmXMOzQUbSrl6N96r7badG2PVlN\n8uh/0Lea7IqisKWwgFZt2vHF/Hnk5ubRpt3uPTr34c+HX+rs0zU/Q758YeOkhvvcOvl/09nnz4TS\n7Zt5fvxNe+Vcb742iYqK3UvJAtx/z93MnPEZIw49rME0+b67bgfpMnTYcEpLS8jObsLwkYdyxz33\nM2tm4oAAACAASURBVP7fj/DFvLlMmvgcCFi2dBmLFszltjvG8/ILE3nmice48/9uZcSho5j42tvM\nnz+fDpk6XTu04eRTT6NTl65YtkvCkRzQvz/HjzmJ/Q/oT88+/ZB2kl49enHx5VcRymyCUDRy8poy\nZNghHDn6aOrq6qipi1K4eSvFRTs45LDDiUSjaIpKMBTC7zPQVAFSomkqtuMtAwkhcF2JqSokYnUI\nJK702ABSevzchOWStB1vn+3iC6QQj8dYs+JLMjKyeGPSS7w68Wm+XryAZs2aserLpcRiUSwrSWZW\nNrFIhKcfuJPzr7qJ3gcOIjv320Xhb76MJz33DHW1NXwx53Oa5efvMg7z587h/269ea+M+T78ObEv\nnfIXQm7z1px70/i9cq5kIsHrk178r/sHDRpExc4yevXuw23/uIkJTz9J7wP6Mf6Bh8nMyuKCiy5h\n8ptvoCgK8VgUfzCIEArtOnSiR+8+HNC3L9W1dUx6+QXOOm8crVu1Ytumjbzx2it8Pn0aLVu2YktE\nZcEXC/n3PXdRsH4tNTXVhEKpfD7tU954eSJz584lv0UrBLBu7WpuvPJSsjPS8RkaO0uLsR2bpYsW\noghJTnYGbdu2pUl+S159+WVCwSC6YVBXW0MikSCasHEcl6TtepQt4ekp2vU/RS3bRRECQ1NwXUnA\n0PHrGoamkJPqR1MEPlOjtiaMZTssW7aMgnWrGDbqKIrKKnn2yUc45fxL6LR/H2Z9XYhlWZx5wmg+\neW8yW4p3srN4B/M+fX+3+ik33X4PS5cspkOnrhj1v3i+wcDBQ7j1/+7cK2O+D39C7FvY/POiuqpq\nr52rfOdO3pn85i5tHTt3ZdiIQ3fbf9JLL2D6/Ex8/jkG9e3FCWPG0ueAvvQbMJArLjwPgFOPH01m\nZhqlpSUsmD+PyvJyHn7yGcbf/yCxaIzBBw3lyKOPpWDDBt549RVuG38/t94xnpv/dQeKqrBu9UqQ\nNvGkzYChw4jFE1x8+ZUEU1KIR6McduTRnHLKqXwxbw4FmwpZu3YNqZlZrFzxFULVuPiKq1Fcm+mf\nTSctJUSHLt0YedhhCCA9K4vtRUWUVlSjC4GUnpynqiqkpoQ8jrhtYzkuPl1FVb18+DczcaEoWK5D\nbdzCcl2iSYu6hA1SoqoCRdMZc8rpmL4ATz7xOKOPPZ4efQdw9603smnjBjRd57Jrb2TsqWewcOan\nDDxoGNt27ODrtRsbPuN1a1dz/mljG147rkt5ZeU+bfB92AUeT/yXV2wKIdKFENcJIU6s9xX+bvvI\n+n0jhRBthRBLhRBP1xvK7zX8zwXxe267mYryvWPKrGkaodCu4ksL5s8jIytrt/2HDhtOTW0NY085\nlSuuvob5c2dTXVHO59M+4bpb/uX9dFM1XMfmvHEX4NoWjzx4P2+9/ipjjh1Nk5xcTNOkQ4eO5OQ1\n5cVXX+eKi8axdOkSnnroftq2aw+KQlZmJqmpKaxctoScnDzPbGLV1xQX7WDxokW069CRxYsXkd0k\nB0VRWPXlMhRVZf2qr3jtxYnMnT0bOxHhpLPOZ+6smUx66UVuuOkWystKycnOxmdoxCyvoMfQVAQg\nrQQSiaYq+DQVU1MxNAVDeGkUgURIF8eR+DQFU1VxXFAVaJKXV687DhOeeYqKykq6du7IgukfU15S\nTJt27TnquBMZc9ShzJ75GbM+/YCjx57Oy88+SY9evbnhtrsaPuP2HTrRpeu3s/J+Bw7YxQmoMVi/\nds0P2hKJxB6d4/fG5eefWV90tQ//DbKR209gHPCMlPItYOx32scAm6SU9/KtReUIKeUF3zif7S38\nzwXxux96gqzsJr/4PBs3rKeiopxDDhu1S/tlV15NXl5TbNve5Z+oqqqS++6+g1dfepGlX8zjtUkv\n0aZ9R5rm51NQUMCSxYuY9OJEtm/bQSKeZO7MGSxZvISacJjrr74CoSikpaXzwvPP8syTT1C0bSvP\nPPU4nbp0Y82qlQwcOpxXXnweVQhWrFzNjqJiBg8dTlV5CWm6ZNDAgdRFo1RXVYIQNM3NQZc2iViM\nWDzB6ONOpHnLVrhCQVMk/fsP5KnHHsK1LQo2buTGa69k27atlO7cieO46LqGi6gv3BGY/gCKqpCW\nnk7SdbBdl6Tl4FI/K0cgAct2sRxPqlYI8Ok6QZ+P1GAAIaB9y3xOO+tcZDJBapM8Fi5ezFl/v5TV\nq1ai+4IUbFgPdoKFCxfSLC+Xe264DP93DDRUVeWqm/4JwMQJT1JWUsxpZ57dqDG94NwzKS0t4T9P\nPvaDfaeecMzPuEt+P/zjznv36MvLioRxkvFf8Yr+eHBd2ajtJ9BXSlld/7xhhl1vVbmpfnb+TdAe\nI4QY990Z+97AvrL7n4HFixbiWBaKqtKufYfd9pn0wvO4rsvYU06jqqqSvKbN+NvYU1i6eBF/v/Ry\nDMNgQJ8eXHz5lVRVVaNu2cK2rZvZumUzl15+JY8+eB8Dhh5MTk4uzfJb0KJFc+67+3b2696dBfPm\n0Kt3Hz5+923ymjXHMAxqwlVM/Wguhs9PqzZt6b7//li2S99+/SiurGXH9m3ouk5NOMyrL00kIyOT\ngvVrycrO5pqb/8XsmZ9j6Abx2hqMQCq2FEhXooeCaEmb6vJSmjTJIZGIE49GSSSTKPUpCst22VlZ\njaYqRGprcCVYjoNP97RRwCu5NzUFXRM4jsRyJKl+HduRbNm2jWAwRCweoUffARQUbKS6uhLd1GmW\nGWRbYQElWzbSu8f+tG7RgmMnvIrp9zPjo/cYfeLJAGxYv47U1DRy8/IaxmDIwSNo2ix/d8OzW/z7\nkScIBoPc+/DjP9j31gef7MEd8vujSU7uT3f6DsJrF+LLbk6o1f+GNrtkj8StsoUQ36XOPfNd32Ah\nRHp9IE/fzbFjgevr9z9T3/9p4IKfd+U/xP/cTPyXwnEcPnrvXfoPGky//gN226eoaAf79exFuKaW\ngo3r+Wzqx6iqymeffswlV1zFiIMG8tqkl7n/oUfp3KUbO8tKqa2poXeffpx5zrlsKtxE5+7789mn\nn/L044+xcME85kz7CJ8/wMZNmwimpLFjx1bKyqso3LKVWCyG5biEa2rJbZaPI6FL9x6MPuZYdpaX\n8/lnn1IdrsZnmnTs0J4N69aydPFC2rRtz7Zt2/nwnTf54J03KCktIzUjg2QsytfLl6JqOjk5OSh4\nFnBWIk5NTS1Jy0LXVPyGhqmphPw6qiJIDxhEkzaKEChCELcsHFeiqwqGKoglHQxNw2fqSCAcSxBL\nWtRG49SEw4RSUrAdhxVLvkBK6LRfH1p26M6cz6biT8nA1FUmPf0gn384BddxKC3a3vCZr12ztkEh\n8hu0at1mlwXNxx68n1gstkufmooyVsz5FIBgMLiX7pI/H7L7HPo/E8CBPV3YLJdSHvCd7bvG74uB\nzPrn1d99CyHEicDdQGb9DPybIJ/JXsQ+nvhP4P133+GoY47bo2Pu+NetHHP8ibRr355AINDQ/t6U\nt3l54vO89Ppb6LqObdu8NPE5mjZrxtOPPUy/AYPIzc1jzpzZ1NbWEKmrQ1VUotEoWzcXMPyQw/EF\nfBRt20bB+rUkkwkicZv5S5ZhJRIcf/hIKsK1ZGZmkoxHaNWuIyu/WoZP1/CZJiOPOJqU1FSmvvcO\nVjJBcXkFLVu2ory01Js5+3w0yW1KWdF2LMtGUTyt7YqKKprnZbG1tApdVUgkYgQMnaTt4g/40FWN\ncG0tIVOnOhpH03Rc10VKF4FACC+3GDA0onGLoM/Ap6vYrqQubpHi07BciUCgCNA1jZhlE/JpPPKf\nl7jknNMJ+Ex69B2Aasfpf/Bw3nn/E/Jysnlkwgvout7wGZeVFDP9gymcct7feWnCEzRr3oIRhx8F\nwJxZnzNw8EG7pBkiNdUUbVpLh579f9mNsg+/KX4pT7xz03Q54dyhjep70J3v/df3qg/M4/AC+JL6\nxxPxUij31D8uA57GS7e0Babvzbz4vpn4bvDpJx/z4eTXKC8tYdvWrT8qczp/5nS2b9va8Doej9Ox\ncxdWfLmczz+b1tBuWRabCzexetXXXH/lZcyc8RmXjDuXlV8uAylp2botp55xNsNGHEJuXlNUoXD5\nldewfftWaupqOfm0s1i5bBGff/IRG9atJRKLUxeJcMyxx7Fj21aOPXwEmunDsZIcfuRoAoEg8Zoq\nevfui1rPJFkwczrvvPEKvlAqEVuiqRolO3agahrSkRx2+JHURSMEUzOwpYvPHyBSFwHA0fw4VgJd\nU1FVnUjCIm7ZhGsjVIZrAKiOJvAbnla47bg4DvgNFUV4s/S6uIUrQVcFUlFwpSTF1HCldyN2yE0l\nLWAQtx0cx0EDXnn2aYYNG8Zl19yA4lrUJR2at2zD5A8+IbdJE55/9D4sy2LD2tUApGVk0Kf/ILZv\n28pzzz1LfqtviQBDhg77QZ44mJr+mwfw7YUbeeHhvUNp3Yefj71BMZRSVksp763PgS+TUm6qf/2W\nlLKdlPIQKeX19e3Tv8mV782/Y18Q3w2KinaQmp6J6fdz0aWX/2gl55L3X6a6eletaiklp55xJn37\n9Wf8XXdQU1PDxo3reei+e3j48afo0bMn+/XoQbv27TFNk3fffI2WrVphmgZ33/5PFs+fw9crvuSW\nm66nbdu2ZKWGmD93NrfccTdDRxxCxc4y8pvl40qFmTM+48Xnnyc9qwnB1DRat2yBoqoMO/RwOnTq\nQm0kArqJqqk0TdFJxBOEy0vIzMwiEEzBAUx/kIBf5+N3J6MLQXpaEL/PR9IF3R/ggP4DKSkuQUGQ\nSMRxHYeMzEwUIdBVBSkloUAQXVVJJG1STL0+/61gORLblSSSjudQpAhqYhbJpO2VNCsQMHVcCVsq\n6qiOJskOGmSlpZDdtBk7K6soLCpj0KFHYRtBhg4ZzPAjj2Pt18vJzcnhb2eOI1xVycxPPwbANH10\n6r4/zVu05KMZc+nc9Y+nQtisVVuOPf383/sy/qch4S9Tdr8viO8GZ51zHkNGHEpKatpP9r3sgYl0\n32+/htc+n4+xJ58CwNNPPoZpmJx58hgWzJvHl2s3Mvigg1m7aiWLFsynR+8+dOjSjerqKg4ePpJN\nBQVIBOMuvpQHHnmcDp06cfqZ51CyYzuWleSlF15g6scfkhIKUVy8gy6dOtCjdy8MQ6espJi2LVuQ\nSMTIyc3DtmyWLVlESdF2sC1ChkrfUX/jwEFDGTjsMHLSU2jeNIdgIEC4uopWbTvg83t64JsLN5OZ\nkcnf/jaWSHUVmwvWY1tJTL+fzIwMgsEAdjLh0a+kpxseT8RxXBdNU7Fsx6MdSu+fwNNScQkYGiFT\nQ1MFhiZQBShCkLQsFEVBN3RcKfFn5OD3+4nGkvTq04+CNStxXZtrb/gH5151MyXbt7Jy+VJatG6N\nLxAklJLK+Zdd/YOx+S5r5Y8ERVFIy9w9DXUffhz33X0HO8tK98q5/ipStPvYKb8ievbqTXpGBtJ1\nWL1qFau+XsmAQYMY1LML0XAlazcWsmXzJkYfP4aPP/qATRs20qx5PkcfewKWZSEkXDzuLP5+2VVs\n3ryZysoK+vbtx6ijjmXe7FmEqyoJV1WzeuXXZGRkMePTT/CnZvDUA3eh+UIYPh+xaDXt2rcnWVfD\npGefonmLFpgdu9K5a3emTHkHxTBwojH6DRrCwnmCHdu3EfAHsBIxXn/5OTTToLKiHE0ThIJ+Kquq\ncByHoM/wxL5siRRgIkn1ewublut69CwJpu4JXOkKaKpnEqEpgrqYhaJ4C6DfKBtGIjECfh/lZSUE\nfD62lRWzbNkSuvXozQ2XXsiW9av4zxvvU11ZziP33E7X/Xry9deraN6qFaefewEb1qzi8nNP5ZZ7\nH0VN1tJv+BF7bSwXzJvLxo0bOL2RdMU9xZZNBWxcv5YRo478Vc7/V8Hoo48lYy99Af5BlgN/MfbN\nxPcSKisq+HzGdIqLdgDw8gvP0yw/n5SUVObOmUNuXh6KInjl5RfR3QQHDxnEU088xmfTppGbm8eW\nTZuoqNjJueMu5K3XX6Xv/l256Lwz6X/gACY88Qi6rrHu66+YP3cOOTlNKNy0kblz51CwYS3lJcVs\nKCggkrSpqCinsjZKdXUlFeU7MQNBqqqq2Bmuo1mbdhQUbubTD9/nvffew7EskvEk+U3z2L65gJra\nGiqqKjH8PnZWVGG5IO0kGVlNkK4kHkuwf68+qKqCZXnrBEIBIT3Th0jCwnUlUoLP0DB1tWEeo2sq\nrgtx2xOnMnQVTVVIOi6iPsCrqgKuQ5rf4KQzz+GIEUMp27KRcPFmyrZuIjMtxDtvv01R0Q4GHTwS\nn89HZm5TKstKmfTUQ7Tv3JXnJ3/E7JkzqAvvvcpcgO779/hBTcDeRCAYJKvJL69f+KujS7fuaNov\nn3t6+e592in/k4hGo+wsK/tB+/vvTWHp4sUk4l5l38HDR9KpUxeklPz9kks59bQzaNO2LU8//hhL\nt1axqbiCWfMXcu0NN7Fp4zpkrIZ7/v0Ijz/8b8445zyGjTyU7Kb5dN6/F5puMu2Tj6murUMIlUsv\nOB/TMBgy9GAOO2I0Ldu0o3nTHDRVIzenCfnNmxNNWHTr0YsD+/WnpqYW27aIRCKgaiRtG2knsRyX\nWKSOunA1C+bPxTRN9ttvf4TrIFSVA/r2o0mzVmzZtqNeuSrJssWLcB0XR3hMEtvxfpYm6q3XdFXg\nuJK45eDUF/x4EMQtG10RxJK2NwuSnuKgZXlmyyk+HQVJZV2CF559htWrV+EKlSPHnIrl2GiGnw/e\nfZvCdas58++XEQwFef+dyaxYvgTFF0IIQW7TZlz/j38y/LhTefDuvWetlpKSQl5e0z0+bv68uViN\nMJ1okptHzz4/NGveh18P34iy/dT2R8cefaUJIVpLKTf/Stfys7BizlRsy6L38NG/yfstX7aUNatX\ncd64CwG4b/xdDB95CGeefe4u/SY9/wwjDjuCAw70uOQTnnycjp07c9ioUWxYt4Yrr7uRMccdRaS2\nlqHDR/DVmvXMmDaVxQvmc+yQ3hRuL2bE6OPYWVaKY9vU1NV5vpOpKQwYNJgmTXLoc2B/Xnv5ZXBd\nqiqqkLiEq6uR0qVDhw40yc5m5dcriEZqyUhPp0WLVhiax9FWFI2Ro0bx0oSn8KkS4U+jqrKCyooK\nNEVBVRSWLVqA6Q/gM1SPEum6qIrABYT0soVBUyXFZxBJWNiORNdVUjSFRNLCdUV9bpx6/jg438yA\nkBi61kA/lNKjG/pNjZRAgNq6CK1atuSYE8Yy7YO3Oe6kMyjZuoEHr/s/PvtoCi89dh8d9+9Dr4HD\n6NhtP5Z+MZ/iHdtomt+iYQwGDfWclN5/9x3atG3XYI/3W2LJooV069adtPTd1YHsw++HP8csuzH4\nSZ64EOJa4Bsh5j5Syr6/xoX8XJ64lYgjpcTw/T6LWNVVVYRSUhp+4q1fu4ZPPvqAjp06M+pIj588\nccKTfPXVCk4782zOP+t00jIyOHTUETz/zJOgqHTt2pV+AwYSrg579mzJJMuWLqGkaDvDRhzC/Lmz\nsBJJUlJTiceinDj2FHJyc5nw2EP4AiEOHHQQn37yIYauoakqpmmi6xo7iktRVJ2szHQcVyKtBM2a\nt6BgYwE+n6cmWBeJktUkm4pwFNtKMPyQw5j+0fvopg9F2pihNBKROupiMZo2bUZRUREBQ0dRIBxN\n8I2slF3vbq8qAoHA1BVcF6QrMXQFKQSO62JqKo7jkhow0FWVaMKbpSpC4EiJ5QpCpkrSdslITyce\nizLm9LOYNuVNyupi/N8dd3HIUSegKioT/n0HY8+7BMt2eOGZJ1ixfCmvvf9pw9hE6uoIhkKsXrWS\nrOxscnPzdhm7e/95AxdccT3bt26mW49ev/7Nsg97Db+UJ94hL00+ctqgRvU94oGP//R64kullBdK\nKS/EE3X5Q0E3fXsUwKd+/OGuDVUbvO1HsHXL5v+6b9qnnzBj+jTmzJoJQEZmJvv36EmHTp0BsG2b\nd99+h9pwmLNOG0urVq24+577yMrM4IQxY4lH64jFYnzy4YesX7OSo489ntkzP6dFy1accc445s6a\niVB0bvrXHSSSCUKhVGZ9NpWHHrifungSy0rw8Ufvk0gmsWwbO5lg9HEncOqZZ5OZmUXT3Byqa2qp\nrqwgJT2TNWvXIQQkHQiEQmiGQenOCtxkjHg8ztQP3kVVBM3zmxFL2FRXVRGLxbEdSVlJMY7jEE8k\nSSRtfLqGAAQCn66iK0rDQqWiKPh0FdNUURUFXYCQeMwVAbYjiSUtdFVpsHwzNQWf4pFzU/06diKK\nrmsoqkp+s1zaNs1h07o13HnrTUSjUU77+1XcfsMVnH/iEXTq1JF/jn8A8FI0Xy1fygPj7yCZTNC1\nW3fYjRjUmDPOxbYtli1a0NjbZx/+QvirpFMaE8QvFEI8KYQYDzz1a1/Qr401q1bt2hDM9bYfwVOP\nPUJ19Q8Xyj58713atG1HIBDANH1s27qFG669mnYdO9GqdRvAm6mPu/hSjjnhRGZ/sYxkIs7NN15P\nIh7n8NHH0K5DJ/zBIB06dqRXn358+N4U+vbry+CDhvLRlMl079aFlKCfN197hWgswc7KKqRQGTx4\nMKeefia5efkYho5A4KIQTdpUVoeZ8Pgj1NXWUl5RQdO8XNJDfkqLtqOpCk2b5hGtqyPuQOu27Qj5\nfaiaRv8D++MPBEgPmmzZsgVVVXBtG00Fv65i6BqqqqGoXqDOzsoiabtI4WIoAtNQvVm4pqIgiFk2\n0bgNgAsgwNQ1AqaOUc8htxwHhEBKiSIUrxhHCFRNJd2vo7o2Lz/7NKbPT3XCxS8TzJ/6LhecfAz/\nuvpiUrKbcd5l1zL6hJPo0s2jer7+/JO8/Oh4Dhs+lGvO9bRVnn/iQcLfG8PW7TqQ1SSHk88ex923\nXM8+/A/hL7Sw2Zic+OvA9PrnB/+3TvXlpwcAvaWU99Zr5r6JV4p6z96uUvq5uOKa63ZtMFJ/8pi7\n7tvVXDeZTLKzrJSCjRs48uhjeG7C07Ru05YWLfvz7AsvM/7O2/nb2JNITUvj+qsuY8G8+cxbvJwx\nxx7NkUcfTbi6mkgkwoBBgykpKaFw4wY0wyAWT4B0aZXfjJdef5tgwE/A52Pq1Kl8veJLRhxyGDOm\nT6OispLKigoKCgtp07YdlpWkcPMWkokEhqYw5Y3XadG8Kd33b0O4ppavv/6aoKGiKgIbKCktxdA1\nwuVlRGtrSPUZ7NfrAJYsnI9lWRTXWVgOBPwmqiJACAxdwx8IkUxWgYS447CjtIyQ3yu/F4qCdFwc\nKVGkS8J2sR2JqSkkHYeE5S1cqoqXZkm4npJhwpIYOriKAMvBNDQs2yEeTyLxo/oCWNEqVq4tIOk4\nrN1eRvuu++HXVa655XaiSYuPJ7/GjE8+YOghhxMIhujQuRvx2jD5bdrzyEtvA9CsfVeee/Y/XHn1\ntT8Y39qaMIGUfTnr/zX8EeOzEKKnlPJLIURroK2UcsZPHdOYID4dGAEIYCTw7u46SSmrhRCbgEO+\n0zziOzKNfxlsKtjI1I8+JBhKwXVdzjn/W0GyRV98wYljTmL25zNIJpMsX7acfn0PIBaL8frbU1i3\ndg2KonDeWaezevVqxo4dSzyeZPHihRTv2E7nLl1RBFx98QXMnDWTQCBEQcEGfIbBsoXzSA0YHjUQ\njYOHj+T1SS+SlppC0G8STyQIBgMkYnEqysspKvFYNJbjknAU0oJ+6iyH1LRU7FiEurhFfl4zNhQU\nUDJ7Jn5DQ1FUQj6FqkicNJ+GqxhUVteRbhjU1oSRAhzHRVM1pOtVXUopsWwHV3qFPYoCjuPlx1MD\nJkK67LQTCCFQFTA0lVjSJmBoxC2HoKFhu5L0oIGtmWSmpBGtKEYXLiYWGakhfH4fPbv3oHTzBnoN\nPJh2HTvz2isvUbR2OSecfQmTnn2cdp26ktu0GdM+/oAb77gfgPXr1hIOh+nQsSM59fIA34dhmPQ8\n4FdZ6tmHPyg8ctQfJ4oLIdrgaa4cIIRYjBdvM4GfDOKNSaeMwxNtqcYTctkT/Cr6ub82tmzeTMGG\n/54nN3SD/oMGkZGRgRCCxx9+kFgs1qD9PfvzGQRDIU4/+1xOHHMSQ0ccyl2334aqaUx6cSLjzj0L\nv9/HiqULWbZ0KX87+RQ6depEbbia5UsXM2r00axas5p4NEpVxU4GDR6CkDb5LVtTF42TkCoGDu+8\n8RpDh48kadlEYnE0zSBcU0dtPEE0aSOkS8BQCfl00tLTMfwBfKafkpISXMcmmUyyYcN60kMhAqEQ\nCcdFFR5LRNcNiqsi5Lfq5OmAWxaO41EHVUVg2zaK8PLefsObC0hJPdVQIuopiDXRBDVxB7+uehrj\ntktVJEE0YRFLOqT4DRKO57dZm0gSi9RhhXeiaxpJx8WVAhMH17bYsG4NCVfllReep2PXbmxa+SW3\nP/UKg0eO4pyLrqJ123YEgiHOuejKhrFyHBfbthkwcDCjDt998U8wFPqvksJ/ZdiWxcJZ03+6418U\nf6ScuJSyEHgLeAOYjCd3e2Njjm1MEK8GCoGK+q2xF7WpXuzlGfaidu5vgeKiHWzfvqus6ZJFC4nH\nPdF8x3VwbIcTxoxFCEHnrt14fdJLpKal8fm8Lzhn3AWsXrmSNStXkpOTy8J5sznn3POYMfUj9u/Z\ni7q6OmKxBOddfAX/eekV3p78FitWrEDzBRg56kiefug+thQWIhQFpMPShQuwHElhQQEtWrYk4DdI\nTQmhahrrVq8iEYuSlpqCqop6aVgTn2F46n7S448EU9OpqQkjcdENg/LaGKauoikQj0eI1NWiILBd\nMHSVtIBOWtBk9aoVIL3qS1UVBHQNTaioAlzpEolbRBI2KT4DTVPwaZ6TvaIIhBBoqoIQElUIVEWQ\ntJ16iqFAVRRs2yaZtElYDooEL6si8YdS6NJtP4rDEeosGw2bNRsLadehIxlBkycfGE9Wbh5T+3Zh\n4QAAIABJREFUp7xB2Y6tRMMVbN+6lVuuuYzc72iId+nalQEDf5qF8NTD9xP/nkztTyFcVckrzzy8\nR8f8keA4DjtLdrBlcyHTPp36e1/Ob469ZAqxVyCEuLs+kC+VUhZKKcNCiJ/O9dK4IL5YSjkZOIk9\n0MFtjH5ufZ8lQoglO3fuHcu0bVu3/EAzeneIFm0kvGHpbvf1HziIocOG79K2dMliHvjXDewsKcYf\nCOxS+jt85CEEQyEAli9dim3bXHzFlVx/9WWsW7OKW++6l9tuuYlZs2aydMki2rXvwOGjj6JiZxkj\nhwzg75dcSm24ikNGjmDaR+9jBFOI1dVRVxNGUXXiySSqbtCzTx+Ktm/HjsdIJuIMGnIQdbU1RJM2\niXiM/Xr2Rvf5aZbfnJy8pqSmZwIuwUCAWKSOoN+HE4/Qt3dvAqZO3PIWHZNJC11VadepK5F4Ak1V\n0RSFtLQ0FOkQDPgbjI8FEtPU8Jlaw8KPT1epjMZJ2l7BT8DQMBSPMujUa6uomooqBD7dm7Wn+T3e\nueV66ZWMoIleb+tm2zZFJWV89eVyMtPTsRyIuhoHdm9PekY6F1x1E1k5eVRVVtKpew8QKrNmzeK1\nl55j/MNPAuzxgtQdDzyKbw+1VtavWkHfwcP26Jg/Ekyfj9Fjz0TTdXzfM5L+q6Oxuim/Ycqlsp48\nco8Q4u7652/+1EHwIzxxIcR5Usr/1PPEM/FyNG2klGN3e4B3zDjgb3w78260fu7e0hN/5snHGTBo\nMPvt3+NH+yVrKnDiUfw5LX6033dRXlZKLJ6grKyUivJyDh11+C77b//nP3hn8luccfa5zJo+lbYd\nOlNRUY4/EESvty5bvmwplmWTGTKpLirkwCPGsn7VSkpLiigtKSY3I5VgdlO+XLqYpGXRJCeHRG2Y\nvFat2b51m2dCrIHjgoZEM/1EY1Fs22N5BHwGVjKJKyVCM9Ckg6Z4s2BLKOTlNsO2LbZu30HQ78e1\nk/Ts25+lC+cTMFTKahKkhIKkmoKoJYnFYigCpFDJSfWxo7KWgK4StxwStkvzzCCRuE047jn9tMwK\nURe3sBwXKT0PzRS/QWVtHENTsRwH24XsFB91cQvbddFVFVMT2I4kPyNIYXktqiII+kw0IUEI/D4/\nXfbvSSxSw45NG+na6wC+XLqEkaOOpKq6mjadu3PJtTcDsHH9Wq44cwwvvj+DzOzsXcboiJEH8+G0\nz/fYONlxHB67+1Yu/8edDW3PP3ofZ1x01Y/aoFVVVvLmpBcZd+kVe/R++/Dj+KU88XY5qXL82MZJ\nEI95bNpvwhMXQqThkUO+yYkfIKX87CeP+5Eg3ktKuVwIMQKPYUJjT/pz8Ec1hfgGkUiE08aeSNeu\nXbni2utp0iQHgJpwGMM0Kdqxg48/eI+OnbswZ9ZMrrn+Rt6b8jb/efpJevY+gGOOPY6FC+bx2quT\n8JmmV5QjbNZs3EKz/OYMG3EIdXU1fPTB+wSDAQYOHsLUD9+nV+8+rFm9muzsbBKWTSCYQlpaChvX\nrMLvMwhH4iQScXymiW07+HwmjpXEcVxcvDy136eTSFgNBTkZmVlUV1YQ9PtJJJP4TB0raXmWVa6n\nbV5Vus1bvTeDVFVV4rqSFL9BJG4hAVURDT/jBIK47SCR+A0NTVU8RUOh4EiJdCWKKsgMmIRjSbxl\nJQVdFaiqIFhvMJGwHTRFQVMFmipI2JKA38RKWqSnhIjGouiqgmL4EUA8aXFA376MOesCli2YTSgY\nZMy5FxNMSWXjurW8OWkiN/7fT+t2F20tpFnLNj/Zb8OalXTo0n2P7hvLsli14kt69tm3cLo3sTeC\n+N1jDmxU37GPT//Vg/jPmTQ3HNuIis1r8BgqNwGfSin/sxeu+Qf4owfx76O0pJhkMsnEZyfwxby5\nTP7gE66+/BJ69urNk488yJXX3cTqlSto1aYtJ449iYkTnuatN14jXF2NKyW6YZCTnU1VZRV2IkrC\nccnJycNyHIqLi9FwcV2XkN+HjUI0FgXXrednZ1KyswJDkcSSDgf07sXO4u2EwzW40iVhOViuxNRU\nzrngIqZ9+D7hcBU1tbXYjqR5XjYVldVURRJkBE2kgHjCJjs7GydWQ9yWJCzbc+dB4DgOliPRVEFQ\n14jVS82qipfztp16IRQhsF2JqQlSTIO4ZeNIMFUFU1eIWg6OK7EdF7+moWne14AqAOH5cCrCKxwS\nikBTVNrlprKjso6oBWmpQUKhFBKxGPFoLUG/j8w23TmgRzcOP+FUNq5cSrhkK8ePuwb9R9IDLz83\ngaNP+BtF27fx5isvkqypYHtFLSeMOZnRxx7/W9w+/3NIxGPM/uBNDjnxjL1yvr0RxO/6W+OC+ElP\n/CZB/GdPmhuTE19Ovdkn37o2/6nhui6zZ36+S1tlZQWnjW38P3DBxg2sWbmSW267g67d98OxbXr0\n6s0pp5/Jh9Nn8tAD9zLi0MM474K/8+G7U0hNS+PA/gN4YsJE5i3+knPOHUdOTg47y4rp1G0/opEI\n27dvZ8eO7Zx+xpns16M3rutSXVNH3/79UaSkT+/epASDlJXtxLFtNMNH62a5FG7eTGVVGNd1qYlZ\nxC2HrKxshKLw7FNPsKFwM5FIFEVAwNQIBoPEkjb5mSEE4NgOhqYQrQ0Ttxxsy0IRnhKhdF1AkBHy\nkRYMoOg6pqaiax4zJWjqgCRgetWbCiClIBxLEk3YOI6DU78wigRNUbzZuu5Vd2qqJ1VbXuvJJ/g0\n1Vv8dCW6plBa52C7kvz8fOJJm0QiTqv2HcnLb0VJdYRObVuyacM6qirKKCmvZMPqr7HsJACObbNs\n3udMvO+fbNmwpmHsWrdrh2n66NxtP67/553888FnuOPef3P4UX8uR/s/EzRNJye/1e99GQ2QgCtl\no7bf5HqkXF7/+JmUMiylDAMFjTm2sQJYFXh/d28awVv8o8OyLBYt/IKDDv52USozM4uXX3+70ecY\nOPggKsrL+Wr5Uu598BGEEIy78CIA3n7zDaoqK/lgymQ2rFvH5Dde44prrueY4//GBeeeRcsWzSlY\nv4Z+AwYxc9Zsjj7uRC645HK+XL6cN197hddenkjHzl0JBoOomsaMT6eiahrVtTU4VoJgIEAiGUdT\nFCqrq3GFhqoIpBToqsC2JbXVlQDk5uVRWlyCpkAkKXGkS3p2Hi0qdxKVKqYhSUQdTE0lkrAAj0mi\nKQIhwTB0YgmLaDyBLQUBQyXpuGiK5+gTjiZBCIT0mDGW4/1a0DWvnN7QVM/dXnHRVAVdVYgkbFQh\nMQyVaMJGUxXS/AZSgJTe7N9naCgCamtrkEKgY6M6FiWltdTVhL1fG5ZDYWEBJ55yNrlN83ns7ls5\n/oxxfPbum2Tl5LJs4XziNZUMG30iLdp2ZPbHU+jSsy+Dh367aG0YBvFohPzmjV8b2Yc9h6pp9BjQ\nOE/L3wq/VYDeE3xfqwr4yTxcY6Vo++EZfe4pT/wPCdM0ueb6bymY/773bmprdl8IAlBbU8Oc2TN/\n0F5dVcnZp4zlpYnPsWH9Onrv15UF8+by6MMPkpWVhW6YTJv6EW3ad+D1SS9y4Tln0KFDB9IzMqmu\nqqK0tBQB3Hzjdbwz+S0effA+dhRtJx6Ls3nDOvoNHIJuGCA93vXWwk04rvTc5g0/qmEiXYmQNhJI\n2hY+00Q3tIaBjddUYTkO1TGLoGng01RWfrmUyliSaCxBVV0cU/fkaYUAXVU8dx7H9QweLBsFEEKh\naZofXVHqRa4kmupVgYLERaIoXvB2XAkSgqbuzcyFlzc36tknmgKG7l3hN3lxn+HN7DVVxacJIgkL\nBTA0jYyMLBTdIOQ36NKhLS1btaZz505kpYb44ouFvDThcb5eupBQKIVAKMRn0z8jmJLKurVrufKu\nx+jUvScrv1rOlFdfwB8M7TKGUkoeuP6ihtevvzKJt998/Ufvn8KN65k1/c9LyXOsJOtnTvm9L+P3\nRSP9NX+HOL/HWlWNCeJpUsoxUsrDgL9cbfK2rVtYtGAegWCwoe2aKy9r0IB2HIeKinK2b/XMkNet\nXcP7774DQHZODv0HDeaMs8/ltlv+wV333MdLLzzPRZdcytU33MSixUsoKNhEbW0Nbdp3ZNHCLygr\nLWVzYSGqblKwbi1XXHMt6ampTP/0E4R0yWmSA0LBVTRmzZhOWWkpht+PT4VY3EbVDWwrSTQSobqq\nEle6mJrmufE4IB2bjLQ0EKBpKrbjoGo6PkPDdV2EgLqERU00STSeJDUUolWrVmi6QYqpkZnhUSc1\nVSHo0700iSJI8etE4hZ1CQtNUXDrtScSloOqqN65EUjXQVe9NInjSCJJB0PxzCPc+oVVTVGIxG0i\n8SSpfgOkIBxNelrjlk1awEfANElYDroCqh2jePs2fH4/imbQcf8+pGVk4kqXSy6+hBat29B5/z6c\nf/U/WDh7Bief/3d6HDiE3gcOZMPqlUyb9ATvvTGJa8c/Ruh7lntCCG5+9IWG1x3btOTIo4/90Xsm\nEAySkZVFRWkxdTXhH+37R4RQVIIZOb/3Zfyu+Eb++Jdqpwgh0oUQ1wkhTvxuUWN9+8j6fSP/W7/6\nvr2EEOPraYU37KlWVWPSKe2+8/wvZwzYomUrXnvng13azj53nFcoA7z28osUblhDl+bZLFwwnzbt\n2tGiZStqamo454zTmPyud+y/H3mMnNxcRh1xJC8+9x9Ki4q55robGDTkICY+O4FHH7iHA/v3Z8b0\nT2nWvDm14TDpmZlsXLcWv7CxFIFpmIQryggF/WSlhSi34riW9FQEFYFuqMTicVRVJWSoVNUlUHQV\n3e8nHouTkZZKVTiMW1tL0pYYwuP4mRpoQpC0XUzDJCMgqI0nURD4NEksGsG1bVxdRff5SEsaVNbF\nibue8p+uKUTiHpdcVRRCPo26mEXMcvAbKq50UNVvdMpVNEVg2Q6aqpLq10nYDq7tYkmHpO3lwA1N\nRUpJSU2MoKmRGfRh6ioJy/EWRF1JwNBQVYXtVVH8qiQWqaOoIkxNuJpb7n+cS886mWkfvEN1JE5h\n4Sa2bFzHhZddQ8jvp3BTASNGH8e911yAm5JL+7x0MjMyfvRecByHNcsX0mvAkB/tl9s0n9ym+cz5\n5F0ymuTSvU/jqGp/FCiqSn6Pgb/3Zfzu2EuT7HF41ZXVQoh7+DZbMQaPWj1dCDENmPZf+n2Du3dz\n7kYtpjYmiL8lhHgD72/+w0q9Lf70HfYfchimP/CLz9Wt+7c0stLSEtq0bUfRjkIGtGxJTk4uOTme\n6uHkdz9g+qefsGH9WrKzm7Bi6WLGnnEOp5xxFgu/mM8D4+9m6ZJFvPf2m1x4yeVs374VwzDZuH4d\nTqyWgvXlrF+/ng7t2tNEUdi6dRNJy0YFioqKyMxtRrKsGNV1EELBtmyklAgg4rqkhQLYtkVVZRVJ\n2/UEs0wdvyaorg+iVr34VGpmOmYygWno1EWiOPUVmLGERciXIGAaJK0k1eVlID3zB68qUxJJOARM\nDVdKFAHldfFv0yqKwLJAdT32iiIEuqqQSDrEHIc0v4aQEhQveLuuixQQTThkhHxYMQvLdlA0MDQT\nn/HtbD0cTZKWlkZa0MulVyUcNOmQrlhcf/6p+H0+LCtB1wP6s2PLZvzBFNYtmUs0UsuSlev5z4uT\neOytaT8Y30Qsutv7RFVVTrnomh+0X33p37nx1tvIbrLr7HXIqF93IfSNlydiGAbHjjllt/v/7+Yb\nuPrGfzQUmu3DnmCvLVr2lVLeW/+8bcPZvUp16mfdm/5bv/q+y795/n02YGMuoDFB/Dop5Zj6N2hU\nGejvgUBKOsqPFF00FolEAvM79LSrrrsRx3F2W9CxuXATteEwhx1xFM2bt6D/gEE8/shDRCIR/KbJ\nF/PnsmPHNmrDNYSrq5n6wXskE14hTEpKkHbNWrNp00aKi3aQTCYa5FjjjsS2XSorK5CKjouLroBp\n+qiLxryCH1UjXBclxa+TtB0vXWIlUFWVlKxcEk4Jlm0TMHXSMjPZWVaMlNAkPQUH0BSBKyUBQ6Wy\nLkYkFiNquQR0z2LNdlz8poaDIIBAUQR1UQufruHXNaSEVL9O3HJQcNA1QTTp4Dqux2rBK/SJJr2i\noJCq4SBRVRVTUxqcfjxJWpeAolAdSSAENM9MoaIuTrOMIBV1dZi6Bo5DQIFQk3widpS8vFzadOzO\ngnmzuPn/xlO6Yxsdu3ZnzYqveP3Zx3j8mWcRQlBUuIHq8lK69h3cMG5vPX4XR5xxCRk5nknEtoL1\nVJSV0HPAQbu9J/7xf3eSkdHoYuW9hjGnnbVrQ+GH0OZbI+WTTj+z0QE8mfQYO4Zh7K3L+1PDm1A0\nOohnCyG+y3/+Rk4E8FIn9UJ/u0s3f8Psm/AT/b7Bd9mAP128QONy4qK+DPRaYEJjTvprIxaL8eSj\nu2pWdBswDN1oXOlwLBbjyot3L+dy2glH89yEXVNRt1x/NWtWr6SkpJinHn+sof2j96dwz5238e87\n/8VVl1/Ml8uXceCAgfTq05fc/Hz6Dx5CfrNmpKWlsWTxEhTNJJSaQiKZBKFgGDp+08ROJqDeFScQ\nCqGqGiAQrsS2EuTk5pCSmkY8kcBUFUCiayp+XUURCulBE8eVHl3PddmxowhfIIRQVaSiUlNThSoU\nAoaGLSGSSCKlpzRYE00SiycI+AxPrlYVSASW7VIZTeIg8OkKtTELrX4RM2k7JG0H25EogE/zUih+\nXSPo875UJHjiWLpG0NQx6imFIVMjlrDx1WuOp/oNUv1GgxqiriqUhWPUxBJUReIEfSaWZePXvDx8\ni5YtyO/cA1f1sWb5QrLzWzP+nzfx8G03cPTBB/L6c4/TsWt3kskkhZsKeP3VV1AN3y7jeeo1dzQE\ncABfIEjwe7lygMqirVQUbdklgDuOw9IFcxp1nxWsXYWzGzOKn42cXd2HOnbu0uhDP5wymQ+nTN57\n1/Jnx54tbJZLKQ/4zvbMd860mG9lRXZRbBVCnIiXJsn8sX67QWX9Y5/G/CmNmYlP41s98T8ET1zX\n9T26gb8Pv9/P1TfcvNt9L731LgsXzNul7a77H+K5p5/AH0yhvPxbjZeLLruKuto6+vYfyCljjida\nXcW6dWvofeAgcnNzKSvaTvOWrVFUFSuZQOASCqUSisYxTZNYLIKCS4dOndi8eTOWZRGprUNRPHGp\neDxGTpMsqioqSFoWquIxR1J8Orqmgmbg2AniCRdNgKrr2PEEKT6dRKQWv+mnprYOQ1expcSxXGw3\ngq6oqJpKImljORJT99IgIb9JbSxJRlAnZtmETB3HtRGqSkbQoDZuYTsuqqpgqJCVYrKuqApN8QSv\nbOnpq/jrqYNJW2Bo3ownWr8gWh1NoigCRQWheEqHKpKklIRMT5I2btkY9dZtjpToioqLp02+adVy\nHAmK7iM7O5srbx9PbWU5S+bPoWDyGwwfdRQ5Obk89q+rSWvenn5DDkZVFFbM/xzHsbHiMfodcvQu\n49ukaT5NmubzfURrKpGuJKvZt/xm20qy5usv6fMTeXOABTM+oUleM1LTfzwX32gEm/3sQ48bc/Le\nuYa/ENy9kxV/BhgnhKgG7q73UTgRL1beU/+4DC+YN/T7byerL+75DEAI8cu0Uxo6/AyR8p+DP0rF\n5vSpnzDysFE/aP9y2VJatGxFLBZjxVfLadWqNS8+/x9mTPuEO+59kEQ8wSvPPc0Rx53IM08/ydCD\nh2NbFnNmzsCyHaI11WRmZVFeFcZJRPD5g1TX1CCEIC0tg3sefIi7/nUL2zcXYksQrouqCmrjnoWZ\nz2cibatBGdCyXSzHRdV1pGMDkoDPhxAKsXgcgcRyPHGqtICnWpiTnUXpzgo0VcF1PZlXV3qz6dSA\nQcxyUYSXJklYnhaLW081TDoupq5iqB4f3XFdhCKgPvhGk069XrgglrSIWRJd8SiLrgRd9RY9A4aG\niiTueLPuuGWTFjAQ0qMiKnjqh0nbxZEuQVOvl7GVBEMB6mqj1MST5OXkkJvfkuqyHRx11FFMfmcK\n/QcPIScjDaGZ/D975x1mR1W/8c+ZeueW7ZveEyAkkAChhCJIlSaIgCCKoHQEEUUUUAQRkSoKP5r0\nooA0URAQBFR6aAmhJCQhvWzfW6af8/vjTEJCgCwQiuibZ5/snTt3bjt75sz3+5bXX3iKrx9zEhO2\n2p7TDt2bMRtuwi77fpNBQ4eTyxdW+379Shknl8O07BXb/v3YP2jp14+x4z6Y1P5/+PjxURWbw1tK\n6rS9+uaQfdR1//xYFZuZSnN/dJWnCb0SF/Qx0/g9yylCiJFZCeWUrNi+P6sGPnwu8fPTfkJXZ+cq\n216ZNpWrLruUhsZGwqDGrddfw2vTX2HCxI2ZMGFjzvvF6cx443W+cfgx7HfgN4ijiHvuvIPnnn6C\nw445joO+dSgDhgwjjBMKNvRvbSVfKLD1Ntswesw6LF68iOOOOIwlCxciLDurjWtLVjvjbgsltaTd\ncbAcD6l0Mk4QBLiWLlWEcbJCri+zBmiQKCpBhGuZ+JUyOcfGMowVE3JLyaU+7+BHKRVf1+WDOKUa\nxqg0pRbrpuaA+jyepZuTlSBGKXR5Ruo6t2EYK+T5tmXRkLNpyLs4lvZSIfMeNw1BKHXcm2noYyip\nSKTCEoJY6mAJy9TKziRVKxqiYc0n55g0N9Sx3qhhGGnAxI0m0dvbS3N9kY032Yy2tnaWtHeQLzUw\ndOwEvvnVPVnU3sW/n36OG6743YoJ/F9//xvTX3x70fDkA3cx65UXV/neGxqbKJZKH/OI+x8+LXyG\n4tmmZNzwY9D19mMynvhP+vLg9yynKKXmCCHu4O3Lgc5MCvq5xmNPPbeCXghw9GGHsmTpUr60626Y\npsmwEaP48em/YOz4Dfj7gw+wyx57snDBQv5y79384fpraGy4mmqlQndnOxttsgm/PusX7LrH7vT0\n9tLd2YEfRBRzPlg2xltzmT17FpZhEPhVFDBgwEC6e3ox0hALKCcC17UJwhjTEFT8ANeM6D9gEG1L\nl+gmpNR2r0qm+JGm7wVxkoUPmyRpQpjoQAalFPmcTTVMMVAs6a4hhE7jaSm6SKDHj8iZAj9RWIae\ncIM40QZaQFPRpRomJEmKZ5l0V0NMQyCX88QNQSHnIBUESYzMJPRGFhSRSkmUgmOaIBRR5ni43N88\nn7NJ/UifyJQ25fKjFCW0nzlS8ubMGfTWAob278e2ex3Ga6+9RrGphRHrjGXmrDlUgog3p7/MZdfe\nyAv/+gez58zimB+dvuJ7HbnuWLyVVuQ77ncIt/zmTAoNzQwaPgohBBtM3OiTGnYfGL3z3wCgbuh6\nn/Ir+WD4/W/OYYttd2DCpL75lnyc+KwINt8xr47Kkn1gbTQ2M5PyF7KDqayk8rnG8gn86ksv5rG/\nP8AFv72Ue/76N954/XUWLVyIbduMnzCRxYsWcsbPTiPnFRg6fASTNtmMJE148G/3MWnTzVln7DiW\nLZxHPp9jwfx5GGjanGka2pMhSVm0YC6u42A7DpZloxR0tLVhoBWPUkGxVEJJvRJtaGzCtU38RLJs\n6RJKpQJpNhBtQ0/mYZIQRAmebWEbApmmlMOUOs8l55i4jgUSWks5XMuiqZijIe+A0mEQUipcQ2AY\nBobQqszeWkQ11CeCOEmphQkCQAjttSK1pB4AJXQiilQrItgsU59YpFR017QSU4B+rXGKlJAqRRBL\ngjilveyTSkmiNG1RAfV5B9c0qNYiZBKzpLOMaRjMX7iIm668BNKIv9x5B/fdcwd77nsASRwxb85M\njj1gT9ralhElcpXvecjwkTi53Crhybt/82j+cc+tLFs4b62Np6lPPcaCWW+steMth2G7GHZuzTt+\nxvDt4076bEzgsMLvfk0/nzAeBs5D19P7FLvUF3bKjgBKqV7ewW/8POM7x36PL+68K8WMwvXzX/yS\nQYMHk6YpN159Jf36D+DGW/7I3vt8ld6ebk7+6c+YOnMukzafTM0PWLJ4EW/MnEVnVzdvzX6LAQMG\nsN6663L0sccxaOhwKtUKKpXINEVKSaFYxCuWqEUxNb9GNUqxHJcwqBHHCTnbYtmydgwh8GzNmzbS\nmJylb1uWgWdb5GwbIXStG6FXx6WcjZIS1zTprASUw5DuWkSipJbZG3pV0tbr48eaTimVrk0HcUo5\nSkiVbizGqaIWxTiWQZSk1BccLMOg4NrkHQvX1GUTP9HGVZYhMIWhU1KAgmtSyDmYpvZWkVIb71uG\nwLEMRGaCJYCWkke952jbW0MQRAmGAZZt01KXZ8stt2KPAw+lo6ubtxYuJfLL3PLQ03R1tpOENaa+\nPJXvn/YLqrHiyBNOXu07fvbJJ3j8kbf/Thpb+3Pw90+j/5C1Z9TU0NLvXZkvHxXFASMoDvjsGEr1\nFdZKV7mfKvpYSvkU0u4VOgyiT6UU6CPFEJ06UUcfKS+fBxjGqh9NY5NmB+39pR147tmnWbpkMSce\ndzQ7bjOZSqVCd1cXr05/hXPOOoPOjjaGDRvBhI02wfd9OtqX8dKLL9DdvoT5c+fwjUO+Q11dPblS\nHWEU41rQ091NUK1Qch1KxYL27vZ9XNNAKUktlriula18FUoYBFFKNZKEiS43aKWjrmEPaSpkND7N\nDEmkpBolOJZJnefi2QaGMCgHMd2+Nr4q5Bwcy8A0BZYBdlbntjVdhpxtYpsGxZxDlKTkHYvuSoRS\nio5KQJJKEAovoxYC9Gb+4wpd60Zpy1nP1kk/Jc9CKsi7NoYhyOcsgiTFsky6egMqobbENQ0D0zRJ\npaLmh/T6EXNnv8n0qS8wfPgI+jU10LFoAcce9BWGjhjNxptOZuKYoQwcOoyx4zdk9szXueikw5n1\n2jSm3nU5MonZ8Uu7sde++3+s42jYOuNobB2w5h3fiTSE2tK1/4L+hxX4jHqn7AyorMTSp0VzX9Pu\nz+Uzrtj8JHDGaSczbMRI7JxHfX0DEzfZlLvvuJ1FCxfyg+99FwNF0cvxxquvkMt55Iu0UONNAAAg\nAElEQVR1rD92LJM224LbbrmRN+ctYWH7g9x7992YlqkFM3lX+4vbNkEQEosYQkUqIYlTpND+HnWO\nqQUygOdY+HGCMAWOJUglREmayeK1YnJeZxXbECgDNhjSxPQFnZRyNnU5m6xag0Bmqw2wLZM0lfhx\nQpJKLFPT+hzTQCLxbIcgSsjZJmGSUgkT/DilmLOphskKqb3IcjrrPJsB9R6VMCGKddiDQmEa+jK2\nqxpiGIIoTGksuAila+W9fkzBsajzHMq+DrcwTYMg1p4sSsGgxjyBNOjq6eHlKc9S7WojVoLGUoFq\npcJJh+5La30RZVhIw2aHfb+lw5K/tDdX/Pp0Nhm/LovvvpXx48dh1rVy3e/O59TzLunzOHhlytM8\n/8RjHHJCnxdLHxxRGcrzIN//Ix9KyRQZ1jC9/zVpV8ansMruK1ZeNH+0tHshxEZAR2aAdYBS6q21\n8xo/GM770dF0tS9b434P/fUebr66T54xfcbycGSAo777fQ457AgqlSqWZdHT1clR3/0ex51wIrvu\ntjuVSoW5CxbjujbNLa0YSNqXLeHOP96EEIJRo0fiujlsxyGKYqI4IhYmEoFMYiSKgmOjlCRKssak\nbWFkxlC2KVAoymFCkqYUCiUc06TgWriWDjd2LVOrJQUkaQoS3lzSQ7/6/Ioan2ubCCFwLQuyunMp\nZ+NYJgKB51iEcUK/kkvRc0hSgW1lDBSpaX8Fx9QhyGiKYcmzqc+7mpOepnRXQ6IkxbUM/DgliLXP\nec6xidJ0RTMzSSVRnGp1aDb5FzyH7mqAlNrtUAuKBAXXwjYNps7vpOr7VGs1hCE49YIrWG/kUMIw\norWpATNXQBSbKA1bn4OOP4Vnn3qCuXNms+XOX+b8G+7m6yefy/Z77kv7vJkMHDKMH59z8bt+922L\nF/L6S8+ttn2DTSf3eQJ/5h9/+zDDDrwW6L920oDCZXPpeqFPCu7/GihA9vHnE8Zyyf3v6WPG5vtR\nDH8EHI2Wi36qbfqTz7+CxpY1u67tsudX+ObhR69xvyAIeOapJ9e432vTX+GXP//pitsDBw1iyrPP\nMGL4cLbcaH2232ln/nrXn/jWgfvhuDnGbbAByAQMh0KpxFe/diBRGOEVipz041P5wnY7sKytjURK\nDANKOQdDSqJI87wNYZBiIKWWxedsk5xlYAtBwTUJooRaLClkYQy+X6USxvhhQiIlYayTcaI4xUDQ\nWPRwbZNCzqYSRLoUk0qCKAGh1ZeWaZK3TVIpIQuN8BwLxzRQaHdB0HVwz7HwoxQ/TkgluvMKJFKT\nvA0hsA0dhuzYFj21mDDR7zXv2JiGoLsWYAmDnGnQWHBxTJMo1StzU+jmL0rqBqzQzBTPtUEIQmXi\n2iZ516KrEhLFKU7YwxnfP5xFCxYwcexoCnX1tLW34+Y8Djn0ULo623nunw+z2eStmDntBX5x5H4A\nOF6ejXY9EOA9MzKTOCYM1hy6fcWF59C2dMm73te+ZNEaH/9xIzdgFC1b7/tpv4z3RJokffqc1zY+\nS2n3K+Hk5Ytm3lZuvi/ebyU+O+MuHsCqTob/8ahUykx9+aV3vU9KyeuvTgdg/fEb8MtzLwDg+9/V\nJ4fGpiaOO/EkTjrldB575BGOPeFEGuvruPzS37JwwSK8fAHP0KKZB+7/C4W8S6m+gXN//SvuuutO\nDAT9BwzANHV9WcoEpXR6TiolYZqSKvByLtUwZl5nFYROy0EY2IaB59iYpqlTdxSEqaTec/EcC4R2\nHYylpJb5tARxgikErmPSUNAslTCbiE0DWuvygFgxaHv8CMc2ae8NyDm6iaoUVMMYz7WJEy388VwL\nx7JoKrhIKcm7FjnHXJHl6dgGYZQgENpwK2uAJkrnfwZRgmsbeLZuklq2RQrEqSRJJKlU9AYxtVDz\n0vsVNee8wXOwTUFDIUdH2Sff0ErOdahiM2fa82wxaWMa8y6L5rxBU3MrZ1x4GSd+bRfmvjmDTXdd\ncw38rRmv8dCdf2DgsBFMnPzufiorY/evfo2mltZ3vW+Pgw5738cmSfJZvqz/RDB9ypM8+udb+7z/\nvLlzPvqTqs8UT3xlfGCbk/ebxLve+funvSJfW2hpaeWQ7xzOJRedv9p9nR0dnHnaqpfKtVqNY44/\ngZdemMKjf3+QW264lssv+Q2Nzc3MmzeP4394Mv379WPAgAHUlYqU+g2itbUfG208ia6eKj0dy2hq\nqCcNKlgm1CoVtpo0jhTBfgd+Q/uHS4ljGVgo4jSlXPUz21dt5VoO9UTckHdY2lvTf/zoCdsxoasW\nUg0iqkGCVAolJWEikUqv6hXgCC3ykVKvQgwdbomfsU/CJAUBRcem4NoctOVoQJ8Q3Ezun7METUUX\nxzKJEr3yby/71OKYrmpAIhUiM6YIYwlC4JgGpoBSTqcEmULTLHVsm0IYguZSTvuV+xGeYyOEluX7\noQ5g9qOE2cvKWlxkGAxoKOJmjJyJG4xDmCbLFs6nt+qTdxySoMKm2+4CwLQnH+GQ757IsPUnEqs1\np9z3HzKMCVts3efxNGzk6PdNvF+OJI5Jk2SVbTddffnHHjCRxNHHevyPigmTt2XXA779vvvMnf0m\nc+fMIkkSLr3g3I/8nAp9IdmXn08Yfwd+jZbz396XB7zfJH6VEOJBIcRDwHnZ/32q0fwn4PeX/x+V\nSnW17aW6OvY/6JsA7LXLF3lxynPssfP2HHXowcz467X84MenMXDQYB554jm+su/+vPjCFK79/RXM\nmfUm0556FNN2mDv7TV5/7VUef+wfxEmEHwRUqlXSVNLQ1ExvTzdTXnydJEm58cYbcUxBqa4O29R2\nr6bQ8WSWKTLTKV2msJfT+jwnW+1ClE3UBdciTPUVgGUYOBk7JO9oD3BDGHiurSl92TwmpcIPE7qq\nIUXHpN5zdBqPAUt7alzz+OvUgoicZZLIlDiRVMKUUk43HYVQICR1nkvJdfAcK0u71+yUomshpUII\nXXKxTYOi52IIqM+7pKkuAZWz1bZlGuRdG0ugm7QIBtTnMISgqeBioCjlHCwTFndXEYZFQ3MLi+fO\nIghCio3NOK5DR9sSukKwc9pudmFbF62jxrHu2PXZe9+vEUcRyBjSkCSJ+dGBu/DK80/z/D/u4/lH\n78fLF3jo3rt48ZknVhsfK+PVqS/x4rNPv+t9SRzz5muvrLLt0b/eyRMP37/Ktm8ffTxf3Hl1m4e+\n4PEH76O7s2ON+113zk/o6Whb437vRK1aJvBrH+alrXV0tLfR1dGGZVmcd8na6XupPv77JKGUunN5\nxqZSqk+OZe/HTtl/ZZ9b0AkUH+kVfobw3RNOfNft/378Mbq6uli6ZAn1TS0snD+Xn/38TIaPGkOd\nqvH0tNe49vdXsufe+/Ct/b9CLOHY44/nyX8+Tkfo8IX11mPeW3NoyjvMWdRDU1MzgR+QJJEWsKQJ\n9fX1lLt7MAzoV3Rpr4QEPb06wszUPt2G0GwPNLsP17JQSiGlLmsUHAsMHU0cJil5R4c/uLZJR9nX\nKkyBNq2SSvPJM5dH1zYxQFMObS2l7/H1JJokaWZwZWA4BrZpEsQxMhWkMgWliBOJZQpSqSj7CS0l\nj14/JYq0EKjkObqmrhTCsFGZ0rO97ONaJk1FlzhVpJaBH0ss8bZAKEFSDsA0DSxT0FWNaCy4pBkN\nsbsWUnQs+teZlHImtZ5OTMOgPu8ikpAd99qPRQvmM2POfG78vwvZfLudGDdxE7qWLeKmB+7mwKN+\nwC0X/JRYBXzzGwfgF8Zw8Imnc82Vl9NQX8/Ou+0OwIGHH4tta9vWX5/5M751+FEMGjxklbGSLxax\n7Hf/E+rt6ebpxx9hzPpv+67svM+BH2nMvhOWba9GhX03HHH6RR/q+FMefxivUGSz7T59t41NNt9y\nrR/z81LFej/Z/YsAQojLM00/9DF9+T8ZC+fPZZcv7Uq//v05+7wLqa+rZ9GihYweMwaA6Pmp3Hb3\nX7Asiw0nTiCRgjv+dDuDhwxh86224fVXprLxpM0wk5Al3VXa25fp0SIEAwcNIqhW6C2XUegJWQgd\nfRaECWEqMW0TP9IqSJSiPu8RpwmeY1Pxoyw9xyBR4BkGaZrQmHfoDWJMoevaAoFSEtPQ9WknS5Tv\nLPtUI0lDwaGQ+Z54jkXZjwhjiTAVhiEouA6VICRKNT9bKO34liSSnC1Y2lMlTBWDG/JUshKIYxnZ\nqltQ8SMsU6tTk1RSci0qUUIUS/KORTVIME1BcynPoq4qpcxwy7YEnm0Tp5JKGJO3LW2YZQjCKMFz\nLXr9iPrGAm29PgVHr/SlU6CaKtYfuwEDh41k3qIlDHBTQmWQRBGj1lmP+196jodvv57nn3uGc6+8\nhV/+7CcslQPofGM6hmHym6tu4PXpU3lz+sssmPU6tXIv9f2H8K9HHuTgw45kwMDVHQRHjBrznuOo\nqaWVbx59wsc0SjW23mGXj/X42+6+z8d6/E8Xn0q9+2NBX3jiQghxDrpTuinasPxzi003n0xzSytP\nPfEEU19+kX888jBDhwwmny9w9HHfo76xhYnrjebyq6+ls72Tt96ag+PY1Go+D517IVdd+luu/O35\nDB42gh132on7772Hml8jZ1t0Ll0CwqCYM6nFKYYQdPkRSSq1kGa59D2j/dXCRGdP2jZBJdBSYalo\nzDuYhoFEy9lr2QTpOSZ1ORvbEtrB0NLJ8pbQNrGNBZe8k4JhIIT2DE9lhGsKHMvBypJ+XNvAdTzm\nd1SppBHNRTc7GTj0+hEF16TZdVAKPNeiFsRUMym+l4Udp2mqV+uppMePMQxBQ8HFMnViz/IEoJxj\navqirqSTKF0SUpkfjGu/bdYlhA5bntfeSy1Kmd9ZobGQo1A0KFiKaS+9zBvTXuaca+/gtycfyazp\nL7F0+jNssPGmvPXqS4wYP4nykjncf91vOOuC3wEwYtTbPfux4ycwdvwEli2ci5KSuvp6xm4wgcFD\nhr7neLnvztsYte5Y1t9w4sc6LldGx5xXEUDTyHFr98BpBOZ/R2jEctn95wF9UWx+4EL7fzLGbTiB\nfKHANVddzjbbbsftd/2ZM88+l0mbbs6lF53P+AkTOPuCiyhXKsyYPYfTzjyLpn4DaWhoZOH8+dxy\ny82EyuLN2XP46913YFgOrlfQXHClCJKEsh9ja4YfDZ6DZYhM7QhBqBWQQmi+tm0KLKG9SRKpG4Hl\nMKbHj4iTlJytpe4IbTIVZIEMSkFXLaYu72IYOnyhoxoRSkV3NSRM9CSbs03i9O1GpJTQU4sIgoS8\nJRjaXMQw9Mq+qxaRSoUfS8p+RCIlcSLxI92scy0tt7dMAYYgSnRQRVPRJU4kHZWA7lqAY1sEcUIt\nTEgSycLOKqGUGIag5OqszljqRqtm8CjaygG9QYwfx9iZM2K/+hJF18JREWEYsbSnRhT4vPj3uzjg\nxDPYcJNN2W63vfnThafQr7mRYeuuz95HnczOB+kLS6UUyxbOI0niVcZAv8HDGbrOOLx8gQ02Wl2k\nHEUhpx57CAATJm3O4GGfrPzd9grY3lqOZKsugfkfi8v0ZxN9VGv+J8zza1yJv6O4/l8TDXLNjbes\n+P38X59NHNS47tprWLJsGeXeXlpaWjjzl79i0YJ5DBw0iL2PP4Gdt9sKL5fDIGXo8OG8MWMGnuGj\nFARRjOvYFIAkTZAZpbCrGhAl2pdECEiUQCpJ0XLwQ50A5EcphgFF1yRKFHWeTSoVUSKphAnlIMLK\n/Enq8g5hnCBMSd7RtWlpm5QDLbaRgaTBc4lTzd82DAM/iShmkWzlMKbgmDiWSz7nsKzXx8m8VCyh\nqCUSzzBJZUoqTVIF9cUcaarwbIPYl5gGOJnZ1/LJenBjnh5fUx6VkqQKfZWQ9QDSRFJLJbUopuja\nNOQdDKGFQi1Fl46KRKkYNyvRaBtcHVhhG4IY2GT9MYRJTBWX3/36DLbcaAOmPfEwcSLZZPeDeO2+\nu+nq7mHujFfZeqfdmPaX61H912Wrnb9M/yHDVvn+rz/r+wwbO4Ftv3Lwan4fjuNy2nmXklR7GNSv\nGTP/yaUWPnLDxUze+2AKDR8ws1xJEO+zZisMgMKHa7D+p+LzUk7py0r8vwKvvTod3/dZtHAB11x1\nOdVKhWVLtXeFIbQf9j777sdX9z+Q2bNmsWzxYs75xc949umn2f/Ag6j29rLBBhvQ0dVJQ1MzTs6j\n/4CBeIUSjusyYvhw8ram1iVSM0jCVGXJOiY5xySV4JgGedui7Mc0FHLIVGpVYyJpLnq4lqAWJYRx\niiGgqeCQs0xsS0/25SAiVaywns3ZJnGmnLQMQd6xSbOSRRRJuqqhfk0KYinJ2xYSgWVqvrYf6Sg2\n09DqySFNBSzTwBDanjaME3prEQpFlGr1ZpytwPXjDBIp6aiEOJZBaylH3tExbvWeTc42qPdsrSJF\nq0il0qIfx9Q+K23lkJytbQqKOQfXMig4Wb3cskiUts3t6VyGYVncdsVFRLUKc9+cgWHn2Pqrh3DN\n2T9i1ptvsuSNl9h8+9244LQfUsm1ss+3v4ttW/R0vK0IfvRvf2bYxl9g4Ih13jO3tVAs8afrLuOW\nq1aV69fKvR/fIAUm7fY18vXvnfeplGLWzBmr3zHjAeh66+N7YX3BtBshjde83yeAzzDF8AOjLzXx\nzz0qlQp/uPF69jvg6zzz1JMMtANMAef/6kxOO+OXRHHErTffwPEn/ogNN9yQ311+JWee9mM222Jr\ncp5HqVjCsW0WL1pIc0t/XDfHzFen4kcpqVQUczYdHZJakFCXsxBAJUyo81wKtkFXNaDXjzPetMS1\n9T7t5UD7mpiavdFVDajL6wi1KJG4lp7shaFXtc0FF9swaK8GGELXkGuhjnVLU0VD3iZKFXGiqIXp\nCj65ZWgRkTJNXMukGsYs6wkY1lLQbm/o+rRUEMfaRzySUHBMIpkilc7rzDsGjmUSxwlR9r614lMn\n+0ipqEUJKC0yMi0TlUqEEHRXIwqeDSiCSOK5NmGiY+ccE4RhYRgpqVKUPJf5HRUGNRWwUERSImTK\nvHnzWPDS62w4tAnb72bJvG5GbbwVV/zuIgqWwxU33kmtt4uBw0fzg7MuIJGKWy/8KQOHj6Zj8XwG\njRmHYefYdKvtMEyTQvH9vUa+ctj3V1nNdS5bzL3XX8ahJ5/Folmv07VsEeO33GGtjtWGfu8f0Vbu\n7eHWm67jtF+8IwFsvd3X6uv4UBh3IJhrdjF84blnGD5iFM2t7y6gWlv430r8Pxg//N53mfXmzBW3\nzz3rDI7//g8ZO24ce35lH96Y+iJ/vfcezv/tZTQ0NrHxxIm09B/InX+8iYvPPZvb/vgHttxme158\n/jlmz5rJ9GkvYzsOe+69D0uXLmbnXXfHEAamYdDY0ECUpMShj5SaE63tWSXVMKLsh0i1POlGfx22\nIbBNQWPBpanokHOszLtbr2gR2rbVNgRCQF3OxbNNwiRBZvFq/eo8DKGNrWphgmEKevyYvGOSdwwK\njvZccWyToU0FSp6tvVmEXqUMasxraT06YajOcxBCl18aizlKrkUxZ9O/Lo9tQKq013hb2UfCCjqk\nbRk4lsCxLWxLq0ylUniZw6KT1fqbii5OJj5SgC0U9XmHapwSS4UfxbiWSSWMKQchQ5oK2iY3iOms\nhCigf12OCcOaqWtqIfBrzFjcwYMPPEDS086oESMhiXjjyb8z/cXnuOHCM7Ftmy32+ibb7fdt7FIz\nbqmJ/kNHUKpvoFAsUatWCP0aUr67g4bneeTz+RW3m/oN5NCTzwKg2NhM86Bh7/q4+2+/iSgM3vW+\nj4q6+obVJ/DPCvrYNF22ZAnVauXjfS0K7evfh5/POv5rJvGVU8d/df5FjB6zzorbhxx2BE8+9hBT\nnvwXgwYN5qRLbqFcrlDp1YEb+x10MDvvtidhFPOvf/+LF5+fwl/v/hODhgzhO0ccRbVa47jDDuaG\na6/BMU2uuepKDNcDYVDzfcJYKy4d0yRNFdUwRjMIFZHUQcWlvINr6hl0udgnlZofHmTS7LxjM7A+\nrydIpUU7jm3oPEzEigZlKedQDXU257KyT31ee35Lhba0tW3qCy6x1F6GHeWAXJZjaRsGjZ5DJYiw\nDIEQBp5j4kcxQZjQUwtZ2uPrMOQopr3i4zk2RdfBtTUHPIjTjEkjs8tRkYUuS+JU4dgmQSwRQq/c\nq0FMOYiQUrNyGgsu1UiuMNtyLQvH0ta1QghMtLd4a8Gh4DnkHYvGgsfu3z6BcpjQf9hItt7zQBo9\nmyEt9Ww2eTKTJm/NpaefwPNTnmPEqNHstf9BjBk3gZyrufO7fv1wNtp6e4avp3ndSRzzy5OO4eE/\nXc+c115e4/j6y5234a8kjKlramXA8NUpiEopGltaMYw1Kzw/EJY8A8F7W23cdtP1XH7Rr9fuc35A\npGnKnbevWV6/65f3ZtiIPoXafCSsjcamEKJBCHGyEGI/IcQm79i+kxDi5Oz2KCHE80KIK7Mw5bWG\n/4pJvKO9jSMP/caK267r8tDf7mfay1rLNGbd9dhlr/2Y9tob/PiHWgT00nNPcevVl3LfvX9GSsn+\nBxyI6bjsuufe7LTzLmy/005865DDOPF7x3HdNb8nTlJa+7Vy0imn0b9/P8o9vXhenjiOsAxoyjsU\n8y4F18QwBBI0q0QpXMsmjHSTzzAEQZLixynVMEJKiR9pr5IkTamEcSa20d7aOdvS2ZU6s5hyELO0\nVzdTDZGVPJIUKRVNeRvXNBBC0VUJMIWg4JgkEt5qK1PvuRgGKCFwLIucbZFKiWdrjxQrY4U0FFwK\nORvXtihmxlZF1yJOdU6mkVEB866VuR2a+GFClKQESUo1iLFNLRsNsvddzpKDklQSxtpkK5YSzzbo\n8SNMQ2R1fYNEKbr9SPPpUwUCHAvuue5yqn7A4489Rsf0Jyjkcpgqprkuz7QnH8YUkJbbeeLuGyi1\n9OfRO67nnusvo2PhXGzXRaYpZ39rZyq9PfzhnB+w0w47sMe3jmX0+FU1bstP7ivDth0Ea5b0X/PT\no5m01XarNUs/8qV93Qiw35uxMuvNmXzrqOM+2nOsBdRqq6ukPw0o+rYK78NK/Eh0LuYdrES/Vkp1\no6MtV+5A76iUOkopNXttvpf/ikm8uaWVa25adQUwdNgwWlrf9mpWStHU1MxWW2/NX++8jS9/5ats\n/IWdOev0U3nx+Slce8X/MWzkSObNmsHjDz9AQ1MLt/3hJjZcdzShX8UrFAmqZa6+4jJq1Rp1no2l\n9GrYdXMow6a36pNkAp+cbSHRtMBKGBGlmkY4qKFAzjYI44T6gkuYpAysz5O3TYQh6KmFeI6lFZ1x\nSk8tpLMaUMzZNBddGvIuOdsi71pIpSdT1zIZ2JgHYSBAUwLFct61RVPRpS6nQxksw9CGW0BHNaDg\n2lnCj8QUCtsyiWLtEZ6mCj9JdQM01g3QjkqYpdXrpqdtaQa4bQqiVJF3baSC7lqEH+mJuqno0q+U\noz7vZJO/SWvJwxTac6VfKUe95yAMgUSRpoqcrQVPUkr6lzxcy6TB1aHSw/o3s86OX2Pd8RvS5NmY\nuRIBNk++MI2Xp7+G2TSImVOfx3Fz7LDbXsx/Xa+0a9UyzetNIl8osuuh32fUJtusGB+LZr1Od9ti\nOtuXcdm5Z6w2xnbdax9ynrfa9iSOOfXob664ffjZV+LkVt3v4nPO5Nc/+R4zpk/9QOO6Z9kiZr2Y\nuXHm+68oV9x85e9WOymceubZFAp9pyX+45b/Y+m097cd+KAwTZODD31/Q7BPEqqPP2vAZtmEDWsO\ncfiaEOLIlVfsawNrbRJ/5+VDtu3IbNuRa+t5Piruuv1Wbr35BtYfvwEDB73dJCoUCnztwK8zctRo\nhgwfydjxG/L0k0/wvR/+iGee/BeHHn4Uu+7xZRYvWcoe++zLH2+6nuefn8LC+fMY2K+FWrVKT28F\nx7GplruJU4UpdEkgimPiOMuTVEqrFsMYlBbvpFJ/Eb1+TDWKV+RBdld0vTyVkt5AB0IIQ2gpfSpX\nOAMObipSyNmYhkFnJSBvmyh0Xd00DGKZ0tZTJUmlVnwiVqTwSCVZ0uPT4+vySXvZZ3FXDSUlaaro\nKPskqcI1ddiDa5mUPIco0XRAJSVlX5dDSjkH1zJ1OpBhEieSnlqMEOA62ktFAEVXl2iCTPAUJzps\nwjYEBiKLl9Mnh1iS2eYK/Owz0MwCgR9FCEPok45UpDJlnYENvLmonUduvgRqXXTXAma8+DTSa2Dd\njTZn6+13odqxjHkLF7P1lw+kt3MZ7UsXc/d1lyGl4shTzsYwTTrLNX512g9ZPH8uAOXuDvxKmaaW\nfpx89m/6PN4s2+ZnF131vvscfPgxHH7iKaw7fsIHGMkgTAvLXr3OPGrdsQix5quC98PYCZMorlTv\n/zxibbkYCiEasl8b3msfpdRspdRVSqmrgKPW0lsA1uIk/s7LByHEfmg724ezF/6ZwF5f3Y99Dzjo\nXe877qjDmfPWHDbYaBNuvfWP9O/fX18mGxZJ6PPqc0+BTPnnP/7BxIkbUfBcttp+J4STQ6WpViN2\ndlJfcHBsPaHGiSRNJUEUZXVdMsqfXiGbhoHOvBEMqPMARUNes0wMQysyLdPUdq9oum9TwcXN6Ipx\nKumtRSzp9vGz+DUzcw2USlELE1zTIor1St+xDFL0ihWljakaCg5jBzXS40eZW6GFZRhESYLn2gSx\ntqB1LS3fr/McLNOgMe/SWldgQGOBVCkSqYOOl79n17Eo5XRNu9eP0P1BQS1KM7aLwrUEUapLP2Eq\nSZVkcXeNOH2b3qWkFv3YlkF3LdJGWK6OdQvjhN5AX8mU/Zg5bWVGNOVRaUL9oFEMHTMOt7E/1fmv\nY9Xa6Zg1lUpXO/988mn2nDSGaf98kN5ymafuv527LjuHS4/cgwUzXqHWsYg9dt2F+XNm0t2+jGce\n+ztOfs3JONddcsFqjVBvDZNhc2s/rv71afz59xeu8fgro665H8M32HS17Vtt/1oOAukAACAASURB\nVNHl+IM2nExh9MZ0zp7OzL//8SMf77OID1ATbxFCTFnpZ+VF6XPAcs5n9zufYzmyBe3ySf69OaIf\nAh8nxXBn4PlsMu9WSvUpufnjhmW991s+/KhjuPjCC1BS8sUddqKjbSm/ufBClixawPjx41m0YC6l\nUh0zZ7yB7/ugFG1LlyATXZ7YaNKmTHnmKVSiSw6phHrPxo+1wCXv6FxM27KwDEksJQJIhYlrCRzb\npBzoSUoYgijRbJaia2v+dyqJkhTDcDMOqzajMh0LU4Dn2hgiQaGpfJUgprGQw48TlNCrb4FCZja1\nCuhX79BWDrANbWhlmQax1OlBUkExp088WpSU4tomXVXN+a4ECTlb0w/rPJtKENNUdFFKZ2v2BjEN\nns7j1B4tMY5pUHAdUqmwTZ1qhJJEicKxdE+g3nMJooSBjQXKfkQtTgmSKnU5l5aSRylnEyYJedei\nHCRIqUilpDtIsQQU6+oplkrMfuUF/DSl36Dh5AcOJS53cOCpF/LKlKcoVypsO3lz9vn+WQR+jcFj\nN2Liplsw65lHKDU0YiQhwwYNomHYukx9+G4GjxhNqaERgD9cdQljN5zIJluu7jW+4aTNV5hS/fOm\ni9lwp6/SOPDdWSor45SLr3tPFszKCMrdLH3zFYZvvM0a910baBwxlrrBn2w+etvSJVR6exi5znof\n23Oo7Cq4j2hXSq1+ttS4CjhSCNENnJM1LfdTSp0H7ARskm17GNg0+32txlx+3DXxKVnB/1PJ5rz5\nvFM/0P4NjY18Zd992XDCRFpaW1lv3AYMGTaMMIp4Zfp0xo/fgAXz5zN5y62oq6vHsQz6DRiMX9Os\nhGefeooklfSGMU4uR86x8DNRTpQqwoz/bAgd+1R0LKJE4ghNu2sv+wigxw9JpWJUSx4za3QKIOfa\nFDybhoKLY+jVuWMZSCU1hTALK9ae4jCytYiBrkPXeQ71BZdqpKPRLEvL+jsqIUpKKoEW1MhMs+85\nJsOaCriWiUARpdrvPO9YFFyd8QnQWHDp9iOCUE/UALFMcEyBbUAliDULJ9G1b8sSKKW0ASM6hds0\n9TDUfHM9kQ1sLICS5Bwt8im4uibeXHCIU4kjBJ5tkbMNuqohvUFM3tYukOXebha29zBu4y1wVYLf\nsZidDjyS48+/hnUmbsZuBx3OJVdczUabTOKiM3/CX646j9uvvZxEKoZttBVzpj7Lw3++jZYxG+p8\n06ZWdtz3YBw3p1/b4EHUyqs3NwE23ertiX3yfkf0aQJ/9I4bmPv6tD45EsLa4zeHlR6e+8O7R9Mt\nhzBMLHf1Wv+7oau9jfmzZ655x5VQ7ll98frwHTdywek/+kDH+TBYG1a0SqlupdR5Wankhaxscl52\n31VKqZ2zbSuqEv9Jjc01Oh5mlxhThBBT2to+uN/xmvClb645qm1ljBg5isUL5tO+bBkLFyzgwnPP\n4Yvb78Cx3/sBJ/34NObMeYtiXR2Tt94WGQX0VAOmTptKd083cSpJEQwdMUrbxdYCqkG8YsWbswxK\neQeUohxERElKeyWkf50HmQVtKhUpipGtJQqOQSVMtdtgKnEdkySVNHouPbWIRCl6/Qjb1MEIlSAG\nQ2dheo6JKRQd5RDPtTKfiJQk1Qn1/Uo5SjmXvGPSkHdwbIuco3ngoCfVKElZ2utTDWJyjuadSwWd\nlZDF3VXKfowhFJ2VkP71Hq11HkIpqlGMUiJjmqgsTs3GEIKyHxIlCjK3RVCYwiBKJKaprXdTpUtI\nXdWQbn95f0AfD7SnilL6isrMGsQNeYc6z0Eh+OKX98VxXGxD8eS/H2dpj093Ty9+12KuOPUYLv7R\n4dx/56384IiDWVyJ2W2/g3h+6muMGz2MYqkOhMG/7riOg046m/lz3sS0HcZM3hGAoFbBL/ew/R77\nss0uX17jeHK8Qp/G3aY77sngUX1bdeZKDTz55FN92ndNcIv1jN/tG2ve8V3w1pzZ3H7rH1bZ1tm2\nhAVv9d3oNAoDLvr56pP1Vw45mu987+R3ecTaxefFO2VtT+IrXz5cBeyUlVPeNYojOyttqpTatPVj\nUGe1vofYYjmq1dXpTgcd8m3uves2Nt18Cw46+BDenDmD3150Hpf/7iI6F85h5MjRXPV/FzNqnXVw\nHJtlS5aQdywthFEpnUsXIVXKgMFDGdDSuIKfbRqCShBhmAYFV3OeHcugy4+Is5R3yzQQyqCtNyBI\nFV21iLZygGWAZ1sUHItU6UnMjxLcbOJfziFXUtfIOyphpo6MKfsxCEU5SFnWG1AJYhKpV8ieY+va\nd5xmx4EoSVFK0b8hT86xKEcxXZWQJDsZqcw2wLFMXFvL5VFkjc5EX6IqyFmaAlkO9ERcDSP8WNvg\nmkAp71AJY3qCiHrPxhQ69EIqRRwnWV1eUO85KKVoLuYIIh1gUfFDev0YP9afQWPRo8cPkank0Qfv\nwyw2kZchA4smLUWXukKeFx+5l232OpB5M6dz342XMmJgC6J7IZO33ZEjf3oOr8xZzB/O/QnzX38Z\nIRQvP/U4Tzz0V55//EG6Fr4FwILXXmLO1GcAePT2q1k0+40+jcM0TVaUSpIk4cr/W1WqX2psxnL6\n7h64yTbb93nfd2LmKy/xyJ/f9rHLN364v7v6+gZGj16VBz96/Q3Zcoe++684bo6fX7x6AplXrGPS\nlquXix76233Mmb123LB1c/x/Yp/V8I7Lh+WXGXd8nPVwpRTPP/fsh3rsdw7+OnEcU61Wee6Zp5nx\n2qv8+7FH2eoLO3DQPnuy5dbbcMYvz+GII49mwJDhPDVjEd867CgGDx7KkoULiaJYi08sh6ZSnuZS\njjAKCaKUziULGdcvj2sZuKYgjFM9kfsxUQL5jC+es0wKjoVj6RAF2xSkSiLSlKZiLkum16tVidQ1\n4FSSyEyeL7SEP+/oEOF6T5tHSSBnaS9uP0wRQqcEeY6pu6NAInWpp5DTNXW9Ta6otedtkyENBU1v\nFIKi62BZBkObCjR4Nr1+RG8Qkab6vaVSM2+0l7jAtU2Kro1pGiv+GKRU5Fx9MirmHOo9basbp/pE\nlkotgPKjhPqCQ2c1xLaMTNmqaC3mKHkulqlLMUma4ocx9Z5Lzjbo6emhVVRRKBZ1+3h1TfjK4q3F\n7dx3+00UbZMvTN6C/m5K26xXueeyXzFsxCjWGT+BsZt/ARkGLFvWxtMP/4W9DvoOHYvmMW+aTu8Z\nM2kbxm2tm4ab7fJV+g9btVb8wjNPcvcfb1xtnD12181Me+oxQCtehwxbc4nl/TB2o80+9GMHjxjN\nhM37Hj33XmhsamLSZpt/5OOsjOf+/SgP3PXeYqDGxiY8b+0xZv63Ev+MwPd9Hvzb/Wve8V2w7XZf\nBKBS7uXVV6ZhWiZdnZ2cfdaZjFxvHLVajcO/eQCbbLIxd977V156YQpJErG0bSlxKhnQ2kycZHS/\nJEUoSGK9Yo1TyZQ5bdpvJFVIBJVAN/bCJCVM9AjJWYZ2MMyEIgo9gfqJtowtunqCj5IUyzApeTYi\nk+X7cUqd55KVlEmkohwkeI5Fa8mj4DnYpkEl1KtWw9AxZ4WcQ0PeoeLH9NQiolhm3G5JnecwtKlA\nLUyoRanmZhuCVClSpW1npVR01iIa8g4txRwSQa8fYpgGZPRF2zQy90NJJYhoLuboX++xoKvK/M4q\nVV9TElWWKZqkCtPQn4JUkkRBGEnqPBvHNLPwDAcFCKFPAAgtTEqlwsmShizDYHZbL0t9qGtsZllH\nN0mth+env0FnZxezlnQyYed9+dbZ11Cp1bD7jeDm665mxx13RGJw3AkncNotj3HID07n2l+cwC5f\nP4KJux7IM48+yD03XInMlL/FhiYevvNm4jAE4K3pLzBsxHC23XlX7vvtaUTB2+rNHfc/lIlbaw8V\nwzDY48t7v++4XDRzGvFalOU/eNkZK37PF0u0Dhy81o69NrGsvZM358x9z/s3m7wlAwYOXEvP1teK\n+Gd/Fv+PN8DK5/OcevoZH+qx640dh2manHDMEfzxrr8ghGDQkGFssfU2rLPuepz9izOYM3cu5559\nNldfdRUvT3kGJSUjRo1mTns7CQZRFDN0+EjaFs6nvRqSKKjPWYRST+QShQAsA8AEA9zlYp68Q3s1\npC5nE0uJbRh6cgLtH54m1Odz9NQCvSq3TBZ0lDFMHepgoOisBtiGkfl4a1tXpbSS049THEtL2+s8\nrcDsrkWYhg5jCLKrg6JroYSmCAph0lOLdSp9Kim6NqWcTWelhikM1hvUSJro5qhrmURpSpSmFHM2\nUgmiMCYWYCtDhy/HkqHNRcJEB1zUezqEWaYSzzbp8XXTVqnMudAyMYQiiFMCwyAnTJTSbo9d1QCF\n5pLnXS3Jty1WBGiYpqAuZ+OYAksluGmVkpUSKZN1RgzllAsu495brubvt13D9l8/knJxKM/+61Gs\nsExv9yRyhTp223kHejvbOfEbX+awo48lSRJ+ctTBnHXx5TR7Fuf/4Dt871eXEccRA4ePYv4zD/Dn\nJ15mvZFD2GqH3Who6cdOR5yCk1t9xTjnzZksW7KILbbZ7n3H5bI5M6hrGYidNVL7in8/+ncWL1jA\n/gevGjo8eb/PjEzjfVHp6WKX3fdcdWOtDXKNYKzdqeoDslM+0/iPX4l/FOz0pV2pVStce/NtK8QR\nixct5JyfnswlF57HnnvtzR/+dA+5Qp6pzz/DxptvyZgxo9lh512oqythC0nOtSh3LqPsBxRdC0No\nEUZ9zsY0tQgFFK5l0q/eI4xSzUixbWzTpLWYI4hTklRiGZCkkm4/oqeqm4BtvTXdDATmtPdiWVoU\nM7ApD0LQWsrpZqBpUMrZyFSxqKtGNUoIohSZSmxL0OC5lIM4q6fHmIZgYGOBfBawHMSSlqKHZ+vE\noGW9NWpRjOeYLO2pUfYTykFMby1kQVcV1zI1LzxKqPMc0lTX4LvCmJxl0FqXo+ha1OUdOsrapVGH\nOOvPOVU67LjOs1FKamWnITBNgWXqdKM0ldpPxRK4tqVTkVybkmtS5+nsTxQkqSJM9fQeJJJE6hNn\npRaTd20coWi2Ux7945W8NvMtnn3+RW767bkUg2XQu5R5M6bhGLD+xpszcvgwZBJx1I9+Bl4dlmXx\n47MvYs7MN3jqL7dw1Cln8cdzvs89N/2eCZO3o2HoOnxl/wMZMGoczzyr6+Vu/t2VkY7rrKbq/MdD\nD6zGNtlol30pNrbw+1/8gI4lC993DFfLvVx53hkAbL7Vtuy+z/6r7VO/BufDzwomf2F7hrwzYKPz\nDQg/Hnvf/5VTPgco9/Zw7s9+RL7wNovgvnvvYebstyiW6vBch3tuu5ltvrAd0srR2NTETqMK3Hz9\ntTQ3tYAQNDU00NGtB1nesSm6NkmSUA0TSjmXgqsDHBKpRTmWqWPJDHTdV0er6YSeWOr6tlAKuTwD\nUECcakm7LsXoIOIxQ1toLuaohAlpKulf5xElKTnHZN0B9ZT9mPq8o4MAFHRWQzxbl2MKOQcDWNBZ\nQaLIOyZ1noNhAEIwqCFPc9HFNk3ae32CKKGYs0lSvZp2LV3eUFJRl89RDrLnQmCZOsihpxrRVYso\n10Lyrkkxp71CalGaUQa1C2I1jOn1I4IkJYhiojglTlPCLCQ6Z5uEsfZTacq7GELQUHSxDIuWkqc/\nZ097k/ery9GQtzEF5F0LSOmsBHTUIiq1gM6eCtvtsDOt9UVO+uVFDBw8DCUMYiPHnZecxSmHf42d\nDjicc844jVHjJ9I4cAQzXn2F5x/5M/++/w7++dzLhGHIRptP5uCjjwd0RNrIMetC13xGrxT19m54\n/Zl/Eva0r7Jt5huvvSc//Nun/JqHrj73fUsrhVIdu++vZf2O61IoruXEnzUgSRKOOuirK25f/suf\n0LF08Yc61vBRo8m/0xpgyDbgrVVtzAqsLcXmpw3xWXmRm266qZoyZcp73l+tVnn80UfYfc+91srz\n9fb2MOXZZ9hhJ92o6u7uwq/5PP7oI7iOw3lnn8kRxx5Pe3sbDz/4INvvsAO//c1FDB44gHypjjlv\nzqShvp5UgQwrpBL8MMGxlpdKbHKuy8LOCo157RdS8hzCOKUaxplDoPboFgr61Xss7qni2RZ+lJKz\ntdmTn5U86j3trx0kKbYQJAocy6Ahr3MvNV9cnyCCOEFlSkypBGGcIIFBDXmSVGqKopS0V0Ic06Tg\nmKRK88mjJCWIU3qDmH5FF4VBFCdUY908XB70kGZXGEGim6YjmksZdVLXzbUgRzdJ+9V5+FFMVy2m\ntZRbEVZhGPpE0F2LtKWu52AbkChoKeaIkxTD0HpWKRVG1gD1skZrnGjxkm1rib9rmZgmWcEFmus8\n/CSlXA0ZWO9RamgmdQo41TaUZRHGCZP3OYzuik/7G89Rqmtk+wMOx6/0cMeFp9IwbD32PPJk/nX7\n7xGmxRZfPohybw89z97FhB12pzvK03/MeOrqG5j70hPMWbCYyTvsRi6/OrVQKUWtu+P/2TvvcDuq\ncv9/ps/sevpJ7z0hQBJaaCJNwQIiYLnYCyheywUVFewVrNeCUi4iYqGodJAOISEQkpCE9H5622fX\n6TO/P9bkhJjCCebeH/j4Ps/+4+xZe3Y5a9615n2/hXR900HN041P38uYucdh5fadyLb8/utMeu9X\nD3iOzg0v0jR+2rDLM4/e8Ttax05g9tEnDmu86zgYpjh3tVwinf3fdzqSJGnZAQg4rxgtOSs+76jh\nkZiuffSlf+q9/rfjdbMTj8KQcql8yM53/S9/xv13/23o7zWrXuSZp5/kXe+9iKcffwTftdmyeTMb\nN2zg8iu+gmWlOO3005k2YyZzDz8CmZi+wiBEogFYdTwkSfhfmrpKzReaKSldSWq3CsSCWagqMrUk\nUdYSMaruUi2B5wmYoOMLAwRVkRiRT+EFYjea0pShZl5jxqDq+qLqLkkUqg4QkzU1TF1loOrh+AH1\naQOimIGKQ7HmJZorCllTY2xDBlmWCcKYqiN2wlEMjSmDXCpBgcgysiShKhIj8yl0RcbQZDRFJq1r\njMxZRLFgYGYMLWnERoJFqos6vO1HjG3I4AchEFNxXBwvSBY3FVM0DXDCSPh4egFFW+zSXS8kSnTG\nc6aGqkj4YSgWAVnC0hRa8xZRLBQXNUVY3ZVtn0LJpjFromkaxUoFb6CdWqwyYd4JzDzudHpXPkF9\nSqfS30Pnzm3cdcMPWfvwbfznz29n/slnUOneyZFvOJv2bZtRdYtffvernPrpH5OZeAwrH7uH33zm\nQp654wbM1okEQQiSRPvmtRT7uveYb7+47P0svfM6PHv4Kn5RFIGeQtX3T7aZcP4r8+gG2jbjv6zR\n+kpx/FnnMWPescMevyuBA/8nCfxQxC647L/CTvx1k8SzuRwXvufVERN2RVdnJ9+46kriOObTl3+J\nq3/6i6Fjk6dMZd78BXz4oncxfsJEogjKlQr33H03P/jut7j/nr8xMDDA2nXreGHpEsJYGC7ohiGg\nbpFQ+LM0mbLjE4WiUbfLC9MPQ7qKNjVf4LCNZBfdkNFpyBiMrk8L4wVZImOqhKHYocqyQLXsIrtU\nE90RQ5WH5FgLVRfXC2lIG+LvmkvJ9ogjQcapOD6yDKqiIEsSlqrg+iGmpuAEAbIsyhuGrlB2AgxV\nRtNkyraHlAhpGYpC2Q4oOT6OHxCEMaoqkzFVVEWh6gZJQ1WmKWORNjRUVRFok0jUtiuOB0jkUzoZ\ny0RXhJyVKitIslA7bEwbCXNUsDqDKMZI5HQrSU0/jkFXFHRVoSVt4oVix29oKoO2T0exSgTIRGQt\nnSAUKAMN0Y/Y2G+zefkSxk6fS3r0FKRqHwvPfDujmupwB3roqoYsf/IhBgYKSJrBg7//FVYmx+Sp\nU7nxLmEmbGVyHPGmC0jnG2ieOJOnHnmQDc8/RXWwn/JAL3ZlzzrupT+8mVM+fAVxGOJUK+xYt5J1\nzz11wPna3b6DJUufQzP3n8Rl/ZV317PfeO6QpdsX//PiV5SDNawUirqnVG6p0Lef0YcmvvjZT1Gt\n/C8bQbw8hsTnXvnxWo/XTRJ/pXhpzepXXDUbGhvp7GhjzdInufz95/LiC6J8Uy6X+dBF72Hzxg3M\nPXIBuep6pozO8/B9d/PN73yPn/3y1xT6etm+fjWzp05i4QknkkqnkYHe/gJpQyVtaAn8TrjZIEv0\nVsTOuOJ6RGGErkqkVBlZEuiSgYqgqxcqNWxPCEf5oYAYSsIwHqKYmu9j6Soxgg5v6Qqt+RQKEqYu\nBKv8pDmpysKAIYpjLEOjKWsku3phpFCoeXSXa+iqwFkbqkJ30cENIxRgbEMK1w8ZrAoFxfqUQdnx\n6K+Kev6uGFmfIpd854rjD0nc6ppKZ9EmjCKypnj/upSBoYh6uCJL6IpCU0bABf1IJFhTU4kR7kON\nGTPRCRclJ0NVsJKGrhOEuH7EYM3D8X1qfkhGF03imhswUHbIWzq241NzAwEQ04wkKcUEso5XHmTD\nji6WPbuIRQ/dRWbUJP7+1z/hGTlSDS3kG1rIZdK89X0Xk83lWLlxO++57Ft4dpW1T90/9BtMmXMk\nl/zkD0ydt5B3/scH+cS3fk7jyLHMOOokRkzYbTry8lh938389UdfYKC3m5XPPE5PVye3XPfLfY4d\nOXYCF126N3Nx5+b1e8x1u9CNVy4ccO7vis9ecRWpfZR7DhRh4PPn//72K47r7uockqA42Lj0c5cP\nq57/zIN/IwgOjU/nv0pj83VTE3+luPKKL/CFL19JZhgTwfM8PnbRBdz0p7/y2Ys/zI+vvQHf99E0\njW1bt7L1xcU8u2wFOzt7eOyhBzj9rLey6MknMfAoOz69/QVkGQxVBoShb8X1iJEJghBVEcYIIBpz\nRdsTuhiR0A9vypoUk12uldSGg1AYP7h+iB9Fwpw5hlxKw/ECQa13fYJQ4LUVWZgFE8eQ4KslSbyf\nF8Z4gXD7UWRBcU9ritB0UWWB/0YinZBudlH+U4aKpkhU3QAQ9mpVLyClqzi+IB8Rx/RXHeoskzgW\nNXo/iLB0oVRYlxLkHS+IkGWIIpBliTCM0DU5adxCXcrADwIqfkjG0EhrKpEEJdujIaWjaUpiHiHQ\nMvWmJnbcxNSlTILErCKMIgo1j9H1YlEtO8Kr1PFDal5IFEV4Yczc8S001uVxnRrjj1jIqqVPU6jY\nTGzKkc+keM/Xf8265c+y7oFbaJ4+n1rvTo6/4GLWrVrJuuVLOPmc9yKpGjk1onPjKooeTJ4+iynz\njt9jp7xs6RIWP/0El35ueHJBruuwdeN6Zsw5fOi5nu2bWHrfn3jLJV/e52tuu/Ya3nLRxVhJE7Bn\n9TNo6Rz1E+cM6z3/t+K31/+auUfO48j5r56M9Erx9P13cuzpb0XT9H+qTt2UNeO3z5swrLE3Prn+\n3zXx/4v45ne/TyaT4bknHznguO4dW6iUS8iKwJ1qisDXaonTSl0+x8lvfRcV22PxU09wzjnv4JnH\nH6VcHCCUNSqlInEcccyxC5EQ9WrbF4gLQ5UY25AmjmK8MESWBOVelUCVhEphHMdEoaCmR1FEX8Wh\np1jD80MqjkCrpHRN+HDKEiXbF+7vqti9pwwFU1UF5T6OCCFp8ElIcYwbiMafoSqkdKGboikSkiLT\nnLOGShWTWjK01glFQFkAtQmTxUOKY7xAYLNrboiXfL+2/gp1KYNR+TR+osNSlzIZXZ9GUQQb1A/F\neXRlN3mp6vjkLA1NVgSNHqh6PpIs05wxMFQFN4zQZLGweIHQMvcTEwlVFom8IW3QkLaE2USC1FEk\ncedRc3eXnCRJIqUpyR2AzNTWLIQ+XT09bO8ucNttt9M+UGV0UyOWEpNrHcvzj9zN5qfvoVKpsmT5\nakqZMXRsXE3L6DEcf+776F78N5bccQNTjj6F6QvPYPL0mWx+/knc2p4lgCPmH8WpZ7yZP950HXZx\nT7u09pWL6Nmwko3rXhqySjMMc48EbtcqtIyfst8EDnD+xZfxyO2/ZdPqFwBombPw/3sCB3j/Rz5+\n0Ak88A9uV33Cm9+Bqr6y2fJw4l/F7f5fJomDSIrbNx9Yz2LF0w9D4HHjrbcDcMbZb+eBBx5g6bOC\nWn3L9b/kUx/7ID0dbbS2juC5ZxczY+oEZs6aQ3d3D3bS+Hv+2cVIsoKSyuMHIVEkdp19JYdxzTny\nloEiicZkLInGoIwoEfRWHBw/EFR2XWVKa5454xoxVJmWnIWuyJQcT9SYVRnbCylWXQxVRpaFXGvO\n0qizhD9kyhCuPAVb1OJtL4Q4xvWF2YIkSfSUbDoKVWpuSH3KRJUVugZraLJgg45pyg6ZO+wsVAmi\nmEHbJUKoF2YTJ/q+ikPNC/ADUePvK9uiZBHHqAmzsr/qJdhzUT6pzwg/T9sPII5JG0IOIGvp+JFg\nbFZc0eTNmjpBLBQPm7MGtuehSOJ3G7R9OgerDNo+haqLLMkYuhDAqk8ZSAitlqojIItNGZ36tM6O\n/grV5NxRHDOxOcOJ82YzIiMjperY+NJK1q9YSlfvAL1ll0mjGkhHNl1dHcw++gSmTJrI/PM/wX98\n6RoAWibOYNzs+XT3F8g0NBP63tD8UhSFiZOnMH/+PFbdc9Mec69u7BRkTWfS1Om844J34dX2rgH/\n7EvDs1A77YIPMGnWEcMauyvaN6yi1Nc1rLFPPf4YixcduGb/qqLSDlHILTf8mntu/yM3/3L4BhvP\nPf4g5eLwykbDiX83Ng9xhGHIE48deBe9K2696YZ9Pi/LMu/84CcO+Noz3/MxGlp3kx9OO+ttnHrG\nm7nrr3/hphuu49LPX4nnOix68gn6B/pR5JiyHXDqm97M7MOPpKGhgab6PC2N9URhQHdPN1lTE041\nnk8tCOgp1nD9EE1TiaKYEXmLbErjorOPJGOqQ16Ruqqgawol22PZ1l76qw7lmk8QhliaKsyI/RBT\nEZA5P4yR4oi8pVPzBUOzPq1TtB3CMMRUVRpzliiJeMJFyAsC1IRu35A2E+1AQwAAIABJREFUyJoq\nJcejZHvIsbA7UxWF7b0lZFkmpWuMqk+TT+juzVnRpFRkidZ8mp5iDSUh5YxrzNCUNal5IV4QU2cJ\nmdiGtEHe0oa0UcIwEceKY2FGIcVEsSidSIjfoiVr0pK1iOOYUXUZilUPzw/RVRVNVak6YsemqwqW\nrqImCJ3BqouTlGS8KCZjaORMjWkj8hiaQtEJmNCYxdQ1io5PZ7FGcz5DobebnoEiipVi0hHH0tLa\nimVoHHfKqWQtjVwmzdpnHsHK5KmVBll61y30rH+Bl/72awDS+QY+cOWPWfTgXfzhSxcB4LsuN/3g\nSkwrxdTD5nP0ez+3x9xLN7Sy9ol7iHwXv2cb/VtW7zU/v/CTm4Z1DZhWetjStbvCrVYIPHe/x33X\nYfUz4hocM24co8eMPajzDyuKmyF0ecPpb+LUs9/Ghz49fLVCRVX/aceioRhmPfx1kMNfO0k8CkM6\ndmwb1ti/3PYHHOfgtCUcx+GnV+/b7fuUU0/j9DPfhKKqbNm8ic1bt6NaaWqD/YKQU6txzXe/TVdH\nO3bNplwu09NfQDd0wjDBVdu+8JDUVbwgxI9iKo5PxQ1wvYCaE3DL/Sux3YA4ElRxRZaQJHADoTWS\nMzRMQyBIwlAYRiiyhG6oKKqCqQpGohsE1ByfgapLzQnQFZUYmWxKo2uwRtrQaM5ZKJKE7UfU/BA3\nEBR6TVFQZRisuQzWXPxQ+GK25lOkdJmUoWCpKk4QkTFVWnMWpi5ge2lDZUJzjr6yiywptA1UcP0Q\n1w/QFZmuYk1om8sSJUegVYo1jzCKUZIGpRvGxJEQ49IUiZQuDxlYDNqeELTyAiquh6aJhvGu38IJ\nQuHBmbga2X5ASyKBu7WvJJQdVSHH21tyIYrQEzhnd6FCXUrnlAWHocpQdjyQNeJiL51rX6CweTUF\nO2DV8hdo29lO+/InOO/DYlf87L1/YkvvIGUvwmvY7cK++ol76Fi1iJU7C/R3d6IZBm9+j/CQjKOI\nl+7bWwzrxA9cDrLK+q4SI+cMH8Z3KGLSkcfRMGr8fo9HUcQTD95DEARMnDSZceMnHPR7vPDcswce\nMPok0FKMGTf+oMWs5p1wKpncfh3QDipiSPSAXvnxWo/XTBInDhmfH54c5233PoxpHpyuhK7rHLNw\nt7zlr376Q77/ra9z6Ufex0+v+QFRFHHscQvZvHED06dN55fX/Q8jR48lklVGjB2HYZr0dXdAHCRK\ngDHVmis0sKOYtKFgqDKOH6GrCnlTJaUr6IpMMcGDO74wTmjKmyiykpQAJBqyJmEU40YxUSiQKYoi\nM+h4qJKA/zVnTcquT9bUURQFObFa6624KBIYmlAIVBNHIFOVkRTI6AqGIpMzVbb1VSgmO1pVVWjJ\np5CQxa5bllBkhYotGoPlmiACCbRMSBgIHRbPD4gSKKGaUO9dP6Tk+shJTTtnquiKsHKTZYmmnPhf\npU2NOIpQ5F0enxLlRIK3KWvhhREZU8fSFXKWQdbUCKKYbAK9LNkehiachyRZpiFt0l9x2dJXEXK/\nkoQkCws3VZGpBTGteYt8SqfkBLgh7GjroGJ7xL6HLImehOv5uGY9bnmQkQ05mkxw002MOuJkfvbJ\ndzB53vH45SKL77uNdWtWE0URvZ1t1I2cwCnv/gRHzD2M2LMBaB0zAYCBnRsp9gjK/N/vvhPvZazL\nKI6olPemknduWkP7+oMzSz6UYVgp5hx/OoqivKrXh2HIA/fevdfzQRDw2AP3vKpz3vabH7N2+Sss\nDK8y/l1OOcShGRYLz7nokJ1v84a1e/wtyzLHHr87ic+YNZtjjjuOr37nahYccwxbN7zEbb+/iZ/9\n6BpWvrCUP/3uf6hWSpz91nN46onH8f2AWbPnoMgKni9Mir0gTIx6JYIoJowZoqdXvXCIUJDSVGKg\nMWPghzEgyCodg1XyKZ3tfRWaswbjG9I0ZC0qieVYSlVxg5ggjNAVhYa0KdiZsjRU685aGs1ZE0VW\nCUKBNJFkhG5KAOzS6SamNS/0TFpyKbKGuDVtyhrUp4Q7j6HKBFFM1fWpSxv0Vhza+svsLNTwIuFQ\n31f1mNKaEzh3S8dMZHSzhkYcQxTGOH5ExtTQFBlTU2kfqCIhUba9xHBC3HkMlAW0sc7S8IMQSxUM\n1cGqh6ZKghglieQdhTGj6tKJpK4sFh1JolhzmdSSRZLA0sRdRsZUSekyfiDw6Z4fMqk5gxQF5HTh\nqoQsUax6tA1UMaw0IyZNZ0xDmlknn0VGk5k0YRLIMooss371C5QG+ij1dPDGt57HI7/9KY/99Y+M\nn3UEZjqLXSlx49cu3YM+Xz92CvMv/BTlQh933nTtHsnAMEzefuF/7DVndSuNcZDwv0MdJ59xloC/\ndmxnoGP/ioL7CkVR+NLXvjX0d8fO7VTKJeIo4qlHH2LJU48f9Oc590OXMvPIYw76dcOJf5dTXuNx\n5+9/i+fuv/53/Mlv5KnHHsEwDI4/8WSOPf4kzjn/3TS3tHDueRdQHhxg4fEnkpNcDj9yHmNGj6a3\np5uWESPxwog4jpETaJ+fIFEUCcIgJGsZgmUYhoxvyhICGUNgubOmStUNhgg7jh8QRoKE0l2y8cNo\nyAotnzYYWWfheCFtA+UECx7QVqhSdQPylkic7YM2JccVyI4oYrDqYugKmiozuiGNlFD7BTNTJJr+\nikPFFncSdWmdEXVpIUKFGKdIonzSlLMYlbfoKdlkTY20rjJYFZBJNwgJw4h8SkeRIa0rmLpKxfHo\nKtao2YLFGscCV57SBT0eCQxdaKekDVXol8fCP7Rc8zB0mYoTCn9RUxsS5xdmyzFVN2BcUwZJItFs\nEYKhBdsjnzJQJAFlJI6ErIEkGKr1GQNNkUXNPpMhn9YpOAGaIvHw326nZ7DCqmcXoTWPZ+OKJXzj\n/W9m8/Y2Tjr7fI446lhaR47ktht/Rf34aTRPnsWaFcv4/uc/Qa55JDsrsOI54biz+uE76d+xiUJ3\nB0sf/AuXfe17GAcg7OyKxtETaBq7p/5KFIb4rv3qLoJ/iA1P7r1L3l/Y5UHs8n59f/eI9evWUhzc\ne+yKpYvZuW0Lmq7zycu/wuzD5+1xfN1tP6S0Y+1er3t57EKixHHM5nVrhvnpXzn+zdj8P4zOjnZ+\n9qOrD+o1G9es5Myz34ZuGPsd88uf/YSTTj2Tth3buPXGXzPrsMNZsuhpOrZv4bGH78fK1fH3Rx/j\nd7f+kbZtW+jq6qKnt49CYYDpo+ppSBuoCb5alYFYKOchQaHqkU8bpE2xm03pKpYmMzJnYScCT7Ik\nLMeKNR9LUxOHeknU04Mo2QHEDFSF32YUC22REfkUaVNlbEOarqJNa96kIWMwpSUv9EgkiYZEGTGO\nY3b0V9B1VSRdP0CWZWw3EExKoOYKUavtvSUGqx6GJrw7gyjCDyKqjo8fxYyqSzFQcXGCgKaciRsE\n6KpCfdoULFBdQ1Nl/DCkaIuSS3M+BTGMb8rihyFpQ6chrQ+hU/KWTir5bLIkoWsqsiyhSDJagm8v\nVB1MTRhNWJpCHEHO0ugYtNnaV6YupRNGoCkKE5uzBIEwmRY67AoNaR1TE32KMIwJohjH9XEqRYpV\nm8NaU5gKtOYtDENn+arVbFq/hikL3kBrzmTsqFYqvR0EvdsYO/soJo9pJVXXyPLFTzFzzlw++fmv\nUJfP844L30tWCXni+u8wZs5RaGaarSuWcOq7Psrkw+bxzEN38cNLL6DQtZPQ9yh1btvv3Gxfu5zQ\n93j2gdtZ/Psfs/z2fZOB9hXF7ja8WoXVy3aXIH7wzatYu2YVkjJ8OdfR0w9n9PTDKfd3Uxk4sHXi\n8mXP097ettfzZ533LmYeJhA0jc0trFu1nLv//Puh49OPPYlcPrvPc6554Vk+f9FuC7y7b/oFd92y\ntwvQPxPRMB+v9XjN64k3NjVz2hnDt3wCyOTqhkxtXx5PPf4o6UyGeQuO5sMfE2w8x7FZsewFHnnw\nfnq7OykVi2gELHv6MebMmMXaDZtQNaEbougatWqVnbaoU+uqRhT6+JEoEQhXGwiRSOnCnsxUBDQO\nYmoDFVRF1KBtLyKMBMGnZPukdA0JqLOMIcLPQMWjaLs0ZgyCICaMRINSQqLkCBecgYpL1tJpGxBw\nNVkSteEIUUu2gxDHDUAC2w+xVIWS69GYtQiCCC+KqTcUtvd7zBnTgBdGgpCTOAH5QUTWkrF90QvI\npzWqric+TyxIQbqq0D5QEb+JIjO6zhJu9qpCZ7FGEMXUp02CKBxKyBUvQFVlaq7QQNnl/KMpEmXH\nI2WoBCGEEeiqRMbUCaNIfA5ZxfUC6hLT6DCGsu2iyjJVNyAMQ2qeRBRL+JHwNI1iUQ6qOgG2Kr5X\nLq1jKDJtfYPUNTSxeH0b86aPp62rm7GTB5FzLahhjUf/52oGyzV8FE754GX8/c+/ZcG4Blbf+j1W\ndVSQVY0jZs3DKQ+iNI2jfetG1j1xN77VwJTONppGjqFULNJgKtSPGEu1r5MND9yM3jiWuW/74F7z\ntHf7RhrHTmbmUScTB8fucy7vL7o2rKRx3FSeX/QEM+bOQ9U0LvnMZWQyWSTpsIO6jgD62rYgyzKZ\nhv3buD3z5OMcd/zedmr/GHOOPIpps3Z/Bmns/jHls+cdwyVf2Q1EOPEtF/CmfB5g2HcIB4zXyS57\nOPEvw9gcTmzcsI7169ZRKZd5bslirvrWd8nn8/zhd79FIsbQNUaMHM1df/sLj9x3F67v4/sB1WqV\nbC6PHjkMVl2Ktk9LzqScuNO4oSDEZAyNmheCBI1pQXe3fbFjrTg+YRiRMlRShjBoKDsexBKqKnaZ\nNc/H1DTSupxYm+ms6xxkVF2ajsEqzVkTNxD0fV1VsTQVWRLqfv1lm7q0QWehihtGQ9BA1w/oKjqk\nTVUQjWJBNJIlmYwlmJiaIsosMqJ+jgSuLxqsWUOlaHuUHWFiMSJv0VdyqEtrdJdcVFnC8UNkYhRF\noSFjkLU0egZtpMTtXlNUIV8lCUKQqcpUXQGRtL0AUxfH9cSXE4RwFohFyQ9C7ESlcGJzlrLtUawJ\nt6KRdUKQK44FekXozcCIrIksKwRRyPjGLF0le2iR8cKYHX1l0qZGnaXjxRIzpkwksKt4vodKRE/Z\nJ5s2ybWOor2zlxENWbbuaONdn7yCrq5O5N5NVJtnMtCxEynyOea0s5l71EIAav1duI4Dikz9CGHF\nFscxW557jMlHC4efcs9O+nZsYeKCk/EcG30f5ZbqQA/phpZhz+9CTxfp/L43MK8mysVBQt+jrunA\nnyGKooOGOx5sXPfL/2bajJmc/MbTCIMAVdP+KRZlfdqIT501PIejO57fut/3kiSpDvgYsAXYEsfx\nCy97fgEwL47jH+xv3KGI13w55VDG1GkzmL/gKI4+9jg+8enP8q2vXwXAuy96P++66AOce+F7Oe6k\nN9C2bQsDAwVMy0LWdE5dMIvpkyfRU3KIgZSuULR9ghhs36chbQgavKURxREtWaE3Ukzc6MuOJzww\ndQVVkXE8AUt0gxhZBkuVhasOEn2lGiUnYFNPmZU7+2nMGrQVxC5XlmUKNRdFEsmvs1ijp+LQXbLJ\nmBplW7Ag8ymDgaorZGdtH0WG0fVpMqY6hJCJ4miI1l51fBpSBv1VJzFjiAV7Ulcp1wSRxfEFhX1r\nbwkvjLD9iFH1KWpegCwxZEyhSBI7+wS5plTzaciY5FPCJcgLQhwvoOwK42BVkSi7wixCU4TIVWsu\nJXbqtmhuCrlb0YPIGhqDVRdJAkmOac6aOJ7QHhfkKciaKhObs6RMjda8hSLJdJVs8pZOxtToKtl4\ngbCwqzk+FVdooXd1dlAsFUnXN5O2VDJaTKlUIqoUaVIcUukMge/Tu20dlWKBEz70JU454yyaGuv5\n6BXf5rD5x/LQH67j8ovexvbli2h7cRF9G1YA4Hse2zeswS4VuOxTFxMEAdmWsUxccDJOtcJt3/8v\nQCT6u7//acp9XRS723jxrhv3msOLb/s1nr1vfZKVT/+dnp1b9zv/wyA44PUx2LmDwc4dQ38/99Qj\n3Pa9zx3gFSJ2JfDlj93HQFc7t998PQDP/P2eA2qLO/b+a/1hEFAe3E3s+egnPsXJbzyNv993N/Kr\nRM/8Y8TDfLxCfAz4TRzHtwMXDp07jgcRCbvxQOMORbxuknjtVd5Cbdm8ke9/Y7fe8shRo5k0eQqT\np0zl6h/9lIH+fi7/zKf46x9vZvlzSzjrhPkEgc9h84+mdfQ4jj36WE445310tW+nsT6HJCukUiks\nTewoVUUISOVNjSCpQ4NEGAu0hKVrqJLALmcNobHthwJbrckSdSkz8dUUxhFZy8APBaFnRD6FIgkr\nNUkS5RRVFkgYLwwZ15BBAZoyBqaukjJFszBraIyqS1G0fVF7TwStqk5I1tQFXT+Kqc/oRImrzs5C\nFUtXmNSSI4wimnImRcejLmtSdgIUCUbWp6k4ojRUqjo4fpiYR8iYmkIsCRihJIEfRUiyJLTLk0Zt\nXcoYUo/Lp8T3zJkaQRRR84RXadlxiKOIppyFHwhRL02W0GQJWRE9A9ePKNviDidtCunbOHEpdZI6\nfhjBoO3RlDNpsHSqri9keyPIpwwmtmQ5bFwjI+vTYlF1XMIgpKd9J3WNWXRTo765lRETpzJ6/CTW\nbdrKpOPO4PQP/hcrlz6DYaWob2ph3AihD9617gW0vk1ccMYJoOqkR03Btm02P3M/bZvW8Mjvf8Wc\n087j0s9ezuP33sHmtavYsup57vjRFbzrSz8FhFzAKR+4nJf+dA2V/i6O+8AX95rP42YcjoAd7R1v\neMdFjJkyc7/XwhO/vYbBrp17PFctl/jdf/8AAKc8SKm3g+fv+h0Ab3zLeXz0mluGeaVBXctIzHSG\nhiZResnVN6IfAAp83TXfoH37vhed9q0beeTOW1i2ZNEeZY/O9rb9mmgcbBwit/ujkoQNcCCB8uGO\nO+h43STxx269lmLv3pRhp1Zh1dN/B6A80EPwMgo0wISJk/nwJfunMj/0wL2oioSuGxw+/2h+feud\nxLJGZ0c7zbkUq1cs5btf/zJdfQN09xcJgxDfE6QURRY7VEMT0LjukoNpaJQcHylJ4v0Vm/q0QRBG\nWIZwos9YOmldFVKwfoAbiuZoSy5FSpNpyVoUaqLWXXE8UrqKrqg0ZSxacikKNZeGtEnF9ZBkWZQl\n3ID+sqDEd5Vsuoo2kgQZS2VMYzrxzxTu8J4fYmgKYQjN2RSqLFOX1qlLmazrGMQPIwoVF00SML5R\nDSkacyYSEpObs0RANqXjByEdgzZOEBLGAhPeW6qRtXQMRaBj4hj6yy5eGKPIUJ9opVRdn3Ri6JzS\nVQxVIZIkqm6IG4qFzvGF9rie/FamKtNbEpIFKUPFDUIkScDALF00dfOmcPaJY6FJHkZQdgPBMkUS\njkFxTM0JKCSsUMdxRS0dYaqxfccAhFBvyAz29/Di6peIFYMT3nA6V3/m/cyZMY1br/oYj/zo01iJ\nDnro1hi98O2sWbYEI5tn0uHH8NCiZdRNmUckaRzz5vPEfJw0mUnTZ7FpxWIqhX6OOvfDKNpuLZAX\n/n4Hk87+GKNn7btSoIQO1e5Xhv7ta7f+xg9/kboRe7IwrXSGE84UvpYjps1l9KwFjJ5xcHT+XTFx\n9pGksnnsaoU4jpkzdy7ZTXsjYir9XURhyKeu/C6jx0/cx5lg3NSZnPOhT7H0macJgoAffe+brFy+\njPd99JJXjWN/eQh0yrAhhk2SJD3/sscepqVJqQTggEyk4Y472HjdJPGzP/5F8s0j9no+jiI2btoE\nwEuLHqK/fRs33bC7iy3LMk3NYmdg12rYdo2+3l5Wr1zBrb+9gZfWrOHt513AokVPI8syrS2tpGKH\nyKux5NmltDY3Ybs+USwRR8IH0/WDIRf78Y0ZIfuqK9SldeIoRpUglmKQQJUFAcj2Q7xAOP+kdZVY\nksinRP1YkYSH5GDNJWVqmAlBp+b42H4ibiVJVNyAiuMxMm9RdX38UDRHw0iA7FRFQZcldEUmpSu4\nnk9X0aHqCUJSEIZoqsCbh1Gc+HD6lB2fmuMRROEQpK+jWKN9sEZXsYaaKB5u6y3RU/GEMmIsMVh1\nyVs6QUKrj8MIWZLY2lPG0BSIYhozOs15E1kGxxe78yCM8KKI/opLX9mmbPsEUUze1EgZKrqiMFBx\nqU8ZyOzWnBmouuTSOroi05o1KNZcbC9gsJbozGhC9MsLYwxNEK3KjkvRdkESYkbjG9O4fkhf2aFo\nu/RXRL9AUwQiZrBq01Os0lWyqZQKNDc0Up/LEqsKq5+4FzN2cQZ76NuxkYFSjdSoKRS72+lct5xU\nvo7mOQt57vZrqZZLvP288+la/Qwblz7Ghgdu4bprBIZ67ISprH/yXuaedCY3XH89pVJx94TWTCrF\nAZ56+H6ee/pxbr3mK3tADOumHcUtv/n5Aa+V4kAfV1/8jmE17mRZZuK0WUN/K6rKyGkH3wB9eSiy\nwhN33Ax6Gg6/ANizlLPx6fup9A9Pw+WSz30BTdP42KWf4bDDj/ynPtceER8UxLAvjuMFL3v85mVn\neg7YZbt0oHLBcMcddLxukvj+IkJm3fZOXMdh3Lw30DphGuY+mkSbXnqRn371Mv50840UBvrZvm0r\ny5c9zznnnY/rumxb/xLvPf8cbrjuWr7zq98ycfJUps4+nJ07dpC2LAxVJqULTPOIvEUYisTdW3ZI\nJzXyoi1KAmEcI0uCIm/pClEc0Zg2kCWZ0I+IQbgA2S41RyRF2wsSUzGxOGiqQj6tM7YhjZ+YHtie\nT9bUsIOIKBK332lDo7/iEMWQ1lUkBApjoOphmSrNGZM4ErtgS9cYkbUEvtrSyVkasiwMlrOWTtH2\n6S3bgkGZMRPLN5IFKGJ8U4asoZLSVFw/RJKEqYShqmSThamU+G0Kn8yIzqKDpakJUQcqtk+M0CkP\nowhTV1FVGeFoHzFQcQlCofviBkGC7VYIwhhDVYfKNm4QCfNkxPfOmBohUKi5xLEorvSWbcEmtT0q\ndkAURwSxWBBMQyT5IBAN3RGjR+OEMXUpHaKYlqy481i7/FlGjx3D7EaDoNTH+ElTGDt2LAM1jy2l\nmN6dm3jmT7/k7keexA8CqsUCTY1NVEtFNjz0B8rtW5kyaw6nfPTLnHLW23EG+3jpth9z0jsFPf/7\nP/k5uVye8kAfd/3u15x04ceZePixzJhzOFNmzmHS1Gm0rX6eOI5x7RpmKs0nvi3MTK69ct93mLn6\nRi7+wY2HTmfkFWLdg7fSv2U3hvvMd1xI06hkx6+Kcsoj13+Hcl8XX/rURxl3/NnkWobXVNwVmUz2\nkDdPDxHt/jfAO5Pd+XclSZokSdIuQZjTgHmSJE36x3GH8nu87tAphZ4O1ix+jBPevqfLz7o1q3hu\n8dNc9JFL9nqN67rouo4kSQwWBmjbvJ66llF8+hMf5y1vO4c3nHoaq1et4r67/0ahp52nFy9l0tiR\n2NUqMw87nOcWLyJjmfQUSyiyTGPaZKDiDJkC+0FEylDIGjq9FYe6lC4acaaOoghWYRyDqcpkLYOu\nwRplzxdGw5ZOXVp4V2ZMlWLNRZMVql7IiDqLmuPjBBE5S8f1AnRNwfZEYy6IQsbUZ2kvVCnaHiNz\nKVRFouqKhmUYCyd4TQEkmdF1Qja3q1jDC0IMTWijbOgaTGzaxI67p2RjaCpSHFOoueiqQtYSOjED\nVZeMoWH7ISPyFm0DNYHJ1kRdOqOrVF0PWVboKdYSNI6G64sFLUxUCzVZsEhThkYcCQndnKkiIeFF\nEUEYY+qKgGGqKnUpA1kSi1sQJk1ROxCs2aQWn9ZUobAYCRZozQtpG6jSmk9Rn9bFbxaEyLJCfVqn\n4oakdQk7iIgj6K14TGzJsbOviKYK6v8p517EY3f/kZaUhmJlqZRLjB87mtCtscNPk0tbmIbGzPnH\n0zppJmNmHoksy3RufonvffbDHDFvAZOnTqOxpZViXw8L3y0S7451L/L8g7dz+kWXkm1o4ZdXXMyU\ncSM54xLRvyl1biM3cgIAlc6trFu1ku3btnDex3Y3GsuDA2TrGgicGoNbV9E089AxG91iP06xj/y4\n6a841ndqqLrBr7/zJT50+TcIA58ffftrXPC+DzF1xqw9x3oemj48eY0DxT/rsZlPGfEJ0/a+s99X\n3Ldyx2taT/x1l8R916G3fTujJr3y5NoV/3XJh3jj6Wdy8uln8YZjjuCy//wkrhewY8c2xk6eztqV\ny6jVbB59/AlOPeNMcqbKhR++lPeefy6GoRMGPr5dxXY9AYkzNNKawmDFIUJCTnweJQlqXoAqi1qw\n6wekTZWyE1CfMl5m4CBRdXyBPVckDFXFD2PGNqTY0V/GUAVtPGvp5FIG7f1VYknQx90EOTKuKSuQ\nLwk+OibG9iMcLyBnafSUXZHI3IAgEsmyzlLJWgKFEoSiTKMoMqosUXE8/CgmowvikSbLiQN9QH/V\n5agJTXQV7aFuvReENOcsyo6fyNDKFG0fTZYJCRmoeGiygE4aukoQRkOlml2NzpShMVBxcYOQ5kTB\nsCljYvsBtheQ0hQGbB8j0WExNJUgDBiRT2P7PqqiECV3KVkzKeski2oupWGqCoamoAkaKu2FGhOa\nxGJlqAoRMjIRXcUapq6St3SUfAs97TuxPZ+FR8wincnR3tZGHIcQhcw54Uxs26Zz+yZGjZ/C6jWr\nSPkVZhx/Jo0zjuL5O3+DEjiMP/oM6sZOYtUTD3D2+z9Bccc6eras20PZ8MavfBy5NsBxbz6PpplH\nUTdiHBJQbNvEtiUPMPaIE+np7qb3xcfZsqOdd3/1WsxMjljchg3ttHs2rMAb6GLMsW8iCkNkReHB\ne+/Gtmuc885XB4So9rZh93XQNPNoerq7+Pk13+MbV//kgK/p7WyjeeQY/nDtjzniuJOYefj8V/Xe\nw4l/Ponr8fFTh5fE739x57+T+HDiUOHEd27dzNiJe1KXt6xbTbZlI3LtAAAgAElEQVSugfXr1tLT\n1YWVSlPubWfB8Sfz3z+6BpmYqdOn8cKy5znyqIX89Y838eFP/he/uubbWLkGOro6qFaq2J7wePSC\nEEuTMXUNNwipTxkMVByacxYdhSpSwpqsJGJTcRyTMdSE4RlhaioZQ6W77BBE4nY+Zag0pg16Sjaj\n6tP0VxwUWSZlqHQXbdKGRkqXkZAo1DwmNGdx3BBVk+kr1YhjCUmKqboh4xozQlArCNEUYYdWcnxS\nmkLF9QHhPBQnIKqUoZExNAFfBEFcMrWENCSUFPvLNtnE9s3UhXuQJEmYqkLN9dEUiQjhB7rrMzfn\nLFpzFl2JNG9d2qC/Iuq7pqbh+AEtWZOy7TO2KYssxRSqProiUCZ1KdEQ3oVllyUhRStJojFlqgoj\n6lJs7SlhaCpZUwMJmhLGqh+EqKoom2iKzLa+MqMb0rheiK5JhJHoWTSkdSIY8isd1ZBhzc5+4QVq\n6uRTBq2tI6hECtVKmdGtLbTt3MFA1WXS9NlMas7QMOd4nnzofopuxEmnv4m/3HozRy44muNPexN9\n65cx77yLAWjfsY0dWzaSq+2kUe+FsWfidaync+c2WqcfyajZx7D58TuY/ub3semJv+GpaZ5/7lnG\n5nTe+JEv0r/2WVY8u4hRU2Yz60RBgtuy9BEaxk6lbuQ4fvvNz/DO//wqKBphFJLL5fd7rdiDfVh1\nTcO6rgr9/dQ3Nr7ywCSWPPM0URSz8IQTh/2a/cXODatxqhWmHrlb9fFQJPGFU4aXxB9Y9dpO4q/7\nmvg/xh+v/zlL7vszlZeJx0+aMYfmEaOYe+QC5h+7kBtvvJ4NOzq47vobeHHdRq749jWse2kNU0e3\nYFeLnPWOd/Otq65ge2cvZcfDdT08P3HYUSRacxYZyxCekopMZ7FKGEPN8UVzzVDxA6FtYmpCKCpM\nQKc1L8QLQ4Iwpj5tEIZiEQ2jiI5iFceP6CraaIqEqav0VRwyhoqMMFz2woi0odJfdjB0mZrjJeYQ\nwrZsTEMaQ5NxPIFRl2VRb07riiDbaCqGJqOrEoamkDJ0DFXQ0oMwwg0EvM/SVDpLVWxXkJRsPxQu\nQpHAiGuqTM316S3ZKIpMyQkYrLloqkiYDRmDlCZKM7Yn0CFpTaHmhQhTO9AUmUHbR9MUeks12gs1\naq5H0fWpeSE9xRq2H6Il9m62F0AMri9MJGKgc7AKkoSqiJKKpsj0V2z6Kw495RoxMUXbo2OwysxR\ndWiKTD6l0ZhOzJwVAQeNopjuYg0JiMOI0Q1pmjKGWAQ0hezoybzpk1/DrZZp7+pmRGsLs+cdxeGn\nnM1AqYLXsYn3X/UTxsyYS+fSB/nUVd/FTKd57t4/MPrwE/jbNZex4t7foxsG6UyOdK6JW+9cyouL\nHqZx6hFMPuZUupbcz8alj9Ld3U2tOMCKh//KzIWn8v7PXcWx538U4ghnoIv0yEm0TJ69e34ffSp1\nIwWp6P1X/oR0vp50JkMul6d/yxpe/Muv93mtrPnLtURRxJc/vmdp8s+33MTD9++pOrivBD7Y203t\nHwyhg6SB2dIygtbWV06SSx59gLatmw44Jp2rJ9vQxOoXV+B53gHHHkxExMN6vNbjXy6JX/7tH9My\nbvKQ4FAYhvzg298AIJfPUy0NcvLCYzju+JP4yCWf5Kj587n2e1exbds2jjn7Qh575FHWvPQSgzWP\niuuzedNGdCkibeoC962pOEFAyRYNuHzKIKWqmKpM2Q0SzQ4ZTVUTHWyJIN6lLigQKXUpkyCOBXU8\nZSBLML4xi66o7OpFOYGAHQZ+iCQJ+rhlaDRmrMTiLKav5DBY8+kp25SShqHjh0ItUBVNv3LNpa1Q\nRUtgfXFy/gjh9xlFEZYmU3F8+iuCzNRdqvFSRwFdEaJdbhAyfWQdcRTjeOLOI45jmrIWqizRU6wR\nRkJ2IK0LRqqE0OwWzdFYaIebGjlDI58sOoosCSRPHDN9ZD1RFCNJMk0pi6aMQT5tkDM1whg0VWDu\nJUVCkREN4hjiaBfJSCYIBU5fVRQGqx6eH/P8ll6hKSPLlGyPQtlmyeYeyq5PX0XIw/aVHTw/ZHxj\nhta8Rc0PhL2dodGQ0mkaNZ7t27bz6C+uxNQ0SoMDtNegZ+MqejeuxIhdlr+wjBu//mladB+3dwcb\nH7mdcZOnM+qIkwgiQNHo27mJ3pVPMbDheSpSinlzZnHUmy8kPWY62dHTyMxaSLZpJMecfzGZhhYu\n+M7vUFRNNLDrmpBkBWnkDAY7d6DEIasfvoPFN36LwHP2a/bQOGk2c8/9+D6PLfjgV5BlmSt/fB1R\nGBBFIQBvfccFnHTqGft8TRzHQ4l0/Ypnadu0bo/jN/38h6xYuphJU6Yweeq+zaJfHk0jRpHO7v9u\nAaBhxGhGjJ/C4488zGBh4IBjhxvDhRe+RgoVB4x/uSQOMGnOfHynShQGKIrCwhNPGjo2a+6R/M/1\nv+EbV13BqJGjmDp1Mh+//Epue+AJxowaydatW3lh0WPkDYn6XI6W+iyGqpDWVVqyFvVpgziWGJlL\n0ZixKNkeqqrg+QGyLDDJxZpPsebSX3WRJcTOO4pozpn4QUh/xcHQhBa344fU/JBN3UUySfLTFQkZ\n6C87jGsSAkGDtV0Kfj51pooqC5XBxozQ3c5ZmmBByhIVJ6AhpQspAU0hnzLorzikdIW0oVKXFlos\npZpHEEPFFXcQo+rStObF4jemPsW0EXlShpYwITUiYgZrLms6CnhBRHuhgpzs9iVZkHlKtosXhAxU\nPapeQH/FxUrKL9t6K+RSOkrSEPb8gCC5SF7c2YeSmE/bgcBwC+GvmJyl4SQNTF2RcQPRWwhDURBK\nGSopXRU2djUfxwtozBrIMkxoyoo7AQm6Bqs0ZS3OPGyswKlrKm0DVbGjL9lUXJ+eskPVFeYSUuI+\nXezaQf/OzRSKZToKFQwrRdi7lXKtyqN/f4DN23cSSjKjR7TS9fzfCSWFcXMWsOHB39P2whM8fsN3\nKOzcSOvUw7Blk8HI5L6bf0ENnbBaIApDbv3OZ9FlkIjZ/vxjQ/O1d8tLrH3kjqG/R02azhsv/AiZ\nplaax00hO2ICO5c/RceqJYSBR8/aA5ck4zjm0Xvu3OM5M5Vi7VP3s/yhO3BKA6y49Wr0lzUffWc3\n5nzJ009ywy8EOemY09/GtCOO3uNcRx51DBtXL8fp3ZNU9I+xbtFDBJ7LlFlzqW9qpr+nk4H2fRN/\nHrvrT3S37eDSz15GyzB298ONKIqH9Xitx2s2iT98yy/Y8uJz+z3+ra9+hc9/+pP7Pb728bv5wqc/\nyZbNm/jaFz4LwJolj+PaNb78ze/z+S98GZmI5oY6vnbFZbzvvLfw0QveytzZM8llc4xsrCOthhQq\nDl0lh7ZClf6KO2TO21WyRT1bEnXgfFpA0ppzFpauoMgSGVNFUoVrveNHwpy3IU3a0Kg6omTgBAHN\nGRNL1+irOIxrzBJHYGliVztY81AkiUktOUAIPXWU7IRGr9GUNUkbolwTJQnZ0sXO09JVZo2qozVv\nMaY+Q3M+he0G9JUdsimBipGTyripiR20HwoXeTUhVCgyjK5L0zZQEaYQYUxjWqgPtuZSxFFM3tLJ\nJqqBZTugt+QwIi9gmcQxqizo+FEcUXUDoYmuKkxurUOVQEKUeVRZwQsDXD/CUGUMVREO95GQ8hXi\nXD6aKr5b1Q8Evd/2aStUsf0QWYpRFBlDlUmbOkgSiWcziqzghBEbOov4fkQ+rdOSt9AU8JK6e0PK\noDVrkrNMXtjWT1tficGqzYSWDJJXYVKjSXfvAFlTY+asuYxtyuEpGfJ1DXSuXYZeP4rGURPoXvsc\nShxg5Bu44Bs3cNHVf2TG8W+i2tvBpqWPsvDs8znlPy5l2X1/ZP0LS3BCqG16ntGzj8Je+xTlzm28\neNM3aBg7hQkLThma15IkYaTSaIZFvnU0O9avIj96MtmR44mDgJWP/u2AFmxxHOPsgwg0ccEbWPbC\ncsxcA8d9ZDfDOYpC7vz6x9m4+CEAjjvxZC757OV7vd51bHo62znyuJM456KP0Lv0XuJkZ7+v8ANv\nD+z49d+/ik1Ldy9e5UIf5UKf+GwzDiNX37DXOf7ZOESMzf/v8ZpN4qf9xyeZNHf/Kmfnv+s9fP4r\nX93v8flv/wAfufQzjBs/ge/9WOBqnVqFKAx4x4Xv4exzz6MyOMBTD9xNpVhgVGMOq76V089+O4WB\nfrZ29TNu2iw0KUKTIZVA+gYqLhNbslj6LveaGF2TkSVBNukerKBKwl3H9iL6Sw5G0oyDmNVtA3hh\nIFiHCVPR9oUdWc0LqXgBQRRRdj16ay5F2yMipmQLFIesiLJNZ6FKoebihxExMRlDCGhVnIAxDVkB\ncbR0nCCmt2RTqNr0Fm2QJJqz5pCRg5GIc72wvY8d/VUKFVe4fCcEHi05Xqh6uH5IS87CS4wbGjMm\ndWmdQtUZMlYWcrRCOVCSRMPW0JQkwSZCWxKYmowkSUSxMHEY35SlIWNgaZpQmEP4cfqh8AkNoxhd\nlRmRT5ExVCquoPPXWzopTXiI7locSKQPJjVnacoYGJpKT9khZWoUKi4ZQyUCtvaWIYpJ6TrTR9Xh\nBdHQHVTnYJVjJ7cQxUKMK0YR7FQvoKkuS0/JodrXScO0BWgEGKFNSg7p7e7ECUK6C2Xmv+vTnHHR\npay++Zs8cfNP6N+5iZPefTFvPfUkVtz/B1Y98Ae8dDOBarK96LF4Uw++50EUYuTqqRs5jmX3/4mu\nrfs2/zbzTehWCrdSpDbQjWqmGPRgsK97v9eFLMucdf7ehhSpTJaPfWlv+LIsK1z43d/RteLApslb\n1q/h6YfuRZZlNE1n7NkX09/bwx2//RXlUpH/+siedfdtXQMsuudPbF/yAABf+OF1HH3uh4aOt218\nifZNa/EcmwnTZmGlMwd8/4ON4eqmvPZT+CFM4pIk1UmSdNouoLskSe+UJOn/sffmYXaVZfruvea1\n571r11yVSmVOgDCEhBlkUBFFQERQAVFUcDzatraKto2o7YDSziI2iiAIyiAiiMwEwpiEBELmVIaa\nxz2vefj98W0CiLTYnT7HPr/+uOoKe+2prqpa337X+z7P/ayRJOleSZLu3Vfv88JatGQ/WttepKtF\nUUS1/PIk7AvPexcz01MsP0LQ5Q498VRu/e1NrH7iUUpTEziRxF2PrmbzziFShso5p57E8888jWEY\neGHE+nXrMHUREgwgSwrZhMaGIdFO0DWVQspAV2T8iCZSVahHVEUmm1BJ6CqZhErGUAGxcc7UPfwg\nFJtWUwud0FRaUiKx3QkiZEkmqarIEpQaLkEYU7OE2aclZYIs4/oRA5M1IMbUNVRZJpvQsJouTBDP\n9cIIL4RIEk2+yZrDlrGK6IHHMWEUM6ctQ8ZQ0FUZRZZIJjQmajblustIxaIlpdNdSJFN6HQV0kzV\nbbaNl6g5gmGiyjKeF9KZT6KpEqWGhwLkEhpqMzAjJqY9k0BTRTjE1tESSDGJZvIRMaQMTZihkKg0\n04YsP0RRJDqySQanajTckKyhk0/qe4OevTCiLW2iayJSLo5ipuuu+CBSJLKGSmc2garKBHGEDBRT\nJrtnGmRNhZrto8gSi7sLJDWFtoxJFInh83TdZbJms2x2C0lDpauznd5illAzSbplzMhmcGArYRjQ\n3TeHpSedSbdp8eztV3HN1z7D2l2TjFYsovEdPPGLr5Pd/2h65y0Gr8HuZx5lv6UH8tmvf49z/ukb\nGIkkNz43zZYnH0LvnEf3vCVUn7iF5++7mcqffvyK6vbwsy4Sf68l4YA8/uwPUGjv2nv/8K4dPPHA\nH//q+bT18ft47v7bXnzePb/AmRree/uw8z/9iuc8cvfvWf+ksMXf+r1LOXrZi8PWW67+AX+85Qbu\nvPlGhrZv4pjDD6UyOcoDt/+a73zqQhQJhjc8Qcu8F92hnuvyZDP9Z8lhx7F4xbH89luvfN99tf43\nFOLP1l+gdg3EcXwo8A7gs/vqfV5t7dq+lRt/fuXe27+78TrOO+9cvvgZ0Ur53ncu547f3Ypklbj/\ndzfxkfeczZWf+wDHHHsMS+bM4onHVvH006sZ2rGF4Zka+aSGpqiEMTieL6LCFLDdkIQuo6kSru9T\nt30KKYPk3jBhjVxSoz1roshCqWF5ITGxSP6JRe5jVyFNw/aJmjI3N4jEJqqJBJpjFnaSTQiYVT6p\n0ZYV1fNkxUaWJLrzSQxNYU5rBkmS0GWJlClMMQ0nxPF8NgyVsD2fhKaQSagkVJWEoYpNuzWLIkto\nmkpPIY2myMxuzQhtuqmKeLcoJpagNZPA8iImag626zNesckmhANVlqCYMXECn0xCw9QU0brJmCiq\ngq6pzZ67SnvWpOGKzTKMoT2XJIxgvGozVm4QEWN5ProiotlkCbrySdFDd33KDZf2nEDPJg0V2wua\nSUUSiiyi5RwvpGSJdpGxF/Eb0pI2abgBCVVhuu6iKhKmKjO3LcNwyRYBADFMVm0iCepugKkruL4w\nVqkSDM40mHRkGuUZZGKC6hQTe7ZTbOtAN0y6OzuYGRtk57rHqKspcmZIv1KhNSrz1rPPx9rwILJh\n8NiNP6Hv6NPpPOptdPTNYcuqP/HLq3/GwMAATr1KcmYHQW2SwLV59g/XMla2SGRb2FkNKQ/t2Ps3\nHkUhmdYupqcmSPctYezZR9l49w3Up5uV+OgTpNIpdj71AJODAwA8fsVHXrYx/fgSIX1ccPiJ7H/C\naXuPdx53NmZrD08+8EemxkYw0q8cPi5dcRTz9zsQVVX54D98nuLCF23xb3//x3l2wyY++KkvsviQ\nwzl0xeFMbF1PrtDK//ONK3nDGefw5ou/QKbtReem57kM7hrYe9t3HU772KUAPHD1N/dmlu6T9f+j\nweY+1Yk37aUXx3H82ZccO6uJX/wP11/TiQ/v2k5P//zX/L2MjQyRaSZjh5Vxsj3zOOGIQ/jKly8j\n39ZOx6w5/OE3v2Lrc88wPTbEtqFJfN+lXKpw+LKlDGzbypzFB7Dm6afob01DDM8Nz2B7gZCmNTna\nuqY0edzChKMqMjXbpy0jcLAJQwzcNEXApCRJagYjCLmh0FVrSIghSi5l4HgCqjVatsmaGoosM7s1\nzWTNJpNQcXxRuUdRRBBGTDc8CkmBWrW8gI5ckpmGQxTFlC2fjKHiRTEpVSaSJCaqFn0tKcp2QDEt\nwppnGi5BFNGZS9BwfNxAKEoUWUCjXpjvtGdN4lhipu4gSULuZ/k+YRjTmU8RRQKypSpC8eIEIX2F\nlJBYSjFjFQcpFkqbjKlieSGuH5JN6ALV27Tut2VMbNcjnTCa8Woyrh9Ss3268wmcIKJiCY6ME4Qk\ndZW6G2CoMroqWl+mKvr6uqqQ0FVkIJbYS3L0g4iErlJqOOQTBooi4XgBSUPHC0QK0ki5QTZh0JNP\nUvdCunImdVf04SVJJmMo2EHMop4i1VoDNdPCtimbTGsHwehWFh53Kmv/dDOJQgcZbDw1jS7HOJLB\noUccTaZ3IaO7t7PitAt49OqvEcgq+bhOW7HI+MQ0Wkc/TmmcuSeejVceR45D+g4/mcrwAHt+910W\nnPclJgd3km3rpjG0mVTnHLREimRLB+HkBnYO1ZhzwPK9kK0tf7qBRSe/e+95YjdqrLrzFuTGFEuP\nP5W2OYtfdh5tXPMEvXMXkC0UufDtp/DzW/5yVT+w6k6Ss/ajs28OcRgy9PBN7LBN8sVWDj5CCAu+\n9Y8f4NOXX/Wa7fMj2zYwNrCZZSefhe/YaC/BafxXdeIZU4sPmf3aNPKPbB37v1cn/hJq139prVu7\nht9c/doiqp58bBVRFJHL5fn1jy/HNHTG1j4AwOVXfI9jTn4rXX1ziaOIuYv24+g3nsphRx3HBR+4\nmHq1iiTB8xs3MTRdpTEsqp4NI1W2jlUxFEWgYEOR2J7UVTG4Q6KnkMTUZKZqNpmEhheGZEyNhhOI\nFkQ+Rd0VlaOhyth+hOsHFFIG+YROS8okQmyKcRxRanikdEHq88OQsUoDxwvQFAVi0SaRZBkkibSp\nIcsiBUeWJCoNF7MZGmH7wrFpqjIhYhOe15YjYQj1wUTVIohCWjMGbWkDPxAhE+mERkc+2eSeC632\nZM0hCGO2jpWxfR/bC7H9kCAQvBfPD8glNaLmILc1Y1JM6XhhhKnJlBsuCztySE2tvaEptKRN2rNi\nCCoBQRiR0hUxYDV0Go6owMNQtBEKKYHdTWgKbVmRIKQqCn4UUWhG5jWaahtFhqShoSoyUzULRZbY\nM11naKZOueERhDGyJEiOYSw+ENOGBsSkTR3HC1nSVSCf1Jpae5ntY1VsTyB+JVWl6gT05hOMVD3G\nogTp9l6mSiWGBraSKnZjD20mljUUr0ZbLs3E2ChK4LD/iqPYvX0LD9z0M55//AGu+Pg5HPjWC1hy\n3FtYcs6nmfvOz9N+1OmsXbeOhQvmUx8dYGTPTvoOPxmAx269GjoXM7DqLtzJPeQ6evjt9b/EqUzh\nWzUAgvRcJod2vYyS+MIGHgU+ke/hj+9kwUHLOfIdF+/dwEPPZvuv/xWA7rYCA3dfg2/XeetppzG8\nS5wT7u7n8MZF1bzp/lvYumMnG54X/BRJUcjNOZDj33Lm3g0c4KOXXvE38U+6FxzAspPPAuDSL36e\nyp+1S/8rK2afsVP+P1//3fFsr+c/IHY1YTAXAfT19b3qi4yOjvL6d1zwV99szeqnueonP+SgQ5bx\n71/9DG9+z4dRNJ2Fb/0gAHPmL+C7X/wEcbad7Zs20trZzerHVlKrlJETaRpugCZFzLhiQDY8XUYC\nCgmVXEJjz1SNnkKahK6Q0BU2jlRIqgo116c87iJJMooi9NheIPTVSV1BVST8MEJXFQxNFghZSaYr\nn6Ziu4RxjBSLEAYvEFAoQ0MQE4OYjmyS6VqD3pY0JVtwwU1N4dk908xuzaAAUSRh+0EztScmDoXc\nMKGJ91cliYQhQppLDQ+IyacM4igmaag4XoAiCzVJIWHQcHx2TFTx/JC2rElXLoWmKmyfqJLURbbm\nrskaSUMlZYrMzPGaw0xDaMMNVSWIIjRNpW571NwAYomq49GdT+GHIX4Qk04oOEFIzfLRFQVNVXB8\n0c/vyCYJY5Evunm0wrz2LEEoJJqaoqApEpYfkG1CwUQOp7xX2eKHEXXHpyVtoGkqfhxTSOqULI9C\nSqc9azJTd+lpSWP6Mo4fMlKxySZVSpaYQwxM1shnUsyULYopA0mSyCcMak5AFAckEwoNP8SyLAzN\noFIuYdWq5AstyE4FfVY/S+b7lCdGqNYtFnWkKUUKU8O7yLV3suJt7yfX3sXNl5zLyp98kaPf/wU2\nP3g7h73jYtyJXRRzGdpPugBp1xa2r/ojvnMua2/6PkOj43SrDp7r8ExVwpy1H+/9x0spbXocw1jK\nyn/7JMd84gqOfOu7mB4b5q5rf8j5//R17rj8U8w/6HDaurqJfBe/Os3s41+05ruOw1MP38fR7xAK\nlMGn76frsJPREmmOfsNbaWnOoeR0ARSNwHNZfMLb6Bp4lkzfEv7lHz/KP37pa2TnHPCK8zOVyf7V\nc/jV1kc+8Sly+cJ/+vl/af0P2J9f09rXm/healccxwMI9OLAqz24iXS8CkQ75dUed8pbTv0P39R1\nXf7w+9s54cST+MKlX+Hm39xEy5IjmLfkQABGBrbQPXcRsmpw5BtPR0ukWbhoCfPmLyBp6qxf/yyP\nr1pFayGHEnsMTlQw0NB9Eac2VrEJg5BIkkTPNI6x/bAZ+CvkeZbn05oxmKq59LakGS41MFSZQtN+\nP+GJdBlTU6naHooc0ZIycJrJOKosaIGqLCpSQTqUcQOPPTN1NFlU2VIsYerC2LKoK48sSViejwpo\nsngOUtwMXDDozCbxwki4Dk2DpKkyWbMxVLUZSafScMT3MFxqoMgS/W0CEZAxVCRTa2Z5OoJwKMvI\nMVRtn1nFDLoqsaS7wOPbxjFVRcgFdWGpH5rx6CkkSJk6GVNkiCZ0IWMcr9ooEjTsCEPXcNUIP4xI\nKQoSKgldIZvUqZcFj2VOWxZTE31wy/FJm5JQjcSSqO7ikMmqRSFloikK41WbjKlTtjyKGUNAyxoC\ne9vfmqFqeyR1DSeIGBivoKsKJdsTaALbJ5vQqEcRizoybBwpM789y5axihjcyhJuGLGwI8OuyRp+\nIk0+JSpMpTHF/Ln9ZKIGC449lZW3XUt+/iHkFhxOVzFH3yHH8Luf/5Dxwd2MbF5PY88mil199C45\nFDmRJIoilhx5Ao99/5O0zD2QUy/+HA//9MsccMq7WbHiMBTdYNfmDVzwhR8xvuUZMi3tLOtfQm33\nJsY3PcX8E9+FJMtsn6wzff3VvO38D1Ls7OFN51zI6NbnOPrcT1Do6nt1wmEcE/g+sqoxvH0jlllk\nxzOPMz0ySLqlDbW7l69//Dwu+PSXiawJtqy8kllzFpIwdK699U4OP+4ksrk8U4MDtM4S2Qc7Nj3H\n9ufXc/JZL1fFRGFAeWyIDU88REtbOwuWH4eRfFGFUqtWuOzzn+byH/2MnlmvXuT9Z9f/hKHla1n7\ntJ0Sx/FVcRy/obmBv3D7b8qSc+rVV/3hRlFErVb7S++L57m0FIvMnTefhfPncva7xGVj4Pusvvd2\nAD7z0Q/y0yu+wbIVh3HQsuX84AffZ8WRxzC89Tne/OaTWbh4CW867SzSpoYUR9iuYKXkEhp1T6Tq\n9BbTxJJEZy6B6wU0vJC6K4Ib6k6AqkhULZdiyiCbNKg0XCREVSnHwrauqWIAWrM9kobCTENICXNJ\n4cTUVJm2TAJZligkdAopgyiWGKtaeEGA44VoisKsljQJTUJtRrJJTWmjoSgirEIV/0qSxFjZYrRc\nZ6TUEAk7iD7zWLmB5frU3AA3CMknDTxfbKh+k18iSyJNKJfQObivBdNQiaKYhCaohM/snhJSxxhU\n5QWHpgia8JqBFwA118dyA8oNFz+IhJU+EGCvQsqgkDRImWrst+AAACAASURBVBq5pI4bxDTcgDmt\nWUxdOEsbrjAAuc32jO2FaKq0twefbGaKJg2F/rYMDS+gLSvYLJIEKV2jK59CVcQQ1AtCCkkdQ1fx\nQ4EDiKOYOIY90xa5hEoYx8JlmtRpSZsYikQ9FHjhiuWSSZqE9TKaHFOq2yiaQeRYWLbN5JO/53WL\neygPPMe2Jx9i99qH2X3HT2jJZeiZu4BksYup6Snyiw/noNPey0MPr8Sqlnjwtl9TmHcw/cecyq7b\nvotMwLobr8Ccf5joDUcu1fE9bF/5e1Jd/VR2baSx7Sna9zuc0fuvJbCqRPkeii/h7yuqBrLE4PbN\nPPPgnS87f5598A5mRkQsm5FIcMKpZwKQa+2kZ9GBHHb6Bcze/1DaeudiV2Y48rgTaGnrpHfJMo6/\n8PNk2ntZec+dXPihj3HKaW8D4Jl7b6G8ayPW5BA9/fNYdvQJ/Pmya2W2PnE/8w48TOAhoohvf/Qc\nHrjtBuI4prxzA5dc9o1XPG9frLjpq3gtX3/v6+9OJ37ZJy7k19f8+1+8b+2a1fzwe//2iuOmafKu\nc8/HqUzj1ss8v24tYRjwzS//M7t37eS0i/+JWnmGd5z2Ji77vuC5987q4/IrvscBBx3CsW88FSWO\nMRMJHnt0JYoMxVyGtqzJ/I4sluvjhiFBEDFSatCTT+H5MaauoqkKUQRd+RSHzC6SUBXsIKJsezie\nCFnwAqF1DuKYKIxI6sJdOdMQ7kAZEU8jIZFN6FQsj7LlQhwTIaLJ2rOm0HR74V7zzO7JGltHaxw4\nq0hSV2hJJ9AVibrrU0hoNLwAVW4Cu3TRfzaaYcR9xTQNx6MtY9JVSCHHMV05oXjJmBqaKrNnpk4u\noZFL6LRmTPqKadKmLoaQmsxIWfSWWzMJsXGrMpIEhqbQ3ZJCb0KrJMTA0HZFX9sLhfY+nzJw/ABV\nkZmuu3hBgKmpTNddojiianvsnq5TtnyRTeqH6KpMa9IgisHUFMIwxg1ipmoOTpOnYrsBNccn2eyt\n236I7QZYnhh8jpUsLC+gagmWe7nh4vohXhQyVnOEmiWlM1lzWb97mi3jFUbLNqYmoykKcRRgKgpT\ndY/2rIlmaEzXHAopA8e20HSNquUTKQZW+xJO/uBnaU3K1Gs1ike/A6k2gVUp0Uh3oxsJJp64nY2P\n309OCdl816/o0Rp4G+5l3S//FctoIXRsxiamuewfLmJqz3YqtkdjYoi+5SfwvY+fw+7VD9D1hvcS\nOA2U4iw8q8bxRx3O0a8/hTU3X8meO39KKpPhvhuuYuGhR7P06NfvPXeGV/2eNu85kunUy86pH172\nWdL5FnZu3sBzT6zEGVnD83/8Ma5Vo3/ufMykeLyi6XQdfCznfu3npLO5vRX+Gy78DBLQKE3z7O9/\nQVvXiyqUa37+M8bGRknlWzni7e+nZ8F+7HfMmzDTWT709as46KjjieOI6tgghWKRwG4w+syD7OsV\nv8b//t7Xf3dP/LWvZvX9r1e/upBl+YrDWL7isFe9vzK4FUUzeN9HPwnARR//B/KFAs+sXcOCBQtY\nvOxIevv6Afjapf/M5k0b2X/hPO6++25ySYMDVxyBohsosoSkakR+wNaxCr3FjKgew4goipiuW+hN\nlKkXCvnghqEZ8kld0PxiUCSJ5gySYlrwwqM4RtMUIEZRJGwnpC2XJI4dVEXC0IS7sz1rEkQxYRjh\nhiGGpuOHIlewmDJxfLEZtedEVNt03cb2QyqWR9X2SZsqDVdwyB0/ImnItJgJ6q7PrqkaaUNlUVee\nwemY6gukRUkSFuMYqrbHZN1hUWeeyZqLH4aMVyw680kkSUJTJIppk9akTskREKzWTAIJkABFgZrt\nCwen5UIs05VPULE88ikdWZYYr1iUbY+ufJIgFNZ6XZFQFanJWdGRohBFUTHTou2UNsVzvTAiIUtU\nbY+qE9CeNekrpggiBLY2FtzxOIa0qTFebjRDl4WEM5/WsRzBZneDkEJCJwSysoRekNFVwULvyicZ\nKTU4Yk4bdhihK2KQLMUCjeSFoq0UOA6j5QZJU0XTTMzaFDU5plSzGFq9isLmtczuLDI2U2bDPb+h\nmEly12OP8YEz3sik0YmZSJBqa6O/Pcfk+CiSBO37H8tMuU6/5FNYciSxNcPBZ32DZx66izl5A9w6\nE+se4ciF3ez3VhEwUZx7ABseG+OWL3yA2bP6mH/Mm1l25kXUt69h6y3f56x/+Mpe00zoeyiaTnVq\nlDknfAgz20K9UiaZziArCqefL+ZIx576jr3nV/GAUwDId83ee8y2LK7/3lc4+uD96F6yjFz//lTH\nB2nMTNK1ZBl71q1iZODlfJXFi/cj8yr98XQ2RzqbI45CslLTeSrLKPqrZ3X+Z9f/BEv9a1l/N5V4\n4Aor8LatW/jyP1+y9/gvvvXPwsX2GtYUaTaufQK7KmapsixRmpnhjttu4ZPvP5fZi8SwZXT3ADs3\nPYumqqxb9wzLDjmYKAp5+pEHCWyL1kySBB6yLDNdd9g+XsFyffwgomJ7WK4YLAbNoN/OXJJZLQna\nsglMXYQcpE3tZcM1RZaQJahZnuCgxDEpQ6PScPCjiDCWIAJNlSk1XFGNIpE2RIqO7Qv3pC4LNOxk\nzUEBlvYVSeo6qaYp6eDZRbKmjqYqpA0Vramrs1xhAJrfnqO3kKbueIAYeCqKguUFWL6If4uR0FUZ\nXVNE26JpSd82VmWqJljkdddntOpQc0RMXDahEUYRXhji+yIfc6bhkEkYhHHESLmBrsrsmaox0xAn\nZxTBeNXBbw6Bg1BgcHVVxfV8EUwhiZNNVUQVLEsiFahie9Rcn2KTBNnwBEd8tGLR355HkWXBXoli\nUqaO2lTyxJFIAuotpig0Y+pUTTw2jGMMVWG01MDQVDRZtL0m6o6YBcgyXiB+F7YfUm44jM7U0FSV\ng2e3ESMTNlOJenv7adg2c1rTpFq7WXbhl1nY005Qm6YlZXDmScewfetmCp2zGB3YwvCjtyOHLp2z\nZuOVxylvWIVan2DuaR9m16O3E+VnseGBWynoUCsuoCyl0awpUp19rL/mMiZ3bWHzyj/gzExw+JHH\ncMy7RfjE+qs+S2bhCgrzDkIOPex6lev+5UM8+YuvUV97F8WO7r0a8Id+dz17tgqFyaw5Qs47PriT\nB2+59mXnWW1oG7vv/zUAqqoyb+lyEl3ziI005ZFd7Fp5G3aTndK16CDe+MFLXvb8I446mlTq5ZX/\nny9JVki29RK6NqqRoH3/IwF44r4/7JNedsz/tlP+21YymeSYlwCrTnnX+19zEshNN1zP7GXHYqaz\nWI0Ga556kh9855vEns3Q0CDf/5aQTaWyeZTKELl0iuef38Tap57E8wN6W3OobkW0OqoWM3VbGHg0\nBUNT8aMIPxRqjqm6KyBWzVACRVYYq1iMly3ySR1ZEr1qET4QU3MDGm7A4UtnkdAU6k6A7fnUHKF9\nnqnbKLJgiICEoYg2SlIXcKvufApFgqjJ1E6bGqNVW8SghSHTDZeMKX5OVdsjnzJQFFkApySYqNr4\nvghjUBWJUtPwoqkKpqbQkTEF+S+MaUnpFBIGcQyTNYe2jIEfx3S3pJpJ9SY5U7BXkppC2lQZnK4T\nRBGzW7O4oYife4G22FNIkU1oaJoiJIKa4Jn7UYRMxJJeMaDVNZnpmoNERHs2ia4qhLHonSuShBsE\nzDQ8DFVBVWQKCYPWtMFU3aUjYwrjUdpkeLpGwxGmIUUWA9Cy5VFuuGweK1O2hGFJlWNGSgJ329uS\nxPMDLDcgbWoUMwamroiYNkkmbYgrqCiKsRzxgZ5Ligp+stJgvGLTcHzGxicIw4jtAwOU6zYTpRrZ\nQit3/dtnGJ0u02LETI6Pku7sI5dOMdHwqTRsto6V8TJdHLFiOXNXnCiu+uw6137m3fiex7xF+yE1\nZgjHdzC3JYleH2Oi6tJ36Ik4toVTGmfg3l+hTG5j8akXkpm1CKdRY+PQFLWB9fQedybsfArZbdA5\nbz8Ov/CLpA4+mbbDT2Vg2xZ++p2vUezpZ82tV1Ea2sG6e2/lt1f9G6WJMaa2PQPAbT/8KmO7t5Pu\nnkfP0acDoOk6J5z6djZv28HI7p08d/9tPLj6ee69/koC30NLpEm2Cgfpb399Pc+sWc0j135n7+xr\ncrOInhsdHnrF+SxLMZHT2Hv7jz/5KuWJ15bN+dfXa3Nr/k8Yfv7dhULsGtjBroEdHP/6V6Iwx8dG\n6egUfxBBEHDn72/n9DPf/orHVStlLvnUx/nh1dcBsPHZdVx84QXcef9KsjlRday662a+9s1vYySS\n9PX3c8D++3Prb29iz+7dqJHHRM2hJ5/C8kR/NZ3QUBESOV1TmrFnQsGRMnVRLXs+pi602QlNWOYX\ndxfYNlamZPlkTZUDF3by5AbhPDNUmWImQcX2kSWwXJ9ZLSmGyxataYOGG5JL6qQMIT2sNFyGKzbz\nWlKMN1xmFdMCN2D7qE13qKkrTFRsihkTXVUoWy4ZUyOIIkxVIZYkSnWbpK6R1BRxqQpECN1sFIXE\nsRgkxhK4XjMlR1NoeD5+EFO3xc/DDUKQJDQJ4ar0QlpSRnPgp+P4EW1pEXQRxaLV4gYhMYKX4vsR\nQSQSf+JYJBPlEwaO7wvsLBDEwkLfkjapux4pQ0NGsNCjOCKMQYph+dw21u6awvYDjlnQyZM7xnFD\n8UESBCIg2g0EhzxjarSlDbwgZtpyyZoaxbTJ3PYcNdtj+3iF/mKaWBa9fDeI0BRx9ZMyhMM1Ywg3\nr67IlNwQOYroyKdouB6SpICmUa/WMJIpPNchYxqQKlCULSLFYHb/LJ7eOMDo6Djz2zKEiTyZeQeT\nCusERhY7gI5Z/aiNafasX0WqZwHlHc9i2TatHZ0Y81bgTQ1x3Icuw54aYfDZx5nZtZm+I09hctcW\nuuYsZGr3FhYceTJbN65n9Q3fpburk1nFNE9v2cMhrzuFrWsf44wvXYUkyTTqNVbd/TvC8hgnnH4O\nd/383zj0bR9k8uGbmLPsaIqHvpGhlTeTP+j1GIaBnki+4ryzalXq1TKF1nbKk2O09fYD4NoWT993\nB7OWHkYun0cNPdLFDqIwZO3tPye75Chu/80NfOZfvvaq+0MUhgyteYC+w94A/NfNPklDjRd2vjYb\ny/o90//3mn3+M6t/7ry/uIEDXHzhexkZFhtgHMeMjQ7z4H2vxLJkc3k++bkv8YH3ns8nPngBH/rA\n+/jSB87kmbt+xfvedRY3XfcLjn7zWfzg33/Jr26+nZGdO7jqe9+mM2OQTadoyyZJmwbTlktrRvTi\n4lCE7JqaihRLuC9pGYyWG0gSIgczCIWVvqmGGK9YVB2BNm3NJHhyw7CIKIvEyGSm7pBvhkcUMyZe\nEOP4IWEs2iYVy6Ni+bhegKapzG5JoxsavS1pFMDxAjKmqHiRYHC6jtnMyazYLl35RNNAFDNVd8gY\n6t4PDl1TKTVcqq5P1REGlmzSJIrFAFJXFfIpHT+M2TPdQFfEFUCu2eMvNVxSunBDarLQZw+VLCw/\nxFAU4iik6ohhY0KXmdueppg2xevoGpIs4UWxQMjaPqos5JMJQ2umBwm4V8XxMHSZ7nyKtKmjazKu\nHzBRtXG9AEOT2TJaRpEFwmD1rimQZFK6QjFt0JlPsl9PgZSukjN1ZhfTBBGYukpbyiQIIjRFZs3O\nCUxVpquQwmmy4HdP1YX6yW+agXSNOIbhsrjqQoJ6w6Fqe9RsF9cPqDZsGvUGsZGi3rAIw4haKBGU\nxxmdqVEtl9i5bTspM0F7IUsmX8BBwdq1nl27BymND1OdHmf7g7diZFsw0nnKG5+gvW8u26dt1m/b\nTXHWXI770GUM3Hc1jdIoU1vXEsoy21f9kX61ij28hZnNq0FPkC12saC/j3T/gaQWHM55l9+EbiZY\ntPwYxp57AkmSSGeyLDvkEIzybrR8J2//3He5/uqfUDzqDFqXnywe09nPo3fcwLPXfZWJbc8ytG6V\nODciMa8Z3rSGLWsfRzNMHrj1Vwxu2whAZFXY/dgfeGbVg0TVKQYfvJHplb/GGtxEoJpMT4zt3cBf\neK0/X3EUIgevTmf8m1f8v+yU/9fX6Jr7eOOxhxGFAl+paRpnnfMuKpWXu7gqk2MEvsf8BQu44vs/\n4vP/chkf/vCHueYPD1HzYr76zW9z2BFHceFbXsePv/xpPnHB2URRQLbYTlCdoV4tY2byOJ7PrEJK\nBCooMmlT21vRSs3LdimOcIMYRZII4xjLD1CafebJusdQqUHV9unJJ/F9wfXwQpEHWUyLtPe2rIkT\nRNSdgDCKCGMBcnJ9gbmNYhEs3PBCXE8kyUuSqGhrji+S23WFrnySfNKgq5CikNLpyifJJXRGSpao\nwjWVbMKg7gSU6i66KmN5Ad2FJAlVpuF4TFYtGk6Arqh75Xq7pqrC1Zg1aTg+aUMlDiO8QBANwyhu\nXq14tKRFS+OFaLiErqE02z+KrDDdEJJNQ5WZbrgYitw0DhkozeBkVZH3qlrKloupyORTBlXLY8dE\nDQnx80YSWFZVVXC8kLob4AYxbRmTrKkxWbMwVBVNUYiA50dmSJmacLeCuLoJQ6qORyYpcAX5lM5Q\nqcGGwWnKDZeRUp2ufJIoilFkmaothsCmJjWt/ApV2yNtKOSSGhOVBqoshrNZU2X38CjEISldRtYM\n/CDi8e3jhC19WJbF9MQYWVOlZjvUGzbGvBUsfet7qY0PoVTHqEyOsfn+m5l7zKkEcczE4ADHHjCP\nAxcvYmrD49x25bdQ0x2s/sNN7Ny5m6nNq+k55DiGXIOoZTZzli7nkZ98kVw+zxEf/w5Tcg6nc3/C\nIGDns09ieFW6lh7B2NN3M/HMAzzz4B846LT3oepCgnpod4on/yB631Hok+lbwpve/SHmH38mmfZe\n8r1CB37PTy6lPjNBMnZYetDB/PHKr3H2x7/AhnVrGNq5HbPQzts//Q1kRcFXE8x6w3sIky2EssK8\ndMScOXNwrAa+53HjZR/BsxvN93wRVatoOr1H/cd+kb9l/W9P/L953XzjDfz2xutfdmxg5e0QeHgv\nYSUXW9s44+1n771tWRaP3nEj13/zs+x47mmy2SydvbN51/su4nXLD+Z1bzufnr5+auv+RNaAsZkq\n69avJ5HJkVFjNgxP4/oh45NT9DUDdUWeZpOMF0PDDZrMEB83FPjZQlInDCNMVSWMQuHARAwup+oO\ncQy6LuP5wj2YT+oQS00FStwc4ik0nJA4imnPJqg5Hg03eLEKjCLSCZ3BmXoTFfsizc7xQ8YqDrOK\nKZEspMhEUcxM3cXxQlGBJg2yCZWK7ZHSFRRZbrK6Y5BE9WlqKjMNl/GqzUzDZaRs4YXi+9FlmTAW\nqfQzloflCa66oam0JA1aswmm6zYdWZNCUqfm+ExUHSaqDjN1hxhhy5+o2WJIq8i0ZRNosoTlCtKg\nqSpM1RxGynVa0gnaMgayLGOqCsVMAlWBmbpLEMWULI+6E+B6ohXl+iFSE6AVxjE9hQwl20Mhotxw\nRQumOQ+wmzF3L/R4egop/CCi3BBO3e5ckqrjC56KLJHQFRZ05jhhcRdz2rO4gTBq6ZqKH4QEkdC0\nm5pKxtDIJHRkSSab0DFlaMumMLw66YTGnPYc9dGdWJJBpCfJ6goJRWZOR47attXsvvsXqDJ0zN0P\nM19EShWoP7+SegDVhkPsOyi+hZbOM7P+Ifas+gOt/Ys468OfZvb+h/Lc768m2buIbfdcj4VOuVZn\n+yO/Z899v2Jxm0lrIc9z992CpRfIH3QSm/90Azt2D9N+yImccNZ7mdm9hX/9xPuIo5BDX386h7/h\ndB6+9ToGH/kdG67/Bvf9+zdojO0ikWsh3ex1n/zRy0i3tLNp1T0kil0ceeb7qNeq9DiDJHSFwe2b\nMYs9nPbOC+jo7uWWH3yFloNOINe3hLYTL2Dr+qfZcefPqEyP07p4BXoixdTurTz26x9RLU1j1V/u\nDXn+vlvYF2tf8MSb9NZ/alJbl73acUmS5japrj9tMqb22fr7kRi+ZJ125lmvOHbA2z9Gy2O3M3f+\nwr3HhocGuf6aqznjLafwg8u/ilToob8tz8FLlzExsIX+JYfsjSkbKlusvf/3rHx6PSNj45zzkUuY\nGR3kp9/7NmueeJyu1hyGIiGnk8xrTTJcshgr27SlTWYsT1zqh8IMU0jp6LKE5deFBd/QKJUtGq5w\n+qmKQhRrVCyPuGnwUWWFybqHqUoQieCCjKlTs30aXkBKFhrrIIaxik1SU9CaKNqUqe011/S1ppmu\nOQIalRT0REUCU5WZqtoMjFfJpnSiCOZ35ITz0BHKlOFSXQQvRJBNqHsJgrmEgeeHtKR1SnWHpK4T\nxTH9rVnqtseWsTIxokVELIaWKUMjnRBxa14YsWW0RF9rFs8XsKxcQkeTheLFkGX8IMJyfQ6Z3cpw\nqU7DEUx1TRUfAmOVhuCxV4WlfqzcoOoEpHUEPjYI6cwmURUxqKy7PrmEjuX61L2AfEqkJoWRRNpU\n8IMQU5GQZJneQpLRioPrC/RB3Ny8rSCiLZ1guubS15pmsmoL16ipoTU577mUaB3tmKjQ35phomJj\n6ALZW7M9ypZPRy4pVCmIhCDXD/EkvQnlihiarpFIpbBdB9lM88imXfS05mhP6wwnevDL41QHx2kv\n5olVSCsa1sBakqbJQWe8nwd//CXae+dQHt5JvqMHvzCbnY/fTUdW47ktW1nSfxRG/8E8f+c9HLD8\neIZ2bGFgzxBv/OxZ7Ny8ga6lR9PS1csd//pRjoocvJHtvPnCf2D9/bfTPmcJ6tQ0I889xu6n7ufI\n9/8zn2rNQujTdsBR5D2X1jmLGbj9R3Qffw7hU/ew/bG76Tnu5efot7/yJTqMBI9d9x065y8lLPSx\naXCCjiBgaPsm+ha+iKm94ItXALD6F1/FUGWOO/8SrD0bSXbN4g3vfD8fOPcd/PTaGznmvE/w6N23\nk87lOfjI1+19/uxDjt4n+8w+apVcBFwVx3FZkqRvAmtf5fhPgZOatNd9uv6uKvHGrmexBjej6/rL\n4qEAcn2LWHz2P77sWEdnFye/6U1Up8f5wQ138P0fXcl+c3vRxzehEOO7Nj+95CIAPv65S2nJ54lK\nI7z3gx9m+7Yt/PRH32e85jFnVg9GMsdkzSb0RABC2XKJiSlkRGUZRiJJPogixsoWIxWHfFLHC4Qp\nxQ8jqk7ASMnG9sKmg1G0FbaPV/GCkKSpIMkKMTBds6g6Pq25BIu6CuRMjTltWaJYaLdf6KNnErrQ\nPTdNPK4vgpK780nCKCKlN0MXvABVVTCNJts7DKnaLgOTNSZrNtN1Fz+MaW22cYZLNg1H0A/dwAcJ\nFEkhiCVmtaQghprjk03oLOwQ+Zo1N2Sm7mCogpMehjF+FFNqeOSTJmpT+hgDA5NV2rImizrz2EGE\nH4QU0iYD45Vm+pCK7Ylh60jZoiWdQJFl+oppOrImcSxog5IsCV57GOMGAXEMUzV3r6U+Y4pE+nxC\nJ6nKBFFEEDYVNCmTLWNV6l5ILqmSSWqEYcyumTrFlMHc1jQLOnPkU+K10qZG1fFY2JmjtyVFb0ua\nrCnMTmEU03B8UqZKHMfUbI/BUh0vFFdJmqqI9lYc054xWTK7Ay+IWNCVQ1MkHNtismqh+3UOm9vO\ngbPbmdPdRjZfYL/DjuOwxXOoWR6oBlIMQQSZhIaz7m4S2TzV8gyTrsQ9Tz3H5pV30L7/UbhSgs6k\njvXcPUxtfJLT9m+ntaMLvTHJu79yNQAnfuDzZIvtbL3tx7T2zmHLo3fRvewk5ChgxZK59B94GIef\neArDax7GshrsWfcodttiUMT5t3nDerZufBbz4DdBfYptOwc59MPfBuCZVQ8SBgHP33Mjxx1/Amd9\n9BJ2b9vAxNQMSw48mJPefj5hbZqj3vzyDd8pT3Dlp9/DAWd+mKXnX0KjWmbouScJXZupgY1c8eOf\noTSTpUZHhqn/mUs7Xdw3EW1/A4q2VZKk1S/5uuglL7PiJRvz3L9y/GxJki56acW+L9bf1SauZltR\nMyKGqTa+B2t8N37jxTRtSVb2/r9r1Rkf2MRBy4/g0JPeSqNeB+BN7/kYlUgn3ngvv7ruWi689AcA\nlEozjE6VaHHHiSqj3HHjtZz37ncT2HUalRlK05MEgSD2uX5AW9rA1FQGpxpMVm0SukJrxiSpiyCI\nfFJnUWeeXEKnI5ekmDZozRgUksKQUrFcTE20NjqzgmV93MJugigijAU6NWNqjMw0UBQJXZHYPlFB\nbbozW9IGIOE07fCuH2E1HaC6IvrZrh8yVXcYnKlj+yFDM3VShlCSmKrMdEOET3QX0iQNhZ58AiSJ\njKHxxgN6cYKI4ZLFTF1AqQxdaeIDfDpyCdozJkgQRKIlYyqiFI/jmLobkDBUgjAkaai0pE3GqzaG\nphCGIcWMMCyV6oKRoioy5YaHE0aokoyqyIRRxETNJkboyEfKFhMVq6l0ER9iSV3DDSO8wMcPY+qe\njyxLaDJ055NkmvCt4Zk6XhzTktTxwoCyZaPLMW9b3o+mSPTk03h+gBMEzGvLoigKkiSxdaxCwxG9\n+jCMySZ0phsOZdsliELR0opieluSDFcsypaL3cTnzsqniKKIpK6gazLz2rOs2T3Nzqk6jfIMs4tp\narZQM5UaAnQWRjFJQyPyXeQwwJvYyeTu7Wwvh7SmDdQo4Kmtuxkcn+KRNc8RyDqW7dLZM4vFBxxA\nZy5BPmkSVsboKGTI9S4gN3cZybYe3EI/Y+sepvPItzB0y+WsvearTG9by70/+iK9J76TZ59cSXbO\n/rTOX8rP/+k8nr/nJqqjuykPbuee54ewKiW2rLqHtdd9k5ldmwB4/KH7KBTbaSm0kLLGWfC60zES\nAgm77dnVVEvTdMw/kMhtUBrazuylR7Ju01YUTUdLZRl99HfidW65mj0bBGpaCjwSusoPLv0MAxvX\n06iWqTk+kqqy55mVLzMCHXvECjpUm0a1wuqV+y5bW+uPGgAAIABJREFURtjuX3M7ZSqO4+Uv+brq\npa/1Elpr/tWOx3E80MSQXAX85eTq/+T6u2qnGC3dTE9OUAT2PP5H1m0f4ozz3o+WevGX6js2Qzu3\ncf8DD7FiST9uaYzS1CTfufxb/PLOh9FSObY1VHbRz+I5c9F0gzAMqZZmSOcKTEg50sUe9l+8iB99\n/7ssXb6CtY+tor0ly+tffxLPPPkYhZTORNUhiCJSpkouqeL7MTVHyNMabkA6Ie+NRosisbFlTR3P\nj8gkNPxAKEJyCR3LD1nQmWXTcImOnMilDKKQ9mySqbrDRMkindCY15YVOZKOUGoYmtjohmcceloS\n+H5E3XaouyESYPtBU0stlDFhFGNqIgItlzSoWoLnvWe6hiKBH8tIkXAqrt81QdpQ9ypqHE8wxKM4\npma5xLJCw3VJ6hphrJE0NfxQYGqdIKAtZTA4XcfQhKnICyLCKKIrl2Jwpk5GU6k6It0HwA4ELsDQ\nFBKaiuX6xIjg52LKFO5WBfxIhCzrMnhhjByJD7ya7dOeMYWkUBLSw4YrAhsSmsqYI5QioxWb9rSJ\nqsiULJ+NwyXiGJ7eOSUctbJwdMZRjOMFFJIGCV0Yeaq2j+2GNLSQOIZ1e2ZY1t/KdN1hrBqyZ6bO\n8Ys6URVh95+u2yzsypExdEYrFtNVh8WdOVRZImMqzDQ8MSvRxAe21nSbtqZ1ulpybB2vkMwV8SOZ\nxuRuxlyxkR996AGYsw9kbnmM2sRu+luz+OM72DxWx8Cnkskxe8nhDK66Hd2M8XetYeh+j6kt60jN\nWcoPP3MhfT1ddGkuDz69jtevWMr6W69im5OifdNG+l83zoXf/z1bb7+Sh37xLabtgOXSKMd/8joU\nI8G6W66ktuVxjNDiok8Jo04Uhsj9C1i8cz2VLU8zsHUTcxcuwS5P4ay+jarWR9sxJ1HZ8AgnfPnb\neFad3WtWcsj7LwPgsNPfw641D/PE767hiDPei43GO9/xVmbtdxAA7b2iuD3kbR+kXp7CiySKrW10\n7reCzv1WUK+WGbj/Nyw/7g2M/pWouNe69pHw5GkE6K/My4mtLzverN5/06zO92lg6N/NJm6VpyiN\n7OKbl13Kt668hv3PuJhN1/+SVFCFnU8yk53HLTfdyNve8kb++Ivvsf8p53LQCSey7ZE7OeSUc7ii\nrYA9vgtt7kE0bJeDoj3M7z+b1Q/djSFFPHLfvXz0i9/gmmt+zlMP/ZFnn34cPZVh/VNPcsTiWZSc\nmIcffIDZbTlURaFme7xt2RyeH55m00iFOBZkQDcIgXjvsExTFeqez1TNpT2XoO75lGyPtrTAuWYS\nOulYDNeSuo7tiQ16stneeEFpUbF9HC/EDSJoQrDyKQNNltEUFcsNMDUFJ4hJ6iq7p+t05xJ05pNY\nXkg+pTM4VafsBeSS4teaMhSQIAxBNVQhdbMDJC/gqPldbBiawQ0E33zbRA1TlckkNIJQor81zdYx\n0bNO6SoN26Mnb1L3RZvIC0MWduSYqNrUbR9dlZGRCOOI/XsL1Cyh6a46PpLkEYYx+ZRgzbh+iKLI\nLOjIsHrnNHXXpyVlkNDFYDUOI5IJg6rtE0aiOs4nYaLJJ1GA6bp4zbQhNOZtGRNNU2jPJig1HaFh\nFDNaatCaMVg6q0Dgh2wcrXD4vHYsLwBZopg1cP0QXVXIATIxETG5pMGR85I0XJ/2bILJqsP8tiyF\nlEksSVTqDvmkgYRgsou2lICbuV7I9rEquqYixSJ4ImsKDLChKnhBxIbdEyLc2h/HKHaTTiaQJYek\nadCRVNix5l7MTJ7MiuNQpDSlDSsx4hlyxTYy2RwD916Hm+1lXm8Po9s2MTYyQqCnCYe2ccKiTpLF\ndhpdB7NciYhG19I3dyknLh5j1oFH8qdffBe5Yx5+GLFkwX4cecwZtLS1Y02PcdelF2Ie8Q6efOxR\n/Ftu5uRzL6ZuOUyXKrzupDdgDW9FynejahoHHPk6vNIY2n7Hctz8ZUzs2kzHIQJ0pZlJ+g49HlnT\niWsTyKrJ1Np7kdNtABzy+jPoPfJNr9gHfvC5i+lRLJxZh3LuR15sn6azec74zOUArLzt2lc8729f\n+ywE+SrgIkmSysDXm0PLs/78OGIzX968f58mnf3dbOJRHOM0Gnzrymv2Hjvr3AuoP/YbzPnLSaUz\nHHTIMsafvAszW+CY44Src8GxbyH0PTKLj+Kid57Oeeedz0c/9jFu/ck3KbR34ToOXd3dtO8Y4pfX\n/JwUPtf95jY+/6VvcMmnP0FEzK7JKo1Y9JJd1+PZ6SrduSR3P7cH2xNBB5UmLAkklKYTMGUI1Yrj\nheQSGqMli2LaYFl/CzvGK8SSxETVJmmoVCyf2UUNRZGaYQjG/2HvvYMsvcs738+b00l9Tnefzt2T\no6RRQjkAElkEgwBjk4wXzAVjGwwYvGABa1xmsY2NvSwywbIx0QaRRZKEJJQZjcLk1GE6nz75vDns\nH7+j4d51ba1voVvWUvetmuqpU92np3u6n/N7n+f7fD64YUyaCoqfqshkEly+pcr+uXVKikEUJZys\nt8lbOpIMpEKKsNp0hXrM0FjpI12bvQDHUljf8M/2nJ/0cS7VuxQsHUWTz57ef3JokU1DTv80IpEk\nKcWihamqrLfEtmqcZKSpgH5lWUo3gpmhAjODDkeX28xvdFEUCctQmCznQZJY2OhQ6/i0vIgZXSAD\nZIQ+brXtoSoyO0aLeP1Y4GhJLCWZqkqWpXhhjKbKZ5eDkjQlb2qcWvdwDA1bV3HDmPEBh5VWj44f\nMeAI1G3LD4iiVMigc8KTWusGSJLEWss7K/PY6PqoskQQJmx0hNGokjNZbQowVt7WyfSUuQ2X7SMl\nji7XyZsGU8MFkMTHdcJYwL9yBrIsIfdBWlNDeVRJpmTrtL0QWRH43BTBwlFkiQyJYs5ClyHLVXDr\na3R6HrO1NmU/oWxpNL0QO9pg4d7vC3SCYuJYBuc87zUcu/1rLKcFLthzIfHJe+k0aqx2fGrra1y+\na4by2BRJc4XxzTuxNRkrmOOBlZDh866l9ch3mRzZgqy5mKpP4svc/1dvR916Kc9+zVvIlcrsCw6R\ndwLWchpuo8bUzn2c/9zzecvzL+Zdr3kRu994I0FjlfWvfYThGz+AYubwOqKvHbRqpF//FJXLX8E5\nl4uC7h/9GYsnjjB8zWswVImNA7dzybOez/4v/zWDm3cx9QyxFxKHAa/+vQ/gPXE7U895AwB3fv9W\nrn7ui5FlGT1X4o5v/wuzG/+WZPr/9sr6NeeXfh5xsv7Y//Twx/6nt09e/0ss9y9zPW164naxzL37\nH+XYkcNnH+t2O1jnPYfD370FJY14xmWXM331S/mt//xxlo88Qqu2ykPfvIVbP/o2Hn/wbt705rew\nevoIuUKJN/zxxykMVNh+3kW0VhdRF/YzXLCZb3p02m3yg1X2XXgZeUOh5/lsKigUbNED3jxUQO3r\n1BRZppIzCWPhfNRVGUfXMDUVWZZZaXkEUUxCxnDBJExSVAnOmRxAkUGREWyQKGZho8tCo0u1aBNF\nCaYiM1K0UFURY5sZzLPedTE1lTjLONP0iNOMtheSN3RqXR8vfHKBRiLLMkqWxom1NikZciYxnDcp\nmhrnTpYpWBq1jk/R1tE0mdWWhxsIc7xjKHhhShRnqLLMYF60azQFkCU6XsQFM4NUCxblnEmSSURx\nyqNzNR44WSPJxHNU8ibVgsP+uRpHF+ustjxxejZVokywYJAkmkEszPaGxnrLY2Igx2rLw9KFvef4\naouWF7F1pIQqixebSt4kb+msdwSSIAhjNvpaOEWW0GQhrm65gvcyXHC4fu8EwzmTBIHl7fgRXiTe\nyrLEgKPTdAOWWx5RKl6kVpoejV5A3tEZK+cYzAvl22jRRlPlvo0mQ9cUVppdmm6AkoGmiJbacN7k\n2EpLWJW6Pm4Qc3K9gxul+FHC/EaPhVoPS1cZLlhoikSzK/L7C4uLJIn4XBdvGqJaKVDJG1QLFhmw\nnhWwK+M0zGGaXQ/vwG0kvSYXX3UtWRpxcqkmBtSWyky1zHqoMHb+teTPeRa7zz2PdG4/x+sew73T\nFJqn+PFjs0SKxfbn/ib75+rMtkJC2cBYfoz5wwd44X/+e6zN57Nl63a2X3A5u697ORvLC0QHb+cv\nP/dVdr7uTzj4k3+luuN88lf+OopuYlVGOX7Pd5nZeQ5Du55BuPN6vvv5TxD6HvOP3Y927gt48EyP\noeoIc/ffhqSIs+PQzgsZ2vGLRcgff+omSuVBpp/7xrM0RLcrlq3SPlvpxEN3MjP0lAjDfmUcm0+b\nIi7LCrv2nMPQsDCH1Dc2eP8734HilDjnjR9CtQR9LTcyQ+R1OXbH12lvrNFYmufyl72BzTv2cPWL\nbuT6V7yWB2695awG6sg93+ehr3ySY22Jf/7MJ3nrm97Ajs2TvPk3X0l78Ri+4qDaBe49vkIQxax3\nfII4ZaXlYepCGHxqrY3S13hpsoQbJgJQlWZcPDNEyREr3EMFE1tXOLneoefHVAsWA46JrsjMDDpo\nisSu0QGOL7fIQDgdo4SOH5MkKestjzDO6AYRuiIzUbIYLdoM5k1WWj0myzaqDJuGC4wXbYaLNl6U\nMFXJYfdTKrqqUHRMVlouHS+k5QbIksi7G5pKtWhRtHQmyzlKtk7ZMQgTMdBteeJr2jNRJmeKYlfr\n+szVOowUbbp+RC+I2DFaYt9kBVVWcP2Yk6vC9pNKEnlTpVqwWG56tHoBuqpQdgyGcibjAzZ5U8Mx\nxHKNbWhEUYyExGhf/LzWEhq1imOKO58sI05TbFU5y2JPM5GI0VWFMPqFManjh9x5ZIlOEOEFEZqq\nUMmZZGmK049CPrm0NWBpbKsWCOKYRh8JvNHxMTUZN4hQZYVuGHN4qUnREiwcW1dpeTHTgzkkCay+\n1LodxAwXbIYLFpahkWQpRUunZGtYmkI5p9P0QkxdoeNFWJqK1GftKGlCyTHQNdg6UqKoJJzZ6OA4\nOfaOl+h1O9x14DDtRgNDV+k21liud1j42bdRTYd2p4NTqhCGIbHuUN5zGZNXvISj9/6QO27+Lxhh\nh5HBMlGnyXov5v2f+CyRrLN84G6uLfe4QFnmuv/rJq79z7ew9eJrAWjPHyUrT7Pj+a/l0S9/Aq1X\n4/id32Dl9n/i1Nc+zvIjdyFLcOctf8kjf/M2Tvz4yxyZXWJw96UcfvheLr/ofN74/o+hyBK12aP0\nOi227tpLrlIlNErYm0U4Y3LflZj5Ep01wU153u/9GXd+7xv/j7rwght/E5mM9Z/8A+25Q/zWB/+K\nV/zRJ56SmvOrgqJ92hRxv7WOu//bFItFOo0a7uosf3jjs8nSXyy1/NG7fp+lxTNoVo6DfoHK5Gae\n89YPsLD/Tm79/N8yd+ok4zvO5YpX/iIBlNlljuubOXZ6gUFH5u6vfZbDJ+ZR04QIhWatxogesmkw\nz2jRouIY1LsBiiThegG6prB5uEC16LDc6GLoggyoqaJoLLV6yLJEFCcs1XtCyxZF3HdyTejeFOHA\nXG0HlHIG9a5PzhRpjidFwzuqBVRVIW9prLY89k4MMDHgECbCjblpsCA2GTWVuY0eI3mL0bLNoTMN\n0j6ytmRpIqrnGGRZv2/dFwGriowqy5iKxEbHJ04SGt2Q07UObhTT8gRZcbxkE6YZq60eDTfACxIc\nU2W6kqPpBhRtg7EBh42ux+HFBotNF70vf5AlidGizVQlf3b7UZGFli5vaVSLFiVb9L23jw6w2urR\ncgOiTMihq0ULMnHXIcsSKy0XL4wZKlgM5y1kRRTRlZZH3lAZK1k4hkrB0hguWLhhTNEUn1NVFJJ+\nVHO8ZAuCIRlDfeN9JWcSphkHFxuUHZOdYyV2jBbZNTbAettnvROw3hGDyryhYqoKjqWzUu9SMDUa\nvYBK3mL3xACuH7HRCbA0IXgOIkEx1DWFoi367TODeS7dMkzRMogymcNLQrhtaQqDeYsTqy1may5+\nGFI0VWpdnyPz4ucn8drMVHJMaz3yukIvP8n2vftAVjFtm1f+6T9SGpuk03Nx5BT7xJ184z0v40yt\nxeP7H0Kxcqz7EmplgtrsMR68+QMM+UsoCw+TKhK9VObRu3/MwS99nB/91bt44q7vo41uY/n44yK1\n1fWQY497T20w8/I/wNx5BRe/4neQtYQXXLqD/I7LGb/oOl73hx9GURSueeWbOPmdT7N8283M/fif\nueDFr+df/+6jOLrGj2/5a7ZdeCX6/014XJs/xtHbxfLOYw8/wB3f+GfC1joAH3zHb1M78Tide75I\n6cpXc+bhOwh9j5WD9/3yBeffeQr/P+Ek/rTpiZvFIUYvei69douffOlmnvGs56MUBxHNYHG9873v\nY3i4StRtcvUzr8Ps/zDMPOtV3Peht9E98TAblkplcsvZj/nOd75NfW2FD3/8E8wdPcTp40fJ9v8d\n5+3YTFGXaYwU6Sh5Wt4ybgBuEKPIEmXHpBfG2LqIsqmyaEu0vZCOH7NttIiExNGVFrqcib60G2IZ\nIsXx5OmtF8SAYGTrqkKaZIyXc3S8iPl6hyhO+z1vickBR/BYuqFIpiQi+TK73mb7SJGWFzE+4HB0\npUnRFn3galEwr3VNYbTosNLyyLIUx9QZyltiEJtlmLoifKFINL0IS1UoWQbLLZexksN62+fUepdq\nyWKp7rJ1pNDfgEz7ZEUhZ67kTKYHc5xeExQ6N0r6fHGL1bYnZMV9GmPO1LB0ldWWoC0qsoSmKDx+\nZgMvjCk7Jm4QshGlnFprc9GmISpJysk10Zogy5BlcbcSp0KJJyMxu97lnCkDQxMZ890jRUxN8MP9\n0BOsFk3B0iQaboBlaHiu6I0rsszsepckTegE4nkNReHBU2vsm6qwdaTEkeUGRUvDCxMW6j32jJcI\nkwxUMQ9Zb3nYpsbPZ9c5d7KMLKcYqoatS0CK12+d6apMtWjT9gImBwssbHTIkpQ0hbKtY2kQJSkz\nlRyOqVPvRXhhTCWnkyHwwJW8hSIlHFlqcnSpxQ1DI5SKOXJFi/k7vsLDX/vvDFZHMeWUzG0RGAUm\nCjq5kRlW1jc4euQwcWOZYnmQi668ViyODW2lPXeAsLPG/LFZenOrHHYDtMEpcounSZIO3TDlsfvv\n4pxn3kB1+3n8zrUvhdwAd337q5z7rBfz1b/9PK9401vZvuuqs79r9/3o21x2/Q0oz3wlR378NbZf\n/hKO3ftDDh54kENPPMZLb3wNY1t301ic5djhJ7jkuhdx/IlHqPVEpdy8fQfvfO1L0KIehx44xAf/\n4lOomgZbzwHAHt3Ejz73F6xExi9db56qnvjT4XraFPHjx44yuvN8vFadZ7/qTeQr1X/zPtkDX+FQ\n5lDoznPeaz509vHhqa1c9+o3UR7fRLx8HCa3cPc3v8SFV1/Hs666nI9+5EN8+I/eCX6HvRdfheM4\nDBZsHjh0iksuuZz7H3oQOctwfcGKHnBM2l7Yby9AL1wjDBOiNMXRFBxTwwtjml5EtWCyWO+RZDBc\nsOj6EdtGihxcbBJEsUinGAqqYlK0DAYsg1pHDB9NVaXsCEa2ocgsNHpYuoIkSxRNjZYXYxsqGRnz\nG12iJENXJGaG8szXumiqTNTPZNe6AbaukqQpbhiTZCklWwziojjB8yN2jJU5dGaD4byJIsucqXfJ\nyFhreWwayve3ECO2jhQJk4RUFplyXZXRJOns8k/PF8IHW5NpeQHTlTxhnDBRsuiFCar8i1+StZbo\n8XtR3D+ZJ3R74u+9ICJOQUKssa/0WyljAw7LTZeGFwHS2RcFRZLRHCGkWGu7uEGCLEnMNXposkzL\nDcTgWBXziqG8xWLDRVdTBvMmZ+oulbyBocmEMWwbydENYrp+TJSI71sYp6y3fUqWLrAAitg2bbkh\ncaIiIQtDU5YymDORZJmyo7Le8bF0RURTk5QoEYCwtWaPkZLDUr2LIsl4cUQmQZKl+LFM3lRpuoJl\n8+SdSK+fRMoyC9vQKZmycLzGGWtraxw4Mc+AY1At5ugEKYVOHcs0qPd8xgYz2onBOefs5TnPezP/\n9Ac3UJRTmu0OspeyceLnTO8zKHnLzHVSjKFp4pVZrnzBy5jZdwULX/kogWJy1Wtv4uaPvp/Ww9+j\nsuVcqlKTqV97Nze+9+PUVhZ5xrNfxM9/egdTG22m915ML8rotkTCLkTjtnsfYfvzamRpzHP2bWeF\nHLmBCv7aHLd/8o9IhrfxjGe/kC1jVY6cPkN6x99wZK5FXN5MpTjGkW9/kMfuuo2XXX81xr7nszx7\ngsG9l3NeHJBbWn9Kas7/L4V4iq9t23cgyzL1pVk2Fk4S9jo89Nmb+MynP0Wr2STxXYoXvxAvjCjt\nu/7ffPye61/JicV1fnj7HQDY+QKPPnAPp48d4YqZEudv38yF1zyPe+5/gD1lmeWNJnq+iL96gqol\nCYt5TkeSJJx+4bQNjSgVfJHN1SJ5SyMlI4gS2l6I1rfYS7LEaFH80kuITcexAZvBnEmSpKy1fdIk\nxQuEg1OVhUVn01AeRRLr9r0gZqUlVv0dTebwSptuELHW9vDDmLW2TzknHJRHlhost1wmBxxWWx5r\nbZ/pSp5uEJFmGSNFC01RCeOEgqVh6ApIEo8v1IizjI4fYmpiddzWVXKG6N2mKdiGBsDEgMNo0cHU\nZHRVZtfEAFIGTv+5Roo2fpyiKwol2xDy444vClc7EBE+JJJUbJtmZEwPFlBk0X4Z7d91TJUtglg4\nPZ/Mtwex4IeXTJ2cpaNqCm4Y9dNB0PJCLE34Lw1NRpXF80dJxkjRxjHVswjhsqOzaTCPqsii1YTI\n1KuqjOdHBKG4Q5gq23ihyIePlSxMTWGqkqNg6dimRob4P1JksA2VOMmQZai1PRYbPco5A11RmCrn\nGCvZbB4uoEgSuiaQvYuNHistl5GSw86RIh0/YqPr40fCWGSoCrIkJCBpluHHCU3XhyTm8fkN/DCh\nnupsGR3gxgsmOW+swIABPc9jo+2iqjrTgwXWI43jc4uc2X8Xj/zdH+DFCjPPfT3GxC6MhYcoDo8S\nrhwjam5QyVsMhKsYUkTSaXL8O59h6IqXYSQ+rTMnGFN7DI5NY0gpcXGSQ5//AEmvxf4vfIx/+Kcv\nMrL7IhaPHyKJI/7+Y3/C9a94LQDDW/bwro/dTHl6J65s89wP/gOvftefMWTJhMfu5+o3f4Cde3ZT\nXzgOfhs1S3gommbnc17DoJ7w9Y/8DpdfcRmvfvef4WcKD//w6/z0i59k5cgBpq9+GTsuvPIpqTnZ\nv/PP0/162pzEn7wm9/xiWn3hGz5AcM/dmJbFyt1f4dbv/4gb3/perNFxHv/8B/n60Q7POncLV73m\n7fz0k+/BLAzwgjf8LrUTj/P4/gcJTjzMphf+J/Ze8wI+/607eOPrr6ZWW+fh+9vkbBO9fpI7H+vw\n/POmCeOEIJGIInEiGy7YaLJMEKXkDI3DS3WG8zauH1OwFfKmWJJpuT4TA45IjGSZGHImGbos0UtT\noj61zw0jJgdzzNe66JqMLEmstz1yhoYXxUxVcgT9toGhqQxYCkGSEScJk2WHrVUFL4yYqORYqPfY\nO1GmF8YMF20UYL3dY+tQAT9JyZKE1bZPGKfUewG6ItPyQnKGJuBQRYdEgkrOQlNlbE1ieqiIBBxb\nbiJJElIGdVcArCp99nYlL06ecRgTAfumB5GkjLbrY2qCpFhr+8RktHo+S80el22tst5yIYVTa22k\nfsqj0RVDxNkN0Vcf6qc2Tq61COKUS7dUWWu7RElMsxdRsjVkSRRrU5FEPLFo0/VDCpaBpkgsNV1W\nWi5Of27xJNddVcQiT5plGLJCydbRFbmvsEsYG7BJkgxVlWl7oVhcyqDthiw1e8wM5pBlYRRyo5ic\noTHX6FHJGfj9XH8YJyKSmWWQSRQs8eKWJMK3qskyBUcHKaNadDi83GT3WAlFknnw9JoYwPYTL5sH\n82RISDL4ibjD03Qdq1VjudYgjBxW6j1K45u4dJdGLKvEgU+uWEZuNxgrWiyt1TCbLXS/ww8+91eM\nD1dwq5PkFhdoNpvsmqzgpD0kS2Xdy5PMHyBLU1peTF0qsvKvf82Oi6+lu3qGaPU4lSvexorbZf6h\nHzG572p+85IcqaTQwySUdEbGxnnw7tu56PKrqT34XU4++hDLsUWyfJT2vV+j0Wyyacduxn7z/Ry/\n9bM0lhcolgb56be/wY6tm2k0W7R+9hUeaeXY+tzXMnLFM4mjgPvu/DGzp45zerlOvlBiaGSUXOf0\nL11nsqcuJ/4ffj1tTuJpf4B54tixs4/JisJlV1xJb2OZ8etex41/8Kd89+tfQbZL7H39n/D7734/\ntdkj/PWf3cRl/+lDXPL69zF/Zpkvf/VfqDeaXHvlpZTyFrOrTZaPPYpz4nauPn83r3rlqzg9N0fb\nDXnR+TPcdXQJ29AYsDX0vsyh1nY5vtqmF0Q0vZC8odL0AuhjaHOGiqXJJJlEwdQYLdoUbe0sQbDs\nmIwWbUYLJmNlh93jZVZaArEZxymKIjM1mMfqDzlNTcGPYmxdZqkpWgWWpuDoYrvRCyIGbJ3pcg5L\nU0RUr+0LEbEESSqKVrPnM193sfqn1wzBJ1dlSSzPxCmz9S5uEFOwVGxdxYsyluo91tqCe9ILY06t\nd0hSibyhMpAz6foh9V4gipIb0AsFCneh7rLYcukGMVGSUev5DFg6q+0ATZaZXe8Idrkq+N55y0BX\nZCxdFRugpOwaL7HRDWh5goViaTIdV2TNn2xjRXF29nMstf0+H0Yw3qNYFPSKY+DoGluqJTYNFcib\n4kVrueVSsnSMfv687Yc0egEjJZtNQwUUWaETxBRtA1mCcp+X3gkihgs2sqwQRMLqlPblGluG8lww\nM8hEycbSRN//yXZTztKwdA0vEJHIKE7ZNJyjZBv0/JjZWoeOH7LWdslbOi84d4qhgsX4gEPeEiz1\ntY5L3Q05uLAOskIUhhRMlZYbIpFRckwGwg05X1OjAAAgAElEQVTiKCbxesQT57PR6tDtdhgtOew8\nbxovDEmyjK1jFTaPVylIAQU1ZbHR42RH48HTbcI4xVAlkkKVZruNubifJA7oDO7hxOkF1k49ThD4\nLN3xRbZc+2sc+sFXcI/cg/HYN5jZvpuxaInOQ9/iummLfRddCllKErjYtkVluMpLP/hp2qnBC9/3\nKSp7r8L9wjspjc2QH9/M4OQmXvbW93HR695HzjYxrnoD23bswrbFrEvVDHZd82Jmxkd5zVvfyXO2\nFEhWT6B35n/5gvMrNNh82hRxd32RM0cO8Pef/m/0ej3e84630um08dp1Tj10JwBD45NMlx38TgNJ\nVlj4+Z285L2f4NKrnskX//w9/Ojj7+BD7383k1OTPHM4ZnrHXp5x7XP5yje/yzU7J/j6wQ3+5q//\nhodu/QdsQrpRwg8OLeNHCYv1LvtPr+GFEWGcoSgKjq6gKTJSlmEbOoO2QJOutz1maz3cUCyWNP1A\nnDwb3lkg0lLLFbfzOYvDS00WG10Kps6eiQGSNGO0ZLHWcun4IV0vOptjXm37dP2Ict5EliQSQFEU\nLto8RDuIOLrSFFb1fv82jGMmB/MULJ3T6x3oMyHcKIY+TTBnilOsuHVX2VktstryxJp5GLHadvug\nLJcjyy0a3QDbULEMCT9MxFYpYtHormPLXLltVCzNdMQJPG/o/b5xwIBtsGeiwoUzFYZyBm6YkDc1\nokzAstJEfM/IwNYVMiSOr7RQFEF3FDxxhVo3II4FEdHWVSYqOYYKJoYikLKylJHEcd+vGfP4mTqK\nIvrgq22X+XqXjU5AFAloFv02WdnRcXShVfMjwX1Zbbk4hkqz47FluICpKZQsnU2VvPi6ev7ZtE0Q\nZyw0eizUu5xeb2P3h7clxyBNU9bbHqoMbhT3H8vwwphukPDEmTq2rmJrCpuGimiKyvxGm7W2R5bB\nZCWHTMbcRg9bU8jrKhdMV5Ay0fLxooyCbZDr3z3NbnSZGrSpVgeZah8iJ/nkCwWO1gOOHVtDVjSG\nh4YgTRhSA4hDDs6tc/7mUYq4qLkCZ+oukVFk5669DFZHaZ85QRpH3PD2D3L5C36NIJE40oSfHpxF\nG55h+zOuIbOKHFzqcOstn2LbM67l4B3fQhvfwV+85y00Fk7SlR1GNu3g3IkStfu+yXRJo3PyEY79\n7Pus1ZsMj09xyUtejzk8jVwY5PF/+BA7w5M8fuvN7LvqerZMjtNbnQNg+oIrGbrgOpBAvf4daIVB\ncH/5ZR/41ZFCPG3aKXEGR+/5AX/+FyID+od//Cd9EE6Bi17yBkBIAOSoh4TMbX/1Hk4dPYgfRdz1\n8EEcJc+td9+DLstccvWzyTsvYqnR5ujXv8zzz5ngdCdlZWkNS05YbYfsnh4BWWFttUa5YKGSMlx0\nKNk6P3lino1eSD5vUnR0Tq622VwtkiYpeVsnzVKabkSzn9aod0SaJAxiTFVGksStmhRlLDZiBnPm\nWcXaYt980+gGIiueiR5rztRZ6yRUciZDedFLf2i2xkUzg/hRzCPzGwzYIv+dM1TCJOWaXaNsdD2a\n3YCGG+AYKt0gFqdeQydKhDxYVWQmK3kMzWOwYOEHMbIs4YYxmqxR7wYM5y1abkTOUBgrOyzWu2d7\nz+MlmyASjtDJco65jQ7DebFynpFR7/ssVVkiZ2jcefgM9V7InvESqRthGgqKJFHvBewaG8BQJBbq\nLn4QE6firsTuiyA2uj62pvajeuDHCQVLZ7HeRVVlskxwxwuWzkrbo5IzqPdCHFOlmrdIERu0hiLT\nCCMMTcMNIrS+bzSMMyQpYsAxiPqLVEN5k5NrLUBidqPHRMVBUWROrLUYLVkcXKxTdixUXWLPeJl6\n1z+btlnY6KDKYm4QpxmWJGxMfpziBwJYlmRCBLJtuEicCYb8xIBDkqbUuj5BnOCFMWfqXTRNRQ8S\nVEXBUBXypsZaz6feC5gsO8jAXDulYqRUCjZtNyJTAtJUoTJQ5Mhyi4ImcW7VYSnQIehQHRqk4UVU\nd1xIq3MvzUhhKi9RW12g3esyOrmTQ3feSlaZoSlLJO1V4rlHcVafQLccBjKPc6++Hm/lNCuzx/Fd\nj5GCxcBkhQe+/ll2XvdKVN3it298EV/4xIfpNev80ae/Tnz6IWp3f5MtL/497vvcR5G3XY4+dD56\noQLA1//y/VTHp5gZn6bbXmf3M1/KrR/9XaykR2bkGLzo+VxwzXOYmZxg4We34k1Pkt9xCWw9n6di\nc/3/gPr877qeNkU8PziCo2YEvTZeGDFcHaG9dJr8yDS12jof+eP38P43vIzLXvduDKfAhS99I/W/\n+yDkB8+qnU7+4B+Z3Hsx+vgm7vj0TXz19oeQFI09e3aTP32KuiOxbaJMKW7yo0eOsrDhcsnWUX74\n2Cw5Q2MgpzFWzNHoBVQLFoam0Qu6lHMG8+sdRko2pibT6Ar7+3DRouWG5E0FSRJLKBVHZ6nlU7DF\nKT5OMixNZqXlI0sSlZzBlqE8p2ttLE1j02Aey1DwoxQZITn2IrFVOTHg0A1iMjLyhgZkYlHF0Vlp\neXSDkFrHxw8FIrcbpoJUCDiGRsNNMTUFRZHxoxiJTKje4oSJAYvNw0XmN7pMlnM4poofxhRMm2bX\nZ7yc68OiUh4/U8NQNQxdDGFPrbW5ZucYectgodHlnMkyR5aaeLEQYggJBCxs9IhTIciIE6FAm11v\nMZwXGW/TkFGRMXWZuVoPQxXFPm+qhGmKJiv0AtGjbroBtqExNiAKV87UKEYJJcdkoxdgqgob3YCx\nkk3OVBktOsRrLQxVuDDNvuCjaBscWWowXLTIUsiylImBHHECGQJ4ZqmK2Iz1Qo6vttk63M/pqxJd\nP+TwYoPtoyXm6z1USWLbSA4vSPD0FMfSyNIMXZWZHCyw0fHwooRmzyfX58KHaYYRJyw3e0Rphi6D\ngkTXj9g/V2Os5KAGEbkwYtNQlaMrTbZWC6JFFgnE8MHFLm6YUC06TA86rHVdcmqG04eKLdQVFjdW\nBc9HVfDVHN0z86iFQZI4ZN2YpttbYmu1QtRZx7r81cz+4BaONuH5l+3j6Hc+i2wXqdga5tQeDp0+\ng93+Ipu3bCUZ3k4cBgydcwVxc4XcxFbu+NEPuO7ivdzwil+nMjFDa+4gA7uuZev2Kznww68hybBp\npIx/4Fs8vnSG4p6rONOJufE3fpeV+75FpFuUqhNMKC1mRge4I9nCfV/9J4pJk3ov4Oen6tTPfIzd\nr/h9Tjxy/y9dbzKEU/ZX4XratFMUVWfXzh0cv+s7vO+tv8Xa/AkWH/kpkddjaLjKX37qs6yuLHH6\ne58THzC7nxe85f3c9o1/IQpDvvSRt/Gj732bTmbw6IFHUE2H9777PezdNsPDd9zG69/+Lqq2xOYd\ne3DkBKs0gq6rdGOJq3eMMZDT+/1B5WzW+8Rqg5WWS5pl5C2dhXqP1baPpMhoskQvjDA0FdsUw8k9\nYwN0AsFBieNEMKoNFS9O8MMY2xBmecEkkRjKm/hxwqm1DqosESUZm4YKQtnlR+QtlXpXSBKWmi71\nrpAe27rGORMVHpmtUeuJPr2gCCbEieCb17o+siSTM3VabkCcJEiSGJwVLJ3xgTwtL8TWhUczjFIU\nSSZIUnpRTMcL8cOYxaZH0xXY2fGSw4BjoKsyTyxscHCxQRilZGlGkqZiENjoESYZ508N4YUx1YLJ\nRCXPlqECJVunaIsWi6kpeEEiTs5xRiVvoGtijT7XN+NIQMuLSbOM8ZLNcN6i7YZsqxapdX3WOyLd\nMV0Rg8DFeo+WG7LS8jix2kJCIk1TKrZOL0zE1macIvexAlGSAhKLTZedY0UMTWGmksONYmptT2CG\nCxYlR0grekFCkkLRMSjnDLYNFxjMm8iSwAQM5kzSFM6ZrDC73kLuI4NNVWZmqEDbDSnYYrGpE0Sk\nGZiaSrWUwzQkojTl8q1VtlULTJRz7Bgr8/hiXfhHVQXb0Flv+wRxzPTuC9g0XMALQwxFZiwvRBZx\nmmHqKq4XsWWkSN6xObQuDE1FekwVE2S3SWP2CKeW65Q376FQGSKtL1EYnuDimQFOnTiG57scOXEC\nOfYoaSmWZVPaei4HH7qHTn2Vbc96OYblMHHp82g8fhdXXPss7N1Xct9PbuPwdz+D7p0hay4jPXIr\nnfljFIpllk4eoXLDO6G1QnDkbl71spfQe+IOsiRh+cHbaP7ks1zwrs/ync4oF11+Le/8s79lcGIT\nI5Mz/Mbb3sPlN7wai5ALrnvxU1JzflXaKcpNN930H/1vAODmm2++6TmXX8T8sSd4+bUX4i0eZ/uL\n34zS9/3N3v4VclO7WF1dYfKcS9AqEzRPPspFu7dS3LSXnZc+i2q1yuwPb2ZlfYnGiUPkxzZz13e/\njlMqkwHf//5tyK0l7jq6zMJanbxl4GUyFRNOrLTQ+r90miyf1aoJOJKFqSoYqkQQixaFJAnkq4iG\nQRgJKFOSZAzlLY6vtqk4hrDO2PpZDGsvTBgp2jTcAEWRKVoaaSYWhQxNiAV0VRUigVjEASdKFvum\nKzx4ap3towOcXG326XmBKIi6ysyQwPUWbAM3iBjKW6RpiipLTJbzuEGEogihwYmVJk0vpGz3IVyJ\nMPWcafQwVAlZEsXFjxJypjDUGKosDEH95aXF/nYoWcYFm4Y4ud4hyRIGbJPBnMlSy0OWJbIM8ULW\nl1lUcqbwcvbbM2TCfDQ14NANxQvgcN6k2QvRVDEATdLsrAItZ6g03KC//alQ7wXIksymQedsrNHS\nVRRZRpElbEPDj1PKOZ1OP/c/M1ggTjJRTL2Ysq2J7VhNRlFkjq+2Rbum5bJ9pMhKy6doG3hhjCzD\nWMkhjlOOrbYwNZUoTIiyVGTV/YDFpvB7rjR7SJJE0w0FW16VWe/6DOUtzjR6IgWVt5AlcMMEKRPz\njycF2a4fEqcpoyUbGTEb6PkRnSBkAJckzSjnDMZKDkuNHkNFwUwf2XspcncNTZHpGGW8doPRiQl6\nG6soWcZoTieSNUbyBorfYlDPiDZfxhP334Vl56lIPdqpTlocY2bvhWzacz65ie10/YRmo8HCoz/D\nGBhh6adfoJJ2OHXocdS1Y5w6+Bjn3vg25tZ7yJ062cpRnGvewEP791Nwlyntu458Z44CLmdmT3HX\nvfehaRqtpdOsq4Psed6v4935d1TDBj/+xhcZ0wOO3/YFjv38bs5lDnXqXLL8CA9++x+55bt3Ld90\n0003/y/Kyf/2+vCHPnSTrir/+3cEgjj5pT7X/9fX06adArDzeb+BZt3K2GXPZ+3QA3TXFtCtHLVD\n9zG//y623LALxREo3js+81F2XnApo1e+hAd+9C3CwOeiK59FZ+U0l1xzA62NDe780qfwVZsrpysc\nfeQ+as02k5fu5vlveidyr8Fn/v5mjs0t0i5NYeg1nr17nLWOzwMnVynYBs/aNcb3Hp0jA7xYJCN0\nVQIUZBncKEVTUvKWSc7UGciJLc8sg9GiKeh1GRxZbjFStHC7MdtGS+iqxLbhAhmczVRLEpiaTi+K\n8cKETcM5VFmmaGpEacYTZ+psGS5wfLkBksCybhsp0nR9dFXlzEaXpabLTlWh7JgsNnqMlhwsQ+OR\nuRoZGeMDNpamUc5Z7BkvcabuoqkyCbBS77FnrMTRlTY7R/I0/YTRkhigPvEkB50MU1VIkpQ9YwMs\nNETa5vuPLbBrrIQXxmRIrLZctg7nOLXe5fzpCsfXWrTdmC3VPM1eSBAn1LsBA46BnGWs9wJOpl1M\nTSZNEuZqXfw4QZYFXTFNoeRoRHFKkopb4VrXo1q0ObrSwo9FOylKM7Io5pxqmeW2AHFliPX7NE0x\ndZWNroheSlLGqbUOF8wM0vFDRiyDZt8vumd8AF2RKOcM1ro+pq6SM1W6vogKpllGJ4iEpzRO6SUx\nVl/4jJRBJjjw9Z6PrSnkDIdeEFDJixfvhY0OmowQiCgSvVDcQRUdsQg2mDfF4pgXMuCYdIOoP8RO\nKOUNhooWiqETN7s4usr+2XXWOz6dPtwsfOIBnNHNzJ86wnA+RMlitg0YrFvb2Thzmm8cmGXf9CC6\nYdPpdMjigHT9FlK/y+kFlyFbwynEVN15yptewuNPPMLeK5/L0vJJStM7aa/MMfu9m6nFBj8/PIfr\nBUz0Ep5z4w0cf+wesrmHGbruBehhm96tf8rO0RGO3D7P+NRJnrjnVs5/1e+RZmNcqNzFKOso51+D\n99PvsHH3l5FXZ0nQeM6Nr6N63jV4qcxF+66gdd+X0BKDn3/2w0xPjf7StUZkwJ/+p+x/z/W0aadE\nnQYHvnEzx7/3eZLQx66MoFl5FMPGqc5w7Xs+xQNf/AS9jSXaa4s80rGoZzZpBhc/+0Vc8fyXY5UG\n2X9knsP33YkWddnxzJdxfKXFM9/0RwwWHN7yoit48NEn+O//5X184Utf5PTSKqacsb44T9ePObTc\n5MGTq3ihkO4+MlvD1ARPu94JiJIETVHRFJlNgzkkBC+11vHY6AXcc2yZxUaPWtdnpGSTANWixdZq\nAUWWyJs6iiSxWHdxw4TZWhdZhpKjc9WOKh1fSA5UGdbaPs2ez0jBpNkLMFQFQ5XJWzp7x8uUbYOO\nL3jbaZpSsHW2jRTxooSiozNatBkpWLTdAMsQWNpnbK4iSxJxImBYBVtDkgTtz9Dks1b7tW5Iy/Vp\nuQET5ZwY2iVCsdboBRQsHS9KyBkqeUNlfMBmsdFjo+Mzu96iWrDp+ALsdHipiYxEnKTU2h4tL2C0\nZHPV9hEMVcY0VAbzQu325Fq8H8UM5wTJ78rtVTYP59FVhV3jAziGKtoypk6rF3DOeJnhvImlK8KH\n2gsYyBlMVXLUewESEiNFkzQToKucqbHU6JE3dSo58T1c2Ojxo0NnyKSM86Yq5E1xMrd0lWreZsAx\niBPhHO307x7qvYDdYwNU8oZY/knFdmrREvHEY6stqgWLQ0tNwUo3dA6dqVPOmeRMDccUrSVbU2l7\nAaYm4qmOqSH1CYxZ/xQPgAR+lJLXNfKWhprFqH11naUrTFfyFB2DmaE8lq7QWZlD7tMCJSTuO3ic\n4kCFetdjYnQQx7LQlZShqU105DwyGZduG6OkZVQLJkXJBdXg4X/+rzjLj+HXlzhzzzcoDI/y8NwG\nxpaLmRkqcP4FF/GKd3yA0c07iI7fD61Vjhw+xPEf/wvxxiwnqlci5au89B0f5sIxh3IxT3Dq58wM\n5vHaPU66OZzSIOe9/K3UnSmO2zsYu+alaO3TRIe/RTHcwF08zjwV5m7/IgU5RNp0yVNSc35VbPdP\nm5N4GvnEeo5zfuO9JEisrq2zfXI76/d/myzyiQYqTF38bNYP3MHjX/gom7OIKPAAYZvp1ddYOryf\noeltjJ1/DQcPPsbpJ37OJ9/3O7hRygOPHmJbNU8pn+PYYo32mQWu3DXJHQdOoCgqhipxeLGBrYu+\ntRcmrLebOIaOKsGmoTzDBYtT622COOHoShuQeMF5U/zs2DIF2+D+E2tMVXLkTa2/Um8QJRnr/eia\nRMZKq4cqK0R9W70XJOwZc4hiGClYNL2QnKVTMDWCOGW17aGoCqsdD0NROF0T8aqCpSFFMbauESYJ\nnY5PnIrBZ6PrMzaQY7kl8J0bHZeZoQL7Z2vkTJWibbDedrF1jZbnYqoKYyVBKdw3NciJ1SYtL2R+\no0vDFWTDHSMFVlseB+bXgYzBJ4eTmpAWn1xtc85EGVVVWGp0CWJBVxRS+UwU5nyRk7UOtU7AXK3D\nYN7i1HqH4ZyBZWqstl0cQ2O8nGN+o4vt98USXoipydQ6rohwJilFR6cXxtS6YtlqqemSNzV2j5eR\nJAlVkqjYuhAyWBpdL8LSUgbzFpX+MNQNhIhjqCBIk3q/p5ymGTlTI04yVFmi44cYqsrptRalPj/c\nUhU2egFZKpRwGbDe8Tl0ZJltI0W2DOVp+RE7RoocmN9gwNao5C06Xki1YKHI4Og6i01XOEEdHTeI\nsTWFMElwg4hNQwWavYBeGJPPGRRtC0dXmKt1iFMR6XySUX7epiGOLzVY64aULbHrMDWQp+7FlPIK\nJxbXKecfo+0HbB7M41tVRnMhba3M9L7dtA78hMAVEVW1OITXWGF1uUZBSSmOTdPudBm7/nUcufNb\nXLVjnPaJn1OQExTT4Oi//g3FPdfgpzVG1ZgL952H7lh0M4uSpeE3azzyzR9QcHRmpqcoTG8h0UyW\nApU9pSLufV9ipZthumtc+Jo/wRyeYmH/E0w4Ka5WYvSSF+Gc06M3d4isV6e079lPQcXJfmXW7p82\nRVyx8lz0wtcA0G3W8XtdusceRFJ13Lkn8BePsvs5b2YBn5FzLuOix7+F8byXAzB36gT/+oXPcc24\nygve9GF+9rfv5qg0xjXPfiG3fea/Uhk7hpSrcGRplUYkM+DobNu+g6zX4lU3XM/a/Gl+vP8ojm0w\nWXaYKJkcX22JXnciIE/dKIU+T3zAMZjfEKvW39w/y0jRZrXpMVnOsdb2qBas/uKKRppl5EKVXhgT\nRqK94qYJuiqSHooisdYLWF1qYmsquqbg+hFtN6RatLBMof6aGSzw+PwGjqERpwkLdSGpGC05SJH4\nHuZ1laYf0XJDoIdjahiKQssThL+iI057J1ZjMZnPApquz56JCmmaYGoqKy2Xrh9TcUwAZImzWWdV\nVciQ2Tk60Oepw8m1Fm0vYmu1QM7UOXJqlYs2D3PVjjG+c2CO9Y5L24u4cGYIL0oYK1n4UUqcqgw4\nJiNhDMD0YI4ojjF0YeqJkxRN1ji53oYsQ5F1VttuvwgbtP2QwbzA1bphjCxBlCS4fsSRpSaWptIL\nE2Q3YPNQHj9MyIDlpsuFM0P4UcLm4TxLDZeuF7F9tIQfJdT6oufHFja4aNMQbn94LUlQdHS2VUss\nN10cU2MwJ1JCvTBhsuKwZ6KCG0TUez6OrnH/yTU2D+UZKtokSYosCZlIx4/wowQvTMjpCqqcQSbY\n82XHJIgTFCQRDR0u0HFFCyqVDfwoIM4yBhyTuY0OIKGpMo+cXGOwXGK4rJLXZTZaXfGzNzzBQFij\nkHN4+NQaSxstkjhjy4iEUp2AlTOszz+BKkMzVhndtI3u6pyQV2Rgj0wz8ZLf58EvfJzNRYWdu/ey\ncOBurtg5ybH5Neqriyz4OtdMbubBL34DOYu45rf/mOzgt1ibPcRgWMfwI8qbJjFKI9SO3sPcgftw\nN1bYXS1RdJcxkzYmOh3JInR7xLNPMDY+zsmDjxBoDoMHfszJ224hKm9m08QIG4/98mUr4ymz3f+H\nX0+bdopq5fAXj9I7/iC5Upm92TykKXLrFIHm8ODjR+nU12mFGXf+tw/QLP5CLD21aQtvfPu7mHrm\nq4ijiNXiTtqP/oScFLJlz3lUxiYpVsd53dvfw40vv5E/v+WbvOMjn8C0DGS3QQObsZFhwjCg54vi\nW+y3HyYGHHKmji5L1PsM8SCIIcvYOlzE1lWWGj38KKbW8bh+zzhrbZeVtsd62yNJMiGNSDJURSFn\naqiKjKOpeEFMvSd6tFuHi+iKhKHISLKMrYuFl6YbMFI0afREW8TWZCo5i4kBh4KlU7bFEpKhKKy0\nfc6fHqKU0+kEQpJc77psqxZZanrce3yNejdE62+VIomhp4TERLkgFlokMDQFRYairXFyrSXAUEnG\nYr3LzGAeCZBlOHimTpZKXL9nAlWRqXU9xgZsFBnuO7GMLGXoisJw3iJniuy3H6ZEccrVO0RfU5Vl\nTFWh3vWRJInlZg9TVZgs5xgumuwYKREl4uQ/Wc4z0t9sLJgaXiR65I2eEO8O5S3ytkbRUtFUifEB\nh6Ybst7x6flicLzWdnlsvkbe0tAkiZlKjn0zg4RxQsURkuvFeoeJsk2SpuwYHSBOUiQEkXKx0WOl\n2fsf7Z17kFzXXec/5z67b7/fPT0zGs1oRvLIlmyPR7axk9jGzgNSeClsEkJtqqACyS4b8scWFQO7\nVbDsbrFxFbUUELIOyx8UJBQ4FQqCl0CEIfHGj0R+yLIlS5ZGmve0uqff79v3nv3jtrNer+2k0ETW\njO+nqmumz5zuPqen69e/+zu/3/dHNmIymYxgqiqu9Jo/fOnJc/zzmQ1WSi1c1+VDi/sZj4exhw6Z\nSJDlcpP1aptCwvJEvQyNZCQ4atnmMJOJkY4EqHYHFNIRTlwss1pq8tJqhW5/SEj0afYGpEIm4YDO\neCLMXD42OiyGTruNtG1sF2KhAKlMBDOe4+RajQA2haDCe6bT5CZnyB7+EZLtFmG6uKrq6cLYPYbt\nGqauERmfxY3k6XY6rD/+ZaKKTXvlNIPlk0yNJ6nrGeITsyTiMcaUFht/87tMJoMc/rnf4sLjX6G2\ntk4okSU5doDsvgNUKjWIZIkt/BSZmXksQ6HV61EnwFo3SCKZIpuIsvL3f4wbybBSt9m48ApBTWGr\ntI2x8K+45dgxXAnZxQ/uiM1xf8Dbtc41Y8Sxe2BY1Fe9sntx288Qvu52olOHiE/MkoxFCMVTuCsn\nuemn/x3L5db3HiqEYNhusPzow5x+4u+YCvS4++57+J1f/wzDrfP802Nf5frZKe744E/wwC98mkf/\n+2/yzc//B0LxNAvzczSbLX7tt/4bN85OYRkqm7UOPdvLTV6cThEJaJ5yoW3T6g5YH2VebLd71Dpe\nFoWuKsQsgxOXSkylo6RDBkJKHAlB04tj1jsDuoMhE8kQE+kwmUgQKQXH9meQErIxi1p3QKM7wEXQ\nH9jomsKJi9sMh453qe1Cq2cDXoiiN3Q4W2wggVum06xut4gFTBKWiSuhNXCotHpUO11mMlEaXU+B\nsBAPsS8VGmmNCy4U61TaA9Yqbc5sVNiodegPHTKRAF3bJaB7bdUiQYNis0urZxMK6MRDBr3BEAW4\ndSZDImTyarHJ+WKDVs87sJXSU0rMRIM4rqRre80WNipNMmETiaTZ8w6OF6YyrNfauNKlM3BZKtUx\nNIVOz8YeOoQMjXObNdYqbVYqLTQFDnVDr0IAABEySURBVGSjuK5Xll9rD6h1h8xkorhI0uEA3b5N\nLmFxeCzO0YkEybD3/piGTrnV5dmlEgFdpdbpkwyZDCXkoxYHsjFe2ahRavZ4frlEJhIkH7MQisCR\ngv91coWhlNx7eILJZBgX73wjHQsycCVnV7Z5YaVMNhJks94hHfE6R1mGxlg8RNwyObtVw3VBVRW2\nWx0uNzqYikq53mYsFiBmmSRCJlIR9G2X9kjbpWMPSYYDrNXaKEIwMxbFdl22am3Ob1Q4X6zTbrsM\nLj7HxXKTYlcyVFTS0QBRe5uzzz1JZTCk2u5zZCqBrkrisQC3TQZxhSAeCZHob+K0q5jJLNPv/1mK\n9Q6oKmPX34Fmd5gw2sTTWRTdIHvzvYhQgsDJLxOTVbpakG51lY2TT1CLz7Hdk7z64ncJzy0SG5th\nbGI/h2f2Mf+hjzP34L+n4eiUN9foShWx8izxjWeIFPZzw7E7maq/zMG4QLvlJ4nc/IHvxfqvCLl3\nUgzFTi1SCBEHFoEFKeXDo7EFRp2dpZTH3+7xi4uL8jvPPMWpf/gKl5s97rn/I2w98RWKZ76LkZuh\nGNzHU49/ncM3HOG9P/4A/comkzccA+DP//MvE4rGmb3tbg7ffi/Vl57g1KO/D8lxdNPisSdfBFUl\napksXVrhvvkcL6xsY0zdSN92OP3SKZLREJ/40K386dceZ3OryOK+JMsdlc3N9dGluORiuUUi5Ikn\nRQIGY3GL4dCh2OjyI3N5Tq6UcR0XF++SGQG6opCPB3lls04koNPuD0mGTEzNEz0JGxqmqqJpCueK\nDW6aSlFp9QiZOroqiAUNLpWaGLrGpXKTwdDh8FicnutSa/WodWzm81FsCYcLCb5+ao1qp8dcNoZl\n6sSCOpoq2Kx51Y1CKHT7No6UGJqKIiQBXWfoeprosaCnky0UhblcjICucb5Y5+hkkoHj0uh6yoCl\nUfbHZCqMqXtZH69JFqTDJiuVFrGgSbvbZ6XWZTxuoWuCgOY1QxZCcGg8jiYUXt2sslxpc+dcHk0R\nLJcaJCNBzzsUELcMHAn1zoB9KS9ejoBoQEdVBEMXkiGduGWydLlBJhJkqdwkHwsSMr2DTFVVuGky\nxcVSk9lclHTYa6WXjQV55sIWY7EQ260+05kwxUaHhBVkKh1mq9alOepm1Oj0GQwd2oMhQgIKBDUN\nV8B2vUffcSjWu8zmYoynvXzzTmdAImRSavSYykTYqHhhjlQkiKEJHMfbV3tgE7cMmr0hAU0lEjJA\nUQgAQ8el0hkwFrfYqnYwTZVivUs6EiAW1BGKRrnRIRGxKNdbWKZOuztgpdJmJhNhMpeg1uzS7g1Q\nVYWIqeGoBsNeh+4QCgmLvtAxUjm2S9sY/TpSwGbTRUl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"text/plain": [ "<matplotlib.figure.Figure at 0x115730750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#------------------------------------------------------------\n", "# plot the r vs u-r color-magnitude diagram\n", "u = data['modelMag_u']\n", "r = data['modelMag_r']\n", "rPetro = data['petroMag_r']\n", "\n", "plt.figure()\n", "ax = plt.axes()\n", "plt.scatter(u - r, rPetro, s=1, lw=0, c=data['z'], cmap=plt.cm.copper,\n", " vmin=0, vmax=0.4)\n", "plt.colorbar(ticks=np.linspace(0, 0.4, 9)).set_label('redshift')\n", "\n", "plt.xlim(0.5, 5.5)\n", "plt.ylim(18, 12.5)\n", "\n", "plt.xlabel('u-r')\n", "plt.ylabel('rPetrosian')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11576f0d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#------------------------------------------------------------\n", "# plot a histogram of the redshift\n", "from astroML.plotting import hist\n", "\n", "plt.figure()\n", "hist(data['z'], bins='knuth',\n", " histtype='stepfilled', ec='k', fc='#F5CCB0')\n", "plt.xlim(0, 0.4)\n", "plt.xlabel('z (redshift)')\n", "plt.ylabel('dN/dz(z)')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Now refer to galaxy angular correlation functions and power spectrum from two micron all sky survey by AH Maller DH McIntosh N Katz" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NSIDE = 512\n", "ORDERING = NESTED in fits file\n", "INDXSCHM = IMPLICIT\n", "Ordering converted to RING\n", "NSIDE = 512\n", "ORDERING = NESTED in fits file\n", "INDXSCHM = IMPLICIT\n", "Ordering converted to RING\n", "NSIDE = 512\n", "ORDERING = NESTED in fits file\n", "INDXSCHM = IMPLICIT\n" ] }, { "ename": "ValueError", "evalue": "total size of new array must be unchanged", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-12-732d6b0eedc8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;31m# Fetch the data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mwmap_unmasked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfetch_wmap_temperatures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmasked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0mwmap_masked\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfetch_wmap_temperatures\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmasked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0mwhite_noise\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.062\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwmap_masked\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/rohin/anaconda/lib/python2.7/site-packages/astroML/datasets/wmap_temperatures.pyc\u001b[0m in 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fetch_wmap_temperatures(masked=True)\n", "white_noise = np.ma.asarray(np.random.normal(0, 0.062, wmap_masked.shape))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# plot the unmasked map\n", "fig = plt.figure(1)\n", "hp.mollview(wmap_unmasked, min=-1, max=1, title='Unmasked map',\n", " fig=1, unit=r'$\\Delta$T (mK)')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# plot the masked map\n", "# filled() fills the masked regions with a null value.\n", "fig = plt.figure(2)\n", "hp.mollview(wmap_masked.filled(), title='Masked map',\n", " fig=2, unit=r'$\\Delta$T (mK)')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#------------------------------------------------------------\n", "# compute and plot the power spectrum\n", "cl = hp.anafast(wmap_masked.filled(), lmax=1024)\n", "ell = np.arange(len(cl))\n", "\n", "cl_white = hp.anafast(white_noise, lmax=1024)\n", "\n", "fig = plt.figure(3)\n", "ax = fig.add_subplot(111)\n", "ax.scatter(ell, ell * (ell + 1) * cl,\n", " s=4, c='black', lw=0,\n", " label='data')\n", "ax.scatter(ell, ell * (ell + 1) * cl_white,\n", " s=4, c='gray', lw=0,\n", " label='white noise')\n", "\n", "ax.set_xlabel(r'$\\ell$')\n", "ax.set_ylabel(r'$\\ell(\\ell+1)C_\\ell$')\n", "ax.set_title('Angular Power (not mask corrected)')\n", "ax.legend(loc='upper right')\n", "ax.grid()\n", "ax.set_xlim(0, 1100)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
GEMScienceTools/rmtk
notebooks/vulnerability/derivation_fragility/R_mu_T_dispersion/SPO2IDA/spo2ida.ipynb
1
13335
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# SPO2IDA" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "This methodology uses the SPO2IDA tool described in [Vamvatsikos and Cornell (2006)](http://onlinelibrary.wiley.com/doi/10.1002/eqe.573/abstract) to convert static pushover curves into $16\\%$, $50\\%$, and $84\\%$ IDA curves. The SPO2IDA tool is based on empirical relationships obtained from a large database of incremental dynamic analysis results. This procedure is applicable to any kind of multi-linear capacity curve and it is suitable for single-building fragility curve estimation. Individual fragility curves can later be combined into a single fragility curve that considers the inter-building uncertainty. The figure below illustrates the IDA curves estimated using this methodology for a given capacity curve.\n", "\n", "<img src=\"../../../../../figures/spo2ida.jpg\" width=\"500\" align=\"middle\">\n", "\n", "**Note**: To run the code in a cell:\n", "\n", "1. Click on the cell to select it.\n", "2. Press `SHIFT+ENTER` on your keyboard or press the play button (<button class='fa fa-play icon-play btn btn-xs btn-default'></button>) in the toolbar above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from rmtk.vulnerability.derivation_fragility.R_mu_T_dispersion.SPO2IDA import SPO2IDA_procedure \n", "from rmtk.vulnerability.common import utils\n", "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "---\n", "### Load capacity curves\n", "\n", "In order to use this methodology, it is necessary to provide one (or a group) of capacity curves, defined according to the format described in the [RMTK manual](../../../../../rmtk-docs.pdf). In case multiple capacity curves are input, a spectral shape also needs to be defined.\n", "\n", "1. Please provide the location of the file containing the capacity curves using the parameter `capacity_curves_file`.\n", "2. Please also provide a spectral shape using the parameter `input_spectrum` if multiple capacity curves are used." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "capacity_curves_file = \"../../../../../../rmtk_data/capacity_curves_Vb-dfloor.csv\"\n", "input_spectrum = \"../../../../../../rmtk_data/FEMAP965spectrum.txt\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "capacity_curves = utils.read_capacity_curves(capacity_curves_file)\n", "Sa_ratios = utils.get_spectral_ratios(capacity_curves, input_spectrum)\n", "utils.plot_capacity_curves(capacity_curves)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Idealise pushover curves\n", "\n", "In order to use this methodology the pushover curves need to be idealised. Please choose an idealised shape using the parameter `idealised_type`. The valid options for this methodology are \"bilinear\" and \"quadrilinear\". Idealised curves can also be directly provided as input by setting the field `Idealised` to `TRUE` in the input file defining the capacity curves." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "idealised_type = \"quadrilinear\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [], "source": [ "idealised_capacity = utils.idealisation(idealised_type, capacity_curves)\n", "utils.plot_idealised_capacity(idealised_capacity, capacity_curves, idealised_type)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Load damage state thresholds\n", "\n", "Please provide the path to your damage model file using the parameter `damage_model_file` in the cell below. Currently only `interstorey drift` damage model type is supported." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "damage_model_file = \"../../../../../../rmtk_data/damage_model_ISD.csv\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "damage_model = utils.read_damage_model(damage_model_file)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Calculate fragility functions\n", "\n", "The damage threshold dispersion is calculated and integrated with the record-to-record dispersion through Monte Carlo simulations. Please enter the number of Monte Carlo samples to be performed using the parameter `montecarlo_samples` in the cell below." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "montecarlo_samples = 50" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "fragility_model = SPO2IDA_procedure.calculate_fragility(capacity_curves, idealised_capacity, damage_model, montecarlo_samples, Sa_ratios, 1)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot fragility functions\n", "\n", "The following parameters need to be defined in the cell below in order to plot the lognormal CDF fragility curves obtained above:\n", "* `minIML` and `maxIML`: These parameters define the limits of the intensity measure level for plotting the functions" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "minIML, maxIML = 0.01, 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "utils.plot_fragility_model(fragility_model, minIML, maxIML)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "print fragility_model" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Save fragility functions\n", "\n", "The derived parametric fragility functions can be saved to a file in either CSV format or in the NRML format that is used by all OpenQuake input models. The following parameters need to be defined in the cell below in order to save the lognormal CDF fragility curves obtained above:\n", "1. `taxonomy`: This parameter specifies a taxonomy string for the the fragility functions.\n", "2. `minIML` and `maxIML`: These parameters define the bounds of applicability of the functions.\n", "3. `output_type`: This parameter specifies the file format to be used for saving the functions. Currently, the formats supported are \"csv\" and \"nrml\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "taxonomy = \"RC\"\n", "minIML, maxIML = 0.01, 2.00\n", "output_type = \"csv\"\n", "output_path = \"../../../../../../rmtk_data/output/\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "utils.save_mean_fragility(taxonomy, fragility_model, minIML, maxIML, output_type, output_path)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Obtain vulnerability function\n", "\n", "A vulnerability model can be derived by combining the set of fragility functions obtained above with a consequence model. In this process, the fractions of buildings in each damage state are multiplied by the associated damage ratio from the consequence model, in order to obtain a distribution of loss ratio for each intensity measure level. \n", "\n", "The following parameters need to be defined in the cell below in order to calculate vulnerability functions using the above derived fragility functions:\n", "1. `cons_model_file`: This parameter specifies the path of the consequence model file.\n", "2. `imls`: This parameter specifies a list of intensity measure levels in increasing order at which the distribution of loss ratios are required to be calculated.\n", "3. `distribution_type`: This parameter specifies the type of distribution to be used for calculating the vulnerability function. The distribution types currently supported are \"lognormal\", \"beta\", and \"PMF\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "cons_model_file = \"../../../../../../rmtk_data/cons_model.csv\"\n", "imls = [0.05, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40, 0.45, 0.50, \n", " 0.60, 0.70, 0.80, 0.90, 1.00, 1.20, 1.40, 1.60, 1.80, 2.00]\n", "distribution_type = \"lognormal\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "cons_model = utils.read_consequence_model(cons_model_file)\n", "vulnerability_model = utils.convert_fragility_vulnerability(fragility_model, cons_model, \n", " imls, distribution_type)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Plot vulnerability function\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "utils.plot_vulnerability_model(vulnerability_model)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Save vulnerability function\n", "\n", "The derived parametric or nonparametric vulnerability function can be saved to a file in either CSV format or in the NRML format that is used by all OpenQuake input models. The following parameters need to be defined in the cell below in order to save the lognormal CDF fragility curves obtained above:\n", "1. `taxonomy`: This parameter specifies a taxonomy string for the the fragility functions.\n", "3. `output_type`: This parameter specifies the file format to be used for saving the functions. Currently, the formats supported are \"csv\" and \"nrml\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "taxonomy = \"RC\"\n", "output_type = \"nrml\"\n", "output_path = \"../../../../../../rmtk_data/output/\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "utils.save_vulnerability(taxonomy, vulnerability_model, output_type, output_path)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 0 }
agpl-3.0
mattgiguere/doglodge
code/create_ta_db_tables.ipynb
1
5429
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# create_ta_db_tables\n", "\n", "a notebook to create the database tables for the ta data." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import connect_aws_db as cadb" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "engine = cadb.connect_aws_db(write_unicode=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "conn = engine.connect()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### create the hotels table" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cmd = \"DROP TABLE ta_hotels\"\n", "result = conn.execute(cmd)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cmd = \"\"\"\n", " CREATE TABLE ta_hotels\n", " (\n", " hotel_id MEDIUMINT AUTO_INCREMENT,\n", " hotel_url VARCHAR(512),\n", " hotel_img_url VARCHAR(512),\n", " hotel_name VARCHAR(512),\n", " hotel_address VARCHAR(1024),\n", " hotel_city VARCHAR(512),\n", " hotel_state VARCHAR(32),\n", " hotel_rating INT(11),\n", " hotel_latitude FLOAT,\n", " hotel_longitude FLOAT,\n", " hotel_price FLOAT,\n", " business_id VARCHAR(256),\n", " review_count INT,\n", " dog_review_count INT,\n", " PRIMARY KEY (hotel_id)\n", " )\n", " \"\"\"\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<sqlalchemy.engine.result.ResultProxy at 0x10668c990>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conn.execute(cmd)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### create the reviews table" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cmd = \"DROP TABLE ta_reviews\"\n", "result = conn.execute(cmd)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cmd = \"\"\"\n", " CREATE TABLE ta_reviews\n", " (\n", " review_id MEDIUMINT AUTO_INCREMENT,\n", " hotel_id VARCHAR(256),\n", " business_id VARCHAR(256),\n", " biz_review_id BIGINT,\n", " biz_member_id VARCHAR(128),\n", " username VARCHAR(128),\n", " review_title VARCHAR(255),\n", " review_rating INT,\n", " review_text VARCHAR(5000),\n", " review_date VARCHAR(512),\n", " PRIMARY KEY (review_id)\n", " )\n", " \"\"\"\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<sqlalchemy.engine.result.ResultProxy at 0x1066da990>" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "conn.execute(cmd)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<sqlalchemy.engine.result.ResultProxy at 0x106709910>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cmd = \"ALTER TABLE ta_reviews MODIFY COLUMN review_title VARCHAR(255) \"\n", "cmd += \"CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL;\"\n", "conn.execute(cmd)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<sqlalchemy.engine.result.ResultProxy at 0x106709e50>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cmd = \"ALTER TABLE ta_reviews MODIFY COLUMN review_text VARCHAR(5000) \"\n", "cmd += \"CHARACTER SET utf8 COLLATE utf8_general_ci NOT NULL;\"\n", "conn.execute(cmd)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
WenboTien/Crime_data_analysis
exploratory_data_analysis/.ipynb_checkpoints/SVM_Regression-checkpoint.ipynb
1
6188
{ "cells": [ { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import sklearn\n", "import pandas\n", "import numpy as np\n", "from sklearn import linear_model\n", "reg = linear_model.LinearRegression()\n", "reg1 = linear_model.BayesianRidge()\n", "reg2 = linear_model.RANSACRegressor()\n", "reg3 = linear_model.LassoLars(alpha=.1)\n", "reg4 = linear_model.RidgeCV(alphas=[0.1, 1.0, 10.0])\n", "reg5 = linear_model.ElasticNetCV()\n", "reg6 = linear_model.SGDRegressor()\n", "reg7 = linear_model.PassiveAggressiveRegressor()\n", "reg8 = linear_model.TheilSenRegressor()\n", "reg9 = linear_model.HuberRegressor()\n", "from sklearn import svm\n", "clf = svm.SVR(C=1.0, epsilon=0.2)\n", "models = [clf, reg, reg1,reg2,reg3,reg4,reg5,reg6,reg7,reg9]\n" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\n", "\n", "dataframe = pandas.read_csv('../datasets/UCIrvineCrimeData.csv')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Cleaning the data" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\n", "dataframe = dataframe.replace('?',np.NAN)\n", "dict1 = dataframe.isnull().sum().to_dict()\n", "non_zero = []\n", "for a in dict1.keys():\n", "\tif dict1[a] > 100:\n", "# \t\tprint a\n", "# \t\tprint dict1[a]\n", "\t\tnon_zero.append(a)\n", "\n", "# print non_zero\n", "for elem in non_zero:\n", "\tdel dataframe[elem]\n", "\n", "# Perhaps its better to remove this row.\n", "# No reason in removing whole column.\n", "dataframe= dataframe.dropna()\n", "cols = list(dataframe.columns.values)\n", "\n", "cols = [ x for x in cols if x not in ['fold', 'state', 'community', 'communityname', 'county'\n", " ,'ViolentCrimesPerPop']]\n", "# cols = ['numbUrban', 'NumInShelters']\n", "for i in xrange(len(cols)):\n", " for k in xrange(i,len(cols)):\n", " cols1 = [cols[i], cols[k]]\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Getting Training Data and Training Labels. (Training = 2/3, Test=1/3)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.2 0.67 0.43 ..., 0.23 0.19 0.48]\n" ] } ], "source": [ "X = dataframe[list(cols)].values\n", "total_val = len(dataframe['ViolentCrimesPerPop'].values)\n", "percent = 2/float(3)\n", "edge_val = int(total_val*percent)\n", "# print\n", "# print reg.fit(X, dataframe[cols[-1]].values)\n", "Y = np.asarray(dataframe['ViolentCrimesPerPop'].values)\n", "print Y\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Performing Regression" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "for model in models:\n", " model.fit(X[:edge_val], Y[:edge_val])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Predicting on Test Values" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "y_predict = [model.predict(X[edge_val:]) for model in models]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Calculating Error of Estimate" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Error of Estimates\n", "[0.019638199850900737, 0.017404206681288809, 0.016869644773174153, 0.14612669450461907, 0.046194355413773855, 0.016994298317673882, 0.016962308052769402, 0.017372763574437011, 0.018487657969123761, 0.017071350383533343]\n" ] } ], "source": [ "error = [0] * len(models)\n", "for i in xrange(edge_val, total_val):\n", " for k in xrange(len(models)):\n", " error[k] += (float(Y[i]) - y_predict[k][i-edge_val])**2\n", " \n", "print \"Error of Estimates\"\n", "error = [er/float(total_val- edge_val) for er in error]\n", "\n", "\n", "print error\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
cathalmccabe/PYNQ
boards/Pynq-Z1/base/notebooks/video/opencv_face_detect_webcam.ipynb
4
284967
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# OpenCV Face Detection Webcam\n", "\n", "In this notebook, opencv face detection will be applied to webcam images.\n", "\n", "To run all cells in this notebook a webcam and HDMI output monitor are required. \n", "\n", "References:\n", "\n", "https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml\n", "https://github.com/Itseez/opencv/blob/master/data/haarcascades/haarcascade_eye.xml" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 1: Load the overlay" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pynq.overlays.base import BaseOverlay\n", "from pynq.lib.video import *\n", "base = BaseOverlay(\"base.bit\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 2: Initialize Webcam and HDMI Out" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# monitor configuration: 640*480 @ 60Hz\n", "Mode = VideoMode(640,480,24)\n", "hdmi_out = base.video.hdmi_out\n", "hdmi_out.configure(Mode,PIXEL_BGR)\n", "hdmi_out.start()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# monitor (output) frame buffer size\n", "frame_out_w = 1920\n", "frame_out_h = 1080\n", "# camera (input) configuration\n", "frame_in_w = 640\n", "frame_in_h = 480" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Capture device is open: True\n" ] } ], "source": [ "# initialize camera from OpenCV\n", "import cv2\n", "\n", "videoIn = cv2.VideoCapture(0)\n", "videoIn.set(cv2.CAP_PROP_FRAME_WIDTH, frame_in_w);\n", "videoIn.set(cv2.CAP_PROP_FRAME_HEIGHT, frame_in_h);\n", "\n", "print(\"Capture device is open: \" + str(videoIn.isOpened()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 3: Show input frame on HDMI output" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Capture webcam image\n", "import numpy as np\n", "\n", "ret, frame_vga = videoIn.read()\n", "\n", "# Display webcam image via HDMI Out\n", "if (ret): \n", " outframe = hdmi_out.newframe()\n", " outframe[0:480,0:640,:] = frame_vga[0:480,0:640,:]\n", " hdmi_out.writeframe(outframe)\n", "else:\n", " raise RuntimeError(\"Failed to read from camera.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 4: Now use matplotlib to show image inside notebook" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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IXXaNUiXEJAH4DDJFb3lxccHZ+Zrghbqu2WxWzA8P+Pjjj5kt5ggbipU+qhID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lrx6Gc/4F/+s39KMZlx98F7GJ2SBk2WCbKsJC8mNHVHUe5y+vwR68Wcx59/iVGaJInMMDZ46nrN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\nrT38ZsgPEZXAHJ2QvN4UEgc6OYHECNQwnEZnLC8vMxwOuXLlSv16KvwcyM/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"text/plain": [ "<matplotlib.figure.Figure at 0x2e64bdf0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Output webcam image as JPEG\n", "%matplotlib inline \n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "plt.imshow(frame_vga[:,:,[2,1,0]])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 5: Apply the face detection to the input" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [], "source": [ "import cv2\n", "\n", "np_frame = frame_vga\n", "\n", "face_cascade = cv2.CascadeClassifier(\n", " '/home/xilinx/jupyter_notebooks/base/video/data/'\n", " 'haarcascade_frontalface_default.xml')\n", "eye_cascade = cv2.CascadeClassifier(\n", " '/home/xilinx/jupyter_notebooks/base/video/data/'\n", " 'haarcascade_eye.xml')\n", "\n", "gray = cv2.cvtColor(np_frame, cv2.COLOR_BGR2GRAY)\n", "faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n", "\n", "for (x,y,w,h) in faces:\n", " cv2.rectangle(np_frame,(x,y),(x+w,y+h),(255,0,0),2)\n", " roi_gray = gray[y:y+h, x:x+w]\n", " roi_color = np_frame[y:y+h, x:x+w]\n", "\n", " eyes = eye_cascade.detectMultiScale(roi_gray)\n", " for (ex,ey,ew,eh) in eyes:\n", " cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 6: Show results on HDMI output" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Output OpenCV results via HDMI\n", "outframe[0:480,0:640,:] = frame_vga[0:480,0:640,:]\n", "hdmi_out.writeframe(outframe)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 7: Now use matplotlib to show image inside notebook" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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vvcZsMUexIVvpgwjBQwjrDqy8y2ohSeqVmqoskkFRJ5tDzv6fjWCeQqdMR5LP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\n39ab01qzsbHBzvr3LCj+G2yj1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oIHDx5wfHxMWQYS6yeZ0ye9Ob33uNrwzz7zWV7+wucR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\ndRG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2du6TJ4LNzXU2hqsEFxhs9en3+xHob1rtKsX62hrDtVUunt9GGIt0gb3DQw4ODzk4\nOODy+sXurHd2+SYcmt+4Xc/k1Eu4tHl1vZjTXiulZDAYcOHChVqzMj527doNXnnlVb74+c8RjMWH\nioODI47H01q6L6frvGqdMhyscuPt38xwfQOQHB2Oef7WTS5fPs8/+a1P8GVeBSKWfTyeUD7GaHY3\nDSEEr732GteuPEOexP7qs2IUS0pF3U6lyboLWtx6a2ubQW9A1oE/9g/u4fWU9e11kqzHcDjk1s3n\n2FxdYVYWuCKG9tZVqKXOpNV0wge+64+w2h8gNlaxCA5nYw7395mNxgzrktAbV6+12fCzrhFwohUL\nQKKjgEo/jxqig8GgTf542TQWrD23EHHfw/Ex/+8//gSF9YxnMKsCPhywPxrzHd/xHvqpYpj3SLvq\n92HRgbDWYirT1rQPBn2U0tFgirnCWaQlNp7m6ec2h/7mj0d7cfY9dtZ4yxvNiD3KqEPoFc5ovFP4\nEEm9zfqJkxg9IKnijjT3fGQtVFDjZxiEUAs3QSOFVhQVxk7RiUNpz3Q6iscRovHc2z9iMhlH4Nvf\npjQVW+cW63YPDvbY3X3I4eFhiw9FAds+585Fo/nMM9fZ2dmp37G40y7U4tKc3yKUsHyRlTSs9gKp\nAhkKhhursc66n8RsLBrrozdVVSZSdTLFanIOaQTmaI88DWxvpqzkelHJusPTbL41iPli9N6Dkoym\nEypneVzNRBevbbzu1qPWDU/Xtq2EV1ZW2N7e5urli2RCMNSag51H7O7tUZnAaDrDiyhSnPV6kb9b\njxe+5R3s7u6zub7K9tZFKitQasDm5gbj2QHP3rjavvbyjWu8fv/+qcfczHlbN02kzXz1jQL5m5/h\nfe98AYVgcmDBS7JcYcqYPNEeVAi4UPNeBynPvvPtmMmc/tPr5YwOR2xtnqefrkG+grMlK5sz0jLB\ny7ip+tKAdHg/59DefP4G2WAF3R9gncCXBaODfaajA/r9PjKrj3d9hbKTH1AhtsUIQhG8IkkyjDEM\n+isRf3cWWZc0Sh0Q0rK6krCykqC0B62opCTIRqQjQYaEsiqZFhX3Hz3C91e59fw3Y0zC7dcfMh6N\nONw75o1Xb/PCC9dATdFpP3JBSaKosnBoAt56psdTHu7scXA8QgrNO9/9TehMo9SikVRC1kyWxfYV\ny9HRPIyPo3G+4HRP9Kzxljeay6NNjgS3zMR5rLdzyicx37EFjRTaeBzVrnu1JJwQ/x937xprSXbd\n9/3W3ruqzuue++zndA97hhyKr6EkgiYp2XIUyx/iREgQGzASQ4mjGPAXwwqcOJYVwAgcQIEQB4nh\nBEigIHEkWFFiBXAkJTak0IJovUhLIm2KIofkkPPu6de9fe899zyqaj/yYVXVqXO7e7pnKAUcbeDi\ndp97Tp2qXbXXXuu//uu/hKOjI27evMnx8XGTOFKV6bIsuXPnzgP8tyzLuHTpEvv7+10fbYCrV69y\n6dIlQD3NnZ0dgIdie2935PmAy5euMzu5Byi1ZTAYkDI12D4q/zSzBo8C7cSa3GUQYOmVOzicTCgG\noweO/ygaTt/QPyp73vfmHyhGYL1JZE1vcWsgVAtuv/4yW5IYVCX3Y4UbWjJnef7D38GXvvxl7txd\nAELuMvJ8hLUFff7ezs4OZ2dn3H7jNU6P7jEeTdna3ufW4g4+1jx1cb3ZtV7tYrHcOPd+IuF8ltxk\nhjfeeBMTPZcu7PLKK6/wZ7JCW9H2Fu75xfuhjz7P733+d7rvOLhyiW++9BLLumRgLUUxRcyCKgou\ns0iT3DL1HIY7+LrHrBBDPhwxHE04m5f4IJzNl4SUmO7sUDdJyp2dnQ1hFw2J13S8ug5NtwIt2TRm\nrYZkTSQzwmQ0ZDIekVuDlaTlktYoxpg0VG7LiyeTCU8dXCQPkeAr9nfGXNzdIdZnLOdLfAlx2HqN\nICa0SrrEqH3iF4szYqopCqU/3b59i2J0g2Gm/aG6BnnNmlSj2eYuHj/6/dbfzhp8VxpN71U/0LkH\na7LfqdFsQ+QQakajAcNC67X39va4dOkSBwcHvPzyy4Cwv7/HwcEBu3vbpJTY2ZluHPm5557jxo0b\nHdn89PQUay3vfe97u+TP1pYmaODtZe4eNXyd2J4eICGRUkVVL3B5jYlTYvBUpUIFVaYcutGwUFES\nXxProAkAI+TDCdENiPLg3J4fG8mC9Ghy+3kIBNZlfu0YDAY8s3/ApYMDdnamFEUGEnFOGI4KRiNh\nMHY4O2RrPCbPhN/5/JzlasZ0XODMGGKOkbXR/MAHPsAzz97g/r0jFos5gYTLMi1/nG5Sjq5fv86V\nK1e4e/fexrn38bbYZMdbek1dLfC18OVvzPnyN15uiiAchrWYbztH/bD36nuuNzqbSu+Z7O3gX4VP\n/9pn2N27yN7uFapywcHOkBs3bmCKhv6WT3BZscHWqCLM5iWrYDg5XvC1r3yZe3dv8YN/5gfIC8ud\no0MAtrenG+dgjLb71evyDIZwenqf595/nVdffZXT01PKUhXap6Mdrl+5wKWLe2QScRKIocLZjBC1\nNr+VRWxbguR5xuX9KYilDHB8tqD2kELOvdt3WJzN2dneVX5wqiBWGKNdK+s6kKJlOCoYjnLECUYy\nhqOptpGOymJpni5Ion1+3iaHs52H8y1bHje+7Y2mKtUEEE+ixAdtfqXGcx1+iwjONqWIjYqO1jm2\n2Tfb/bvLorP2eNoF/PTTN1SPsK5xmWG6reTd7Z0t9vZ3WKzmZFnG/v4+u7u7OKd9tvvD5RmT6RaD\n0ZCt7SlXmlB0NBh2i7AlNwMbFUft2PDi2MxctxScvrEVC27oGIac+fEJ+AC1J6UREiNF6zWkRDHI\ntJ9MsUXll6RUIiZQZNuMh5dw2T4pbmKWfRFnPY82u22Un4dltaywpiCEegMn2kgEkUiNsr1tQ92k\nwg/TyYS9vSn7B7tMd6ZkmdbOa2NOg8UQQkXlTzHUTIoJ9XJFYRziaq007J13lo+wboS5PGS/N7f9\nLpDdPcNz9cI2X0mi0mzNqIk4MVgC1kQ+9fHnMQnu3H6TV16/xXJVE41VdR6xrHykWNVYm0EyKphr\nLbEx5tqrCC5cXGPGzk34yIe+m+n2BQ7v3+Pk+Dbee26872mK7R1ViQdtVJaVG47Uoqz4jd/8BU6P\n73H3tOLihat890e/k9Fkh+VySV2tvbFN7m8kJQOidB5fBQqx/Gv/yvfywu9v84UvfIHZWcJmOU9P\nt3jP/kW2iym5GRCxhBSRUGPNFEuuRHRJiLU4a5EiI/gMk8AlYTAasVpWxAi721N9JrLY9FwvSFHw\nQbFr3+pK1BFE8KsakwkhLtnLxs152+46RCCkVu5uU/Slf80iAkFJ84IWeFS1hxCJ9buu9vzJx3nO\nn3Ky9HdMoQGllQu4+d63rjZqF9t4PObq1au88frrrFYrlZtzrsmqjzEN7tYavYdl3X7kr/zVx15H\ne9z+d/dHHy9MZjPE64uTbM6NUi7a6p66ipik52dEGPS8JKIQVxWhrPB1RUjChQsXmezsqBjsufPp\nstgbD2OvFWovGfRWCaE1Bi0PLOIstwwGg0ZZfsRwVDDeGiECyYjWhZcLUjrppPdaUYp2ofQ3nz/3\nb/+Fx96HdrjMUAyyRkeg18+pKXAyxvCx7/pOvucTfwwjiddff51y9c+5e/+EZVU3MmcRklHwI216\nL+cXb6s/CVrCOJlMePbZZ3k6XicExU+3Gv5iW/ZKTEgcIL1+NlcuXyf7xJ9gfnaKKUYMh2MOdve6\n467nYzNsXd8rSFEIJuJTAEl86MPvZzzMeOFrLyIiHIynDIYqydfOiXQ/ii8amnlq71UyuCZvoGr4\nGrq3XmJKCeyaVtYPkeu65mR2yuHhfY4O73NyNkPE8r1//PtY1Z5RkRC+terBdg7a73wXCnY8+egw\nzRixTnt9aKuAgF6O0TJI0B3lIRnbh9FiWgOWZdoqtq7rrl/1aKQyVSq6Oure31ZNvF33HvQmtQvn\nUaFvR4mIa2GL/i76MBymrhJ5PqLIjarCBC31Wy5KrFWFJn1PzmBSE32FrwO2GJIPJ5i8oAp1952w\n5rBtZo+V87ZBE+mFsg9LWLV4oDOW856oCkUsCbHCmITLDFlmyfOMlEIDF7T9tg+5d+8e8/kcl9nO\n827H//WLP6vf3SzW89I3/Sqx1aKiXs44qm/SVXf1EiaSDA6hcI697SlFbnEmcvXyPs9/4H28/Nob\nvPTGLRZljZgHOzqu56DvdZuNKjCtpFEVLeOGiF0LZusG19bkJ0xwSK9iaTgZcsk2lCSjSZHM2A4W\nMq71qM9hfcmqTJv0qmwkYk2knM94+qmLHOxOuH37NnHpyU0kc4JtuKISDRK1OR+pJokKR3ffJxCa\nZ0TsGgbDpCaKMUjPaTj/HB3eP+PwaMFrrx1yfDqjrANP3XiTD2w1Xmpv832nQ73UNRf4Sce7zmiG\noL16UmoqBUS5Zrog2yy6jjZD3i7W9etrmkT/NaArQ9zZ2eHevXvMZrMHRD9asYX2XPrE2P/lp/4+\nR0dHncfUihFUVcXibM5sNmOxWOCNf8B7Oz86z6n3gLTXct6rAiAZ9vf3WZ7cpywXECGjpFqtKFdL\nBsUEZzLqcoUkS5GVCAFBGG7tYPOhJoyM4VyR0wOGfW0w155nPBfSP8zrXFM/Nj3Ntj3Hcjmnqld4\nX5FSDg1JPISALwOLxUKJ+42K+GRru2MnrDcvNaKK2UnjPa5He5567yrKaklV6XfG5Okvi9aryp1h\nOMixolJlk0nOe566DEY4mS8o79zvUlARg+A35qBjaqWGH9m79r/6o/81TzR8BBeIYQ0H2ayiMBaS\nQ6wjNaFmCIpTb493epPfl9JroKpkmuSb0m8yq0pBEksy6xkPLKvakznIbIKgikdBHJIElWdrRLyt\nbUL09rlujGaTpHFYYmw51hDPwTftnFhrKQYjLl4aY+2UVRU5PDrm9dfv8tz7308rNt13iN7J6N+L\nwWDAYv7o2vz+eGKjKWqNfgd4I6X0gyKyB/wfwA3gZeDPp5TuN+/9MeAvoXHzj6SUfunx39BfpX0i\nurr/G+FfM/G+3lRjBxqeXquZWXUJB2sNbbvO9rjNnOm3N9lz55QjeOHCBZbLJbdu3eqMlIaQuT5o\n0Wvb0ah9X9rxH/7FH37SKe1GJ1DRLOY+figiGOvOvf/BY5RloI4JYy3JOU6OFmTWsFeMGA4t+WiC\nWP2e7XyISbDyS6Ix+FSwv3sByUe0aufGbCYNHs6pbOcuUlZLVuWioXNt8hn7XkQmimXmzrFs+l37\nGBAj3Ll/yPiNjEFuGGaCSzUm1iTnqH2kXJacnVS88fIJh/dOiZKwhaNOkChBHKRMPagoXVOz5Dfp\nZa2BjTFSre5RrpaEaDg5q1mFSDTr+xmTpw6J0juKobIphoMhq3LBZGvE1vGA7WLAPM85PptjJWkB\nRC/aU8dV58DERKBEEH7yf/hbvPjC1whlxbPPPst4MsHkBhlaRAyhjizP5iznK92kt0DV1tfH/vd+\n6D9/3OPVXkmD8Tf3jgokQ0zQncHqmhi5SE1JDAp9ZMOChEcyJbDXMWlrEanRTvVaoRfxCAUmJSRq\nUohWv6BBZdrNw5hEJGjL9hBIlVejnWcY5xiMcq5dO+DkZImIZb7wbG2NWVYnWGcawWLXuzKVi1w7\nNu3aPr9pG4xo5JJSzWiUU1Ve6WrFozms58fb8TT/I+ArQJsq/pvAP00p/YSI/M3m/z8qIh8C/h3g\nw8BV4NMi8v50HuT5Fkc/LG7Ds77n1V/k51/Xn4dnh9sM6XA4ZDgccnJywvHxsTbwMoas0b1sicLG\nGE5PT/lr/8lfZzQaUVdaPfT8888zGAyoqoqqqlgul13rixhjl0waj8ePvQtPspOqxmjFwOWEUHPr\n1k12trcYjgoQw2h7C7HqVazOFpSrkhQNIQqjrS0Gg9EGYPGo+XvYOT1p9rw9Votj9T1tEWE+r7lz\nOKMYHLG1tQ0o4J9M04K49Bwf3ef20T0WVUmeOVKMSIikZEnRbEAGrVivs5F4DnPtFIkqT71cEaua\nk8Oj9U5wbrS4tmbPDdbbTp2/hRf619pfsOfDc2NUsyArhoxGI27eO2QxOyUzgksFNst1gw+J6AO+\nqrFisLJ2Ev7ef/uf8iN/7e+8xROxOfTebGLIpEhosvz9xGJZlpSLJbsHF9hxjlOJCA6M8iBDjKSg\nurLSJmICJBGsTY3gCCSTPWKzbeYmgTSJm1j7jnmejDAaZ9jMKNY8L5mfLdkf7zMYGhDlAqduTgOp\nqRxKbWn1EzBpyrJU7L9pdPik44mMpohcA/4N4MeB/7h5+d8Cvr/5908Bvwr8aPP6/55SKoGXRORF\n4BPAbz3xWb3F2PQ410TpB5Mim+5+qwTTxwQfdfw23B6NtJpmtVpxenqKc47BcNg1dxPRnuZf/OIX\nCSHw9NNPs1rOSSnx/PPPd4uzruuub1DbhrSfPX/czX2i8EMU3w2xxBghyywvvfQNxoMxO3t7GDS7\nm0jkNifEivncg3NcuLxDiqbXX+WtZeEedY5rbOrhxPz2fR1Wd+7vnoyTs5r8zjHTrXsQE9Y6nLPU\ntadeldy9e5d5uSIYwDUGq6qbdgqm6cWu5xFipe0gkDWsmRIxBP3RFYOpI4vTM44Pj5D4cEihf6/O\nl/11NLhzWG73w6bRDElIYnBi2N7e5V7+JqvlnCJ3jB1Eb8hshjGWzDnqVQkh4kcV2WgtrvE//vd/\nQyumXEYVIlKtWJ7NWc1XzE6X7O0dsHNwQBCDN4mUHn1f+/fl+PgYiz7bJsuo6xF1k8luDWdDy2Td\n1xxiMphGBEevWIUx+lFTn4YV64BFCJVnsVjg8oxsOCAfFISGUjie5JhMGI4c0+09nM2beN833mTT\n0SBp22OTNBn6JM9sG4E+KpJ61HhST/PvAn8D6BdZX0optSUUt4BLzb+fAj7be9/rzWt/IONRO8jD\ncMu2fLL/mXWY9ugMr7PqQQyHWtI2m826Xj6tWGtdq4DHarXigx/8IHfu3GE2m7G7M+Xpp5/ujG/r\nabYiFK2hHQ6HD0jCfSsjxshiOWeQjckLx7XrV7l3901+61d/g49+93exe7DPeLqFiLA4nXF895DT\nxYJ8MuW6KO5qOsHbwMPwyf54K4rUW432fa2vmP6eAAAgAElEQVSMGPSy79FQ1onTkzn37h4zcI5B\n7iiKomsNcvfuXZblCpfluFwpLaH2dAulPQfRNhEh1KQ6YnoLt27qnUMIpKpGQmRxOuPs5FRD6fjg\ndbQUpXZT6Otn9g3p+cWXUjq/h1BHZU6EyjMajZqe6SXLbE5eWGRlIIcis1iE5XJJuVoxHk6oiyHz\n2Yz5fE6eNYao+f7Qe9ZAmSBK4UlduNoOEaXc9A1aC10Mh0MIOj/iHDbP8NGTjCjeKUIy+oNpcUsN\n7/W4D9739h5vsC9iIqTI8fExL/z+l7n41BWuXteklslHiDFkrkBsxnBgGA4npOh6x2+eOYmNDsWD\nNexvZQzVu0ydLuqTjscaTRH5QeBOSul3ReT7H/aelFKStykqKSJ/GfjLAHv7B3RNkjoPUTHNGLWx\nWN+TSUSQc0osKT3AvevfoD61QUSIwXXYYGLTUw0EimHOjtsmRs3qpuAJdUXwnhQji/lcQ3YR9vf2\n2N3ZIUbtMLm1tUVdB0JITVmfIQTVvbGZwzhLVuT6f2O0fbUT7ecMSjHq4biZWct0tZtDv1IFFDdb\nrc7wW5ZxMWA8PeA97/kw/8/nf4b54k2uXb3MztYFxsMD7i9OOJrdoSgG7GfXSE4IJiMYh41gMJub\nSttyYfOub4TlLbvgYRSs9u8igtiMOiQQ2yUCfKMVgC0RDCd14u7smGLoGBU5o2FFVa24ezLj3uEx\nNgoSBRMTpS8xucr9kVyXERVRbmzto3L3UmwYFREJARM0K13iWaSKO6cnzDysjCP1pMVCUm/Eh4A4\niysczgl1DMSUkMxRSSQ4ZWxY0dLJdE70Wsx6IRckLVPVDBP7V57i9M27pIWQBoaYB5INVLJg5Wd4\nf8rZ/SNcfUo+32Y2X2CMY3DxAgQLdaO6tapxdWTpA+PpFuSOWhJl8J0H1t2TGlKqsDkINYQcAoSU\nk+dT8sxS+UReOBxDollhxADq0Tmx6pVHIVnX8KANISZiFJwzRGpEIkJGSoZWgi2SiDFgYoMtE3ju\nQ+9na2uLzFlm94+Q8YqdRi/AYjWqkAhSKw8gBi2jTkEhmmQwOFT0eTOy6YjwSajRnkQGTYZJskiy\nZLIudHjceBJP848D/6aI/OvAAJiKyD8AbovIlZTSmyJyBbjTvP8N4Hrv89ea1zZGSukngZ8EuHHj\n2XeU/jof9rVKNeu/aXOq0Og7tu0EtOIgbRjNdmg4HRjmBc7mTKeB2WzOarEkJemEflssczabce/e\nve741lpOTk5wTlVcdnZ2GI1GG+o+Ki6Rd+e5sfuypmB0nlHyHQbUScWFTX7nX/h3//1HzNJ/9/iJ\n/HtPMtubozXc7bm2XpfIZm/p8+NBrG89qihKUE/C4ckpKXoyMYyGOcvlktv373Pn3n3KkEixovIl\npc85ma+o6xmg9KmTkxnGwO7ehMxmrFaL9QbbSLVF7zt1m6qquHPnztr7fQjU0/J0lWvYRA3O4vKM\nla+JAtGKZo5FiGFNjRP0p503UeBV+3gbYXt/j9Vszmy+JJ6dMpScVNdI5ghBuHDxKS7uX0FCwhu4\ncDBlOBzqs+FLfEiUVUlIwiqCHQ7ZPdgHCyGo1mdM67LD/n3oMF4DMQWyPGdnb5fBIKdKgTL4LjHZ\nMjnasNbadReA/rytHRyFSoQmfG4img7Pzpz2zrp4oXveq6oikNgZFGSiCaoYYkMA9RASoTtvv5YC\nTIJs+kxPNNqGiE8EgTXjsUYzpfRjwI8BNJ7mX08p/ZCI/B3gLwI/0fz++eYjvwD8byLy36CJoOeA\nf/42ruOJRz/k7hufdQ20IYpm26URkY3SekaWTqG751Wtb7bWwBb5kPFoi7r06i029KGUErPZjJdf\nfrmrPW8xoZ2dHfb397tEQXtj2iRTv3zvYRhra5C6z9VLJTb3rrM1VD/9U/8zwc+ZH9/j9isvMpmM\n2L1wkfHuAdEVZBTcfvNrHN55BQme3E4oV4EkHmctNh/xzIe+m/H+00RrMUE1v33y3SJ7GEZu7GYY\n1Pc4eQuzef4zG8cURx0VqyqrmtPZnNl0SQiKeZ00fD1tOaabx9l8ye9+4V9w4eKUg4NLZG6IIIyH\nE5wxiHWQFxB1LkOzQFocsjVss9ms8QjtA0bTWtsp+1hrVerNGIrhAFfkhKTNwGJKBDTk7M+JkZab\nqPfWilFvqw0fjeCGqrbvU4QU8KUmD63NICSKbEg+cJhMuZB1XeNTpepVWUZIkZC0R1A+GGAyFWhJ\nKWBFBZ03aJrnjGZsIpv5aomTQGEEYxwx1N3mbIwmX9b9px6NXetm0TbZU++vv8maJsQXlBLWOTvW\naDQmCV+XLfOalKIKE7cFFe021MIOSWvhTXry7kF9/PntjG+Fp/kTwD8Ukb8EvAL8eYCU0u+LyD8E\nvgx44K+kJ8icnwel2yGinQ43wNokBK+K7ApEG1ICa9s+OE04y3rX6ytRp6SqLf3RHruuE2ITy2rZ\n7K4w3hqxXM2Zz+dkBYiEpvfzkOeeew7BMh5PmodKF+Vyuex4mu3iNMY0dbl55622u65pyMXN5UH0\nmFATzmYsZ7ewbCYZuo3BQFnOmR/fZ37nFvF0hLOWWoTRwQEky/b+NfJ8ymp+SqpL8vyMGCz4EqiJ\n9VIxsWS1rUESYqiI/cqYHoG8ldlT58I0FCxBuxsmAo8XdO1vFu1vR91EkBHvobSGk9MFJo5ZLQMr\nn6hIRCOEUJIXBdEnTmZnfOVLn+ejz3+Ma08/w3S6h7EZdayJGGwxanifkRRiZ8AkCktvWAXL4WxJ\n6T3RbG5izhkyE9nbHjPKVcwi2gI33GNVHeGc0c0IwTpLSB4vgdTSn1IipCUd3hYNVQyYzHTkb5sp\n/SoFVac3oy2yUHN2/5jbr71BLGu2hiOuXryEHRWcns1YrJYEZ9jZ32P74gEhBuoqIuIYjIaUISLW\nIl6xQ4NsdgCQ0PQmyjBSYESoRbVYJSYKK/gY8CQWtUdsU5AxzLVizEVqIxQm1/BZLNZYQpMRx1hc\n3axVE4gGfPIYsUiMJJ9wonBKkEiIHrHKFTVOMORawR+baqvYlBtJjkaQul4SnoSAKPUoxc0wW+1B\na34SDlV9p2XQSGQwHIN9clP4toxmSulX0Sw5KaVD4Ace8b4fRzPtf+Bjc6GZ3u7ZUg0aTyc16tlp\nnUF/lGHu/z+ERm2l1eBMKhi8tbVFWZYcH58ynapWo7W2oSt45o3UV0xqIAeDQdecqk0I7e7vdGoz\nrbHsQtqUOs9OkoEYmM+OOT26y8CtQLR3Tb8iSkSxvVSVpLKkPFuQKs/kZAvJcopiSLalDACbIhJr\nohWC8fi6QjKhrBPLxYxtCViTNd/hN7psnp/3895xH9t8GO1o00N++H3tsKemP06USFV7ZvMzTJd1\nFZyxGGcYFAMyazi4cJnv/s6PcOPZS+xdvMxgsqvsQbEY5yB5Qr3mjrYeuq9qoldv8+joiJOTEy1E\nOHd+ulnHThZONzqUj9hjbmhCog8XKSm/j8+3IxsUeF8p3SZFIPLZX/sNCrF8/OMfJ66EmDmKfIvd\nvcukyjMejiiTgbJkNJ6QMgfGsLU1ZbUqwQhVWVHVK8bTLTKnavshJSSlhtv44PO/zmR7UohUwTPK\nLKuqgpgoz5aU1ZLBcLjmQovFWqfVRGJJaNuJJAYjgNG5Cg3HOrJWwSJETcSVHotFJOljb0SFOUwj\nLmzX5xlDUDGP1FfQav/VXNc5zPatR2TNEIn4oJj5k453XUUQnM9QrmtrlaenXC19oNWb7IeOXQuF\n88U0fdC+B2ulqBVA0+mU+XzO6ekpWVYyGGgZZNuvvA2/rbVMJpOu9LLtI13Xded5pqQUlo3kTo9f\nKFEoVytO79+hWhwy3R101I5AaMBvr2sgGgoRamNI1Nw7PIbMcrUYIKMpYRQwedaFlmItqVUYioFI\nxdnxIXF5TDbYJoojCmAt9tz8PKyCKfbCzrYm/vyGdB57Pj/az5gkRGkAfyx1ipyslkTR9hoGYZDl\nSO4YFgVXLx/w4Q+8l4988P1kWyMyN1QVnGQ1+UAk+AbLNmtqSUcRaiKYmzdvqu6pK6j9g+feJrke\nBgcZo1qeMc6670gpEZMntthnTzg44VlVygsc5QVxVXHntZsc3bzN5YsXuXPzTfYvXUYGkLuCre1d\nyrKkGI+V5mbUiy9Kp4UWxkEKVKuK2d1jTmZzJMFTzzwDSYsdSFFxwZ6nuZFYbaqnYoQ37x5yaW/K\nPMyxIbE8PaOqqp7RNI2SU0FqGCqIqDcoKlwiIqrTSd14iwmTINY15cmSarkiea2ccs5hnJAkKk7a\nPKsMrK7B1MIYLbdTy1DVUELrIOn8Pk7ebV3YgiRiUD3euv4jbjQ3PZ11VgxQN7330+8xcz5xcX5P\n2vSkHny9rU0/OZ5RVyrMcHBhj/F4zN7eDnnumgZpikWenp4yn2tIf3R0xGKx4Okb19eCGWzq+cUk\nTW9pIMLi9ISTu28yGVbE2iCSIWKwyawFOeqaGGtcljE+2Ob6B57l5W++wgvf/BrL4MlHY7YPrjYd\nD9Vw04D6bS+gPEVmx7d58StfYP/y02zvXSGZgpTOVyFtKitt8O0a3mA7x+fFD/rZ84c92GtszSAJ\nYkNtST6ySpGqnml7Baf30wVBYgXlipsvfYOJ1Fx+z1UyN+T07m2Ggy3GW1OKYY6LnjpB6UvK5ZJ6\ntcRXNcF7qmXFa6/d5nOf+xwiGiJLNnjg3PsYcwhhQ4C5jUJEDjU7385PKhtPU0jRdpt0SoFk9N4d\nHp3we5/7HU5u3+P6wSV2pjusTk9ZDAzJjzDbU5KLTCYD6lASXSBHE0DT3S2qqiImYXW2VEWjVcl0\nMCIsVtx5401G21tkedElQPs8Tb2uNW/SRUeMji99+QXSB9/H9iDHlp7kE1nWp1spUyFFB6IRgRGI\nxnXlt6bhcsamxDmmSIiJelVy/9YdWNVk2QDjBsRCiHXCh4okomT2osAayAeZUsVYb7YpJSTpRqF/\nsMD6vB7rbcq62DWmqOfaqOw/6fj2MJpdeSOktPYcSRYkKm1B1p6CGLSPdPB0XSdFs9HWSEf0jqK4\nm8qxtIGCKNk4bU5u3xhIUomr5oDKTTMGyRyzJgy31ig2aTKqMqJNoUrAk5KlKgOLecnsdM7du7c5\nPj7ie9wnqOuyMZxVY4iaa688tqFt+rBkuTzD1zUYiAPARcSaxoOJOBMxREKsSFWFLYZMt/a4dh3m\npWdZrZhMh9phMdSEUBOi17AtJqwzhNpirbA1soTFbWZ3loRwwmD3MvlwDyMqShuDUqZEUEHcFFtI\nvvEkvKquS2SxXGDNw1qytvSwh3majWxaiz3FNugSTWAAtURSI9+Vx8hitSKsKo7uWI5u3+XDb95l\nOp2SD0cMxiNMXjDa3mUwGHC8PCEvnCZEViUkx9msJknGr/32l1n4vIkAwITNlsyxVqytyDKsJKIv\nMZlDUtBuAmilEBKpV576rMYMMiQYfBWo6xKkxDSLvp6XnB7d5ejeIceHp0i0TLcP1JCIYzTIiVVF\nbYSlEbLhgFBXDIYDsiLHOC2qCFVFMp5yucKvFtgYyIcjMlfgihx8YHEyoxjWuCInWbuRuEkpUNce\n57RlcOVKfPC88o3Xubi9z+jKBULy2CxgZEgUQzJWyx+lBhGMyTQcR5qoRQlC0hg6j0EITU/6yGCQ\nc3BlH1/XxCT4lJMXQ0y0LBcL6rpkfrYg+MTWaIAZ5MSk31unSJYycsnQTgL6vdLgorqIW92AtzCc\nScV9EgozhEa/c1CMKFezR3+uN749jOY7GCKCsSoAkDaIu30hi81EQx+LO0/ObrN3+nvtGbXHbcPp\n1msTkQ0hjzzPcZkhhBJwHN+fcXJywtHRIbPZrMPS2mO0nM7WU4P1NfQ9tQ5aSIY+INh60MYYQooN\n6b7g4OCAZGzXtE2/d42DGmfBWpxTQ4gtSDFiWVH5E+qjktofM9h7liyfUuRbiFHj4D0k2/DmUr0R\nij9Kru78eLuZym5IhORJyVAGQ2YcZ7VVuo2vSWdfVA+l0cpsz+U973mG/fc+SzEZMcwzymVJSjWr\nKnBydsrt27cf4LyeH60n3VZ1GaVjkGWWLLPs7GzjnGGVIiHU3cYWfMTX4LKCEPSe3r51l+X8Lmfz\nGZOtAp9n1MuKOgNjDZIbJDOEJpNdpEjm1VA60YRI1TyDoQrNs2Iw1lBYDXddrlzgYKCuSwIBkxfa\nX6cZIbTepra/TkkFlMuy5uYbb7I3GbGzNUAkIU1LC5I6MilapRAllEbR0o4MRFVJgcZgdrdP1NPN\nxkOMz0g+adRkFJfPB1o2uVydsVgtGdRjBt25hk434Vt6huhFNU143x7qj7Ry+0YSIvXxsr6B1Jno\nl02ep+ucX9x9o9n3amGdPOi3tfDes1wuuX//vtakZxnWCSKBuk7cuX3I0dERxgjj8ZiyXIetrZFs\nqUt9vKy9hr5BijEiMTaiI+tzku4aDatKOx9aa9na2mIwGPQSNGvKE9iOMiLONq3mAskYnE1Kj5mf\nMvcvYwcT4nCPYrCNuJF6kEEXtJjN8z1fe/1W9+8djWRUqSQKmRG2iozrFy7y7NUrbA1ztg+GbG1P\n2N7exuWWRbmi8iXT6RTDEF/XrFZLCMJ8VbGqAr/7hX9J3UQrKT1YHHF+tFioEcjynPFkQFmvuHLl\nEjs7UxaLFW3bXhEhz0cYWzQGpxUhjuRZweULI3wdqWxgZSpsVpNZwWZCNnQbG3bwntV8Qb0qcc7g\nvW6CPminUJflSu+hxlpp7FjCORUL9r5qYMdeP3CGwGp97c6xXJaIWObzJXUdlBWR9JnWOv4ByRtl\nSbiamEpCUGK7dohsIQAHTRKsddpFBC+pkfITgg3YlDTikowg4PKc3QsXCVW5sQakkXpsnZT4LRrN\nh43H3fv+eNcazTUOBtDjebX2tJdkab3Bdjd5KEew+duP/+2/9Yd27p/5zJO9r/WU+j90D5C+p280\nk2wqEbWCIDFGLGtc7vyxjIFgDUKBkEHw+KqkyAx5tqReLJkt5izcfYrhDlvTi9hMYZCI7T3Ua2zs\nceOdGs0kUCdtUmbwZNEwoOSgSFzdHVLke2Q+w5wlyrSk8iVuawDJ4X1N8gmJay/+yy98lZdeebWZ\nN12MdS/Lfv6cW6y2qipliVqLa6hjBwcHXLx0gdu37zZJRP37YDBAjKouBa+VUtvbu1RzIERsrtl9\nwROygLVqVMSsuhC3i5JiIqVAVWmRhRiHM07rw21OAJykxmiKalhaIc8KAoloLMPhGq8dDrcA1Sy1\n1hCSKCPEOBaLFatlhd/yFIUQY4202e+UcK6XdcdrXkVaZawm0YKha97TvNI6B8k2yl2pVsV1gaLQ\njp5FUSCDnCRraT1pMNJ3vOGef5YaT1N6NervQk9T6Otg6mQFUqP1uH5NCbdFPtJw09U940hnHKGl\nJ5jutZYysk5e9MN2PcZ/8V/+V5qlEw0urLWYpGoor7z8Mi+++GJnkGCtiDSZTDqKEcl0tax5njOd\nTpvsakRMYGtr3ZumTcYAXL92lfFkSohNNs+XhKRJhyiaIkpRuWUxCRAIcYW1gh2MGWZq8LxXXBU7\nIGUZPqzAB+JqhQnagdAVFi8gwWGS9giqy2UTBgF5xlCmuACemqq8T1mdcjq7S5FP2d67guSTZt6a\nbG5myIxgXUFZb/I0H5c9f1TY1f+ciYlBsgQxLAVqHzl5/U1ePzzmyv4OO9mAvAAziGRDx4XLF7h2\n8D5qPGX0DAZDojhW84xPf+YzfP2br5CsI2DXWVjjetIaOqKowW7DwxSiikx4zyolijyjnM+5sj/l\nm5lmk32KOHG44ZbqkqZEq0/8n/3tv/uWK+H/rzEc75AVk6aqx0FSGt3BxassZvepUoBM6WkYIcUa\nQo2zUFUBmypsiIgpCOJJJoI3DVVNDZBtKXSihYu6wbebuyHFDClCI7aSGA1bjqXe51qa8teQyLNC\n8UsMpEerrMdeUcYmLQnW5cBNXC4GZ13TTO/dZjTTo93mRw0RaWphe/9vqDR93LJ9vY9zxhhxrldu\nKW2Cokk4uawRgdAQWsVxlwyHQ93Je9QVNcShoTc1clUSGU+Gjbxc0QgRa0a/9Wb6qjlduCzSZB5N\nj/NouvBcE2JrGCLRVpiIahFGT4i+qWW3KqhAJCUPEtbz0HBZlWqi9f2xaZBlnPZasrYgAjYJEg1Y\nqOs5s9UJxQAyew1nM0gNJ68JGY19UHHqcWM5ezIAvj88sAJOZ/DSnce9+50PTdapiEVH22quL4RA\naAoX2vvZpyJZYS1f9k5q/P6Qxk//g/+VEFqZPn3+SZEQIleuXOEOHmczjHFYo6ruMUJIK0h145gk\niBYxWnoa22c4S1rPLaKHbfp1iW2k+5rulUm0GZoY1b5cNwpoDK3RMNyIUfqb0xYiqev79U5Hw9EU\ndcxEDHVd4X312E+249vDaLJpNN9q0W0YRtkMw41peF8tCdesj7cZvgp9Vfd2ItsHPIQa70OX/Ikx\nMplMGA6HxFhtUGuqquqU3BVHcjg36MolWwk5WIeGfd5oG0afn4t+ON2G1C1q3cd1Y2wJyblilMGC\nCbg8wzrXVEw0uqMWLY2LgbohGmt5myXPil5bZEOiwlinIgi0VC1PuVpx/86bTLM9plu7xGgIoWlf\nYGgexAfbPjxqDCaTjWs5f6/7w5sal4SMnEGRsTedcOnCLu993w3e997n2L94oVHU97jcUvuFCnqU\nkdnZkq9+45v8nz//T4iosSewWSXzkCFiu2KFNsnUblhd2xVrO3WhflLpPIH8p376f2J+esThrVeI\nVan3uWE20PAoDZZkNKlobCNG3SDPQoa4tSB2C0l13FBfa/M1DDZTr02sRj/5zh7jyXYjnuG6zbpd\natYJ9cpz48YNCKXS55LFSMsUqEipJqYag9KTJFpCYzTFKuYcY8SEptpNcpJJmuzRmVDoQtYqSUnQ\n0tXWv2+9UzQCtNZhsqYcOTbydC3F6B2N2G0SygNTzDfEx1extePbxGiuqShtlq0jfcdNFaO+IQtB\nvUPT7IbWWlxmG8MJukNGrC26cJ02ZDf9naUVMVYDt1wuyQcqOlxkIwbDnL19VTGKoey+v00MtC0t\n2mZp7WIfDoebVxnWFTMion18UlLah7GsfCB3BiORPDNUeBbZkMw5LaNMHiMGaywYRx0LqmpFVi9h\noOIHsdQ5ycQzNBU1I1bBox5nxKaaHE8eaurUNpv1ROeR5BGTcNRIBCNZtzBDCiSbcFY4mx2zFU+J\nMcfaHU5PFtQhKc+uMIRF/oBhbxc60qipNwkIIw7ts+1wjZpVa4x8vS5IsNYiAXyIDAbCwFluPHWd\nZ5++zvVrVym2IpgzMDlF7tbk9WA4XpW88I2X+eVf/hXqOuCsahJgjHrhbG5EnbGLFrxFJCcFUYMm\nkUCNMZahhbL0BGrunx4TTCTUkUmR42PEJFXUiQQwysPNx1PyvACzrgprmQfquTaKPQb1xLpNXTe9\ntuQg2oZvbE3XeK8oxjqf1hDaYrncEYHxZJuEClLHaNXmNArqghAqS5KM3cv7HM8PlJju0L/bRs0o\ngZbYAhIUZ5UERjBBFYacy4lRDSN2gbFCtA5LhhiHZaAShEmQoER5n7yuf6NEJZ8iiQZOawVQEoAl\nhXYDVlk3HaKYfMoaj3UTsmv5qdpWuTW4Co2lCPP5XLP+Tzi+TYzmk4+Dg4MezaVvbJuyxNQQkFPo\nFlvL5WyxFhFtt9Af/WTJ3u7BA8miLhPPWvAhJe0t0v9/X3fxvIfVpxf1Ew8iQrSCNVq/HSk4OHgP\nO9uXMDZizkkQdNVNo0Aeam1ZkGWYlBhkNVkIFAPFNEPMoMixUmAkNbXV6mWaFhZJAYnaMiChdcIx\nG4O4zl1PLuHTito6gl2RYkaMgrWRPHfcPz5kPB6zKGdg7xMitOyT8Vj7YUevcEBrMEmGwShrvB1P\nCB4B2t5crS6kMQYxBlKNcRHjIvkwB7fCjSO1zBmnXagNvo4EqREs9SpycjLj6y/f5Jd+6Z9RVbA1\nPmC5LIlBMfI83ySzb0Q5FmJYEkJJiCswUb1p0TuSmYLKa91+AuoaRuOBGsmHhJDt85gVOakRPLHW\n4hrPr914u8Zp4jv4p/18yy82aALQWovJFIslaoKoFdZIRml55hym/FD2iBMQy8WLB8xnR1RzLStV\n3rMHq0bTQOctKlNXCz1i9CC2aVFsMSYiMRCTYEIiOlRhKrPY5vqcy7u8Q2wERURMk6DRMF7XR8+r\nTG2p7eZ4VKTaX4dt8vT85540idmOd53RPD8JrUp3V0vallshBI9KwNk29FtnR+25h6ZtdyoiVNXa\nSLVycyKtOkt7wxSjrKrYCHCs8UlYl2P2RxC0Vg1AFDtsRyZ1k/wSxGS4wS7ZUAnj1m5m/X3TNzyX\ntuopdHMy7JU6tuc7bM7HOUd6CJ+yv5C6uQ2+M+jt4h6n1PUPN1iMs/hQM54UfPzjH+NTn/oEGDBp\ntLEJiUhXUQVKjdLvVX5piMqB9PVaZq5tP9DyI9Ujz/BlRVUumC9mkDyFy5DMUqa5iozEdhFHlsuS\nl15/hVduvsqHP3qdxWJBSJG6HlA3Wo7LRb3h3batD0IIpAjZaAu/WpGPCuzAIVmuWHJKxOSwxYjy\ndAaZxQ0My3pOkY+BBzHMLgufapBas8KWjSRljBkptPdEzy02m0wSQaLXunpjoKuxHyBiSW5tMExq\nOhD0YR56xqP/cErU6AVhsjVkb3/K3fKYhFf6kgHXqHURNGJRLlETdpvWk0v44LtyZiRhKqU6JdFe\n8BKteq5GNNS3mSpcJkHIle+JNLkDpTNpX5+2ICU1+QOta9d5NWtGSE9jos9/7oxizzbqfY89Ot6T\njXed0Tw50Z7XuijpDEZKjS5eT71ZcFRVxVde+BJ3797tJXuE7Bwva8NbNAMuHFzi2rVr7O/vd383\nRkPn/gOX5zmXLl1iNBop/6xRMzJiHvzgzmgAACAASURBVLgRIcW+zaTPLfWp3UBVSiyI8v2cNcRU\n0WE4Ai5r3mcMxqS1yAh016cPkMHEJuQUgxVHspa6DkRRZt+aZdB6ncqLM9Z2XnybkKKTBXOEKhE0\n94X3FfmgaIwbxNRI5Zk1bSYmvWfWDIFBc98Czgm5GSKSiCFvuH6tJ+J6XoC2jFU6T6IqlxRF0ySL\ngMu0vLAqfQOT5CyXKz74kSFE1XN0znE2nyvu2fXGMcSwNtTGmK7DaIgeySHVSwoXKJxyCSOi+Frw\nTIocsYZPfOITPPPse/EpMHBCOgeR6TXoM5YVTW+j3mYiIprAjwoH6MPQRjG+YV8kiHOMcYhxCI6Q\naBR6TCPS20AiPay//f7Eo4sP+pqZ0+mU+fGIwniseFVySposjM0GZkVbW8QYiR3GmJrzUCqgSUog\nj2jFXBUqxVQzMFaBC5MSSRyma3/cMGnahCdtu+feeZ/zNlsPsv29sfn38wLtAuqNR4nMvNX4tjCa\nif6ktDimzltMNUaUUJuSMDtdcnY2byhDD7rU7c333rM8O+YrL3yJL/3+FwDFQJ3LmhtRbpDf++0X\nok+ECPv7B3zfn/x+9vYOcPmAGFPnLYbgVTQDz4VLF6lDhREhiIYhIgLnNCclBaxZv9bd3ISGrLQP\nfNV9pqr8A20x2vBcQznNPvYXCKmdxzVvFVG+XYyxs7+KCiVaInIz/fqr3yvcNtiWkQYDi5CvHzQR\nuya3J1grTtH9To0xjqnaeIjX3FnBWL+xACI1YnuGtwmDA2AHBk9Tf4ylDE3hgTGIEeo4xxWOOq4U\nvgDKWOEGAB4nSnvR9SLoUmgNWN4ZkORrjMkbQQuljRkbEBOoV3pek3HiYPdZvuv5QknnywCuKdcV\nSOg5aTGDYXfvfbSixA8bLe9WE5K+i6YSLT1n7VEOGy8VIPq+VxnxVvFjay2kRoEoRYzpSy1qokma\n50aSZW/7EqPntjtHQcJx99y1EEKnm+DbqCFpqW4ItOpOoVo2ivGoofMeH2uS12gAl2FSwKCwg4UG\n41bhlj5Lof0OfT4iqX3GmtexkWTqxknYNIwb+DpaaCBJN/hBkTM7OcPEP0Lk9vO7ZXvjYuQBpebW\nUwgxcnZ2xr/8wud56aWvU9dlI5orhCA4tya7t1nwDldBw0aXZdy5c5vf/K1f51Of/F72L17C2XwD\nF1Gj13P/eztcfzwMR2pDwj53tE8nag1jm2Q6f7z196cmO94sgHOexLqW/w9vnL9mkXVo1E/k9K+5\nzUK3C+JhHtADm8tbfOf5cFjDw/Wx+/O7iVXr79ajHQ6HlGVJSmirEnHKqzBKmwkx4YMg0XZG3liL\nzRxlVSkOmDUeYG/Udd0lMc9nfvvPgEloE7ik1T5Cy7h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wyXxCz3L/wT5WWbRnORz2yXJ4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\nYoklngH/G2GlmH3S433xAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x302e4490>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Output OpenCV results via matplotlib\n", "%matplotlib inline \n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "plt.imshow(np_frame[:,:,[2,1,0]])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Step 8: Release camera and HDMI" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "videoIn.release()\n", "hdmi_out.stop()\n", "del hdmi_out" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
NathanYee/Video-Processing
jupyter/keynote_data_fixer.ipynb
1
8509
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This is a one time use notebook to count an process result data to make nice graphs of my results" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-08-02T18:19:55.918631Z", "start_time": "2017-08-02T18:19:55.893827Z" }, "collapsed": true }, "outputs": [], "source": [ "test_imgs ={'BENTHOCODON': 286,\n", " 'CYSTECHINUS_LOVENI': 40,\n", " 'ECHINOCREPIS': 26,\n", " 'ELPIDIA': 13,\n", " 'FUNGIACYATHUS_MARENZELLERI': 13,\n", " 'LONG_WHITE': 3,\n", " 'ONEIROPHANTA_MUTABILIS_COMPLEX': 3,\n", " 'PENIAGONE_PAPILLATA': 10,\n", " 'PENIAGONE_SP_1': 2,\n", " 'PENIAGONE_SP_2': 1,\n", " 'PENIAGONE_SP_A': 453,\n", " 'PENIAGONE_VITRAE': 93,\n", " 'SCOTOPLANES_GLOBOSA': 63,\n", " 'SYNALLACTIDAE': 5,\n", " 'TJALFIELLA': 66,\n", " 'bg': 0}" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-08-02T18:19:55.949438Z", "start_time": "2017-08-02T18:19:55.921286Z" }, "collapsed": true }, "outputs": [], "source": [ "train_imgs = {'BENTHOCODON': 569,\n", " 'CYSTECHINUS_LOVENI': 174,\n", " 'ECHINOCREPIS': 82,\n", " 'ELPIDIA': 493,\n", " 'FISH': 8,\n", " 'FUNGIACYATHUS_MARENZELLERI': 64,\n", " 'LONG_WHITE': 8,\n", " 'ONEIROPHANTA_MUTABILIS_COMPLEX': 6,\n", " 'PENIAGONE_PAPILLATA': 14,\n", " 'PENIAGONE_SP_1': 111,\n", " 'PENIAGONE_SP_2': 12,\n", " 'PENIAGONE_SP_A': 1108,\n", " 'PENIAGONE_VITRAE': 360,\n", " 'SCOTOPLANES_GLOBOSA': 365,\n", " 'SYNALLACTIDAE': 30,\n", " 'TJALFIELLA': 230,\n", " 'bg': 0}" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-08-02T20:55:52.627350Z", "start_time": "2017-08-02T20:55:52.622688Z" } }, "outputs": [ { "data": { "text/plain": [ "3634" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum([train_imgs[key] for key in train_imgs.keys()])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-08-02T18:19:56.027504Z", "start_time": "2017-08-02T18:19:55.982261Z" }, "collapsed": true }, "outputs": [], "source": [ "imagenet_ap = {'BENTHOCODON': 0.805137016516,\n", "'ONEIROPHANTA_MUTABILIS_COMPLEX': 0.458815192744,\n", "'SCOTOPLANES_GLOBOSA': 0.899604662246,\n", "'PENIAGONE_PAPILLATA': 0.7725,\n", "'PENIAGONE_SP_1': 0.0206598918637,\n", "'ELPIDIA': 0.0442176870748,\n", "'ECHINOCREPIS': 0.42133689736,\n", "'LONG_WHITE': 0.2,\n", "'CYSTECHINUS_LOVENI': 0.590778912159,\n", "'TJALFIELLA': 0.494639502854,\n", "'PENIAGONE_VITRAE': 0.292665667963,\n", "'PENIAGONE_SP_2': 0.166666666667,\n", "'SYNALLACTIDAE': 0.821111111111,\n", "'PENIAGONE_SP_A': 0.978279800281,\n", "'FUNGIACYATHUS_MARENZELLERI': 0.386287047543}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-08-02T18:19:56.087021Z", "start_time": "2017-08-02T18:19:56.044382Z" }, "collapsed": true }, "outputs": [], "source": [ "most_epoch_ap = {'CYSTECHINUS_LOVENI': 0.643499906799,\n", "'ONEIROPHANTA_MUTABILIS_COMPLEX': 0.248599910394,\n", "'SCOTOPLANES_GLOBOSA': 0.948805921186,\n", "'PENIAGONE_PAPILLATA': 1.0,\n", "'PENIAGONE_SP_1': 0.0500641025641,\n", "'ELPIDIA': 0.102745995423,\n", "'ECHINOCREPIS': 0.786982162631,\n", "'LONG_WHITE': 0.183501683502,\n", "'BENTHOCODON': 0.757451612353,\n", "'TJALFIELLA': 0.389716451493,\n", "'FUNGIACYATHUS_MARENZELLERI': 0.424597600764,\n", "'PENIAGONE_SP_2': 0.166666666667,\n", "'SYNALLACTIDAE': 0.627380952381,\n", "'PENIAGONE_SP_A': 0.987186066252,\n", "'PENIAGONE_VITRAE': 0.376280487988}" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-08-02T18:19:56.100827Z", "start_time": "2017-08-02T18:19:56.089953Z" }, "collapsed": true }, "outputs": [], "source": [ "keys = set(most_epoch_ap.keys()) & set(imagenet_ap.keys()) & set(test_imgs.keys()) & set(train_imgs.keys())" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-08-02T18:19:56.157245Z", "start_time": "2017-08-02T18:19:56.115750Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ,Number of Training Images, Number of Testing Images, Imagenet AP, Most Epoch AP\n", "SCOTOPLANES_GLOBOSA,365,63,0.899604662246,0.948805921186\n", "ONEIROPHANTA_MUTABILIS_COMPLEX,6,3,0.458815192744,0.248599910394\n", "PENIAGONE_SP_A,1108,453,0.978279800281,0.987186066252\n", "PENIAGONE_SP_2,12,1,0.166666666667,0.166666666667\n", "TJALFIELLA,230,66,0.494639502854,0.389716451493\n", "PENIAGONE_SP_1,111,2,0.0206598918637,0.0500641025641\n", "BENTHOCODON,569,286,0.805137016516,0.757451612353\n", "LONG_WHITE,8,3,0.2,0.183501683502\n", "PENIAGONE_PAPILLATA,14,10,0.7725,1.0\n", "ELPIDIA,493,13,0.0442176870748,0.102745995423\n", "PENIAGONE_VITRAE,360,93,0.292665667963,0.376280487988\n", "FUNGIACYATHUS_MARENZELLERI,64,13,0.386287047543,0.424597600764\n", "CYSTECHINUS_LOVENI,174,40,0.590778912159,0.643499906799\n", "ECHINOCREPIS,82,26,0.42133689736,0.786982162631\n", "SYNALLACTIDAE,30,5,0.821111111111,0.627380952381\n" ] } ], "source": [ "print(\" ,Number of Training Images, Number of Testing Images, Imagenet AP, Most Epoch AP\")\n", "for key in keys:\n", " num_train = train_imgs[key]\n", " num_test = test_imgs[key]\n", " imgnet_ap = imagenet_ap[key]\n", " max_epch_ap = most_epoch_ap[key]\n", " print(\"{},{},{},{},{}\".format(key, num_train, num_test, imgnet_ap, max_epch_ap))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-08-02T18:19:56.183934Z", "start_time": "2017-08-02T18:19:56.170798Z" }, "collapsed": true }, "outputs": [], "source": [ "all_keys = set(most_epoch_ap.keys()) | set(imagenet_ap.keys()) | set(test_imgs.keys()) | set(train_imgs.keys())" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-08-02T18:19:56.193619Z", "start_time": "2017-08-02T18:19:56.187680Z" }, "collapsed": true }, "outputs": [], "source": [ "all_keys.remove('bg')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-08-02T18:19:56.218503Z", "start_time": "2017-08-02T18:19:56.196041Z" } }, "outputs": [ { "data": { "text/plain": [ "['PENIAGONE_PAPILLATA',\n", " 'PENIAGONE_VITRAE',\n", " 'FISH',\n", " 'FUNGIACYATHUS_MARENZELLERI',\n", " 'ECHINOCREPIS',\n", " 'ONEIROPHANTA_MUTABILIS_COMPLEX',\n", " 'ELPIDIA',\n", " 'TJALFIELLA',\n", " 'SCOTOPLANES_GLOBOSA',\n", " 'LONG_WHITE',\n", " 'CYSTECHINUS_LOVENI',\n", " 'SYNALLACTIDAE',\n", " 'PENIAGONE_SP_A',\n", " 'PENIAGONE_SP_2',\n", " 'PENIAGONE_SP_1',\n", " 'BENTHOCODON']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(all_keys)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Environment (conda_video-processing)", "language": "python", "name": "conda_video-processing" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
petrasovaa/amos-visualization
scripts/nonspatial_analysis.ipynb
1
9499539
null
gpl-2.0
ledeprogram/algorithms
class6/donow/Skinner_Barnaby_DoNow_6.ipynb
1
17436
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Import the necessary packages to read in the data, plot, and create a linear regression model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import statsmodels.formula.api as smf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Read in the hanford.csv file " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv(\"data/hanford.csv\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>County</th>\n", " <th>Exposure</th>\n", " <th>Mortality</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Umatilla</td>\n", " <td>2.49</td>\n", " <td>147.1</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Morrow</td>\n", " <td>2.57</td>\n", " <td>130.1</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Gilliam</td>\n", " <td>3.41</td>\n", " <td>129.9</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Sherman</td>\n", " <td>1.25</td>\n", " <td>113.5</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Wasco</td>\n", " <td>1.62</td>\n", " <td>137.5</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>HoodRiver</td>\n", " <td>3.83</td>\n", " <td>162.3</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Portland</td>\n", " <td>11.64</td>\n", " <td>207.5</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Columbia</td>\n", " <td>6.41</td>\n", " <td>177.9</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Clatsop</td>\n", " <td>8.34</td>\n", " <td>210.3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " County Exposure Mortality\n", "0 Umatilla 2.49 147.1\n", "1 Morrow 2.57 130.1\n", "2 Gilliam 3.41 129.9\n", "3 Sherman 1.25 113.5\n", "4 Wasco 1.62 137.5\n", "5 HoodRiver 3.83 162.3\n", "6 Portland 11.64 207.5\n", "7 Columbia 6.41 177.9\n", "8 Clatsop 8.34 210.3" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Calculate the basic descriptive statistics on the data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4.6177777777777784" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Exposure'].mean()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 9.000000\n", "mean 4.617778\n", "std 3.491192\n", "min 1.250000\n", "25% 2.490000\n", "50% 3.410000\n", "75% 6.410000\n", "max 11.640000\n", "Name: Exposure, dtype: float64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Exposure'].describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4. Calculate the coefficient of correlation (r) and generate the scatter plot. Does there seem to be a correlation worthy of investigation?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Exposure</th>\n", " <th>Mortality</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Exposure</th>\n", " <td>1.000000</td>\n", " <td>0.926345</td>\n", " </tr>\n", " <tr>\n", " <th>Mortality</th>\n", " <td>0.926345</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Exposure Mortality\n", "Exposure 1.000000 0.926345\n", "Mortality 0.926345 1.000000" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.corr()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10e652cf8>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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MhojYAtwInAo8AfxpZv7BMvsZDJLUo3ENhtOA0zLz7og4CbgTuCQz71+yn8EgST0ay9tV\nM/PrmXl3sf0fwH3As6uoRZJUVvldSRFRA84GPl9tJZIkgA1VHrwYRvow8JbiyuH7zM7OHt2u1+vU\n6/U1qU2SxkWj0aDRaAzs/Sq7KykiNgCfAD6Vme9bYR/nGCSpR2M5+QwQETcCj2TmW4+xj8EgST0a\ny8nniHgF8HPAKyPirojYHxEXVVGL+ueqp9Jk8gNu6ournkqja2yHkrphMIymcV/KW5p0YzmUpPHm\nqqfSZDMY1DNXPZUmm8Ggno3zUt6Sjs85hh650ueT/LOQRpOTz2vIO3EkjQODYY14J46kceFdSWvE\nO3EkrRcGQ5e8E0fSemEwdMk7cSStF84x9Mg7cSSNOiefJUklTj5LkgbKYJAklRgMkqQSg0GSVGIw\nSJJKDAZJUonBIEkqMRgkSSUGgySpxGCQJJVUFgwRcVFE3B8R/xgR76iqDklSWSXBEBEnAH8EXAi8\nENgZEVurqKVKjUaj6hKGapL7N8l9A/u33lV1xfCjwIOZuZiZh4EPApdUVEtlJv0v5yT3b5L7BvZv\nvasqGJ4NfK3j+T8VbZKkijn5LEkqqeT7GCLi5cBsZl5UPH8nkJn520v288sYJKkPY/dFPRFxIvAA\ncAHwL8A/ADsz8741L0aSVLKhioNm5uMR8cvArbSHs+YMBUkaDSP91Z6SpLVX6eRzRMxFxIGIWOho\ne2ZE3BoRD0TEpyPi6R2vXRMRD0bEfRHxE9VU3Z0V+nZdUfvdEfGRiNjc8drY9A2W71/Ha2+LiCci\n4uSOtonoX0S8uejDvRHxno72se9fRLw0Iv4hIu4q/vsjHa+NTf8iYktEfCYivlT8f/rvRfuknFuW\n9u/NRfvgzi+ZWdkDOA84G1joaPtt4O3F9juA9xTbLwDuoj38VQP+H8UVzyg+Vujbq4ATiu33AL81\njn1bqX9F+xbgr4GvAicXbc+fhP4BddrDnxuK5/95wvq3F/iJYvvVwN5ie6z+fgKnAWcX2yfRns/c\nOkHnlpX6N7DzS6VXDJl5O/DokuZLgBuK7RuA1xbbFwMfzMzHMrMJPEj7g3Ijabm+ZeZtmflE8fQO\n2idRGLO+wYr/7wDeC1y9pO0SJqN/v0T7ZPJYsc8jRfuk9O9fgCO/RT8DeLjYHqu/n5n59cy8u9j+\nD+A+2v/WJuXcslz/nj3I88sofo7hlMw8AO0/AOCUon3ph+IeZrw/FPcG4JPF9kT0LSIuBr6Wmfcu\neWki+gecBfx4RNwREXsj4tyifVL6907g9yLiIeA64JqifWz7FxE12ldGdwCnTtq5paN/n1/y0qrO\nL6MYDEtN3Ox4RLwLOJyZ81XXMigRMQX8KnBt1bUM0QbgmZn5cuDtwIcqrmfQ5oA3Z+YPAb8CvL/i\nelYlIk4CPgy8pfjNeum5ZKzPLcv070j7qs8voxgMByLiVICIOA34RtH+MPCcjv228OSl7tiIiCuA\nnwQu72iehL6dSXv88p6I+CrtPuyPiFNo9+WHOvYdx/5B+7eujwJk5j7g8Yh4FpPTv5dl5scAMvPD\nwEuL9rH7+xkRG2ifNP8iMz9eNE/MuWWF/g3s/DIKwRDF44ibgSuK7dcDH+9of11EbIqI04Hn0v5g\n3Cgr9S0iLqI9/n5xZn6vY79x7Bt09C8zv5iZp2XmGZl5Ou31r16Smd+g3b/Lxrl/hY8BrwSIiLOA\nTZn5r0xO/x6MiPMBIuIC2mPRMJ5/P98PfDkz39fRNknnlu/r30DPLxXPrt8E/DPwPeAh4ErgmcBt\ntGfabwWe0bH/NbRn1O+juHtiVB8r9O1BYBHYXzx2j2PfVurfkte/QnFX0qT0j/ZQ0l8A9wJfAM6f\nsP6dS3us+i7gc7SDfez6B7wCeBy4u+jLfuAi4OQJObcs179XD/L84gfcJEklozCUJEkaIQaDJKnE\nYJAklRgMkqQSg0GSVGIwSJJKDAZNvGIJ8Bs7np8YEa2IuLnH93lxRLy6i/3Oj4hbiu3XRMTbi+1L\nImJrr/VLa81g0HrwHeBFEfGfiuc7KC8qdlzR/jras2kvN9CNBMjMWzLzuqLttcALezmuVAWDQevF\nJ4GfKrZ3AkcXGCu+wOX/RMQ9EfHZiHhR0X5tRNwYEX9P+xPP/wu4NCL2R8TPFl9s89mIuDMibo+I\n5y09aES8PiL+MCJ+jPbyx9cVP39GRNzZsd9zO59LVTIYtB4k8EFgZ3HVsI3yMsXvBvZn5ouBd9EO\ngSOeD1yQmZcD/xPYk5nnZOaHaC8vcF5mnkt7VdnfWun4mfk52mvWXF38/FeAf4+IbcU+VzLmq5lq\ncmyougBpLWTmF4u163cCf0V58bjzgJ8p9tsbEScXSxoD3JyZh1Z422cANxZXCknv/57mgCsj4m3A\nZTy5mqlUKa8YtJ7cDPwOHcNIXfjOMV77deAzmfnDwGuAp/RYz0doz1n8NPCFzFzuG/GkNWcwaD04\ncnXwfuDdmfmlJa//PfDzABFRBx7Jji8+6fBtYHPH8808ua79lV3UUfr5bC+N/Gngj4Hru/h5aU0Y\nDFoPjtwh9HBm/tEyr88C50bEPcBvAr+4wvvsBV5wZPKZ9tdfvqeYNO7m39IHgauLyerTi7YP0F5C\n+daueyMNmctuSxUq5hc2Z+YkfyWqxoyTz1JFIuKjwBkU3wonjQqvGCRJJc4xSJJKDAZJUonBIEkq\nMRgkSSUGgySpxGCQJJX8f0fUiE0KaUwfAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10e652400>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.plot(kind='scatter', x='Mortality', y='Exposure')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 5. Create a linear regression model based on the available data to predict the mortality rate given a level of exposure" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Intercept 114.715631\n", "Exposure 9.231456\n", "dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = smf.ols(formula='Mortality~Exposure',data=df).fit()\n", "lm.params\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "intercept, Exposure = lm.params\n", "Mortality = Exposure*10+intercept" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "199.93541569980266" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Mortality" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 6. Plot the linear regression line on the scatter plot of values. Calculate the r^2 (coefficient of determination)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## 7. Predict the mortality rate (Cancer per 100,000 man years) given an index of exposure = 10" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "intercept, Exposure = lm.params\n", "Mortality = Exposure*10+intercept" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "207.03019352841983" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Mortality" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
mne-tools/mne-tools.github.io
0.20/_downloads/133b333d981b1511cd3e323ca3305f51/plot_virtual_evoked.ipynb
1
2210
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Remap MEG channel types\n\nIn this example, MEG data are remapped from one channel type to another.\nThis is useful to:\n\n - visualize combined magnetometers and gradiometers as magnetometers\n or gradiometers.\n - run statistics from both magnetometers and gradiometers while\n working with a single type of channels.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Author: Mainak Jas <[email protected]>\n\n# License: BSD (3-clause)\n\nimport mne\nfrom mne.datasets import sample\n\nprint(__doc__)\n\n# read the evoked\ndata_path = sample.data_path()\nfname = data_path + '/MEG/sample/sample_audvis-ave.fif'\nevoked = mne.read_evokeds(fname, condition='Left Auditory', baseline=(None, 0))\n\n# go from grad + mag to mag\nvirt_evoked = evoked.as_type('mag')\nevoked.plot_topomap(ch_type='mag', title='mag (original)', time_unit='s')\nvirt_evoked.plot_topomap(ch_type='mag', time_unit='s',\n title='mag (interpolated from mag + grad)')\n\n# go from grad + mag to grad\nvirt_evoked = evoked.as_type('grad')\nevoked.plot_topomap(ch_type='grad', title='grad (original)', time_unit='s')\nvirt_evoked.plot_topomap(ch_type='grad', time_unit='s',\n title='grad (interpolated from mag + grad)')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
ecalio07/enron-paper
dev/min_samples_split_parameter.ipynb
2
2674
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Tree Classifier - Focus on min_samples_split parameter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing Modules" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "from sklearn import tree\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run Variables Setup If Necessary" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if 'features_train' not in locals() or globals():\n", " %run ../dev/environment_setup.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Tree Classifier - min_samples_split default value 2 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "clf = tree.DecisionTreeClassifier() #if nothing is specified default value is 2\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train and Predict Data " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "train_predict(\"Train and Predict Data with min_samples_split = 2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Tree Classifier - min_samples_split with higher value" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "clf = tree.DecisionTreeClassifier(min_samples_split=40) \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train and Predict Data " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "train_predict(\"Train and Predict Data with min_samples_split = 40\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
tensorflow/docs-l10n
site/ja/r1/tutorials/keras/basic_text_classification.ipynb
1
28854
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Ic4_occAAiAT" }, "source": [ "##### Copyright 2018 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "ioaprt5q5US7" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "yCl0eTNH5RS3" }, "outputs": [], "source": [ "#@title MIT License\n", "#\n", "# Copyright (c) 2017 François Chollet\n", "#\n", "# Permission is hereby granted, free of charge, to any person obtaining a\n", "# copy of this software and associated documentation files (the \"Software\"),\n", "# to deal in the Software without restriction, including without limitation\n", "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n", "# and/or sell copies of the Software, and to permit persons to whom the\n", "# Software is furnished to do so, subject to the following conditions:\n", "#\n", "# The above copyright notice and this permission notice shall be included in\n", "# all copies or substantial portions of the Software.\n", "#\n", "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n", "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n", "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n", "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n", "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n", "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n", "# DEALINGS IN THE SOFTWARE." ] }, { "cell_type": "markdown", "metadata": { "id": "ItXfxkxvosLH" }, "source": [ "# 映画レビューのテキスト分類" ] }, { "cell_type": "markdown", "metadata": { "id": "hKY4XMc9o8iB" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/r1/tutorials/keras/basic_text_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/r1/tutorials/keras/basic_text_classification.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "nBk9hleL9kon" }, "source": [ "Note: これらのドキュメントは私たちTensorFlowコミュニティが翻訳したものです。コミュニティによる 翻訳は**ベストエフォート**であるため、この翻訳が正確であることや[英語の公式ドキュメント](https://www.tensorflow.org/?hl=en)の 最新の状態を反映したものであることを保証することはできません。 この翻訳の品質を向上させるためのご意見をお持ちの方は、GitHubリポジトリ[tensorflow/docs](https://github.com/tensorflow/docs)にプルリクエストをお送りください。 コミュニティによる翻訳やレビューに参加していただける方は、 [[email protected] メーリングリスト](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs-ja)にご連絡ください。" ] }, { "cell_type": "markdown", "metadata": { "id": "Eg62Pmz3o83v" }, "source": [ "ここでは、映画のレビューをそのテキストを使って**肯定的**か**否定的**かに分類します。これは、二値分類あるいは2クラス分類という問題の例であり、機械学習において重要でいろいろな応用が可能なものです。\n", "\n", "ここでは、[Internet Movie Database](https://www.imdb.com/)から抽出した50,000件の映画レビューを含む、 [IMDB dataset](https://www.tensorflow.org/api_docs/python/tf/keras/datasets/imdb) を使います。レビューは訓練用とテスト用に25,000件ずつに分割されています。訓練用とテスト用のデータは**均衡**しています。言い換えると、それぞれが同数の肯定的及び否定的なレビューを含んでいます。\n", "\n", "ここでは、TensorFlowを使ってモデルを構築・訓練するためのハイレベルなAPIである [tf.keras](https://www.tensorflow.org/r1/guide/keras)を使用します。`tf.keras`を使ったもう少し高度なテキスト分類のチュートリアルについては、 [MLCC Text Classification Guide](https://developers.google.com/machine-learning/guides/text-classification/)を参照してください。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "k6HBIXfqD6ZB" }, "outputs": [], "source": [ "# keras.datasets.imdb is broken in 1.13 and 1.14, by np 1.16.3\n", "!pip install tf_nightly" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2ew7HTbPpCJH" }, "outputs": [], "source": [ "import tensorflow.compat.v1 as tf\n", "\n", "from tensorflow import keras\n", "\n", "import numpy as np\n", "\n", "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": { "id": "iAsKG535pHep" }, "source": [ "## IMDB datasetのダウンロード\n", "\n", "IMDBデータセットは、TensorFlowにパッケージ化されています。それは前処理済みのものであり、（単語の連なりである）レビューが、整数の配列に変換されています。そこでは整数が辞書中の特定の単語を表します。\n", "\n", "次のコードは、IMDBデータセットをあなたのパソコンにダウンロードします。（すでにダウンロードしていれば、キャッシュされたコピーを使用します）" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zXXx5Oc3pOmN" }, "outputs": [], "source": [ "imdb = keras.datasets.imdb\n", "\n", "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)" ] }, { "cell_type": "markdown", "metadata": { "id": "odr-KlzO-lkL" }, "source": [ "`num_words=10000`という引数は、訓練データ中に出てくる単語のうち、最も頻繁に出現する10,000個を保持するためのものです。データサイズを管理可能にするため、稀にしか出現しない単語は破棄されます。" ] }, { "cell_type": "markdown", "metadata": { "id": "l50X3GfjpU4r" }, "source": [ "## データを調べる\n", "\n", "データの形式を理解するために少し時間を割いてみましょう。このデータセットは前処理済みで、サンプルそれぞれが、映画レビューの中の単語を表す整数の配列になっています。ラベルはそれぞれ、0または1の整数値で、0が否定的レビュー、1が肯定的なレビューを示しています。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "y8qCnve_-lkO" }, "outputs": [], "source": [ "print(\"Training entries: {}, labels: {}\".format(len(train_data), len(train_labels)))" ] }, { "cell_type": "markdown", "metadata": { "id": "RnKvHWW4-lkW" }, "source": [ "レビューのテキストは複数の整数に変換されており、それぞれの整数が辞書の中の特定の単語を表します。最初のレビューがどのようなものか見てみましょう。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QtTS4kpEpjbi" }, "outputs": [], "source": [ "print(train_data[0])" ] }, { "cell_type": "markdown", "metadata": { "id": "hIE4l_72x7DP" }, "source": [ "映画のレビューはそれぞれ長さが異なっていることでしょう。次のコードで、最初と2つ目のレビューの単語の数を見てみます。ニューラルネットワークへの入力は同じ長さでなければならないため、後ほどその問題を解決する必要があります。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "X-6Ii9Pfx6Nr" }, "outputs": [], "source": [ "len(train_data[0]), len(train_data[1])" ] }, { "cell_type": "markdown", "metadata": { "id": "4wJg2FiYpuoX" }, "source": [ "### 整数を単語に戻してみる\n", "\n", "整数をテキストに戻す方法を知っていると便利です。整数を文字列にマッピングする辞書オブジェクトを検索するためのヘルパー関数を定義します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tr5s_1alpzop" }, "outputs": [], "source": [ "# 単語を整数にマッピングする辞書\n", "word_index = imdb.get_word_index()\n", "\n", "# インデックスの最初の方は予約済み\n", "word_index = {k:(v+3) for k,v in word_index.items()}\n", "word_index[\"<PAD>\"] = 0\n", "word_index[\"<START>\"] = 1\n", "word_index[\"<UNK>\"] = 2 # unknown\n", "word_index[\"<UNUSED>\"] = 3\n", "\n", "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n", "\n", "def decode_review(text):\n", " return ' '.join([reverse_word_index.get(i, '?') for i in text])" ] }, { "cell_type": "markdown", "metadata": { "id": "U3CNRvEZVppl" }, "source": [ "`decode_review`を使うと、最初のレビューのテキストを表示できます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "s_OqxmH6-lkn" }, "outputs": [], "source": [ "decode_review(train_data[0])" ] }, { "cell_type": "markdown", "metadata": { "id": "lFP_XKVRp4_S" }, "source": [ "## データの準備\n", "\n", "レビュー（整数の配列）は、ニューラルネットワークに投入する前に、テンソルに変換する必要があります。これには2つの方法があります。\n", "\n", "* 配列をワンホット（one-hot）エンコーディングと同じように、単語の出現を表す0と1のベクトルに変換します。例えば、[3, 5]という配列は、インデックス3と5を除いてすべてゼロの10,000次元のベクトルになります。そして、これをネットワークの最初の層、すなわち、浮動小数点のベクトルデータを扱うことができるDense（全結合）層とします。ただし、これは単語数×レビュー数の行列が必要なメモリ集約的な方法です。\n", "* もう一つの方法では、配列をパディングによって同じ長さに揃え、`サンプル数 * 長さの最大値`の形の整数テンソルにします。そして、この形式を扱うことができるEmbedding（埋め込み）層をネットワークの最初の層にします。\n", "\n", "このチュートリアルでは、後者を採用することにします。\n", "\n", "映画レビューは同じ長さでなければならないので、長さを標準化する [pad_sequences](https://www.tensorflow.org/versions/r1.10/api_docs/python/tf/keras/preprocessing/sequence/pad_sequences) 関数を使うことにします。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "2jQv-omsHurp" }, "outputs": [], "source": [ "train_data = keras.preprocessing.sequence.pad_sequences(train_data,\n", " value=word_index[\"<PAD>\"],\n", " padding='post',\n", " maxlen=256)\n", "\n", "test_data = keras.preprocessing.sequence.pad_sequences(test_data,\n", " value=word_index[\"<PAD>\"],\n", " padding='post',\n", " maxlen=256)" ] }, { "cell_type": "markdown", "metadata": { "id": "VO5MBpyQdipD" }, "source": [ "サンプルの長さを見てみましょう。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "USSSBnkE-lky" }, "outputs": [], "source": [ "len(train_data[0]), len(train_data[1])" ] }, { "cell_type": "markdown", "metadata": { "id": "QJoxZGyfjT5V" }, "source": [ "次に、パディング済みの最初のサンプルを確認します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TG8X9cqi-lk9" }, "outputs": [], "source": [ "print(train_data[0])" ] }, { "cell_type": "markdown", "metadata": { "id": "LLC02j2g-llC" }, "source": [ "## モデルの構築\n", "\n", "ニューラルネットワークは、層を積み重ねることで構成されます。この際、２つの大きな決定が必要です。\n", "\n", "* モデルにいくつの**層**を設けるか？\n", "* 層ごとに何個の**隠れユニット**を使用するか？\n", "\n", "この例では、入力データは単語インデックスの配列で構成されています。推定の対象となるラベルは、0または1です。この問題のためのモデルを構築しましょう。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xpKOoWgu-llD" }, "outputs": [], "source": [ "# 入力の形式は映画レビューで使われている語彙数（10,000語）\n", "vocab_size = 10000\n", "\n", "model = keras.Sequential()\n", "model.add(keras.layers.Embedding(vocab_size, 16))\n", "model.add(keras.layers.GlobalAveragePooling1D())\n", "model.add(keras.layers.Dense(16, activation=tf.nn.relu))\n", "model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))\n", "\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": { "id": "6PbKQ6mucuKL" }, "source": [ "これらの層は、分類器を構成するため一列に積み重ねられます。\n", "\n", "1. 最初の層は`Embedding`（埋め込み）層です。この層は、整数にエンコードされた語彙を受け取り、それぞれの単語インデックスに対応する埋め込みベクトルを検索します。埋め込みベクトルは、モデルの訓練の中で学習されます。ベクトル化のために、出力行列には次元が１つ追加されます。その結果、次元は、`(batch, sequence, embedding)`となります。\n", "2. 次は、`GlobalAveragePooling1D`（１次元のグローバル平均プーリング）層です。この層は、それぞれのサンプルについて、シーケンスの次元方向に平均値をもとめ、固定長のベクトルを返します。この結果、モデルは最も単純な形で、可変長の入力を扱うことができるようになります。\n", "3. この固定長の出力ベクトルは、16個の隠れユニットを持つ全結合（`Dense`）層に受け渡されます。\n", "4. 最後の層は、1個の出力ノードに全結合されます。シグモイド（`sigmoid`）活性化関数を使うことで、値は確率あるいは確信度を表す0と1の間の浮動小数点数となります。" ] }, { "cell_type": "markdown", "metadata": { "id": "0XMwnDOp-llH" }, "source": [ "### 隠れユニット\n", "\n", "上記のモデルには、入力と出力の間に、2つの中間層あるいは「隠れ」層があります。出力（ユニット、ノード、またはニューロン）は、その層の内部表現の次元数です。言い換えると、このネットワークが学習によって内部表現を獲得する際の自由度ということです。\n", "\n", "モデルにより多くの隠れユニットがある場合（内部表現空間の次元数がより大きい場合）、または、より多くの層がある場合、あるいはその両方の場合、ネットワークはより複雑な内部表現を学習することができます。しかしながら、その結果として、ネットワークの計算量が多くなるほか、学習してほしくないパターンを学習するようになります。学習してほしくないパターンとは、訓練データでの性能は向上するものの、テスト用データの性能が向上しないパターンです。この問題を**過学習**（*overfitting*）といいます。この問題は後ほど検証することになります。" ] }, { "cell_type": "markdown", "metadata": { "id": "L4EqVWg4-llM" }, "source": [ "### 損失関数とオプティマイザ\n", "\n", "モデルを訓練するには、損失関数とオプティマイザが必要です。今回の問題は二値分類問題であり、モデルの出力は確率（1ユニットの層とシグモイド活性化関数）であるため、損失関数として`binary_crossentropy`（2値のクロスエントロピー）関数を使用することにします。\n", "\n", "損失関数の候補はこれだけではありません。例えば、`mean_squared_error`（平均二乗誤差）を使うこともできます。しかし、一般的には、確率を扱うには`binary_crossentropy`の方が適しています。`binary_crossentropy`は、確率分布の間の「距離」を測定する尺度です。今回の場合には、真の分布と予測値の分布の間の距離ということになります。\n", "\n", "後ほど、回帰問題を検証する際には（例えば家屋の値段を推定するとか）、もう一つの損失関数である`mean_squared_error`（平均二乗誤差）の使い方を目にすることになります。\n", "\n", "さて、モデルのオプティマイザと損失関数を設定しましょう。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Mr0GP-cQ-llN" }, "outputs": [], "source": [ "model.compile(optimizer=tf.keras.optimizers.Adam(),\n", " loss='binary_crossentropy',\n", " metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": { "id": "hCWYwkug-llQ" }, "source": [ "## 検証用データを作る\n", "\n", "訓練を行う際、モデルが見ていないデータでの正解率を検証したいと思います。もとの訓練用データから、10,000個のサンプルを取り分けて**検証用データ**（*validation set*）を作ります。（なぜ、ここでテスト用データを使わないのでしょう？ 今回の目的は、訓練用データだけを使って、モデルの開発とチューニングを行うことです。その後、テスト用データを1回だけ使い、正解率を検証するのです。）" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-NpcXY9--llS" }, "outputs": [], "source": [ "x_val = train_data[:10000]\n", "partial_x_train = train_data[10000:]\n", "\n", "y_val = train_labels[:10000]\n", "partial_y_train = train_labels[10000:]" ] }, { "cell_type": "markdown", "metadata": { "id": "35jv_fzP-llU" }, "source": [ "## モデルの訓練\n", "\n", "512個のサンプルからなるミニバッチを使って、40エポックモデルを訓練します。この結果、`x_train`と`y_train`に含まれるすべてのサンプルを40回繰り返すことになります。訓練中、検証用データの10,000サンプルを用いて、モデルの損失と正解率をモニタリングします。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tXSGrjWZ-llW" }, "outputs": [], "source": [ "history = model.fit(partial_x_train,\n", " partial_y_train,\n", " epochs=40,\n", " batch_size=512,\n", " validation_data=(x_val, y_val),\n", " verbose=1)" ] }, { "cell_type": "markdown", "metadata": { "id": "9EEGuDVuzb5r" }, "source": [ "## モデルの評価\n", "\n", "さて、モデルの性能を見てみましょう。2つの値が返されます。損失（エラーを示す数値であり、小さい方が良い）と正解率です。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zOMKywn4zReN" }, "outputs": [], "source": [ "results = model.evaluate(test_data, test_labels, verbose=2)\n", "\n", "print(results)" ] }, { "cell_type": "markdown", "metadata": { "id": "z1iEXVTR0Z2t" }, "source": [ "この、かなり素朴なアプローチでも87%前後の正解率を達成しました。もっと高度なアプローチを使えば、モデルの正解率は95%に近づけることもできるでしょう。" ] }, { "cell_type": "markdown", "metadata": { "id": "5KggXVeL-llZ" }, "source": [ "## 正解率と損失の時系列グラフを描く\n", "\n", "`model.fit()` は、訓練中に発生したすべてのことを記録した辞書を含む`History` オブジェクトを返します。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "VcvSXvhp-llb" }, "outputs": [], "source": [ "history_dict = history.history\n", "history_dict.keys()" ] }, { "cell_type": "markdown", "metadata": { "id": "nRKsqL40-lle" }, "source": [ "4つのエントリがあります。それぞれが、訓練と検証の際にモニターしていた指標を示します。これを使って、訓練時と検証時の損失を比較するグラフと、訓練時と検証時の正解率を比較するグラフを作成することができます。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nGoYf2Js-lle" }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "acc = history.history['acc']\n", "val_acc = history.history['val_acc']\n", "loss = history.history['loss']\n", "val_loss = history.history['val_loss']\n", "\n", "epochs = range(1, len(acc) + 1)\n", "\n", "# \"bo\" は青いドット\n", "plt.plot(epochs, loss, 'bo', label='Training loss')\n", "# ”b\" は青い実線\n", "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n", "plt.title('Training and validation loss')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Loss')\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "6hXx-xOv-llh" }, "outputs": [], "source": [ "plt.clf() # 図のクリア\n", "acc_values = history_dict['acc']\n", "val_acc_values = history_dict['val_acc']\n", "\n", "plt.plot(epochs, acc, 'bo', label='Training acc')\n", "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n", "plt.title('Training and validation accuracy')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Accuracy')\n", "plt.legend()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "oFEmZ5zq-llk" }, "source": [ "上記のグラフでは、点が訓練時の損失と正解率を、実線が検証時の損失と正解率を表しています。\n", "\n", "訓練時の損失がエポックごとに**減少**し、訓練時の正解率がエポックごとに**上昇**していることに気がつくはずです。繰り返すごとに指定された数値指標を最小化する勾配降下法を最適化に使用している場合に期待される動きです。\n", "\n", "これは、検証時の損失と正解率には当てはまりません。20エポックを過ぎたあたりから、横ばいになっているようです。これが、過学習の一例です。モデルの性能が、訓練用データでは高い一方で、見たことの無いデータではそれほど高くないというものです。このポイントをすぎると、モデルが最適化しすぎて、訓練用データでは特徴的であるが、テスト用データには一般化できない内部表現を学習しています。\n", "\n", "このケースの場合、20エポックを過ぎたあたりで訓練をやめることで、過学習を防止することが出来ます。後ほど、コールバックを使って、これを自動化する方法を紹介します。" ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "basic_text_classification.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
mortcanty/SARDocker
tutorialsar.ipynb
1
4824934
null
mit
WormLabCaltech/mprsq
src/automation/kallisto_bash_script_generator.ipynb
1
7215
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "toc": "true" }, "source": [ "# Table of Contents\n", " <p>" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "button": false, "collapsed": true, "deletable": true, "editable": true, "new_sheet": false, "run_control": { "read_only": false } }, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "# params\n", "directory = '../input/rawseq'\n", "length = 180\n", "sigma = 60\n", "btstrp = 200\n", "thrds = 6\n", "\n", "# sequences:\n", "seqs = next(os.walk(directory))[1]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "button": false, "collapsed": false, "deletable": true, "editable": true, "new_sheet": false, "run_control": { "read_only": false } }, "outputs": [], "source": [ "# params\n", "directory = '../input/rawseq'\n", "length = 180\n", "sigma = 60\n", "btstrp = 200\n", "thrds = 6\n", "\n", "# sequences:\n", "seqs = next(os.walk(directory))[1]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "button": false, "collapsed": false, "deletable": true, "editable": true, "new_sheet": false, "run_control": { "read_only": false } }, "outputs": [], "source": [ "def explicit_kallisto(directory, files, res_dir):\n", " \"\"\"\n", " TODO: Make a function that allows you to systematically \n", " set up each parameter for each sequencing run individually.\n", " \"\"\"\n", " \n", " if type(directory) is not str:\n", " raise ValueError('directory must be a str')\n", " if type(files) is not list:\n", " raise ValueError('files must be a list')\n", " \n", " print('This sequence file contains a Kallisto_Info file\\\n", " and cannot be processed at the moment.')\n", " return '# {0} could not be processed'.format(res_dir), ''\n", " \n", "def implicit_kallisto(directory, files, res_dir):\n", " \"\"\"\n", " A function to write a Kallisto command with standard parameter\n", " setup\n", " \"\"\"\n", " if type(directory) is not str:\n", " raise ValueError('directory must be a str')\n", " if type(files) is not list:\n", " raise ValueError('files must be a list')\n", "\n", " # parts of each kallisto statement\n", " \n", " # information\n", " info = '# kallisto command for {0}'.format(directory)\n", " # transcript file location:\n", " k_head = 'kallisto quant -i input/transcripts.idx -o '\n", " \n", " # output file location\n", " k_output = 'input/kallisto_all/' + res_dir + '/kallisto '\n", " # parameter info:\n", " k_params = '--single -s {0} -l {1} -b {2} -t {3} --bias --fusion'.format(sigma, length, btstrp, thrds)\n", " \n", " # what files to use:\n", " k_files = '' \n", " # go through each file and add it to the command\n", " # unless it's a SampleSheet.csv file, in which\n", " # case you should ignore it. \n", " for y in files:\n", " if y != 'SampleSheet.csv':\n", " if directory[:3] == '../':\n", " d = directory[3:]\n", " else:\n", " d = directory[:]\n", " k_files += ' '+ d + '/' + y\n", " # all together now:\n", " kallisto = k_head + k_output + k_params + k_files +';'\n", " return info, kallisto" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "button": false, "collapsed": false, "deletable": true, "editable": true, "new_sheet": false, "run_control": { "read_only": false } }, "outputs": [], "source": [ "def walk_seq_directories(directory):\n", " \"\"\"\n", " Given a directory, walk through it,\n", " find all the rna-seq repository folders\n", " and generate kallisto commands\n", " \"\"\"\n", " kallisto = ''\n", " #directory contains all the projects, walk through it:\n", " for x in os.walk(directory):\n", " # first directory is always parent\n", " # if it's not the parent, move forward:\n", " if x[0] != directory:\n", " # cut the head off and get the project name:\n", " res_dir = x[0][len(directory)+1:]\n", " \n", " # if this project has attributes explicitly written in\n", " # use those parameter specs:\n", " if 'Kallisto_Info.csv' in x[2]:\n", " info, command = explicit_kallisto(x[0], x[2], res_dir)\n", " continue\n", " \n", " # otherwise, best guesses:\n", " info, command = implicit_kallisto(x[0], x[2], res_dir)\n", " kallisto += info + '\\n' + command + '\\n'\n", " \n", " if not os.path.exists('../input/kallisto_all/' + res_dir):\n", " os.makedirs('../input/kallisto_all/' + res_dir)\n", " return kallisto\n", "\n", "with open('../kallisto_commands.sh', 'w') as f:\n", " f.write('#!/bin/bash\\n')\n", " f.write('# make transcript index\\n')\n", " f.write('kallisto index -i input/transcripts.idx input/c_elegans_WBcel235.rel79.cdna.all.fa;\\n')\n", " kallisto = walk_seq_directories(directory)\n", " f.write(kallisto)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "button": false, "collapsed": true, "deletable": true, "editable": true, "new_sheet": false, "run_control": { "read_only": false } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "button": false, "collapsed": true, "deletable": true, "editable": true, "new_sheet": false, "run_control": { "read_only": false } }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": true, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 0 }
mit
bioasp/meneco
meneco.ipynb
1
46339
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "meneco.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "xgB9x4323imj", "colab_type": "text" }, "source": [ "# Meneco demo\n", "\n", "First you need to install Meneco. For example with `pip` ..." ] }, { "cell_type": "code", "metadata": { "id": "Vd7YkD5WyG6Z", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 624 }, "outputId": "a85711db-7f98-49fe-b88d-b031ca25a364" }, "source": [ "pip install meneco" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Collecting meneco\n", " Downloading https://files.pythonhosted.org/packages/2b/75/cbe49e47c0067bcf0e4b1117329eab27dc94f4ef889e35c2e118478705e2/Meneco-2.0.0.tar.gz\n", "Collecting clyngor_with_clingo\n", "\u001b[?25l Downloading https://files.pythonhosted.org/packages/c5/7f/33858be8415afdda144479faae76e91e12880bcee5792f383c218ee613a6/clyngor-with-clingo-5.3.post1.tar.gz (5.8MB)\n", "\u001b[K |████████████████████████████████| 5.8MB 3.8MB/s \n", "\u001b[?25hCollecting clyngor>=0.3.15\n", "\u001b[?25l Downloading 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in /usr/local/lib/python3.6/dist-packages (from pytest>=3.2.1->clyngor>=0.3.15->clyngor_with_clingo->meneco) (1.15.0)\n", "Building wheels for collected packages: meneco, clyngor-with-clingo, pyPEG2\n", " Building wheel for meneco (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for meneco: filename=Meneco-2.0.0-cp36-none-any.whl size=15271 sha256=d28903c1811afe873239b6ec9b0fcd2138147fa632880785465f3ad895843834\n", " Stored in directory: /root/.cache/pip/wheels/f2/b1/da/9add58eff34821feb52c528bc86820bf98bc18fc97ed075e27\n", " Building wheel for clyngor-with-clingo (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for clyngor-with-clingo: filename=clyngor_with_clingo-5.3.post1-cp36-none-any.whl size=5798483 sha256=60fdfe7689f7ca04884d291580bc0901bdda5c6beacd3f53dafd0057fb49432b\n", " Stored in directory: /root/.cache/pip/wheels/38/f3/21/5ec422631f80e8905910c5ed4cabc43740db8404be904a4eb5\n", " Building wheel for pyPEG2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for pyPEG2: filename=pyPEG2-2.15.2-cp36-none-any.whl size=22777 sha256=72923dbefd65f82931b17662f0b727ee4e432ca4472bf6e304c3ab8ed5bb9881\n", " Stored in directory: /root/.cache/pip/wheels/4a/57/58/03557d6f87c87f518b56561f8ee036d46554824e08832e06f3\n", "Successfully built meneco clyngor-with-clingo pyPEG2\n", "Installing collected packages: pyPEG2, Arpeggio, clyngor, clyngor-with-clingo, meneco\n", "Successfully installed Arpeggio-1.9.2 clyngor-0.3.31 clyngor-with-clingo-5.3.post1 meneco-2.0.0 pyPEG2-2.15.2\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Qzas2YD6395Y" }, "source": [ "then you can import the necessary modules ..." ] }, { "cell_type": "code", "metadata": { "id": "T--PJq4oy0LD", "colab_type": "code", "colab": {} }, "source": [ "from clyngor.as_pyasp import TermSet,Atom\n", "from urllib.request import urlopen\n", "from meneco.meneco import query, utils, sbml" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "PqAWmNmTzkOA", "colab_type": "text" }, "source": [ "Next, you can load a draft network from an sbml file ..." ] }, { "cell_type": "code", "metadata": { "id": "-OgubHemzTDn", "colab_type": "code", "colab": {} }, "source": [ "draft_sbml= urlopen('https://raw.githubusercontent.com/bioasp/meneco/master/Ectodata/ectocyc.sbml')\n", "draftnet = sbml.readSBMLnetwork(draft_sbml, 'draft') " ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "yVi8_PK9zqYl", "colab_type": "text" }, "source": [ "load the seeds ..." ] }, { "cell_type": "code", "metadata": { "id": "Pkt42CXLzWPE", "colab_type": "code", "colab": {} }, "source": [ "seeds_sbml = urlopen('https://raw.githubusercontent.com/bioasp/meneco/master/Ectodata/seeds.sbml')\n", "seeds = sbml.readSBMLseeds(seeds_sbml)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Ktg_p47Kz5bL", "colab_type": "text" }, "source": [ "and load the targets ..." ] }, { "cell_type": "code", "metadata": { "id": "8Lyy9PjMz6Ke", "colab_type": "code", "colab": {} }, "source": [ "targets_sbml = urlopen('https://raw.githubusercontent.com/bioasp/meneco/master/Ectodata/targets.sbml')\n", "targets = sbml.readSBMLtargets(targets_sbml)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "EPrjgIXI0iO4", "colab_type": "text" }, "source": [ "Then you can check the draft network for unproducible targets ..." ] }, { "cell_type": "code", "metadata": { "id": "jXxpLcxbz9Nm", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 936 }, "outputId": "0347ed41-6d2d-48fd-ee65-626182e80ee4" }, "source": [ "model = query.get_unproducible(draftnet, targets, seeds)\n", "unproducible = set(a[0] for pred in model if pred == 'unproducible_target' for a in model[pred])\n", "print('{0} unproducible targets:\\n\\t{1}\\n'.format(len(unproducible), '\\n\\t'.join(unproducible)))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "51 unproducible targets:\n", "\tMET\n", "\tL__45__ORNITHINE\n", "\tASN\n", "\tOLEATE__45__CPD\n", "\tCPD__45__7836\n", "\tILE\n", "\tCPD__45__13814\n", "\tCPD__45__8117\n", "\tSTEARIC_ACID\n", "\tLYS\n", "\tCPD__45__8120\n", "\tDOCOSANOATE\n", "\tTHR\n", "\tCPD__45__9245\n", "\tPRO\n", "\tSUC\n", "\tL__45__ALPHA__45__ALANINE\n", "\tTRP\n", "\t_5Z8Z11Z14Z17Z__45__EICOSAPENTAENOATE\n", "\tGLY\n", "\tL__45__ASPARTATE\n", "\tGLUTATHIONE\n", "\tCPD__45__8119\n", "\tCPD__45__8121\n", "\tCIT\n", "\tTYR\n", "\tCYS\n", "\tHIS\n", "\tGLN\n", "\tGLYCOLLATE\n", "\tARACHIDIC_ACID\n", "\tGLC\n", "\tD__45__ALANINE\n", "\tHOMO__45__SER\n", "\tVAL\n", "\tPHE\n", "\tGLYCERATE\n", "\t_4__45__AMINO__45__BUTYRATE\n", "\tPALMITATE\n", "\tGLYCEROL\n", "\tGLT\n", "\tARG\n", "\tCPD__45__14292\n", "\tTHREO__45__DS__45__ISO__45__CITRATE\n", "\tSER\n", "\tLINOLENIC_ACID\n", "\tMANNITOL\n", "\tLEU\n", "\tLINOLEIC_ACID\n", "\tARACHIDONIC_ACID\n", "\tCPD__45__9247\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Ih3_CNwW0pWr", "colab_type": "text" }, "source": [ "You can load another reaction network like metacyc repair data base ..." ] }, { "cell_type": "code", "metadata": { "id": "R2jr_U1K0nuX", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 52 }, "outputId": "1ee3a315-04a1-422b-ce12-062659de7c5f" }, "source": [ "repair_sbml = urlopen('https://raw.githubusercontent.com/bioasp/meneco/master/Ectodata/metacyc_16-5.sbml')\n", "repairnet = sbml.readSBMLnetwork(repair_sbml, 'repairnet')" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\n", " Warning: RXN__45__13206 listOfProducts=None\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "LBTOly6700CP", "colab_type": "text" }, "source": [ "and combine the draft network with the repair database ..." ] }, { "cell_type": "code", "metadata": { "id": "LZtKqgXZ0y_L", "colab_type": "code", "colab": {} }, "source": [ "combinet = draftnet\n", "combinet = TermSet(combinet.union(repairnet))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "vC50yfMP1Kb2", "colab_type": "text" }, "source": [ "and then check for which targets producibilty cannot be restored even with the combined networks ..." ] }, { "cell_type": "code", "metadata": { "id": "e4QAmoFR09i_", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 121 }, "outputId": "d4f4c319-761a-49ad-bfb0-4ee87e596e6f" }, "source": [ "model = query.get_unproducible(combinet, targets, seeds)\n", "never_producible = set(a[0] for pred in model if pred == 'unproducible_target' for a in model[pred])\n", "print('{0} unreconstructable targets:\\n\\t{1}\\n'.format(len(never_producible), '\\n\\t'.join(never_producible)))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "4 unreconstructable targets:\n", "\tCPD__45__13814\n", "\tCPD__45__8119\n", "\tDOCOSANOATE\n", "\tCPD__45__14292\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "yKMjGZr21SD6", "colab_type": "text" }, "source": [ "and for which targets the production paths are repairable ..." ] }, { "cell_type": "code", "metadata": { "id": "ExlQXta41Pl4", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 867 }, "outputId": "20e18749-3b1f-47b5-9101-1090b4c0ac1a" }, "source": [ "reconstructable_targets = unproducible.difference(never_producible)\n", "print('{0} reconstructable targets:\\n\\t{1}\\n'.format(len(reconstructable_targets), '\\n\\t'.join(reconstructable_targets)))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "47 reconstructable targets:\n", "\tMET\n", "\tL__45__ORNITHINE\n", "\tASN\n", "\tOLEATE__45__CPD\n", "\tCPD__45__7836\n", "\tILE\n", "\tCPD__45__8117\n", "\tSTEARIC_ACID\n", "\tLYS\n", "\tCPD__45__8120\n", "\tTHR\n", "\tCPD__45__9245\n", "\tPRO\n", "\tSUC\n", "\tL__45__ALPHA__45__ALANINE\n", "\tTRP\n", "\t_5Z8Z11Z14Z17Z__45__EICOSAPENTAENOATE\n", "\tGLY\n", "\tL__45__ASPARTATE\n", "\tGLUTATHIONE\n", "\tCPD__45__8121\n", "\tCIT\n", "\tTYR\n", "\tCYS\n", "\tHIS\n", "\tGLN\n", "\tGLYCOLLATE\n", "\tARACHIDIC_ACID\n", "\tGLC\n", "\tD__45__ALANINE\n", "\tHOMO__45__SER\n", "\tVAL\n", "\tPHE\n", "\tGLYCERATE\n", "\t_4__45__AMINO__45__BUTYRATE\n", "\tPALMITATE\n", "\tGLYCEROL\n", "\tGLT\n", "\tARG\n", "\tTHREO__45__DS__45__ISO__45__CITRATE\n", "\tSER\n", "\tLINOLENIC_ACID\n", "\tMANNITOL\n", "\tLEU\n", "\tLINOLEIC_ACID\n", "\tARACHIDONIC_ACID\n", "\tCPD__45__9247\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "lKFp46GR1dSC", "colab_type": "text" }, "source": [ "You can compute the essential reactions for the repairable target ..." ] }, { "cell_type": "code", "metadata": { "id": "ww43a56D1Ygp", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "7018f57f-0ef3-487f-b45f-aa1aaef396c2" }, "source": [ "essential_reactions = set()\n", "for t in reconstructable_targets:\n", " single_target = TermSet()\n", " single_target.add(Atom('target(\"' + t + '\")'))\n", " print('\\nComputing essential reactions for', t,'... ', end=' ')\n", " model = query.get_intersection_of_completions(draftnet, repairnet, seeds, single_target)\n", " print(' done.')\n", " essentials_lst = set(a[0] for pred in model if pred == 'xreaction' for a in model[pred])\n", " print('{0} essential reactions for target {1}:\\n\\t{2}'.format(len(essentials_lst), t, '\\n\\t'.join(essentials_lst)))\n", " essential_reactions = essential_reactions.union(essentials_lst)\n", "print('Overall {0} essential reactions found:\\n\\t{1}\\n'.format(len(essential_reactions), '\\n\\t'.join(essential_reactions)))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\n", "Computing essential reactions for MET ... done.\n", "0 essential reactions for target MET:\n", "\t\n", "\n", "Computing essential reactions for L__45__ORNITHINE ... done.\n", "0 essential reactions for target L__45__ORNITHINE:\n", "\t\n", "\n", "Computing essential reactions for ASN ... done.\n", "0 essential reactions for target ASN:\n", "\t\n", "\n", "Computing essential reactions for OLEATE__45__CPD ... done.\n", "5 essential reactions for target OLEATE__45__CPD:\n", "\tRXN__45__9520\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\tRXN__45__9655\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\n", "Computing essential reactions for CPD__45__7836 ... done.\n", "6 essential reactions for target CPD__45__7836:\n", "\tRXN__45__9520\n", "\tRXN__45__9533\n", "\tRXN__45__9655\n", "\tRXN__45__10727\n", "\tRXN__45__9537\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\n", "Computing essential reactions for ILE ... done.\n", "0 essential reactions for target ILE:\n", "\t\n", "\n", "Computing essential reactions for CPD__45__8117 ... done.\n", "5 essential reactions for target CPD__45__8117:\n", "\tRXN__45__9520\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\tRXN__45__9655\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\n", "Computing essential reactions for STEARIC_ACID ... done.\n", "3 essential reactions for target STEARIC_ACID:\n", "\tRXN__45__9520\n", "\tRXN__45__9655\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\n", "Computing essential reactions for LYS ... done.\n", "0 essential reactions for target LYS:\n", "\t\n", "\n", "Computing essential reactions for CPD__45__8120 ... done.\n", "10 essential reactions for target CPD__45__8120:\n", "\tRXN__45__12968\n", "\tRXN__45__9520\n", "\tRXN__45__12969\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\tRXN__45__12777\n", "\tRXN__45__12971\n", "\tRXN__45__9655\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\tRXN__45__13435\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\n", "Computing essential reactions for THR ... done.\n", "0 essential reactions for target THR:\n", "\t\n", "\n", "Computing essential reactions for CPD__45__9245 ... done.\n", "4 essential reactions for target CPD__45__9245:\n", "\tRXN__45__9550\n", "\tRXN__45__9520\n", "\tRXN__45__9655\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\n", "Computing essential reactions for PRO ... done.\n", "0 essential reactions for target PRO:\n", "\t\n", "\n", "Computing essential reactions for SUC ... done.\n", "0 essential reactions for target SUC:\n", "\t\n", "\n", "Computing essential reactions for L__45__ALPHA__45__ALANINE ... done.\n", "0 essential reactions for target L__45__ALPHA__45__ALANINE:\n", "\t\n", "\n", "Computing essential reactions for TRP ... done.\n", "0 essential reactions for target TRP:\n", "\t\n", "\n", "Computing essential reactions for _5Z8Z11Z14Z17Z__45__EICOSAPENTAENOATE ... done.\n", "0 essential reactions for target _5Z8Z11Z14Z17Z__45__EICOSAPENTAENOATE:\n", "\t\n", "\n", "Computing essential reactions for GLY ... done.\n", "0 essential reactions for target GLY:\n", "\t\n", "\n", "Computing essential reactions for L__45__ASPARTATE ... done.\n", "0 essential reactions for target L__45__ASPARTATE:\n", "\t\n", "\n", "Computing essential reactions for GLUTATHIONE ... done.\n", "0 essential reactions for target GLUTATHIONE:\n", "\t\n", "\n", "Computing essential reactions for CPD__45__8121 ... done.\n", "0 essential reactions for target CPD__45__8121:\n", "\t\n", "\n", "Computing essential reactions for CIT ... done.\n", "0 essential reactions for target CIT:\n", "\t\n", "\n", "Computing essential reactions for TYR ... done.\n", "0 essential reactions for target TYR:\n", "\t\n", "\n", "Computing essential reactions for CYS ... done.\n", "0 essential reactions for target CYS:\n", "\t\n", "\n", "Computing essential reactions for HIS ... done.\n", "2 essential reactions for target HIS:\n", "\tHISTIDPHOS__45__RXN\n", "\tHISTCYCLOHYD__45__RXN\n", "\n", "Computing essential reactions for GLN ... done.\n", "0 essential reactions for target GLN:\n", "\t\n", "\n", "Computing essential reactions for GLYCOLLATE ... done.\n", "0 essential reactions for target GLYCOLLATE:\n", "\t\n", "\n", "Computing essential reactions for ARACHIDIC_ACID ... done.\n", "3 essential reactions for target ARACHIDIC_ACID:\n", "\tRXN__45__9520\n", "\tRXN__45__9655\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\n", "Computing essential reactions for GLC ... done.\n", "0 essential reactions for target GLC:\n", "\t\n", "\n", "Computing essential reactions for D__45__ALANINE ... done.\n", "0 essential reactions for target D__45__ALANINE:\n", "\t\n", "\n", "Computing essential reactions for HOMO__45__SER ... done.\n", "0 essential reactions for target HOMO__45__SER:\n", "\t\n", "\n", "Computing essential reactions for VAL ... done.\n", "0 essential reactions for target VAL:\n", "\t\n", "\n", "Computing essential reactions for PHE ... done.\n", "0 essential reactions for target PHE:\n", "\t\n", "\n", "Computing essential reactions for GLYCERATE ... done.\n", "0 essential reactions for target GLYCERATE:\n", "\t\n", "\n", "Computing essential reactions for _4__45__AMINO__45__BUTYRATE ... done.\n", "0 essential reactions for target _4__45__AMINO__45__BUTYRATE:\n", "\t\n", "\n", "Computing essential reactions for PALMITATE ... done.\n", "4 essential reactions for target PALMITATE:\n", "\tRXN__45__9520\n", "\tRXN__45__9549\n", "\tRXN__45__9655\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\n", "Computing essential reactions for GLYCEROL ... done.\n", "0 essential reactions for target GLYCEROL:\n", "\t\n", "\n", "Computing essential reactions for GLT ... done.\n", "0 essential reactions for target GLT:\n", "\t\n", "\n", "Computing essential reactions for ARG ... done.\n", "0 essential reactions for target ARG:\n", "\t\n", "\n", "Computing essential reactions for THREO__45__DS__45__ISO__45__CITRATE ... done.\n", "0 essential reactions for target THREO__45__DS__45__ISO__45__CITRATE:\n", "\t\n", "\n", "Computing essential reactions for SER ... done.\n", "0 essential reactions for target SER:\n", "\t\n", "\n", "Computing essential reactions for LINOLENIC_ACID ... done.\n", "0 essential reactions for target LINOLENIC_ACID:\n", "\t\n", "\n", "Computing essential reactions for MANNITOL ... done.\n", "0 essential reactions for target MANNITOL:\n", "\t\n", "\n", "Computing essential reactions for LEU ... done.\n", "0 essential reactions for target LEU:\n", "\t\n", "\n", "Computing essential reactions for LINOLEIC_ACID ... done.\n", "5 essential reactions for target LINOLEIC_ACID:\n", "\tRXN__45__9520\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\tRXN__45__9655\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\n", "Computing essential reactions for ARACHIDONIC_ACID ... done.\n", "11 essential reactions for target ARACHIDONIC_ACID:\n", "\tRXN__45__12968\n", "\tRXN__45__9520\n", "\tRXN__45__8346\n", "\tRXN__45__12969\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\tRXN__45__12777\n", "\tRXN__45__12971\n", "\tRXN__45__9655\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\tRXN__45__13435\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\n", "Computing essential reactions for CPD__45__9247 ... done.\n", "6 essential reactions for target CPD__45__9247:\n", "\tRXN__45__9520\n", "\tRXN__45__9558\n", "\tRXN__45__9655\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\tRXN__45__9555\n", "\tRXN__45__9557\n", "Overall 21 essential reactions found:\n", "\tRXN__45__12968\n", "\tRXN__45__9549\n", "\tRXN__45__8346\n", "\tRXN__45__12969\n", "\tRXN__45__9533\n", "\tRXN__45__9550\n", "\tRXN__45__9558\n", "\tRXN__45__9655\n", "\tRXN__45__13435\n", "\tRXN__45__12777\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\tRXN__45__9557\n", "\tHISTCYCLOHYD__45__RXN\n", "\tRXN__45__9520\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\tHISTIDPHOS__45__RXN\n", "\tRXN__45__12971\n", "\tRXN__45__10727\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\tRXN__45__9537\n", "\tRXN__45__9555\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "EGHtOzN51n7k", "colab_type": "text" }, "source": [ "You can compute a completion of minimal size suitable to produce all reconstructable targets ..." ] }, { "cell_type": "code", "metadata": { "id": "DiIhAg8u1h2V", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 849 }, "outputId": "b36a540c-e5e0-4d95-a5f4-ce18927e2f94" }, "source": [ "targets = TermSet(Atom('target(\"' + t+'\")') for t in reconstructable_targets)\n", "model = query.get_minimal_completion_size(draftnet, repairnet, seeds, targets)\n", "one_min_sol_lst = set(a[0] for pred in model if pred == 'xreaction' for a in model[pred])\n", "optimum = len(one_min_sol_lst)\n", "\n", "print('minimal size =',optimum)\n", "\n", "print('One minimal completion of size {0}:\\n\\t{1}\\n'.format(\n", " optimum, '\\n\\t'.join(one_min_sol_lst)))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "minimal size = 45\n", "One minimal completion of size 45:\n", "\tRXN__45__8349\n", "\tRXN__45__9549\n", "\tRXN__45__8346\n", "\t_4__46__2__46__1__46__61__45__RXN\n", "\tRXN__45__9533\n", "\tRXN__45__9655\n", "\tGLUTDECARBOX__45__RXN\n", "\tPREPHENATE__45__ASP__45__TRANSAMINE__45__RXN\n", "\tRXN__45__12755\n", "\tRXN__45__13435\n", "\tRXN__45__12777\n", "\tMANNITOL__45__1__45__PHOSPHATASE__45__RXN\n", "\t_3__46__4__46__13__46__17__45__RXN\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\tHISTCYCLOHYD__45__RXN\n", "\t_4__46__2__46__1__46__58__45__RXN\n", "\tPHOSPHOGLYCERATE__45__PHOSPHATASE__45__RXN\n", "\tACP__45__S__45__ACETYLTRANSFER__45__RXN\n", "\tRXN__45__10727\n", "\tRXN__45__9634\n", "\tRXN__45__12968\n", "\tRXN__45__13261\n", "\tRXN__45__13242\n", "\tFORMYLMETHIONINE__45__DEFORMYLASE__45__RXN\n", "\tSHIKIMATE__45__5__45__DEHYDROGENASE__45__RXN\n", "\tRXN__45__12969\n", "\tRXN__45__9673\n", "\tRXN__45__10663\n", "\tRXN__45__9550\n", "\tCYCLOHEXADIENYL__45__DEHYDROGENASE__45__RXN\n", "\tRXN__45__9548\n", "\tRXN__45__9558\n", "\tFATTY__45__ACYL__45__COA__45__SYNTHASE__45__RXN\n", "\tRXN__45__9557\n", "\tRXN__45__9520\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\tHISTIDPHOS__45__RXN\n", "\tRXN__45__12971\n", "\tRXN__45__8347\n", "\tRXN__45__9537\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\tRXN__45__12766\n", "\t_3__46__4__46__19__46__7__45__RXN\n", "\tRXN__45__9555\n", "\t_3__46__4__46__14__46__1__45__RXN\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "baXsQE4D1wXW", "colab_type": "text" }, "source": [ "We can compute the common reactions in all completion with a given size ..." ] }, { "cell_type": "code", "metadata": { "id": "_uA97Wk911zo", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 589 }, "outputId": "960b8429-f712-4be9-a62e-c0ff0a60a3da" }, "source": [ "model = query.get_intersection_of_optimal_completions(draftnet, repairnet, seeds, targets, optimum)\n", "cautious_sol_lst = set(a[0] for pred in model if pred == 'xreaction' for a in model[pred])\n", "\n", "print('Intersection of all solutions of size {0}:\\n\\t{1}\\n'.format(\n", " optimum, '\\n\\t'.join(cautious_sol_lst)))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Intersection of all solutions of size 45:\n", "\tRXN__45__9549\n", "\tRXN__45__8346\n", "\t_4__46__2__46__1__46__61__45__RXN\n", "\tRXN__45__9533\n", "\tRXN__45__9655\n", "\tGLUTDECARBOX__45__RXN\n", "\tRXN__45__13435\n", "\tRXN__45__12777\n", "\tMANNITOL__45__1__45__PHOSPHATASE__45__RXN\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\tHISTCYCLOHYD__45__RXN\n", "\t_4__46__2__46__1__46__58__45__RXN\n", "\tRXN__45__10727\n", "\tRXN__45__9634\n", "\tRXN__45__12968\n", "\tRXN__45__13261\n", "\tRXN__45__13242\n", "\tRXN__45__12969\n", "\tRXN__45__9673\n", "\tRXN__45__9550\n", "\tRXN__45__9548\n", "\tRXN__45__9558\n", "\tRXN__45__9557\n", "\tRXN__45__9520\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\tHISTIDPHOS__45__RXN\n", "\tRXN__45__12971\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\tRXN__45__9537\n", "\tRXN__45__12766\n", "\tRXN__45__9555\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "n6u0SWY418Hm", "colab_type": "text" }, "source": [ "We can compute the **union** of all completion with a given size ..." ] }, { "cell_type": "code", "metadata": { "id": "RpcZ9CUP2AEE", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "db3c9648-9da8-4996-da9a-93e5676f6206" }, "source": [ "model = query.get_union_of_optimal_completions(draftnet, repairnet, seeds, targets, optimum)\n", "brave_sol_lst = set(a[0] for pred in model if pred == 'xreaction' for a in model[pred])\n", "\n", "print('Intersection of all solutions of size {0}:\\n\\t{1}\\n'.format(\n", " optimum, '\\n\\t'.join(brave_sol_lst)))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Intersection of all solutions of size 45:\n", "\tRXN__45__8349\n", "\tRXN__45__9549\n", "\tRXN__45__8346\n", "\t_4__46__2__46__1__46__61__45__RXN\n", "\tADENOSYLHOMOCYSTEINASE__45__RXN\n", "\tRXN__45__12794\n", "\t_3__46__4__46__13__46__19__45__RXN\n", "\tRXN__45__9533\n", "\tRXN__45__13431\n", "\tRXN__45__9655\n", "\t_4__46__3__46__1__46__25__45__RXN\n", "\tGLUTDECARBOX__45__RXN\n", "\tPREPHENATE__45__ASP__45__TRANSAMINE__45__RXN\n", "\tRXN__45__12755\n", "\tRXN__45__13435\n", "\tRXN__45__12777\n", "\t_3__46__4__46__13__46__17__45__RXN\n", "\tMANNITOL__45__1__45__PHOSPHATASE__45__RXN\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\tHISTCYCLOHYD__45__RXN\n", "\t_4__46__2__46__1__46__58__45__RXN\n", "\tRXN__45__8389\n", "\tPHOSPHOGLYCERATE__45__PHOSPHATASE__45__RXN\n", "\tRXN__45__7968\n", "\t_3__46__4__46__13__46__4__45__RXN\n", "\tRXN__45__9548\n", "\t_4__46__3__46__1__46__23__45__RXN\n", "\tACP__45__S__45__ACETYLTRANSFER__45__RXN\n", "\tPREPHENATEDEHYDROG__45__RXN\n", "\tRXN__45__10727\n", "\tRXN__45__11243\n", "\tRXN__45__9634\n", "\tTRANS__45__CINNAMATE__45__4__45__MONOOXYGENASE__45__RXN\n", "\tADENYLYLSULFATE__45__REDUCTASE__45__RXN\n", "\tTYRAMINOTRANS__45__RXN\n", "\tRXN__45__12968\n", "\tRXN__45__13261\n", "\tRXN__45__12361\n", "\tRXN__45__13242\n", "\tSHIKIMATE__45__5__45__DEHYDROGENASE__45__RXN\n", "\tRXN__45__9673\n", "\tRXN__45__10663\n", "\tRXN__45__9550\n", "\tRXN__45__5682\n", "\tRXN__45__9558\n", "\tCYCLOHEXADIENYL__45__DEHYDROGENASE__45__RXN\n", "\tFORMYLMETHIONINE__45__DEFORMYLASE__45__RXN\n", "\tRXN__45__12969\n", "\t_2__46__3__46__1__46__180__45__RXN\n", "\tMALONYL__45__ACPDECARBOX__45__RXN\n", "\tPREPHENATE__45__DEHYDROGENASE__45__NADP__43____45__RXN\n", "\tFATTY__45__ACYL__45__COA__45__SYNTHASE__45__RXN\n", "\tFATTY__45__ACID__45__SYNTHASE__45__RXN\n", "\tRXN__45__9557\n", "\t_3__45__PHOSPHOGLYCERATE__45__PHOSPHATASE__45__RXN\n", "\tPREPHENATE__45__TRANSAMINE__45__RXN\n", "\t_4__45__COUMARATE__45____45__COA__45__LIGASE__45__RXN\n", "\tPHENYLALANINE__45__AMMONIA__45__LYASE__45__RXN\n", "\tRXN__45__8350\n", "\tRXN__45__9520\n", "\tRXN__45__12793\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\t_3__46__4__46__13__46__5__45__RXN\n", "\tRXN__45__12971\n", "\tRXN__45__9697\n", "\tHISTIDPHOS__45__RXN\n", "\tRXN__45__12487\n", "\tRXN__45__8347\n", "\tRXN__45__9537\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\tRXN__45__12766\n", "\t_3__46__4__46__19__46__7__45__RXN\n", "\tRXN__45__701\n", "\tRXN__45__9555\n", "\tTYROSINE__45__AMINOTRANSFERASE__45__RXN\n", "\t_3__46__4__46__14__46__1__45__RXN\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "_Sho_Bll2DkX", "colab_type": "text" }, "source": [ "And finally compute **all** (for this notebook we print only the first three) completions with a given size ...\n" ] }, { "cell_type": "code", "metadata": { "id": "TptWH9g02HtS", "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "267be30b-5ae6-4f24-a80a-ed6957a0ee21" }, "source": [ "models = query.get_optimal_completions(draftnet, repairnet, seeds, targets, optimum)\n", "count = 0\n", "for model in models:\n", " one_min_sol_lst = set(a[0] for pred in model if pred == 'xreaction' for a in model[pred])\n", " count += 1\n", " print('Completion {0}:\\n\\t{1}\\n'.format(\n", " str(count), '\\n\\t'.join(one_min_sol_lst)))\n", " if count == 3: break" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Completion 1:\n", "\tRXN__45__8349\n", "\tRXN__45__9549\n", "\tRXN__45__8346\n", "\t_4__46__2__46__1__46__61__45__RXN\n", "\tRXN__45__9533\n", "\tADENOSYLHOMOCYSTEINASE__45__RXN\n", "\tRXN__45__13431\n", "\tRXN__45__9655\n", "\tGLUTDECARBOX__45__RXN\n", "\tRXN__45__13435\n", "\tRXN__45__12777\n", "\tMANNITOL__45__1__45__PHOSPHATASE__45__RXN\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\tHISTCYCLOHYD__45__RXN\n", "\t_4__46__2__46__1__46__58__45__RXN\n", "\tPHOSPHOGLYCERATE__45__PHOSPHATASE__45__RXN\n", "\tRXN__45__7968\n", "\tRXN__45__10727\n", "\tRXN__45__11243\n", "\tRXN__45__9634\n", "\tADENYLYLSULFATE__45__REDUCTASE__45__RXN\n", "\tRXN__45__12968\n", "\tRXN__45__13261\n", "\tRXN__45__12361\n", "\tRXN__45__13242\n", "\tRXN__45__12969\n", "\tRXN__45__9673\n", "\tRXN__45__10663\n", "\tRXN__45__9550\n", "\tRXN__45__9548\n", "\tRXN__45__9558\n", "\tRXN__45__9555\n", "\tRXN__45__9557\n", "\t_4__45__COUMARATE__45____45__COA__45__LIGASE__45__RXN\n", "\tPHENYLALANINE__45__AMMONIA__45__LYASE__45__RXN\n", "\tRXN__45__9520\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\tHISTIDPHOS__45__RXN\n", "\tRXN__45__12971\n", "\tRXN__45__9697\n", "\tRXN__45__8347\n", "\tRXN__45__9537\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\tRXN__45__12766\n", "\tTRANS__45__CINNAMATE__45__4__45__MONOOXYGENASE__45__RXN\n", "\n", "Completion 2:\n", "\tRXN__45__8349\n", "\tRXN__45__9549\n", "\tRXN__45__8346\n", "\t_4__46__2__46__1__46__61__45__RXN\n", "\tRXN__45__9533\n", "\tADENOSYLHOMOCYSTEINASE__45__RXN\n", "\tRXN__45__9655\n", "\tGLUTDECARBOX__45__RXN\n", "\tRXN__45__13435\n", "\tRXN__45__12777\n", "\tMANNITOL__45__1__45__PHOSPHATASE__45__RXN\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\tHISTCYCLOHYD__45__RXN\n", "\t_4__46__2__46__1__46__58__45__RXN\n", "\tPHOSPHOGLYCERATE__45__PHOSPHATASE__45__RXN\n", "\tRXN__45__7968\n", "\tRXN__45__10727\n", "\tRXN__45__11243\n", "\tRXN__45__9634\n", "\tADENYLYLSULFATE__45__REDUCTASE__45__RXN\n", "\tRXN__45__12968\n", "\tRXN__45__13261\n", "\tRXN__45__12361\n", "\tRXN__45__13242\n", "\tRXN__45__12969\n", "\tRXN__45__9673\n", "\tRXN__45__10663\n", "\tRXN__45__9550\n", "\tRXN__45__9548\n", "\tRXN__45__9558\n", "\tRXN__45__9555\n", "\tRXN__45__9557\n", "\t_4__45__COUMARATE__45____45__COA__45__LIGASE__45__RXN\n", "\tPHENYLALANINE__45__AMMONIA__45__LYASE__45__RXN\n", "\tRXN__45__8350\n", "\tRXN__45__9520\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\tHISTIDPHOS__45__RXN\n", "\tRXN__45__12971\n", "\tRXN__45__9697\n", "\tRXN__45__8347\n", "\tRXN__45__9537\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\tRXN__45__12766\n", "\tTRANS__45__CINNAMATE__45__4__45__MONOOXYGENASE__45__RXN\n", "\n", "Completion 3:\n", "\tRXN__45__8349\n", "\tRXN__45__9549\n", "\tRXN__45__8346\n", "\t_4__46__2__46__1__46__61__45__RXN\n", "\tRXN__45__9533\n", "\tADENOSYLHOMOCYSTEINASE__45__RXN\n", "\tRXN__45__9655\n", "\tGLUTDECARBOX__45__RXN\n", "\tRXN__45__12755\n", "\tRXN__45__13435\n", "\tRXN__45__12777\n", "\tMANNITOL__45__1__45__PHOSPHATASE__45__RXN\n", "\t_4__46__2__46__1__46__59__45__RXN\n", "\tHISTCYCLOHYD__45__RXN\n", "\t_4__46__2__46__1__46__58__45__RXN\n", "\tPHOSPHOGLYCERATE__45__PHOSPHATASE__45__RXN\n", "\tRXN__45__7968\n", "\tRXN__45__10727\n", "\tRXN__45__11243\n", "\tRXN__45__9634\n", "\tADENYLYLSULFATE__45__REDUCTASE__45__RXN\n", "\tRXN__45__12968\n", "\tRXN__45__13261\n", "\tRXN__45__12361\n", "\tRXN__45__13242\n", "\tRXN__45__12969\n", "\tRXN__45__9673\n", "\tRXN__45__10663\n", "\tRXN__45__9550\n", "\tRXN__45__9548\n", "\tRXN__45__9558\n", "\tRXN__45__9555\n", "\tRXN__45__9557\n", "\t_4__45__COUMARATE__45____45__COA__45__LIGASE__45__RXN\n", "\tPHENYLALANINE__45__AMMONIA__45__LYASE__45__RXN\n", "\tRXN__45__9520\n", "\t_1__46__14__46__19__46__2__45__RXN\n", "\tHISTIDPHOS__45__RXN\n", "\tRXN__45__12971\n", "\tRXN__45__9697\n", "\tRXN__45__8347\n", "\tRXN__45__9537\n", "\t_3__46__1__46__2__46__14__45__RXN\n", "\tRXN__45__12766\n", "\tTRANS__45__CINNAMATE__45__4__45__MONOOXYGENASE__45__RXN\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "ZSpZ-sOt2LnL", "colab_type": "text" }, "source": [ "That's all folks!" ] } ] }
gpl-3.0
google/eng-edu
ml/cc/exercises/intro_to_neural_nets.ipynb
1
32645
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Intro to Neural Nets.ipynb", "provenance": [], "private_outputs": true, "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "code", "metadata": { "id": "wDlWLbfkJtvu", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Copyright 2020 Google LLC. Double-click here for license information.\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "TL5y5fY9Jy_x", "colab_type": "text" }, "source": [ "# Introduction to Neural Nets\n", "\n", "This Colab builds a deep neural network to perform more sophisticated linear regression than the earlier Colabs." ] }, { "cell_type": "markdown", "metadata": { "id": "7RDY3EeAluPd", "colab_type": "text" }, "source": [ "## Learning Objectives:\n", "\n", "After doing this Colab, you'll know how to do the following:\n", "\n", " * Create a simple deep neural network.\n", " * Tune the hyperparameters for a simple deep neural network." ] }, { "cell_type": "markdown", "metadata": { "id": "XGj0PNaJlubZ", "colab_type": "text" }, "source": [ "## The Dataset\n", " \n", "Like several of the previous Colabs, this Colab uses the [California Housing Dataset](https://developers.google.com/machine-learning/crash-course/california-housing-data-description)." ] }, { "cell_type": "markdown", "metadata": { "id": "tX_umRMMsa3z", "colab_type": "text" }, "source": [ "## Use the right version of TensorFlow\n", "\n", "The following hidden code cell ensures that the Colab will run on TensorFlow 2.X." ] }, { "cell_type": "code", "metadata": { "id": "lM75uNH-sTv2", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Run on TensorFlow 2.x\n", "%tensorflow_version 2.x\n", "from __future__ import absolute_import, division, print_function, unicode_literals" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "xchnxAsaKKqO", "colab_type": "text" }, "source": [ "## Import relevant modules\n", "\n", "The following hidden code cell imports the necessary code to run the code in the rest of this Colaboratory." ] }, { "cell_type": "code", "metadata": { "id": "9n9_cTveKmse", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Import relevant modules\n", "import numpy as np\n", "import pandas as pd\n", "import tensorflow as tf\n", "from tensorflow.keras import layers\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "\n", "# The following lines adjust the granularity of reporting. \n", "pd.options.display.max_rows = 10\n", "pd.options.display.float_format = \"{:.1f}\".format\n", "\n", "print(\"Imported modules.\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "X_TaJhU4KcuY", "colab_type": "text" }, "source": [ "## Load the dataset\n", "\n", "Like most of the previous Colab exercises, this exercise uses the California Housing Dataset. The following code cell loads the separate .csv files and creates the following two pandas DataFrames:\n", "\n", "* `train_df`, which contains the training set\n", "* `test_df`, which contains the test set\n", " " ] }, { "cell_type": "code", "metadata": { "id": "JZlvdpyYKx7V", "colab_type": "code", "colab": {} }, "source": [ "train_df = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\")\n", "train_df = train_df.reindex(np.random.permutation(train_df.index)) # shuffle the examples\n", "test_df = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_test.csv\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "8ldP-5z1B2vL", "colab_type": "text" }, "source": [ "## Normalize values\n", "\n", "When building a model with multiple features, the values of each feature should cover roughly the same range. The following code cell normalizes datasets by converting each raw value to its Z-score. (For more information about Z-scores, see the Classification exercise.)" ] }, { "cell_type": "code", "metadata": { "id": "g8HC-TDgB1D1", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Convert raw values to their Z-scores \n", "\n", "# Calculate the Z-scores of each column in the training set:\n", "train_df_mean = train_df.mean()\n", "train_df_std = train_df.std()\n", "train_df_norm = (train_df - train_df_mean)/train_df_std\n", "\n", "# Calculate the Z-scores of each column in the test set.\n", "test_df_mean = test_df.mean()\n", "test_df_std = test_df.std()\n", "test_df_norm = (test_df - test_df_mean)/test_df_std\n", "\n", "print(\"Normalized the values.\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "b9ehCgIRjTxy", "colab_type": "text" }, "source": [ "## Represent data\n", "\n", "The following code cell creates a feature layer containing three features:\n", "\n", "* `latitude` X `longitude` (a feature cross)\n", "* `median_income`\n", "* `population`\n", "\n", "This code cell specifies the features that you'll ultimately train the model on and how each of those features will be represented. The transformations (collected in `my_feature_layer`) don't actually get applied until you pass a DataFrame to it, which will happen when we train the model. " ] }, { "cell_type": "code", "metadata": { "id": "8EkNAQhnjSu-", "colab_type": "code", "colab": {} }, "source": [ "# Create an empty list that will eventually hold all created feature columns.\n", "feature_columns = []\n", "\n", "# We scaled all the columns, including latitude and longitude, into their\n", "# Z scores. So, instead of picking a resolution in degrees, we're going\n", "# to use resolution_in_Zs. A resolution_in_Zs of 1 corresponds to \n", "# a full standard deviation. \n", "resolution_in_Zs = 0.3 # 3/10 of a standard deviation.\n", "\n", "# Create a bucket feature column for latitude.\n", "latitude_as_a_numeric_column = tf.feature_column.numeric_column(\"latitude\")\n", "latitude_boundaries = list(np.arange(int(min(train_df_norm['latitude'])), \n", " int(max(train_df_norm['latitude'])), \n", " resolution_in_Zs))\n", "latitude = tf.feature_column.bucketized_column(latitude_as_a_numeric_column, latitude_boundaries)\n", "\n", "# Create a bucket feature column for longitude.\n", "longitude_as_a_numeric_column = tf.feature_column.numeric_column(\"longitude\")\n", "longitude_boundaries = list(np.arange(int(min(train_df_norm['longitude'])), \n", " int(max(train_df_norm['longitude'])), \n", " resolution_in_Zs))\n", "longitude = tf.feature_column.bucketized_column(longitude_as_a_numeric_column, \n", " longitude_boundaries)\n", "\n", "# Create a feature cross of latitude and longitude.\n", "latitude_x_longitude = tf.feature_column.crossed_column([latitude, longitude], hash_bucket_size=100)\n", "crossed_feature = tf.feature_column.indicator_column(latitude_x_longitude)\n", "feature_columns.append(crossed_feature) \n", "\n", "# Represent median_income as a floating-point value.\n", "median_income = tf.feature_column.numeric_column(\"median_income\")\n", "feature_columns.append(median_income)\n", "\n", "# Represent population as a floating-point value.\n", "population = tf.feature_column.numeric_column(\"population\")\n", "feature_columns.append(population)\n", "\n", "# Convert the list of feature columns into a layer that will later be fed into\n", "# the model. \n", "my_feature_layer = tf.keras.layers.DenseFeatures(feature_columns)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Ak_TMAzGOIFq", "colab_type": "text" }, "source": [ "## Build a linear regression model as a baseline\n", "\n", "Before creating a deep neural net, find a [baseline](https://developers.google.com/machine-learning/glossary/#baseline) loss by running a simple linear regression model that uses the feature layer you just created. \n" ] }, { "cell_type": "code", "metadata": { "id": "QF0BFRXTOeR3", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Define the plotting function.\n", "\n", "def plot_the_loss_curve(epochs, mse):\n", " \"\"\"Plot a curve of loss vs. epoch.\"\"\"\n", "\n", " plt.figure()\n", " plt.xlabel(\"Epoch\")\n", " plt.ylabel(\"Mean Squared Error\")\n", "\n", " plt.plot(epochs, mse, label=\"Loss\")\n", " plt.legend()\n", " plt.ylim([mse.min()*0.95, mse.max() * 1.03])\n", " plt.show() \n", "\n", "print(\"Defined the plot_the_loss_curve function.\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "RW4Qe710LgnG", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Define functions to create and train a linear regression model\n", "def create_model(my_learning_rate, feature_layer):\n", " \"\"\"Create and compile a simple linear regression model.\"\"\"\n", " # Most simple tf.keras models are sequential.\n", " model = tf.keras.models.Sequential()\n", "\n", " # Add the layer containing the feature columns to the model.\n", " model.add(feature_layer)\n", "\n", " # Add one linear layer to the model to yield a simple linear regressor.\n", " model.add(tf.keras.layers.Dense(units=1, input_shape=(1,)))\n", "\n", " # Construct the layers into a model that TensorFlow can execute.\n", " model.compile(optimizer=tf.keras.optimizers.RMSprop(lr=my_learning_rate),\n", " loss=\"mean_squared_error\",\n", " metrics=[tf.keras.metrics.MeanSquaredError()])\n", "\n", " return model \n", "\n", "\n", "def train_model(model, dataset, epochs, batch_size, label_name):\n", " \"\"\"Feed a dataset into the model in order to train it.\"\"\"\n", "\n", " # Split the dataset into features and label.\n", " features = {name:np.array(value) for name, value in dataset.items()}\n", " label = np.array(features.pop(label_name))\n", " history = model.fit(x=features, y=label, batch_size=batch_size,\n", " epochs=epochs, shuffle=True)\n", "\n", " # Get details that will be useful for plotting the loss curve.\n", " epochs = history.epoch\n", " hist = pd.DataFrame(history.history)\n", " rmse = hist[\"mean_squared_error\"]\n", "\n", " return epochs, rmse \n", "\n", "print(\"Defined the create_model and train_model functions.\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "f47LmxF5X_pu", "colab_type": "text" }, "source": [ "Run the following code cell to invoke the the functions defined in the preceding two code cells. (Ignore the warning messages.)\n", "\n", "**Note:** Because we've scaled all the input data, **including the label**, the resulting loss values will be *much less* than previous models. \n", "\n", "**Note:** Depending on the version of TensorFlow, running this cell might generate WARNING messages. Please ignore these warnings. " ] }, { "cell_type": "code", "metadata": { "id": "tsfE4ujDL4ju", "colab_type": "code", "colab": {} }, "source": [ "# The following variables are the hyperparameters.\n", "learning_rate = 0.01\n", "epochs = 15\n", "batch_size = 1000\n", "label_name = \"median_house_value\"\n", "\n", "# Establish the model's topography.\n", "my_model = create_model(learning_rate, my_feature_layer)\n", "\n", "# Train the model on the normalized training set.\n", "epochs, mse = train_model(my_model, train_df_norm, epochs, batch_size, label_name)\n", "plot_the_loss_curve(epochs, mse)\n", "\n", "test_features = {name:np.array(value) for name, value in test_df_norm.items()}\n", "test_label = np.array(test_features.pop(label_name)) # isolate the label\n", "print(\"\\n Evaluate the linear regression model against the test set:\")\n", "my_model.evaluate(x = test_features, y = test_label, batch_size=batch_size)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "3014ezH3C7jT", "colab_type": "text" }, "source": [ "## Define a deep neural net model\n", "\n", "The `create_model` function defines the topography of the deep neural net, specifying the following:\n", "\n", "* The number of [layers](https://developers.google.com/machine-learning/glossary/#layer) in the deep neural net.\n", "* The number of [nodes](https://developers.google.com/machine-learning/glossary/#node) in each layer.\n", "\n", "The `create_model` function also defines the [activation function](https://developers.google.com/machine-learning/glossary/#activation_function) of each layer." ] }, { "cell_type": "code", "metadata": { "id": "pedD5GhlDC-y", "colab_type": "code", "cellView": "both", "colab": {} }, "source": [ "def create_model(my_learning_rate, my_feature_layer):\n", " \"\"\"Create and compile a simple linear regression model.\"\"\"\n", " # Most simple tf.keras models are sequential.\n", " model = tf.keras.models.Sequential()\n", "\n", " # Add the layer containing the feature columns to the model.\n", " model.add(my_feature_layer)\n", "\n", " # Describe the topography of the model by calling the tf.keras.layers.Dense\n", " # method once for each layer. We've specified the following arguments:\n", " # * units specifies the number of nodes in this layer.\n", " # * activation specifies the activation function (Rectified Linear Unit).\n", " # * name is just a string that can be useful when debugging.\n", "\n", " # Define the first hidden layer with 20 nodes. \n", " model.add(tf.keras.layers.Dense(units=20, \n", " activation='relu', \n", " name='Hidden1'))\n", " \n", " # Define the second hidden layer with 12 nodes. \n", " model.add(tf.keras.layers.Dense(units=12, \n", " activation='relu', \n", " name='Hidden2'))\n", " \n", " # Define the output layer.\n", " model.add(tf.keras.layers.Dense(units=1, \n", " name='Output')) \n", " \n", " model.compile(optimizer=tf.keras.optimizers.Adam(lr=my_learning_rate),\n", " loss=\"mean_squared_error\",\n", " metrics=[tf.keras.metrics.MeanSquaredError()])\n", "\n", " return model" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "anH4A_yCcZx2", "colab_type": "text" }, "source": [ "## Define a training function\n", "\n", "The `train_model` function trains the model from the input features and labels. The [tf.keras.Model.fit](https://www.tensorflow.org/api_docs/python/tf/keras/Sequential#fit) method performs the actual training. The `x` parameter of the `fit` method is very flexible, enabling you to pass feature data in a variety of ways. The following implementation passes a Python dictionary in which:\n", "\n", "* The *keys* are the names of each feature (for example, `longitude`, `latitude`, and so on).\n", "* The *value* of each key is a NumPy array containing the values of that feature. \n", "\n", "**Note:** Although you are passing *every* feature to `model.fit`, most of those values will be ignored. Only the features accessed by `my_feature_layer` will actually be used to train the model." ] }, { "cell_type": "code", "metadata": { "id": "4jv_lJYTcrEF", "colab_type": "code", "colab": {} }, "source": [ "def train_model(model, dataset, epochs, label_name,\n", " batch_size=None):\n", " \"\"\"Train the model by feeding it data.\"\"\"\n", "\n", " # Split the dataset into features and label.\n", " features = {name:np.array(value) for name, value in dataset.items()}\n", " label = np.array(features.pop(label_name))\n", " history = model.fit(x=features, y=label, batch_size=batch_size,\n", " epochs=epochs, shuffle=True) \n", "\n", " # The list of epochs is stored separately from the rest of history.\n", " epochs = history.epoch\n", " \n", " # To track the progression of training, gather a snapshot\n", " # of the model's mean squared error at each epoch. \n", " hist = pd.DataFrame(history.history)\n", " mse = hist[\"mean_squared_error\"]\n", "\n", " return epochs, mse" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "D-IXYVfvM4gD", "colab_type": "text" }, "source": [ "## Call the functions to build and train a deep neural net\n", "\n", "Okay, it is time to actually train the deep neural net. If time permits, experiment with the three hyperparameters to see if you can reduce the loss\n", "against the test set.\n" ] }, { "cell_type": "code", "metadata": { "id": "nj3v5EKQFY8s", "colab_type": "code", "cellView": "both", "colab": {} }, "source": [ "# The following variables are the hyperparameters.\n", "learning_rate = 0.01\n", "epochs = 20\n", "batch_size = 1000\n", "\n", "# Specify the label\n", "label_name = \"median_house_value\"\n", "\n", "# Establish the model's topography.\n", "my_model = create_model(learning_rate, my_feature_layer)\n", "\n", "# Train the model on the normalized training set. We're passing the entire\n", "# normalized training set, but the model will only use the features\n", "# defined by the feature_layer.\n", "epochs, mse = train_model(my_model, train_df_norm, epochs, \n", " label_name, batch_size)\n", "plot_the_loss_curve(epochs, mse)\n", "\n", "# After building a model against the training set, test that model\n", "# against the test set.\n", "test_features = {name:np.array(value) for name, value in test_df_norm.items()}\n", "test_label = np.array(test_features.pop(label_name)) # isolate the label\n", "print(\"\\n Evaluate the new model against the test set:\")\n", "my_model.evaluate(x = test_features, y = test_label, batch_size=batch_size)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "wlPXK-SmmjQ2", "colab_type": "text" }, "source": [ "## Task 1: Compare the two models\n", "\n", "How did the deep neural net perform against the baseline linear regression model?" ] }, { "cell_type": "code", "metadata": { "id": "hI7ojsL7nnBE", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Double-click to view a possible answer\n", "\n", "# Assuming that the linear model converged and\n", "# the deep neural net model also converged, please \n", "# compare the test set loss for each.\n", "# In our experiments, the loss of the deep neural \n", "# network model was consistently lower than \n", "# that of the linear regression model, which \n", "# suggests that the deep neural network model \n", "# will make better predictions than the \n", "# linear regression model." ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Y5IKmk7D49_n", "colab_type": "text" }, "source": [ "## Task 2: Optimize the deep neural network's topography\n", "\n", "Experiment with the number of layers of the deep neural network and the number of nodes in each layer. Aim to achieve both of the following goals:\n", "\n", "* Lower the loss against the test set.\n", "* Minimize the overall number of nodes in the deep neural net. \n", "\n", "The two goals may be in conflict." ] }, { "cell_type": "code", "metadata": { "id": "wYG5qXpP5a9n", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Double-click to view a possible answer\n", "\n", "# Many answers are possible. We noticed the \n", "# following trends:\n", "# * Two layers outperformed one layer, but \n", "# three layers did not perform significantly \n", "# better than two layers; two layers \n", "# outperformed one layer.\n", "# In other words, two layers seemed best. \n", "# * Setting the topography as follows produced \n", "# reasonably good results with relatively few \n", "# nodes:\n", "# * 10 nodes in the first layer.\n", "# * 6 nodes in the second layer.\n", "# As the number of nodes in each layer dropped\n", "# below the preceding, test loss increased. \n", "# However, depending on your application, hardware\n", "# constraints, and the relative pain inflicted \n", "# by a less accurate model, a smaller network \n", "# (for example, 6 nodes in the first layer and \n", "# 4 nodes in the second layer) might be \n", "# acceptable." ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Pu7R_ZpDopIj", "colab_type": "text" }, "source": [ "## Task 3: Regularize the deep neural network (if you have enough time)\n", "\n", "Notice that the model's loss against the test set is *much higher* than the loss against the training set. In other words, the deep neural network is [overfitting](https://developers.google.com/machine-learning/glossary/#overfitting) to the data in the training set. To reduce overfitting, regularize the model. The course has suggested several different ways to regularize a model, including:\n", "\n", " * [L1 regularization](https://developers.google.com/machine-learning/glossary/#L1_regularization)\n", " * [L2 regularization](https://developers.google.com/machine-learning/glossary/#L2_regularization)\n", " * [Dropout regularization](https://developers.google.com/machine-learning/glossary/#dropout_regularization)\n", "\n", "Your task is to experiment with one or more regularization mechanisms to bring the test loss closer to the training loss (while still keeping test loss relatively low). \n", "\n", "**Note:** When you add a regularization function to a model, you might need to tweak other hyperparameters. \n", "\n", "### Implementing L1 or L2 regularization\n", "\n", "To use L1 or L2 regularization on a hidden layer, specify the `kernel_regularizer` argument to [tf.keras.layers.Dense](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dense). Assign one of the following methods to this argument:\n", "\n", "* `tf.keras.regularizers.l1` for L1 regularization\n", "* `tf.keras.regularizers.l2` for L2 regularization\n", "\n", "Each of the preceding methods takes an `l` parameter, which adjusts the [regularization rate](https://developers.google.com/machine-learning/glossary/#regularization_rate). Assign a decimal value between 0 and 1.0 to `l`; the higher the decimal, the greater the regularization. For example, the following applies L2 regularization at a strength of 0.01. \n", "\n", "```\n", "model.add(tf.keras.layers.Dense(units=20, \n", " activation='relu',\n", " kernel_regularizer=tf.keras.regularizers.l2(l=0.01),\n", " name='Hidden1'))\n", "```\n", "\n", "### Implementing Dropout regularization\n", "\n", "You implement dropout regularization as a separate layer in the topography. For example, the following code demonstrates how to add a dropout regularization layer between the first hidden layer and the second hidden layer:\n", "\n", "```\n", "model.add(tf.keras.layers.Dense( *define first hidden layer*)\n", " \n", "model.add(tf.keras.layers.Dropout(rate=0.25))\n", "\n", "model.add(tf.keras.layers.Dense( *define second hidden layer*)\n", "```\n", "\n", "The `rate` parameter to [tf.keras.layers.Dropout](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Dropout) specifies the fraction of nodes that the model should drop out during training. \n" ] }, { "cell_type": "code", "metadata": { "id": "tflt9TZEDARW", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Double-click for a possible solution\n", "\n", "# The following \"solution\" uses L2 regularization to bring training loss\n", "# and test loss closer to each other. Many, many other solutions are possible.\n", "\n", "\n", "def create_model(my_learning_rate, my_feature_layer):\n", " \"\"\"Create and compile a simple linear regression model.\"\"\"\n", "\n", " # Discard any pre-existing version of the model.\n", " model = None\n", "\n", " # Most simple tf.keras models are sequential.\n", " model = tf.keras.models.Sequential()\n", "\n", " # Add the layer containing the feature columns to the model.\n", " model.add(my_feature_layer)\n", "\n", " # Describe the topography of the model. \n", "\n", " # Implement L2 regularization in the first hidden layer.\n", " model.add(tf.keras.layers.Dense(units=20, \n", " activation='relu',\n", " kernel_regularizer=tf.keras.regularizers.l2(0.04),\n", " name='Hidden1'))\n", " \n", " # Implement L2 regularization in the second hidden layer.\n", " model.add(tf.keras.layers.Dense(units=12, \n", " activation='relu', \n", " kernel_regularizer=tf.keras.regularizers.l2(0.04),\n", " name='Hidden2'))\n", "\n", " # Define the output layer.\n", " model.add(tf.keras.layers.Dense(units=1, \n", " name='Output')) \n", " \n", " model.compile(optimizer=tf.keras.optimizers.Adam(lr=my_learning_rate),\n", " loss=\"mean_squared_error\",\n", " metrics=[tf.keras.metrics.MeanSquaredError()])\n", "\n", " return model \n", "\n", "# Call the new create_model function and the other (unchanged) functions.\n", "\n", "# The following variables are the hyperparameters.\n", "learning_rate = 0.007\n", "epochs = 140\n", "batch_size = 1000\n", "\n", "label_name = \"median_house_value\"\n", "\n", "# Establish the model's topography.\n", "my_model = create_model(learning_rate, my_feature_layer)\n", "\n", "# Train the model on the normalized training set.\n", "epochs, mse = train_model(my_model, train_df_norm, epochs, \n", " label_name, batch_size)\n", "plot_the_loss_curve(epochs, mse)\n", "\n", "test_features = {name:np.array(value) for name, value in test_df_norm.items()}\n", "test_label = np.array(test_features.pop(label_name)) # isolate the label\n", "print(\"\\n Evaluate the new model against the test set:\")\n", "my_model.evaluate(x = test_features, y = test_label, batch_size=batch_size) " ], "execution_count": 0, "outputs": [] } ] }
apache-2.0
ZWMiller/svdRec
svdRec_example.ipynb
1
9928
{ "cells": [ { "cell_type": "markdown", "metadata": { "run_control": { "frozen": false, "read_only": false } }, "source": [ "## Demo of svdRec, a Python3 module for Recommenders" ] }, { "cell_type": "markdown", "metadata": { "run_control": { "frozen": false, "read_only": false } }, "source": [ "Download the movielens dataset [here](http://files.grouplens.org/datasets/movielens/ml-20m.zip) \n", "\n", "** Before loading in the data, I highly recommend cutting out some of the \"ratings.csv\" file. It's 20M rows long and can take a long time to process. In bash you can do something like: **\n", "\n", "`cat ratings.csv | head -100000 > ratings_small.csv` \n", "\n", "We'll also load in the movies.csv file to a DataFrame - this will act as a dictionary to translate between MovieID and Movie Titles." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Note: load_csv_sparse expects a csv in the format of: rowID, colID, Value, ...\n", "Created matrix of shape: (702, 128594)\n" ] } ], "source": [ "from svdRec import svdRec\n", "import pandas as pd \n", "\n", "svd = svdRec.svdRec()\n", "svd.load_csv_sparse('data/ml-20m/ratings_small.csv', delimiter=',', skiprows=1)\n", "svd.SVD(num_dim=100)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "movies = pd.read_table('data/ml-20m/movies.csv', sep=',',names = ['movieId',\"Title\",\"genres\"])\n", "movie_dict = {}\n", "for i, row in movies.iterrows():\n", " movie_dict.update({row['movieId']: row['Title']})\n", "svd.load_item_encoder(movie_dict)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3114, 0.12452773823527524)\n", "Toy Story 2 (1999) \n", "\n", "(1, 0.096984857294616089)\n", "Toy Story (1995) \n", "\n", "(2355, 0.043104443630875205)\n", "Bug's Life, A (1998) \n", "\n", "(2987, 0.041949127538017023)\n", "Who Framed Roger Rabbit? (1988) \n", "\n", "(6377, 0.040522854363774369)\n", "Finding Nemo (2003) \n", "\n" ] } ], "source": [ "MOVIE_ID = 3114 # Toy Story 2\n", "for item in svd.get_similar_items(MOVIE_ID,show_similarity=True):\n", " print(item)\n", " print(svd.get_item_name(item[0]),'\\n')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ID: 356\n", "Actual Rating: 5.0\n", "Title: Forrest Gump (1994) \n", "\n", "ID: 1961\n", "Actual Rating: 0.0\n", "Title: Rain Man (1988) \n", "\n", "ID: 1270\n", "Actual Rating: 0.0\n", "Title: Back to the Future (1985) \n", "\n", "ID: 1097\n", "Actual Rating: 0.0\n", "Title: E.T. the Extra-Terrestrial (1982) \n", "\n", "ID: 1307\n", "Actual Rating: 0.0\n", "Title: When Harry Met Sally... (1989) \n", "\n" ] } ], "source": [ "USERID=25\n", "for item in svd.recommends_for_user(USERID, num_recom=5, show_similarity=False):\n", " print(\"ID: \", item)\n", " print(\"Actual Rating: \", svd.mat.toarray()[USERID][item])\n", " print(\"Title: \",svd.get_item_name(item),'\\n')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "run_control": { "frozen": false, "read_only": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "User #3's most similar user is User #[282, 488] \n", "Items for User 3 to check out based on similar user:\n", " [0, 1, 5, 4104, 9, 2057, 6153, 6154, 15, 16, 18, 20, 24, 28, 31, 33, 2081, 35, 38, 46, 49, 57, 2107, 61, 4160, 4161, 2114, 6217, 86, 2141, 94, 2151, 30824, 109, 110, 2158, 116, 6265, 2173, 4224, 4231, 6280, 2187, 140, 139, 4239, 149, 4245, 4247, 152, 6293, 6296, 156, 160, 164, 8359, 8360, 171, 172, 8372, 2230, 184, 6332, 8382, 193, 197, 4298, 207, 4305, 4307, 2268, 6364, 6366, 2272, 4320, 6372, 230, 231, 4328, 4326, 234, 6376, 240, 241, 2290, 2288, 4339, 2293, 246, 4342, 4343, 6385, 6391, 2299, 2301, 255, 2305, 259, 261, 2312, 4360, 6409, 2317, 4366, 271, 2320, 4367, 4368, 2323, 276, 4369, 6415, 2328, 6428, 2334, 287, 2336, 289, 4385, 291, 294, 295, 299, 2352, 306, 307, 2354, 4405, 315, 317, 318, 328, 2383, 336, 338, 2389, 343, 2392, 347, 348, 349, 2395, 4445, 4446, 2401, 354, 355, 356, 2405, 6497, 6502, 360, 361, 363, 366, 369, 373, 376, 2426, 379, 6533, 4488, 6536, 6538, 6549, 2454, 412, 419, 431, 433, 434, 439, 2489, 6592, 453, 2501, 456, 2504, 467, 2516, 470, 479, 480, 2532, 484, 2538, 491, 2541, 499, 506, 2561, 6657, 2566, 519, 2570, 526, 2579, 4627, 538, 540, 4637, 4638, 4640, 4642, 550, 2599, 6710, 579, 2627, 584, 586, 588, 589, 591, 592, 2639, 596, 598, 4700, 607, 2670, 2671, 4718, 2676, 4727, 6775, 2682, 4731, 4740, 647, 2698, 2699, 2705, 4756, 2709, 2711, 664, 2715, 2717, 2719, 2721, 8865, 2723, 4798, 2760, 713, 2762, 719, 4815, 2769, 8911, 4822, 6872, 732, 735, 2787, 2790, 742, 2792, 746, 2796, 749, 4847, 8948, 8960, 4866, 8968, 777, 4873, 779, 2828, 4877, 783, 784, 785, 787, 4885, 8982, 8983, 4888, 4889, 2857, 817, 2870, 2881, 834, 836, 2889, 2890, 4946, 857, 4957, 4962, 2915, 2917, 4972, 4973, 4974, 4978, 4989, 4990, 4991, 4992, 4993, 2946, 2947, 2948, 2950, 903, 2958, 911, 912, 5008, 5013, 2967, 922, 923, 2975, 5026, 2984, 2992, 2994, 2996, 5047, 952, 5050, 3004, 5059, 31684, 967, 3015, 3016, 968, 5063, 31693, 5091, 5095, 7146, 3051, 7148, 3059, 1019, 3074, 7172, 3081, 1035, 1036, 5134, 1041, 1046, 1058, 3112, 3113, 5170, 1078, 1079, 27705, 1088, 1089, 1093, 1096, 1100, 3151, 3156, 7254, 3159, 1119, 5217, 5219, 3173, 1126, 3175, 1128, 1125, 3174, 5224, 1134, 1135, 1146, 1147, 7292, 3197, 7302, 3207, 7310, 1172, 38037, 1174, 1175, 7322, 7324, 27807, 1185, 5281, 1187, 1188, 5283, 1192, 1195, 1197, 1198, 1199, 1200, 7345, 5298, 3252, 1205, 3253, 1207, 3256, 1209, 5308, 1213, 1214, 1215, 7360, 1218, 3266, 1220, 1221, 1222, 3270, 1224, 3268, 7365, 5323, 5324, 1229, 1233, 1239, 1240, 1244, 1245, 1246, 1249, 3300, 5348, 1255, 1257, 1258, 1259, 1260, 3309, 1262, 1261, 1264, 1269, 1270, 1274, 1275, 3327, 1280, 5375, 5376, 5379, 5381, 1287, 1290, 1297, 7443, 1303, 5400, 3358, 3362, 5414, 1319, 5417, 3385, 1345, 5442, 1347, 5443, 1349, 5444, 5448, 1353, 5451, 3407, 5457, 5458, 1366, 5463, 1369, 3420, 3423, 5478, 5480, 1386, 1392, 1393, 34161, 1395, 3447, 5501, 1408, 3467, 3469, 3470, 3480, 1436, 3497, 3498, 1465, 3523, 5571, 3526, 5576, 1484, 3537, 3542, 1499, 5601, 5602, 1512, 1516, 3568, 5617, 5619, 5620, 1526, 3577, 3580, 5635, 1541, 1543, 1561, 1563, 3614, 3616, 1572, 5668, 3622, 5672, 1579, 3628, 1583, 3632, 1586, 1587, 3638, 26171, 1609, 1613, 1616, 3675, 3687, 1640, 3696, 3701, 3702, 5751, 3716, 1672, 1675, 3725, 1679, 1681, 1689, 1692, 1700, 1701, 3750, 1703, 3751, 3752, 5799, 1712, 5809, 3763, 1720, 1721, 1725, 1728, 1730, 1731, 3792, 1746, 1747, 1759, 3820, 5871, 1776, 3825, 1778, 3824, 3830, 1783, 5880, 1800, 5899, 5901, 1808, 3861, 3868, 1830, 3881, 1834, 3892, 5942, 1847, 3896, 5944, 5956, 3910, 1875, 1883, 1887, 5988, 5990, 3947, 1906, 6002, 6005, 1910, 1918, 3966, 3967, 3968, 1922, 1923, 3976, 3983, 3985, 3987, 3989, 3993, 3995, 4001, 4004, 1960, 4010, 6059, 4013, 4014, 4015, 4017, 4021, 4022, 4024, 4026, 1981, 1982, 4033, 1996, 1999, 2001, 4060, 4068, 2027, 6124]\n" ] } ], "source": [ "user_to_rec = 3\n", "print(\"Items for User %s to check out based on similar user:\\n\"% user_to_rec, svd.recs_from_closest_user(user_to_rec,num_users=2))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" }, "latex_envs": { "LaTeX_envs_menu_present": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "30px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }
mit
ray-project/ray
doc/source/ray-air/examples/analyze_tuning_results.ipynb
1
28322
{ "cells": [ { "cell_type": "markdown", "id": "c62191af", "metadata": {}, "source": [ "# Analyzing results from hyperparameter tuning\n", "In this example, we will go through how you can use Ray AIR to run a distributed hyperparameter experiment to find optimal hyperparameters for an XGBoost model.\n", "\n", "What we'll cover:\n", "- How to load data from an Sklearn example dataset\n", "- How to initialize an XGBoost trainer\n", "- How to define a search space for regular XGBoost parameters and for data preprocessors\n", "- How to fetch the best obtained result from the tuning run\n", "- How to fetch a dataframe to do further analysis on the results" ] }, { "cell_type": "markdown", "id": "41abda7b", "metadata": {}, "source": [ "We'll use the [Covertype dataset](https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_covtype.html#sklearn-datasets-fetch-covtype) provided from sklearn to train a multiclass classification task using XGBoost.\n", "\n", "In this dataset, we try to predict the forst cover type (e.g. \"lodgehole pine\") from cartographic variables, like the distance to the closest road, or the hillshade at different times of the day. The features are binary, discrete and continuous and thus well suited for a decision-tree based classification task.\n", "\n", "You can find more information about the dataset [on the dataset homepage](https://archive.ics.uci.edu/ml/datasets/Covertype).\n", "\n", "We will train XGBoost models on this dataset. Because model training performance can be influenced by hyperparameter choices, we will generate several different configurations and train them in parallel. Notably each of these trials will itself start a distributed training job to speed up training. All of this happens automatically within Ray AIR.\n", "\n", "First, let's make sure we have all dependencies installed:" ] }, { "cell_type": "code", "execution_count": 1, "id": "db506971", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.4 is available.\r\n", "You should consider upgrading via the '/Users/kai/.pyenv/versions/3.7.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\r\n" ] } ], "source": [ "!pip install -q \"ray[all]\" sklearn" ] }, { "cell_type": "markdown", "id": "ec82829d", "metadata": {}, "source": [ "Then we can start with some imports." ] }, { "cell_type": "code", "execution_count": 2, "id": "d45b4f69", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.datasets import fetch_covtype\n", "\n", "import ray\n", "from ray import tune\n", "from ray.air import RunConfig\n", "from ray.train.xgboost import XGBoostTrainer\n", "from ray.tune.tune_config import TuneConfig\n", "from ray.tune.tuner import Tuner" ] }, { "cell_type": "markdown", "id": "a93b242c", "metadata": {}, "source": [ "We'll define a utility function to create a Ray Dataset from the Sklearn dataset. We expect the target column to be in the dataframe, so we'll add it to the dataframe manually." ] }, { "cell_type": "code", "execution_count": 3, "id": "3875df98", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-05-13 12:31:51,444\tINFO services.py:1484 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265\u001b[39m\u001b[22m\n" ] } ], "source": [ "def get_training_data() -> ray.data.Dataset:\n", " data_raw = fetch_covtype()\n", " df = pd.DataFrame(data_raw[\"data\"], columns=data_raw[\"feature_names\"])\n", " df[\"target\"] = data_raw[\"target\"]\n", " return ray.data.from_pandas(df)\n", "\n", "\n", "train_dataset = get_training_data()" ] }, { "cell_type": "markdown", "id": "90dd6fc5", "metadata": {}, "source": [ "Let's take a look at the schema here:" ] }, { "cell_type": "code", "execution_count": 4, "id": "936c9b26", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset(num_blocks=1, num_rows=581012, schema={Elevation: float64, Aspect: float64, Slope: float64, Horizontal_Distance_To_Hydrology: float64, Vertical_Distance_To_Hydrology: float64, Horizontal_Distance_To_Roadways: float64, Hillshade_9am: float64, Hillshade_Noon: float64, Hillshade_3pm: float64, Horizontal_Distance_To_Fire_Points: float64, Wilderness_Area_0: float64, Wilderness_Area_1: float64, Wilderness_Area_2: float64, Wilderness_Area_3: float64, Soil_Type_0: float64, Soil_Type_1: float64, Soil_Type_2: float64, Soil_Type_3: float64, Soil_Type_4: float64, Soil_Type_5: float64, Soil_Type_6: float64, Soil_Type_7: float64, Soil_Type_8: float64, Soil_Type_9: float64, Soil_Type_10: float64, Soil_Type_11: float64, Soil_Type_12: float64, Soil_Type_13: float64, Soil_Type_14: float64, Soil_Type_15: float64, Soil_Type_16: float64, Soil_Type_17: float64, Soil_Type_18: float64, Soil_Type_19: float64, Soil_Type_20: float64, Soil_Type_21: float64, Soil_Type_22: float64, Soil_Type_23: float64, Soil_Type_24: float64, Soil_Type_25: float64, Soil_Type_26: float64, Soil_Type_27: float64, Soil_Type_28: float64, Soil_Type_29: float64, Soil_Type_30: float64, Soil_Type_31: float64, Soil_Type_32: float64, Soil_Type_33: float64, Soil_Type_34: float64, Soil_Type_35: float64, Soil_Type_36: float64, Soil_Type_37: float64, Soil_Type_38: float64, Soil_Type_39: float64, target: int32})\n" ] } ], "source": [ "print(train_dataset)" ] }, { "cell_type": "markdown", "id": "282a6078", "metadata": {}, "source": [ "Since we'll be training a multiclass prediction model, we have to pass some information to XGBoost. For instance, XGBoost expects us to provide the number of classes, and multiclass-enabled evaluation metrices.\n", "\n", "For a good overview of commonly used hyperparameters, see [our tutorial in the docs](https://docs.ray.io/en/latest/tune/examples/tune-xgboost.html#xgboost-hyperparameters)." ] }, { "cell_type": "code", "execution_count": 5, "id": "60fdd48d", "metadata": {}, "outputs": [], "source": [ "# XGBoost specific params\n", "params = {\n", " \"tree_method\": \"approx\",\n", " \"objective\": \"multi:softmax\",\n", " \"eval_metric\": [\"mlogloss\", \"merror\"],\n", " \"num_class\": 8,\n", " \"min_child_weight\": 2\n", "}" ] }, { "cell_type": "markdown", "id": "228ae052", "metadata": {}, "source": [ "With these parameters in place, we'll create a Ray AIR `XGBoostTrainer`.\n", "\n", "Note a few things here. First, we pass in a `scaling_config` to configure the distributed training behavior of each individual XGBoost training job. Here, we want to distribute training across 2 workers.\n", "\n", "The `label_column` specifies which columns in the dataset contains the target values. `params` are the XGBoost training params defined above - we can tune these later! The `datasets` dict contains the dataset we would like to train on. Lastly, we pass the number of boosting rounds to XGBoost." ] }, { "cell_type": "code", "execution_count": 6, "id": "bbece53a", "metadata": {}, "outputs": [], "source": [ "trainer = XGBoostTrainer(\n", " scaling_config={\"num_workers\": 2},\n", " label_column=\"target\",\n", " params=params,\n", " datasets={\"train\": train_dataset},\n", " num_boost_round=10,\n", ")" ] }, { "cell_type": "markdown", "id": "e436035b", "metadata": {}, "source": [ "We can now create the Tuner with a search space to override some of the default parameters in the XGBoost trainer.\n", "\n", "Here, we just want to the XGBoost `max_depth` and `min_child_weights` parameters. Note that we specifically specified `min_child_weight=2` in the default XGBoost trainer - this value will be overwritten during tuning.\n", "\n", "We configure Tune to minimize the `train-mlogloss` metric. In random search, this doesn't affect the evaluated configurations, but it will affect our default results fetching for analysis later.\n", "\n", "By the way, the name `train-mlogloss` is provided by the XGBoost library - `train` is the name of the dataset and `mlogloss` is the metric we passed in the XGBoost `params` above. Trainables can report any number of results (in this case we report 2), but most search algorithms only act on one of them - here we chose the `mlogloss`." ] }, { "cell_type": "code", "execution_count": 7, "id": "c5d00628", "metadata": {}, "outputs": [], "source": [ "tuner = Tuner(\n", " trainer,\n", " run_config=RunConfig(verbose=1),\n", " param_space={\n", " \"params\": {\n", " \"max_depth\": tune.randint(2, 8), \n", " \"min_child_weight\": tune.randint(1, 10), \n", " },\n", " },\n", " tune_config=TuneConfig(num_samples=8, metric=\"train-mlogloss\", mode=\"min\"),\n", ")" ] }, { "cell_type": "markdown", "id": "7ba9cf69", "metadata": {}, "source": [ "Let's run the tuning. This will take a few minutes to complete." ] }, { "cell_type": "code", "execution_count": 8, "id": "ab642705", "metadata": {}, "outputs": [ { "data": { "text/html": [ "== Status ==<br>Current time: 2022-05-13 12:35:33 (running for 00:03:37.49)<br>Memory usage on this node: 10.0/16.0 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/6.73 GiB heap, 0.0/2.0 GiB objects<br>Current best trial: 4ab2f_00007 with train-mlogloss=0.560217 and parameters={'params': {'max_depth': 7, 'min_child_weight': 4}}<br>Result logdir: /Users/kai/ray_results/XGBoostTrainer_2022-05-13_12-31-55<br>Number of trials: 8/8 (8 TERMINATED)<br><br>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[2m\u001b[36m(GBDTTrainable pid=62456)\u001b[0m UserWarning: Dataset 'train' has 1 blocks, which is less than the `num_workers` 2. This dataset will be automatically repartitioned to 2 blocks.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62456)\u001b[0m 2022-05-13 12:32:02,793\tINFO main.py:980 -- [RayXGBoost] Created 2 new actors (2 total actors). Waiting until actors are ready for training.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62464)\u001b[0m UserWarning: Dataset 'train' has 1 blocks, which is less than the `num_workers` 2. This dataset will be automatically repartitioned to 2 blocks.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62463)\u001b[0m UserWarning: Dataset 'train' has 1 blocks, which is less than the `num_workers` 2. This dataset will be automatically repartitioned to 2 blocks.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62465)\u001b[0m UserWarning: Dataset 'train' has 1 blocks, which is less than the `num_workers` 2. This dataset will be automatically repartitioned to 2 blocks.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62466)\u001b[0m UserWarning: Dataset 'train' has 1 blocks, which is less than the `num_workers` 2. This dataset will be automatically repartitioned to 2 blocks.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62463)\u001b[0m 2022-05-13 12:32:05,102\tINFO main.py:980 -- [RayXGBoost] Created 2 new actors (2 total actors). Waiting until actors are ready for training.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62466)\u001b[0m 2022-05-13 12:32:05,204\tINFO main.py:980 -- [RayXGBoost] Created 2 new actors (2 total actors). Waiting until actors are ready for training.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62464)\u001b[0m 2022-05-13 12:32:05,338\tINFO main.py:980 -- [RayXGBoost] Created 2 new actors (2 total actors). Waiting until actors are ready for training.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62465)\u001b[0m 2022-05-13 12:32:07,164\tINFO main.py:980 -- [RayXGBoost] Created 2 new actors (2 total actors). Waiting until actors are ready for training.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62456)\u001b[0m 2022-05-13 12:32:10,549\tINFO main.py:1025 -- [RayXGBoost] Starting XGBoost training.\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62495)\u001b[0m [12:32:10] task [xgboost.ray]:6975277392 got new rank 1\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62494)\u001b[0m [12:32:10] task [xgboost.ray]:4560390352 got new rank 0\n", "\u001b[2m\u001b[36m(raylet)\u001b[0m Spilled 2173 MiB, 22 objects, write throughput 402 MiB/s. Set RAY_verbose_spill_logs=0 to disable this message.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62463)\u001b[0m 2022-05-13 12:32:17,848\tINFO main.py:1025 -- [RayXGBoost] Starting XGBoost training.\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62523)\u001b[0m [12:32:18] task [xgboost.ray]:4441524624 got new rank 0\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62524)\u001b[0m [12:32:18] task [xgboost.ray]:6890641808 got new rank 1\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62465)\u001b[0m 2022-05-13 12:32:21,253\tINFO main.py:1025 -- [RayXGBoost] Starting XGBoost training.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62466)\u001b[0m 2022-05-13 12:32:21,529\tINFO main.py:1025 -- [RayXGBoost] Starting XGBoost training.\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62563)\u001b[0m [12:32:21] task [xgboost.ray]:4667801680 got new rank 1\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62562)\u001b[0m [12:32:21] task [xgboost.ray]:6856360848 got new rank 0\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62530)\u001b[0m [12:32:21] task [xgboost.ray]:6971527824 got new rank 0\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62532)\u001b[0m [12:32:21] task [xgboost.ray]:4538321232 got new rank 1\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62464)\u001b[0m 2022-05-13 12:32:21,937\tINFO main.py:1025 -- [RayXGBoost] Starting XGBoost training.\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62544)\u001b[0m [12:32:21] task [xgboost.ray]:7005661840 got new rank 1\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62543)\u001b[0m [12:32:21] task [xgboost.ray]:4516088080 got new rank 0\n", "\u001b[2m\u001b[36m(raylet)\u001b[0m Spilled 4098 MiB, 83 objects, write throughput 347 MiB/s.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62456)\u001b[0m 2022-05-13 12:32:41,289\tINFO main.py:1109 -- Training in progress (31 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62463)\u001b[0m 2022-05-13 12:32:48,617\tINFO main.py:1109 -- Training in progress (31 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62465)\u001b[0m 2022-05-13 12:32:52,110\tINFO main.py:1109 -- Training in progress (31 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62466)\u001b[0m 2022-05-13 12:32:52,448\tINFO main.py:1109 -- Training in progress (31 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62464)\u001b[0m 2022-05-13 12:32:52,692\tINFO main.py:1109 -- Training in progress (31 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62456)\u001b[0m 2022-05-13 12:33:11,960\tINFO main.py:1109 -- Training in progress (61 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62463)\u001b[0m 2022-05-13 12:33:19,076\tINFO main.py:1109 -- Training in progress (61 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62464)\u001b[0m 2022-05-13 12:33:23,409\tINFO main.py:1109 -- Training in progress (61 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62465)\u001b[0m 2022-05-13 12:33:23,420\tINFO main.py:1109 -- Training in progress (62 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62466)\u001b[0m 2022-05-13 12:33:23,541\tINFO main.py:1109 -- Training in progress (62 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62463)\u001b[0m 2022-05-13 12:33:23,693\tINFO main.py:1519 -- [RayXGBoost] Finished XGBoost training on training data with total N=581,012 in 78.74 seconds (65.79 pure XGBoost training time).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62464)\u001b[0m 2022-05-13 12:33:24,802\tINFO main.py:1519 -- [RayXGBoost] Finished XGBoost training on training data with total N=581,012 in 79.62 seconds (62.85 pure XGBoost training time).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62648)\u001b[0m UserWarning: Dataset 'train' has 1 blocks, which is less than the `num_workers` 2. This dataset will be automatically repartitioned to 2 blocks.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62651)\u001b[0m UserWarning: Dataset 'train' has 1 blocks, which is less than the `num_workers` 2. This dataset will be automatically repartitioned to 2 blocks.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62648)\u001b[0m 2022-05-13 12:33:38,788\tINFO main.py:980 -- [RayXGBoost] Created 2 new actors (2 total actors). Waiting until actors are ready for training.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62651)\u001b[0m 2022-05-13 12:33:38,766\tINFO main.py:980 -- [RayXGBoost] Created 2 new actors (2 total actors). Waiting until actors are ready for training.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62456)\u001b[0m 2022-05-13 12:33:42,168\tINFO main.py:1109 -- Training in progress (92 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62456)\u001b[0m 2022-05-13 12:33:46,177\tINFO main.py:1519 -- [RayXGBoost] Finished XGBoost training on training data with total N=581,012 in 103.54 seconds (95.60 pure XGBoost training time).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62651)\u001b[0m 2022-05-13 12:33:51,825\tINFO main.py:1025 -- [RayXGBoost] Starting XGBoost training.\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62670)\u001b[0m [12:33:51] task [xgboost.ray]:4623186960 got new rank 1\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62669)\u001b[0m [12:33:51] task [xgboost.ray]:4707639376 got new rank 0\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62648)\u001b[0m 2022-05-13 12:33:52,036\tINFO main.py:1025 -- [RayXGBoost] Starting XGBoost training.\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62672)\u001b[0m [12:33:52] task [xgboost.ray]:4530073552 got new rank 1\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62671)\u001b[0m [12:33:52] task [xgboost.ray]:6824757200 got new rank 0\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62466)\u001b[0m 2022-05-13 12:33:54,229\tINFO main.py:1109 -- Training in progress (92 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62465)\u001b[0m 2022-05-13 12:33:54,355\tINFO main.py:1109 -- Training in progress (93 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62730)\u001b[0m UserWarning: Dataset 'train' has 1 blocks, which is less than the `num_workers` 2. This dataset will be automatically repartitioned to 2 blocks.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62730)\u001b[0m 2022-05-13 12:34:04,708\tINFO main.py:980 -- [RayXGBoost] Created 2 new actors (2 total actors). Waiting until actors are ready for training.\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62466)\u001b[0m 2022-05-13 12:34:11,126\tINFO main.py:1519 -- [RayXGBoost] Finished XGBoost training on training data with total N=581,012 in 126.08 seconds (109.48 pure XGBoost training time).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62730)\u001b[0m 2022-05-13 12:34:15,175\tINFO main.py:1025 -- [RayXGBoost] Starting XGBoost training.\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62753)\u001b[0m [12:34:15] task [xgboost.ray]:4468564048 got new rank 1\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=62752)\u001b[0m [12:34:15] task [xgboost.ray]:6799468304 got new rank 0\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[2m\u001b[36m(GBDTTrainable pid=62648)\u001b[0m 2022-05-13 12:34:22,167\tINFO main.py:1109 -- Training in progress (30 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62651)\u001b[0m 2022-05-13 12:34:22,147\tINFO main.py:1109 -- Training in progress (30 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62465)\u001b[0m 2022-05-13 12:34:24,646\tINFO main.py:1109 -- Training in progress (123 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62465)\u001b[0m 2022-05-13 12:34:24,745\tINFO main.py:1519 -- [RayXGBoost] Finished XGBoost training on training data with total N=581,012 in 137.75 seconds (123.36 pure XGBoost training time).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62651)\u001b[0m 2022-05-13 12:34:40,173\tINFO main.py:1519 -- [RayXGBoost] Finished XGBoost training on training data with total N=581,012 in 61.63 seconds (48.34 pure XGBoost training time).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62730)\u001b[0m 2022-05-13 12:34:45,745\tINFO main.py:1109 -- Training in progress (31 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62648)\u001b[0m 2022-05-13 12:34:52,543\tINFO main.py:1109 -- Training in progress (60 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62648)\u001b[0m 2022-05-13 12:35:14,888\tINFO main.py:1519 -- [RayXGBoost] Finished XGBoost training on training data with total N=581,012 in 96.35 seconds (82.83 pure XGBoost training time).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62730)\u001b[0m 2022-05-13 12:35:16,197\tINFO main.py:1109 -- Training in progress (61 seconds since last restart).\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=62730)\u001b[0m 2022-05-13 12:35:33,441\tINFO main.py:1519 -- [RayXGBoost] Finished XGBoost training on training data with total N=581,012 in 88.89 seconds (78.26 pure XGBoost training time).\n", "2022-05-13 12:35:33,610\tINFO tune.py:753 -- Total run time: 218.52 seconds (217.48 seconds for the tuning loop).\n" ] } ], "source": [ "results = tuner.fit()" ] }, { "cell_type": "markdown", "id": "2c7c444e", "metadata": {}, "source": [ "Now that we obtained the results, we can analyze them. For instance, we can fetch the best observed result according to the configured `metric` and `mode` and print it:" ] }, { "cell_type": "code", "execution_count": 9, "id": "4f4e5187", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best result error rate 0.196929\n" ] } ], "source": [ "# This will fetch the best result according to the `metric` and `mode` specified\n", "# in the `TuneConfig` above:\n", "\n", "best_result = results.get_best_result()\n", "\n", "print(\"Best result error rate\", best_result.metrics[\"train-merror\"])" ] }, { "cell_type": "markdown", "id": "53d71b08", "metadata": {}, "source": [ "For more sophisticated analysis, we can get a pandas dataframe with all trial results:" ] }, { "cell_type": "code", "execution_count": 10, "id": "50e76e91", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['train-mlogloss', 'train-merror', 'time_this_iter_s',\n", " 'should_checkpoint', 'done', 'timesteps_total', 'episodes_total',\n", " 'training_iteration', 'trial_id', 'experiment_id', 'date', 'timestamp',\n", " 'time_total_s', 'pid', 'hostname', 'node_ip', 'time_since_restore',\n", " 'timesteps_since_restore', 'iterations_since_restore', 'warmup_time',\n", " 'config/params/max_depth', 'config/params/min_child_weight', 'logdir'],\n", " dtype='object')\n" ] } ], "source": [ "df = results.get_dataframe()\n", "print(df.columns)" ] }, { "cell_type": "markdown", "id": "b8a05668", "metadata": {}, "source": [ "As an example, let's group the results per `min_child_weight` parameter and fetch the minimal obtained values:" ] }, { "cell_type": "code", "execution_count": 11, "id": "94b017b4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Min child weight 1 error 0.262468 logloss 0.69843\n", "Min child weight 2 error 0.311035 logloss 0.79498\n", "Min child weight 3 error 0.240916 logloss 0.651457\n", "Min child weight 4 error 0.196929 logloss 0.560217\n", "Min child weight 6 error 0.219665 logloss 0.608005\n", "Min child weight 7 error 0.311035 logloss 0.794983\n", "Min child weight 8 error 0.311035 logloss 0.794983\n" ] } ], "source": [ "groups = df.groupby(\"config/params/min_child_weight\")\n", "mins = groups.min()\n", "\n", "for min_child_weight, row in mins.iterrows():\n", " print(\"Min child weight\", min_child_weight, \"error\", row[\"train-merror\"], \"logloss\", row[\"train-mlogloss\"])\n" ] }, { "cell_type": "markdown", "id": "8e135ee9", "metadata": {}, "source": [ "As you can see in our example run, the min child weight of `2` showed the best prediction accuracy with `0.196929`. That's the same as `results.get_best_result()` gave us!" ] }, { "cell_type": "markdown", "id": "2f478e4c", "metadata": {}, "source": [ "The `results.get_dataframe()` returns the last reported results per trial. If you want to obtain the best _ever_ observed results, you can pass the `filter_metric` and `filter_mode` arguments to `results.get_dataframe()`. In our example, we'll filter the minimum _ever_ observed `train-merror` for each trial:" ] }, { "cell_type": "code", "execution_count": 12, "id": "afa83cf6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0.262468\n", "1 0.310307\n", "2 0.310307\n", "3 0.219665\n", "4 0.240916\n", "5 0.220801\n", "6 0.310307\n", "7 0.196929\n", "Name: train-merror, dtype: float64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_min_error = results.get_dataframe(filter_metric=\"train-merror\", filter_mode=\"min\")\n", "df_min_error[\"train-merror\"]" ] }, { "cell_type": "markdown", "id": "3f4525cb", "metadata": {}, "source": [ "The best ever observed `train-merror` is `0.196929`, the same as the minimum error in our grouped results. This is expected, as the classification error in XGBoost usually goes down over time - meaning our last results are usually the best results." ] }, { "cell_type": "markdown", "id": "47236b0a", "metadata": {}, "source": [ "And that's how you analyze your hyperparameter tuning results. If you would like to have access to more analytics, please feel free to file a feature request e.g. [as a Github issue](https://github.com/ray-project/ray/issues) or on our [Discuss platform](https://discuss.ray.io/)!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 5 }
apache-2.0
wilselby/diy_driverless_car_ROS
rover_ml/colab/RC_Car_End_to_End_Image_Regression_with_CNNs_(RGB_camera).ipynb
1
45572
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "RC Car End-to-End Image Regression with CNNs (RGB camera).ipynb", "provenance": [], "collapsed_sections": [], "toc_visible": true, "include_colab_link": true }, "kernelspec": { "name": "python2", "display_name": "Python 2" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "<a href=\"https://colab.research.google.com/github/wilselby/diy_driverless_car_ROS/blob/ml-model/rover_ml/colab/RC_Car_End_to_End_Image_Regression_with_CNNs_(RGB_camera).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" ] }, { "cell_type": "markdown", "metadata": { "id": "7Ob-YPAvx7Nr", "colab_type": "text" }, "source": [ "# Development of an End-to-End ML Model for Navigating an RC car with a Camera" ] }, { "cell_type": "markdown", "metadata": { "id": "uPcv0EFav6LH", "colab_type": "text" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/wilselby/diy_driverless_car_ROS/blob/ml-model/RC_Car_End_to_End_Image_Regression_with_CNNs_(RGB_camera).ipynb\">\n", " <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />\n", " Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/wilselby/diy_driverless_car_ROS/blob/ml-model/rover_ml/colab/RC_Car_End_to_End_Image_Regression_with_CNNs_(RGB_camera).ipynb\">\n", " <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />\n", " View source on GitHub</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "EPxyR5SargY9", "colab_type": "text" }, "source": [ "#Environment Setup\n" ] }, { "cell_type": "markdown", "metadata": { "id": "lZbZCuOD2JLH", "colab_type": "text" }, "source": [ "## Import Dependencies" ] }, { "cell_type": "code", "metadata": { "id": "AqUpKnu52L5S", "colab_type": "code", "colab": {} }, "source": [ "import os\n", "import csv\n", "import cv2\n", "import matplotlib.pyplot as plt\n", "import random\n", "import pprint\n", "\n", "import numpy as np\n", "from numpy import expand_dims\n", "\n", "%tensorflow_version 1.x\n", "import tensorflow as tf\n", "tf.logging.set_verbosity(tf.logging.ERROR)\n", "\n", "from keras import backend as K\n", "from keras.models import Model, Sequential\n", "from keras.models import load_model\n", "from keras.layers import Dense, GlobalAveragePooling2D, MaxPooling2D, Lambda, Cropping2D\n", "from keras.layers.convolutional import Convolution2D\n", "from keras.layers.core import Flatten, Dense, Dropout, SpatialDropout2D\n", "from keras.optimizers import Adam\n", "from keras.callbacks import ModelCheckpoint, TensorBoard\n", "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n", "from keras.preprocessing.image import ImageDataGenerator\n", "from keras.preprocessing.image import load_img\n", "from keras.preprocessing.image import img_to_array \n", " \n", "from google.colab.patches import cv2_imshow\n", " \n", "import sklearn\n", "from sklearn.model_selection import train_test_split\n", "import pandas as pd\n", "\n", "print(\"Tensorflow Version:\",tf.__version__)\n", "print(\"Tensorflow Keras Version:\",tf.keras.__version__)\n", "print(\"Eager mode: \", tf.executing_eagerly())\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "7vy5iiwR19nJ", "colab_type": "text" }, "source": [ "## Confirm TensorFlow can see the GPU \n", "\n", "Simply select \"GPU\" in the Accelerator drop-down in Notebook Settings (either through the Edit menu or the command palette at cmd/ctrl-shift-P)." ] }, { "cell_type": "code", "metadata": { "id": "_0h6Afcy2E9l", "colab_type": "code", "colab": {} }, "source": [ "device_name = tf.test.gpu_device_name()\n", "\n", "if device_name != '/device:GPU:0':\n", " #raise SystemError('GPU device not found')\n", " print('GPU device not found')\n", "else:\n", " print('Found GPU at: {}'.format(device_name))\n", " \n", " #GPU count and name\n", " !nvidia-smi -L" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "H2sPUxOR3FlN", "colab_type": "text" }, "source": [ "# Load the Dataset" ] }, { "cell_type": "markdown", "metadata": { "id": "8YsJN12w3aug", "colab_type": "text" }, "source": [ "## Download and Extract the Dataset" ] }, { "cell_type": "code", "metadata": { "id": "SfRnrsuuRXBL", "colab_type": "code", "colab": {} }, "source": [ "# Download the dataset\n", "!curl -O https://selbystorage.s3-us-west-2.amazonaws.com/research/office_2/office_2.tar.gz" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "HV6z9-T4hd-G", "colab_type": "code", "cellView": "both", "colab": {} }, "source": [ "data_set = 'office_2'\n", "tar_file = data_set + '.tar.gz'\n", "\n", "# Unzip the .tgz file\n", "# -x for extract\n", "# -v for verbose \n", "# -z for gnuzip\n", "# -f for file (should come at last just before file name)\n", "# -C to extract the zipped contents to a different directory\n", "!tar -xvzf $tar_file" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "9FogCRzr3sUF", "colab_type": "text" }, "source": [ "## Parse the CSV File" ] }, { "cell_type": "code", "metadata": { "id": "KeQ8c-9s3v8y", "colab_type": "code", "colab": {} }, "source": [ "# Define path to csv file\n", "csv_path = data_set + '/interpolated.csv'\n", "\n", "# Load the CSV file into a pandas dataframe\n", "df = pd.read_csv(csv_path, sep=\",\")\n", "\n", "# Print the dimensions\n", "print(\"Dataset Dimensions:\")\n", "print(df.shape)\n", "\n", "# Print the first 5 lines of the dataframe for review\n", "print(\"\\nDataset Summary:\")\n", "df.head(5)\n", " " ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "3BnBpo6-F2oR", "colab_type": "text" }, "source": [ "# Clean and Pre-process the Dataset" ] }, { "cell_type": "markdown", "metadata": { "id": "ub8CkShSJEkV", "colab_type": "text" }, "source": [ "## Remove Unneccessary Columns" ] }, { "cell_type": "code", "metadata": { "id": "PDth_K-3JINP", "colab_type": "code", "colab": {} }, "source": [ "# Remove 'index' and 'frame_id' columns \n", "df.drop(['index','frame_id'],axis=1,inplace=True)\n", "\n", "# Verify new dataframe dimensions\n", "print(\"Dataset Dimensions:\")\n", "print(df.shape)\n", "\n", "# Print the first 5 lines of the new dataframe for review\n", "print(\"\\nDataset Summary:\")\n", "df.head(5)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "o-zof8fUDz2C", "colab_type": "text" }, "source": [ "## Detect Missing Data" ] }, { "cell_type": "code", "metadata": { "id": "ffOXGmmQD2om", "colab_type": "code", "colab": {} }, "source": [ "# Detect Missing Values\n", "print(\"Any Missing Values?: {}\".format(df.isnull().values.any()))\n", "\n", "# Total Sum\n", "print(\"\\nTotal Number of Missing Values: {}\".format(df.isnull().sum().sum()))\n", "\n", "# Sum Per Column\n", "print(\"\\nTotal Number of Missing Values per Column:\")\n", "print(df.isnull().sum())" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "cKBJ4sODIFOC", "colab_type": "text" }, "source": [ "## Remove Zero Throttle Values" ] }, { "cell_type": "code", "metadata": { "id": "QAk-fsbkIJrh", "colab_type": "code", "colab": {} }, "source": [ "# Determine if any throttle values are zeroes\n", "print(\"Any 0 throttle values?: {}\".format(df['speed'].eq(0).any()))\n", "\n", "# Determine number of 0 throttle values:\n", "print(\"\\nNumber of 0 throttle values: {}\".format(df['speed'].eq(0).sum()))\n", "\n", "# Remove rows with 0 throttle values\n", "if df['speed'].eq(0).any():\n", " df = df.query('speed != 0')\n", " \n", " # Reset the index\n", " df.reset_index(inplace=True,drop=True)\n", " \n", "# Verify new dataframe dimensions\n", "print(\"\\nNew Dataset Dimensions:\")\n", "print(df.shape)\n", "df.head(5)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "6XZJLwqKCbE7", "colab_type": "text" }, "source": [ "## View Label Statistics" ] }, { "cell_type": "code", "metadata": { "id": "AgOG94fnCeDB", "colab_type": "code", "colab": {} }, "source": [ "# Steering Command Statistics\n", "print(\"\\nSteering Command Statistics:\")\n", "print(df['angle'].describe())\n", "\n", "print(\"\\nThrottle Command Statistics:\")\n", "# Throttle Command Statistics\n", "print(df['speed'].describe())" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "nae5TmmFFJ5T", "colab_type": "text" }, "source": [ "## View Histogram of Steering Commands" ] }, { "cell_type": "code", "metadata": { "id": "Bh3DSasZQKCi", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "#@title Select the number of histogram bins\n", "\n", "num_bins = 25 #@param {type:\"slider\", min:5, max:50, step:1}\n", "\n", "hist, bins = np.histogram(df['angle'], num_bins)\n", "center = (bins[:-1]+ bins[1:]) * 0.5\n", "plt.bar(center, hist, width=0.05)\n", "#plt.plot((np.min(df['angle']), np.max(df['angle'])), (samples_per_bin, samples_per_bin))" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "cwunrGrDQweC", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "# Normalize the histogram (150-300 for RBG)\n", "#@title Normalize the Histogram { run: \"auto\" }\n", "hist = True #@param {type:\"boolean\"}\n", "\n", "remove_list = []\n", "samples_per_bin = 200\n", "\n", "if hist:\n", " for j in range(num_bins):\n", " list_ = []\n", " for i in range(len(df['angle'])):\n", " if df.loc[i,'angle'] >= bins[j] and df.loc[i,'angle'] <= bins[j+1]:\n", " list_.append(i)\n", " random.shuffle(list_)\n", " list_ = list_[samples_per_bin:]\n", " remove_list.extend(list_)\n", "\n", " print('removed:', len(remove_list))\n", " df.drop(df.index[remove_list], inplace=True)\n", " df.reset_index(inplace=True)\n", " df.drop(['index'],axis=1,inplace=True)\n", " print('remaining:', len(df))\n", " \n", " hist, _ = np.histogram(df['angle'], (num_bins))\n", " plt.bar(center, hist, width=0.05)\n", " plt.plot((np.min(df['angle']), np.max(df['angle'])), (samples_per_bin, samples_per_bin))" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "1uqfxZ4uGoNX", "colab_type": "text" }, "source": [ "## View a Sample Image" ] }, { "cell_type": "code", "metadata": { "id": "L2nwHnC4Gq1m", "colab_type": "code", "colab": {} }, "source": [ "# View a Single Image \n", "index = random.randint(0,df.shape[0]-1)\n", "\n", "img_name = data_set + '/' + df.loc[index,'filename']\n", "angle = df.loc[index,'angle']\n", "\n", "center_image = cv2.imread(img_name)\n", "center_image_mod = cv2.resize(center_image, (320,180))\n", "center_image_mod = cv2.cvtColor(center_image_mod,cv2.COLOR_RGB2BGR)\n", "\n", "# Crop the image\n", "height_min = 75 \n", "height_max = center_image_mod.shape[0]\n", "width_min = 0\n", "width_max = center_image_mod.shape[1]\n", "\n", "crop_img = center_image_mod[height_min:height_max, width_min:width_max]\n", "\n", "plt.subplot(2,1,1)\n", "plt.imshow(center_image_mod)\n", "plt.grid(False)\n", "plt.xlabel('angle: {:.2}'.format(angle))\n", "plt.show() \n", "\n", "plt.subplot(2,1,2)\n", "plt.imshow(crop_img)\n", "plt.grid(False)\n", "plt.xlabel('angle: {:.2}'.format(angle))\n", "plt.show() " ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "gTQywtOyGvLv", "colab_type": "text" }, "source": [ "## View Multiple Images" ] }, { "cell_type": "code", "metadata": { "id": "ODmdWWpsGxK2", "colab_type": "code", "colab": {} }, "source": [ "# Number of Images to Display\n", "num_images = 4\n", "\n", "# Display the images\n", "i = 0\n", "for i in range (i,num_images):\n", " index = random.randint(0,df.shape[0]-1)\n", " image_path = df.loc[index,'filename']\n", " angle = df.loc[index,'angle']\n", " img_name = data_set + '/' + image_path\n", " image = cv2.imread(img_name)\n", " image = cv2.resize(image, (320,180))\n", " image = cv2.cvtColor(image,cv2.COLOR_RGB2BGR)\n", " plt.subplot(num_images/2,num_images/2,i+1)\n", " plt.xticks([])\n", " plt.yticks([])\n", " plt.grid(False)\n", " plt.imshow(image, cmap=plt.cm.binary)\n", " plt.xlabel('angle: {:.3}'.format(angle))\n", " i += 1" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "BgFvPAZl9vfP", "colab_type": "text" }, "source": [ "# Split the Dataset" ] }, { "cell_type": "markdown", "metadata": { "id": "clqhrZpXtYQy", "colab_type": "text" }, "source": [ "## Define an ImageDataGenerator to Augment Images\n", "\n" ] }, { "cell_type": "code", "metadata": { "id": "FcAb3NLiten6", "colab_type": "code", "colab": {} }, "source": [ "# Create image data augmentation generator and choose augmentation types\n", "datagen = ImageDataGenerator(\n", " #rotation_range=20,\n", " zoom_range=0.15,\n", " #width_shift_range=0.1,\n", " #height_shift_range=0.2,\n", " #shear_range=10,\n", " brightness_range=[0.5,1.0],\n", " \t #horizontal_flip=True,\n", " #vertical_flip=True,\n", " #channel_shift_range=100.0,\n", " fill_mode=\"reflect\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "m0iJWp1YE-u5", "colab_type": "text" }, "source": [ "## View Image Augmentation Examples" ] }, { "cell_type": "code", "metadata": { "id": "fxKea6xhVO2T", "colab_type": "code", "colab": {} }, "source": [ "# load the image\n", "index = random.randint(0,df.shape[0]-1)\n", "\n", "img_name = data_set + '/' + df.loc[index,'filename']\n", "original_image = cv2.imread(img_name)\n", "original_image = cv2.cvtColor(original_image,cv2.COLOR_RGB2BGR)\n", "original_image = cv2.resize(original_image, (320,180))\n", "label = df.loc[index,'angle']\n", "\n", "# convert to numpy array\n", "data = img_to_array(original_image)\n", "\n", "# expand dimension to one sample\n", "test = expand_dims(data, 0)\n", "\n", "# prepare iterator\n", "it = datagen.flow(test, batch_size=1)\n", "\n", "# generate batch of images\n", "batch = it.next()\n", "\n", "# convert to unsigned integers for viewing\n", "image_aug = batch[0].astype('uint8')\n", "\n", "print(\"Augmenting a Single Image: \\n\")\n", "\n", "plt.subplot(2,1,1)\n", "plt.imshow(original_image)\n", "plt.grid(False)\n", "plt.xlabel('angle: {:.2}'.format(label))\n", "plt.show() \n", "\n", "plt.subplot(2,1,2)\n", "plt.imshow(image_aug)\n", "plt.grid(False)\n", "plt.xlabel('angle: {:.2}'.format(label))\n", "plt.show() \n", "\n", "print(\"Multiple Augmentations: \\n\")\n", "# generate samples and plot\n", "for i in range(0,num_images):\n", "\t# define subplot\n", "\tplt.subplot(num_images/2,num_images/2,i+1)\n", "\t# generate batch of images\n", "\tbatch = it.next()\n", "\t# convert to unsigned integers for viewing\n", "\timage = batch[0].astype('uint8')\n", "\t# plot raw pixel data\n", "\tplt.imshow(image)\n", "# show the figure\n", "plt.show()\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "LUE19jPetzBs", "colab_type": "text" }, "source": [ "## Define a Data Generator" ] }, { "cell_type": "code", "metadata": { "id": "TgtIMpoSt1LW", "colab_type": "code", "colab": {} }, "source": [ "def generator(samples, batch_size=32, aug=0):\n", " num_samples = len(samples)\n", "\n", " while 1: # Loop forever so the generator never terminates\n", " for offset in range(0, num_samples, batch_size):\n", " batch_samples = samples[offset:offset + batch_size]\n", "\n", " #print(batch_samples)\n", " images = []\n", " angles = []\n", " for batch_sample in batch_samples:\n", " if batch_sample[5] != \"filename\":\n", " name = data_set + '/' + batch_sample[3]\n", " center_image = cv2.imread(name)\n", " center_image = cv2.cvtColor(center_image,cv2.COLOR_RGB2BGR)\n", " center_image = cv2.resize(\n", " center_image,\n", " (320, 180)) #resize from 720x1280 to 180x320\n", " angle = float(batch_sample[4])\n", " if not aug:\n", " images.append(center_image)\n", " angles.append(angle)\n", " else:\n", " data = img_to_array(center_image)\n", " sample = expand_dims(data, 0)\n", " it = datagen.flow(sample, batch_size=1)\n", " batch = it.next()\n", " image_aug = batch[0].astype('uint8')\n", " if random.random() < .5:\n", " image_aug = np.fliplr(image_aug)\n", " angle = -1 * angle\n", " images.append(image_aug)\n", " angles.append(angle)\n", "\n", " X_train = np.array(images)\n", " y_train = np.array(angles)\n", "\n", " yield sklearn.utils.shuffle(X_train, y_train)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "PAVmOpT8HEg0", "colab_type": "text" }, "source": [ "## Split the Dataset" ] }, { "cell_type": "code", "metadata": { "id": "lZWDPKBnvGZI", "colab_type": "code", "colab": {} }, "source": [ "samples = []\n", "\n", "samples = df.values.tolist()\n", "\n", "sklearn.utils.shuffle(samples)\n", "train_samples, validation_samples = train_test_split(samples, test_size=0.2)\n", "\n", "print(\"Number of traing samples: \", len(train_samples))\n", "print(\"Number of validation samples: \", len(validation_samples))" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "g5u_LimyvOkv", "colab_type": "text" }, "source": [ "## Define Training and Validation Data Generators" ] }, { "cell_type": "code", "metadata": { "id": "ux8mS7YRpQaX", "colab_type": "code", "colab": {} }, "source": [ "batch_size_value = 32\n", "img_aug = 0\n", "\n", "train_generator = generator(train_samples, batch_size=batch_size_value, aug=img_aug)\n", "validation_generator = generator(\n", " validation_samples, batch_size=batch_size_value, aug=0)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "-_WW4C27_4HO", "colab_type": "text" }, "source": [ "# Compile and Train the Model" ] }, { "cell_type": "markdown", "metadata": { "id": "Z62Wkaj4ADbB", "colab_type": "text" }, "source": [ "## Build the Model" ] }, { "cell_type": "code", "metadata": { "id": "AK578kaYAE1_", "colab_type": "code", "colab": {} }, "source": [ "# Initialize the model\n", "model = Sequential()\n", "\n", "# trim image to only see section with road\n", "# (top_crop, bottom_crop), (left_crop, right_crop)\n", "model.add(Cropping2D(cropping=((height_min,0), (width_min,0)), input_shape=(180,320,3)))\n", "\n", "# Preprocess incoming data, centered around zero with small standard deviation\n", "model.add(Lambda(lambda x: (x / 255.0) - 0.5))\n", "\n", "# Nvidia model\n", "model.add(Convolution2D(24, (5, 5), activation=\"relu\", name=\"conv_1\", strides=(2, 2)))\n", "model.add(Convolution2D(36, (5, 5), activation=\"relu\", name=\"conv_2\", strides=(2, 2)))\n", "model.add(Convolution2D(48, (5, 5), activation=\"relu\", name=\"conv_3\", strides=(2, 2)))\n", "model.add(SpatialDropout2D(.5, dim_ordering='default'))\n", "\n", "model.add(Convolution2D(64, (3, 3), activation=\"relu\", name=\"conv_4\", strides=(1, 1)))\n", "model.add(Convolution2D(64, (3, 3), activation=\"relu\", name=\"conv_5\", strides=(1, 1)))\n", "\n", "model.add(Flatten())\n", "\n", "model.add(Dense(1164))\n", "model.add(Dropout(.5))\n", "model.add(Dense(100, activation='relu'))\n", "model.add(Dropout(.5))\n", "model.add(Dense(50, activation='relu'))\n", "model.add(Dropout(.5))\n", "model.add(Dense(10, activation='relu'))\n", "model.add(Dropout(.5))\n", "model.add(Dense(1))\n", "\n", "model.compile(loss='mse', optimizer=Adam(lr=0.001), metrics=['mse','mae','mape','cosine'])\n", "\n", "# Print model sumamry\n", "model.summary()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "USKQYVmhAMaJ", "colab_type": "text" }, "source": [ "## Setup Checkpoints" ] }, { "cell_type": "code", "metadata": { "id": "BS6R3FVoAOPc", "colab_type": "code", "colab": {} }, "source": [ "# checkpoint\n", "model_path = './model'\n", "\n", "!if [ -d $model_path ]; then echo 'Directory Exists'; else mkdir $model_path; fi\n", "\n", "filepath = model_path + \"/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5\"\n", "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='auto', period=1)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ImbsdKhSOKw3", "colab_type": "text" }, "source": [ "## Setup Early Stopping to Prevent Overfitting" ] }, { "cell_type": "code", "metadata": { "id": "Rhi1wb2yOLvY", "colab_type": "code", "colab": {} }, "source": [ "# The patience parameter is the amount of epochs to check for improvement\n", "early_stop = EarlyStopping(monitor='val_loss', patience=10)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "ZDwl4ZB9boi2", "colab_type": "text" }, "source": [ "## Reduce Learning Rate When a Metric has Stopped Improving" ] }, { "cell_type": "code", "metadata": { "id": "m1xTmpV-bywp", "colab_type": "code", "colab": {} }, "source": [ "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,\n", " patience=5, min_lr=0.001)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "BVIGONgjSy7V", "colab_type": "text" }, "source": [ "## Setup Tensorboard" ] }, { "cell_type": "code", "metadata": { "id": "-YWTAEeyS0tw", "colab_type": "code", "colab": {} }, "source": [ "# Clear any logs from previous runs\n", "!rm -rf ./Graph/ \n", "\n", "# Launch Tensorboard\n", "!pip install -U tensorboardcolab\n", "\n", "from tensorboardcolab import *\n", "\n", "tbc = TensorBoardColab()\n", "\n", "# Configure the Tensorboard Callback\n", "tbCallBack = TensorBoard(log_dir='./Graph', \n", " histogram_freq=1,\n", " write_graph=True,\n", " write_grads=True,\n", " write_images=True,\n", " batch_size=batch_size_value,\n", " update_freq='epoch')\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "v83cZRQMxBQi", "colab_type": "text" }, "source": [ "## Load Existing Model" ] }, { "cell_type": "code", "metadata": { "id": "acaXlXpUxDxM", "colab_type": "code", "cellView": "form", "colab": {} }, "source": [ "load = True #@param {type:\"boolean\"}\n", "\n", "if load:\n", " # Returns a compiled model identical to the previous one\n", " !curl -O https://selbystorage.s3-us-west-2.amazonaws.com/research/office_2/model.h5\n", " !mv model.h5 model/\n", " model_path_full = model_path + '/' + 'model.h5'\n", " model = load_model(model_path_full)\n", " print(\"Loaded previous model: {} \\n\".format(model_path_full))\n", "else:\n", " print(\"No previous model loaded \\n\")" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "71O-pWk9AQy3", "colab_type": "text" }, "source": [ "\n", "## Train the Model" ] }, { "cell_type": "code", "metadata": { "id": "3nkossmrAUo2", "colab_type": "code", "colab": {} }, "source": [ "# Define step sizes\n", "STEP_SIZE_TRAIN = len(train_samples) / batch_size_value\n", "STEP_SIZE_VALID = len(validation_samples) / batch_size_value\n", "\n", "# Define number of epochs\n", "n_epoch = 5\n", "\n", "# Define callbacks\n", "# callbacks_list = [TensorBoardColabCallback(tbc)]\n", "# callbacks_list = [TensorBoardColabCallback(tbc), early_stop]\n", "# callbacks_list = [TensorBoardColabCallback(tbc), early_stop, checkpoint]\n", "callbacks_list = [TensorBoardColabCallback(tbc), early_stop, checkpoint, reduce_lr]\n", "\n", "# Fit the model\n", "history_object = model.fit_generator(\n", " generator=train_generator,\n", " steps_per_epoch=STEP_SIZE_TRAIN,\n", " validation_data=validation_generator,\n", " validation_steps=STEP_SIZE_VALID,\n", " callbacks=callbacks_list,\n", " use_multiprocessing=True,\n", " epochs=n_epoch)\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "OdaGfWNxBT4T", "colab_type": "text" }, "source": [ "## Save the Model" ] }, { "cell_type": "code", "metadata": { "id": "jWE--9r4BZEk", "colab_type": "code", "colab": {} }, "source": [ "# Save model\n", "model_path_full = model_path + '/'\n", "\n", "model.save(model_path_full + 'model.h5')\n", "with open(model_path_full + 'model.json', 'w') as output_json:\n", " output_json.write(model.to_json())" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Y-pCmYO1A89_", "colab_type": "text" }, "source": [ "# Evaluate the Model" ] }, { "cell_type": "markdown", "metadata": { "id": "O7o-6SbBD9zx", "colab_type": "text" }, "source": [ "## Plot the Training Results" ] }, { "cell_type": "code", "metadata": { "id": "t-M9fEvWD_ZJ", "colab_type": "code", "colab": {} }, "source": [ "# Plot the training and validation loss for each epoch\n", "print('Generating loss chart...')\n", "plt.plot(history_object.history['loss'])\n", "plt.plot(history_object.history['val_loss'])\n", "plt.title('model mean squared error loss')\n", "plt.ylabel('mean squared error loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['training set', 'validation set'], loc='upper right')\n", "plt.savefig(model_path + '/model.png')\n", "\n", "# Done\n", "print('Done.')" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "fl5JppdwCRWD", "colab_type": "text" }, "source": [ "## Print Performance Metrics" ] }, { "cell_type": "code", "metadata": { "id": "podNBS9cBRNW", "colab_type": "code", "colab": {} }, "source": [ "scores = model.evaluate_generator(validation_generator, STEP_SIZE_VALID, use_multiprocessing=True)\n", "\n", "metrics_names = model.metrics_names\n", "\n", "for i in range(len(model.metrics_names)):\n", " print(\"Metric: {} - {}\".format(metrics_names[i],scores[i]))\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "OHL1otavPhX0", "colab_type": "text" }, "source": [ "## Compute Prediction Statistics" ] }, { "cell_type": "code", "metadata": { "id": "xlv7PUnPeSYd", "colab_type": "code", "colab": {} }, "source": [ "# Define image loading function\n", "def load_images(dataframe):\n", " \n", " # initialize images array\n", " images = []\n", " \n", " for i in dataframe.index.values:\n", " name = data_set + '/' + dataframe.loc[i,'filename']\n", " center_image = cv2.imread(name)\n", " center_image = cv2.resize(center_image, (320,180))\n", " images.append(center_image)\n", " \n", " return np.array(images)\n", " \n", "# Load images \n", "test_size = 200\n", "df_test = df.sample(frac=1).reset_index(drop=True)\n", "df_test = df_test.head(test_size)\n", "\n", "test_images = load_images(df_test)\n", "\n", "batch_size = 32\n", "preds = model.predict(test_images, batch_size=batch_size, verbose=1)\n", "\n", "#print(\"Preds: {} \\n\".format(preds))\n", "\n", "testY = df_test.iloc[:,4].values\n", "\n", "#print(\"Labels: {} \\n\".format(testY))\n", "\n", "df_testY = pd.Series(testY)\n", "df_preds = pd.Series(preds.flatten)\n", "\n", "# Replace 0 angle values\n", "if df_testY.eq(0).any():\n", " df_testY.replace(0, 0.0001,inplace=True)\n", "\n", "# Calculate the difference\n", "diff = preds.flatten() - df_testY\n", "percentDiff = (diff / testY) * 100\n", "absPercentDiff = np.abs(percentDiff)\n", "\n", "# compute the mean and standard deviation of the absolute percentage\n", "# difference\n", "mean = np.mean(absPercentDiff)\n", "std = np.std(absPercentDiff)\n", "print(\"[INFO] mean: {:.2f}%, std: {:.2f}%\".format(mean, std))\n", "\n", "# Compute the mean and standard deviation of the difference\n", "print(diff.describe())\n", "\n", "# Plot a histogram of the prediction errors\n", "num_bins = 25\n", "hist, bins = np.histogram(diff, num_bins)\n", "center = (bins[:-1]+ bins[1:]) * 0.5\n", "plt.bar(center, hist, width=0.05)\n", "plt.title('Historgram of Predicted Error')\n", "plt.xlabel('Steering Angle')\n", "plt.ylabel('Number of predictions')\n", "plt.xlim(-2.0, 2.0)\n", "plt.plot(np.min(diff), np.max(diff))" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "pyalDh4uIvIT", "colab_type": "code", "colab": {} }, "source": [ "# Plot a Scatter Plot of the Error\n", "plt.scatter(testY, preds)\n", "plt.xlabel('True Values ')\n", "plt.ylabel('Predictions ')\n", "plt.axis('equal')\n", "plt.axis('square')\n", "plt.xlim([-1.75,1.75])\n", "plt.ylim([-1.75,1.75])\n", "plt.plot([-1.75, 1.75], [-1.75, 1.75], color='k', linestyle='-', linewidth=.1)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "5jVE6WfNGDEM", "colab_type": "text" }, "source": [ "## Plot a Prediction" ] }, { "cell_type": "code", "metadata": { "id": "GSJ08rg3QDP7", "colab_type": "code", "colab": {} }, "source": [ "# Plot the image with the actual and predicted steering angle\n", "index = random.randint(0,df_test.shape[0]-1)\n", "img_name = data_set + '/' + df_test.loc[index,'filename']\n", "center_image = cv2.imread(img_name)\n", "center_image = cv2.cvtColor(center_image,cv2.COLOR_RGB2BGR)\n", "center_image_mod = cv2.resize(center_image, (320,180)) #resize from 720x1280 to 180x320\n", "plt.imshow(center_image_mod)\n", "plt.grid(False)\n", "plt.xlabel('Actual: {:.2f} Predicted: {:.2f}'.format(df_test.loc[index,'angle'],float(preds[index])))\n", "plt.show() \n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Nw3xUyrWU0_Q", "colab_type": "text" }, "source": [ "#Visualize the Network\n" ] }, { "cell_type": "markdown", "metadata": { "id": "IDmn0c0AU3w_", "colab_type": "text" }, "source": [ "##Show the Model Summary" ] }, { "cell_type": "code", "metadata": { "id": "8VTkX6ofU3Ko", "colab_type": "code", "colab": {} }, "source": [ "model.summary()" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "KsDr5kvMVfut", "colab_type": "text" }, "source": [ "##Access Individual Layers" ] }, { "cell_type": "code", "metadata": { "id": "4b70eI0mVnH3", "colab_type": "code", "colab": {} }, "source": [ "# Creating a mapping of layer name ot layer details \n", "# We will create a dictionary layers_info which maps a layer name to its charcteristics\n", "layers_info = {}\n", "for i in model.layers:\n", " layers_info[i.name] = i.get_config()\n", "\n", "# Here the layer_weights dictionary will map every layer_name to its corresponding weights\n", "layer_weights = {}\n", "for i in model.layers:\n", " layer_weights[i.name] = i.get_weights()\n", "\n", "pprint.pprint(layers_info['conv_5'])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "zrqmP6unW99W", "colab_type": "text" }, "source": [ "##Visualize the filters" ] }, { "cell_type": "code", "metadata": { "id": "lgACkEzLXAw3", "colab_type": "code", "colab": {} }, "source": [ "# Visualize the first filter of each convolution layer\n", "layers = model.layers\n", "layer_ids = [2,3,4,6,7]\n", "\n", "#plot the filters\n", "fig,ax = plt.subplots(nrows=1,ncols=5)\n", "for i in range(5):\n", " ax[i].imshow(layers[layer_ids[i]].get_weights()[0][:,:,:,0][:,:,0],cmap='gray')\n", " ax[i].set_title('Conv'+str(i+1))\n", " ax[i].set_xticks([])\n", " ax[i].set_yticks([])" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "R1SFhmyiuh29", "colab_type": "text" }, "source": [ "##Visualize the Saliency Map\n" ] }, { "cell_type": "code", "metadata": { "id": "sQmQVfJoVfTK", "colab_type": "code", "colab": {} }, "source": [ "!pip install -I scipy==1.2.*\n", "!pip install git+https://github.com/raghakot/keras-vis.git -U\n", "\n", "# import specific functions from keras-vis package\n", "from vis.utils import utils\n", "from vis.visualization import visualize_saliency, visualize_cam, overlay" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "cWwL58SWup5M", "colab_type": "code", "colab": {} }, "source": [ "# View a Single Image \n", "index = random.randint(0,df.shape[0]-1)\n", "img_name = data_set + '/' + df.loc[index,'filename']\n", "\n", "sample_image = cv2.imread(img_name)\n", "sample_image = cv2.cvtColor(sample_image,cv2.COLOR_RGB2BGR)\n", "sample_image_mod = cv2.resize(sample_image, (320,180))\n", "plt.imshow(sample_image_mod)\n", " \n", "layer_idx = utils.find_layer_idx(model, 'conv_5')\n", "\n", "grads = visualize_saliency(model, \n", " layer_idx, \n", " filter_indices=None, \n", " seed_input=sample_image_mod,\n", " grad_modifier='absolute',\n", " backprop_modifier='guided')\n", "\n", "plt.imshow(grads, alpha = 0.6)\n" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "QMGEoYbVHnaJ", "colab_type": "text" }, "source": [ "# References:\n", "[Keras, Regression, and CNNs](https://www.pyimagesearch.com/2019/01/28/keras-regression-and-cnns/)\n", "\n", "[Regression with Keras](https://www.pyimagesearch.com/2019/01/21/regression-with-keras/)\n", "\n", "[How to use Keras fit and fit_generator](https://www.pyimagesearch.com/2018/12/24/how-to-use-keras-fit-and-fit_generator-a-hands-on-tutorial/)\n", "\n", "[Image Classification with Convolutional Neural Networks](https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l04c01_image_classification_with_cnns.ipynb#scrollTo=7MqDQO0KCaWS)\n", "\n", "[Keras Image Processing Documentation](https://keras.io/preprocessing/image/)\n", "\n", "[Attribution.ipynb](https://colab.research.google.com/github/idealo/cnn-exposed/blob/master/notebooks/Attribution.ipynb#scrollTo=jqSOW0pQniCw)\n", "\n", "[A Guide to Understanding Convolutional Neural Networks (CNNs) using Visualization](https://www.analyticsvidhya.com/blog/2019/05/understanding-visualizing-neural-networks/)\n", "\n", "[Visualizing attention on self driving car](https://github.com/raghakot/keras-vis/blob/master/applications/self_driving/visualize_attention.ipynb)\n", "\n", "[Exploring Image Data Augmentation with Keras and Tensorflow](https://towardsdatascience.com/exploring-image-data-augmentation-with-eras-and-tensorflow-a8162d89b844\n", ")\n", "\n", "[Tensorboard Documentation](https://keras.io/callbacks/#tensorboard)" ] } ] }
bsd-2-clause
radaniba/QuotaWatcher
QuotaWatcher.ipynb
1
24985
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This is a documentation for QuotaWatcher utility, a small cron job developed to monitor disk usage on GSC servers\n", "In this notebook we will explain every part of the utility in order to have other people maintain the code easily\n", "\n", "**All the code is heavily pep8'd :) **\n", "\n", "![alert](http://i.giphy.com/GJXKS0ZZQUnv2.gif)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Importing needed Libraries " ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division\n", "\n", "__author__ = \"Rad <[email protected]>\"\n", "__license__ = \"GNU General Public License version 3\"\n", "__date__ = \"06/30/2015\"\n", "__version__ = \"0.2\"\n", "\n", "try:\n", " import os\n", " from quota_logger import init_log\n", " import subprocess\n", " from prettytable import PrettyTable\n", " from smtplib import SMTP\n", " from smtplib import SMTPException\n", " from email.mime.text import MIMEText\n", " from argparse import ArgumentParser\n", "except ImportError:\n", " # Checks the installation of the necessary python modules\n", " import os\n", " import sys\n", "\n", " print((os.linesep * 2).join(\n", " [\"An error found importing one module:\", str(sys.exc_info()[1]), \"You need to install it Stopping...\"]))\n", " sys.exit(-2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I like this way of importing libraries, if some libraries are not already installed, the system will exit. There is another room for improvement here, if a library does not exist, it is possile to install it automatically if we run the code as admin or with enough permission" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The Notifier Class" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Notifier(object):\n", "\n", " suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']\n", "\n", " def __init__(self, **kwargs):\n", "\n", " self.threshold = None\n", " self.path = None\n", " self.list = None\n", " self.email_sender = None\n", " self.email_password = None\n", " self.gmail_smtp = None\n", " self.gmail_smtp_port = None\n", " self.text_subtype = None\n", " self.cap_reached = False\n", " self.email_subject = None\n", "\n", " for (key, value) in kwargs.iteritems():\n", " if hasattr(self, key):\n", " setattr(self, key, value)\n", "\n", " self._log = init_log()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We init the class as an object containing some features, this object will have a *threshold* upon which there will be an email triggered to a *recipient list*. This obect is looking ath the size of each subdirectory in *path*. You need to create an email addresse and add some variables to your PATH ( will be discussed later)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ " @property\n", " def loggy(self):\n", " return self._log\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to inherhit logging capabilities from the logging class we imported (see later the code of this class). This will allow us to log from within the class itself" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ " @staticmethod\n", " def load_recipients_emails(emails_file):\n", " recipients = [line.rstrip('\\n') for line in open(emails_file) if not line[0].isspace()]\n", " return recipients\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to lad the emails from a file created by the user. Usually I create 2 files, `development_list` containing only email adresses I will use for testing and `production_list` containing adresses I want to notify in production" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ " @staticmethod\n", " def load_message_content(message_template_file, table):\n", " template_file = open(message_template_file, 'rb')\n", " template_file_content = template_file.read().replace(\n", " \"{{table}}\", table.get_string())\n", " template_file.close()\n", " return template_file_content\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Inspired by MVC apps, we load message body from a template, this template will contain a placeholder called `{{table}}` that will contain the table of subdirectories and their respective sizes" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ " def notify_user(self, email_receivers, table, template):\n", " \"\"\"This method sends an email\n", " :rtype : email sent to specified members\n", " \"\"\"\n", " # Create the message\n", " input_file = os.path.join(\n", " os.path.dirname(__file__), \"templates/\" + template + \".txt\")\n", " content = self.load_message_content(input_file, table)\n", "\n", " msg = MIMEText(content, self.text_subtype)\n", "\n", " msg[\"Subject\"] = self.email_subject\n", " msg[\"From\"] = self.email_sender\n", " msg[\"To\"] = ','.join(email_receivers)\n", "\n", " try:\n", " smtpObj = SMTP(self.gmail_smtp, self.gmail_smtp_port)\n", " # Identify yourself to GMAIL ESMTP server.\n", " smtpObj.ehlo()\n", " # Put SMTP connection in TLS mode and call ehlo again.\n", " smtpObj.starttls()\n", " smtpObj.ehlo()\n", " # Login to service\n", " smtpObj.login(user=self.email_sender, password=self.email_password)\n", " # Send email\n", " smtpObj.sendmail(self.email_sender, email_receivers, msg.as_string())\n", " # close connection and session.\n", " smtpObj.quit()\n", " except SMTPException as error:\n", " print \"Error: unable to send email : {err}\".format(err=error)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`notify_user` is the function that will send an email to the users upon request. It loads the message body template and injects the table in it." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ " @staticmethod\n", " def du(path):\n", " \"\"\"disk usage in kilobytes\"\"\"\n", " # return subprocess.check_output(['du', '-s',\n", " # path]).split()[0].decode('utf-8')\n", " try:\n", " p1 = subprocess.Popen(('ls', '-d', path), stdout=subprocess.PIPE)\n", " p2 = subprocess.Popen((os.environ[\"GNU_PARALLEL\"], '--no-notice', 'du', '-s', '2>&1'), stdin=p1.stdout,\n", " stdout=subprocess.PIPE)\n", " p3 = subprocess.Popen(\n", " ('grep', '-v', '\"Permission denied\"'), stdin=p2.stdout, stdout=subprocess.PIPE)\n", " output = p3.communicate()[0]\n", " except subprocess.CalledProcessError as e:\n", " raise RuntimeError(\"command '{0}' return with error (code {1}): {2}\".format(\n", " e.cmd, e.returncode, e.output))\n", " # return ''.join([' '.join(hit.split('\\t')) for hit in output.split('\\n')\n", " # if len(hit) > 0 and not \"Permission\" in hit and output[0].isdigit()])\n", " result = [' '.join(hit.split('\\t')) for hit in output.split('\\n')]\n", " for line in result:\n", " if line and len(line.split('\\n')) > 0 and \"Permission\" not in line and line[0].isdigit():\n", " return line.split(\" \")[0]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a wrapper of the famous `du` command. I use GNU_PARALLEL in case we have a lot of subdirectories and in case we don't want to wait for sequential processing. Note that we could have done this in multithreading as well" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ " def du_h(self, nbytes):\n", " if nbytes == 0:\n", " return '0 B'\n", " i = 0\n", " while nbytes >= 1024 and i < len(self.suffixes) - 1:\n", " nbytes /= 1024.\n", " i += 1\n", " f = ('%.2f'.format(nbytes)).rstrip('0').rstrip('.')\n", " return '%s %s'.format(f, self.suffixes[i])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I didn't want to use the `-h` flag because we may want to sum up subdirectories sizes or doing other postprocessing, we'd rather keep them in a unified format (unit). For a more human readable format, we can use `du_h()` method" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ " @staticmethod\n", " def list_folders(given_path):\n", " user_list = []\n", " for path in os.listdir(given_path):\n", " if not os.path.isfile(os.path.join(given_path, path)) and not path.startswith(\".\") and not path.startswith(\n", " \"archive\"):\n", " user_list.append(path)\n", " return user_list\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we need at some point to return a list of subdirectories, each will be passed through the same function (du)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ " def notify(self):\n", " global cap_reached\n", " self._log.info(\"Loading recipient emails...\")\n", " list_of_recievers = self.load_recipients_emails(self.list)\n", " paths = self.list_folders(self.path)\n", " paths = [self.path + user for user in paths]\n", " sizes = []\n", " for size in paths:\n", " try:\n", " self._log.info(\"calculating disk usage for \" + size + \" ...\")\n", " sizes.append(int(self.du(size)))\n", " except Exception, e:\n", " self._log.exception(e)\n", " sizes.append(0)\n", " # sizes = [int(du(size).split(' ')[0]) for size in paths]\n", " # convert kilobytes to bytes\n", " sizes = [int(element) * 1000 for element in sizes]\n", " table = PrettyTable([\"Directory\", \"Size\"])\n", " table.align[\"Directory\"] = \"l\"\n", " table.align[\"Size\"] = \"r\"\n", " table.padding_width = 5\n", " table.border = False\n", " for account, size_of_account in zip(paths, sizes):\n", " if int(size_of_account) > int(self.threshold):\n", " table.add_row(\n", " [\"*\" + os.path.basename(account) + \"*\", \"*\" + self.du_h(size_of_account) + \"*\"])\n", " self.cap_reached = True\n", " else:\n", " table.add_row([os.path.basename(account), self.du_h(size_of_account)])\n", " # notify Admins\n", " table.add_row([\"TOTAL\", self.du_h(sum(sizes))])\n", " table.add_row([\"Usage\", str(sum(sizes) / 70000000000000)])\n", " self.notify_user(list_of_recievers, table, \"karey\")\n", " if self.cap_reached:\n", " self.notify_user(list_of_recievers, table, \"default_size_limit\")\n", "\n", " def run(self):\n", " self.notify()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we create the function that will bring all this protocol together :\n", "\n", "- Read the list of recievers\n", "- load the path we want to look into\n", "- for each subdirectory calculate the size of it and append it to a list\n", "- create a Table to be populated row by row\n", "- add subdirectories and their sizes\n", "- Calculate the total of sizes in subdirectories\n", "- If one of the subdirectories has a size higher than the threshold specified, trigger the email\n", "- Report the usage as a percentage\n" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def arguments():\n", " \"\"\"Defines the command line arguments for the script.\"\"\"\n", " main_desc = \"\"\"Monitors changes in the size of dirs for a given path\"\"\"\n", "\n", " parser = ArgumentParser(description=main_desc)\n", " parser.add_argument(\"path\", default=os.path.expanduser('~'), nargs='?',\n", " help=\"The path to monitor. If none is given, takes the home directory\")\n", " parser.add_argument(\"list\", help=\"text file containing the list of persons to be notified, one per line\")\n", " parser.add_argument(\"-s\", \"--notification_subject\", default=None, help=\"Email subject of the notification\")\n", " parser.add_argument(\"-t\", \"--threshold\", default=2500000000000,\n", " help=\"The threshold that will trigger the notification\")\n", " parser.add_argument(\"-v\", \"--version\", action=\"version\",\n", " version=\"%(prog)s {0}\".format(__version__),\n", " help=\"show program's version number and exit\")\n", " return parser" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The program takes in account : the path to examine, the list of emails in a file, the subject of the alert, the thresold that will trigger the email (here by defailt 2.5T)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def main():\n", "\n", " args = arguments().parse_args()\n", " notifier = Notifier()\n", " loggy = notifier.loggy\n", " # Set parameters\n", " loggy.info(\"Starting QuotaWatcher session...\")\n", " loggy.info(\"Setting parameters ...\")\n", " notifier.list = args.list\n", " notifier.threshold = args.threshold\n", " notifier.path = args.path\n", "\n", " # Configure the app\n", " try:\n", " loggy.info(\"Loading environment variables ...\")\n", " notifier.email_sender = os.environ[\"NOTIFIER_SENDER\"]\n", " notifier.email_password = os.environ[\"NOTIFIER_PASSWD\"]\n", " notifier.gmail_smtp = os.environ[\"NOTIFIER_SMTP\"]\n", " notifier.gmail_smtp_port = os.environ[\"NOTIFIER_SMTP_PORT\"]\n", " notifier.text_subtype = os.environ[\"NOTIFIER_SUBTYPE\"]\n", " notifier.email_subject = args.notification_subject\n", " notifier.cap_reached = False\n", " except Exception, e:\n", " loggy.exception(e)\n", "\n", " notifier.run()\n", " loggy.info(\"End of QuotaWatcher session\")\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that in the main we load some environment variable that you should specify in advance. This is up to the user to fill these out, It is always preferable to declare these as environment variable, most of the time these are confidential so we better not show them here, it is always safe to set environment variable for these" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# That's it\n", "\n", "this is an example of the LOG output.\n", "\n", "```\n", "2015-07-03 10:40:46,968 - quota_logger - INFO - Starting QuotaWatcher session...\n", "2015-07-03 10:40:46,969 - quota_logger - INFO - Setting parameters ...\n", "2015-07-03 10:40:46,969 - quota_logger - INFO - Loading environment variables ...\n", "2015-07-03 10:40:46,969 - quota_logger - INFO - Loading recipient emails...\n", "2015-07-03 10:40:47,011 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/amcpherson ..\n", ".\n", "2015-07-03 11:21:09,442 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/andrewjlroth\n", "...\n", "2015-07-03 15:31:41,500 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/asteif ...\n", "2015-07-03 15:40:34,268 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/clefebvre ...\n", "2015-07-03 15:42:47,483 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/dgrewal ...\n", "2015-07-03 16:01:30,588 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/fdorri ...\n", "2015-07-03 16:03:43,850 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/fong ...\n", "2015-07-03 16:16:13,781 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/gha ...\n", "2015-07-03 16:16:38,673 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/jding ...\n", "2015-07-03 16:16:50,820 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/cdesouza ...\n", "2015-07-03 16:16:52,585 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/jrosner ...\n", "2015-07-03 16:27:30,684 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/jtaghiyar ...\n", "2015-07-03 16:28:16,982 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/kareys ...\n", "2015-07-03 19:21:07,607 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/hfarahani ...\n", "2015-07-03 19:22:07,618 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/jzhou ...\n", "2015-07-03 19:38:28,147 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/pipelines ...\n", "2015-07-03 19:53:20,771 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/projects ...\n", "2015-07-03 20:52:45,001 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/raniba ...\n", "2015-07-03 20:59:50,543 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/tfunnell ...\n", "2015-07-03 21:00:47,216 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/ykwang ...\n", "2015-07-03 21:03:30,277 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/azhang ...\n", "2015-07-03 21:03:30,820 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/softwares ...\n", "2015-07-03 21:03:42,679 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/sjewell ...\n", "2015-07-03 21:03:51,711 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/kastonl ...\n", "2015-07-03 21:04:52,536 - quota_logger - INFO - calculating disk usage for /genesis/extscratch/shahlab/amazloomian .\n", "..\n", "2015-07-03 21:07:43,501 - quota_logger - INFO - End of QuotaWatcher session\n", "```\n", "\n", "And as of the email triggered, it will look like \n", "\n", "```\n", "** THIS IS AN ALERT MESSAGE : DISK USAGE SPIKE **\n", "\n", "This is a warning message about the disk usage relative to the Shahlab group at GSC\n", "\n", "We detected a spike > 2.5 T for some accounts and here is a list of the space usage per account reported today\n", "\n", "\n", " Directory Size \n", " amcpherson 1.96 TB \n", " andrewjlroth 390.19 GB \n", " asteif 2.05 TB \n", " clefebvre 16.07 GB \n", " dgrewal 1.61 TB \n", " fdorri 486.49 GB \n", " *fong* *9.67 TB* \n", " gha 50.7 GB \n", " jding 638.72 GB \n", " cdesouza 56.15 GB \n", " jrosner 1.82 TB \n", " jtaghiyar 253.84 GB \n", " *kareys* *11.26 TB* \n", " hfarahani 1.09 TB \n", " jzhou 1.19 TB \n", " pipelines 2.1 TB \n", " *projects* *4.09 TB* \n", " raniba 2.03 TB \n", " tfunnell 1.02 TB \n", " ykwang 1.71 TB \n", " azhang 108.4 MB \n", " softwares 34.67 GB \n", " sjewell 24.53 GB \n", " kastonl 118.51 GB \n", " amazloomian 1.71 TB \n", " TOTAL 45.34 TB \n", " Usage 71.218% \n", "\n", "\n", "Please do the necessary to remove temporary files and take the time to clean up your working directories\n", "\n", "Thank you for your cooperation\n", "\n", "(am a cron job, don't reply to this message, if you have questions ask Ali)\n", "\n", "\n", "\n", "PS : This is a very close estimation, some directories may have strict permissions, for an accurate disk usage please make sure that you set your files permissions so that anyone can see them.\n", "```\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The logger" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true, "slideshow": { "slide_type": "notes" } }, "outputs": [], "source": [ "import logging\n", "import datetime\n", "\n", "def init_log():\n", " current_time = datetime.datetime.now()\n", " logger = logging.getLogger(__name__)\n", " logger.setLevel(logging.INFO)\n", " handler = logging.FileHandler(current_time.isoformat()+'_quotawatcher.log')\n", " handler.setLevel(logging.INFO)\n", " # create a logging format\n", " formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n", " handler.setFormatter(formatter)\n", " logger.addHandler(handler)\n", " return logger" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Before you start\n", "\n", "```\n", "export NOTIFIER_SENDER=\"[email protected]\"\n", "export NOTIFIER_PASSWD=\"passwordhere\"\n", "export NOTIFIER_SMTP=\"smtp.gmail.com\"\n", "export NOTIFIER_SMTP_PORT=587\n", "export NOTIFIER_SUBTYPE=\"plain\"\n", "export GNU_PARALLEL=\"/path/to/your/gnu/parallel\"\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How to run the program\n", "\n", "```\n", "python quotawatcher.py /genesis/extscratch/shahlab/ dev_list -s \"Hey Test\" -t 2500000000000\n", "```" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
ioos/notebooks_demos
notebooks/2018-03-15-ssh-skillscore.ipynb
4
193383
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Investigating ocean models skill for sea surface height with IOOS catalog and Python\n", "\n", "\n", "The IOOS [catalog](https://ioos.noaa.gov/data/catalog) offers access to hundreds of datasets and data access services provided by the 11 regional associations.\n", "In the past we demonstrate how to tap into those datasets to obtain sea [surface temperature data from observations](http://ioos.github.io/notebooks_demos/notebooks/2016-12-19-exploring_csw),\n", "[coastal velocity from high frequency radar data](http://ioos.github.io/notebooks_demos/notebooks/2017-12-15-finding_HFRadar_currents),\n", "and a simple model vs observation visualization of temperatures for the [Boston Light Swim competition](http://ioos.github.io/notebooks_demos/notebooks/2016-12-22-boston_light_swim).\n", "\n", "In this notebook we'll demonstrate a step-by-step workflow on how ask the catalog for a specific variable, extract only the model data, and match the nearest model grid point to an observation. The goal is to create an automated skill score for quick assessment of ocean numerical models.\n", "\n", "\n", "The first cell is only to reduce iris' noisy output,\n", "the notebook start on cell [2] with the definition of the parameters:\n", "- start and end dates for the search;\n", "- experiment name;\n", "- a bounding of the region of interest;\n", "- SOS variable name for the observations;\n", "- Climate and Forecast standard names;\n", "- the units we want conform the variables into;\n", "- catalogs we want to search." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "\n", "# Suppresing warnings for a \"pretty output.\"\n", "warnings.simplefilter(\"ignore\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting config.yaml\n" ] } ], "source": [ "%%writefile config.yaml\n", "\n", "date:\n", " start: 2018-2-28 00:00:00\n", " stop: 2018-3-5 00:00:00\n", "\n", "run_name: 'latest'\n", "\n", "region:\n", " bbox: [-71.20, 41.40, -69.20, 43.74]\n", " crs: 'urn:ogc:def:crs:OGC:1.3:CRS84'\n", "\n", "sos_name: 'water_surface_height_above_reference_datum'\n", "\n", "cf_names:\n", " - sea_surface_height\n", " - sea_surface_elevation\n", " - sea_surface_height_above_geoid\n", " - sea_surface_height_above_sea_level\n", " - water_surface_height_above_reference_datum\n", " - sea_surface_height_above_reference_ellipsoid\n", "\n", "units: 'm'\n", "\n", "catalogs:\n", " - https://data.ioos.us/csw" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To keep track of the information we'll setup a `config` variable and output them on the screen for bookkeeping." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving data inside directory /home/filipe/IOOS/notebooks_demos/notebooks/latest\n", "*********************** Run information ************************\n", "Run date: 2018-11-30 13:25:17\n", "Start: 2018-02-28 00:00:00\n", "Stop: 2018-03-05 00:00:00\n", "Bounding box: -71.20, 41.40,-69.20, 43.74\n" ] } ], "source": [ "import os\n", "import shutil\n", "from datetime import datetime\n", "\n", "from ioos_tools.ioos import parse_config\n", "\n", "config = parse_config(\"config.yaml\")\n", "\n", "# Saves downloaded data into a temporary directory.\n", "save_dir = os.path.abspath(config[\"run_name\"])\n", "if os.path.exists(save_dir):\n", " shutil.rmtree(save_dir)\n", "os.makedirs(save_dir)\n", "\n", "fmt = \"{:*^64}\".format\n", "print(fmt(\"Saving data inside directory {}\".format(save_dir)))\n", "print(fmt(\" Run information \"))\n", "print(\"Run date: {:%Y-%m-%d %H:%M:%S}\".format(datetime.utcnow()))\n", "print(\"Start: {:%Y-%m-%d %H:%M:%S}\".format(config[\"date\"][\"start\"]))\n", "print(\"Stop: {:%Y-%m-%d %H:%M:%S}\".format(config[\"date\"][\"stop\"]))\n", "print(\n", " \"Bounding box: {0:3.2f}, {1:3.2f},\"\n", " \"{2:3.2f}, {3:3.2f}\".format(*config[\"region\"][\"bbox\"])\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To interface with the IOOS catalog we will use the [Catalogue Service for the Web (CSW)](https://live.osgeo.org/en/standards/csw_overview.html) endpoint and [python's OWSLib library](https://geopython.github.io/OWSLib).\n", "\n", "The cell below creates the [Filter Encoding Specification (FES)](http://www.opengeospatial.org/standards/filter) with configuration we specified in cell [2]. The filter is composed of:\n", "- `or` to catch any of the standard names;\n", "- `not` some names we do not want to show up in the results;\n", "- `date range` and `bounding box` for the time-space domain of the search." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def make_filter(config):\n", " from owslib import fes\n", " from ioos_tools.ioos import fes_date_filter\n", "\n", " kw = dict(\n", " wildCard=\"*\", escapeChar=\"\\\\\", singleChar=\"?\", propertyname=\"apiso:Subject\"\n", " )\n", "\n", " or_filt = fes.Or(\n", " [fes.PropertyIsLike(literal=(\"*%s*\" % val), **kw) for val in config[\"cf_names\"]]\n", " )\n", "\n", " not_filt = fes.Not([fes.PropertyIsLike(literal=\"GRIB-2\", **kw)])\n", "\n", " begin, end = fes_date_filter(config[\"date\"][\"start\"], config[\"date\"][\"stop\"])\n", "\n", " bbox_crs = fes.BBox(config[\"region\"][\"bbox\"], crs=config[\"region\"][\"crs\"])\n", "\n", " filter_list = [fes.And([bbox_crs, begin, end, or_filt, not_filt])]\n", " return filter_list\n", "\n", "\n", "filter_list = make_filter(config)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to wrap `OWSlib.csw.CatalogueServiceWeb` object with a custom function,\n", "` get_csw_records`, to be able to paginate over the results.\n", "\n", "In the cell below we loop over all the catalogs returns and extract the OPeNDAP endpoints." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "code_folding": [], "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "********************* Catalog information **********************\n", "URL: https://data.ioos.us/csw\n", "Number of datasets available: 13\n", "urn:ioos:station:NOAA.NOS.CO-OPS:8447386 station, Fall River, MA\n", "urn:ioos:station:NOAA.NOS.CO-OPS:8447435 station, Chatham, Lydia Cove, MA\n", "urn:ioos:station:NOAA.NOS.CO-OPS:8447930 station, Woods Hole, MA\n", "Coupled Northwest Atlantic Prediction System (CNAPS)\n", "HYbrid Coordinate Ocean Model (HYCOM): Global\n", "NECOFS (FVCOM) - Scituate - Latest Forecast\n", "NECOFS Massachusetts (FVCOM) - Boston - Latest Forecast\n", "ROMS ESPRESSO Real-Time Operational IS4DVAR Forecast System Version 2 (NEW) 2013-present FMRC Averages\n", "ROMS ESPRESSO Real-Time Operational IS4DVAR Forecast System Version 2 (NEW) 2013-present FMRC History\n", "urn:ioos:station:NOAA.NOS.CO-OPS:8418150 station, Portland, ME\n", "urn:ioos:station:NOAA.NOS.CO-OPS:8419317 station, Wells, ME\n", "urn:ioos:station:NOAA.NOS.CO-OPS:8423898 station, Fort Point, NH\n", "urn:ioos:station:NOAA.NOS.CO-OPS:8443970 station, Boston, MA\n", "***************************** DAP ******************************\n", "http://oos.soest.hawaii.edu/thredds/dodsC/pacioos/hycom/global.html\n", "http://tds.marine.rutgers.edu/thredds/dodsC/roms/espresso/2013_da/avg/ESPRESSO_Real-Time_v2_Averages_Best.html\n", "http://tds.marine.rutgers.edu/thredds/dodsC/roms/espresso/2013_da/his/ESPRESSO_Real-Time_v2_History_Best.html\n", "http://thredds.secoora.org/thredds/dodsC/SECOORA_NCSU_CNAPS.nc.html\n", "http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_FVCOM_OCEAN_BOSTON_FORECAST.nc.html\n", "http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_FVCOM_OCEAN_SCITUATE_FORECAST.nc.html\n", "https://opendap.co-ops.nos.noaa.gov/ioos-dif-sos/images/tide_gauge.jpg.html\n", "\n", "\n" ] } ], "source": [ "from ioos_tools.ioos import get_csw_records, service_urls\n", "from owslib.csw import CatalogueServiceWeb\n", "\n", "dap_urls = []\n", "print(fmt(\" Catalog information \"))\n", "for endpoint in config[\"catalogs\"]:\n", " print(\"URL: {}\".format(endpoint))\n", " try:\n", " csw = CatalogueServiceWeb(endpoint, timeout=120)\n", " except Exception as e:\n", " print(\"{}\".format(e))\n", " continue\n", " csw = get_csw_records(csw, filter_list, esn=\"full\")\n", " OPeNDAP = service_urls(csw.records, identifier=\"OPeNDAP:OPeNDAP\")\n", " odp = service_urls(\n", " csw.records, identifier=\"urn:x-esri:specification:ServiceType:odp:url\"\n", " )\n", " dap = OPeNDAP + odp\n", " dap_urls.extend(dap)\n", "\n", " print(\"Number of datasets available: {}\".format(len(csw.records.keys())))\n", "\n", " for rec, item in csw.records.items():\n", " print(\"{}\".format(item.title))\n", " if dap:\n", " print(fmt(\" DAP \"))\n", " for url in dap:\n", " print(\"{}.html\".format(url))\n", " print(\"\\n\")\n", "\n", "# Get only unique endpoints.\n", "dap_urls = list(set(dap_urls))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We found 10 dataset endpoints but only 9 of them have the proper metadata for us to identify the OPeNDAP endpoint,\n", "those that contain either `OPeNDAP:OPeNDAP` or `urn:x-esri:specification:ServiceType:odp:url` scheme.\n", "Unfortunately we lost the `COAWST` model in the process.\n", "\n", "The next step is to ensure there are no observations in the list of endpoints.\n", "We want only the models for now." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Could not access URL https://opendap.co-ops.nos.noaa.gov/ioos-dif-sos/images/tide_gauge.jpg.html\n", "OSError(-90, 'NetCDF: file not found')\n", "************************* Filtered DAP *************************\n", "http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_FVCOM_OCEAN_BOSTON_FORECAST.nc.html\n", "http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_FVCOM_OCEAN_SCITUATE_FORECAST.nc.html\n", "http://thredds.secoora.org/thredds/dodsC/SECOORA_NCSU_CNAPS.nc.html\n", "http://tds.marine.rutgers.edu/thredds/dodsC/roms/espresso/2013_da/avg/ESPRESSO_Real-Time_v2_Averages_Best.html\n", "http://tds.marine.rutgers.edu/thredds/dodsC/roms/espresso/2013_da/his/ESPRESSO_Real-Time_v2_History_Best.html\n", "http://oos.soest.hawaii.edu/thredds/dodsC/pacioos/hycom/global.html\n" ] } ], "source": [ "from ioos_tools.ioos import is_station\n", "from timeout_decorator import TimeoutError\n", "\n", "# Filter out some station endpoints.\n", "non_stations = []\n", "for url in dap_urls:\n", " try:\n", " if not is_station(url):\n", " non_stations.append(url)\n", " except (IOError, OSError, RuntimeError, TimeoutError) as e:\n", " print(\"Could not access URL {}.html\\n{!r}\".format(url, e))\n", "\n", "dap_urls = non_stations\n", "\n", "print(fmt(\" Filtered DAP \"))\n", "for url in dap_urls:\n", " print(\"{}.html\".format(url))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a nice list of all the models available in the catalog for the domain we specified.\n", "We still need to find the observations for the same domain.\n", "To accomplish that we will use the `pyoos` library and search the [SOS CO-OPS](https://opendap.co-ops.nos.noaa.gov/ioos-dif-sos/) services using the virtually the same configuration options from the catalog search." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "********************* Collector offerings **********************\n", "NOAA.NOS.CO-OPS SOS: 1229 offerings\n" ] } ], "source": [ "from pyoos.collectors.coops.coops_sos import CoopsSos\n", "\n", "collector_coops = CoopsSos()\n", "\n", "collector_coops.set_bbox(config[\"region\"][\"bbox\"])\n", "collector_coops.end_time = config[\"date\"][\"stop\"]\n", "collector_coops.start_time = config[\"date\"][\"start\"]\n", "collector_coops.variables = [config[\"sos_name\"]]\n", "\n", "ofrs = collector_coops.server.offerings\n", "title = collector_coops.server.identification.title\n", "print(fmt(\" Collector offerings \"))\n", "print(\"{}: {} offerings\".format(title, len(ofrs)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make it easier to work with the data we extract the time-series as pandas tables and interpolate them to a common 1-hour interval index." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>station_name</th>\n", " <th>sensor</th>\n", " <th>lon</th>\n", " <th>lat</th>\n", " <th>depth</th>\n", " </tr>\n", " <tr>\n", " <th>station_code</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>8418150</th>\n", " <td>Portland, ME</td>\n", " <td>urn:ioos:sensor:NOAA.NOS.CO-OPS:8418150:A1</td>\n", " <td>-70.2461</td>\n", " <td>43.6561</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>8419317</th>\n", " <td>Wells, ME</td>\n", " <td>urn:ioos:sensor:NOAA.NOS.CO-OPS:8419317:B1</td>\n", " <td>-70.5633</td>\n", " <td>43.3200</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>8443970</th>\n", " <td>Boston, MA</td>\n", " <td>urn:ioos:sensor:NOAA.NOS.CO-OPS:8443970:Y1</td>\n", " <td>-71.0503</td>\n", " <td>42.3539</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>8447386</th>\n", " <td>Fall River, MA</td>\n", " <td>urn:ioos:sensor:NOAA.NOS.CO-OPS:8447386:B1</td>\n", " <td>-71.1641</td>\n", " <td>41.7043</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>8447435</th>\n", " <td>Chatham, Lydia Cove, MA</td>\n", " <td>urn:ioos:sensor:NOAA.NOS.CO-OPS:8447435:A1</td>\n", " <td>-69.9510</td>\n", " <td>41.6885</td>\n", " <td>None</td>\n", " </tr>\n", " <tr>\n", " <th>8447930</th>\n", " <td>Woods Hole, MA</td>\n", " <td>urn:ioos:sensor:NOAA.NOS.CO-OPS:8447930:A1</td>\n", " <td>-70.6711</td>\n", " <td>41.5236</td>\n", " <td>None</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " station_name \\\n", "station_code \n", "8418150 Portland, ME \n", "8419317 Wells, ME \n", "8443970 Boston, MA \n", "8447386 Fall River, MA \n", "8447435 Chatham, Lydia Cove, MA \n", "8447930 Woods Hole, MA \n", "\n", " sensor lon lat \\\n", "station_code \n", "8418150 urn:ioos:sensor:NOAA.NOS.CO-OPS:8418150:A1 -70.2461 43.6561 \n", "8419317 urn:ioos:sensor:NOAA.NOS.CO-OPS:8419317:B1 -70.5633 43.3200 \n", "8443970 urn:ioos:sensor:NOAA.NOS.CO-OPS:8443970:Y1 -71.0503 42.3539 \n", "8447386 urn:ioos:sensor:NOAA.NOS.CO-OPS:8447386:B1 -71.1641 41.7043 \n", "8447435 urn:ioos:sensor:NOAA.NOS.CO-OPS:8447435:A1 -69.9510 41.6885 \n", "8447930 urn:ioos:sensor:NOAA.NOS.CO-OPS:8447930:A1 -70.6711 41.5236 \n", "\n", " depth \n", "station_code \n", "8418150 None \n", "8419317 None \n", "8443970 None \n", "8447386 None \n", "8447435 None \n", "8447930 None " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "from ioos_tools.ioos import collector2table\n", "\n", "data = collector2table(\n", " collector=collector_coops,\n", " config=config,\n", " col=\"water_surface_height_above_reference_datum (m)\",\n", ")\n", "\n", "df = dict(\n", " station_name=[s._metadata.get(\"station_name\") for s in data],\n", " station_code=[s._metadata.get(\"station_code\") for s in data],\n", " sensor=[s._metadata.get(\"sensor\") for s in data],\n", " lon=[s._metadata.get(\"lon\") for s in data],\n", " lat=[s._metadata.get(\"lat\") for s in data],\n", " depth=[s._metadata.get(\"depth\") for s in data],\n", ")\n", "\n", "pd.DataFrame(df).set_index(\"station_code\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "index = pd.date_range(\n", " start=config[\"date\"][\"start\"].replace(tzinfo=None),\n", " end=config[\"date\"][\"stop\"].replace(tzinfo=None),\n", " freq=\"1H\",\n", ")\n", "\n", "# Preserve metadata with `reindex`.\n", "observations = []\n", "for series in data:\n", " _metadata = series._metadata\n", " series.index = series.index.tz_localize(None)\n", " obs = series.reindex(index=index, limit=1, method=\"nearest\")\n", " obs._metadata = _metadata\n", " observations.append(obs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next cell saves those time-series as CF-compliant netCDF files on disk,\n", "to make it easier to access them later." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import iris\n", "from ioos_tools.tardis import series2cube\n", "\n", "attr = dict(\n", " featureType=\"timeSeries\",\n", " Conventions=\"CF-1.6\",\n", " standard_name_vocabulary=\"CF-1.6\",\n", " cdm_data_type=\"Station\",\n", " comment=\"Data from http://opendap.co-ops.nos.noaa.gov\",\n", ")\n", "\n", "\n", "cubes = iris.cube.CubeList([series2cube(obs, attr=attr) for obs in observations])\n", "\n", "outfile = os.path.join(save_dir, \"OBS_DATA.nc\")\n", "iris.save(cubes, outfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We still need to read the model data from the list of endpoints we found.\n", "\n", "The next cell takes care of that.\n", "We use `iris`, and a set of custom functions from the `ioos_tools` library,\n", "that downloads only the data in the domain we requested." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "**************************** Models ****************************\n", "\n", "[Reading url 1/6]: http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_FVCOM_OCEAN_BOSTON_FORECAST.nc\n", "\n", "[Reading url 2/6]: http://www.smast.umassd.edu:8080/thredds/dodsC/FVCOM/NECOFS/Forecasts/NECOFS_FVCOM_OCEAN_SCITUATE_FORECAST.nc\n", "\n", "[Reading url 3/6]: http://thredds.secoora.org/thredds/dodsC/SECOORA_NCSU_CNAPS.nc\n", "\n", "[Reading url 4/6]: http://tds.marine.rutgers.edu/thredds/dodsC/roms/espresso/2013_da/avg/ESPRESSO_Real-Time_v2_Averages_Best\n", "\n", "[Reading url 5/6]: http://tds.marine.rutgers.edu/thredds/dodsC/roms/espresso/2013_da/his/ESPRESSO_Real-Time_v2_History_Best\n", "\n", "[Reading url 6/6]: http://oos.soest.hawaii.edu/thredds/dodsC/pacioos/hycom/global\n" ] } ], "source": [ "from ioos_tools.ioos import get_model_name\n", "from ioos_tools.tardis import is_model, proc_cube, quick_load_cubes\n", "from iris.exceptions import ConstraintMismatchError, CoordinateNotFoundError, MergeError\n", "\n", "print(fmt(\" Models \"))\n", "cubes = dict()\n", "for k, url in enumerate(dap_urls):\n", " print(\"\\n[Reading url {}/{}]: {}\".format(k + 1, len(dap_urls), url))\n", " try:\n", " cube = quick_load_cubes(url, config[\"cf_names\"], callback=None, strict=True)\n", " if is_model(cube):\n", " cube = proc_cube(\n", " cube,\n", " bbox=config[\"region\"][\"bbox\"],\n", " time=(config[\"date\"][\"start\"], config[\"date\"][\"stop\"]),\n", " units=config[\"units\"],\n", " )\n", " else:\n", " print(\"[Not model data]: {}\".format(url))\n", " continue\n", " mod_name = get_model_name(url)\n", " cubes.update({mod_name: cube})\n", " except (\n", " RuntimeError,\n", " ValueError,\n", " ConstraintMismatchError,\n", " CoordinateNotFoundError,\n", " IndexError,\n", " ) as e:\n", " print(\"Cannot get cube for: {}\\n{}\".format(url, e))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can match each observation time-series with its closest grid point (0.08 of a degree) on each model.\n", "This is a complex and laborious task! If you are running this interactively grab a coffee and sit comfortably :-)\n", "\n", "Note that we are also saving the model time-series to files that align with the observations we saved before." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/Forecasts-NECOFS_FVCOM_OCEAN_BOSTON_FORECAST.nc \n", "[No Data] Portland, ME\n", "[No Data] Wells, ME\n", "[Water ] Boston, MA\n", "[No Data] Fall River, MA\n", "[No Data] Chatham, Lydia Cove, MA\n", "[No Data] Woods Hole, MA\n", "Finished processing [Forecasts-NECOFS_FVCOM_OCEAN_BOSTON_FORECAST]\n", " Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/Forecasts-NECOFS_FVCOM_OCEAN_SCITUATE_FORECAST.nc \n", "[No Data] Portland, ME\n", "[No Data] Wells, ME\n", "[No Data] Boston, MA\n", "[No Data] Fall River, MA\n", "[No Data] Chatham, Lydia Cove, MA\n", "[No Data] Woods Hole, MA\n", "Finished processing [Forecasts-NECOFS_FVCOM_OCEAN_SCITUATE_FORECAST]\n", " Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/SECOORA_NCSU_CNAPS.nc \n", "[Land ] Portland, ME\n", "[Water ] Wells, ME\n", "[Land ] Boston, MA\n", "[Land ] Fall River, MA\n", "[Land ] Chatham, Lydia Cove, MA\n", "[Water ] Woods Hole, MA\n", "Finished processing [SECOORA_NCSU_CNAPS]\n", " Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/roms_2013_da_avg-ESPRESSO_Real-Time_v2_Averages_Best.nc \n", "[No Data] Portland, ME\n", "[No Data] Wells, ME\n", "[Land ] Boston, MA\n", "[Land ] Fall River, MA\n", "[Water ] Chatham, Lydia Cove, MA\n", "[Water ] Woods Hole, MA\n", "Finished processing [roms_2013_da_avg-ESPRESSO_Real-Time_v2_Averages_Best]\n", " Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/roms_2013_da-ESPRESSO_Real-Time_v2_History_Best.nc \n", "[No Data] Portland, ME\n", "[No Data] Wells, ME\n", "[Land ] Boston, MA\n", "[Land ] Fall River, MA\n", "[Water ] Chatham, Lydia Cove, MA\n", "[Water ] Woods Hole, MA\n", "Finished processing [roms_2013_da-ESPRESSO_Real-Time_v2_History_Best]\n", " Downloading to file /home/filipe/IOOS/notebooks_demos/notebooks/latest/pacioos_hycom-global.nc \n", "[Land ] Portland, ME\n", "[Water ] Wells, ME\n", "[Land ] Boston, MA\n", "[Land ] Fall River, MA\n", "[Water ] Chatham, Lydia Cove, MA\n", "[Land ] Woods Hole, MA\n", "Finished processing [pacioos_hycom-global]\n" ] } ], "source": [ "import iris\n", "from ioos_tools.tardis import (\n", " add_station,\n", " ensure_timeseries,\n", " get_nearest_water,\n", " make_tree,\n", ")\n", "from iris.pandas import as_series\n", "\n", "for mod_name, cube in cubes.items():\n", " fname = \"{}.nc\".format(mod_name)\n", " fname = os.path.join(save_dir, fname)\n", " print(fmt(\" Downloading to file {} \".format(fname)))\n", " try:\n", " tree, lon, lat = make_tree(cube)\n", " except CoordinateNotFoundError:\n", " print(\"Cannot make KDTree for: {}\".format(mod_name))\n", " continue\n", " # Get model series at observed locations.\n", " raw_series = dict()\n", " for obs in observations:\n", " obs = obs._metadata\n", " station = obs[\"station_code\"]\n", " try:\n", " kw = dict(k=10, max_dist=0.08, min_var=0.01)\n", " args = cube, tree, obs[\"lon\"], obs[\"lat\"]\n", " try:\n", " series, dist, idx = get_nearest_water(*args, **kw)\n", " except RuntimeError as e:\n", " print(\"Cannot download {!r}.\\n{}\".format(cube, e))\n", " series = None\n", " except ValueError:\n", " status = \"No Data\"\n", " print(\"[{}] {}\".format(status, obs[\"station_name\"]))\n", " continue\n", " if not series:\n", " status = \"Land \"\n", " else:\n", " raw_series.update({station: series})\n", " series = as_series(series)\n", " status = \"Water \"\n", " print(\"[{}] {}\".format(status, obs[\"station_name\"]))\n", " if raw_series: # Save cube.\n", " for station, cube in raw_series.items():\n", " cube = add_station(cube, station)\n", " try:\n", " cube = iris.cube.CubeList(raw_series.values()).merge_cube()\n", " except MergeError as e:\n", " print(e)\n", " ensure_timeseries(cube)\n", " try:\n", " iris.save(cube, fname)\n", " except AttributeError:\n", " # FIXME: we should patch the bad attribute instead of removing everything.\n", " cube.attributes = {}\n", " iris.save(cube, fname)\n", " del cube\n", " print(\"Finished processing [{}]\".format(mod_name))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the matched set of models and observations time-series it is relatively easy to compute skill score metrics on them. In cells [13] to [16] we apply both mean bias and root mean square errors to the time-series." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from ioos_tools.ioos import stations_keys\n", "\n", "\n", "def rename_cols(df, config):\n", " cols = stations_keys(config, key=\"station_name\")\n", " return df.rename(columns=cols)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from ioos_tools.ioos import load_ncs\n", "from ioos_tools.skill_score import apply_skill, mean_bias\n", "\n", "dfs = load_ncs(config)\n", "\n", "df = apply_skill(dfs, mean_bias, remove_mean=False, filter_tides=False)\n", "skill_score = dict(mean_bias=df.to_dict())\n", "\n", "# Filter out stations with no valid comparison.\n", "df.dropna(how=\"all\", axis=1, inplace=True)\n", "df = df.applymap(\"{:.2f}\".format).replace(\"nan\", \"--\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from ioos_tools.skill_score import rmse\n", "\n", "dfs = load_ncs(config)\n", "\n", "df = apply_skill(dfs, rmse, remove_mean=True, filter_tides=False)\n", "skill_score[\"rmse\"] = df.to_dict()\n", "\n", "# Filter out stations with no valid comparison.\n", "df.dropna(how=\"all\", axis=1, inplace=True)\n", "df = df.applymap(\"{:.2f}\".format).replace(\"nan\", \"--\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "# Stringfy keys.\n", "for key in skill_score.keys():\n", " skill_score[key] = {str(k): v for k, v in skill_score[key].items()}\n", "\n", "mean_bias = pd.DataFrame.from_dict(skill_score[\"mean_bias\"])\n", "mean_bias = mean_bias.applymap(\"{:.2f}\".format).replace(\"nan\", \"--\")\n", "\n", "skill_score = pd.DataFrame.from_dict(skill_score[\"rmse\"])\n", "skill_score = skill_score.applymap(\"{:.2f}\".format).replace(\"nan\", \"--\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Last but not least we can assemble a GIS map, cells [17-23],\n", "with the time-series plot for the observations and models,\n", "and the corresponding skill scores." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "import folium\n", "from ioos_tools.ioos import get_coordinates\n", "\n", "\n", "def make_map(bbox, **kw):\n", " line = kw.pop(\"line\", True)\n", " zoom_start = kw.pop(\"zoom_start\", 5)\n", "\n", " lon = (bbox[0] + bbox[2]) / 2\n", " lat = (bbox[1] + bbox[3]) / 2\n", " m = folium.Map(\n", " width=\"100%\", height=\"100%\", location=[lat, lon], zoom_start=zoom_start\n", " )\n", "\n", " if line:\n", " p = folium.PolyLine(\n", " get_coordinates(bbox), color=\"#FF0000\", weight=2, opacity=0.9,\n", " )\n", " p.add_to(m)\n", " return m" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "bbox = config[\"region\"][\"bbox\"]\n", "\n", "m = make_map(bbox, zoom_start=8, line=True, layers=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "all_obs = stations_keys(config)\n", "\n", "from glob import glob\n", "from operator import itemgetter\n", "\n", "import iris\n", "from folium.plugins import MarkerCluster\n", "\n", "iris.FUTURE.netcdf_promote = True\n", "\n", "big_list = []\n", "for fname in glob(os.path.join(save_dir, \"*.nc\")):\n", " if \"OBS_DATA\" in fname:\n", " continue\n", " cube = iris.load_cube(fname)\n", " model = os.path.split(fname)[1].split(\"-\")[-1].split(\".\")[0]\n", " lons = cube.coord(axis=\"X\").points\n", " lats = cube.coord(axis=\"Y\").points\n", " stations = cube.coord(\"station_code\").points\n", " models = [model] * lons.size\n", " lista = zip(models, lons.tolist(), lats.tolist(), stations.tolist())\n", " big_list.extend(lista)\n", "\n", "big_list.sort(key=itemgetter(3))\n", "df = pd.DataFrame(big_list, columns=[\"name\", \"lon\", \"lat\", \"station\"])\n", "df.set_index(\"station\", drop=True, inplace=True)\n", "groups = df.groupby(df.index)\n", "\n", "\n", "locations, popups = [], []\n", "for station, info in groups:\n", " sta_name = all_obs[station]\n", " for lat, lon, name in zip(info.lat, info.lon, info.name):\n", " locations.append([lat, lon])\n", " popups.append(\"[{}]: {}\".format(name, sta_name))\n", "\n", "MarkerCluster(locations=locations, popups=popups, name=\"Cluster\").add_to(m)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "titles = {\n", " \"coawst_4_use_best\": \"COAWST_4\",\n", " \"pacioos_hycom-global\": \"HYCOM\",\n", " \"NECOFS_GOM3_FORECAST\": \"NECOFS_GOM3\",\n", " \"NECOFS_FVCOM_OCEAN_MASSBAY_FORECAST\": \"NECOFS_MassBay\",\n", " \"NECOFS_FVCOM_OCEAN_BOSTON_FORECAST\": \"NECOFS_Boston\",\n", " \"SECOORA_NCSU_CNAPS\": \"SECOORA/CNAPS\",\n", " \"roms_2013_da_avg-ESPRESSO_Real-Time_v2_Averages_Best\": \"ESPRESSO Avg\",\n", " \"roms_2013_da-ESPRESSO_Real-Time_v2_History_Best\": \"ESPRESSO Hist\",\n", " \"OBS_DATA\": \"Observations\",\n", "}" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "from itertools import cycle\n", "\n", "from bokeh.embed import file_html\n", "from bokeh.models import HoverTool, Legend\n", "from bokeh.palettes import Category20\n", "from bokeh.plotting import figure\n", "from bokeh.resources import CDN\n", "from folium import IFrame\n", "\n", "# Plot defaults.\n", "colors = Category20[20]\n", "colorcycler = cycle(colors)\n", "tools = \"pan,box_zoom,reset\"\n", "width, height = 750, 250\n", "\n", "\n", "def make_plot(df, station):\n", " p = figure(\n", " toolbar_location=\"above\",\n", " x_axis_type=\"datetime\",\n", " width=width,\n", " height=height,\n", " tools=tools,\n", " title=str(station),\n", " )\n", " leg = []\n", " for column, series in df.iteritems():\n", " series.dropna(inplace=True)\n", " if not series.empty:\n", " if \"OBS_DATA\" not in column:\n", " bias = mean_bias[str(station)][column]\n", " skill = skill_score[str(station)][column]\n", " line_color = next(colorcycler)\n", " kw = dict(alpha=0.65, line_color=line_color)\n", " else:\n", " skill = bias = \"NA\"\n", " kw = dict(alpha=1, color=\"crimson\")\n", " line = p.line(\n", " x=series.index,\n", " y=series.values,\n", " line_width=5,\n", " line_cap=\"round\",\n", " line_join=\"round\",\n", " **kw\n", " )\n", " leg.append((\"{}\".format(titles.get(column, column)), [line]))\n", " p.add_tools(\n", " HoverTool(\n", " tooltips=[\n", " (\"Name\", \"{}\".format(titles.get(column, column))),\n", " (\"Bias\", bias),\n", " (\"Skill\", skill),\n", " ],\n", " renderers=[line],\n", " )\n", " )\n", " legend = Legend(items=leg, location=(0, 60))\n", " legend.click_policy = \"mute\"\n", " p.add_layout(legend, \"right\")\n", " p.yaxis[0].axis_label = \"Water Height (m)\"\n", " p.xaxis[0].axis_label = \"Date/time\"\n", " return p\n", "\n", "\n", "def make_marker(p, station):\n", " lons = stations_keys(config, key=\"lon\")\n", " lats = stations_keys(config, key=\"lat\")\n", "\n", " lon, lat = lons[station], lats[station]\n", " html = file_html(p, CDN, station)\n", " iframe = IFrame(html, width=width + 40, height=height + 80)\n", "\n", " popup = folium.Popup(iframe, max_width=2650)\n", " icon = folium.Icon(color=\"green\", icon=\"stats\")\n", " marker = folium.Marker(location=[lat, lon], popup=popup, icon=icon)\n", " return marker" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false }, "outputs": [], "source": [ "dfs = load_ncs(config)\n", "\n", "for station in dfs:\n", " sta_name = all_obs[station]\n", " df = dfs[station]\n", " if df.empty:\n", " continue\n", " p = make_plot(df, station)\n", " marker = make_marker(p, station)\n", " marker.add_to(m)\n", "\n", "folium.LayerControl().add_to(m)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<iframe srcdoc=\"<!DOCTYPE html>\n", "<head> \n", " <meta 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mit
furstj/NSP
lessons/04_navier_stokes.ipynb
1
910401
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Algoritmus SIMPLE pro Navierovy-Stokesovy rovnice" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Navierovy-Stokesovy rovnice\n", "\n", "Řešíme stacionární laminární proudění nestlačitelné tekutiny s konstantní hustotou popsané systémem Navierových-Stokesových rovnic\n", "\\begin{eqnarray*}\n", " \\nabla\\cdot\\left(\\vec{u}\\otimes\\vec{u}\\right) - \\nabla\\cdot(\\nu\\nabla \\vec{u}) &=& - \\nabla p, \\\\\n", " \\nabla\\cdot\\vec{u} &=& 0,\n", "\\end{eqnarray*}\n", "\n", "$\\vec{u}$ je rychlost, $p$ je podíl tlaku a hustoty (někdy nazývaný *kinematický tlak*) a $\\nu$ je (konstantní) kinematická vazkost." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aproximace rovnice kontinuity\n", "\n", "$$\n", "\\iint_{\\Omega_c} \\nabla\\cdot\\vec{u}\\,dV\n", "= \\sum_{f} \\int_{\\Gamma_f} \\vec{u}\\cdot\\vec{n}\\,dS\n", "\\approx\n", "\\sum_{f} \\vec{u}_f \\cdot \\vec{S}_f.\n", "$$\n", "\n", "$f$ probíhá přes všechny stěny buňky $\\Omega_c$, $\\vec{u}_f$ je aproximace $\\vec{u}$ v těžišti stěny $f$. Označíme\n", "$$\n", "\\phi_f := \\vec{u}_f\\cdot\\vec{S}_f.\n", "$$\n", "\n", "potom aproximace rovnice kontinuity v buňce $c$ je\n", "$$\n", "\\nabla\\cdot\\vec{u} \\approx \\frac{1}{|\\Omega_c|}\\sum_f \\phi_f.\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aproximace vazkých členů\n", "\n", "$$\n", "\\iint_{\\Omega_c} \\nabla\\cdot(\\nu \\nabla\\vec{u})\\,dV\n", "= \\sum_{f} \\int_{\\Gamma_f} \\nu (\\nabla \\vec{u})\\cdot\\vec{n}\\,dS\n", "\\approx\n", "\\sum_{f} \\nu \\frac{\\partial\\vec{u}_f}{\\partial n} \\cdot \\vec{S}_f.\n", "$$\n", "\n", "Na kartézské síti s krokem $h=\\Delta x = \\Delta y$ označme sousedy buňky $C$ indexy $N$ - soused nahoře, $W$ - soused vlevo, $S$ - soused dole a $E$ soused vpravo. Potom je (pro kartézskou síť) ve vnitřních buňkách sítě\n", "$$\n", " \\nabla\\cdot(\\nu \\nabla\\vec{u}) \\approx \\nu \\frac{\\vec{u}_N + \\vec{u}_W + \\vec{u}_S + \\vec{u}_E - 4\\vec{u}_C}{h^2}.\n", "$$\n", "\n", "Diskrétní verzi operátoru $\\nu\\nabla\\cdot\\cdot$ je tedy možné reprezentovat (na kartézské síti a při správném očíslování neznámých) pětidiagonální neostře diagonálně dominantní maticí. Při vhodně implementovaných okrajových odmínkách je tato matice symetrická." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aproximace konvektivních členů\n", "\n", "Konvektivní člen je nelineární a proto je třeba provést linearizaci. Označme horním indexem $n$ novéhodnoty a horním indexem $o$ stávající známe hodnoty rychlosti. Linearizaci lze provést různým způsobem, pro algoritmus SIMPLE se používá tzv. Picardova aproximace\n", "\n", "$$\n", "\\nabla\\cdot(\\vec{u}^n \\otimes \\vec{u}^n) \\approx \\nabla\\cdot(\\vec{u}^o \\otimes \\vec{u}^n)\n", "$$\n", "\n", "Potom aproximace tohoto členu pomocí MKO je \n", "\n", "$$\n", "\\iint_{\\Omega_C} \\nabla\\cdot(\\vec{u}^o\\otimes\\vec{u}^n)\\,dV\n", "= \\sum_{f} \\int_{\\Gamma_f} \\vec{n}\\cdot\\vec{u}^o\\otimes\\vec{u}^n\\,dS\n", "\\approx\n", "\\sum_{f} \\phi^o_f \\vec{u}^n_f.\n", "$$\n", "\n", "Tedy\n", "$$\n", "\\nabla\\cdot(\\vec{u}^o\\otimes\\vec{u}^n) \\approx \\frac{1}{|\\Omega_C|}\\sum_{f} \\phi^o_f \\vec{u}^n_f.\n", "$$\n", "\n", "Použijeme-li schéma **upwind** (např. pro tok mezi buňkami $C$ a $N$)\n", "$$\n", " \\phi_f \\vec{u}_f = (\\phi_f)^+ \\vec{u}_C + (\\phi_f)^- \\vec{u}_N, \n", "$$\n", "dostáváme\n", "\n", "$$\n", "\\nabla\\cdot(\\vec{u}^o\\otimes\\vec{u}^n) \\approx \\frac{1}{|\\Omega_C|}\\left(\n", "\\sum_{f} (\\phi^o_f)^+ \\vec{u}^n_C + \\sum_{f} (\\phi^o_f)^- \\vec{u}^n_F\\right),\n", "$$\n", "kde $\\vec{u}^n_F$ je hodnota řešení v buňce sousedící s $C$ stěnou $f$.\n", "\n", "Diskrétní verze konvektivního členu je tedy reprezentovaná maticí s diagonálním prvkem\n", "$$\n", "\\frac{1}{\\Omega_C}\\sum_{f} (\\phi^o_f)^+.\n", "$$\n", "Mimo diagonálu jsou na odpovídajících místech (sloupce $N$, $E$, $S$ a $W$) příslušné hodnoty $(\\phi_f)^-$.\n", "\n", "Pokud je splněna rovnice kontinuity, tak je výsledná matice (při správně implementovaných okrajových podmínkách) **nesymetrická** a neostře diagonálně dominantní." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Algoritmus SIMPLE\n", "\n", "Algoritmus existuje v několika variantách (např. řešení rovnice pro tlak nebo pouze pro korekci tlaku, detaily diskretizace, ...). Na tomto místě popíšeme variantu používanou v sofwarovém balíku OpenFOAM.\n", "\n", "Algoritmus je iterační a používá jako hlavní proměnné $\\vec{u}$ a $p$ definované ve středech buňek a jako pomocnou proměnnou $\\phi$ definovanou na stěnách. \n", "\n", "Na začátku výpočtu se určí $\\phi$ na stěně $f$ jako $\\phi_f = \\vec{u}_f\\cdot\\vec{S}_f$ kde $\\vec{u}_f = (\\vec{u}_C + \\vec{u}_F)/2$ je lineární interpolací rychlosti do středu stěny. V dalších krocích se $\\phi$ počítá jinak.\n", "\n", "Nejprve diskretizujeme rovnici pro rychlost (gradient tlaku zatím ponecháme formálně zapsany jako $\\nabla p$):\n", "$$\n", "a_C^o \\vec{u}_C^* = \\sum_{f} a_F^o \\vec{u}^*_F + Q_C - \\nabla p_C^o = H(\\vec{u}^*)_C - \\nabla p_C^o.\n", "$$\n", "Protože byl použit *starý* tlak $p^o$, nedostaneme řešením této rovnice $\\vec{u}^n$, ale pouze odhad $\\vec{u}^*$. Ten však nemusí splňovat rovnici kontinuity. Vyjádříme proto\n", "$$\n", "\\vec{u}_C^* =\\frac{1}{a_C^o} H(\\vec{u}^*)_C - \\frac{1}{a_C^o}\\nabla p_C^o = \\hat{u}_C^* - \\frac{1}{a_C^o}\\nabla p_C^o.\n", "$$\n", "kde $\\hat{u}_C^* := \\frac{1}{a_C^o} H(\\vec{u}^*)_C$.\n", "\n", "Tento odhad rychlost interpolujeme na stěnu $f$\n", "$$\n", "\\vec{u}_f^* = \\hat{u}_f^* - \\frac{1}{a_f^o}\\nabla p_f^o.\n", "$$\n", "\n", "\n", "Pro $\\vec{u}^n$ pak požadujeme splnění rovnice (pozor na horní indexy $n$, $o$ a $*$)>\n", "$$\n", "\\vec{u}_f^n = \\hat{u}_f^* - \\frac{1}{a_f^o}\\nabla p_f^n.\n", "$$\n", "\n", "Tato rychlost by však měla splňovat rovnici kontinuity, takže\n", "$$\n", " 0 = \\sum_f \\phi_f^n = \\sum_f \\vec{u}_f^n \\cdot \\vec{S}_f = \\sum_f \\hat{u}_f^* \\cdot \\vec{S}_f -\n", " \\sum_f \\frac{1}{a_f^o}\\nabla p_f^n \\cdot \\vec{S}_f.\n", "$$\n", "\n", "Diskretizovaná rovnice pro tlak má tedy tvar\n", "$$\n", "\\sum_f \\frac{1}{a_f^o}\\nabla p_f^n \\cdot \\vec{S}_f = \\sum_f \\hat{u}_f^* \\cdot \\vec{S}_f,\n", "$$\n", "což je diskrétní verze rovnice $\\nabla\\cdot(\\frac{1}{a}\\nabla p) = \\nabla\\cdot \\hat{u}^*$.\n", "Z nové hodnoty tlaku $p^n$ určíme novou hodnotu rychlosti a objemového toku $\\phi$\n", "\\begin{eqnarray}\n", " \\vec{u}^n_C &=& \\hat{u}_C^* - \\frac{1}{a_C^o}\\nabla p_C^n, \\\\\n", " \\phi_f^n &=& \\hat{u}_f^*\\cdot \\vec{S}_f - \\frac{1}{a_f^o}\\nabla p_f^n\\cdot\\vec{S}_f.\n", "\\end{eqnarray}\n", "\n", "\n", "**Jedna iterace algoritmu SIMPLE**\n", "jedna iterace algoritmu SIMPLE zahrnující relaxaci v rovnice pro rychlost a explicitní relaxaci tlaku tedy vypadá následovně:\n", " \n", " 1) ze známých hodnot $\\vec{u}^o$ a $\\phi^o$ sestavíme aproximaci levé strany rovnice pro rychlost (tj. bez vlivu gradientu tlaku)\n", " $$\n", " \\nabla\\cdot\\left(\\vec{u}^o\\otimes\\vec{u}^*\\right) - \\nabla\\cdot(\\nu\\nabla \\vec{u}^*) \\approx\n", " a^o_C \\vec{u}_C - H(\\vec{u}^*)_C,\n", " $$\n", " \n", " 2) provedeme relaxaci s koeficientem $\\alpha$\n", " \n", " 3) sestavíme a vyřešíme rovnici pro odhad rychlosti $\\vec{u}^*$ se gradientem tlaku počítaným z $p^o$\n", " $$\n", " a^o_C \\vec{u}^*_C = H(\\vec{u}^*)_C - \\nabla p^o_C,\n", " $$\n", " \n", " 4) pomocí lineární interpolace určíme $\\hat{u}_f$ ($F$ je index sousední buňky)\n", " $$\n", " \\hat{u}_f^* = \\text{interpolate}\\left(\\frac{1}{a^o_C}H(\\vec{u}^*)_C, \\frac{1}{a^o_F}H(\\vec{u}^*)_F\\right).\n", " $$\n", " \n", " 5) sestavíme a vyřešíme rovnici pro tlak $p^*$\n", " $$\n", " \\sum_f \\frac{1}{a_f^o}\\nabla p_f^* \\cdot \\vec{S}_f = \\sum_f \\hat{u}_f^* \\cdot \\vec{S}_f,\n", " $$\n", " \n", " 6) nový tlak určíme z $p^o$ a $p^*$ pomocí relaxace s koeficientem $\\beta$\n", " $$\n", " p^n = p^o + \\beta(p^* - p^o)\n", " $$\n", " \n", " 7) určíme nové hodnoty rychlosti a objemového toku\n", " \\begin{eqnarray}\n", " \\vec{u}^n_C &=& \\hat{u}_C^* - \\frac{1}{a_C^o}\\nabla p_C^n, \\\\\n", " \\phi_f^n &=& \\hat{u}_f^*\\cdot \\vec{S}_f - \\frac{1}{a_f^o}\\nabla p_f^n\\cdot\\vec{S}_f.\n", " \\end{eqnarray}\n", "\n", "Výše uvedený postup opakujeme dokud proces nezkonverguje ke stacionárnímu stavu." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "using PyPlot;" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "include(\"mesh.jl\");\n", "include(\"fields.jl\");\n", "include(\"operators.jl\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vazké členy jsme již implementovali v minulé přednášce. Zbývá pouze konvektivní člen. Ten zapíšeme jako *div(phi, U)*" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "div (generic function with 2 methods)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function div(ϕ::ScalarList , U::Field{T}) where {T}\n", " @assert length(ϕ) == length(U.mesh.owner)\n", " \n", " A = spzeros(length(U.values),length(U.values))\n", " b = zeros(T, length(U.values))\n", " \n", " mesh = U.mesh\n", " \n", " for f in internal_faces(mesh)\n", " o = mesh.owner[f]\n", " n = mesh.neighbor[f]\n", " \n", " α = max(ϕ[f], 0.0)\n", " β = min(ϕ[f], 0.0)\n", " \n", " A[o,o] += α / mesh.volume[o]\n", " A[o,n] += β / mesh.volume[o]\n", " \n", " A[n,o] -= α / mesh.volume[n]\n", " A[n,n] -= β / mesh.volume[n]\n", " end\n", "\n", " for p in boundary_patches(mesh)\n", " name = mesh.patch[p].name\n", " bc = U.boundaries[name]\n", " for f in patch_faces(mesh, p)\n", " o = mesh.owner[f]\n", " c1, c2 = boundary_coeffs(bc, f) \n", " α = max(ϕ[f], 0.0)\n", " β = min(ϕ[f], 0.0)\n", "\n", " A[o,o] += (α + β*c2) / mesh.volume[o]\n", " b[o] += β*c1 / mesh.volume[o]\n", " end\n", " end\n", "\n", " \n", " return Equation{T}(A, U.values, b)\n", "end " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Při spuštění výpočtu musíme vypočítat $\\phi_f$ pomocí lineární interpolace" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "create_ϕ_by_interpolation (generic function with 1 method)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function create_ϕ_by_interpolation(U::VectorField)\n", " mesh = U.mesh\n", " ϕ = zeros(Scalar, length(all_faces(mesh)))\n", " \n", " for f in internal_faces(mesh)\n", " o = mesh.owner[f]\n", " n = mesh.neighbor[f]\n", " ϕ[f] = dot(mesh.surface[f], (U[o] + U[n]) / 2)\n", " end\n", "\n", " for p in boundary_patches(mesh)\n", " name = mesh.patch[p].name\n", " bc = U.boundaries[name]\n", " for f in patch_faces(mesh, p)\n", " o = mesh.owner[f]\n", " Uf = boundary_value(bc, f)\n", " ϕ[f] = dot(mesh.surface[f], Uf)\n", " end\n", " end\n", "\n", " return ϕ\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "V průběhu výpočtu budeme $\\phi_f$ počítat z $\\hat{u}$, $p$ a $1/a_f$. Máme\n", "$$\n", "\\phi^n_f = \\hat{u}_f\\cdot\\vec{S}_f - \\frac{1}{a_f} \\nabla p_f\\cdot\\vec{S}_f = \n", "\\hat{u}_f\\cdot\\vec{S}_f - \\frac{1}{a_f} \\frac{\\partial p}{\\partial n} ||\\vec{S}_f||.\n", "$$" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "calculate_ϕ! (generic function with 1 method)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function calculate_ϕ!(ϕ::ScalarList, Ubar::VectorField, p::ScalarField, ra::ScalarList)\n", " mesh = Ubar.mesh\n", " for f in internal_faces(mesh)\n", " o = mesh.owner[f]\n", " n = mesh.neighbor[f]\n", " S = mesh.surface[f]\n", " δ = norm(mesh.centre[n] - mesh.centre[o])\n", " pn = (p[n] - p[o]) / δ\n", " ϕ[f] = dot(S, (Ubar[o]+Ubar[n])/2.) - (ra[o]+ra[n])/2 * pn * norm(S)\n", " end\n", "\n", " for pa in boundary_patches(mesh)\n", " name = mesh.patch[pa].name\n", " bc = Ubar.boundaries[name]\n", " pbc = p.boundaries[name]\n", " for f in patch_faces(mesh, pa)\n", " o = mesh.owner[f]\n", " S = mesh.surface[f]\n", " Ub = boundary_value(bc, f)\n", " pb = boundary_value(pbc, f)\n", " pn = (pb - p[o]) / norm(mesh.centre[o] - mesh.facecentre[f])\n", " ϕ[f] = dot(S, Ub) - ra[o] * pn\n", " end\n", " end\n", "\n", "end" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "SIMPLE (generic function with 1 method)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function SIMPLE(U, p, ν; fix_pressure=true, iters=50)\n", " mesh = U.mesh\n", " α = 0.7\n", " β = 0.3\n", "\n", " ϕ = create_ϕ_by_interpolation(U)\n", "\n", " for iter = 0:iters\n", " \n", " UOld, pOld = copy(U.values), copy(p.values)\n", " \n", " \n", " UEqn = div(ϕ,U) - Δ(ν,U)\n", "\n", " relax!(UEqn, α)\n", " \n", " solve!(UEqn + grad(p))\n", " \n", " ra = 1 ./ Ac(UEqn);\n", "\n", " Ubar = VectorField(ra .* H(UEqn), U.mesh, U.boundaries);\n", " \n", " pEqn = Δ(ra, p) - div(Ubar);\n", " if fix_pressure\n", " pEqn.A[1,1] -= 1/ν #length(mesh.centre)\n", " end\n", " solve!(pEqn)\n", " \n", " p ← β*p + (1-β)*pOld\n", " U ← Ubar - ra .* grad(p)\n", " \n", " calculate_ϕ!(ϕ, Ubar, p, ra)\n", " \n", " if rem(iter,5)==0\n", " nxny = length(mesh.centre)\n", " pRez = norm(pOld - p.values) / nxny\n", " URez = norm(UOld - U.values) / nxny\n", " println(iter, \"\\t\", pRez, \"\\t\", URez)\n", " end\n", "\n", " end\n", " \n", " return U,p\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Proudění v kavitě\n", "\n", "Popis problému viz minulá přednáška, tentokrát však použijeme úplný systém Navierových-Stokesových rovnic." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "function create_fields(mesh)\n", " U = VectorField(mesh)\n", " set_dirichlet_patch!(U, \"left\", Vector(0,0));\n", " set_dirichlet_patch!(U, \"right\", Vector(0,0));\n", " set_dirichlet_patch!(U, \"bottom\", Vector(0,0));\n", " set_dirichlet_patch!(U, \"top\", Vector(1,0));\n", "\n", " p = ScalarField(mesh);\n", " for name ∈ [\"left\", \"right\", \"bottom\", \"top\"]\n", " set_neumann_patch!(p, name)\n", " end\n", "\n", " return (U,p)\n", "end;" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "cavity_ns (generic function with 1 method)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function cavity_ns(ν, mesh)\n", "\n", " U,p = create_fields(mesh);\n", " U,p = SIMPLE(U, p, ν)\n", " return U,p\n", "end" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\t0.003954386650172449\t0.0031633014957489266\n", "5\t0.0009313427996707473\t0.0005820193295719034\n", "10\t0.00013548980578324712\t0.0002550687683337278\n", "15\t0.0001784724800357114\t0.00015129097069981444\n", "20\t0.00010612779116953121\t0.0001061374671015328\n", "25\t4.453713224426026e-5\t7.866576767180216e-5\n", "30\t1.7553394031876378e-5\t5.9975488044014416e-5\n", "35\t9.895685271580443e-6\t4.658787470759525e-5\n", "40\t7.548678374507451e-6\t3.66352814513691e-5\n", "45\t6.353603839820342e-6\t2.9026630573720365e-5\n", "50\t5.382903568904233e-6\t2.309688894509734e-5\n" ] } ], "source": [ "ν = 0.01;\n", "msh25 = cartesian_mesh(25,25);\n", "U,p = cavity_ns(ν, msh25);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <Figure size 640x480 with 2 Axes>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_contourf(p; cmap=\"jet\"); colorbar();\n", "plot_arrows(U);\n", "axis(\"equal\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Jemnější síť a menší viskozita" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "ν = 1.e-3;\n", "msh50 = cartesian_mesh(50,50);" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\t0.00039379457172875656\t0.0011611564177153332\n", "5\t0.0001336644019491234\t0.00018143208796904552\n", "10\t2.2293425535828883e-5\t0.00010599819220560722\n", "15\t1.92492455701407e-5\t8.185950976471176e-5\n", "20\t1.7066576891442317e-5\t7.089598420492306e-5\n", "25\t1.2710589849258206e-5\t6.466429272635908e-5\n", "30\t1.0287541290045031e-5\t6.0413375212725364e-5\n", "35\t9.037281288737101e-6\t5.6633566019180126e-5\n", "40\t8.260620382094667e-6\t5.3002191145150027e-5\n", "45\t7.656750089871438e-6\t4.947518798854899e-5\n", "50\t7.11313871941009e-6\t4.604726111025421e-5\n" ] } ], "source": [ "U,p = cavity_ns(ν, msh50);" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <Figure size 640x480 with 2 Axes>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_contourf(p; cmap=\"jet\"); colorbar();\n", "plot_arrows(U);\n", "axis(\"equal\");" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "component(f::VectorField, c) = [ v[c] for v in f.values ]\n", "\n", "n=50\n", "uu = reshape(component(U,1), (n,n));\n", "vv = reshape(component(U,2), (n,n));\n", "pp = reshape(p.values, (n,n));" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "streamfunction (generic function with 3 methods)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Ψ_x = v, Ψ_y = - u\n", "\n", "function streamfunction(u, v, Δx=1, Δy=1)\n", " Ψ1 = zero(u)\n", "\n", " Ψ1[1,1] = 0.0\n", " for i=2:size(u,1)\n", " Ψ1[i,1] = Ψ1[i-1,1] + Δx * (v[i,1]+v[i-1,1])/2\n", " end\n", " for j=2:size(u,2)\n", " Ψ1[:,j] = Ψ1[:,j-1] - Δy * (u[:,j]+u[:,j-1])/2\n", " end\n", "\n", " Ψ2 = zero(Ψ1)\n", " \n", " Ψ2[1,1] = 0.0\n", " for j=2:size(u,2)\n", " Ψ2[1,j] = Ψ2[1,j-1] - Δy * (u[1,j,1]+u[1,j-1])/2\n", " end\n", " for i=2:size(u,1)\n", " Ψ2[i,:] = Ψ2[i-1,:] + Δx * (v[i,:]+v[i-1,:])/2\n", " end\n", " \n", " Ψ = (Ψ1 + Ψ2) / 2\n", " \n", " #Ψ[end,end] = (Ψ[end-1,end]+Ψ[end,end-1]) / 2\n", " return Ψ\n", "end" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <Figure size 640x480 with 2 Axes>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Ψ = streamfunction(uu,vv,1/50,1/50);\n", "\n", "pos = range(0, maximum(Ψ), length=10)\n", "contour(Ψ', pos, colors=\"black\");\n", "neg = range(minimum(Ψ), 0, length=5)\n", "contourf(pp'; cmap=\"jet\"); colorbar();\n", "contour(Ψ', neg, colors=\"red\");\n", "axis(\"equal\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vypočet na nestrukturované síti\n", "\n", "Řešíme problém ustáleného obtékání eliptického tělesa v kanále nevazkou nestlačitelnou tekutinou popsanou systémem Navierových-Stokesových rovnic. Při řešení uvažujeme následující okrajové podmínky:\n", " - levá hranice (vstup): $u=(1,0)$ a $\\partial p / \\partial n = 0$\n", " - pravá hranice (výstup): $\\partial u/\\partial n = 0$ a $p = 0$\n", " - horní a dolní hranice: $u=(0,0)$ a $\\partial p / \\partial n = 0$\n", " - obtékané těleso: $u=(0,0)$ a $\\partial p / \\partial n = 0$\n", " \n", "obrázek oblasti viz níže" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "gmsh_mesh (generic function with 1 method)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "include(\"gmsh_mesh.jl\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "gmsh = gmsh_mesh(\"domain.msh\");" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <Figure size 640x480 with 1 Axes>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_mesh(gmsh)\n", "axis(\"equal\");" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5-element Array{String,1}:\n", " \"TOP\" \n", " \"OUTLET\" \n", " \"BOTTOM\" \n", " \"PROFILE\"\n", " \"INLET\" " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "patch_names(gmsh)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "U = VectorField(gmsh)\n", "set_dirichlet_patch!(U, \"INLET\", Vector(1,0));\n", "set_dirichlet_patch!(U, \"TOP\", Vector(0,0));\n", "set_dirichlet_patch!(U, \"BOTTOM\", Vector(0,0));\n", "set_dirichlet_patch!(U, \"PROFILE\", Vector(0,0));\n", "set_neumann_patch!(U, \"OUTLET\");\n", "U.values[:] = [Vector(1,0) for c in cells(gmsh)]\n", "\n", "p = ScalarField(gmsh);\n", "for name ∈ [\"INLET\", \"PROFILE\", \"TOP\", \"BOTTOM\"]\n", " set_neumann_patch!(p, name)\n", "end\n", "set_dirichlet_patch!(p, \"OUTLET\", 0.0);" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\t0.01320064755067597\t0.0023074718509579536\n", "5\t0.00802833169108605\t0.0005953211697410505\n", "10\t0.0035927633033289164\t0.00037123646477498336\n", "15\t0.0009435004753181014\t0.0001496983687921182\n", "20\t0.0002838994518982319\t6.170762805817095e-5\n", "25\t0.00026411717328420694\t2.467994095292978e-5\n", "30\t5.8001984240376655e-5\t1.3214375203446303e-5\n", "35\t1.2634339558913047e-5\t6.464143263012179e-6\n", "40\t8.27905538091566e-6\t2.873321673740732e-6\n", "45\t3.758162123910306e-6\t1.5465536656263301e-6\n", "50\t1.780497887249392e-6\t8.863326173833504e-7\n", "55\t1.1107180544857576e-6\t5.977561343311247e-7\n", "60\t9.044482283054537e-7\t3.7969259033145676e-7\n", "65\t6.647126903481037e-7\t2.5007725441437235e-7\n", "70\t2.933342738325574e-7\t1.765284685991397e-7\n", "75\t1.2317469293832773e-7\t1.2985302179181044e-7\n", "80\t1.1658603528929908e-7\t9.29175507602859e-8\n", "85\t6.603097281104495e-8\t6.83581838447192e-8\n", "90\t4.145791337885607e-8\t5.069535038809639e-8\n", "95\t3.0719380714308483e-8\t3.769858268139703e-8\n", "100\t2.271213560577168e-8\t2.8122455393788428e-8\n" ] } ], "source": [ "U,p = SIMPLE(U, p, 1.e-3, fix_pressure=false, iters=100);" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <Figure size 640x480 with 2 Axes>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_contourf(U; cmap=\"jet\"); colorbar();\n", "plot_arrows(U);\n", "axis(\"equal\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Výpočet na síti se zjemněním u stěny" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "gmf = gmsh_mesh(\"domain_bl.msh\");" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <Figure size 640x480 with 1 Axes>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_mesh(gmf);\n", "axis(\"equal\");\n", "xlim(-1.5,0); ylim(-0.1,1.1);" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3988" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "length(cells(gmf))" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5-element Array{String,1}:\n", " \"TOP\" \n", " \"OUTLET\" \n", " \"BOTTOM\" \n", " \"PROFILE\"\n", " \"INLET\" " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "patch_names(gmf)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "Uf = VectorField(gmf)\n", "set_dirichlet_patch!(Uf, \"INLET\", Vector(1,0));\n", "set_dirichlet_patch!(Uf, \"TOP\", Vector(0,0));\n", "set_dirichlet_patch!(Uf, \"BOTTOM\", Vector(0,0));\n", "set_dirichlet_patch!(Uf, \"PROFILE\", Vector(0,0));\n", "set_neumann_patch!(Uf, \"OUTLET\");\n", "Uf ← [Vector(1,0) for c in cells(gmf)]\n", "\n", "pf = ScalarField(gmf);\n", "for name ∈ [\"INLET\", \"PROFILE\", \"TOP\", \"BOTTOM\"]\n", " set_neumann_patch!(pf, name)\n", "end\n", "set_dirichlet_patch!(pf, \"OUTLET\", 0.0);" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\t0.038589726480548446\t0.0017800536137290459\n", "5\t0.01935677792910062\t0.0006796930119209985\n", "10\t0.005675544667418478\t0.0005761775772204923\n" ] } ], "source": [ "Uf,pf = SIMPLE(Uf, pf, 1.e-2, fix_pressure=false, iters=10);" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\t0.002743989579133127\t0.0007287874017782403\n", "5\t0.0011430280876983151\t0.0004799918024760951\n", "10\t0.000460386824278761\t0.0002610821293494769\n", "15\t0.0001252732545679363\t0.00014093897905312735\n", "20\t9.443214275967843e-5\t8.961099486556777e-5\n", "25\t4.507412044495838e-5\t6.0568955122828145e-5\n", "30\t1.9753486398563542e-5\t3.973068466177771e-5\n", "35\t6.956636339999515e-6\t2.466381532749566e-5\n", "40\t6.4011833108226405e-6\t1.52768978936901e-5\n", "45\t3.674592491566292e-6\t9.664242565035096e-6\n", "50\t2.0950872679181326e-6\t6.71575877856642e-6\n" ] } ], "source": [ "Uf,pf = SIMPLE(Uf, pf, 1.e-3, fix_pressure=false, iters=50);" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <Figure size 640x480 with 2 Axes>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_contourf(Uf; cmap=\"jet\"); colorbar();\n", "#plot_arrows(Uf);\n", "axis(\"equal\");" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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jXHzD/El6pClqLXoGSXvLKZGBwDizHdGLk7t+ns7E+8eAI9VAmB9fg0AfegYkBgOTEumD3x3wUjiHJDRo2L42tp/nWhqP+4wDzSRUqbgTf3YXd4svTfacLoyhZu2kzTqg+B5ZqOcIDIs2teqq9dyojY+XX1t3hEIQBKHjm7kGSqUSYWFhaFjaenfqCdAZqAh2dMmhQUNCEuQLFCuBfx7mRLUBEY59HE+BwcgANCPZs+18VxxlGUVtIOF7ZQ8zFlOS+NwWDAFi3Chz8WsR8M0ZjvidmtQ+OVXpgIc3MVV/aQonqcpoH4UNsnV7Z6DUAGFvAQ0NDQgNdU46QopL2wB0JXHZBGACnHuujkKvyyh0F3y9nRO4zNOcSaHAc27Q/qY3us6wxduLg5M8Hafr+DXAm0HXz5tjdUfGcqx0tItr4OsKgbk/sJwgOT826TgTItyfpavFOZY/B0G+wDszHXs+Kh3w3mGKCnvCsLR1hcwKSoOf/nOE5kg3iYOfBsr1ehkuhEwUehGqVcCWUqa0HSHaVOuBOjXwwAaKNyckmIShfT1c3FWl4s7e0TXSZp3lTo8XzPQbaj1FpEPdyBRnRgpw4T5+r1AAG4uZBZmVzm6P7sgSzPzKREp9vIBtpdSnDIsGrssA7s91r6yLvWjRs0bfogeq1Zyu+d+j1FH9brjjHDXVer6Ocopfhq2QiUIPx4katijWiALLNw+IATCEs92fGW//4rq7HJjxFXe9EQHsnS5u5FQ7g5HiyM4SBbUemPs/6iYmin4L3dXRsK2UPeCNWhKf3/3KqYdTEplen51hv3BNazCRDrWBQq23DrCcMDOFnvttX/8AH/ezWfZSAF5mGbKZKTy6E69PpRZFbyThurWKr98VqSQr7kwSDEZ+hq3NpbikPw+AGZvxpzjmOy3MMZ/9r0+T1PfxZdZi6QbO/pg/mBkrT9XCyOgeyETBRry2l3XD8fHA+AQGWnfZ7bWHggamNPsGstc/IgCYEAnckc2BMJ3xkRgZBzQ8YFr0PjkBRPhzUE9nShBv7gde3kP9hY8Xg0Gzjh4F8wYCD+QBg7vYUlml4n22V/f9+35+DfblgurrxUAfFQgMi2JrpL3YWwEs+tk0lAcACpXAxydI3hQA5mTKXvu2wJw8qfVA4d3uHeB2l1O9H+jD9/fOn0k4bxoIjIqzvn4oFMBNgxx7LtUtwMlaXgNNWm4aPj8FbCwB5gzghsHTs4AynAdZzNgOHt4IrC8Calu4qLfo+fux/YAnxwBXp3f/OXUFeiPbClPcTDfToGHNO9SPmoPndjKLcHUa4N8FKru7HKjTmDIED24gWZqcyCzBDZnWU+b1auBgFW/ryOmE+yqZ5bkm3fMEur0By7ezbe9aBxgqLdsIHK8hqWnRA4erTe3SNw8CXp4MxLqg86NKRSHqtCQgs4c5ispiRufAqUThhRdewLfffouTJ08iMDAQ48ePx0svvYSBAwfa9P+uJgr5osAsIoC721O17DBwpGGQDOdh3g/MCIT6AyG+wH+O0idi/iDg1iHul953NJQa4JHN3MVekuLeO8ayJjp2XtrfsW6dder2TbBuWw18e4aZHX9vccqiHvBW8DV7cVLnzMbaokEDvLibBHVSouc5kuoMwJengVlp7k1wZaLgHDiVKFx++eWYN28eRo0aBb1ejyeffBJHjhzB8ePH0adPx1Ta1UTBmahXc4dvy67l85McDTsmDkgP71k7gO6CUkP7aEdnCNwJBiOQ+xE/VzojF/fyZn6vAC2zX5rsHrX82hbg/l9E7YOC5/fpSZYSZiZTDzJ3IB377MHOMqCsmbt2Xy/g9X3skLh+AEttqVZKT0YB0OiBe9eTVF7anwSrN5spPbaZzqMhfizHbSgCmvUkC7cMpi6kKxk/Z0AmCs6BU9/mNWvWtPp5xYoViImJwb59+zB5ctddfbaXMmgOj3G/D6wltOi4iFWpgKJGYMFqmgSN6cdyxi2DTbu+/9sPnGtgbfFINQ14AKB/KEVdszNkwmAPQv258PdkeCmA768Vg6Q3icIdP9NB8LqMrus8HIkgX+DWwQzQBoHmTF+foXvjtCQ6HwZ3Ytd94AKwvUwkSkbgbD2vo2M1wM5y4JFR1Ni0hZeCnicfXtH159ZT8EAeyVqjlu/P2vPAgHAgNsikI/KEdVdG19Gtb3NDQwMAIDLSclOwRqOBRmOyc1MqlRfd5uAFtmbVi+513+UzZTg6jjum27Pdd8e47jzw5+0Ux0UFUOTUoOGFNz25dWrY15t1+ugg2hEH+QB3DeWFK7c1ydhVToGluaGXQtHaU0AQgHU3dv+52YIAn9Yanzo1NSP2ZhDa4r5cHhL+e4TX0eh+smDUXiSEmCzcm3XA2Tvdu3wlw3noNqIgCAKWLVuGiRMnIjs72+JtXnjhBTzzzDPt3k+dmgNaIvyB9DAK4G4aSJLgKCtmZ+GaDB4Aa+erzjKNZ4mVL84xfX9pikwOejs2FrODRWugkv7TkyTNkxP5+bgq7eKMgTtfC23hrGFadwx1zv26Gs06ptn7iB06HxwDCpQUSQ5xQuZIehwZvRPdRhSWLl2Kw4cPY+vWrVZv8/jjj2PZsmW//axUKpGU1DpfPC2ZBwCUNNIRzhmTGZ0NP2/uoGyBTBJkHKsGTtQyeya1BCoUTM/HB7ObQ0bvweYSGp0163hIBPLZnRy09OwEz+vKAlpPGtUb+XxmZ/A5eRLx7WnoFqLwwAMPYNWqVdi8eTMSExOt3s7f3x/+/rarhxK7MNlQhgx78cEx1v+nJHZtqmZnsCSv9c9bS4DcGJlE9lZckQrk32n6+fW9NDyblkQtjjsIVjuDRzcD7xxiCSrUDyhpAp7ZAWT1pabl9mzXtJT2djiVKAiCgAceeAArV67Exo0bkZraA0z5PRx7KrrmnNib8N0Z4H/naFDTpONCfEZsmc2OAl6dQlc7V2Cidb7tVBgF99UA9WYsG+nqM3AMXp7C6bVNWnq+zFlFQjw5kYc80t01cCpRWLJkCT799FN8//33CAkJQUVFBQAgLCwMgYFypOoO6I3A4SrqOi6oOFp77Xm2WUoCUEcHu8/FNrcxcd2/83YkkkLZSx/sy53716fZdTJ/EFvt7G2dK21kz/6MFGBwN9lSdxXVKkBrNA2CWraJ5Y+rUvm5iZQvYxkOhpeCXUp9fIFjizzjOunpcCpReOeddwAAU6dObfX7FStWYNGiRc58aBkiWvScWRAdyHSkrze7LSbEA3cP5SyFrqK0EfjsJFs5q1roPii1c2ZGAK9N6bhe+vwufh3Xj/a27pBSHxHLQ8KYOPv0MA0a4MmtoomPjkY+PxWQvMUGAfMGAcvHuffY5bvXsbXQC1zAL6joUvrFKZKo5yYAebEd3o0MGXbDW+5ScRs4vfQgw7nYVEzBz6g49oePiqV2Q2LhIX7Alnmm2x+8APS/wrHBSWdkr3VCMNOEQT7cdd41lB0plsyyNhYD7xzktLwaNSfk1ar5t+ExwN+mMdXoKKh0zAiMj++8aVVHJOFsPUnBkL4Mqn5eJBqBPnxNvL2AvZXAFf1JEqYmub5lT7pErb0eK69t/fPLuzlR84rUnm1GVNBAv5P2ZjL0Fmj0zBAqFOy0eHYnP795MfJr01sgz3qwE4LAFk1XplyXb6dXe2UzJxI2avn78fHAPcM4SMmVRij16o6JyLl6+uD3DaSt8ofHuQjdnu24Bejr05xqWa+m78Yn4iCm2CA67z07oWtjpGta6I3RrKOGoaQReHUvfTKmJtFhcGGWafBWi45kwdWDjFQ6ZjUkB8fbVgMDIoArU3neQd3YRSQI9IQYGeda0pRfBzy1nQTX35tlln8d4fC3GzKBGzPpxdAb9RkzvuJ75OtFgaHk9jkokgLDJbnukxWTnRmdA9lXy0boDEytX1ABD/7KADQqlovHzOT2/eAFAXhoI3cpo+LEI7bzZOO2IcCCIQx4dWrglb0kCNlRnbs/R8OWRSMtvLU50LMTHL87ya+j5Wy4PwnBz4Wc77Awi5a+lsx9jAJ3krYoq+s1JAp9fKlj8BUDXWYEMDXx4umc7tLGO+oTfm68FSQu1S3Urfz7CAdxvTgJyIhw3uP/4yDLMgow8L5/DKhU0Qvi2nRqH7q79NQ3kARbI7YZnhe93qIC+b42i+SqMyQvv47luKvTPdOL4BfRtEtrILm/42duSqS5Fe5CEgBe4zIcD5ko2Ii8j7h7jwnkRMKCBqBfH+DKQO7GLOGqbzmBsEHDxaZeQ7/0+YO5AHWWKJgH2GA/4M3pnbsfd4IzUpiPjTF9Lwg0JkqyQNzr1Cx7NGh4/H4jd92TErgQTk+2PHEzPRz4z2Wmn8ubgHtz3G86Z1scW2T63igAT2zhjv6y/l13RrQVRlEcKYAWzio934MLKr4fnSUKggD8cROJ+9Vpths5RQSYzNAAZofuGdY5lX1hA3Dt96aSk583A1iQDzArnYTkilTPsz/286afwf4Frj4TE/LrgKtWsgwWJmY7ZDgeHvZRdR2OLjJ9/30+kBEOZHWwg39nJj/AoX7sBGjWsbbnDkI9S+jJrW8KhWWSAABLf+E8jTB/HiodkF9PrcdEgRkJW9DPA02PvBRsR+tO3G9msSwInOswrl/nMy6DVwBFSpYufL2YRVLr+fPVaRznbI3MW0NXPFoSgoGV15D8qHTcLOwsZ5ZpQjzPxRnlp5M1HHx2bYbzMhfupknoHwZsuJGbsAYNsHwHxbcyHAtZoyADgkASs3QDtQOSKHJ0P+6aexu+PMVBXe6WGTAYZSW4NQjicCmNAVjwE6dEXtafWSFXl3zq1czUOPq9q2wGRn/C+w7xY5lrYzEzF7MzOGTukhR2OvUWnK0HMv4jaxQcDTmj0Itxqha4/BvWqIPEGntpE3CqThy01c/6/56tZ60yL4Z+DKPimGVxtx1HZzB3oKvPgDAY2UWhMXCH/NVpU1vizBROHHW1MNJdoFAAPgpmEb69tuPbdxXPbGdG8crUjsWfzqrhRwcBe2+lkLZJS1vnY9XAlCRqCJJDXUcsza2YzXG6lp/ZMH+KsJ/YSr3VzGTHnGt0D/f12Lx5M1555RXs27cP5eXlWLlyJWbPnm319t9++y3eeecdHDx4EBqNBllZWVi+fDkuu+wyq/9jCTJR6MVIDQN23QL0DeBF+s1p/n5WuuUA9PBG1lovqBjAWvRcnLL6AncPAxZl9eyWOWfip3NcWCclmEpTbx0EXthF0hbgw3r+mToq0DcWA3dkA7dlub5cZDBSvyOhpBF4cAPr8LPSe0ZW6rKvgW1lQID4Xii1DHRBPsA16cBT47p/jLeXgmQhWvw5JZRlHVeQda2Bn0lJK7CjHFhdQOH1Zf1NWY0/bwOO1rBMIL2Gn5yg3mtxDvDHka7PALkzmpubkZOTg9tvvx1z5szp8PabN2/GJZdcgueffx7h4eFYsWIFZs2ahV27diEvL6/D/5cglx5k2Iz8OhKKmCAa8PxhE3DrEO5snbU4WduZtHd7wP0zG6vygQ3FJsKVX8fF1deLr+cDeWzLM38em4rpsjknk4Og3AUHLwCXfdP6d1UqEht/b+DBPODP4zovlDQKJCOOSKE3aID3DrPrxV4CYxToKdCiBxasBuL6cCc8PVmeP1DbAty51iQIvqDinAaAJODFScwcmH+eD1QCz++mRmVSAjuSutoi64ntkcXFxa3O1daZRwqFosOMgiVkZWXhpptuwlNPPWXz/3h8RqEnC/AsYVspmbrkGpgU0n1BsW3L3Nsznft4gsAdx/WrKCgc24/HiFjruw6dEVi0hun68fEUyQ2Pbd2m6KxzbdDYnmaODGAmJtCHx+YSulreNJBHdtTF7+uUJB7uhtwYoPI+08/FSuAvOykknJnSOWHdzjJ+9fFiG+c967hjvy6DO1RbvR7q1cDEz5kh8/OmCdaeSuCRzUBuNMnYfTm2dSB5Kfi5C/ABfrjO/cloV2AvQY8MbG3O9eUpCoQnJfCatbTRy4sFvprV9XP1dLSdkPz0009j+fLlTnkso9GIxsZGREZG2vV/HksUBIG1uWUbgdN1tNcd04+HJ02VVOm4i2xvt/TVKe4+y5vI0vdV8vfh/sAfRvLwtFar9rBsIxeaeg1Tu1ojg+D29aoAACAASURBVPDOcpY34oNbt4g+sx3471G2rXoruMDVqdmKevMgBpXcdnwubEFNy8WDtBq1PK8mLdCoY2lGqWV/+dQkOktaG741MbH1YKcpicDfp/WM4JMUCvzr0q7dx0t7+PpKxlBn6ulq+e0Zpvqfn0jFe0cI8WNQ1xr4OapuAW79ibvYy/pT72Fvm7Kr3iON3nnX+dl6oKzJ1Pnz2l6SqgVD2veIsYa5A91H6+MsZHcx063UAHjLckbBWXjttdfQ3NyMuXPn2vV/HhleHt4IvHuYJjc+ogDvaDV3kwMjPYMoGIzcQZ6qBWZ/z5apEbHAiBi2N5n3b4f7c2ccJ6Y3n94O3DuMF2J3uuh1Fx4bDfxpDHfnXgouWkP60gfBkuBpSS6wNI+vk7cXraEjAqj87mwmoVFLstGoZfB/cis/Z5MSSACmJAEP/UoDmmBRdV7WzAVXZ2QqOjfG9imdUd04Fa9BQ/J5ear7Xivmu1O9kbNAJsQzwNsTLL29WhMKtR44f7dru0fKm9g6KRl1/d8B/jx/UGudw+lazkwJ8ePtfjhHsjR/EEsnnRVJNmppKGVu0LangsZXSg3QoKXORKkFXtvHtefN6eyGkuF4hIaGdkvXw2effYbly5fj+++/R0yMfezPI4nCcxOBV6YwiHx7hq6JszOcx7bfOkATFclVMTWsa7uKb04zPR4dxPodwNrz4EgqqaPaBJdL+pu+1xtpP9yTEdMmaD7cwQjdtkH2vlzLt7MHD2wAdpdzkQ71AyqambkK9KF1rY/iYnX9pydIaHKiu3fXaTDy8ayV4KpVTLUbBH5+DALwv7OAah2DxdVpwLIR7jvC18eLYkFHwNklKFvw0XHgk5PMRDXrmDnTGEwzW16fwmxTaRPwY4FpzHlhAzuSfikCXtwDvDKZ2RVbPmtLf+FjhIqk9uU9wMAIZgxuHkR/l3mDeFtBoOgwIoDEOC+md7VY9kR88cUXuPPOO/HVV19h5kz7a8ZucNnYD/OL/foBznmMv+8D1hSyV7m4kTMCAF7IT4zmrr+zweC6ATxvhYKL/HuHmR2wZffp6iFCvQXvX97652/PAAPCgaHRlm8P0HGzOyAIDC46I1Pqaj1w84/MSl2aQiJpLnYM9GWaXar3AyzLTEtmzX9WuvuShJ6IR0bzkPDUNhK4aUnA+ASTpmNaMg8Jn5zg+z01iePO7Vl/ZmdQYKrUci3TGljOWVPIjpX7c0wZCoUCeHZil5+mDDfBZ599hjvuuAOfffYZrrrqqk7dh0cShe7ApESWAmKDKDTbf4FjmR0xUtd85+ft5ZgdsD0QBODTk/Q9GBYltyPZAkcQ0tO17ATIjOh6xmHYB9zl+YlDpooamZZef55ajkdHM/MFMPDcNMj0vy064Oxd3WfXLKN9/GWCbbe7pQtEdGaK6fuKZmozcqPlTIGnoampCfn5+b/9XFBQgIMHDyIyMhLJycl4/PHHUVpaig8//BAAScJtt92GN954A2PHjkVFRQUAIDAwEGFhNoh8RMhEwQpGmBGCARHAXR4uMqtWAZtK6IVe1sQd8qk67jDHxQN/m9r5GqQg+vbLroGtUdgAbC01WfkWNABvHuBuf3oSF+t5g+w3TVIogOJ7TT8bBeC5ndROTEzo+H2QiWHvRlwfk95Jhmdh7969mDZt2m8/L1u2DACwcOFCvP/++ygvL0dRUdFvf3/33Xeh1+uxZMkSLFmy5LffS7e3Fb2WKKwtBL7LZylhdBzrztYWWE9Ror93mPqG4TE0XzE/7woVn2+/PpxJkB5OEd59OQxWnfWGl8ZuL1zDryPjgJGxbIly5gRCT0BFM4lCkC+7N6SyUVoYu3Mu7e8YZ0UvBX0KZMjoTkieEq6eHnmihuu3p6zTXcHUqVPRnvVR2+C/ceNGhzxuryIK688Df9tHM5DyZoqF3jnEoPrQCNbpPCkVJ7malTdRcb+5hII7ABgaBbw9gyUUgKK1j640/e+NmV1TvH9+klP66jXsNhDA17O8mWTE0w2ytpawbt+VMsHYeB4SipTA74dbH07VU9CgoW7C3UyIBHFaZW/yXXE03jlInUO42Eb58CYKHhdmMUPmCA1VR6Zp68/TzjzCn34kX55mxm5RFrVjMhyPXkUUMiOAP46iqr66BXhjP0fJOspnvLthFGjp268PMC2S4rbkEGoeZqW1T3q62hZ3VRqFcxEBvKA/PUGf9Rkp7rsQf5/PFrAJ8TRjGhFr6pQ5Vk2DGGkK3aYSGltFBzKdf+sQCv+6smtJ7qEEoUgJrDvP910BCvMe/JU18GvSuXi7ase3v5LBK8Cb7/VDv/J9uDGTJTd7P6u9fTBXqL/Y1l3HDGJ1C+ePrD8P3DgQ+Ov4zrX6Hq3mBi4ygCTkd7+K193gi6eypoaRnNSqeQ41LUChEnj7oMljRoZj0auIQnKoabEWBNZ0PRlh/sBrU00/X5XWfbMW2grhHKH4L20EtpSyHJTWxRZUAFhXCDy2hUZMdWoSqWYdSzDDY4B7czgvwceLo4A3FJl2Sn0D+BxnpdOvflJi70htdgZKLfv9Ja2KALZg7r/A1zLEjzXxCBekqJ/camo/1BjYwdSo5Sbh0hTgjWnAoHZmNJysYbYuyIflufVFLFvOG8ROgt4228RcUFnRzGA+MaHrAt1d5cDKfF6ndWrgbAOw6iyv37mZbIeXNjfp4Sb7bb2R7cgj4/h3pYYZBhmOhTzroZvhKbMIHImaFgYLS/X4nwtJECpVPP5xkAE9IoALxHMTbTctaos6NRezyADe36EqlkwWZtFXvj2cqQMSgnumoZU51HoOnpqezCyLI0pv1SrOrZie3HntizNgFIDHt9DrYmYykGBDVu2DY/Q9kQSpF1poqgVQlPrSJAbP3nQ9OxuNWs6RyYvhZzKrr+1ZnO6c9dDVuNQd5+ooeARRKLjLNrtWT0BNC3DFtwxeudG8GCYkuN4h75fzXAzzYhgg2y58LTruFG0NnILA1GClivf9h81k/iNiabd98yCmgu9bT/Oi2D4sCS3fztfjnqHuXcZwF5yo4WfJVj3AqVq+5gZx0JJBYNq3SUcznusGAM9NsC2IOhtv7mdXzuwM9zgfgKThvJIkaFi07Z9PlY673z6+DHrrzwPby+iyaKvo93gNv4aKJmCPbOZO/uZBF6fneytkouAceETpIdLFqlpbcLiKi3BuDNNi5gtIZTMw5QsK/QJ8uGOuU7MmNyDCNc9PbwTWFDCtWtrIQTmrC/i3+GDWGhdlcxd2QQWcqweu/Z4p5LwYpu5vGmh5AVdqgPh3KTaKEwmA1sC2zKtS2dMtaQPeMTMJ0xlY0+6prVvf5zNgTE3q/MK+vZRGORoDVeeHqoD/HGXQujSFR3sEKyEYeGESA7A0F2PhGmBULAPyjGTb2yc/OMahZBMTHNO9MXgFrxEfBTMbLXrqRZZu4M7yhUnOLxe26Np//nMyO3e/n54AntpOQmYU+HrVqWnHPjqO46EXDGmfeLy5n+UcpTiiuaqF1/EfN9Nd89UpXE/aYmcZU/OyWZuMzsIjiIK74lw9Ow3KmjkmeMUx/j7ED3hoOGcWBPqyZXHtDQyAft6sj46Oc+545o6gAK1k44MZPEbFkuzcOwy4exjP9f71vE1sHyC+Dxeacw0894kJ1kcdh/gBjQ+YnltpI0VGV6a1v1j5evcskvCfI/TQb9JRG1HYQC0EQLvup8fRkdOez8DxGgYLf28eOiN/H+TD125ARPvBJtiPrZkS9EZg/62WU7vbS1tP3rz1J2bEpBRkfj3T8KF+dH783XBmgzqL44tMQ6D0AvCnrQyIl/fv3PAmW/DjOWZWpCmeL+zm6zp3IPUpjiqd3DWMB0CisOIoa/DSALEcG7IT/7zE9L3OwMxQejiFfaPiTOR7+XZ2P0l6m58KuFm5ZTD1Np0Z8iSjd8MjSg/uqlHYUAR8fZoBMyoQeGIrU4n357IO6kmoUrGObx7ItQZOtlQoWEp4/xh3/J3VDPQ2rCtk6UUa/vNrMbsDbh5kPRtjL07U8L4d0XL5t30MYtLx+Umq22ckUygbF8T3Xopnr+6l6v2adAZVezobtpWyZbc9MaC9o47NodYzvT8zpf35Dg9vZKarRfQEOFItZjW8aIr18mTPC6wHKunU2aChkPNNcVbN8FgS/LuHth4+1ZMglx6cAzmj0AVMT+YBUIBzy2DPtcW15PVvnk5WKIDbs7vvfHoCzId5AfTxf36SYx+jowW/SElhpi1TF2vVgBe4s/USyxIKkCwG+vA+zFvfcmJs/7z/ZQezKt7ifW8sBnZX8PqZnUGy0TZDZQ9JOK+kUNHPzNb6s5PMuFydBtyQycxEW42NedeQIAB/389hSZMSbX9uKh2zEFJW5tU9zO5cm+Ga9SAv1mQ1bzCyLDUqzr2EpTI8CzJRcBA8lSD0BuwsY+ljbDxnW9ir7DcKHHW9qQSYKI46Hhln/yTC7liod5VTa6IzMj3domd/eaAPBwxd3p/GNMEWPq9/bTNzYHoytSjWavb2fOaTQ0z1eYNAsuAtDqkyGJkB6ArC/YF5AwGt2aCsj45zVkuEPx9Hb2z/PhQKGq/Zi2u+I+kxCnydtQaWTAJ9SFIeHd3aEr474e3F0oazsKcCCPFtv8VUhudDJgoeig1FnE8/LIoTDYdE9lwP/8NVXOSH2hjkT9eyJ7u6hUdJI/vfAabpnxwDLBvZvgDvsc3ckdZrGAD8vTl1b3UB6/H3DO3aBFFnwceLAdzHi5kAvZHfj4qj0PGadMskwRK6ojdoi0VtslGTEtj21pnPrCAAL+7m+U2IZzAM8weuMXPla9IyQKaFd+28bcH6G03n1aIH7lrL135qEidC2tqxVdBA0hTqz+D7+j4SnPmD3cesa2sJsGSD2HLszyzR2vPUWy3KYknN1ZbOMhwPmSh4EL48BWwpYf3xXAPb2gCOnP39CM5tcIT63NVo0NApsVLFjot9lcC/jjBY58awxr8k17owUjLXSQuj8FJrBAqUNFdaOMQ2bcDDI4E/jGQA8vUmQThRw0XbkuBy/Xm+N5MS6fjn6OzB9lKWGToyLRoR23r3WtNCc6AYNxsjbc8AsioVMPZTBl9fb34tVFITFBPE0sUjo0wmPADJkK2EyFFQKJhF+OTKzhHI/zvADYBSy1JmgzhK/PGtzO68MBEY3a/j+3EmRsYB38wSXRE1JNP+3iQ0Z+uBXRUkpN1NoAUBmP9jz/c9cRVkMaMHYeUZXqDJIVww/7IDeDCPQrLutJU9Wg2kh1nfDTZpGSg7u1hsK6VyW/JW8FGwdnxdBi23pyVfrBBX6bhIW3pMlY5lAkd6Mnxzmn7zDWKrWnWLac5GqB/T+Pe3Q2ba4qtTdKWcngRMSWI3TZ2aO1SVngr5raUsBUxP5mthPkeiJ8NgZCugzsDAqTUAC1bTyvey/gxMPXF+xsMbGYAnJzJ74gpny45wrp5dUx3pX5wNo0AyX9IILF4vixkdDZkouBle2s366tBoptrTwiyTgK56zpc10R51cCQfZ2g0CYi14N6o5UVY3Mi07+YSmr3kRNMKeWoSL9YqFTMfy3dw958TzWmSN2R2PttR1sSAa2lXrNEziKwt5PMZ3Y9ZhNFxTPs6awE7Wk0leag/iUF+PfDuIc6EuH5Ax/X7x7fQlbJZRyLTqCPpAJixGBbFHXKQD3dJO8po0HNNOtO7l6far5FwJs4rOVytO2AUuIPszTMXehrUemYPu1pikbsenAM3WmpcA0EA7lnHIJgXw+A2IrZ1GtPZeG4n65PnlcDRGtoOA+wdf3w0d9Bt0ZlFclU+sKaQj1PUyFS6QWAAvi8HWJpreaDLl6eABzYAicE02DGIi/SoOOCBPCAjHEh5j9mOqEAOqapVM5iOj+eOqCslEUt+DRuLgdnfU5UfE8THrWohgUkU21XtecxC8fUfFWdb+jI7ioeEYdEkQ7ZicQ5wexYzL0G+wKZi4H/nKMiblnxxJmJfJdsP3UG5rjMA355p/bs3D/D1n5XGDNcEB9lBW4KXAqYeTRkei7/uYCtq30DOVvnrTl5/d2QD16a7Pkshw4Re+1Y8somp4+oWLmh1ai7Gdw+jMKc7EeLHXvXkUAbAokaWFLKiOv5fexDow532jZl8rEc3m3ao7V2UcwfykLCjjERK2uELArDnVgZnLwUD7rZSYM4A513sE+KBqvtMwahYScHijZm21aZrWtghUKumSLG8CXhpDwN0TjRwRSrr3rYq++0ta7Tdfc8ewMMaXKWatwSDQD8Ic9Sp2Ya5ppAagsgA0zwNo0DDH3e2GV5XSO8Td7GKdhTKm6jnCPdn6WJrKdeYhVnM9LlSjDs1iVqrmhZujpRaZtnO1jN79tRY55hsybAfHlF6qFvCHcvWUga6UeJhzRnQFhQrueOMDgK+OMnF78ZM2wJbRTPbrpxxkRmF7plv0BuHU5ljfyVLKJEBPML8WS6ZGE+/iOsyPL+LpLOfpbWFrPdemcpskC3XxDenmZFLCwfq1XQD9FKYfBOe2MpgdW0GxYeDnTx2ukXHazrAh+TvSBXwziFe45MTgUVrWOoJ8mV552gNsLeCra83DWR2yNb5Ge6MtYXAy3vEqYwabowatfzbkL7sALp5kHPfi5JG6nemJln/PJY1AQcucA5MZ8ZUS5BLD86BR2QUEt7lBV3dQsHX3ExTiruzH3Bz8dPNNoxIFgQuLCVNFBGuPc868rBo9kpfntq582gLR5CERi2wcDVfHylFPjSqdduSuxEEtZ6zMob0dV7K2hzDY4EvZ5l+Vmq4YLpLG1p7OPya5d/rAWjFrzoApQCeBTAGwDgAwwFY0sP9GcAOAEbx0ANoBAWkoQAeADAHQN7D1s/JfAaCSs9dq+TwaBD4mTxdR01Gg4ZW4c5sXfznYVHvYzD5JzTrSBZig0gEbhnMv6n0wPlGXh+S8ViDxn6iUNlMX4HL+jvmM1zWZH8JrS0u7c9DwoqjJEQTEnhIuiSjwPJXhEicjQLw8Cbg1sF05bTnHAQBGPI+s3F9RRL+xSmKT2/PYjaj7XUWH9y1jZ8M58IjMgr1Szi29mw9L25X9One+TN3WUnihbWrnDuTpbncIXVHcGsPm4pprHNeyaO6hQt0cgh1BHcOtU81/dFxzioYKpKhoVHM4nRGG3G4iufR9n3T6IEKFWdBlDYBD21knTu7L9uw/jDS8pCb3gZrxKAtPgLwXwC+ZkcRAAOAfgCuAnA7gLbrcbN4G2/QmXEXgNUApgGYAKC9bPywdsiDBIORn6dpyd0neGyLbaU0zZqZwhHTAyJak+XTtSyn2fP5Xl0A/HkbA2KIL0teX5yiv8DcgVyrxsfbTspXF3DSapg/sy/FjcB/j5LA3ubkUoFKB9z0A0txtWpmICpV/FtUIInd42Ns08gIArOuNWqWFfZUAI9u4YZlXD++Jh2VOzsLOaPgHHgEUXCHrgdz3/ltpTTucScP+DN1wMlaLsQpocDzu1imuTbDthY9gxG48X/URxQp2YZWr2G9ed4gaiZs7X0XBC4QJU1MO/7rCNv7UkJZ/78zm+Y4kz/nYhLfh7uJPRUkOHcNZRukp40WNxj5PMf06/ruyFZy0B5qAHwMYAaALHRd/2cE8DyAVACTALTV2NpCGroKjV50QHSDslCTlsG8UfQ9ONsALPmFJZgpidxIzEi2vbf/4AXqfxo0QIOWcyd+PMdrcGQs9VMLs7qnNHmsmmvIuHgG9mHRnZ8+Wd5EEtUd7rUyUXAOejxR+OU8sLOcF29ejHsLqlyNdYUUcyWFkCx8fZqtj/ZOdHzrAOuiicFAYghTu2sLuctamstAamlntKGIIkVPUTurdCQGSi0v+gYt8PFxisfSw4HJCcAfR7Wex1DTwp2apRq9veRgNYDdAMYCGA3A0cmX0wDuAqdFSocaLE0AzDY8CiDFwv86ijQUKalN8hOnZfp4Afeu4870hkxOJHWHThAAqFbxs+uogLilhKRodJx7ECNPgEwUnIMeSRRO1gDvHmaNr0DJnSrA2tuTY5iKd3WpoDfh0AWSje4Qhz29jYF4ZCwzIFl2aB7snVZYr2YdN9SP6eJQP+CTEyRGC4ZwkmigD0svTToe9WrgD5uBMD8grwUYAeByALaU6/8I4CCoQ9CC5QLRegGxoJbgatiWOVgFCpTGwTrBkPQK5t2IzwBIAssSGTY+FtB54nCgkroCjYFGSxoD9UHNOhLR2RnAn8e5n/OkDNdAJgrOgYfs3eyDvw93cdOTubC8soeWvzdmyszcFXBmieb5XSwFSXbPVS0URv5bwdbP3w2n06EtqFQB131Pv4Jx8WyTzeprvW4dHgD857LWv0vfBPQHoNgKKLcCG8D0fxCAPuJXXwA+LUAiSBRs1fT9CQzMfuKxGdQTXAIgF9QXWMNDMGkR9ACqQLGjAsBQAPeDpMEcXuLjmOOvNp5rWxx+DfgSQD1IMq5fZhspy4sF3rvU9HODBvjgGD1GrI21PlIFbCuj8ZU7EIjaFmZELLXtljXxOfXUsc/dga6MI5dhG3pkRsEcWoP7zj/QGqjMTg6l2n9wZPf707srjlWzbBHWwfu+pYSizdggBoXv8imkuj27Y53AiRpg3o8sBzRo2ErXrKNtcno4jV/uyO649GJPyUAF4BSAHLQf2B2NQyAp8AZ3B1+BRGE6GLgdbNkBiPfbIn7vBZIUtfhzGoA/gOULoGulij9vYyYnwNvUDrmngrX8qUk0sVqYZfs6sLmEmp/rB1wsAF5XyDbDq9Osaw/2VLBsF+rHA+C8hmvTKQreWkqSGerHcsrf99MnY8EQChc7Ije9PTAKAg2/YsRr/lgN8H0+dU3ZfYHwt+WMgqPR44mCu6FFB3x8gnXsggbglyLuhAH2rb8yhaSht8EoUMhY1sTj/WM0xEoPp7Zk/iCmmaUFsqaFpaSuLJgtOnZb9A0kIVGA1spXpnG6oaX71hn4fiWEOEZw6CrowMxGd0AAyxjvAygDhZCjweyKNdhDHFRiWUet57GllOZZM5PZ5TAtqf2On3vX0QdCcslUADhRSxFhbBDvu28A0MeP7ZTHayhmvjaD7YOX9b+4g2LteZN2pULFLIgCvLZHx7GdukVPQvLcLrYyT0pgFnRhVmt7bq2BGp+IAB5BPsD9v5jM0sL8KRiMDGDGVKMneZo7kATEk0mFIFAwat5OqdbTTfeCmEUsbTKtoZkRbMOViYJjIRMFB2JDEXfCWVFMWcdYMGXS6IE/bmZPcWoop6+F+XP6oyMIwrZSLhjp4V3PpJypY8vioAieW5box+CMCW2PbQa+PM0OiH7BXAxWF5Ac3J/LxR7g4lvexLax945wSNKIWIogr8noWBH++l5g/wUu1mP6sQujvZkJBiM7M+rU7AKp1wB3fsPglwsgD9wVO3Ais1vjBIC+ALq74cce4qDW2zcHQxBYomzW8fj4BInDtCRgchIwIoafq2YdtU/rzptGSE9K6Ng98HgNNwUT4i9uES5oIElOC7Me0OvVJDN1GpNxUmEDM2n+3iTRm0qYFVMbuJ7UtPA5ZUdx/POiLBJiT8C7h4BDVVw/owM5bjsjnBmDWekXr2ufneDUylFxwOAIYMQnMlFwNGSi0EUcreaI4UIlScL6Iv4+MgD401gKJ9trK2rRdV03Ua9ma9bZeuDD42yp8law7vnX8dz52LqreHk3cLCKC1hBA5m6ACA3mqOX5w60jYB0teRz6AL7tyVL3df2cqJksB93X8G+3DlmhHNuwsIhlh3dntkO/FpM7UKVisOX1Hou/FekAvfn8Ku116ewAZj2JRCgpJ9AKICTACrBnfE14teeKn35CNQ2SDgDYD2AIQAmA5gCYDA633ppAPANgPGgIFMqjyhA/cWvYHtnHkyCKme3YeoM1gWw9pIQZ6CiGXhyK4nuhHhe5xJBNgok+Df/SAI9XmxvzIzo/szCWXGypL2v175KrqsXVLxm/3mY7aeRASQKL0y03r0mixmdgx4pZuxOlItpryF9qbQvbmR24LYhtu28u0oSdAYg+wOgfyizCOGi+n5xDr0P7PWuTwihGU1qGE2SHt7E5zI1yfaFpkUHfHSC6c+svjzyYjhZ0Vby0FYAuSQX+P1wk7Dw0AUG/+kWRk6bY1Y6iUC0WM/8uZCL0O1Zto0mVv4b+L7N79YCGAXHtyPaihPi14Fwvs7BF4C0FirEn71hEmf2gX0k4TCAN2HSSniLv3sOJGIa8DkpYGrH/AxAJIDFAG6AqeTjLMLQXpeMq0kCQM1MWxGtBC8FScH+BY55rHP1fM62+IJsLAb+to/XWWwQPVl+PMf14+5htmdMR8SaZpvUqbl2jO7XftZFhnMhZxQcCJWOF1V3GKJYQ34dL9Tufr3O1NE2ukDJlGdKCNOHfQO5a78v134/BlehO7QHJwD8C8BIkHSkw3rQXwegAmyJVAMoBj0UIsT/nQzgSjDo2gINGPA7QzLOgDv/jjjWGbCNsq00oBFAIZhJkDowlgOIB0s4EwEMAInCFgA/ib8fB5Y8LMESYahW8RpwVyGzu2JHGce1RweSXOuNtH2+MpXurlemWidS1SpmI6UOpDUF1Gr08WXgf2i4fdnNzkDOKDgHbsCPew6cUbu3FxnduM3VGVjPHRBO18V/zGRmIzyAvverzlLsZUvW5JntHDkr2UW3nU3hbDibHLwFtkq2gJ0PavH4Bdwt3wRgESzPYrgAoAHc2QeDHQp+oEZiOkgU2ouHdWBbohSclWCr5XAwCI8Xz8EWWBpwuR/AjyAx8AGD/B5wfsQ4AFPB8kxfMGsw1Ox/dWAHhqUN6yTx6AiHXwM2ATBMpW9FoA/T8y/s5nCvmwYB05O61zvl0xMUUtrTnrm20JTNcxWyo4BXp1BYXKViB4hBoGmdAP5+UZblluGoID5nCXkxwGtT2c3VGet3Ge4DmSj0Eig1FE5mRgADI1sP1DIKnNw2ILxjhlzRTFXxG2gK5AAAIABJREFUuXqOiP3gGFOMAC1r/zGDAT62D9ON1vD1aZYBikXL6KJGisV8vaiDeDCP6UZno7s6F24EMBtM2QcBqAbwLoArwA4A8wtRAwZzcUozbmlzX2VgGt5Wk9EfxcMbppR/HbhbPwXOg7gFgK3x6WVQoyFtDOsA7BXvNwm0eJbIUDnYhlkByxkBaSZFV6EDcGIjEDKC3QQVzRSefngcOFXHToTFOZbJgsHYuUD20XGKYpNCgNyPWhtv7a8EblvNjogFQ9ih0HYjccMqIL+e5cJwf1qe76uk/fPCLDpPhvhRbNmk6x4L5BA/igIl5MUAT47lBkChYMmvoIHZhmBf4MENFBFfknJxJnWqjf4lMtwfcunBDaE38mLsH2p9F/TPQ4CflynwRwVenNKrUrEen1/P499HOPAFYE/3q1P4fX499QR7K0kghkUDz4ynWKotHtkEHK7mrictjApwnRF4eATbCjsqu/z3CN/LRg0X5/6iFmJjMQWh9w6z3Wa7spntpXkxfB1sWewLG4A7/00xXjY4A6EzSb/TAGrB3XFXKypKMPirwYxDCzj1MQQkEaPEr44atqgE8C3oc9DWflkHUzujEdztnwR39uPFc9gPEgGIt90H4H/ibSaDltKnwHJCrIPO2V7obiVZmJxoCrAGI0Vx5c1sqevjy6OkkS2K8wcBNw60nAUwCkD/f/H2oeLcgrP1/MyOiAGuTqdext+b3Qf3rKOvwySzmQ9t6/y1Lbwe68Vuhpf3kLCPj2f3Qm40r8WIAOCZHfyML8rqWJfTFsVKoKyZpKaraf95P7B1tErFDiSNOJ0zJZSeI4tzXGtyJZcenIMeTRQaNGS9npL2EkQvgTN1XGhO1TEYZ0cBj43mxXmmnjqE9UUcAgVwB/DiJC4g5nj7APBjATsDMsI52S4xmPMH8uv5GCmhQHoYWwAPV9kvgixtbP+2p2o53Oa8kpmDX4pokALQWe8fMzhV0BY062jpW9rEha+0EXj7EJXoQT7MRLw0ufVCtaUEePMAX7viEgb3evFv0QCuA7AAHZOFw+DOWykepaANsheYjr8CwK3o3O54K6hXCAAQKB57xPOcCFo8T0b7vgMVYMtiVz7qRQDmi/chCQp1oK4AIKl4EOxCMI83tSCB6OixjSCx6K4KQAWAM9O5kw/04Wfk4U0M8l7gV7WB68QJ8VoK8mG30rIRrWeOCAKDeaPONATq/vV8PpMTTUOgooNYkqtV229ZXtjA61EAu3ykKY61al7vdWp2M12TDrw+1frQtC0lwOL13Dz0DeDz/PA4RcV3ZDPDEd3mw6TS2V86rVIB83/k+iS1G6e6WHAoEwXnoEeWHvRG7ibWnec0t6FR7JcfGQcssLFm3t2oUgEDV5DYDIjgziTAmw5wD41g8Jv/IwN+bgxbLvuHAo+O5iJl6eJcksdDwjwz17ecaJq1SP+3q5y1RHs/+B0Rip8KOBUvOZQLisZAgePvhnPBs9Q6qjPw922fU5ESeHaXadpkWjgzIHkxFExOT+b/aMVdTpAv08JTTrMGHwnu/l8EcBkYhG29AA4BKAAJRSiYXg8Ag+a14K6/7VMpBYN3Rx+3ieIhwQjgZ1A/YGn5MAA4BtPMBy2ANQB2grv5saA2IAq0j84HswFjYerU0EGs64uPZwA1FArxfyeK/7MDLB9MhfXZDtb0DQ2g8FIqd3gB+Aso3JwOZknsyaYLVh7fGrQA9m4AgnM5f0OlIxHPr+d1cHUaM2elTcAP5+iLMK6f5fVBoaBfguSZIAjAhrmWjZx8vTs310QK/AqQzEto0QFvHeSGYFRcx0OwxvQDfryOz7VGDewu5+91RgqMQ/P5WhhBwhAdCLy4m0H+zmzal9sS7KODgHU32v88m3XOG+RV0tjxbWTYjx6XUahpYYqwXzB7eLeUMDDdO4yHve2CHcEoOKbLQRC4M5YWqY3FDObWHOWatJ5p91yvbl+kqDfSj+LKlcx0DI1i+nV2xsWLr1Fge6r0nhrF8dalTcBVH3EHPgTs858MzlZwFGrBIGeeTa4Ed99NoPfAGrBjYRgoHBwlfu3qx0ULYKn4+L7i1wpwYJTkQyAFZwGmMkEGODhqCljmeA4M3t7i13NgWUEK5NPB18/S49vy0dsP4G2YRJQGAOfB18cXJB+/h/X3pQQmB0k/kKz9KJ7/GDCL4Q9qOv4KvseTYb0UlPEgFfxTkri77i2tdsVKkmZzw6X/HqH3SpU4H2VziakseWUq8OZ0tls7Aqvy2Q0R1weIC6JZWoAP12NLbddVKm6YOrOhO1EDDHlfzig4Gj0uoxAZACgf4IevWgX8fB64YYDzRhdXqYDpXwER/kylD4oE5mTar1xWKFpfGB0JgTyRJACWSUJZE3Dj/yhsrNcwY9CsA7aXMWOQHcUd4BNbuEvKieYxLJokobwJGPkJU8hhOorm9GDgSwAFdu3pIgUwACWD3gS2XPuWdtEPiV+DxaMJ1Bs0iD/3R9dJAsCg+V6b3+0CA2c6Wl/Uq0ASMROcryAhENRBmGM3gDjwdTDHaTDIS+e+AcB2MGBPhfVMw3AA/zH7WQ/gn+CcixFov5QC0PL5KEhMdOBrWQWaMCnE52A+Q+In8L2bBRKitnEu/01giZPNmixh5Rlm/3JjWgfF4zXcWac4OUZY8gu5w6z1RKMHXt3L62l0nOOnvEYGUE91ogb4tQjYXUGdyBengIkJwKuTgVf2kkj060PC//o+6jHuHQYMssOxNsFWha8Mu+AxGQUBrNGlhLrHTuDSr5nGA0w2v1emMmVorRQgwzK0BmYRkkO5qAAclNMgOk6WNJFElDYx6+LvzTLKA3nszzYYKawqetsUsFaBO3hLBEEDptRrQcV+HdizXwgG2VxwpyutpZ+AxGAoSDxsfWsPg8Eww47Xwh2xBMyQSAuFEiRh0uTJm0GNhrM/8ttA7cY48D2SCF0dOP46DyyvDED7eomuGjV15Do66mP6CIQHsJuhSkUNRGwQ9QKZEQyITToGy6lJdBa9IdNzNwC2wmA0WTKPjOXgN52R2qXyJs7FOHiBc14ArvcPj6APS3sOtxJkjYJz4BFEYf0NTIvd/jODxPAY1uoeHO46E5+TNayTh/oD7xykkCnLGSP4uojODk9S63lhWrs4Sxqpa8iM4EXvDN3Ht2f4NTGYWoMfzzFNetfQ1mIse1scm8BUdYTZsQVsWZwFUyagXjxOgl0JADMOj4NBqbdiM/i6TIB1EyR3hyWyoDeyPbFtd8C2UupjQsXWx3MNwNsHqXe6ycK0R50okKzXAA1aDhorUlJLkxHObGOgDwWK/z1KbdCEBLZSXj/AcinTUSVOT8DOMr5uI2Is27K3B5koOAceQRTGTQRSq+nZbxQYKO4e5vyUnafAUlAXBKb3HvqVnQ9pYaw53p7Fvuf2YDCyA2L6VwzIGeHAkEjg3hyqvc/UsePiuV3cDSjAsc4vTbLtwv7mNH0TBkfSpz4ppHOLoCM9EC6AwkMNmC4PAwlEGKg1aAR9EOwRQHYWSjC7McSGx/oW7N6wJZXvDrBXkOjMx9Xdyu6CYF/u5IN9gX8dYRCfPwi4ZTA/n5+e4Nqj1DBzValiGyPAjMEz4zm4zBqprlNb1hqdqCHJsDSs6UgVsxCRAexcyBfnuNyRDVyV5l6Ok46YV+MoyETBOfAIjcKXq3wREgJsvlGHy/q710XiahgF1jqnf8V2qMRgBvEWvdgeFch6f0IwW74mtaPoe2QTuxSKGnk/ft5sQbw6jfMrrl7JmuqACJozJYcyu/PoaJM3uyUUNAB7K7gTK2jg9MY9FfxbWhjw57HAzYOAP21jejYrir7wySHWMyGONkqSRkv4A7i/zd9GwLJjoqNwGiQGkoeCCjRj8oJJBDlL/Nt/QTIj2TmfA0V/PmCWYymYljfHDgCZaH/3vxUkRVlw7PyIapBkSQJLLWjZPBYUS2bCOcRBCQonpXbTALDbZSDYbpoIQPkxMO9algCklscWPbMBu8opuLvDH5g/mIeEX4tIFCYlMkvQUUrcmiB5cDtvSFkzp6fWqpkVLG/m9fPDOXYpPDOe0xSttX4fqeJ15IwsxA9n6aUSG8SM7lenKa6+dxgww4LxkgzPh0dkFIprfREaavr0hT6qdeFZOR5GgWOWfbwYONPCgYER7XdofHGSFrUFDSyB1KrZ8rQ4B7gqFRgbz4VubwWDbXuBXMKBStZQ+4fxXFYcBWalWc4SCAK9Eaz1cpvjw2NMs6aJJk3NOrrajYvnjum8UiQTlUzX+npRg/D46IsX082vMaB1tBbpAXwIChkHgoGhu+00BFCnMAjtCyS/A02LAs2OT8Hnebl4pAOoAVsg/cXDD8DXYBngUrBVUwXaQutBAaAeJAEnwO6P8WCN/2eY2iKNIFE5DGZRxgO4CiwtWMK/QdIxGR2XHj4CRYbSXAc92DaqB7sZrgG9GdoT2CthnylWLTjIazNIqlpAUlUNEhWAIsw/gp8LqQzRqGUr4dAoEmxvL2YDlm9nK7EtbYOr8vmZnpBgf7lPEKz/zw9ngR3lbN8c2+/ia/KONfR4iAokkVh3ni3id2Qz25fYZi05IF5rMeKwtG/O0MDsjuyOh6VtLuFR2UxNwe5ykztrXgy9UcbGm57Tynzqt7pjoJacUXAOPJIoSOgKYWjQAP84yPR3VhQDmDV2/o+DtCwe0pfH4L60arUVO8uo9E0PZ8tfahi7MP5zhM6JJ2q5w65VM+25OIcCnvaIQrGSO6GMcPZtv3+UkxI9Yea8lKr83QYubKlhVIV/eJyljiW5F7vYqfXA+jcYpDaAgrUBMM0SaALFbhdAZXwVqOIXvZ0wEsATYMBVgUFkMEgkHEUgJJFfk9nxCeglkAVmJq6A5XkJ5tCA3gdDQEJkBAPdG6BwbzTYHpgIBkHzmHEe7ODwBbMMPmCXwDnQiXEGKEDcDJOxkjeAjSBZmSYew8X72A3gabP7V4jPs1H8fgSAh2G5jdISGsDOh7FgpqQtB60CMyBSS6QvmEXxBnCJeP7Rbf7nM7A8JBlW+YOjsP3F53KF+HxeA1/7UWAHiPmKIpGFj45TU9Co5YyDPr7c0RsErhELhjAzJwWInA8pVgwTbZiVWmb4pNsuGGK9zfBkDe3QIwPoz1DaCPzfAQZ2e8sLBy+we6iqhZ1B7x3mfSeH0OnxiTHAULMX7t1DwP/OUXR5QcVyivn49YdH2GaEJgjAG/t53Y6MZbZx5lcUIMeJGYdtZbxvqYsh09bBIp2ATBScA48mCoD9ZOGrU3QKLFQC3+fzwgaoOH5tCtPpABnzJydIEE7UUnEPUHC0bAS9zdvbMVQ2A1tKWQY4eIGMHSDbf3gkRya/sJvpu8F96ZugM/L3bV3TPAGCQBtoo0BL6YGitXREAInBl6cpfBwUaT0Va8lzf9EapmDVKtoBBwA4Agam+aBxkh/oYfA2WEKIAYPJ92BP/dVgAKkGiUM1aE1cB9okjwO7HBI6eI77wJ1tGiy7C+4BiUyw2bEHFEJeKp5HLkzEpAnW5zWcAnAXmBVQiM/bAAZaH5AoLAJJgzl2gMFd6gzwAyc5psJUZzwE4HMwczAezApcAF+zi+r44nkCpq6Hv4n3NUV8fEd9XM+Ar80XMBlJ6cD3uxym9+oetCZb0nsqZQ6qwC6IITBZYJt3SUg4CmZaLgEzGm0FjlJZ7951bOObnMiv5psEo8C0e72Gm4/lO6g9mJjA0sTEBOut0qsLmG2rFd0XL6hMO/PoQODxMcDSXOs27pXNzPy13RwYBc5RyYuxXez99DZ2bo0WTZ0GRna+hKAzkLBUNFP0fOtqboBGxvK4N8e2sdWdQU8nCps3b8Yrr7yCffv2oby8HCtXrsTs2bPb/Z9NmzZh2bJlOHbsGOLj4/HII49g8eLFdp2rxxMFwD6y8Pd9Jm/ytw7SeOV3w3lhmONAJXC0BkgNpZBoSykdErNt7GzYWMyMwaBIsQ6/ndbIdw21bJXqiarmrSWcKifNkjh4gYuNrxcwZwAzHEaBnvjvHmadFQAuTQHemsHdhzXUqzk4KiYICPzeNDPgKBgwh8EU1FRgAI1D60CnAoOYFMD7gm6FfcH2yVBwWNMMXBxEDGBAlTofGsT72QaSj2zxf2ei/TLIfjCb4AeT/qBZ/Pq6eN8jwIxHHkypfMmgyNfs/r8Wv86AyWHxHnB3LwVWNRj0IT6/pQB+QOvRzhBvB/G53I2L7acFsDMkASwxmHsltBUGWhMotidcrALwJUg4pOzHJjCTMhV8XceIf/saLN8MgW32zxKZahsjfw8Or5LKOz4Azopfx4Ojuv/wUGuy2l45wBKUms4Hj3WF1AiNi2eJoW0J4JfzwD8Pm6yZvRXAi3s4IfPO7I71Aa5YYxq1JFAJwV1rGdcZbJv+WdIIJL3Xc4nC6tWrsW3bNgwfPhxz5szpkCgUFBQgOzsbd999N+69915s27YN999/Pz777DPMmTPH5nPtEUQB6FwZwlZ3Q3sXi7bQ6Pn/7iDC/OEs50X0D+WREsodvkLBnZBk5ZomlkhyY6xPrXvvMC9MaZbEr8VcFH43HLh+FdO2UrnlpwLWKP8w0nI2RmtgqrSwgZqFQiXw7l7TnIGZAB6BiTAI4E63EtxtPin+LhMMKreifZOlYrDsYA16MMhKnQ/hYGbgVzCgzAZLFwDtlKPQ/gCkvWBNvA9IXvqAu+Ai0IjpSvGwd+DeGZiCrS+oM/gBfL2mgGRBDZOY0AvcRX8GEo6xIEn6FpwKqYApcyBpHCDe7nHxXJUgKVGIRwNo3jQMJD39xXOqBvCSeB7T0DrdXyOep7mWYhuA4zBpKS6B6TV2FCTjphawPPEdSNJGiucuVQm66rXgDFQ0U0RZLZYXjtdw0JuPF7MHNw2kt4i0znx3hrMsogOZpXzvMEn8XUPds5XbHHVqljQSg1mCPVULrD3PDMvlqa0JzwO/8DlLOoxlGz2LKBQXF7c6V39/f/j7d3zHCoWiQ6Lw6KOPYtWqVThx4sRvv1u8eDEOHTqEHTt22HyuPYYoAD1P5OgIGIwsnRyt5jCmbaUM6AD1Ftekszf8ZC0Xnn2VDBRDozgcZ84A24dqtbdjKWm8WFBljnP1wLwfTeTFdy+wEgyct4CpY+muD4GdCQFgcI4BU/VNAK4Hd/qOtGv+7TmAxksFMA2HUoLp/l/AjEYOTMLCjj6xq0EXxCE23NZWdLb90FKW4Fnw9Z8KBn8JL4G+E9KQJwHMYkjujSEwBdxi8e+BAG4CMyDWsuG2dGc4EpI9tCV0hSh0dWNhKw5XcbOTF2O5PfHN/czyVbXwOF3HAAwAkxKAv00ziZz1RmYozGe/tOgtm8f9cp5ZD3uHSNmDOjXwr8Ms+ZY2ce2ShuClhQHPT+QQOIWCJZxC0ZjtUBX9VjyJKLTF008/jeXLl3f4/7YQhcmTJyMvLw9vvPHGb79buXIl5s6dC5VKBV9f297EHkUUJPQUwtDegiMIwF1rKZJMCaVoaVY6hUjmUOuByV+QFGT15e7i9X1sSZyTCTy5lSZWQ/pS2PnUdrrEXZPhulKI1PpYDe7U20InfjX/iG8EU9X2ajnzwUBmXsMHmMloBomI+cugA4NdGBgQQ8Hd8Xpw13wtuPt2lsC7Dgy+bT0WDGB5oa1IrztQA7ZzTgA1AdJ7UIb/Z++8w+Oqrq/9zox6712yLNmy3HsvGDDF9N5rEnoJCSSUkEIIP1ooIZBQQ0IgkNAxmGZcwQWMjXuViyRbtiSr99HM/f5Y53JHsmRsYxHC5/M889iacsu595699tpr763sjckorHKoig4uR0Cw1bzCzKsaeAzpVqby7dt/7y9YqGpWRkJ8qNi5uFC4YbY0DRcUHnjRoJ4YtoYoMVw6hOEpHQ39syvhZ3N17KmRYh4Wl0lrFOyWfio7WpqH5eUyypcMkDjxu2AnXlwrRmVYisq3d6fj+ngbHPv6dwMUDtQu7b0di+wEb48yCgUFBVx22WXccccdX7+3cOFCJk6cyM6dO0lP3xfv6oweTVg5GOHFoRh194f8IMDCsnI47S0Jf3JioDBe+dUbqmBtlbpLljWagkcDJbTsPMKC4PMLnb+bvHDJQAcE3DvZ+cyy4I1T/rvlpwPrI3S3/nSFgafu5/b3IO93j3mtQeK5UCSQG4to7yrg10hsOBBpEs5E3u7znba5BdHyB9jeY7+GLYa0jWIzakkdhDI4+iDKPwgZ61Y6dpDsyjtvMa9D1POHRJRR0nlkALd8y203mW2HIaARhuZ7KWJwMO+3o3Pag7QOYcAlwI84cPBoj5UPOWDBbynl8KjsvWPllc3wjzXygqtbJUwsqZeA+ZZ5CgncN/nQN6Q7kOFywR8mdf/5T4bAjwfrHHY3Kftic43Cj0OTpbUqiNdnC3eqC+wXu7RmXDfswPoxHMy4aD/Tasbun937Xo2YmJgezXpwdVrQbW6g8/v7Gj0KFBobGxk6dCiXX375AQknDsX4XwULn5bC/V8oM6PRK0ZgT4u8kxtGwOxiuGaoGIAvd0spfXo3ZV+7GvuiCw8WIFiWFpWsqH1XaLMs+OsKqcYLTNGmwCZRgSBhHTKSecjbT+fQpDHeg4yJLWoMRbH7DKQXaEXgIQFpCXyIdj+W7inxvG7e/6bxPspQGImU+V1pHDailEe7dkKYOd5IxHjsQaGYEMSM1KIQjAtHoLkJiQf9OLUM7kZhj4mo2uQAOs5vC9JjTKBnAND+jmDgHByhZivSpIQAvRCwG4+0DJtQW2070yGli+1hvheG5jAS1ZQoRrUjbEJuGUpHjXoIsgvlab+1WRT9Bf3FutlNngoS4D8nO9uvb1Pq77AUpSYOTd4bXKzdA0EuefjxYfDyej3LF/X/72U9BbbSvnsiPHH03muC1yc9Uv/E/eu9EDiKajQ3w7q7MIfHIR9paWns2rWrw3vl5eUEBQWRmLj/6K5HgcL06dOZPn36t97OI/f7KNsJw0a4GDrcxeBhLqKivtmq1d0vsvO/BRiKaqRQPpCaC1nREiQNSVbq5MNfavGwO7qdV+h8d19VFr+r0dKu2Ocv5klslBElAHDnOJjWS9qEDVX6zqYaxQ83Vuu3Y9PVOW5SFsx5SGGAUvPahuolgAz2hcCP6do7XIdu5Fy6jznb4+FOf5cDF7O3UfEjz3TSfmxzf0Yd6mjY1OnVgoSEmcBVqAJj4Pp7YcfN0ITEgZ07NlrmeEexd8rlQkTXB+HUTfAhQBSKQihfIU89cHsfIzAyFGU+nEzHGgZtdMzK+DajwRxL57kORmAlcAxE2onOj1VflBb5TeMhBLAa0Hw2IwDyDAJMVyGgcbv5Tv16iD1OxtztUnGwWcV6JjOiJFYO7E4bHQJ/O37fx/D4clH7lc3y4i3kGNw6X7qha4fBUfuoY9DTbea7q8cS7OlYj2FfY3M1jPmXusFmmr44r20UeLpumFLSvw8C7x/yGD9+PDNmzOjw3kcffcSoUaP2W58A36FGYX/iKa2trbS2tn79d11dHdnZ2YSEQFsbBAfDKWe4ueIaN+MnHRicDf552yGtR76v+ubtfj0k930hSjI2VAKcX44WDfldU/sHK67693rVvrcLIhUmwKl95EnctwSeW62Frm+88qQX7VRRlVvHOEVmznxHGQ122eevKtQg59YxjlBq8UMySp+g2HMmopUfRXT/GXSszOdD+oVdyLtciGomBCHv/hzzm+9qDXoHeexDkBHryiFsR0DIznoIR17rfOTJBqZ7djda2b8W2HuQge2OzGwFPkLaATv88DJiIzLRvLmQaDELp520hYCNXaipAmVKTDGf20WaatA18qBrUgvci5P9kIRATQ1OUaUWs60jUMbGeA6dpuGbxguIURhpzsFmdtrMMaYgQBF7mdKo/7lWz7itR5i1XVVFLx2oVMVA1m5rrbzoxDAVVlqzB15Zr6JKgbH933ymHirjMlR5cViyAz7a/SoCZWcvpETAvUugTzz8ZNDBVYH8rkazV+HRHQ3wTpHaWefFSiNxfK7mYX/F0vszvss6CodKo3Agx9rQ0MDmzZsBGD58OA8//DBHHnkkCQkJ5OTkcPvtt7Njxw5eeOEFwEmPvOqqq7jiiitYtGgRV1999fc3PXJ/gMLvfvc77rprb5/gsafc7Kl0cfHlbpJTDuzCWJZFdRX8+lYfsz/2MzBItP3ROephcDAPWKNXKTkztsiI9o6Bs/spQ+DJFXD7p3ovI0qK3PMK5WEP6ME43o56mF0i5G6nFEWaBevx5Wp+kxWtzyZnSYTU+QHd3Shgs2aPXpuqVZAqLAiuGCygkxWthetvqwQexqZDbJjCIMnhTsEqEECAjl5DbWtHhmXxQ1LQ/wZR4bnIqzsDGdRALLYNLd73ISOVjgBFGPLKp6OWx98UzqxETEIf9m2M2hH13zfgOGpRTYYaxBLU4gAWzPHcjFL7DvTW2oH0EiNxiiW1BLyeRkLGMeY1FAGO5ciQec2rHHnNg5HBHY/o9nXIOy9AFSP9Aa8SFAZJROGIIxB4sZmWJsSI2ADAMr9bbj7PQoDnMgQwlpr5s0s315jfeNA1SjHn60XApQUBRdB8H4M0BoGObTECKN9EpPnR9dhXAa02BE6n4ogel5n37GJZ0cCfUQnwkxHIGXczPPKlAEBNq9iATdWwwbBkvWPh0akSFrtcevZeXOf0bGhphyaTczomTc/hpQP3bSzbfEqDrDBVFCuaxWjYGQwn58Gfjtq7sFObT9oKu3Ryab1Cl2cX/HeaOG2rFbjqzLD+fbXAT3a0nJDfLhTjMKWLjItvGj90oDB37lyOPPLIvd6/9NJL+fvf/85ll13Gtm3bmDt37tefzZs3j5/97GdfF1y69dZbv78Fl74No3CwF+TzRX7OOrmdqCiIi3exZpXFwMEubsmwOL+wI124P+PNTVIGN7TJEK+sUGGUX48XOna5JBYM9ejB31St9MSeEvpsoTksAAAgAElEQVRYlsq2rqiQEvkPS5xOkoOS5O2sr1IstLRBIOHOcULxXVF+O+qVaz0gUd5OdIhSrG4ZBelRAkjrq2BNpeotrDPpSmPS4MlpMDwgyG4XWjryP1JN94mTN/azkQo93PmGjJMXLfyb0IJ9AWIE7DWvCYn5dqBY/HPIWGQjw3Q5Eh4G072GoN18xy7tvAMnBJGHjK6dtldjvltt/n0C1WrohwSNp6CiT7EBr6Xm2E5FRrmr28pCwCJwLX8CpVoGhiI2IUPnNtuJN8cVjjIxis02piHjNRw1WarHqaXQgMI2LpzaCeD0OUhDLMAgnFDEUmSopyCDWGyOJXDMMcdohyJGI6AwBQG87p7QXcDjCKSMQ/qPN9D8TjO/3Y5qQIxD4Q0/Ajt1CAiGmn2/jlib45BuJN0cwzwcpiYCgbkk1CfjOMQ4zcTRJUSivhleBBZORDqVlWYu7RLcryMgk4PCUFcAUztlQ/xzrYzyxAzd4/sybm9uUqny8elKMRyZ6jAQ9W16bmJCtI1N1ao/clH/jqGAZi88tlyx/lGp3YcJPtkOZ7+rdSLVMBELdshYX9JfFRLtXipf7JI4el9hBZ9f97GtTSiuk0NxXO7BZ0hZluoelNRrjSqp15oGStG+frj0IPu7Vv/QgcJ/a3yvgELn8W0viNdr4fdDaKiLzxf5aWiAI6e5vlZ7Hqh2oapZaUIpETLOe5oVRzyUtF+TV1RmZrTYgeTw7r2N+5YoNDAsRVTlW5v1oP9mnEpQ94qR1sFvKb565ZCDb8xy8UyxI/1Nv4uSetU++JXp/Bjk1n4umqnCTVtqlUrV3C6l9GUDxUjkx8HHD8mrz8YpCDQTLdaBzsa9SGiWYV5piCKfhHQFI9nbOLUgkWAflBUQgRiIBTilnVOAGcjInIuMSTgqvvMMMmTx5t9VyEM/GTgdGc9FyFsfbPZjG9vOw4uMjm14HkCGbzhiDDzI6EcgULAdAaGhyBiuRCGCocgoW0i0N5596yZKUcXB0cjIutH8bkD1HWyAEDh2IcNq3x4rcSo8ggz3H8z5HmFeufs4hsCxHQFAu3iTx2yv2fxdgFiGvjjNrnyoQFU+OucoBKr+ZfY71ryOMO9/gVNIqRz1fmhF1zAPXYNmBFbsEMmvEAAYiSMoDVyuqxCAGs7e/SV6oiDTsyvht4vEukWHKFSxdo9A/Wn5ykqYdhDdGVvbxUR8sA1uXQAjU5S1sLxcXnx6lAz2nBLpB64eKtah81qxuhImvCymMjNK6+D7poX91UPVUMoGLXuadfwHujbOL1X9hKHJ5pWi/ezvOAwUemb8IIBC8XaL2DiIjT24i/Z9yI5o98vAflUBdywQugYBkb8cvXeJadhbe7Cz4dvXUG/0iuLMiOrIOjR6ISLI2d+GKrEEnUHM+1sVdsmL0+9fWa/iLnZq2IG0h26no3dehzz9XgHvNSFDtBsZu11IM2A3g7INwLNI0V6ADGUhMgD7umN8wHXIqNqlnKvNPkHG/HwEcAKnwabpVyP6OsH8W4489QGo78N08xmI5biDjl5xFapWOACni+R+NAHtkdGMGInuMiCKEKNjax5q0Dl3BaB8KMW0FIGeceiazTP7sNtoF6N7wI1CUacjg/1Nc1CCrncKur7NqM5GMQKboxBr1ZsD60oZOHqycqNlqeHbG5ukRRiXAWPTpF0YnHzg2Qb2aGiTkbef4RYD4ssalI306kY5HCNT4djcrkOzliXmY0cDzChSafpBiZAeqfDK5Cw5KMV1Cs1eP0yNsXpSeBk4DgOFnhk9ChS+SXjxTWNfF8SyLEqKYWepxUfv+3noPj+9esPgoS6OnObmsp+4CQr6/hVpsiy48zPpATKNlqAwHk592xH5bDECqD9MUl+E70qo1OwVU3LKW4qDpkaIHn14aveNbQ5kHAhI2N8xH3nhqcgIpCJWoA4Z3ExkMD4zn52DDM4+2kx8Tf2DCinFmu/HI0HibLMNOwFlBhJUBlZrtOPvbhRvvxllFuTxzd0j7bEKGdvcLj5rRmyBfWvUIcZgKPvWXdSbbdrDQhUwB6Dwyv7cao3A33FEjx7E2KxExncwAgxvI4//KJxS0fs7qpEmYzQy7J2X0QNpPW0hZmYguh8O9HHyIfAXOG82UJhfqtBaQpgEi28XKWRw+aCO4N7rE6g+EDag3d81KGhpFwi3t7WgVEzn1OyDDwGU1IlZOBAQUtGk8GawR2Bj4U45OKX1+v/CnfpeVpTWkLMKen4tOwwUemb0KFD4JuHFN419XRC/32LSyHaSUyAh0cUb//EzaoyLK651c9pZbsLCDvwC9iRY+HibqL2vKtS/fU+LHrJfjFazqLAg5yHdWKUsgZ54qO5ZrIc5I1JeQEG8KMm3NkudPDDR6WZ3x9hvF66wx/LdsPZFaRHi6HqhXoAW81xEB/dCRjVw188iA9Ebp75CV4/XBrMd2zDtQUZsCl17uF8hozQAGfHtSNQ20LzX37y6m4aVKJQSE/C6DzEWJ7A3bd15NCGNw1C678RYi1NHoBW1kn4VzVUmAkGvI6M4ChnmI8y27foJPiTO24hTRyIW9VcoM8c5CaVkdgYzXtTEabzZ12t0FEO+hRiCGLOdCAR0ghGrcyzScBzMrVSF7o2wgNcMNOe2VmEtCmX17mYbXQ0/Clvkd3p/A7r+0TjVN/+E7oeT0dwON0DhulnS3Owxz0xFkyNW7BWjLIijc+TJ3zBbwuZLB+zd8OlAxi1z1RwqJkS0fJBbYuI+cRIdXzZI72+sUgrngaRnH6rx5ibdi0OT5WR8V1VeDwOFnhk/iBLOxdstKissRow6NHk2PQEYnlwh+n54ipoeFdVIJBhYcKgnRn2b+jssK4dlu1VqdmeDwgg3jVC65nFv6HtZUdIfnJwH5xbuuziTZSmToqld5aNzYkwGiGEefH6d58znRCnbnQ/jUJ76ELS4bzevrcjQgLzIS1Fcfweiku24ezXy0qchVfzgg5iTvyJjUGleu5HRwGw71uwnFBmis81+XOb9KPatEbAfqO7u2HWIgYhDxr8R+Kf5tz8y9BfQkWI/x3zXrlBYi6h0FwIKhUicl4wYjGNxOlQG4TSFKjW/i0AhmPFIpOhGIsapCGS9YI7RFXAettAvB4GQiwOOcT1iXAK7fFbSdRvqUlSzYRz7lwK5G2kT7OyPKqQTsVtwx6FrUoJYkekoTNO5SN8S1IgqEier4WUEBE5CoC7JzM88c642Q2QLOcPRvffUVfLAO48/fqFW0emRKjns9QlEVLbAnGKT+eFS47Tfjj/4XgGWpYyk3Y1KOZy5FWJD9DzmxkjMXNOq8MX5hRIujkr9/qZRHqpxGCj0zPhBAIWeGF2BhYomuHCmiijZaZEn5B24LuBQNY0pqYMT3oSoYIUJeseqPkFgD/plu+GuRTAiBUakSmT08nqJmdbukWjK4xZdeMNwCQ67U1FXNCk0saoSVlXAvFKFSdwuKbN/PU653XctVKGopDZ5eBko7e90lD6XATyIQEABAg0JiDG4zPztRUAgGXm3Q5CB2YGMaOcUuW9qhtSCvOooxF64zbaTEGCZAZyHWIDNyKicjIxIG04WxHJkfAsReBiCBIL7Mnh7zHHbSvpZKBTShMIiOQgExeJoFzLMOd2LPP/RZn82QClFwOAoM3ctyFB2LsTUecxCBnIkjndfjYx8pdmWG4G3KhzQ40cprPmImZmCIxAcyL6rZv4Gp7aDffwfoWsxBQGbSew9h2+g+2dop+17EbCKRnPnM39vRvM0GjFBW80+bGDwNNJO9Dfn2YSAQj2at9Ho/uusgahDAGMYul+DOHCNwqZqPXfj0mF0mjIPOo+qZlhZKTYgJVwpjD+drUqQ+wor2CnHFU3qurq7USDl3S0KhcSFCiRcOeS7of//m+MwUOiZ8f8tUPjkIz9+H/Tp5yKnF3g8e2/fBgsPGcS+plLeQbtfAr1fm+qDPf3gzSlWbnFKhFIdy032xZo98lia2+GKIcp26MrLCRx+SzRlk1e0YFwYzNwipqPzb8sa4Jx3tQhlRCo1MsSjhW5wkjIf1uyBO8eqjK09alqg6AnHKFSjxT0I1ULYigzxh+bzaOAaZAh3II+uBCnZ7eKjk4CrcRgE2ytOxKHUFyFDlo+M1zhk4CrNdm8y+ypAnud487190eHHIkORgIBFLPI2kxGVfhpOa2ifOadC5JXbxm0GEuvZBisK5eyXIO/0BOQVr0OAyu7p0IKAhZ0tYMfrT0RAqavbzsIRbo7BaVr1CE654ySz/aU49RBA8z0T1VGYbObvj4ixmIhCHUFmv25zbDPNXExFjEQBHYst+ZE+Ix8Z/Hh0LR5C2QcTzDGVo6yKwFLV5SgkkorAxIl0XyNjN4540f77L4ilaTD/lqB7EXTdbzafDUfXpM3s1x4z0P2VjpOhEoWyanYNl8p/SDJ8ugPmlqgeQq9vsd6vqoDbFihDobxJa01zu57Z/DgVV7p6qJ7H/VlzFu+UU5Mft+/vV7fIYbBDn/9aJ21SRqTSJjOj9f+mdrj/c7ETh0Kz1N0orpPzk3AQTToOA4WeGf9fAYW2Nou5syy2FFnM+tDPxx/o1JNT4A8PeDj3QvdejTJibm1jbgmEuJUa+OiXykQ4IrurPRy6sWw3vL0Zvtgtw17ZrAf53AKFB6Zmy9A/s0o1HAIN9cEOy4L/bFDa1PJy6SlqWpUjfkZfpVUt2GGKOkVL33BeoTwdy5LWISUC6l+Ud3gPWljTkRdfggxGLlqAX0Lx8PMQkPgFMro55t8lyFheigyQhYx+sdl2EQ41HoUMerLZngfR1Y1mXynIS65DBm2iOaZ5iLEoREYrDHnS9ms1Mqgg43yhOZ/JSEn/Kg6AcCOPfScy0KNQ1kSfLuZ6KTJW4ebcm5EB/QR528PNsSw330lHhneeOYcMc7z5iJ3wBrzW07HDZhgyiJY5xnNQtsbjAfPnQqEFoz/72tDbBZNA4OokBJosM5/PI4M/CbED75pzaA94VQds4yTUqCnTHNcyHPDXiBNeaEVZIH4EDoage+olnNoIB/IIWkj0Go/YlAiU6mqHIFwoxIR5L9Ycxy40/+Fm7poBrxvaDLpyu1Q3pbld2zgqRzqBs/s5z0XgkvLJdukGBu1Hx8WttXDNLDGXIR7VKxmYCG433PEp/HiQMhM695E40HHWOyq/HmXSHiuaZayPzhFYaW1XM7ogt3QYlgUn5qko0rG5315/UNagDre9YhQ2Cfao8NulA+Gnww+sDs1hoNAz4/8roNDSYvHji9rJ7+PC5YJ/v+Tnmhs9XHaFe5+plf+N9MkZRcpbtlvC/nYh3DL6wD0Wy1LoYU+zyr8mh2t74zKc7/j8TprjTXO0j+Epoj9/vVAgaWejQg7VLdI33DBcQszYUNGqy3bD3YudCnUhOCK6bNSn4SQ6VjgMpnvxHnQsWXwdMppxyGC3mb/HoKJL4YiOP95st9b8zm76VIlS7Nw49RR8yGCPRcZzJPB7BCwSzGsXYgnOQAavxmy7Gnm7puYUYcjjrzHnfQoKIezL8WpBnniQmQe7EuVG814hDvVui/geQUDqWBTyqDfHaJdCDkJhnViz7fHmPH+PwM1Uum+WtMwc+zCzHQv4PwTsjmDvcM8uHIaku9GE6keMQNdosTnWELOPWYjxmGrOyS7NfQxOmqsNHmoRcANdmwvMb2IQWxOEQjXDzfYiEKiqYu/MkVUI5DTg1Lkow9E9TEb3XztO3YuhZl9fHKvnc0KGXqsq4b2tKqI0IUOMm50OOOpFpRImhevZq29Tueex6WIIzi2UN2+Pdr+0TIECxOW7JV7c3ah0xt1NAhEgLcS1w+Dmkd+u2qKteSithzPekfEfnqLXKfnK4JhXouJIw1Oc2i2j0hyxc1WzAI197m9sUurkNzkxflM4bnudXu9tgX+tl5ajMAFuHiXQsD+A5DBQ6Jnx/xVQCBzlu1V7ITS043a//NzPc0/56Z3vIr8v5OW7KBzgIizM9a0Bg13UJCLIqdkeFXzoQheLdkokGRcq5L+tTkZ8bokyHaJD4PYxElLZQkXLgpvnwQtrTGw0QpTqxQMkgnxpvRaEYSkQH6p869vGyCs6422Vje5t+kDMK4W6Ri3e5yIj0AtR6wejdrcrFfZCxi42YDu70OLe2VtvQfFqOxvAC/wU0df5yPieiIDCSuRN7kspX4XAiRsV8Qkyf8eZbT+OQhBTkfHbibz9/Rld6SrWoWZN09i7DXQ7ThhkX8PLoWlkdajHNmSgvQjo7UFevh2qsEMa9lOWD9yIQIoLAcO1CNjlo+vxDLpPGnFqMNilonMR6/IyznU/jq4reDYjgNUXAYO+CDTZ89iEgEo6B6ZPsOsOVDSLFbx3CXy2U0BhbBpEBsGSXWIrUwyQv/1ThTavGKwQQOf1YekueGqldAej08ROHKrmSu1+6ZciD+IGum+JtEntlgo5NXnVVXZ6b2V27S/7YFeJLIg/8Oq5h4FCz4wfJFBYstDPR+/76dffRb/+Lvr2cxERse/fP/KAj2VL/WzeCGtXa0qGjnDx81s9nHya62sNw8GChZoWeRL3fQ6fFOu9fvGq0X5c7kFt8utR1gBLyqSjeGaV3gv1iAa9qL/ETuurVL45OUI1159ZpTTN5eUSL26sFntx2xgdY3iQRJHZ0cqOsAsmBY7Seokoberz3YdUy8B2kPYlMPwr8uYzkadqK/BdaNEvNq8HkJcZhxbwE5GnmIsW7WpEte9EHuFiZGztuHsUMj4tOL0g+u/nvFrIqHUnVOzu/JoQu5HP3umYfuTdx+J4qoGAoBQZsq7CsxYCQb0Q+9HZk1+N5m54F8dcikI5E9g7G8CHwh1j2NuI+uhoMDuPd8327EqTgaPZHEd3NsyPUlMHBRzvSyikZPez6AoUzUWGP62L/d2I5jYDaTEagafQfA5DIONsnP4O3Y1ZaE7iEIsQDTwJTMhRjYTT+xycB3/xTGkabHavzae+DdnR0D9BzMMbm51aCRf1h/smf/vW09vrvp1+4kBGs1eOybnvau0ZkixN0xl9e77F9Efb4LjXDwOFQz16tM30dz2ee8rH8i8ttmyy+GyBjH16Blx9g4erb9h3bYXsHBgw0ENOLvzuDh9XXufhqGNce2kW6u4P6QAWKpvAZ+lB7g4tP/ol3LNE6D8jSsb1rgkqybqvAicvrhUNlxiugi6FCY4mwB4nv6meE2PT9cqMUj+F64c5aNxvOb+pala1xJ0N6gR53xRYtEPMQH0bXPmxPIGmdrigEK4Z2jVIAOkU7LHyIXnxgcM+zFoUemhDXnEDTiOgrUiDMAwZMjsunWNeCchITTV/z0E1BECL9zQEFjKRwTodUfTZwEWIcXgdJzug89hgttcPGZ9e6KHwIo/3VnPc/ZGmYCQCKRYS8mUjKt8u1VxrXg+bc03DSatrwckUaEdGfyQSRCYio/Y5SiUdhFgPu9CPPV+bUb8KtznfMThCvHIzP2HmXG0hns+czwJz3Klm2/1wdAjzzHYLkGZgivmehVo35yDj+yUK59gdJHciQBePtARjkTceZM7nHsQIHI1CM4G21Y1CEoGjc1vt1Sj8ER7wWof0LgMRe5WDimBNRwA0cFHbZc6lH/te7AKLbIFTN8Nu/FWFaVxVLOp/RYXqjASGCCqaxMKdGKAbaPJ2TDN+/niFASua9f3/bBQTl2ZqmoxIVQhjbAYMSereoy6p0/PXHRt5/SfaT3qUhIjvbtEzfe1QpUQfqqZQbb692YzwYAko553bMaxij7V75CQdyq6R9vjHmkO/zcPjB8YovPKiD7cbUlJd/PYOH1df7+bMc92EhOw/6rMsay9w0NWIubWNulZ58ee/p+ps6ZFCzb+f2PEBaTXegcslkVB8WNcPEKh/wqxihRE+3SHqLsQjr77zwgSKZ9o0odenOGNX6Y3NXtVSWFIm0FLVomO+YbiARFa0qMzhKfD8GrWA7g79zy5Wc6y0CC1E0WsUVw9HRjMSGZgqZNg2I6PrR4b7SuTtrUex//nIa7OLAM1B9Hs2ygLwoAJL2cjDewE4C7EDXZXzbWJv7YNdDno2AiflyDCWIzocc/yh5jgtZGxbkbHJR0a9DzKA1cCnqB6AGxnnPgiwxCDDazccsoWII5HBesac5wjU0MnOhghHIGIOMrajzfnl4mgUnjPbOhrNeYn5bjpiVF4K2Gdf85sgMyf34DRwKkVefB/znSfN/hORziLwFipCxjIGAbrT0bXym+OpQWLGHWY/dnEnL2J4LDMvJyAhYwQCAMuQFqEzwxHI1DQjMNIc8PoNAlxh6N5oN9cEdM2uMucJTnpkGbpvTkKhprk4xZRizGcRKIxUyN5M0U4k3jzveucZfGwZ3Pu5KjImGTD/1maFEydnwWl9BPTDguAng8XwdXYmttfpOfomiv1Xn2rtSI8UqFhUJoN79RCFCTvXY1lZIeZwZ6MYx3+uk64hxAMT0uGhqQIlIMdhTJrKrn/TeH61jiM7Wq+/r1Go8/rhToOpzuNPy2TAe8XotbpSadU3msyRA60l0WqUsV3N2eHQQ8+MHxRQsIfXaxEUxH4Z/IMdJx7lZf0XFkOT4atyed13T3DayxbXweubFHdMNa/c2K4BQqMXLn1fKY9VLfr7/EI4Mlstbe+ZpN8e7Lh1vkINQ5MVF11WrkXt/ybt3+Kwq1EL3pcmA2N9lY7R41KXyZZygQEvMqjnIxDQF6c502tIuT4GGYJdyGBvRRX/tpt92Z0Ca9FiPQ2xEXa4oAYZvq4EdH7k8fZCHrN99RuAO9Fi70Le70BkqKNQCuDpKBNiAzI6g5Fxm4k81jpznD4EWrIQUNgNnGm2F4uM3msolh5j9l1uvgsyZOchg9Vqfn8rMk62WC4LgaJm4DEzF6MROLHj7zbDsBAZymQkwLsCR7F/GzKKvc33bMev3ex3I/LwR6DY/RlmflagkMAUZEz/g67dIJRC+T4CI2PM72PMtpsR8BlnPm9GLMYE4FpzTLbo0oNAFmY+jzJzEmXOOQEJUwONtu3170Dshv3+AmT4RyEwloJEn/MRoLB7c9hgIssccx0Cnn4EnOrM50MRM9LPzGUcMHeY5v7yQapJ4nI5PRkqjf5g6S64frZEwAnh0iKV1sMucwCTM+Hxo0XFf9No8mo9sEHJinIoqtWzWNYoMeWKCj2Do9PggSkCJ10Nnx/+vUEZEwMS1bjtg21iOjJMj4btddISXDcMju/dPTs6u1gaguI6pUcvKtO5g0Kofzxi74yOZq+2v82IFR//SmAhPEh1JR48Qv0l9jUe/ELgIj9WYOP2T5100UCQdBgo9Mz4QQKF72JUVVn0uk9JaO9uEd3odgm1f16mh+nxryQMigsVG3D9sI6U37/WqVb8kjJTE8Ev1fQfj5CIaX8LM72+EX6/WJXZYkPFBvx0hEN/VjXrfZvq29XYsShT4Cg2D3RSuOMlraiA51bpYR6ZKvHikBfgmF7Qa7UW83lo8Z5Ox5j0MmSk0swrERmzRBwjNgcZwaOQ19mEFv7L6NgAyh4zkIfY23weidM++nkkgPQgo+RCnqItNMxDBjAZGT4LGZAKpIXYbrYPMlrTkNGoN5//AxnpJGRITkGGZg3wIjJwkWY/YxEoaUYe+0nI0JbhsAiRyLtuNb+pML+xm0PZjaRyzP4GIhBhn9tdCOBUmrkODnhVI+Mcbs71ZMRQuFGJ4pUoLDALgTMLR5dRZObAg0P5g5PuCE7mgt0N0m/2ZzMCRyDQmIiTQZGDgEMTAlRZCCyG4JSnrqBjZ8kfIdBZh0opH49CDoEObAP7zr64D5WZdiGwF4xT3jrbzMtUdM3ikDYmBLB6K0VwsynTDGLenjgaxhgqxA7tVTbJ+NqhBr8FDy8VDT82fd+F2dbvUXZSmmEMksIF8E/tA1cNkXFMiXDWg1c3yKMfkiz6v6i2o7Hdn7WjrlUZGWe+o2O1MxlO7dM9OxA42v1w9yLnOAYmfbMI0m/Bh9ugb5xqMexv+GGFSdfeXKOGdK9u1PtRwVrr7hireT8MFHpmHAYK33J0Fjee+Y4ellGpYhTGpUsg2FUltvuWSNswNl2NXVIjJZLq/IDvbpQXHx2i+u7RIVq4ZpeorsHS3XpA4kLFPvxkcNcPoGXB1bPkjcSFKkTx85Fwel99VlSjbV47S7oLj0uI/Q8ThdqbvPIEUiOdynF2oyfbOLyGYu9hOEbH9hzzUWGlKXRsELQbGW77kC1kKFzme6sRxV6CjOZ6HEPmRt5hX2R0SpC6fgqiwwciI/oRAgShZhsLAn7fGwkA05GxeRyJJuuRUbTj/QkIDPQy28hERiUeGcz/IM//dBQasC95AzK0XYn6WlDzpGl0rcZ/3+yvP1231PbQtdDQj4zqMCQS7Xz72YbVQoYaOtZTeAUZzqlobu19L0LhhCMQy9C5u+fdaA6acMosexHA2I6ugQexGFeac/vKnOexiBV5BoHObUjw6kfXrQXnXvIgwHEM8vrfQ6GuE9Fc2suuXfK7MyFXhIDLUHNMK9B1jDW/fSdIep+z+grAr6/S8zIkGZ5aIcN9Rl+B8rsXqyLpjwZ9cyGid4uUPpgWoefowS9kaE/IEzO5u0lAfuYWZUOAMgDy46RnunaYmMHz3lUYIiNKwOLplTreU/NVwyEnGm6aq3bRZ/TtPiui3S/h5HfV3fFQjOW7Yf4OabYKEzR/NgNyGCj0zDgMFA7B6C4Toqbl2/dyqG4R3XfJ+xIjeVxKMwpxS2MwIhUmZqq98y9HKx1yd5MYhJgQiaY+L9MitKhMD1RJPUzJgkenqvnUue+KAUkKN6Blh/KfbxujY0iO0P42VcPlH6qWPYjqPRcZ5w3mVYa8whBkoE5HpXMvQ4t6Z/yyyPw+w7zsePVqVDshFoGGVFQoqRAt/nORZxmLwgqtyOikIxGjC4GDf5tjiUbGdgry4p/A6SQZGH1pMMfYlcjch6HDckkAACAASURBVCjqrliO9QhYjGJvg74Hee3D2TsTohWl5RUg2jyv0+8fMec/xnxud730IbDzOqL6x3X6bRvwK8RCTDT/Bs79q8gAT0KAJ3AsR8Z9OHuDkD1oLg/UrniR0HAw0lG04KQz2oyOPUIRiIlA515p/j/C7PsIBGZsMNmIBI5274sENB/2XD9hzvNkBESC6aiDqEdgtQ6IP1XPx0/nKCthhNHp5MUpzFDWIPFhq0/vx4YISNiNoE7Mg1+PhQFJTurz+1vl9Z+Yp5LMW+s61kRo98u5OL9Q9RAKE8USztoup8DrV4rzV2aSBiaqT0SfOIUhVlfCrQv0jMaGitJ3u0TzW4iJuGKwnIKDqXb4vzQOA4WeGT8YoPDFYj+bN1n0H6iUyPDw7xZYfJsaC16fMg4igx3R48YqlU8ua4SJGXroQz3SQMwuluARVAzloSPk4Swvh5fWwWPL9VlujAz/vzfo7+gQVXMblwHnmJrvH22TPiIlQoBgYob20TtWCu6zZzhV6FLC1fWuqgXumQj9Z8qTs3s29EOL/FuIco5EnqQLxejDUPzYNrhFyCjZ3r1dutdCxngHMmYXIgq+Ehmc1cjQZOBQ1/U4BZ7SEGCIRrn2kxF4GGuOZV9pe+0IYPRFTEOgx1wD/NK8n2/2HWH2XYHEgjFm//Hme23m80/M/0OQIUtFRrIJ6TXsuyfcbNPOEPEFfGazBy6cyok1Ab+zKy/aD3SrmRMXuhYJOGGCepxiUTHo2tm6jmoULrJrTPRH7IEHhW+eQ4Z3snnZQGuZmbtpCGQEzu9Gs/2u2nt/an47FifN0i6y9JA5Vvs6pOE05XoKZVokmr8zzdx9as4hHwHJt802MxHgOtOc8/8h8HkcMCmgNsIaEz/vHbs3u9faDg98IZp+bLqYuV/OV8bC2HRlKtw4R90TG71iIYJcCg1kRIl1KIiTniA6GC6YqfcnZ0pr8MRXcNlAbStw36+sFys5MgWSOqHYkjoBl7w4x7MuroPbF4h9GJGqkELn332bYVnmfvue+W+HgULPjP95oPDeO35WLPOzbq3FO2/oVCIi4Jd3erjupgPLeAgc8+f4WbPKIiPLRVYWZGS5SE3Tw9veDsHBHbf7bYBCdQtMeFn0pselamePHgExJiXS7YKJL8sLmZKl955fA48eKQHRJe9LJ9E/wQimvoL0CMVU+yVI/zAiBf50pH773lZYuEP94tdVqfxsVDD8boKyLZbs0iI3Jk2LZliQfnt0L1VgHJQE6x/d+zy8SKtg90WIRkZwEwIPdilkDwICo5EX/CKK9fdD6vMwnFh1rPl3j3kvHqe5lM3yfmq21Wzes6/MUvO7SXTMm7dQeGE1ipnHmd95zPffQwbRg4xSHDLoDTg0uu31Zgec60c4xrU3TgaEBwkmxyID2gtHnxCCCkINQQYsGSfDIdh8FoKA1wDzf5sZeAlpDU5FtL0NftwIQFxh9nmm2Zad0uhCtRz8SBPipmOWw3wEbCYjLzsRB2DU4KRg2nNphyzA0RaE4rTpDkNhh9mIcZmA9Chp5rieQNf1GDpWjaxHQCkOXatScx2a0HX6GwIGSeZ3mQhcrMcRx/ZB90svM1/lQGyKsoO21johtqHJCsHlRMMrGwTcLx8oGt8WIG+vE72/r5TmwOG3pA8a/IIEf7EhEjVb6FmflKlnz0LPdnWr09thaLLCfhf179mwwHtbBHayo8Wg/GyuqiCOS98bJD2+XMeXGyNx9e0LFCq9bNCBFWiym1j1xDgMFHpm/M8Dheef8VFXC3Hx8H+/8/GjKz1cfqWblNSDu4ALF/j5YonFkoV+3ntHU5PfF277tYczz3Xj9cJZJ7azYb1FarpAxI03e5gw2X3AYOEvXym9aGut4o1batXY6aaRilFeOFMCnchgxRp/P0G6gPo2xS1tseLaPXp4I4LFTjzypcDGuHR5Ru8UiXlwuRSPvOwDiSUnZmhBenCp8qvjwrRwDErSgulyKctheEpHz6GlHeb/SYt7KFrMtyGl/zM4i7Sd1jgEGaN5yEAEIx0D5v1+yFBtxamwl4Q802NQOt9XyAM+hY7xdgs1QQpCxjkJGa1yBFCeMN8LR2Ag2GwnCGUThKLzsBs5JaIiQh4EMEYhg5aADPEl5nxOpGOufQkq9pOHjJLNaviQd/scMmJZyPD1McfegGonVJtjSzDHYTMfO3Fo+UgETEJxKPsS81koMpR2kyyvOf8W81mqOUe7tPZqRNUnm+McihP2+RSBrDHI0I9GhtiNxKIbzXU42sy3PR5Fsf7pZh/h5jjtNMn5Zm6qzPzafSRs8OVCLNFU8z0bYI1HgGYyzrV/zczHMexdH+EtM49votBWRJju7YgglVsO9eietiw4obfo/7klygiID5NOp6hG93xKuBq/DUpSUbL3t8Jp+WLdRqVJ7PtOkcJxF/fvmtpva1dootyEGn6zUALh0anaxsl5YvnW74FrPxGoH5kqY9o/UZkJoUFw56cwNcukWbrFWqRGdF29cX/HjbMFWorrlErZ6BVQGZ2m9MWzC5w0xLc2ibXcVqc1a1GZwibxYXDlYJV07yo1+9/r1cWyT5xCnU+u0Lpz8yhpLw7lOAwUemb8zwMFe9TXW4SE7F2S+UDH/Xf78HotCgpd3He3j5tvU7Oo7dvg4rPbKS2xSEyCLZvh5NNd3HlXEIUDnH3aYMGy4PNdMtS2ADE6pKOoaH6pPPmhydII+Pzw+mZ1i9zZKPSeHA6PHaX4ZuCoaYG/rdaiF+oRSn9suUSMYR4psh+cIt3BTXO0QEYa0HHVEDgyRx7SlR/p9zGhAiu/GK0wRHfDb8FbD0s/sAvHu01HBrQRGelLkOF7D4UMNiJvewgCBv9CBiAGKfLzkKH/NzJOdpVGe2xBMfcUnP4N0WZfq3GMaQhiCfqY776GPNthyNDYmRfPIeN1NE7b4UZzrM8jg5eCPP8Ms78qFC6x0+yCzDnGm/MoQ+AjDYGS3ubfcNS3IMqcqw0iIs25PGT+tg1vjPlNGBIHepBXHPibEDOHS5ARHWmuhX1MZSgUMhrpMiwcVsGPOiu6ERiqNecfyMSsMNehEofqtxAosjt6hiCgEYcDwIqRl5+HgEaB+d4T5pxOQADQFuhXAj9HoZ5eOCEXu2SynakQbM7dZmnKUUpuHxQ+OMn8vghlrdxyqVT4oPS86hYxBc+u0n1e26aQQHO7mLcdDfpudLCqCF4yUKG4R77UM9gnTg3a5paIgQgLEvBo8uqZq2vTc3RWgdo5T85UufbChL2zHew6BF0Z98omgZogN/z4QzF8ZaYhU32bQgxuFySFQWSIDPawZKU1XtC/Y3En6Fhn5ZvG5mq4apaOuaZFYGVKlrRQjy5TLZehKc45PPiFwNXAJOkmumtPX1qv1NHNNbCpRtleDV6dx/mFSu3cV0bIgYzDQKFnxg8GKBzq0dJi4fE4IYaWFouS7ZDXBxZ9ahEeASNHd81BRv2yjbV74GdzHC3B4CQZ/KkBLe82VwvN2wa8plWexvG5evieW6ViKmEBLpNlKZQwazvcsxgqWyRiCvFowWr3i5WYnCUgMqdEi01dmx7miRkSVhXX68EtrTc6gP5w/+SOVRi31ykDIiJI361pFSBJatXivRsZmgbEFoCMyji04K9HC7oHVR+8GhnfVvPZADqK5ZpQGuHrOP0VInDCELZ3mowM4DDkob+DPPLzkHAvBBmtLaiVsAencVAqMnZlSAOB2U8c8uRTEcW9AUd8OBAn6+EOs/8TkOcbbc6/EqXxZZnzSkWAwI8M3hNmTnIReMgPOOf55jsR5jh643Qq3Ipodsw59MJJJazC6fYYjYxoPDK0bcjYY7aZhoy2DRTmIGq/r7kmw832XQhE2cWiBph5DjO/tUskn4bT9dE27P+HwMQYdN2jcIohrY+WSDDGiPOSI0R5hweJtWr3y+hMzBCdnRyhviKrKwVsj85RmKC+Tdu593NVF7WZuHY/tJgqgdUmjTEvVp7/Rf21r6tnKSvg/EInXm9Zyjh6cZ0M7uwSsXJVrQoXbK2VcbYbFF0yQKD9lnnq3HhBIfxpucTCxfXytnOjVcCsrFEe+Cn5as18TK+9Y/prKuEPi2UoM6IEPJ5aKfHhFUP07MWFiY048Q1td2iy9BP/WKP37SZNR2brmV1cBlmmw+vTK5UVcf3wrmsVNLTp2Q4srtbQBr9b6NQ92Fbn1Eo4MluA4cS8g6usWNkE/1grTUdBvK7R/nS/3N9U8cNAoWfG/wRQOHY63PSLICZO6YGan4d4rFvj56Rp7aRnuujbaDGnROmFVw7pGNvcWqs0xZvmaHGND4N795HaCMofPupVsQTTcrTA7m5So6dT8uHNzXp/dgn85EMtmtEhStXMj9Pr158JNLhdAiFZUQIvWVH6bkMbfFUpDcNnO7UA+kyO9XG9IOwLGaDFyOAWII97DmILopHXXIiM5V8R+5BrzsEubnMXKo4UjrzgEGScslBNggxkcOzQQxIq9Vxo9hmKjFodEsLNN9u3vepEs41SZIR7IVDT3xxXCfBnxDLYHm4rMvgv4tR+sMFFJAIjm3EEhHYNAbsXwB6zL/v7uchwByNRZTDy/BORUY808/EYYgvs47BZCFBGRxwKdfRGxtcuWvR7c0zTEDizzz0IpRu+i4DTFETzB5nvbENakGzzuTVIFLqFyv5+uE3ZMmPTZCSiQ3S/eP0yfvVtMpyRwRLRxoRosf+iDBrbZVD7xQvwpkYq/v6L+fr7R4NEt7f55M0/u0rM2LG5EgraMfv6Nhne7aYKUnSIKgoOSJLxvHcJDE8VCDiv0KkLcsMnMs6TMvV8uF3aVlWLDKKtSZiYKbo8P04tm4el6NjsYkGWBbUt8MsFkGOEwljSEZQ3qUtqu1/zmRUtYN4rWtqh2FBphz4vE5PXO1YCxtP6wHG9BTKm9RKt3+RVltHOBs3tR9uctMiYEIGANp9KqZ/dz3EaLEsAprm9Y+r1st0SIZc2yAn4aLtj5KflqLjRdZ9om3Z1xadXKQxy4wgdY2cNxjubxcYMSlI21KAkaRQCQY9laT4OtuV1V0BgVYWOv3+CtFZ3LBDgOjK7e9BwGCj0zPifAApzFgcxYtT3EyS0tFgsXGARFw9x8S6ioiw8QZCY6Gb7NouMh70khutBuP9zLXz/McVC7IV4UqaqJAaqkkvq4OfGTQ9yycP65SgI8ohBKG/SIpEULkBQXAcfF4tp+KrCdOJzwaUD4IW1KrU8NUuL2Gc74bEjxVr4LXlt09/QYgVOOGREqlIix6Vr8X9lg0rUgijf25BnX4oMV6DD4kdahRsRHW0bNwuHQl+LUt7ORFqARFSQ6C4EGOyiQlGIAbCrJroQcBhqjqMZxaOPMNsahIz+DlT1sNxsOwJ5z16cypAE7KOXOYc9CAwNxqmxkISM/p0IHEw2556GGIUqs68o5KVHm+1ZCDy8idMBM7BVdj3SEphsu6/DGF5zrnY2hwt59RHmu3ZxIrv5VZrpNeIyBr3JK3oXVGI4OVxetYXo6NWGpugdI4HeoCQB1NpWedeJYQKGWdEwOl3hrOXlUvyPz1D2jG1Uvaby31+/UshrbJqMfKhHepYttbrnQDH1Np8ARHSIPMytdQp3jU7TPkemyghe/L5A7o8H6/zr26DeKwP83GoZyGC39pERJbCzxVQvHJWq+gGXDBBLdu8SVSM8p0D7azDgYU+Lwn71Rl5kiwgHJkpLMCxZ4N0OY/gtKG9U+/URKdpnTKhYjF2N+k2TV0BpRKoM8wOfa77t9MrSBm0nMUyVHn8+Us9wXCi8vF7nFxak6/TpTpVjBsX3fz5CIRCbMVhRoeO/wegJvH5H/NjaDie+KWA1JFnX6/YF0mWclKd7ZUUF3LZA8zggUe/fPnb/whXvbJZOIT/WcULsAlExoVrzZhcrzNlVdsQTywXcChM0/zO2wG2jlR4KsG6PsrjWVUmHtd6k6YxPV4O76b33BgyHgULPjP8JoHCwF6S52eKWG30kJEBBoYu+BS76DXARH+9sy++3ePLPfqJjIKeXi5xcF1nZe2c1dDVqay2+WGxxy43tbDUVgKYd5+KeBx3dgvtnbcwpUQhgRpG8mugQsQdXDxVQsAU9liXD/OE2LWqzirWonl2grIP0KH3nsx1w7OsCCm6XREe9Y+UFTsuRYX95vWjXYLcWDzt++OoGLZJLyxU33Fgt4aLHLQ9kQoYe0I01AiRhQVrQJmRC0jppCG5EgjUbulUDFyDv3lbA+5DX7EJAYhzSLdjK/WdQjN0OMdggYgcygG3IaE9Chro3YieqzXvxAfvZiGLZ9rDrJmQi778UxcxH4Ij22lFYoq95LwmBl3oURlmKk1ZoF4Pag6PEtz33VPMKRrn97YhZsUMIMSg88CoKN0xC4Mbuavkp8GYwDE+WN5cfp0XaQpT4H5dqgT+nQMbU9py31cENs/X9swoU2ooNcQSrF82UsO7kPInvfOZ8/JY8yro2eb3nF6o5ES55hE+tUInviZmKs6+uVNy7pV335uo9uqcGJSkEFuSSkfNb2iao6t7AJBme8CBts9Gr4zy1Dwww3bkWl8m4hAeLEVtRYUIuRjewvFzfC3bLSI5PFzB+u0gsxUX9FTIriIc6r8R2Dy7V/ZwUrkJkLe1K73UhQx0ZDFMyZaCPyhHgeLdI8+D1q9dKZYvmZFGZAyBGp0p8NzBJDZdONyAk0JuvahaD4LckGE6LVGgkxKNKgkdm65g/3CbNw/vbxGQMTtIz+dxq9W748WCnW+SLazX/I1LE3lQ0S8NkMwbvFsHmWn03NUJzkRKhUMMRWar5sL5K3y2ul+G1K0leN0whDpdLwOZA2YCVFVrXNlcr/fPj7bqHXOZYJmYKICaGqcbL+YUd9/HGJlWlXbdH1764Xr89swDuGCPGyB4ryhVu6h2rcEVerFJIO2eEHAYKPTN+kEChskIAYUuRxYa1Fi0tkJAIV13n4Ypr3SQmuWhvt3j5n37Wr7F4b4b/a0N//IkubvuNh+Ej981gLP7MzwVntjNqjIv2digtsbjngSCOmd7xd58v8vP4Ze0cmQPxoXqw7p+yt2CwqlnFjOaU6Ht5ccrJntZLoYGLZopWrGiWh5AYLsDx9DGicgOH31Lcc2eDaNYgN5xo0PdfVjiswKBEGYsttQImhQkCJVHBWiR/NkK0a6oxTnMfkrdfg7xdu1xvI/J0yxG1fjmKa4cC9yPa3T7dYJy8+Cpk1IcgMVoh8rTPQd5zHE5fh1pE+1ea7UQj0DEAGfknkNE/AieHvwxlV6w224lAzEILAhx2dkAYEkDa4ZIvUfhjuDmfgWZ7XuAX5vuFCERkme1UmPPEvJ8cI2+zrk3sj21EY0NkCCOCNc9tPihvNmmxqbCjUamtoR79f2utKOhT8sUUpUXI8GytlacVEaR4cXO72IAQj7zWrXV6b2SqvM9VlboPmrwKLzT79NsxadpusNsx9l6/jueoHDEJ0SH6/JUN8gCPzoGjsqXGjw8T+P3NQnn1Z/eVB7mhWkZ6XqkAT4NXzFlVi5oVtfkFeu1CRb1iBCyOzlZM/g+LJe49o68MTFmjju2zHbpXdzaqpsCKCh1DaoTOd0O1zveMvgpJjE5TpsJdiwQghiTr963tOpbyZh2nCzEA1wyVx/2nZQIyF/SXEV9Zoe/uboSVlTKIoR5pHu6eqPN5dpUMfUGC5tjuy/D2Zj13aZHaVmaUnu11VTre/gkCS0vKNBfJ4XDvZIGZZ1cpZJPTjR15aKkAnMelsM/sYmk4QA7DX452DG5RjUDK0GTNQ1d9Z/Y0a22pbBLQdLvEUqZF7t3DIXBsq1U2Q2GCMrCCPQKzl3+gNSIvVk7M7yd2LXp8ZqWuZX6cGIo+8ZqXA83mKP1VcI8b38NA4Xs2DvaCtLZazP7Ione+i3/+zUdunosLL3MTEeFsw7Isbvu5jz59XaxdY1G+y+IXv/IwbMT+hTh8PguXC9xuF5s2WOTmdc9C2JkQPv/e+oN1e9SB8v2t6hbZahiEx46SWvuj7VrYPtqmRXVyplrVljdpEQyMJ/r8yrR4f6sYhc018qySw+WBFMbLg3l7s7whr6UFERQL/es0ofSWdhV+afXpGNYsETjYjuPxpyHx4DRkLB9CnngYMqi2fiAUGexY5E3b9RIaUVXHSJxYf4TZvi2Ai0L0/iScxka2Z24r7aMQY/CVOS7Me/mI+t9sXgPMdgYgr3UXamAUhdiJaBSWqDLnaZy0r0tEpwPRaVrYm72KSSeGweBkiAtROGlJmSMqy4wSG+OzJAyrNiry0WkyviEe0ejPrpK33SdOlL3PFLKpbZWRbW4XJX1qvpoLuZGodPUeebsRQWKO0iMFHhq88iB3NMgQHJ8rz9LuJfJ5mQxdTrQ8/AnpAiRul1J1V1eKdr9oAByTo1obfkuCvaIaeaxnF+j8PinWsWypFTMFMkJpkRLphXh0brubIDIITusrz7VXjM7zhk8UbjspT3qZOcU6/kU7BV6qWnScCWECVMkRMmSrTZvii/oLzGRGKez2yJcSIh6VI8Nlz5HP0nG40HU7qbe81nmlus8L41Uj5F/rZMgrmjUnLaYS6oQMecTTesEVH4ttOKuvGItlu3Vd7GZuoBDfNcMEVt4pEiAcnabrUd6ka7OzQZkE80o0p+mRAlghLrj/C6U/ZkULbFQ2i226fpichhpTh6Azne83z3N2tEImgQWY9jWu/ljH1jtWWR01rQJEW+sEBoPcun+OzxWrcnTO/hnw8iZlT/TeT8HiwY66+x3E810Y38NA4Xs2DsUF2Z+20fX1FtHRPZtV0V2NhRtnyzOZ3lu0Z2qkFn6/BeP/JS9neq4Wo1CPDHzg6Ty8VAaquV2gIjdGi3x6pBbcX4+Dl9arr0S9WSzDgpT2dIxZHM8qkCcU5JYRmV8K/1wrj8eFDPtZyGjPQB53MlK6NyKNwA7z/zCU2mhrBXah4kihOIDAhbx6kEHOR+V17RLFv0e1BWLM76LNtuvMvtworDABiRNjUDZCNE5nwATz/ZUoOwM6Zje4UHvpaMREjAMGHKPv/XyuaZKTLOo8N1ZGYHudmBbQ+6EeI2hz6fsNXnn/49JlOLKipR3ZUiujc0q+VP12/P61Tfo8LRLO6acwwaxik3mwWot171h9ds1QKdhB9PH2OnmZZ/SBH70RxKwPLUpLLN6f4WfDOkhMguNPdHPtTW6Kt8GShX4++sCiaKOFBRw33c15F7uJiFShsoXz/ZSWwp5KGDHKxXEnuElLh2f+6mP3LqirhdZW8Hjg5NPdjJ0ALz5vERoKy5ZqCRk1Bs67yENWtkVpKbw/w8+ShZCapt+cfb6bD2f62bgeViz3s26NzqegEMaOd7H0c4vIKBc7SizKdkK/AXDJ5R76FUJQsIt33/bz4Xt+2n1wbKyuy9/XCAw0GyEkKEQxKVPMwNoqGc+4UBnuVp/muKLJKXIEov/PKoAz+4ru/3i7jPuULPjzchnPXU0CcH5LDMaF/aUvqG2TZ7+zQfqN51bpemfHiAXZVC1v+tpherbrDPOzuUYA8aY5zv0S7JZnfn6hmKIbZuv+8PnFtBxh0hVfWCtdwqUDu29ZDxJ0poSL7Xl+tcKX03t3BBDvbxUI3FYLbxXp/yDG5rfj4dHl0gj0jROrcF6h5mFy5rcvUX+wIxAcdHj/MFDokdG5VskPbuxPq+meBgmgG7srsPDYUfKCdjSInnO5ZAQigmDJhc73AlXBfksCpne3qCb8uiqFMm4fA/d9Ab9dqO+nRyoOOC5Dnm6IR6lNx+bq/20+ZVLc8aleLhyP8ZheYFWJAYhEXvsLiMbfgL6biwDBWMQGXITU+mvNC5y+D/VIHHgeovLrcVT7m1Fef7DZTixOI6DJKE/eg7QID5vPdqP+CR8i6t8y77UjwNHbbGs5Ai/ZQHqmvM+GdhW3aauR7qDBA2vckGfEVW5EJadEQHSok+P9dpF0G0OSZUCavWJ5yhoF0kCLdl6s6N/5hnJvM0rFrXWiZl/dCI2h0OpVSl9LJDROcLNtkosFT/vZsM6i1hxDVF8XEWe72XMafHWrn6VLLOr8+iyxv4uEs934fC5KS/y88qKfuhpoaVF10phYaGqEHaUWL/3DT0M91Nbqs+Zmi4gI2FoEf3/WT7sXvMYj9njg+JNcbN5o4fcLILS2QkoqnHGOixtv9rByuY/xE+FvT1uEhcHwUS4uuNjNiae6+eA9Py/9w8/OUmhoAE8lbN5oUVMNf3vKj9sNxWa+Bg6Biy71MHEKjBgNz/zFR0M99CmAY493U9DPxeuv+li0wKK2BqoNc7EoGgae6+aqdhfF2y3+9Q8/OZlw6hluTj7dzdN/8XH7eqgotygyv9nig1PPdHPu5bB2Fbz5mp8pBS7OudBNcDC89oqP/yxzUVqifX1ZDisqlbF0bC5cN0sg74hsXeO7Fgm8JUeIzQkLEtD/90m6zi+vV3v3vFixSH9YLHawTxzcs0SVH68dptol2+okYJ5bqnvq+TX6/YBEMVSzS2BtpTQhrT4xODfMht8vkmDyov5qQndan461Wu5dorBTmWGNalvFxtw0Uk5HRLCAg72u9IlX6GZAooCYC5iSLWCc/bT2u6pSTMfuJgkXbxrRMa26p0Z34ODw6PnxgwcKPT3uvaudzxdbRMdATKyLE052c8LJe4cvbry6nZf+JiozKRwemSoPxuWSt3rmOxKRgYz008fIa7fHB9vk5bT5ZPyDPerHftcEKdGjQ1RwKS5U+/jpCIUTJmVqAXttg6jSWdvlIZXU62EfkqzF4Nx+WnDSI3Ucz78kA/4AMpx2NcKzUYGcZGS83wVm4lQILEMMxAUoxl+Po2tYjrz4YCToc5tt5KKKjRnI6H+KQMFuVFPhXZQSaVdWjDN/DzT/rwT+bva/B/g0CBa5FXLxtUBLK0QlaxHvG68Fc26x5qlvvDyuyBDFIu4D2wAAIABJREFU9ssbJYwDCbR2NcpzL2sALCfeOjJVtHF1Pxcfv2URHAyDhsA5F7oZNtxF0f0+PDtg9SrRyEOGwq2/D8IdZNH2Ix+t9TKiAL1y4fxL3NTWWsyfY2FZ4PPpd6mpcOx0NxmZFh9/YOH1QlsrBAVBcgocdYybrGwXH830U7ZDBh2gLSOH+nOn8dGINGb85T127FyJv12ef+yAdFLvPJ6Fo9L5fPVSCJuPt6mFoPAg8o7pzcj7pjKrfzIbdm5k45a38LX6iM2NIffUAtzXjuDlXonMfmYuy15djt/fjC8omKrk3nzgyqcsfggfPPkAq76yaDcefmF/+PmtHgoHwtHHuvjHcxbh4QIX51+s52XNKj+PPeSjsgLq66GtDbZstrj8CheNDRCX4GLbVoteuXD62W6uu8nNjlJ48B4fG9db1NVBYyN89IFFappFfZ1FXLyLxQuhT18450IPV17n4u3XLd55w0/xdrEWZTstSkt9nHiKm2NPcPPuW35qqmHqUS5zTeDJJ/387iOL0mJdl6I2OOtcN7OfdlO0yeLZv/qJ2GIxYowbdxBcNt9PXh/YWKz759I+8vy31KqHy8oKGexHl4kZuHaoVPy/WaiwxY3DZag3VItxKqnXGlHerFBQQbzE0BMz4MwZev7/vUFhpZ/OkUjx6iEK67x2iq5Bux/OeFvhpwGJYj/e3yLQkxgu5mFTtbJZOocJ7HLLu69RKGFDNZz+ttaNeaViQ24fI5BxqMdhcPD9GIeBwkGO55/xMfMdP58vlqeUnQN3P+Bh+kkddRB3/sLH++/58fv1sJ7aV10bw4OlWH5wqWi9ggQ9kA8fIRGTzR60tgsgvLhOi4HbpRSpvglaQF5eLy94ShbMPEN6h6NznIe7yauyzB8XO9R17xj1dTizQLHzsf+Sl/ufjdp+nl+efAYSCo5HIYLlKD9/D1Lyx6DCQ8eb7/3afGc9YguCkEEPQ4AgH1H84YgVWIiAQRHK7c9HbIDddyEFpwlQqNmuFwkH6zzQmCBNRUktBJWIAciO1r/hHthQo8UZ5PFtqRVbU9Ek78ku5ds7Rh6Xx+WkluYVwEXXuHn5RT/r10NTk1iaiAgIneQm8lwX83/mY/c8GfaYWJg4xc30kzyU7/ZTtAm2boGwMFH51/3MzYqTfknRR0XUtL5NS0MLnlAPOVOyGfXX4/kwP4GdS8vwu/+B38j0+55aQMHjx/FWRjR+n5/mtgf0GZB3Ul/y/3wcM7JimAFUhP+T1lbBtd5H53LCk8eT0EfpBVFpkfjbLVxBLnpNzuH4J44lub9gqCfYTXNVC2EJYeQemcvE28Z//VnF2kram9sJiQkhc1wmWRMyiUyRsrVhRz3Ne5SgHx4fRv5xeQz70VDcHjdVUTm0t28nJDqE/On5DLljPHOGpvFuRSOvzXgGaMITF0l9bm8+Ds6lLGEgH775MVu3LgM/hESFMOCCAfT97WReS4pg8ebnqd1eS3Ckn6DCbFYm5HD/rjwWPbKIrbPW0dQkFmT6yS6uus5Dfl+YM8vFurUWbjeEhEpsvGAOPPekj7Y2F+vXQl4+XH6lmx9f7eHDmX6ef8rPliLYUQq7yiyqqvwcd4Kby65wc+cvffTOE2g7Zrqbzxb4mTqmnbY2Ab6QELEj51zu4sdXefjn8xYrlvsJCoJVcS5+WwMjRrtoaIBF7/i58jo3V17nZv4cP4/f4efRZWKJ2ny6J28cLoo/MwpOCVcIqjDBdIKtkNr/L0Y8eGF/OR+ztovdmG+0LTePlLag//PBhIS4mNzLx0P3+agKdhGxw+LuxWI6PG6FUrbUqsHVjSOUDdFVWCEuTELM5RfrOeuOsF1mMk76J8KMKUG0ey1OP9vN1iKo/a2XwaYk9sZqhUoDtVbdgYOmJhW961MAn86Tc/Z9TZv/oY3DQCFgPP+Mj5LtFumZLjKzXGTnwKAhri7DF7FxcNX1Hm65HeZ+YnHjLe6vO1bW1lo0msWjcJCLCy8LIiUVFn0GFy5qp9ELk19x0r/GpMGvxiomHh8mL6TVJ3HjDbOVmnVeoeKdPx8pb+TxryQ8CguC+yYrXul2wbG9lCGx3VRVa/BKjHVuP3nBBfGQHaXMhz8bgVqsYSFO7wMXbZQOYAUCAsvMKx2FIc5BgsIPkNF+G5VpjkJGHSQinIjTPXIJThpjMWIaRiMQsRanZHE76iRZab6/B2WrjMzRvHiLoXSHSckztSReXKe/2/yas/gwZY2EBUkoCsoUmJIlcdvqSlMmuF3GPXG6G18kLJ/pZ+0mfb/fADjvYg9XXOvi/ff8WAgkABx5jIuEu65ltt9ia+mztNa14nJD4oS+7J4yhFfz+lKyq5TiHa9gudqJH5xO0k9GsubkgXiQEWypaiE4KpheR/Ti+MeOIT5Prpgn1I3fa0FoKK6TTqHowT/yl7A4qBLo9LsfBY8X1ymnsfWPD/HXiPivW0C21b4L7h24jjqakkce45mEjK8/827YqJtx/HhK73mQv6X2cz774maIjqZ11EQ2XXgNRblTcFVp8fUW3QxBn9E2bAIbhp7ApnEX4vJHQRV4ty3SBpKSafjNfXx8+pnMqtX937bmWYiLwztpCpvO+zGbMyZzG3+keU8zzaZsYmRqFL2m5DDkksG43C6CIoLAD3G5sRSeVci4m8YQnRFNY2UTtdtraa1txRPmweV20efEPqQOTiE8Ifz/sXfe0VHUaxh+ZmbTe0glHQgQSiih9yJVAaUIqKAIWBAVEQFF9IoiIqjYQNErNlAQvSKIAtI7SpHeS0IChHTStszM/eO36YmEquA85+QkmZ22m83OO195P3LzwL2qO9W7V8NvWAO2NAnmo6/3sWbTukIhkyIHoD/QhT31/bk441syEzNxC5RxahTGOoJpfXADM6aqJCVCRrqotRj9jEKve2Smv6ryy1Kd1BRRv3E2QcPkAC1aSyTfI7P7DxEdaddJokEjmbfesJGTDYcOgKbB4KESjz5h4shhnV+XaRw+qHPiGJw4prF8qcbUNxW+2Whi8UKNdat1ataSqFtf4uc/NM7v1PlJl/DwhMefUgi4QyJst875bTrHzujsTNQ4ligMm6rVgI++VGjfSWbEAzZ+SoHDKRJHD+uk1rDy6GiFh0bKDHxA5swpnVkzNEznNCKiIbqW+Ls5rNep6ydqPjYlihqJ8oSAi4O9OPQvMrZb2yssWqBxZJ1O9uc2cnJg2hSV7nfKHMyAQ5tFiiw9DV49AeMnKQwYLGMyVbzT+NMwYoiN+NM6PlXg9MnybfQNrj+3hFB4bJiNZi1k2nWUylWQVqvO/f1s9vCvRL0GMo2biIt9ZdiwVuPH7zU2b9A5fFB0M/QbKPP8S0qhSLBYxN1JwRu574Ci+FzzVkX70nWdQ/t1enURdxuSBA8/KvPSawq1Y6DHPIkdq3UxxMkEU9uIi3xBN8Tei9BigbgjMMmi8jzTLOxs47NEyK9rhMhJ5ljhy+7CoCQpW1RY/++Y+ODIs4niuI+7iHTEmgQRYv/msDhOAz+Y1EK0R874HV7bDguPCp8EH8TcgZaIO/0wxF32CoRfQcEzd0REHuoiLr726daF3RGxiE6DlghB4IB4wx1HpCAKJjqqQFRwUfHg+kQRCrUg6i/S7ZXT+1OEoGkcKCIGXs4iJfC7vVrRpol6j/0eEocO6Ggm8HKHtndKPPMcPDJMx0WCXHuPZe0YGHi/zPcLNQ4dd8RiseAW6EqVuxtyaUQz3nF05YT5S3JzzuLg5kDte2pR58OuOHs6Y75kxmYWsXW9aQtODh7L6XbdIBM06SA227dQpz7JDzzCL3cM4NdLoidMTd0M7v/D1qYdp6dOZ653eOFFW7WcAucFyP0GIE+bjuxa1EMrSRK4uCD1648y9Q0kDw8so4sVQCV5QnAvdI+52F4vPoMR2FMFvFuhu3+E7f1aJR/b7QeuzdCt41AXdURpn13szQw0a47SqzfyoPuQXIr62nSTCQICUabPQO7Vu6SYdnNDqh+L3K8/UpOmSIrC9LQJqH+uRLPOhYgIUka/zIq7+7IyQ2xn3bwWQkLJ6tCV3zsNZKfcCCnNCXXndtRL74nXp31XzkydzuchEUxgOqfWnMHBxQG/GD+8I7xwcHfE5GzCnGEmLzUPZx9nYgbUpvWEVvhU8+b0+jOkHUvHnCHyM+ZMMzV7R/OzfwMS0j4l32ImqJEvSqtw/mcLZtupQH5YuoTkvck4ejpSd1Ad2jzfCq9Ibya2/wrtVAJn7Tk3ixU6d9F5/EmFuXM0VE2nVoyE2Szx8xKV9HQ4ckgnJUVn5CiZ4Y/KQgTH2HB1gwvnwcUF6tSTaN9JQtUkkqpKnFygceE8rPzFxh3dJN6cZSI8AgYNkYiuJREUJFG7rsSL4228+pLKmMdVLl0Snztu7iIyknIRPvtYZc8unclTFBo0kvnP6xIff64UTtdVVR1FKfl5eankO4Vdf2h8NU+jWnWJP3frnD+oM+Y5hc7dyt5MDQGGDFPQdZ1Rw1WSEnUioiSq+IkLe1wzmUnjbGzZpBMSBnt36/j66nS7s+LP7Np1JDbtdCA/X+ee7jYsZp3EBHj1JRtPjVVo3sqILtwobgmhcDZep/udwjSpNO2aWTlxVFzcL12CfXt1RnpItG5XeYWp69CwsUTDxhK/LtOY9IpC3fol33QJZ6BjC5G89qki5jy89qZC1ZCi42xarzFqhBAsIWHg4iLx7hyFZi3FvlSbzrCRCt9Xt3EqU1wUq9nNlr44IDoN9qWI9jGzTdwFb0oSd/0RnjCrk2iTc7X332eaodv3ItKQZRFGMhOaCTOY2XtES2TMPFEw1z4UukeJ9smWVcXd90tbiqqq3REh/64IIZCCSA3kAIcQPgP3IooEzwDvItIRyxAdCh0QhkYbEKLCCeF8eBQhPAqGFYUCsdEidLopES5kCGfDvfZx1oGuImxpkiHCXaQoVF3UV1jtRYFRXmCuJ2FT4fcfxa2+mxv0HSLh6Snx0fsaqiZy+S1agdPDg1gWG0F8xhwyUzKRFInIzpG0/OwuVoZ4cGTHTsxZK3DwcKDe/fXY0W82v8tRkAaW89+Blxdq/3s59NrrHLE5Ft7hq06fQMs4TC/9B7lBQwBx8b7kC54NwWsi6pbeqNuK2dxlRoHvXeiuH2Kb7lvyjZjpCGEj0Swz0MaXCr+a08B/CLrldWzPu5V8TLNBYEeo+SQ4+1MGzxiIGQ8e1cp5rBaE9QG/FkXnX8DRWFBaoW4fiLqzlPHHyQioPgx1xSDUlcX+11Qb5NVEzx2Cuvwe1JXFtjttBvcaEDoV9bcBqKslHD+wezS7uyO1aIHUvTty/VgkJ5E709PSQdeRuvdEmfEWUtWqALyROh7rpS8gwJP4gFYk9niWreHVRbRj9UAICsLSsTN7u/Rjv94IKc0X2+4v0DLm4+TpSOywWNo83wr3QHfObIwn+0IOuqqTGZ+F7KBQ++6a5KTkkn0uG2dfZ0JbhuBR1YOLB1LIz8wn5XAq+RkykR3DaDI6jug7a7BgayKLR/xAnt0zOd09jPw7m5Bc35+1z68jMTGJS4mX+HqeRvxpnYkvKTw5VubIYZ0TxyG2oUTdehJrVon39ML5Gk2aSTw4XKZFG5mFX2uMH2Pj5AkR4Rg2UqZzV5mQUIkvF4r32OqVGm9NU6lVR6JaNXB1k+nVFwIChLw3m3U2b9Bo1bbkxb1AJOi6zvKlOl17SGXavYOrSjRtLnHimM6eXRrHjsDG9TYaxkm89Z5Ck+YyNpteIiogSRJzPiv/MjN15tVdfpycYNlqUxlhA2Cz/WOb+G5pbgmhsGx1xW0oS1aY8PaGuR9qRERKdOkhlfsG+ivad7KHWq06Qx8uWcmTk6PTurGVvFxw9xD5yBGPKTzyhFxmUmWd+hJLVzkQESmxfo34Zyz+z1bFT6L7XRK2uxwJf8bCuWxxUcy2ws7zwqmxT3VRlPj1Qbu/eXPR6z2ltUgRWFTRzrTwiBhFnW4WOcOve4rWvMXHhE/CoTQR+hxQC+Z0hsPpYrlJFt0Sno6i4PHBOvDrZzADIQDmIWoT7kHUHkxFFCrGIzoOQhFRgoJLlTviYr4OERkomNlQBVGkGFVHtG/a0kVq4bwT1FChigkS7DevBZ8rf1wQEQMHWeRO24bCMT8hAOPt4Qovb6g+SGH3To1ff9aRZPDzg8h74wh4qzOHvjtMXt5PAPjX88N3eCvC24YjyzKZWSZx29bjThLeeZc5LiKMbtO/Bc+t2B4ewa5hI5GqVi26YFpbQEQMWvYEtLHFxGNuIlTpD+4vYPukOiVI2QFpg2FzNEJmFecrYBDEn0X0khRnBdARjh6mLPuBODh6opzHUsVf5MA5RDlpcWzAcdidjWgWLY6OSCI1LucxC8JQeyzsOF7qsXTAD07Wgq37ihYPjoX4RVD9YQjrD1KpOzxrthAsof0K49aFr/OpKAjujvp9N9QlxcTF0RTw74Rueh/b68WEjjUX0hXknk1RnhuPFBFZ4mlJDRtDo8bItWoh+QhBpu/8HcLDsfbtx+5hw/nTIUz8/TfMRld1CAnB/NQY/njoYXZaTKgrv0O9OBskyE7KJrxtGFGdIjm0+BDmTDMewR44+zhzcf9FQluEkHMhB1u+jYBYf2rdU4uItmEE1A9g75f7OLLkKA4uDjR6pCFtXmiFd4Q3459awa65e1DNGk6eTji1jcWzVxMOLDxI4vYkki8cY/lSnc0bVCa9AtWjdfq4Srw6WUe1wcZ1OunpKk8/K7N2tc699wnh0LmreN1XLtfIN8NH72ns2WUl+YJO/QYyP/2gUStG4unnFO7qIz4vf/5J47WXVCKj4I8dou31sScVhg6Xsdlg1+86nbtK3DdUQdN0wiMlAgJEVCMiCj6cpXHiuIqTs8R/P7Ix7nmFdh3LT9teK5IkoVTgy/DpHK38BwyuidveR+F6kHhWp2oI/PQ/nbbtJXyrXPu5JAy30GKBEAmuJjHrYVeysK69ZBYVy7M6iF7s+CxRZJSeL+68QXiiPxADfX6C0xli2p1FhZ7V4OlGwshn+SkRmbBowrhmRKyITnx/VFyQc20gaWLOggsiGuCLuLQU/LsFINohAxCdDjriMpGG8ELwRJgY+QN54aK9y6aJFq2WwcK8Z91ZUbAJ4rgBrmKdSxZRYd0qGBI8RBFZpr2HO7YRTPqPwmcfq6xYbt/WSaHt5Da0Gt+C5aN+Zc+nf+Ls40yjkQ3p8Eo73sqdhLpnD+odHaBOHUzPTUS6q1dR+iimD/g2gwavg1zsk+bYR6CaocZIMNkvUt/sRUifaYgJD6U5j4ip1CrnMTNgQtcbVeat8I9EkvYinDLKmzmehhA5seU89i3Cl7N2OY9NQkjP0mxFxLB6wuBSr9mJeeAdC1XiSi6/dBr2vQj1XgTPUsfaMgS860PkfeAaWrR828PIHWSk/gOQYxsg2VM71iceRT90GOXhEUiDBiObxP2TdfIk9DkfQvceOEz+D1It8be2vvQi+ofvg7MzyjPPIo9+CsnZmboT27P7kz24BboR1CiQdi+3JbRFCN8P+h/Hlh0nqHEQgfX9CWwYSMyA2izo9i2WbCvBTYKIaBuOfz1/nH2c+WHg/8i5kEu1blE0fSKOoEZBXDyUwoqnVnJmXTxhrUOZ+dw5uvaUOXYERgy1ceq4SI0+NFLmsSdFpPPrz1XWr9HZvlXjjH2wSXRNOGYvWakeLdH9TolxLyg4OUFCPBzYpzNssA1XV/DwFEW8jeNgzW8QU1fiybEy/QbKhSkLEFGI2e9qHD6ks2eXzt7d4pLStLnEuOcVut1ZvmAo8LhJS9NJTxXnc60YPgo3hlsionC9qYwJU3EKah369K3cNl9+pvLjYtHpoGnQ916ZB4fLSJLEzt81pryocuiAKCy0avBic9hyTvTpZ5qFj8HcLvYBTokiJfHTCVF38HgsvNFOFDKOXiN8EFLyRGrhpRbirvzdXcIYyKxCgBs8XFfUM+w4J2xpLZq4a2+jCcviFETNQEFHQS37l4wwVyrwKHBA3Hu6I6ZFnkOYGiW4Q81wcLIKAQAiTbAnWdROxGeJqEFtH8jXID5PFF7mqxBdDx76j8JH76sc3C+2dfJyosq9LdnVuQlH318MnMa9qht3vNmJ5T2+ZsslsLpZwCcey5ix/DHyUXbmOom70zRP8O8IoTOxrWgkbtIBbDmQHAzJ98PhA6X+YoFACOw+bn9/xMKCgovgogr+yuVdJG8fdP1qn1/J7YTgKOB+yuciohRWsQu0AvIRQ8TjKBvxWAZ0gDMWGFxssS1fCIewviVFAoBTFbQT7WBea1QH96LlO9zB8wHUNX1hq09htENKPY48aDDyk2MKRQKAvmM7xDZAGf0Uct9+hZ8lu9fngrMzeT0HEf/cBOYHBqIl5WNbOQdyrCScc+bck5N4fsBOUg6nkHo4FXOWhczTmXhHelPv/rokbD5LTnIO2edzOLXqFNYcK52mdSDlYArOPs4ojgqWbAuvLqrNd7Za5KXnEfqozslxvxIeKZEQDwu+1GjbHho0knjgIYUPZ4mcXXRNCQ8v8PeH/Xt1ut0pCrDz8nR2/6HTrKWMtw/sP+lAYJAo+I0/Ayt+1vhtpUpmhs7C+aJm4vGniiKqkiTxxBghvJf8oLF3t0ZUdYmoahKR1aQKR0QP6GXj/HmIbSCxaIHGgyNkxk9SCAwqWvn0KZ3wCOGAa/D3cVtGFCwW8ZSKq97iaJrOQ4NtnD6FeDNHibanax1jffyozvwvVH7+Sbjj+fnD2x+a6NO3aL8nT+icPKbTvjMcfdjGmnjRmjikjriYWjVhlZqaL+xiHSRhB3smS/i4Oyii4NHXWbgudgqHNt+IHmsQtQwvtxT+Cd0WC5MfECYvzzURJiojVwrjGBCFiv0QDoXvIi78Bcs7I+oN5tmXSQgPgfahwsEtzW6vaJJFxCI5VwiSAFdh2XshDyRnyL4EgcHw6CiZDes1tm4U/f4uvs60+09bGo9sxHf9vuf48hM4eTpim/4+8oCBSJKEZcQw2LcP04dzsH3euejF3vM8VGkBob3Fp1DhBWY/4i64ZD7+6i98BjeTksIiF1Epc0c5a76IcOgoHYP+AeHSUfrvvQVaeEBoL3Aodvem67D/FQi7F7xiiq5oug6/Pw7h94JfSzDZCznVfNh0L0QMgqp34jhXrK8lJmJ7YDDKwMFIgwcje4niI3XDetQnn0C+fwjyI48ie4vltnffQXt3FvL9DyA/PQbZT9SWWEcOR1++jEYP1KLVhJaFLa6fNP4v5/dcoPY9tWg5rjmhLUPRNZ33Ij8kKyGLiA7hNH+mGdF31kBWZPbf+wb/+06j4x0ST45V6NRF3NWv+kXjvbdVjh8VnhzNW0m4usKyH3UaNpYY85xC154lIwDJF3RcXHU8PK5vsaCq6hw9rPPfjzU+mS3uMLy84OXXZb74VKNGTZn8PJ3ERJg2U6FV28sf34go3Bhuu4iC1apz+iTc3d2Ku4cogKwdI3H/QwpR1SRemWTj0zkazi6QfAE0Fe7ur9Ci9eX/4JeLRJxLEha0n39jYvZ7Gv+ZquDqBm9PV7mUpZOfBxmZsHaVUOWaJgTCxkHCMEnVxGjd5zaI/fWqBl/2EFGCrUmi0FHVRd1Ci2D49jC8uk2MdM20CKOmfBu8skXcrTf0F8Nzgt2EBe3Y9SKKUd9POEHKuuhOmAM0Q4iFAqFwToKlTmJdzorogiwJv4FfzwhXQgdJ9G1LkphOJwGSAvXbSXh66qxeKVIJJhNEDGxK7qg2pB//DbN5Hw5uDphHjWdN/9GszXXC6nQcohXMNb6HDfVQ7a8Bu1whbge2bvGUvKvsCASi6/a/R2EEwBAEtzJlBV2LMusIMfEEZUUCiNhYt3KW74dtnRF9PMVZBkTCfqvYdrD9+OY0IRB8GxeJBIC8JFE46tMQHDyxjLa//y7uB60P6m9NYY9fUQrrZBry3fcg16qJlJsLdqFAZiZy/wFINWvC+fPg549uNoNqQ+rXn72Rddi3tw6SVzv0M6dR3Tch9WrB0RoxHDtUi2fr7ufigYs4+zjhGRZKlZq+ZJ7JJONUBrqmc8qzPpKyl70JPkz82I+6GTHUGRiDtb2VcJcktvZejOKk8PspLyLdksnNhR3bdCaNt7F9q8y4F5TC2ThJicKkysNTZ/hjcoUXx4wMHS+vyrnhgiigjKkrMfxRUfflHwA+viJ6cGdvnW1bdF6ZZOPkcejR0UaffhJT3jARGWVEF242t11EoWVDK7oOmRk6yRdE2H/U03LhNMi0NB0PD1izUjzt0ur5r9j5u8bd3Ww4OonWo453yEx4UWbXH+DoAA6O4OAAYREQESmTkaHz8xKNN6eqnD4p+vZdXEVhuDkf3v5QYcSfKtkW+GA3fHZAeADsTRH2qk0D4flNohPg2SbCtnnefiECZIS3wksthVPjy1tENCLQFSY2E2mHNfHCrAnEJLZavqI7Yney8JtXZGHXapLFHAizfbyvu4ModnRURJTAogpzIi8nkbK4kCvOwcsbWrURtXenT4vnVbM2NJ99P+f3nGfV2NUgQezQehx6bjFScDC2lyajLfoW6s6H4GJ3itsfhpNPUvrD34gEGFwpJaMSBcwARiOqcYrzOkJ0eJVa/jHCcLz08kXQtTf4NilZrHl+tRAR4QNAKeZUdGiGSHvVehoc7daFmgar24BHLYh9FcfPxN2ktm8vtjs6IrVth2nqG4XpDnXux6jPj0dq1BjljRnITZoAYB31GPqib5F69UZ5/AnkZs0BqPF4C+I3xBM3Ko4moxrjWkWIlj+/2MvF/Rdp8FAsboFuuPqJ5ZnxmaQdS8Mj1AO/WmJEZMFNkTXPyvyu33BhTzKWbAteXjBylMzjTyn4+Rd9bi5aoHLimM4vP+tMerlsVOJqyM8XbqReXuDtA94+Er5VwN294v0aEYUbw20nFAr45iuOAvovAAAgAElEQVSVth2Eve31YOhAKxvXiZdKlmHKGwp9+kls2wRjn7Rx5pRwhxv9jMyEyQpubhKdW1mpUVMiNAxOHNeJqiZx/4MKCfE6VUPBy0si+YJO+ms2Pt0nbFwb+Ak752X24qNH6sPYJqJ98osDRcWPncJg10URGegRKYoFD6eBzT6roH0YDKolbGIP2fv0IzxEqsJJEcN08lXRolnfD3ydYNt5EXlwUkQqwdkEZzKFpXGQC7iHied4xF7I3+seeOJpmbu6S9jyVdwC3ej+QVd+6jgPbcsm1L53Iz/zLFr81KLiwd3PweG7EfELgSEGDG4WZUXEDMTw8NKUV3iZC3wKPFVqeToiRdIecC+KSmga7H8Zao8Dx2KC48I6OPsjxE4pmQbZ/yrILhD9GNhrKBzeTcf23LMoXbohdepU1DZ64QLqfz+B4KrIMTFItWsjefugp6dhmzgBUi4i1amLFBeH3KsPE/1n8uPQn9j31X4kRaLhsAZ0eqNDoYjY9Ppm1k3eQMyA2rR8rgUnfj2J4qgQ91gjTM4mVj7zGw5uDvjW8MG3hg8JWxKJvqsGQQ3EDOudH+9i7xf7OLs1EYCqzarSYUo7qnWN4jntrcv8Va4fhlC4Mdy2QuF6E39GJyQUFi3Q6NFLxttb4oVnbRw9ouPsAucSYdYchfoNiu4yClR5To6Om1vJ89+8QaNXFxuqKnL6X3SHzUnw+X5x4d6cBE2CRKp0cxIMiBYGSeM3iPZGgI6hwhJ601lhqJRlEdGAKs6ibdJRgTZVRcskiNRAmIcwbPrxhCiCBCEK6lURBZUZZmFmFG5/3x6xD9S5Z4BE8nnYvFG8Xfzr+9Pn8158EfE21sgwCArE9PUCbO82FRuc+hKcg2FdYLHXwxAEBv9MSgoIMyIhN6bUWkeAzcDDpZavRDQMty61/AOE9VCpqET7JJHWKC4eNBXOrQCfWHAJKaqVsObBgVeEF0b4gKKURvx3kDAO+d5BKGPGFpph2V55GX3VSuQRI5HvHVTY2aH9vAzt4AGkBg0Z1XI9XmGeSMUKBH+bsIatb27D0cMRn+repBxKRTWrOHs70/TJOJo91bQwAvG/B5ZwbNlxzJlmou+qQZtJrQltEcL+bw6gmlU8wzzwDPPEM9QTB9diHiJ2Hr44A1/fG/N5/m8QCrNnz2bGjBmcO3eOunXrMmvWLNq2bVvh+rNmzWLOnDnEx8fj5+dH//79mTZtGs7OlR/9edvVKNwowiPEG2LwkKLQ+OtviZevQESU9m9Y/K3Grt91LmWLgr5uPSVatpF4Y4rGql81wiJEquLRQFGDsDYefuwjPiOeWiNSB/4uopth41lo862Y9ObtJFoL154VLZXPNRU1C1O2iQu9TRUe61ZN7MNJLhqHfCFXCI+M/KLzTMkTRkx2wzqCo+DEaQqH+lRtFkzojL4kvLUdNu+CXr1JnzOXT5/1B3MKBAyCJh9ja7GPgjoCXR96Q/4OBgY3grIitmk50YcEShfJCg4j7MhKk0rZ1MUBWJ+NcCspzhngNAzsUrJF4OxikeYI61dyedouqLsMLbE+WkFAxJIBqd3AvzNyl2rCM8SO9v13aFs2I9/dl4+av4CUIWol7js0muT9F6kzIIbmY5rhHuSGruqsGLMK2STjW8MHnxo+WAtmeAPd3u3CxQMpOHk64urvyqnfTuEe5Ea9wXVLPCNrrhWb2YbJqegyk7A5gbbjAwlvG0abF1rh5OnEOHVm4eMzlXHlvI5FXG5ds2IGbl4E42azcOFCxowZw+zZs2ndujUff/wxPXr04ODBg4SHh5dZf/78+UycOJHPPvuMVq1acfToUR566CEA3nnnnUof918fUdj3pzDuiYgUYiA8UiIoGPLy4HwS+AeCh8flC3Q2rNX46H0Vsxny88S8iKxMWLFceMFPn6Uw9GGZpERYsVxjwGCZX5dpjHtKJSNddCV80gUmbhTRgCcaihGyqfYLequqML4JfHsEVpwW6wS7iS4DsyrGy16yCO8EEGKirq9obdx2TvgiuDuAm4NowQRRPOnlBOlWcHUTYqZRnETLhY/yWdPPMV+yoA0YjGnW+8iKgqXlS+DTGKoNLdHGZkQKDP5NlBUQExF+G8U/I04j5qqOKrXuIsSkk6hiy3TgI+DxUutuR7iatC5KZwCoFsjYK7o0TMVcOs+tgBP/hYZvgHsxQZP4s6inCOqG4+xiFt2AOutt1HdnIbVug+nlV5Cio4vO6sxp8PRE8vFlgu90ADRVQ1Yu332g6zp/fr6XTa9tpt0r7ag3uA6yIrP3q30sffhnNJuGq78rHV5tR6PhDZFNZfdpybGgOCgojhW4K5WDOcvMm15v3bYRhebNm9O4cWPmzJlTuCwmJoa7776badOmlVl/9OjRHDp0iNWrVxcue/bZZ9mxYwcbN26s9Ln+qyMK772lsvN3nR8Xi9acwCB4epzC/Q9JHD8Cg/vaSL4gJgC2aS8x4z0T1aqXfWN8+7XKogUaq1fqODrCY0/K7PpDOKVduqQyYLCMoyO895ZGykWdnr1kRg6xsXePTlxTiYObdbydYOAyYU/8Xkf4eK8YfCQB0d7w50V44BfR8RBTBaZuEx0IAS7g5SjGUMcFiBZLmy6iAxfyRHGjgwxmu7+BgyyKF7OtEBYG/e+Dd2fApSwIbxfGvv9uYL+3L5b6R+BSH9CewvZ0QfvhEHTdbnyzwBAHBv9OSgtjSXqVkiIB4A/KN+M6DvQvtewMZSMPIEzQ7xY/lvCX2AuEACeK1UPYwJoJrb8txxEzC2zZYEnBMrrYHJDEpShj4jCdOI0sl71Qq/O/RpvzIfL9Q3jj8VFIEZHYhj+EFFMHecRIJG+fQgFRZluzypElx0g/mcGSIT+x9c2tdHy9AyEtQ+j2bhecfZxx8XXB2ccZS44FZ6+SYfDc1Fz2frGPXZ/soefsbkR2jCz3OKVJPnCxUuv9k8jKyirxu5OTE072WpTiWCwWdu7cycSJE0ss79q1K1u2bCl3323atOHrr79mx44dNGvWjJMnT7J8+XIefPDBKzrHf7VQ8PSC/oNkThzXeeBBmQdHCAOSVyfb2LBWJyBQwmQSnuz3P1jxZLPUFHhstEJ0LY0hw2TqxRb907XtIDFkgMr+vRoeHvDxFyaat5IY+IDM7M8k5n6g8ecGnT8uiNoBfxcx/XFyS+hdHZ5ZC0czoLqXSB+8ug0G1hSjaLefF/4JIe7g5yzGz5pkkXoAuJAjihULREJ4TUhKFDbUipNCo9d6oPSORvtgLlKLVsR7L4KX7O1b698EHNF3lm4/NDAwKE6heLYjIg5NEMPXi5OKEBSlL8rbEDNVi3PJvm7pPPIaROdGFfFroYA4izBYt7uWFQiIpF/FPA/fxmVPPC8JdZoEX1nB2b9o7oYduUFDpKefEVGGvHwxwXT7dti2DW3nHyiTX2Z6nQll9wtM8J1Ok1GNqd4tCvcgd9yD3HALcscr3JMqo+LK3aY41hwrJ1eeIvVwKl91WkDdwXXo8tYdeAS7/+V2++eXNlP75xMWFlbi95dffpn//Oc/ZdZLSUlBVVUCAwNLLA8MDOT8+fPl7nvQoEFcvHiRNm3aoOs6NpuNxx9/vIzYuBz/+tSDpulYLODsXHb/WzZqNIyTCvuJL0dpn4VFC1QmT1Dp1EVmzSqN2nUgI0MiLUVn0hSFV15Q6dRVJu6UxktbROogxE0Mdfr2iBgLXcdXOC1qQKCLSBVcyBNdD0tPCvGgSKIg0aaLEdVWzd5kKIHJUZgbVfGDOk+0ZdOrm5AUCaZMQxn5KNYnveDkFxD5ACw8YKQRDAyuM0WpikMI6++OxR7VEC2aExDepwVsQzhTdii1tzeB8aWWmRFGU4NLLT+KmLhiHzBWPH1x6ksI7g7OpSaNAqaBy9C+/w75wWHIjYoEhq6qkJ+P5OZWZpvKUFH0IfVoKj7VfcqkNA4uPkRuci4mFxMOrg64B7kR3i78L9PAZ7cnMq/FFzcl9TA+81mcPMve+VeWgjRJQkJCiXOtKKKQlJRESEgIW7ZsoWXLloXLp06dyldffcXhw2VnxKxbt45Bgwbx2muv0bx5c44fP87TTz/NyJEjmTx5cqXP9baLKEwca+NSFjRqItG4iUS9WKlCh0YQ5h4VFX9Wxgns+FGdd95UsdlAVYU96vjJCj4+Ej16yfTpJ7F+jc75c7BmFfj56zzznMLFCzD8UZFbmP6rmImwKxkSc8SchiExsDMZlqZDDR84mi4EgiILr4VNieDtCCn5woQp2yp+t2rC4KhmbTh1QtRa+Nb0Zci6B/hAewH9t/7oPl/C3ig0v712YWAPQ31riAQDg+tNkfgW30vWOJxFRA1KdwdsAx4qtewIEFPOEb4EepezPB4IplAoFEYfcoE6MLisSACwfR4HecEosSULNyVFEWNar5LpaWWjD7qqYntiIFw4z9APGxDetqggb2mnz4GKBUZ5+MeUjuL88/H09KyUqPHz80NRlDLRg+Tk5DJRhgImT57MkCFDGDFiBAD169cnJyeHRx55hEmTJpWbciqP20YozJym8v7bKrk5YLHA4m+F2dKIx2Ximl5dNMJsFoNWSo9bLeDgfo0PZ2ksnK9htcKAwTLPvShEAoCHh0RmJsx8XeXBEQpWq8bGdTqTxqu0bCMx+VWZD9/ReH+uwvxnVGREdEAC5h0UzoznskWUoKD24HwOBLuLDoZsa8nzOW6PHNapB83+O4xj7b6G6CiyflzGrGftRUqbP0DX7YVURjrBwOCmUzxqJ0k24JFSa2QjPFK9Sy3fCPQstSwdMbys9IViO6JI0qPU8jRgKfBgybqHgmjDic+g6p3gG4f16bLn7vhBFtrmTWgL5gtLavt49asmLw/98GE4m8AX7fYh39MX5ZVXkUKK5nQUFxhjXV5j7QvraP9Ku2u6m78VcXR0JC4ujlWrVnHPPfcULl+1ahV9+vQpd5vc3NwyYkBRFHRd50qSCbeNUHh0tMzoZ2S+mqeBDgPuE14H18KZU9CppRVZBl8/iGsqM+IxMXbV0VFMlQwLh6HDJdp1VAgKgtUrNDIyhHVxRKSogVi50YGXn7dx6IBOWARcyhRjW/vfqTJ1psyo4SrPRAs3xK8OiqFNJkmkFtpUFXUHfq5CJGjYpy7KIpIAwiEx1wqSg4yTuwMH31/H4cg6WCNDoc7z2Ooco/A9saDBNb0mBgYG1w9dL1k7IKINJ4Hmpda0IQzXS7dVLqeseNCBTcCz5exjIWW7K4BvVonvg0t7RJTEMtoTMsLgQjPkrCx0qxXJoWQ0RLdasT0yHOWBoUidOv91x5gkYXpnFnh4IXl4gKenGPhQAdP7HUT/9Q+2Lz7P/fPaUO2Oou6R6WkT0LOyuJ3bI8eOHcuQIUNo0qQJLVu2ZO7cucTHx/PYY48BMHToUEJCQgo7IHr16sXbb79No0aNClMPkydPpnfv3igVzeouh9tGKHh4iDfjyMfLf/KapjPsPpXcHJ069STq1peIaypXONp08waNEUNseHpBTo7Yb997JX5YpPPCOFEtGBAI095WGPeCTHa2xGsvqYXDTYY9IoojTxwDL2+dNu1kBtwnM+0VlV+W6siK6DqY+IzGQyMlZn6q0z8amgeLdkZJEvUHf14UJkkFQ59ApBd8QyD1lLA2HbB2OKue+Y3TOYHkh6+C2Z7wzV50fYp9CyNyYGBwKyCiDeWlKH5FjO8ujoromCg9lfMXCrslSrANuK+CI2cBtlLdFZSsayjAuz5418e2EKE7cs9CylYIvQdkEw5vpyDXj0VPS0NKSRH52AqQ3NyQOpU39Kt85C5d0C4mg6axYPwR5KEtkIc8eM120bcKAwcOJDU1lSlTpnDu3Dnq1avH8uXLiYiIACA+Pr5EBOHFF19EkiRefPFFEhMT8ff3p1evXkydWt6494r5VxQzfjVP5fmxKpIEWVkQFg4jRykMHS4XpglKU1CYuOQHjbYdJHx9Jb75SuW3FTo7tmnc0U3m5alKYdTinTdVjh3R2bRew9FRdBfk5MCDI2RmvKsgy/DCOJWgYDh/Xuezj3RsNqhWHc6fg65VYf1Z6FMNfjpZNJkRxDyGi7lCIGiAfwBcTAZJkZA/+i9K335YBh4E3yboC28b7WdgYECBYNiOaLcsno5YCngirKMLyAG+BYZSsu7hHHCA8idxfg/Uofz6B4RYyDkD1kvgXa/s4xfWwoHXRcqi5hMgl4wwlO6muJHoWVlYo8JuqWJGw8L5GikuFNzc4PlnVUJCJZq1kGgYJ+HiUjnxkJ+v4+AA87/Q8PSSuKuPVGGrY2nKc/+6eCil3KIZXdPZ2nMa2zdDbi40aSbh4KBz8SK8PsPEHd0lnhmlcuSQTnAI/LBIR9ehShVQs0Xk4GIuNAmAZaeL9hvoAnmOkJUJbdtDyIR7+XboWmjwPQS0RV9QqadiYGBwi1MUZbACU4DJFBYrAuKiHwUUT2nYEFGGRogWyuKsA6ojBsuXx0XgHAx+4JrO+2aJBUMo3BhuidvPx4bZOHZE52y8uABLEnTpLvHamyZqxVz+gl/Q+jj04crlZC5nI1pRZa0kS4RNHsL9ZxYw90OVbVt0PDyhUxeJmdNU9u+TmTBZYuY0nXWrdVq3hU0bID1dzI9xUoTTYvwlYbtc4H+QpgovFbcgN7Z9sg/rS5HQ/Dj6suhyz8PAwOD2pKAQUpJ2AwMoKRLMiDHbfUtttc/+vbRIOAb427/K45x9/04iJVFeGqKSWEZf3YXwZkYjDCrmlhAKz70gRprOnKZStarE4KFy4eyF683lRMLlCGsdxsrMfnj0T6NB3m/8uRuWfK/TorXE8p800tMkWreDc0lw5jRUqwEnj4tt0/KECErOA9kJyBOCKMk3hgNr0shpuQ9e9kP/BsAQCQYG/1Z0vRHQqFQdw5dAV0q6RCYDG4DSLQxpCCEQTVlTJxA1C1ZEe2Upc6crFQy6XnJORcGyhO/LzrAoRYHAMATD38stIRSqR0t4ekrMmq3c0KKVmco4/pizE1mR8a/nj38dP5y9i/6Jkn5PYn63b3FwdcDJ05GI9uF0mtaxxDoA0T1rENQom/0LDtC0xXl2/q6zbbNOtRqw4Esdk0kmJBQOH9SJrilEQ14uWHVwdoIL+aBr4OLrwrr3D2J9RoXmTug/3Ho9wgYGBjeOogjDXkQRZJNSayxGiIfi2IDfESIguNRjuYArwmq6HDdHuPLowqnPwcELwopFOiQJwktbWVdM8YiEIRpuPreEUCjgeomEnBwdV9eS+5upjGPDq5vY9/V+0o6mARDYIIA7ZnYubMFZOfY3AKx5VsLahOIR6snu//6J4igT3DiIsNZFeT6PYHeGb3+IuQ0/pXXbFLZs1Dl5XLQyzn5Xo2tP0T75526oGgIn7KOgY+pBfJYvmQlZWH5ag/pSJPrS6/K0DQwMblMKuiVKRhg2IQoaSxcprgScKNsNlYuYkOlBhSKhPJJ+AV2FkLvKf7zasMrvqxIYUYabzy0lFK4Haak6S3/UePl5lYaNJRo2lmjURMbW20ZkxwjMGfmkHk2j+TPNiOwYUUJMPLhhCHs++5PABgEsG7GcgwsPITvIVO9WDVu+ytltSdTuWwufKFGZLMkSI3cP56tO8+l4RwJrVgl/BYAtG4UXg80mogkmk/i52oS+7JUfRJ15Cb11i7Ij7g0MDAwqoGSE4QRl2yG3IOZIlPZeyETMiWgA/PU8BUBEFdolwIYwxIAqyjpIX09yzojR2mFFRkOGYLh53DJdD9dr1sPUl22sX6OzfauOlzfcP1RGe2xEYYGiNdeKg6to78lJzsGliksZD/K8tDx2frSL48tPkHokldyUPBw9HKnevRoeVT3wqe5Nk1FxhdtpNo2Ffb6jpnKCX5ZB8Vc8OBiQRPqB9h1w/GEJZt9/dgWsgYHBrUHJCEMiMBcxV8K11PLTiK6I4svLQ0e0arZA1D60K7vK4FjQrGXaJK8Z6yVwKO00KXD8IEsIB2sWLPYyuh6uM5Uzer6NmPSKiXnfmJjzmcKRBAeqvDuxRBdDgUjIS8tj5djfmObyJh9Gz+HrLgs4/L8j6LqOi68LzZ5uyt3zexN9VzTBTYKxXLJw6LvDHP/lBIe+P8LiAT+QsPUsaSfSkU0y9y7pj5ePRHRNKG5k5uYOsRO7gK8v+Cw0RIKBgcF1o8gu2gp8hLCLLi4GEhBCIYaKRULBnc1q+/cCd9dyRAKIaMPCx8CWV/7jFbFzzF8/XoFIgKvvqjCoHP+61ANASKjEfUOVv+xwOLHiJBknM9CsGlVqV6Ht5DaENCuyT3V0c8TRzZEub3cm41Qmvz65kosHLpJ2NI3sc9nYzDZSj6RRo0d1IjtH4Ffbj6hPxxM4aQYLvtRIvmA/l6HtWHv/Ylivoy+u2LrUwMDA4GooSkeMpuRMCBVhF10f8K1g6zxEG6Un0AbRUeFSiaM+Dd8du7Kix4ZvVn5dg5vKv1IowOXbIOsNrot/XX80q0pwXOnK4CJcfFxw8XHhvl8Gsmfenxz87jBnN58FIOVgCrZ8G/u/2U9U5yhR8HjvULyPbCb5p2PIjjKb+32K1ddTFCcbGBgY3CB0vUuxVEQuMBsYQvkiIQsx5noH0AP74Por50paKhXHy69j8LfwrxQKlfVKCIwtfwxreTh5OtHk8TiajIpjxdMrOfzDEXIu5JJxUlQvHl16jLPbEjny0zE6vdEB7ygvfj/oizXG8EMwMDC4Oeh6QWfEm0AXyk6dzESkIkBMsaygk+FKuAajJoN/BreEUNB1nU9ma+zZpdOxi0THzjJV/P55Q0AUR6G6e87uQcPhDdn31T72zNuLJcuCOcOMOcuMNcfKV4l9kV96D4vn5QqHDAwMDK4vQiw8j2iRLCAf0SVxEQhC2EBf4xhnQyDcNtwSQqFWqA2AvDz4+nOIrqXy0msmet0t3VADpmM/H8czzBP/un5lOh/Ko/jcdKJAn2hBvz+B+u8PYf83B9FtOtlpNkz16hlFiwYGBn8but60WBpCB/6w/9wIuJZaqRxocQJcQ67p/C5L+h7wrA1Kea6SBtebW0IoHE104NdlGseO6PTpJ1O3/tULhMqmHTITstgwZRNJO5JwcHMgqHEgjq4OhLUJw8nLiZq9o/GOKJrkVkIk2JEcHZGqV+fwzA1Iww+ivzwZjh7B9lIUfHNVp29gYGBwXRCRhaXAPGAYEHFtOxwcKyxl0x3tHvQ3CNUMqdvBvZohFG4St4RQALj3vqssprFz4pjO3j918rvll7FcLg8HFxNeEZ7knM+mwbBYqtSqwpIHl3JixSkAVjy1CtlBwtXfjexqjZGizyMPGIQcE4NUqidWMpkw1Y+FH5ZgeVRG/+aflzYxMDD4N+IKDKfi6ZHloSMGUO0CgqFZKkgyECu++8bdgPMshuIENR69sccwKMEtIxSuhQVfqqz8Red/32lIyjuEtgyh1t21aDKqMQ4u5ZuCuPq50vmNjnhFeCErMqpF5fAPRwhpUZWds3fh6OFIypFUclNzIWkD+qYNqPM+Q1UU8PGB0FCoHYMUHQ1+/shh4ajf9Ub/7iY/eQMDA4MK0PXOpUyZSjyKKG60ADsRYuIg4AZ4wp1dQDaJO3vVci0n8ZeDoQz+fm6KUJg9ezYzZszg3Llz1K1bl1mzZtG2bdubcWgA6sVK2Gzw268Q1qM2te6pSY0e1SsUCQX4VPMp/Fl2kOm36B4kSaJ6t2oE1Atg19zdrIidAh/PRtd1SE9H37YVzBY4eQqOHkOXZXBxRjU1hbZdKX9Sm4GBgcHfjQU4j/BNUBDFjF6I7odYxACp2oCpbKHi1bQ2WjJh74vg3wYiBl7LiRvcYG64UFi4cCFjxoxh9uzZtG7dmo8//pgePXpw8OBBwsPDr9txNE1n9QqdVu0k3NxKqtPYhjLhkTr33ifzgds9FezBvh9VQ5LL1kAU/z2gnmibbPxII1al1UR+axa6xSIGNmRkQFoaus2KfikLkpNRZyYAkrA1NYSCgYHBPwhRq9AL8dkUhrBnrkXJcdUFXEczX0cviHsPtGuIRhjcFG64UHj77bcZPnw4I0aMAGDWrFmsWLGCOXPmMG3atBLrms1mzGZz4e9ZWZUb9vHHdo1lSzS+X6Rx4Ry0aS/R/U6Znr1lQsPEm93bu3KhrUtJ2XzX93uCGgZS487qRHWOxMnj8oU5kqNdUfv6gq9v4b+YZbQn1LX/8k3BKFgDAwODfxKvUb4wKIcraXvUbJDwPaRsgbh3yz4uSaLmwOAfzQ2d9WCxWNi5cyddu5ach961a1e2bNlSZv1p06bh5eVV+BUWVrkCG2cXCAqWcHAAH18Ij5SIjJLw87/yc9746iZSD6ey+9M9/PTQMlY8vYrMhPIFS3mdDsUp7T9e5LtuYGBg8E/iBtUISAoEtIfIITdm/wY3hRsaUUhJSUFVVQIDS7p/BQYGcv78+TLrP//884wdO7bw96ysrEqJhXqxMnXq6bRsI1G/gYQsV+5Nn3IkFc2q4l/XvzC10OPDbrgFuBLZMYLwduEoDhV3W0zwnf6XYqFwopkdSdpriAUDA4N/IDZuyOVAksAlSHxdKRn74My30GDq9T8vgyviphQzls7367perg+Ck5MTTk5XF4aSZYkGjSqvipP+OMfuT/ew6+PdeIZ5UqNHNWr0rEGNntXp+FqHSu+n4LnoOTng7Aznz6OnpaJbrZCejtI6GXV2kij26f/0VTwzAwMDgxuH6HqYgihgrAo0RwyK+ps7EZwCIHyg0RXxD+CGCgU/Pz8URSkTPUhOTi4TZbjZKE4KTl5OIIFXuCe+0b741fGrMIKg6zq6piMrMvGbEghtGcK2t3egx/6ObfYHYHKA1BT0XTtB08SbW9dBUcDdAxxaG+YgBgYG/1CmIKZJpgJHgQ2AA+AOZCAERBRgE7VWN8Oe2SVQfBn87dxQoeDo6EhcXByrVq3innuKuirujVkAACAASURBVA1WrVpFnz59buShL0tg/QCcRsXR4plmuAe5l7uOzWzjzLp4ou6IxJZvY3H/HwhuHMTer/cjSRKXki6hqWtAs28gSeDgAAGBEBmJVD8WqV498A9ACgxE/eiv2zENDAwM/j4UIMD+VcAlhID4AzFx8gjgCN+shW5tQHEFrxiw5YLJmF1zu3LDUw9jx45lyJAhNGnShJYtWzJ37lzi4+N57LHHbvShL4t3xF97mh/87jArnlyJk6cjEZ0iObMunhO/nhQPSmByNuER4kFmnbbIsfWR7h2IHBaBJJdfI6p8kIXUz4b+fUWz3w0MDAxuHpK0EiEGKppi62H/fof9e0EkwQIr7M6MLAQcYfALJTe1pIOjDwa3PjdcKAwcOJDU1FSmTJnCuXPnqFevHsuXLyci4hp9xW8C7kFuWHIsKI4y2eezCWoUKGY8RHlTo2f1Em2Tf1XUqFutqDt/R3vlZYiPRxqcZNg4GxgY/ANQERf6QUCNK9jOEVHLAGD3wymekkhcCtmnoNZT1+tEDf5GJF3X9b/7JCoiKysLLy8vEtIc8PS8PhfWyg6FAshNycWSY8Ur3LNSQ6hKiwU9Px/91Emqv/0QR388iq7p4OyCad9BrDUir/TUDQwMDK4bJadHbkV0PtQFqlzfA93McdPWLFjsRWZmJp6eN2ZCb8F1aXzmszh5Xr0HhDnLzJteb93Qc71e3PKzHhYvVEk6C33vLTJX+ivGqTMrLRZc/Vxx9av8uUzwnQ6IwsezWxP584u97J9/gCM5VgAkRcLD30Tuvr04+fqS5Xb1XR4GBgYGV4skbaYorSABTYCTwH5EjUIEYmDUdeCbUrMkrkQ4WDLE+Tley+hrg2vlhhou3UiW/KAR5mth7BMqkyeo1I2y0r29lWVLNC4XJBmnzrwh52TNtWLNs/LTsGV823sRu+fuwWoXCW6BbvjFVCGgfgAPVVtCnfHtcOnW44ach4GBgUFFiEjCu8DaYksdEXMc4hDV2QnA5ut74MGxVx5dkJ0gN/76nofBFXNLRBSSzup41ikZLejTV+bO3g7MmqGx70+dth0k2neUqVGzrG9DeYxTZzJDfpa9X+7D2duZ6LtqICtFuknXdKRKGjeBSFPs/GgXR5ceJ2lHUuFy35q+qFaVGt2rEdK8KlWbVWXlmN84ufIUsqOMw8GjWOvUrPRxDAwMDK6WonTDs8DHCHEQXGwNd0T6IQMRWfgB6I3oiLiG9O/Vph9MLuBd/+qPa3BduCWEQvNYGy1bS0x5Q6FZy6KLuckkMe75ip0TL8dnYdNIz3MhLy0P7yhvmo6Oo+HDDchNyeXIkmOkH0+jxbgW+FavuHI3+0I2GSczWD7qV9JPpGO5ZMXJywlJkXDzd6XufXWJaBeOX0wVnL2dyRg/g4ydIuKhWTTaLBqO01M/YxlhQf/hCvIcBgYGBleAJC1FpBQAXIDRwEaK2iKL423/fiewD/AB/BEjpitLPvBb2W4Ig1uOW0IofPq1QtceMl5e17dTYN8JBx4anM+BnCgC6gfg4udK9oUcDi0+zB8f7iT7XDa75u6hzsAYWk1oSVCDIvOP/Ix8clNzWT1+LeknM7iwJxmAKrWrUKWmL44ejrR9sTWOHk54hnigWlUODp7O4YOQmVF0DsnfbaKt292sXr0Tqc8B9CWh1/U5GhgY/LsRUQQr8BkwAjEhEsTHfzRwGiEWyitidAIa2X9eB7RHtFNWovhuYBxkGd4KtwO3hFDo1lO+bl0PxTGZJL7+zgFIZKYyuHB520mtkWQJS7aFKtG++Eb74BFcZMqUczGHrTO2E78xnpRDqZgzzTh6OFK7by28I73wCPGg0fCGhakLm9nGN3cuopoMhw+VPIecbPhzxlrIgvDMVjil7ccy2hN9wXV/ugYGBv8yilINDsATwGxgHCLFABCKuAzsA5ry1xGDDoh2ytNALLQ+CuH9yxYrQlGqwbvetT2ByqDmQ048Dq/mg48v1sU3/pD/Nm4JoXAzKN0N0eb5ViUe17WiAklzloXjv5zg4v6LyA4ytfvVIqpzJJIkEX1XDTxDi9S2zWzjq07zCZUSWVtsYGZQsDBxNJvB1Q1M6RC/PoE+yx9g2d1DkZqmov/+6I17wgYGBrctQiB8Agyk6O4/AOgJLAHuL7Z2EKLD4XdEMaMHFaMA9qLE8yli0eDYv28eg2qB9VHCAddn3s0//r8EQygUo6LWybTjaRxdehxzlpmWzzbHO8KLpk/Eodk0ZJOMbJIJahRIcFxwie00VWNuo/8S4ZbGjl3if8k/ALIy4Y5ucDFZYvdOHWcXsNnENvGzluKetoHspGxMa2NQP2lnRBcMDAwqRVEEAaABsAAo7oLbDJE6WI4QDQV42h87iahPqEQKNKhT0c87R0OTD/96fVsu7B4HsVPAqVQ91tkfIaQ3SJVvxHP8IAsAbdcCpNgGSCYTelZWpbc3qDy3jeGS2ayTlgrTX1O5f6hMk+ZSpbofyqO0WNgyYxtbpm8lLzUP9yA3OrzangbDYkt0SZTm0rlsvuo0nxDnVA7sA1WFGtGgmKBnL5n8fFi/RiMwCLZthrw8sZ2LK+TnCVHh5OWEtm0/eloattd90ZddiXOagYHBvwUhEDYArSh5//cpRdMgC1AR9QbeiAhCcXIRhY7bgJYVH/BquxhsOWC6koLIshQIhPLQs7KwRoUZhkvXmdsmohAXYyUtFXJyYN5cjZq1YcgwhZGjZFxcJM6c1klJ1vlkjsbwR/9aSBT3WZipjKPFM83IPpeNLc+Ki58rlhwLF/5MJrhx+TPWD/94hOq7/kegSWPv/9k77/Coqu39f/Y5M+m9hxCSQOg1JHSQXkVpKoiKDQFFQRERe8eCXQQELFwURUFBEaUr0nsPLQkklBDSQ+rMOef3xwokAWxXvd8f9+Z9njxJ9qlzZmavd6/1rrV2y1ib9oqAAIs6dTVatYWvvzQpK4PkYxUkQQcog2B3yDehJK+U7uPrcdq3AYlbMlEDdoFnRLWHoRrVqAZwqQchCUlh7FRpbBDwCVWJgo7oEXYBJ6nqPbggPmwMHOOKZZ3/SqXFv0ASfosgVOOfxX8NUdif7MLO7SYvPm3Qpp1G63aK+NYKd3chA3cNd5KSbJGVCZ/PM2naXHHPfRq33K5hs/2652Gi8ToomDRV/v+9qo5JK5LpW7yY2WstDu4XD0H/AYriIohvrTP0Fpj+jsmWTVAjQrFlo4WmSWfqQA8oMyDEHfJz5HyrV1jY7IngALf1sTjX7UbdXBdy92H90PxveXbVqEY1ri4otQvYgYQLLuBW4EXEq3AhbTwQ6IXUQxhcaV8fJFUyBwjg8iqMPkiI4hyQDjT920oxVxv8qw//NUQBoGWCxtfLrhwOWL3Rzo9LTZZ8bdKipaJ5nKJpC/WbJOFK+LWqjq/rE5ngmMpt052MW21RWAit2yqCQy2Sj8GDk3SuH6TxyHgnmzZYxMUrvvtGoj7hNSD/LNTyhtR8qO0L+7Mqzu1vA6c35GSX0HZ0AyInDuGru9agunwBNXpVexiqUY3/EVR4EEzgW0SHcMH9bQdacjmBaAykAKlcbOAEQDRSfTGHK5drjgCyAVs1Sfgfx1VRwvnzeQYOx1+XUvTprzHjIxuj79dp20HD0/PPkYTfknNYz7/K7cOc7NltEREJoWGwc7sFlmLxchvXD9IYN8aJhwd06Kix7FsLy4IGjaCgAHpFw4l86F4LfjlV9dxuNijNkzdr8wZYdfciyMmBX3qjt34X1+x8VLfVqJtK/vxDqUY1qvH/NZTai1Lvo1Tlkst2oAOw8ZK9ewIHgLJKY1r5vvuvcPYOwBYg8QrbAALg5qGw/3koTv/1m9w5AdYNhDMrfnWXapJw9eKq8Cj0vU7Dbv+/a8tcUGCx8keT0yfl/9H3X34/Yx/SeOV5i2+/FjIxYIjinZk6pqFwOqU3xaj7NN5+zeCbhRYW0KQZnEiBG4bBss+gTwwcy4GcUrApcFrg4yKhiNwSMA25VlEeRNeG48ng8cx4buywi5XOVaQdCkANXg1uoiiu9jRUoxpXJ6pqD0DCAD9TNXzQC3gN6FppzAMpqJRKVX1BIJIemcHlVRgHI0RhH1W1DFR4Epo8/ds3HNQeom7G5fP6QHk2wi/rsI4dRdWJRbum828fXw6ruBhOnwYPD/D0BE9PlP7vV9+txt+Dq4Io+Pn935EEkHTG75cIWcjLhc/mmrz+rk6Ha8Qhk5xkERUtHSzbdtDIz7PIyxV9wZCbRDR56IDJw/cbuLiAl7ekHB9OhOde1njjFZP7msGZ87DpDOgK3G1Q5IRWYZBeCC46UlwNcNGg+Iz8LsiHb/p8Qkkx2Kwz6Gk1cS5Yip7QGtXgGWjyHCw6jmX9B1u9VqMa1fjTqCAHexBDf12lrb0R/UFloqADMUgBpOhK492BacADl1yhBTAXuPsKVw8BrD8UYrA/cRhz3lyoWxet4zWo4GCEtFSFdfAg1umTaIOH/O45L8Jmw9G/D2RkgKah3X0P+uNPoiplBZg/rcVcu1pSyGw29CE3ourX/+PXqMafxlURergUyUkWiQfM/9j1Imoqps3WGXyTxvARGu06Kn5YajLvI4M+nR1MGu+kZ0cnrq6S+jjsVp3R9+vcMFQnLw86xjvwD4AGjRSnTkJuDtSIgO/X6KxbazHzY53jefBpopAEP1chCdfXketnF4tHAUTT7OcGLjawlb97OdngZoJRalCaW0rsgz0Znng/+on3YGtTbLsUajjyo3b+x55bNapRjd+GUttRau0lHoS6wO5L9tQRfcGlnRR7ASsvPSuiUbh03AYMRFImL8HNXeHmbnBstqxCKsFlWn6VHwwn5ob1KA9PCLxS2WeBNmo0tudeRPn6/eo+l0LZ7ajmLVBt22Fbuw7bK69VIQkAVmYm5rT3MBd/jdauPZPbLebRgFd5NOBVHgp4+w9f62rF9OnTiYmJwc3Njfj4eH755Zff3D83N5exY8cSHh6Om5sbDRs2ZNmyZX/qmleFR+ECNm8wmfGeiasrLPjMpHVbxe0jNQbd+Of1Bn8W7u6Kt6dXfVyGYRFbT3HncCdnTsM1rZxMekLnwUkSmsjNtbDb4dW3NKa9bfLLTxY+PjDpSQ1vH8W6NSZdeyomDjcIdAcLMC3IK4M7G8PRXEjOBXe7ZD4DhHqAuw7ZJeCqC6EAIRgN/CExG44lgho1TzwPaal4De7EbWtuYYb+JM4jo1F9Pwb/JvD53mpPQzWq8R9GVVJwFvgCeKTSmAfgC2RRtf9CRyQ8UFmQGFDpPKGVxtsA3yNhgMqGNhC4HW6oDXYvLkPsPVCYhu22Faj4BJTtCiYioib2JUt/4xUK/t06NraXX4XoGJRSPBrw6mXbzbtM1p3qSPtJbXHx3PZvXeNqxYIFC3jwwQeZPn06HTp04IMPPqBv374cPHiQWrVqXbZ/WVkZPXv2JCQkhIULF1KzZk3S0tLw9v6t6puX46oiCnXqKobfprHuJ/EmZGRYrFlpYVkmN9/222mO/wR0XdGoKbw/x3axeqlSkH4GImvB6hUmk8YbdO6msWWjRc++iuJCmPuhyZgHND6ZY9GqreL+OHh+E5QaEOwGtzWGLw5BjC8MqgvTdsm5g9wgxANOnpeQxOoTMq4p8HUFhwl2BQ4Lkg4J6XB3h9yUPD5tOZ2xd81g2j4N02qGeuJ59MwHUMOBw9Og7hhYcLCaOFSjGn8zKojBfuA0Vd30NYFSJK5orzTeFqlz0KPSWF1gKdLRsTJuQ8jGHZeMN0aKJ5Wjcljh+HwI6wFuVfUKLtPyMTcdwFzyLZqHB6rp5fPBv0sAfg2XkYGAK+93AZpNo8tz1/yt93C14M033+Tuu+9m5MiRALz99tssX76cGTNm8PLLL1+2/0cffUR2djYbN27EbpfPV1RU1GX7/R6uysqMyUkWvr4QGPTvf2Aty8Iw+MfJRXaWxaB+TnbvsHBzg7em6wy9RWPBZya9r4V5H1nMeMEkvQi6Rkpq5KKjMKkV1PGDMStF3BjjIx6E1AK4NgZOFMDWchFyDU/wdYFzJXC+DErK3Q/uNjnuYLb8X6cuZJ6T7pU2G3R7oydNb23MGzU/QDVrhv7xPJwv1JWdywpgUUo1cahGNf4khBgcRVb4lVfzOcAM4NK2ywsQo165gVIh4hG46ZJ91yDG/9KqieeQ8ES5lb1ACs6sALdQ8L9CzZXD74DphOhbcfnQ/bLN5p7dWIcPo+rEomLroHz9MGa8j/nLOlRMbVRsLNqNQ1FeV/BMlONKHoF/Ev+Jaod/d2XGtLS0Kvfq6uqKq+vl5y0rK8PDw4OvvvqKQYMGXRwfP348u3fv5ueff77smH79+hEQEICHhwdLliwhODiY4cOH8+ijj6L/CZHoVeVRuIDadf6acf9xqYmPL7z0jMGQYaI7cHOrOGdJiYWrK8yebjLstt/vXLlmpYmvLxw7atG4qaJ+Q8WSRSZ9+mt8s9DgxVc1cnIUGWctUo/DpvUmq1dYPPOYSYNGCgvoFAHH8ySksOg6mHsQ3t4hJKGBP6QXicfhoZaiUViSJNcOdgdPO2QUQ6wfbKuUwRTqIdvddCEPZ1Lkf81Vzrv3lZXUTV6FzWHh3LKFoNs6Mezbm5ju9iJl1/WDpr1QwxqDppd3iCvGstr8pWdfjWr8t0GpzVxeh2AfIjLsWWnMH5GFOak69bZHSipXJgqeiOEvpGpHx27AXoR0+MvQzc3E4KevhPAmVfslhPeC5I/h7GqIGVG1x4JPQ/SHDVSrK5sB68ABjAfuA08vtNvvQH9wAlZ+PtbaNaiwMMYMPEpArd/p7wAcX3uc70f9gEeIJx7BHoS3DKXdxLbYPey/e+z/z3gr+0GU898nI9KX4g0iIyOrjD/zzDM8++yzl+2fmZmJYRiEhoZWGQ8NDSU9/cqpq8nJyaxZs4ZbbrmFZcuWcfToUcaOHYvT6eTpp38nk6USrkqi8FexfavJ9q0WG9dbrF9n8MrzBmMf1LlrtIa3t6JfVyfnMixyc2DKcwbjJ+qMGvvrOoiz6RbPP2VycJ/URnhoksaRwxaPjDcoLYH6DRVzPpVaCqknLJYsMnn1LZ3VKxSTJxhkF4Fdgzm94I0dcOdyGN0MlqXI+Q/lQNMgeLWTEITpe6DMhHBPcBqQViD1FyykDAuAt10KOGlKQhJQ8XeRQ8hDZjos+EDu2ccH0necZU3/97jrsxLmHtpBSd5GrNjd2KbPQpsWTVm7p1EJ26HufRJjKW8vW+11qMb/Ci5PWwR4AXiJqtrwVkiXxp6X7FsXIRCVUxcjgdwrnDcKIQXtqoYNzMaQtQl8I8GlnCxo5VP5xpuhxVTwrBSv9ogCz9rgEnBJLYO2GG+9gXPIQFTnLtheegVVp87FrVq37vD4k2h3j2Ry7VnAHLLvycU2bjQ+EX88xv39mB/JS8unMKOIDo+1o/W4VhdJQubhLOZ1/Qybmw33ADdCW4TS7eUueAb/tX4QVxOu5FH4LVwa+rEs61fDQaZpEhISwqxZs9B1nfj4eE6fPs3UqVOricLv4cnnbRw7YvHRBwYxsYo6sYo6dRUe5YuC1Rtt/PKTxa03OAkJleZNebkWEybrV/Qu3HybpEreebOTnByL+f8yadVWY9ITGi88ZbBrh8U1CQ5ef0+nUxdF6gmLts0duLqKgX6qqXgLnt0kZCDUE0atgMxiyYCY3Ap2ZsCIH6F9Dbmmuw3OFMrvh+Khphc8sb58mw5NgoRMHM0WoSMWOE3IKYEwTzh1Xuo0ZJaLIm3l9Vn27oKs3rNx5MtxxuJvCDqxkSFfDWZr721se38aBC3FNudjIQ/3OFC174LWs2HBgYvPpJo8VONqx+WkYCVCBrpfMh4OnEEqGV5AJLLqvxSNuDIpuAFYBUPHVRh9mkHKPLCngNW0oo2zpsPZn8A9ooIoAAR3FG9C1mbsj6ZDbGy5GDEBx50jsL67Fqff7eiPPnYxk+COAWlkN+5LwyEN0O0Lq95SAMJ/mFUxFCuhjQsR6z+iV7gvcTS/vLiehPvi8Qiq6nnZPWc3RplBWUEZcSOb0+ah1tjd/5ynIX33WdZMXovN3YZmv/oS+Xx8fP5QmCQoKAhd1y/zHmRkZFzmZbiA8PBw7HZ7lTBDw4YNSU9Pp6ysDBcXlz90j/+TRAEgtp5iyhtXfvlKKdp3gtSsP/YQAWpFKVZvrPoBLyiw6NNfIzoGdu2wyMuVvg6vva0TF694YJSB0wnPnoY5PWFQLNy9Qgz5ngzxEnjo8PRGuLY2rLpB/s4tlfO3CYORzWDjKZizDwoc4OcCNbwgOU+IQfsI+C5Z9jcNCHKHHlEyllEk4wFuIo5cf0pCH+dOQpQP2HU4lA3pO8+SOGYGVr4SheTyH/Hq3JBBnw2gxlfhvOTzJWz9Gdsvn+KcLrFT1eYjiY2uq+pWqyYQ1fj/EVVJQSnwDjDpkr2ikQ6Nl6IR0p454pJxfyAPyWC4gIbQJR2CYsBeaVVuNYF0DQpTZPWvlU/skTfCnslQkgExt4HuJuM+9WFLW/SRo9DuH4cqX4U6n/sRc8tmrCP3oWIrvBb6iDswW8ShoqK4I+9ZAkMCZFUfEE6NhHDKCsvY+NpmfKN8Cazrj4u3C+ueX49vlC8Bsf741/EnrEUo7gGiZbBMi6+GfE3e8Vz8avvhX9uf+gPqUqvT5cp7pSmuebrTZeMAPaZ2J6hhELH96uAV9utah1/Drjm72T5jJ+m70lFK0eLu/97+Ny4uLsTHx7Ny5coqGoWVK1cyYMCAKx7ToUMH5s+fj2maaJqQqCNHjhAeHv6HSQL8DxOF38OfETlmZ1sEBFy+/5FDFrcPdRJRU3H6tEWtWoojhy2atVBs22zx/Ksa/a7TyH3SydRt8HACTEiAAYvF6K9OhRvqQuKd8MVhGLREjHuQuwga92fCxJ/EyxDqAecdkFsGedkQHwq3N5IwxQWEesJ1tcULUVDuQXDVoZaP2P8LdRlQsnjxsEGgG2SVwJ51EOBu0aQZ7N8Lucm5pDw5l+se1rAZGs6UZGzXdaXP691Zfv2nmOuDMG4cgDZ+HGb6axdXSareOGj6HHydVuVZVROIavzTuHLY4EXgyUvGXJFKiJeiJqIZuBRtgPVc1BNcCBNYH8C+p6HhI2CvtGLMyJOSyE2eqUhRVBpk74TzSVA7kgtNnezTSzCedqI6+qB1zL4oHLQy4zHmjgJPT6xNG6FhI1RoKPrESVj3jcaYOR310xpUh05ogwbz2JBtfPP1EvY/f4BPPO3Ej46j45MdcPcXw5+6Lo0tb22lOKsYpSmCGwdx7mAmlmERUC+ADo+2o1bHCtK/aepmTm89zfkz59HsOs3vaEZkB9l+evsZbO42fGp64+rj+rtehxZ3iXG/tI/O7zXgAwhtHsJ1H13L/s8O0HhYIwJi/dk1+9IaFP89mDBhArfddhsJCQm0a9eOWbNmkZqaypgxYwAYMWIEERERFzMg7r33Xt577z3Gjx/PAw88wNGjR5kyZQrjxo37U9etJgp/AfePcuLrA8dTLPwDFc9N0atkYsS30th7zM6Md00en2iQetwiriV4eEJeHjz+sMn2LfC+v5RvfvhnKHFCUi7c3QR6RsGEn6DXIhgfJ56AL49ISOJfB6FfDHzUR9IkH/1F9Af+rvBYG8mC+PE4HCn3ftb1g4RQqfK4JV26VNqAUAOKHXCkRK4NQhjKyscLHVJOusgJbg6wUoVAFDlh+zqYbjeJjTFJOgYluaXse3EZg0NGkZWUzSrDwHznLeoNXErK0wtRUdE4nzQxv6qHvmAGxuIbLj4rFXMHtJ4FXx665CmbWFaLf/BdrMZ/E0zTRNev1NPgZWA8l4sOXZBQwKVFgepwebljd6AWdM+F4E4VoQCA9GIoPAjRt1WMKQW6OyROhQYTwaXcsxDcEXaMg9JMaD4Flw8lHm8e6ISzc0eU7yIpNFS7DqChomMwbhmG2eka9BdeQmvaDBUUhJWUJEWHhg5DCwtDhYaiPD1Rrq74Zh2mTYIPLW49hN1dWt/WH1SP4MZBtBwTh0dA1ecQ3CSYmu1r4l3Di3aPtMUz2IMVE1ZRp3dtGgyuj6ZrOIod6OUEpsPk9oQ0DUGzadTuFXORDJzZlc7mN7dw4PODAHiFe9Hj9e40ubkRj5hv/Mq7dmX8WgO+Kmgpv6wpFkrtJD/f4rU/dZWrC0OHDiUrK4vnn3+eM2fO0KRJE5YtW3Yx5TE1NfWi5wAgMjKSFStW8NBDD9GsWTMiIiIYP348jz766J+67lWZHvl/AdO0SD0B0TEV93Fwv8nPayxeecEgNwf8A+CZl3Ruv1tD0yrtd8CkIE+aP+XnW5w5ZdGylSI3B6zZBsHu0D0KLAtu/h4WHJbjOteEL6+FlAL4cJ+Qg1IDHmwJN9WDzw7B8uMydqYQ+kWLkT+QJWSiaRBsPC1hhGKneByC3SXtcsVxmSINJOraBJkW05C6bjbA3aXiHLoSwaWLLj+FDiETYR6ga5KVYVlSw6FLN4VfgMWGdZBxFjQNmt3dnC7PdWb15LXs+9c+dFcda+wE9ImPoFxdKbvrDti3F9ucj3DOrpQjvXEExE2FxWcveUdSkRWeVu2N+B+DeAaOU7Vs8QU8BzwMXOrGngN0RsSElbEcIQnxVOmSWJoFp76DGv2q1ho4fwLSvoLQbuAfV0EWMrfI/kFtIbQ72MrTDY9/htZsPapZC7RrOqNqiMjIOeVFOJeBatgY1bYtWrPmWA4HzntHg66hNWiIatYc1bUbVuoJjHH3S9+D+g1QDRsxccRRsg5ns/jWJdjcbAQ3CSa4cTCNbmqIZtNYNWk1B79IxKemN4H1A0kYG0/9gfUA2P3hHja8sonAegEE1PXHu6Y3ke1r4hvli0/NCs9HZZGco9jBlre3sfO1n6gdq6hdR9Gxs+L2uzV0vWKue+d1g8QDFosXmowaqzF+ov6XxuJ+DgAAIABJREFU0tj/LPLzLSIDHP+R9Eh7StplVSP/DKz8fBwxkf/ovf5duGo9Cju3m1iWrNr/SWSctRg+2Emb9oov55sMu1Vj4uM6vr6KRk00QkItbDZwcwd3D/DwUKSfga2bTT6ZbdDnWo3vvjEZeZ/OwCGK06cUXds66D9Qo3YdxeeboVst8HeDn9Lg+fbwRmcx9CN+gLZfwD1NxVA3CwJvF9EjzD8ktRaWDICuX4ke4dtkaSA1uQ30j4Hei8oFjbmSIfF4G2gZAiNXSHZECRCMVI8PAy4UP7WQqbNnGbidlka0TgsMQ1Izu9eCH1LEq3CyUAhEsDsUOsFRCombLDztUFpc4X1I/XoPbmH7CEk3cfeA4iID+wdv095/C23Gt2JR6R6SkpPRBvRAm/I2+s23oJSibGQh7GqHbfG7OBf0r3hjdi8A34YQdbMUjfr8glt5N5JOVtUgVJOJ//9xeWigCFjL5QWGAD4EnuHyKawlcBgx/JXRDNrkQ0RNcK1U0cdqCgdeEj1iQKWGSC4BEgpwD4egdhWhA/cwyNoM7jXAPQL7HDeUUpjp/hh3rkRd649+fQMIDkYphfGLL8bEVehBIViurlw0md4+mPPmogUGoSIlrq/sdtB1rB+XEV8jhXYdcvAJ2gFB8KEjmfS16TStcYa21xTh4hlMWFwoRplJ5qGz+Eb5Etk+Ar9oX5SS/jJKU9TsUJP2j7QlPD4cgPUvb2TXrF3kHs/Dys6hex0NvwJ4taOkRgUFw9DhGo89q+PtXWHgx451krNXetjk5Vp07qYx7NaqJAFg/ESdUyctnntZJzTs3yMIH882eP9tg+BgRVAIdO6mcec9l1+rGv85XHUehScmOtm2RUojr19nEZegGDlGY/BNGh4ef/8Hyem02Lvb4tuvTd56reLL9MRzOiPuqqgGeTLN4uwZixbxio8+MBl+u2LLRuka+clsOa7/QMUb70kVxyWLTGY8bZCcJ5qDAFdIyhMiMKsn3Fje42TvOfj0ILy/Wwzu8AbwfnfYlSFk4dsk0S20CYPXrhFSsCYVVp0Q0aO/G4xqKmWg956DDadk9W/TIMEJ9YBMJAkrBfEm1AXikDpx24ELwYAwJBrrh5R9uaAyCHSDHrXAsGDhURlTSHvscE8Jd5Qa0DgAyixIzQenBmVlUK8+PPaMzryPDdaUl6a3udto81ArOj7WgQXXf8XxtSfwCvOkywudWTH4cwAcLz2PNXsW6t6x2MY+gPLyoux+H8jZD9tGQvw0CEyo9EYWwld3c3mxG5DOeVFUdktXE4u/jssN/04u+oqrYAlSnKjtJeNliIbg+Sscsxzwh5tHVh02yuDAFIi5Bbwv8R7smgQBraBGn6pCwm1jwbsORAwAr9oXvQQq7zpUYCDavfeh6tW/uLou690DNIX+wINofftVjLdrBenpqNvvxHb/OFRQEKbDgbNBLOTmopo3R3/2BR4buJnsY9nMbvkRZQVl2NxtdHyiA+0mtuHkppMsvPEbijOLcQ9wJ7p7FD1e68aZHWfY/dFekn5Mxj/Wn5AmwTS/oxkleSWUFZSx4sFV1KltUK+BIi5e45quEByiyEiHO4c7qR2riK2nKC2x6HOtIukYjBqrk7jf4usvTebPM0lorXjwEZ2OndUVdQUn0yy2bDIZOOSfMdoz3zP4ZI5J+hlJTQ+vIfPszbdVXG/bZpNhg5z4+oG/v6J1W8Vjz1Zko1V7FP4ZXHUehYcf1zmVZjF7hsmuHRauLrB3t4V/gEW/66qm61iWhcMBLi7//ofaZlO0TFDY7dChk4afP/j5K/z8q4YoU49bPD3Z4HiKRX4e/OtjxbszdYYO1wgPhx+WWug6vPmaQc1IGDlGo+Bz8RwUOeFEvtQ5sICH1sIzG8UDMKguRHrD0Pri7t9wCup9BOPi4Jm2ImgMcIPDOdDtK+gSCfe3AMOEn05KyGHKViEjIxrJ9qXJ4GqTaXurUzTbMcApJCJbiJR/KaYiQhsK1EDIwQHgQoKOJ1C3BMqOSKTXrolWQlcStmgZAr+cknDI3iwhKF528NTgvA4nk2DBYwbZhRAWDmfTwVnsJGPRJuo12kp9D5PjwPn0QpaPX0nbtGvp9EQHluWtYndBAfYZb9K8ZBVdX+zM29OextiVhdFzKxy5Ee3xJ9EH31AxkZ9Mhaw50OIN0CtlqBzdDKWrod5YcJFYtXgpdgFTgKeu8Mk4ibziJlfYVogEduxXJeGoMPBXit2DVAE8ASRcYdt8hGY2vMK2RVyZKIQBp+Cm+qBfkkOePAh8SyGwVdXx876w62HI7yQZABegu4gg8PhnUOce8KiUiVCWjVbvR1R/O1rrNihP0QY4xubC0c/R+nigDR5SkUHwdAzmrJlYRUXYJk2G8iwC1bYd1rR3MUaPhAkT0cY+gHJxIb67Fztm5OMydyY1EpfQ/ZWuhMeHs7h/BIkLCwi2nyXk2yfZkV6DJrc0JqxFKMXZJYS2CMHN15WTm07hFeqFV5gXrt6uxHSPpsXI5niFeRHUMAij1MCyLPyifXlj7DE6XJPMsSMwbrQTN7sY1xZxGneN0ggIVHRMcJCbbZGTDZs3WBQWSs2X+f+SteHy7y1ef0/n+Vd1brlDo0mz3/bO1oxUBAZplJZyMZX870SnLooevW0EBMEns03uHafh7i7f3UMHLUaOcHIy1cJwQlYm3HOvxt1jtL80v1fjj+GqIwoBAYqAAMXjzyjeel+/IrPNy5MYWVS0YupLBl17Vnx5/l00ba7R9Dcyb9p30li1QWPW+wYvPm1wKs3iliFOHpig89CjGvUaWiz71mLBpya5OfDDtxZzG0FseRp0/28kHXFYA9iXKSGExcfgoZ8kI6GmN8zvBw0DJR3yyyPQ5nNZrdf3h5VDoLafkICPD0howGFC32iY3RPyHfD1UfFC5JSCuwEjm0LXXZLt/QKyfjuJpE/3R/rPXSjAehbxPAQAHRATch7wRkzGKSQRTDdF9xBugWc6WMHgWmn+cdUkIyPWD+YdFJK07qS8dqclhaJKnHAqCda+aFAYCq3bwbbN4ChysOP19cQVbiT4tIR8jKIykudvx1niZMI7Gonph1hiWXD8OD4vP0x7YyHNRjTF7mHnjaO7KSrag6qfiv72u2h+YgSdCyzMiVNQbdOwjR6DiqkNQBlxsMEFvL+Cxs9IW9sLKA4VQ9W0NXhXLp4DnPgCLBM2Nb7Cqvpf5U+wDpdjKdCAqsV4LmAP0g/gSsY5EwkQXSlFygnMBO6/wjYLIUJPXGFbGfBx+XGX5rW7IumALbg4hVyI7Z84DGYJRDUE7ZLjjl4PbJLGQ5WrB9IMto6GtEUQcX3VZkVGEex5HBKmg08lD4FrEJSuQ3N5HH3Ss6haFfXrHcOzwDiLFm9HGzgIFVFTnsQEA/PndWj+/li1oqB2bXHRt4jD/GI+Rk4OltOBPvxWlK6j4uLA4cBa9BX+R3+mz3u9iLqmFoe6n2LRBxru3oqw7Z8S9t4qWj+QQGrXKA4sOIhPTR8i2kRQcOY8Hql5hLcKZ9+nB8hJyqVmu5rUvS4WV29XghoHsXvOHs4dOEfyihTi721J/JiWNB7WiBM/nWDXnN3smrMbXz/RPnW53+LsjTrPPp7C4H5Qr4FJi3jFtDk6WzZaDL5J43hyRWbl2k06mqZ4e6pJWDicSLHYvhWCQ2DcwxoDBld4RJs0k9+nT0mp+SvNlUVFFocOWgzu58TTU1LMGzVWjH1Ip2bkXzfWjZtWfCYmPFq1vHBkFMyeq1O/oeLDD0yGDNWumGlWjX8GVx1RuICw8F//kJQUw/69Ft8vMdm2Raovvj7F4JY7NCY/pRMc8s99wEaN1Rk19vIa2oNvhOsHWTwyHhL3W5BsMSYVXugA7WrAdwNhdwZ8fgi+PCzk4LZG8G5XuO0HqW8Q/ym83EnEjK3DpS/ER/uliFKbzyX88FC89IxIKwAvG+zIgNiPxDj3iZGqjAPriDdk/SlY6gOOfDH8oUBfhAycAT5BCADl24Yi6+fTwI+IxiEdyRLvANRGKtm7ID3r9gM794lZsZX/RDqlToSuoK4/7DkHRYYc0zxIUjUXHxM7uzUdrHTxQrhqEr4oKoKUb0w8Neh3neK7xRZZmeD8cjdnrT3E+ClcXAFLmmFlzv2R5mEr2X/dI9g97ZCZj/rxO2res5vrP+mPb6Qv28zt/FhUhD7vQ5pqW9l76/tojRrjMi2fsrbbIe17tJZpaFNeQStfSlmWG47obyH9CLYHX0OLq1gpl41oCetvhLYTIPKmCmEbQO5dsO8pCY14hFf9kBS4w6G3IK4f2NyqbnPEwJ4noMVQsF1Stc40YP9PUDcM3Ks2+QHgcFMIcwXf+pdvS+oJnhkiwLvU3XwoDvStEHM72C5ZQm6aDWEHIfqWqsfVHATr+oNZBpFDLnpoAInz735YigTVuhGX9ytSEMtuSkF5n0Pr50Tr0fPiat+5Mhjz1p9QagL6Y6+hRYiHwLIsHM1dMTdugBeeQ3/sifIsAUBTWHv2YQYFQ3Q0ms2OCg2Flgkw9xNc500nzmUD7Sa2xSPQgxPtTjDPprCSk9CfeJjWZ7+g4xPtKepRzMfhXpSdL8Mj2J2j3x+jOLuYgLoBuAe4UZpfimVahMeH4+LlgmeoJ2aZScbeDDL2ZlC3fyytx7Uipns0DQbXJ23DSba9t51dM7bTvZdixqs634bBwQOKQweLCD+2kYB/baKnq4XVC2YnQbMWiptv02jVRuP7b01+Wm3h5w8uLjBgsMZdozUCAsFul5TrOTNNFi80CQ2DgEDIzYVWbRQNGioaNlHcNFwjJAw8PVWVRVZensWJFIu1q8QzGhIqFWW79dQY+6DGu2+YTHvLIKaOwt1dWts3bKy4d/yvk4RTJy0eGOUkPEIRHaOIqQ1dumsEBf/5+dfTU9GwsRx3z71/vEdBNf4eXLVE4bcQGqaY+o6N9DMWM98zqN9Q+i/Ua6Dw8vpnSEJ2loVuA19fxc9rTDp1UVUyH1b+YNK2o+LJ53RST1h0/tJZ5XilIC5UfpuWrLRzS2H8WhjfEobUhSc3wLMbJPzQJ1pqK3zZHxoECln4PlkyJn48LmGKLpGwfph0odxwCt4q7x2xOElW9I+2ggGxMH23VIU8j5ADD+AaoDkSvY9FsrqXI9HkfGSd6okQhDaIOHIX4k0wkPVoPaQxroVIz+yIZ2JNopAMKNcyADEGtAmXsESbcMm0OJIrhMLHBRoHScvt3FI4XSj7Be2z6BwhIZa8XFjwsUXLEIuGPpBSJovWA/th4RcmD9R4leWhFqklkHHW5PiaE6wb+D7vzLSRf9xkoy/k5zk5uCCReI8HaRPVGt9IHz4OKeLk0Tz0xfOpW7aVa2f2xc3PjdKCUt5wFGFs30bAhKFkP/I2qjxebZuchbPLAch+GX1ABtrQoShfMZjGhhMYNy9HFd2BPvlFtHoVxts4mo/R/V9oLXPQpkxFuyRuWVb3MxRn0ae8cdk2R/9VULgFi49FaFcZeQdktR7/LvhXhEJcpuXjeO4g7P4U7dpzYqArFWFxTNiFtX8/evNctDvuQrlXEJ6yoWdgz8PoI52yaq9EFsoSjqI856P1Aq17d5S/iAeNNXaMG09BykPodxdhWRXHqeJCrMSDWL6+mD4+F0MDyt8fTBNr6VKchon95VeZ3Fx0KjP9S8k6nEWNs1uI/PI+Gg9rRHjLMLb2cbD8h3TcVy+mUcAh2nZsg3+APylNjvOVryvFmcVsfHUz6TvP0mdaL0KahuBdw5uic0X4RftSnFXE/s8OUKtLLXwivTm99Qxnd50lqGEQ4fHh+NT0xr+OP47kUySvSCF5RQrtOiheeE2j9yfw6SeKVcsttKRjlHyYTK0GMLAJrEi1KPSHrj01brldw9sb3n3DJCAITqTA6ZMmwcEaI+/V+OoLk569Nb7+yuS7b+Rb1buf4tW3bSQf02jSHCaMNXh9ikXDJoppbxqER4Cbm0IpyMqS7925s7DqR4vQMEWz5go/f8Xy700eGOWkfiPxCLRoqfjXxwYe7orIKIWmQXiEYsRdGgltFO+/bVK/IazeYCe6NixeaNKt15VX9JZl8djDBgf3Wxw5ZHE2XTLG2rZXPPKETmDQZYdU4yrAfyVRuICwcMWzU/6+l2iaVhXjXxlrVppMetAgOkZxNt0ioqbijWk6TZtrFBdbfPKhwag7LBo3UWxcZzExAZ5pJyLDC0WUNCWlmF/vUnHexCz45pgQht3nJJXRVRe3/ZYzEPcpvNRByMStjcTwj1wu6ZKHsqDJJ9A8GG6oByl5cEtDaBIIh7Ph00QhH7qS8/aNgTEpki2+DfgSibbvQbwMfRFi8COwAvnwbEYEjxaSrAhSn64zom9wAJvKx4vL/6+JkItsYDFyvUJg1W4hHOXds7EhqZ09o8QL8fNJWJsGBzNFKOnvJhoPL7ukftp1GY/ygfau8MkBKCqBzd9b2HYBeeDIFQ+F04TDifDxbBMfH4voGDh6BIpzS8j5YSs+ftt58FGNzT5Ocr3hfIGDxC8TqVWYyBvv2ZmpRqHZNIxSg4y9GUR9OJk49QWNhzUi1UjjC1cNx5HDBC54nQT/NTS7TcIfqf5pzC0qxLZ5LVHPD6P71G6ENBYvwDmfc8wsKsRc+i1WaQm8/iZaQCBQXjL3fCHW4m8wSkvhjbcubgOw8gtg2zZUpxHoz76DVqnRjPOeLMxlW1BF49AffQGtWaUYWlEh1s4dmP/6BDSF1q1HBVlwdYXduzBsOjgcaHfejSrvY69ME+tcBsbkSVimUUULQuF5rF07MT08wdeXSQO2YXOzkdkkkw/sGuaZ0/i8PIEO1le0uLM5SlOs7GCwedMpfNcvpkFIIgkJ8fgF+FEYV8j73nZK80ph+TJqOg6S8VYPQpqEENW5FpmJmeSl5BJYL4CizCJK8kqwudtw9XWl6FwR26fv5PT2dHq81pWwuFAC6gaQn5aHUWai2TX2zz/Akz028XO4SVKhRVZiJnUDMrmlzz761dZwucZgi02xeWMJ26ftYM8HO7j/IY33n1EsXqTYuV3Sf+NbKZKPwZfzTZxOaSp35jT0udZi+AgbWzaaHIuVz9zM90xmvmdSKxo+mKsTHaP4/juTJQtNTp2CRQssko5CXALUb6AIj1Dc+4BGq7YSRvhxqUlaqsWZ0zInRdaCNu0VZWUQXkPxyluK7r00fH3hs7kWt4/USNxv0berE5sNoqIVhgEnUy1691XYXSyees5Gg8YKLy+Lu0drFBRYbN9isXyZ9KZxOMBuN7j1To2nX9AvIwmLvjTw9BDBZLMWMGCwhAneeNmg73UaHa65skCyGlcH/quJwt+FuR8aeHqB4YTNGy0mP3V56s8Nw3QG3ajxw1LpEXEyzaJTgpNRYxVxCRrTP7Th5gbj4x0YFry6TQjAnF5i9L9LFj1Bfhl42OGtLhDoLpqEhoFSofHO5RI62HNOjOTDCXCqAGbthZMFIn78+SS8eg1El9d2cZqyOl98DPZmyk+4p/SPeL2LuPOv+QJ8XGFVKqzUINQUQpCJeBW6I0b+HCJV24V4BBTSy+5aJKP9guTvNLAQ8ULUREIPWvk+nZHCtk4kkdFECEMuQjDigPrAMiR1MzAPkvbKOS+EQYoNqeEQ6C6VJjOKRefgZRfxZmn59lahsO6U9LX4Llk8NbX9wKdMqk06yiB/rUmoP7QxICZcmm4lHRYvhKcXFJ5XuLiIHsLphFU/wpOTDG6/ayY/RBhkZyE/O1MJXH+Saxsvo1C32BBukHoCcg9mkDNvOfV8VjLkJp17S4bgHuBOcVYxx39OZeWE1fR+pydBDYJwFDrRXXWMoiJYuoTaHKDv+33wifDGNExes5s4Ch1Y3y4hxpJtvpHiWZgXdp7jB5xYP60m4qFrufaDvgTWlZX82kanWf91Cdrm9dR8cQS93upBaFMhJ9sTUvlhznlcd6wj2ucEbWp9T2SHSJRSbIg5zBrTwLZvB7XDsqm3agv1B9bDI9CDJZEZ7AXIPIfbEw/QxVxI3MgWaLrG/DgvkpYn47pxJXW8kzhXry3hcWF4BHngHuhOYXohxVnFHF16DNNh0uKu5phlJppNIycpl11zduModtLxsfZ4BHvgG+VL/sl8HIUOjDKDA18cxHGdg9i8nST5WuScPs+h+XupV7qPXh46d/eBh5caHDwAKUngknmaopmf03SgIsbdoCBMsW+PhZ6SRN8eKTSP07hzlMa8j01OpFhs/AU2/mLQ8RqTgTdotIhXHE8x8PISb0BCG40Vy0wWfGbh7i7CuiOJFkNv1XjkCR3LtPhmoRjYD6ZZrP/ZSWAQXNNVUTNSYZkWnbpq3D5S4+fVJuNvMXCY8rndv9ckZzOMawnXp8LI5uBltxhwm4mbDsEe0BR4PB6+9hU9U1kRrPrGKi/tbtEvBuJ+MYkOgXYZsGKtwffJUtMEwO28xeAY6BwBp1eZzHMqdm4XD2dtX5jWHc6fh5ASaOcHSf6yiLi/BYR7mVCe/XU0Bzb2spF0VMK8O7eLSLJ2LEx9RzRhL73+50zMxpvKyC0VL2iMr6RdF7z2x0sNV+OfQTVR+ANwcYGVP1hsWGeSlgoLPjUZ97DO/RO0KqEMXVe06wB7j9nZ+IvJrOkmC7+wWLLI4JnJBi+/qfNaJ+hfW0SFPWtJL4U6frDtFlh5Aib+LHqEb47Cix1FqOjvJu74DTfLdYYtleqN3na4pxk8uV6yJhrPFWNY7ITr65TrEqKlhfXec1KjwWHKNeclwktbxYORlCfFmqZ2gYYBsv2DT8Q4pyGpk3URj0IfIAmR3HkgxnsOklpZXhWaKOBGJNlNIVI5E5G/nUQIRAQSilDl+/dAQhwm8DMSvjiDaCDsQC3Eq+GClNKpB8Tlgl+uEI81CCE4fV5CFYYlv/1cpKx1tI+Qp+bB4pF5a6e03j6cA3llsl9miXTcTC2AQ4kw6zmTzBKo4QkurhJyKXGCY4fJ2Vzo7AI5AfBtHhQXwfYtJrGxiuYtLRo1AV8/2LUDtm+1cDgMPNxhmOeXbPI1yDEhN8dJ8ooUtt8xi9ffs1FUaPEpBuhgGBZHlhwlsvgYr7xpo3asxVSHE5sdnA44+t0xosre56WpNho0UizJclwkMyfWnmDbbTNp/v6dhMeHU5xVgu6mY5QYHF99nKV3f0/f93tTo1UNNE3hGepJ4dlCkpcng2Xh6udGaJMQgpoEY3e34yx1cnbXWXwifaiREI57gDveNbzwCPGgKKOIkvxSEhceonPxSu4cpbE0y4ndDiU5JRz44iAB6QcZPsVGwyaQc73BR7OA4hL8Mo7S3UiiZ9pqEnqbPLTEJDsLCnJK2Tl9B4HJO3n5DRvHWxgc87DYtsVCP5VGK+cpRtfYyskxOufOGZxIsTh2BL6ab7F3l5PR9+v4B1g0aaY4kWLh4gINGoqqPv2Mzg/fmdStL6v7xyaYzHjX5KZhGrffrfhyvqReN4tTDBmqkZsDU18yMAxIOgbHjpp8+7XJ8BEauw7Z2L3TYt7HJgf3S7bTpx+b7N5pUr+hwssbSkqgBxbDg2HrSovCPKhfAjPelev6uEgIcGICPL9ZEj+aBcv3qMdXcCwXsorFY1fokFLtXnaYvU9qlET7wiMJUD8APkuEB+Lkb4BfTkpYMdpXvuf+btKVdmwLyUL69pgY47bFFs5gaWF/cwPJiLoAy4Je0dLK/lJ8cxT2b3ZS1w/aOMARBI+2lmJw9jVO+VKWI//VXzf2mecsku53cDBbCsFd6Job4SUartsnlaGp3z5HNf5ZXHV1FP4v8dZrBieOWwQFQVCwTATNWigaNVG887rJXaM1/P2r3ufe3SY9Owpbd7MkNPBsW9ieIZqC75JkddsyBKZ2lonhWI54DfaekyJG58vgwXh4sYN4G0wLdp6V4785Jvv6ukoK5Q/Jom9wmBDiIW72QoeEF1x0+KCnaBdAzvPQWnh3l/yvEDHhoFhZka/cIz3tQhADn4EIFMt3Jxy4HmgPBAG3I1kQVvmPhnglFBJaqIecL44KAnEQYatu5T8OhBCklP9uhzTqdUNqMU4pP68nQhg0xBvhRFr5+CCCy5aIyHIOYuBrAYFAgyZi7A9mSSgHJKVTVzIx6Rqkn5fn3itKJsh6/hIWemcXlDplIu8aKYTMMCXMszhJzhXrK1U2d2bAmfNCQi701WgcKATug71CZBy6iDN9/eD6wRpduilmTTdJPW5RUADnC6BBIxh8o86gmxRPPWpwYK9FXh7k50HjpjBwiM7gmzQ+n2ew8AuT/DzIzpYc9P4DNYbdorFnt8XUlwyKikTLoetiNB95XCc5yeLh+w1MU0gGQEJrxdR3ddJSLaa9ZXL0sKTYRdSEwTdqjBmnc/CAwcplErpRClq0FKFcn/4aq5abfDjTJOOsxKjd3KBdR8XDk3UeGivu70MHKS+YBrfeqRHfSrFrB8yZbnDiOPgFQI/eGh07K5YsMtmyUZ5J4Xm5x6bNFXfeo7DZFUcPW3w21yQoCPoP0ujdV2PWdIOkoxZZWZBWHssKC4dBN2i06aDYtd1i8SKTyFqKwTdpKAu++sIgPR3ST8v7oikpLnZPUzGW966CA5nQIUKI5/ObpG9KsAckZksNlBGNpPx6sId48WbvhbNFkiqcWwodasjC4IXNQvJHN5Pv3AV8ngjTdkOLYOkcGxcKA+pA9/Kiai1DhQyvPwWjmsGY5pIRBaJT8r9EA3sBafkQ4H5lg3+uPPT5VyIDGUWy+v8j58h/1YWFXxhknIWY2op9rzjZlSHPyNdVvh+PtYY7GlclLZWPv4CCAotPPzapFa2IihYBZ8MoZ3Udhb8Z1UThL+DL+QYz3jVJTrIoKZbKjJOf0rl7jIbdLvdrGNIx0ney47LjHQY8uBZOnpdJyUWDia2kkyOIgen3tRQo0sqzBKZ1g3qVCst9tE8YuMMUzcL5MjFijYKEnc/sIMZcAAAgAElEQVTpLcas0wIx/iDCyA96gK+bEI7mwUIiLGQi/ClNViz7MitW/AMRXcFyYF/5WCHSOqcEOFb+twtCBAYDzcq33Y4Ydo2K8tCZ5f/nIQ15+wKtEE/DS4juwZ8KD0Ip4mG4IIIMQtI3GyMhiufKrxFdvs2v/H52UlEYSkP0EJGIl+IIEhaJLX9tgUBkT5j0s5CoFsFSiTLGV8hFcp6EZ0BWc14u8nryy8TolRiikWgZIl6hSG+ZQE/kiYaiTwy0Cxdi4WGTcMjxfKmDcVM96RD6c5q813MPyn6R3rLt/jgJMYF4o1LzpUvooLowIV7SYY+11fhxqcmRw+DtA336aYx5QCP9jMXGX0TRnnTMorQEuvdWDBmqExAgbvLtW0xOnxI3et360LuvRmQtxYz3DHJzxUA7yj/C/QcqOncVwZ2rK/zyk3zG23aA4SN0gkMsTp9SfL/EZOtmi4AAuHaApOMtX2Zw7AgcTrQ4lCjni6gJXXsoEg9YePsoko5apKVC8xYwYqRGvfoKw1T88J3Jinkm+aVCdmN84ZP9Qox1Tb4vCjHuHSKkcdqec/DmDjHo19URcrfyuJDzCzVMANqGi47nxrqibfkuWQhh+xrw9g4xwrll8h1ymKKDuauJGPrMYiHbR3OgdZhkLmlKvFPnisVrNaCOrOS71YK8UjnHkG+lUVtqvrzXeaXyPcwsFuLaNVLu380mn8XmIdLozWmKlyCtQLxmHnYJQT74k9RVaREi+/eNkecA8j3feFpeU7Mg+VxXhtOs1BTuCihyCLmp7SuEt3G5Uf8z2JMBm86IPmr9KdheHgqJC4En28oCJbdUPCb230lsWDvExqFESdf8bK5JVqaQlH7XK75fYlUThb8Z/3NEITfXQinJTvg9GIb1hyqQrVlpMm60E/9ARUAAtGmneOhRnaxMmQQ9JznYlVFBAP4IvjwsX97e0TIBphdKNkP7GnBNzfJaCx4yYVVGoQPWpsKULfKlVAgRGNdSJrGnNsCOdLDKJ6IlSaJ/CPGQVVObMJnkHlwLDQJk5XvqPBzeISv6w0iIIQDJ2u+LVAR4GxE2elARgihAivDmlo8nIB6IRoihvhHxQLgg9+mFhDtyEGLgipCNjkjq5T5EYFkfSde0IeTgFCKYLC6/rgtCZKLLtx1GQiXtERKjl1/jMYRQBJbfR2j5vSYhegwoF48iXhXX8vsrUvKcvF1kUg5yF1L280lZFXWPhChfCXFYFjyzSbJNWoUKUXCzS/vw8w7xUpwvk4m3dZg8B6Ugv1QI4HmHCDmvjZFz6EqIybYzkF0qk3X/2hDhCacK5f3fnSEEJNBNyEfbcBGyGpZoObafhTq+4uZuEy4rVAV8dEAMTYMAEb32jpJiWRbwwR5ZNdfyhoGxQlLWpsm9nD4vnjAQQ1LfX67nosvEn1ogzcpuqCfemdp+YnDvXSXP8NoY2bY6teL+92cJ2QpyLw/96GJU089Lym+LYFm9d46UZ/BDitxjbqmMnciHfeekNoebLsTApkm4b0CsvIa1aUIConzkOzUvEQrLJASVVSzG3EUX4jExQcjJyOWiB7q2tqTv7swQj4FhyrNQQL/aMKaZGOplKXKvkT7w8X54Z6c8rzJTjjnvkGcW6yfzQ0EZzNwj3q0oXzieJyRiWAMhGvX85fqnzsP8RCGUGkJavV1kvFOE7OvlIk3h0gokY2hJudfLVZfy74+1FjF1sQPe3gkf7pfPb7MgCVvG+AoxPp4nn6efTwqZ0RXcWE9Co5YlHoCGAeV6qoDLScgFzNwjJKF+gBDvr/8fe+cdHVd1dfE96l1Wly1L7l2ucu9gIEDoNYTyGUILPZCEkgJJKAnEBEIgoYQSIKEkgVBCMdUFbGxj4967LctW75rRzHx//O7NG41GzZZIAJ213pI98/qbd8++++xzzhZ0Vd/q23E244x/8Xvof3G43nrDp8wsGKueOequzNgF9o0BCs/82asVy/zKzXPpoXleXXZVmK66PnTDkmef8iouXiotllYs8+vHPw1X/wEdP/6JR3m0eZNfM5Nx8sf2wVmcNkgqMoP6SaHq7oiB7/kN6Bay4wlDzOwN6/D5QQbqHRWEI64dy4zKms/PrKa+EYez8qC0qwq2IC2WQf3cwQzCD5mOrAkm3rm3mhnD8iKAxANHOTHP+kbpjAeh8quEY/aLlMdYwRKMlHShmO0nS7pPhCrjzLoRAjxUCkYiQjj+EwWA6CtprgAAGcKpx5h1N4kQiMxnA+UwCk+ZbWeaJVIwEE+IrI1Ycw7pIrxRZM7fZ86hpwiNZIuS1V8IIecYOeWsvZJ+ZPY1XDAePc11HZL0T3NuKQJ4pEvyZkgHa6TCWr6LC6BSo82sqayBvyPSAISZccwiS+pwstHhOO3VxQyOMeGst6kM5zcnj5BIhRvHVlqHc6j3wkL1TZRWFRNicXulqgapwcwgLYiICsdxHaxlu+hwHGOPaJbIMBqT+URI5lt9pOwEZn+byqRffAr1fOZgaXI2n9V7pc8KAayl9XQvrXTjxCNcXOMhg+6GpuJkvtUHputni3HUZwyENdlViZNaUQSg2F3F/dpZSWOy7Hic7OpirvmcwdQhGZJKCOCXnxLfH5kuvbkD4WBpvdPcLCKMsMAVo3D0v1sBs3bWYJ7DmmLuTVEt74/Pz7WfMwTtT1kD7+uVo5uD94V7pd8up4Nr/x7cv/Awae7b3GefHxC234RVJmRRL2VOH+nlTQD9vCTDLPyFZ5Fj7v3zGxlXrh7D570TYQ3+vZ1nkhLDNY9Ml37+CUXWLh/Fs5r0V569z8+92VDK51eMBuhsLZfe3WX0PUnSH78A0EzuyXfbK9BKfbCHfaTESFeNBlS1BBas1XkAKZ1hpXVSxO8JR3SXcO4a+8YAha2b/Vq2lG6Pf3sW1W5CArXEL7/aqVAmSU8/4dWSxX59ssinXTuI6Z4/N0w/ui1ceX1Cn4ff71djI4VPdu30KzNLio11yXOdWw98Lt25hPWiw6Gaq9zMJk4dID14dPPBxZrXJ53yqiPw6Zsk3T9bunUhg7HEAPzgUdJbO6WjcyngtKOCAXB0QP0dj1ea/RIgIEzMaoak0L662s3g+ZsZzKxuW8Q2iZGg/h9NYFBIjpaK/tT0HCslXSAcr9eet3DM0WJWP9asM1Y40ackvSiYCZcABGGiJ2CV0BykiHDEBMEQPCNm/EeLctIuEYrYaPZlf8hx5rtswUzsFdkXI0WroGxzvB8LkeZk4fBjzLHnC4YiQrAcWYKlKBGAwGPONUywEZZt2GzuwVChlRggwFKxpFfMOUwz15Jitlku6W8CpBwt6ahTcQgSVPG9y3CI5w7FOaSZAXh3lXTN+8z6zhosFWRCQUs8x4veYoZ5Un++d3tNjQ6f9MMFOKXj+kpnGNDqcvFbe2GTtKSQ39Q0I4KNj+C3sqcKZ+ISznhvFcewAOSgcfr5aVJ+Og4uJoJZe20j/UvONiXJ6738Du8y78WJ/WES7PmXNyCqlZjB5qfTTTUjTnphIwBj7nD2mR0PiHhrByLV9BjA1vIi9hUfycy3pB7Qc2wfdASzcpnl/nMLYKTagJjiOva/6qATmpjWi/btw9Ok6z7gvp0zxNl3pRtgcfX7OOpdlXxe18hxrU5laAr3aUQagG90BoBgSSH39/ujpfGGeax2wxB8sl+6fD5gamwm7+zCfbA7g3pw74akSnd8QvhjbzXhjL1Wz5EOALok33HO7+xE6DghmyUmHNbhvGHO7++3y/idWaAxKIWJxvMbOE5/k5lw60IYj4vzQ2sgOssafWasaCVEUtkgJf9B3UChk+0bAxSsffYpJZT79ncpry8FSlqyP/zOq0MH/eqd61Kv3lQWG54f+le6e5dfM8Z7NHqsS6lpLi1f6tO8UQzS28oZaBMiWaLDpYveZiCMjWBm/6tpoUMTdrAJczFghoeZgkweBuiIMD6raGDQ+2A31GxOPMpmW92xqJYBwAr2JFD9kkIGuKfWMVDFRhArvGUiA+3DqxhAcpNQTi/cJ010E14YJWb0/SU9JulK4VStFUm61PyNEo7VL5xvoqQtYmZ+itAI9BaagrvNPm0INE5kP6wRmgYJRzxCTqbEK5JmCC3FGDnFna4126QLliLdnEOh6Flhf/zRQh+RKRgPm+mRK4SRGQLU3CMGqxlm6WnWL5V0vQAGQwQQsJUMKgQY8Jj9JAuw0CiKXK0234Wb7SzrUScEpLY0V4LZ1i8AUrWcME+apB49AlgDtzNbT4/luWfEsm2VG9pcYhbfKwF6PCIMB/3oan4/J/bHEYzN5Lezokj61aeEKy4dyXdePwD0je3MOEdlAFp2VEqx4fzu91dLS83xUmM4x7wk3oX91QDevEQA7gn90HjUNZLdMzqD33CDF2Bd5WZ2//Q6fqsJxlHnJeHE1hdTkGtWrvSdITRRS4zG6f1rK2GHzWUwCWUNgIJ1xQ4gmJAtXTsGYHLbIpz2Jflcq+S8j6uNgPj4f8BMJEQxK19Xwrs6tSdhgrMGU8xseRHOOCcBB17tbhqiWLIfhqZ/MqBtwBNcb04Cz+ftHTyf5Gieb6XbpPsmAy4uyWeikBTFNnWNhDmuGUsowuWSbl6Ag99TBQA4WBtwzWOl2b0JQeyuAmhsLefaXEKEe0OBI4S25vNzT1pz3p1lB2qkmS8yjgzoAbtx1RiH8ZS6gUJX2TcOKHSl1dVRpOSO27xa/hm3dVZvZvuBM/v2ms/PoGOtsoG47tF50M1ri6H7rGDJWkMjqVWL9vP/CDMj21wm/WSydFNBUzVxfaN03zIGotWHoHN3V/L/2AhAxv+N4LsPjTIwTU7Ph/Vi1hwt6buSLhGO3C1m9AnSf9rr+sXM/GaRQREjJ+uhXDhSm/EwVmQ8DBQswGUCHIwW9L+E1mCJpPfN/8PMvqIE61BsjmeLPOWbfR2Q9LBw9ieJsEW1YAyelPSJ2SZRgIdEAQY2mH3KHCPCHDPRbF8ldA+xcjItokTKp1+Aqx7mu3hzXx4z1ztbgIRUcw3hQtgZL0IzPc19jTb7vN9cxzECzIQJgBQhaYEAT5NF5kijHC3IfkkvmGNNNtdsWo6oWtIK89mYTH4HcRE4gkYfs+M641RTohmkk01oYsFeHNjYTGbMtlBWYTVCu2/1lS7NZ2Za72U/L24EYMzOdcCASzjywhoWCQd4VC5AJDZC+vligMUFw9HYpMZyfjd+hNOd3EtadoD3p8qNwyus4Z2yYZXvj8bZ/HgBDMrcIEBQWCPd9BHX0zuBsI8FYD9bTEgi3wCjn0zmXj25FvBV0yj9fTOTgZhw6Pr7ZrGv3y4HSISHAVYO1ABiXt8O4zEuS7piJGyJyyWV10sPreQ975PEPb3xI9jI4/s1HSOs+f3N4/4vbGT/uYnch98sAyBMMpXEy+pZJy8J4La8CFB2zRhCJq1ZrXlunRVOsFbtlk74J/fRhuoKa8jwunWiNDKj6frdQKFrrBsodLL5fH4tO8+j+EgG0KQo4nWJQSnAFUbdGx4GhZsRi/bg9k8AFzMNur+pQJpqgECdh7bS7+9mKakjLj0uE2FRdDiD1YXDGdDiIhnAD9ZK573pOPlBKdK8mdKkXlC0EoNRXCSxams7KqQpf2VgdAngMLUXDuDTRTieq0ThpH+I2a9bUO6nCif5snDGXkGv9xEO8yUhZgy8LY2iV8TrYubsEg7TK4SUNebfyQIsjBWz9ncEYDhbOE2rg9gu6WrhaBPN3yzhDPcJ0aLMceLMvjIFsNgoRzMx0lxLqnDctkrlDPNvC34uMPsYI0BKtrkfFZJ+a9brL4BCfwEEaiS9Z7ZPNt/Zktl1gnU5YM4xxWwXKwDYIQGqJIfByDD3qEFORcwsc13DzX69orpmpRzgNMYcX+YZ+EQoZIwAN5YRuloAldPNefrNc6sXgtYaIVotNPfcJyc8dFAwExEuYumpMTj9BXthJgoy+e1/ewC6g4dWoq+5fBS/u3rDlOyvJstgcxmz5+3l6BkiwhxBn0SY4NQBvA+pMdJV7zuhGPs++v04o6hwwgU/WQzLUFIHKDpQ4wD2XvEITnsnEgqscZNJsb0CR5YUDSgYkQbIencX53rFKABNYhTx9AO10kOfk1mUFAVjkBAJG3HBcNYfZ6i5+5cDqAqyWDLM+1rlJtQyLhMnHgwWyuvJjhmTAbhKOIwSBKHARiiraICVOfEV2KsRaYQqrhrDtR2J+f0wWUNSuX9/2wADYpvpBVs3UOga6wYKXWBJN7vbXOfupQwCk3sRd/60ULp3ppQchbr4oz38lRjofjND6hnw0tV6SJ3cbrQIbq8zS5vWS3p4jsNi+PyEPxIiGTDiIlAzX/IO/56WA9X80mZp3iwGv3oT7qhrZAAMc5GCtfQAaZh/Wc8gGiGc9jnCUX0qmAK/cA4HhIOxKYkHBMW/SzjZS0XowU5E6gU4CLZdkn4pnF6cHBbikAhXGPJE4cLx9TfrvimAy3AxY+9lPn9OMBHfEs4wU4CNQ8JRrjHnmWC2iZTDKNjwR5Q5XpRw8KWCUUg129kiUXECDLjkiC9zBHCJNcdLlVO1MsV8HitYg0bhmHsIZx5lrv1VSYsEaBlpziXCnGuxpF8LMDVVOGx7vn7R06NOsEKlgl2w7PE6EQ7pI1ieWLOtXwCwkoDr7222jRBAYKs5x0FC0DrUrPc7c70nSzpKDoNxSNIPBCDrJQCOzHFqRIVOv9lHsmiHniSA3gZz3ceK6qDp5tznS7rtkubOpMbjxNA9XmbzZfVQ8We/TqigdwIz7GP6MKu+5gNCA3NyKYi0ptgRmWbHAx5qG0ljLMii5sJ5Q3lndlWyD5cL0D3yGd717HjCFG/tZIJw6Ui0ReOyYBV+ugjA4XI5eglJGp7qaDL2VxMWLK0HbJzQj5BhjccUHqshK8JmYpw9GG1TTqLabW0BhR9+RKZMVBhMzA6TBnxJvnTd2JaduUQo6J9bnEyJ0Rnc8yOt8twNFLrGuoFCJ1t7QIK1KjdCpds/wflKzHh+N5uX/fE1Umo0g05WHIrylhC61yddMZ9CQnGRDB7fH03K0/Qc4pS9EnDw1441dRP8DGaL90u3L6ZCo0Tc88R+fHfvTJpOBVqNh/hn9SKczCHhmHYKJ1IjnOFI4Xx6C2eXK+orfCCKLSWK2W2eGOiHCNp/ttlXHzlpjtuFWPAs4XCs+UXj5lizXq6I7xeKLIl5wjnGC0cTZ863VoCScOF4BgpwkS7CGI0iVFEgHJh1/pcIYHSGcIbhZj/7hEZjkNlXLzm9LspEmMMjR8g4UAzg1SJMUGyuIdXsI0w48l1yQFCScJSxgqmoEoyD5DAiWQIUeISzr5MjyMwXbIBXsEDbzLPJEayBjWB9IMIP08znkwRICRPNvdYLxugYc04+OWzQCknHBRzXbZYvJC0052GBlgTAsIxEmLk/R5vrdAmwM0OAoSlyAOWbgqHKk9PyvEb8Hm8z92KspBNPcGbnN3yIk+8ZD7uWFssMODWGMIWtaOrz49DXlpDO5/YS2ghz8U6kxQDevzeSkN47O2kLPzZQoBNkdpS1jrC0jmZmJ/aV5r4D6D5Qg36h1sPvOiuO/feMp5fLXUulSBd6oZ7x6IoyYnnPj+srzX6RycCQVJiGJ9bCqlw1mslAoBN+ai36pLGm5sLIjOZCxMJq6fh/cqzRhpmY3dsBG4dq2caKaP+yjklGYNGn93cBpPISOf8dFUxuXIKxSY5Gp3Ht2I6BmJasGyh0jXUDhU628Bvd/3nhbKy0NfP5pTe2QcEmmlzotFhmDQ+vRAg1pac0ozcziNsmodhuj3iozkMq2MK9FDjZVcnL2S9ZeugotA7rShBsvbLFSf/aVcH6tnvjFaOow5AQCdhwudh3WYNU/KhzPL8Y2DcKZ1AnHHKccIAbhJP9lqiNkCRmxKuEA9ghmlE1ChAxRDi2PWK2Wi8c/mmSvifCAW6zj+mCrciWE85YYP4GOhhrTwjmo5/Zj3V4JSKMss/sJ0mAh1rh8GvkpFTGCkCQbs53kTmOBTh5wllGSfq9HBq/j9lvvLm+HwsnfrLZV6xgDGKFlsMltB8DzPpWh/CimHGfLtiIMDmsQr2kKwTQOUVOVkm42d995t59S04nUGvLRDhniniOtnS2XzjkheYY9o2052OrZMqc+yjxvGMEKJkv2I3ZAgzYUMdj5rrnBHwmc69fNt/1McepNcsmSY+I510pwFeWud/LBDPRKAd8JUmq7wGzZtMnLx4hDUiRTnmFtEqXEHPWNjopk8f3Rdg7LYeSxw1ewERwxUCfn8UWLWpolM58nYyRST0JWYzLcpxqjQc2z+Xi/c80KZ7psWRVnD6INMriOt7LwhoqNr69g5obBZmcS2wE7IEVhdY3Et45d7B07ThYDomQ5fPrmfHvr4YRKTXVy2b2hgE4YxDN5z7cQzg0KpxjVDQw0bh2LPUntpXDXg5LbX4fPF7CRTsrAQUL9joZW4lRlJu+oYCw0aJ9sA/BYdkjsW6g0DXWDRQ60ZJudmvUM7yMtiHRqHTp1zNan220ZpUNzPj/vhmhlMQs4KGjGbw8XunXn/HvyT15ias9CL2C7VefIqRq8DLDyU9HEHneMOnu6Q5b4fdLf1jJgJkWA3DZU+n0RxiXybZ/+kI6w43TThKz3z4hrqFE0k8lfSZAg62NUC1m3XZmO0LMhIeIWfWnoiZDH0l/Eo69TACHBuGkc81+U822tcI55AjntVyAiPOF43PJKP/NOQdbjRAe5ptzCsRjlZJ+LsDFUHPsKOFMi8x3mcJ5pcrJbKiS9IY553jzXS9zvjXmvtmKkzazwc6ya816Eo4y1py//cxuF2O+t4xBo9nWpqsmmfOKEECnzJyzhJO1WgUJduNDAaBsrYoRZtvdglWYKUIIk8UzlSjt/bFw+COC7t0Oc28CAYk1m5LaR9SiqDdLnRyQaK+xv7l3EUJnkSEYj/XmXMaI39tMc49ulJO5UiNpmIxw9QTCCpN7Sn0SAfRH5xLee/QLNA8XDHc0PI1kVIesXljfyOx6xDOs5/Objqwup2DWxSNgIHITWfedndLNC4m3TzTLnLymjve2hdRD6RlvUkDdVO48ZQD1GkrqABq9EgiB/HIJ7MDcEeiiDtYCCvZVo2l68HNCGkNT0RHERgIQRmaQIuvxEYqJiyQEEx1BiGBOHue9u4rJxpJC0nbDXYCuC4ZROyE6Am3Eqf9iMtLXvGCPr0EQefkoxpKutG6g0DX2jQIKfr+/w61O6+v98vmkuDiXPvvUp7R0lwYMCr2PpJvd8vt5Md/aQe6zxAt17VhSIK2oyONtu0xpoC3YaxpBRRFWSIqCCahrJE99oSmJmm0KpxydSxe4rDgKopw5qCn1eKgWseTdS/l/fCS1EtYVOzOgsZkUqrl+XFO6cdVB6Yk1aBokHP9o4XhGSvq+GIyt+QUlnKKmzsMjBHBvCefoFY63Wk5ooFHMKCcInUGuiINvEY6qRtIdwqn1Fc7uWjGzfVI4PEubDxYOI1vE6IeaY7qEw0k3+7lfxL3PDnEdLf169sgp3BRspYJtGCvAR+A+3JIeNOdiwxyB9rBZZ6LZPi7gu6WiS+dkswQmv3iEpmOonNoNgcd9xfx/mrj2QFsnwESws5eYyS8QrIANn1hbI5x+gZzUVmsl4pnmimeyUgAnmxnzgQAGaeaczzT7+If4PRSI39kTZp0S8eyiBfgolvMsI8XvKUmADJuJUidAmi2I9ZkAH8dKuuFsUiqtKLDKDfU+oAcz6kN10hmvQfVP7sk78oMPqY4ZGQYTUGrKQg9PQ6tQ6YayP7Ef78o17/MbyoxjP5/s530+fxghxxc3wQBMyEIMGRdEgy0txIG3RNEHagp2VUpHv+SAiOx4GMmpvRAZnj4QVrLQAIm1xYxXKTEce1ovutMmRwNIxj5LxkVeIsDlwZWc8/XjEKC2NKweqmUfUR0Y647EuoFC19g3AijMf8unpGQpuYdLv/6VV9fcQKvY9tiij306/6xGpaax/eqVfp17fph+eFu4Bgx0zilYm7CnEmo+K45BJDxMuuxdHPKkbBB+/x7SDeOaDwiHa40+6aRX2He4KaCTm0hO+8ze0gOzmzIb28oZXFJieJnrG6kE+dkBYrafFzllg2f1ln5QIP19C1kPQ1IIYyRGSiteQEPwe+FgwkV44AY5s3Y7uw0e4zxqHhaQmCHeIqcQUrRw5EXmu0DxYrKg5XMkfSQo6AKhvh9pjvGeABdZwsm8Y85JYhY6RQCTIgFc/MJJThVCzd7C4b8k9AmvyslS6GGO8WehtThdgBJrhwTrMFCEXxoFcxAvwMVdYgY8x3weaGVyhH/BtlI49WPVtH6FzDHcckpql5t7ZUMQ/xKajzkCZAQKSHdKWizCGY8JEOQPWNaI5xwnwiv2WstEWMJWzRwk7r9X3OsHBRAaKJ7lEHO9NQIQ9RPPrEroRGyYJF3c6zDBWPQw5/1tAZ6qJT0utBE2s0PmXJ8Sz3aomoKag0LMeoy5jgZJKZfg7Evr0fr88GPWjQ7nHfH4AAITsmEI5vRBz2NZhsdWAwwmZjd1nLas+tXvOwxCfCSpiMnRvEcHa6Hq/eJY38snzGjj/VVuJ021JXt5E9kgNkOiIIv3NDwMTcTeaqczpdsrZTwCgOidCAB5ZSv/v2YM6dAt9XIoqQM8tZUy2dn22lbYjKGpLDY1N9C6gULX2DcCKNx+a6NWrvBr+1aazUjS1OkuXXdTuL71bZfCQiUiB5jf79e+vdL3L2nUksV+5eTS9ezGm8M1Y3ZYuwWMK4vIblhaSFnZSjcv6K+mIY5qj+4guLZC0/N06tPbdX+6iHBDSgyirRsLoEDbuGRJ0JzXfsBA2OgDSOyooH7+6AzKzB7fl0Hx3/NwMJlmeU7k6vcXjiFK0msiXn6FcCZvCFFcR0OUzwnH09cs8cL5bxFOV3JKNocLJx0vHHaCcPXxlkkAACAASURBVGwpAkyME7R3gnDwFSJcMUTSu8KhWJHcITk0eJqgvAcJx7ZLTi2HKMFaDBJOLVrSX8UM3Qr8GtRUgOcXgGmYcIJp5hqeFw5yphAL2vBCtPnc/Jw1SLA6U8x3EaI41ACzz3+ZY1jhYb05b4lnkS0nJVVCNyLBAEwSzy1GgIG7hMAw0tyTHPGcd5nrnGW2DTPXEGn+/ZwcLYJlV4YI9iBCPK8Yc26rzPkcI3QUE8Xva7/5dzC43CmeqdU42BBUe0eNZUK3kSQpbxD1Bt7cTtrmBcOh8xfuBXAH5+6Hsvd2SZe+i1ONjYBF2FzGO3lSf6o6en0mAyIehvD6D/n8zEFOdcQ6D7qhxfulK+ejcxifBeBIjJTuXY5+oajGaSgV5pJO7k94YlMpmoNxWTCE4zIdnVEgA1HfCGt5TJ/2jQ1dYetL6CsxIt3JFnl+g7S5lOJP60rILpEIff5wPGGPwHGzGyh0jX0jgIK1D9/z6W/P+jRosEuDhro0eIg0aIjrP50eWzO/36+DRVJGppoAi5qr3cqORwS1/ED7UnzK6unE1iMatiE9FsFibojfyt83Q0lO7sk6D61EADQsjRxyl1FiZ8eFbsla38iMKPicHvocWnVCNrnZT6ymDKsNjTQ0Nt/f7krKxGbGMbhlx3G9GXHS6nms45NDV9eJAXyrAAmfBewrXTjlFNHb4Uw1T4u0tQVCXFZIKxY6ApuKac0nhG+fChBTZ87RKxzdPjkZELamwABzji+YbY432ywT8fxhYkZ6rJglrzbnO1I44EXCgdrMhUeF8x8jHORlgl0pEQ5qlHBs6815NJptV4gMjngBai4RoMDS9a8LR94gxJw2i8AjAE+VOe5kSReJmbVE6KFI6DeiBcOSIxiCtZKeNccbJwDYdvPdHjk6CZ/5vEgAoAIBAkeYY/zBrDdNlNHeLicTpEIwMPbcpggAESfSXevUFBD4BCtii/BtkNMvpFawK/8QrMQwAdYWCYaiwCw2k6I9ln8jwsFj+jSlzf1+wnV9ktAB9U8mm6LKjc5hSk9CDw1emITUGKdy6h9XSReNCJ25FOi0P94DK/jACsKTWUbouHAfTMSFw6RTBhImzIrjPHISyCb42WLCJGX16BNqGzmPiDAAyFVjnEqN/wu20/SL2FlJ6umDn/N5QiRhjToPFSsH9IDZ/P1KBJEtNZLqBgpdY98ooNDZ5voB4sXaRkRE83cR57xrmlMk6UitpA518JJC6dP9Tm2FuAgGjek5zFg+LUQ0efbg5i9QcD60348AcmmhtKyIAjj7qplJzOxNK9s3dyB4+kEBtGhOggMcNpUiYgpzST9ZxExpZLqU9AmO4T3hMPoGnMNqMRvvKZzFpyJDIkdOet4rwiHmmWW3ABcnSTrPbPcHASryzH5XiFlgL7PtdkHHHy+cjBUuJiq0k/BK+o1wdGly1PylZnlXzKJt7NumU6YIgFEtHL/XnIOtdbDMbDfS7McyLYnCqW0Xs/JKc/1xZtkjnLbNCjhkrrWfOf7dwqkWmHNKNJ9HCoZmrwAvNcLB9zH33COo/7Hm/zsEMHPJYSZskaQIoVuweohS8324ACnHmv2ECZbDpjW+I6cAU+AiERoZa+6RZZAeFezOsYI56BfwXGpEGKm3+X+9+L2EC73KBvG8kwRw+dCcZ5ScMtjrzHdHizDFJDlizsfNPiwYGioHkLrNeU++CUd192dQ96X1hAk+3uu0bLel1T0+APkZgxAItpXtFGjrS5hkFNXyrq0rQeMkoQf42WSYu9+toDZBWT1Fm/68huPGRsBI3j2d8aKwBjDSM553/NNCajVkh1KSHoa9twuNxbC0I9cenPIKz6NfEhUh71xCQawbxjVnbtpqhS11A4Wusm6gcJgWGG4oqydL4OefOLXWLx9FnfW2ftgdsZVFZB7YTIS4CDQI81Y4dRgm95TumCLdv0KaaESJSVHSy5uln09xVNyB9uFu6dv/pF5DVDgCqz2VUpWH9XNMp8rcROhLW1r3V1OhQVcdIr1swSJm2RXCAVnK2JYnDnyCDXJKCgd+tscsu4XjM3rJ/5QnbpRTNXCuSJ/bJihpW1TokHCCY8Qs9R8idn2emmoH2rI6MWMfZraLNseoMse7Vjg3m8+fa76vFCLNauGAegsgFG22fdVcly0KlS+cW50AVB8JR5cpnLqt8Fgn9BNe83myCOvIfL9VAJAY8/1Is3/bN+JF4VzHmvOabu6pT9JDwsnPEGGOvnKezdvm31PVXGNi01894vlFmCVc6Bv84hkECx9tvQ0L+IoEk9BglvUi9dMjQFyqOdYh8VyniWd9tLnv28z/7f62mGW2AAsXmWNUy+liWmnOc7IIO40235eLlNVMSUePkcrdpu1yEgzBLz7lHbl4BCWUH10NYDhvaNMaAu21f2ymAFpmrFMm+qGV6BQuGQl7UViNZsIyeVnxhBWPyiN9eWqv1lkCmzYZbHUems71iCEsMS6L1Mv0gHGivF7aUk7Btb3VFEr6cA8izlEZjDctdcGVpB99TI2IURlOPYZQoKXGQ5XMrCMANN1AoWusGyi0YKs+92nkaJfCw5sfN5QmoaiGGf7hlEo9EvP5oUht/YX0WGi7zw7Q4nfpAZiIwhrWuXkCLIFLDCwxEWRgRIQ5A83GEumRL5y68mFi8JCLHOlbFyKQlFBH3z0dpXRStHTqPAb5OjGjDhfOcajIhpilpg4jMFQRbB+LQb2vmHVvFymIfYSD7iscm2Vy60Qa5WuCHYgXzmON+U6icM9Naqr09wm2I0RPrhbNdpAMNaHyiJBL/xDf+wULMlih0zNXm8+DsxQkQgvLxYw6VLbtW3LCG8E/w73CUY5SaPGodfgtmVs49kBh5QfieiYrdNpjWxaYRbJOCEVj5IhXF5rzPlowEWvN8cPN+b4ngGOWYCSuVNN76pcDkD4QzMFE83exuJ8nCIbnF3IyJWzDsjJzbdecSDMpKzp2e7tWxV9ax7vU2iTDYxpDtZVu2Ogj8+DOJZSULsgC9Fc0sH21x5Sx9kDvXzmKLpCB+316LcWdeicgfNxfzb4uHwVI6R2EHt/dSUrkniqW4jruWXwkWo9rxjQv4tZZ1g0Uusa+EkDhgT+G6bwLw1vt9NgZdt/dXr37b58GDnZp43q/ysv9uv6H4TrvwjBFR3PsjlRebMkCc7L9fqj8I3lx1pcAEDLjAA5LC5vWUfj958Q939zBeqPSycW+dzmzgfODBEHPrAMMRIYxw0iJprTsbZOkS0YguvrHFhiGMRmkaz25lnSqa8dKOx5iP36RubBHzHBtGGCccPL9xKzzKDFg21ne4bwy1ukEZ1GUC2ZikFlSBeX9hZi1Zwjn+qRw3ueJ2eoawRrMUcfFll81C5X2actv28GhWNK9woGnCqdaK1iMCBEKOVc4dZdw8IlqWkAp8HhWR/Fz8bynCCceLcIiQwQwGCae52LzeZwcgeTdIpwQXLXxO+I355VT7rtIAIypgukaItiDasGA3WnOd5wAWh6zX6upeNp0KZzWC0q8I0zhWzukrWWEI0ZndB7IKK+X3t4JqzegR1NG4bfLpPtMn4jMOOonrCvB2X+7P101R2fCNt63jH8f204h47Zyp85CKNtYgvgwNxEQ8cZ2NFxzW8mk6CzrBgpdY18JoJCeIR17fJh+fme4euW0/ktet8anQwelWUe7OlwzoabGr62b/dqy2a+f3+zVvr1SXJw0baZLv74/QuP+7Gl7J+2wRXulk19F9FSQhWDx2D7SL6dRNz09FhHT9nLijz1DiJ8C7baF0jPr2XZitvTOLkqt3jODOKI1j5fBYtkBBq9XtvL5SFMU6oR+TQebwmoYhD+scmKmeYnS749Gub2tHAHSi5sIbUimslwdKvngu18iHPROMejvEA5ZYkCfJpz2JlHEabrZx25BLVs9Q6z5fqSgi3sJKn+jKKxk49qBFugMi4Qjs6Wn9wu1vn0RYoSD2C00B6cIXYTt+WD30yCyCY6XA252i5noqID1ioXTtSmR8SK27xUpff3M9f9aOLDjzHFDmdWAjJczu/eL2gYT1DorIKHxyBSOPV6wIr8R9/0os99IMbtfYa7BZc51kWAV+ovwRaO477MFmEqXk1WxQ9zTfPFcp5p7c6c5jwizTY0cIekgs+4/zTVNFEBglprXeqg364SaUFfJKUAlEQ7bLgBCmshyeUZOu/NEwThIgIOJ4h5XCeBaKUfwKpFRcP/s5r0MKhsQHFY0UJegxNRV2FVJYyOJ0MQ909EMBGc5rTpICrVNoZzUE3BinbfbS2ZCUS0M5sFaOm9WuhFGn9Sf9zjHzPoTImEmJLaraOAdbwvkFNUQ5shPJ9Q4NtNpRtWaPbOOSdDoDKegU2u2rpj7NTKdY3UGiOgGCl1jXwmg0Fbowev1a2AvjzIypZQUl5Z84tewES5dcU2Yzj0/THFxHQMMdXV+fb7Mr/4DXcruKSXf0jkAIdAaGnGy7++WblnIZ1GmatrOCv72TiSscEMBSt8ebcQ/91XhtG8KyP++eYJ066Tm8cn5O1FJZ8U5WQx9k0KnaH64G9q1l0nlCp4RLdjLAJFnZhAxEWRB7BXOIEM4myLhRK3yfp+IjceZ790ibGHwiwaLynoxgj7ea7bxCUq5QTinJLNfKzo8SjjRocIpZglQ8JZgDEaqebx8mdAx5IhZ5jNCIDfCfJ4vMgEWyqme2EM4U8lpHZ0pHFE/ATBOEs70KeEUa+TMYm3VRCtGbDCfRZhruEG0u54vZtqWkl8kmJJ0EZ/vK0SfxQJcHSuc6yuisJTVDIQLwFJvrj/WXEeVOSeXcMw/kqN9sOY292+a2cZttrHdPS8yx+klJ9vjdXNucwTTMFwO8LD2I/FscuWUu35TzOLnCOdeoOaZL17BFI1Ux7IZQplHiGstK1Eufg9JckIRD0maPIWsBduKurwekbGtvVBaj+CwvIF3Kj8NGr/aA+P2vXwyi6xD/GgPbaxtlkJsBHVLGn0AhUtNgykbzqx2S999k3cwK47loVVoJy4fRZ+FcBf7e3M7rGBBFoXZxmchiAzFAvj90s0LKAW9rxqQsa2c1NDUGLKsfjShub5pW7n0wW7KPe+tJty51vRg758sPXAU5xRoH+wm9LGrkvHn+Q18HhEm/WAcOqojCd92A4Wusa8FUJAkt9uvwv3Sow959exTPg3Pd2nEKJemz3LptDPD2qyV0JJ1RqihNdtcyktmO8qlxJBfva4E0dSrxmv2SZIePIpYaWu2swLhUmYcwKIzc6JDpUy2ZtfOQ7hXIZxoTzFTmyEYgbEhtnlVDNZ5cnoiBNshEY9OE7Pb/uazKpHWOEDEncsFOCkSVLTtejhc9E6oEQ41VDXFlgpB+eU4+t2SfignjJIjnP1fBMixrbCPCdqXXzSrspT9aOG0bzfXPUFOAyd7/vVyhH6/FY56oDlunqi30CAc/RSzJJntvAHLL4Wjn27W6SGKHfURs/5QEbBXRJhhlmBrXhfdIK1GI0zc+yo54sAz5DybjphX6DDGBd2z7QK41QUsi8y9SRcswOVqmjkhwSaUCKbkMKuoS+KZjb6p6WfrSxAUppnGUmkxzPDTYwEIVtwYLCTcWcFsPyIM7VBGLOvVNpJ+/H8jnMJIrZ6Tn7LKfZJg/C5+m31kxDKWfFoIKLlwmHTFaGbtLdljq9nOZkv88GNCFOcMbpkZWLAXYaPVLqw+BHC6diwAIRRzccnbaBXyktju5oWEQa4aw2cdtUYfTfVsA60+SdLAP3cDhc62rw1QsFZa6ldKijocdghlXQ0S2rKVRbzoWfHNO7u1ZO3tId8e+8kiqjOOTCcuu66YWUe/JGl2Li9mRhztsr87LLSSefU8HNhuQdM/K2ZoeaJ645kKLQhszVorpWytRE2d3q9EqqNbOOhss06FcIDnCAfXkSSVWjlsgLUicX2hmNoaMWM+kkKcXpHG2Dvos+1qnlkQ+H24nMqK9hoDuzbK/L1fzNQte+GS0xBLIjxwnciMCLTHBJswQ811CSXintvPvQIQWq1KqHPeJ9gZe6+2iGyQwIZZz4r7ebwI18QKJsjWVqgVDIotHpUn6WLxu+toMtJ6Sa8NJR15Wi9++8Hsm99P+fThIbqtHqghDFFch7j4rqXoCiZlS0flAg7aU9K9uBbGIDAkIZG90OgHgLhcZC3N3yWdO7T9Y0fgdRzOGBLYxrs91tAIa9HRyrQ7KqhquauSZdkBwjwuEVr5945uoNDZ9rUDCp1l/22Q0F775xZmEza16cm1DDznDGmZTdhVCRLvl8w6S/YT6ggObRTXEh6xy/xdbOsSMw+vH/FkXSP53JePlG6eGLqgjA1FWNq+M5/mNgEEssWsMUswCgeFsC14Rl8nzuUqs+5Qs0wRoMZ2GwwToYY6EQqw+7CON9D2CRCSFLDtxcKhD5fjJOcJp3ayYA1C3YeV5hjj5Di0g2a/gY/ILxx4pBwAYvs/TBK0fLQIffxVUPlz5ICMlYL6t8WT/HKaMUmEA64RoaDdAhxMV8v6CWufCwBm6yh8LtJYU+SIRzeLUEkvOb0qesipWvmJSI8cb65ltAAbQwPuyWZzzhYUVIvmY0lyKlJGCYB4otAf2PTJQCuTdKt4TpbheUekhVbKYUtMoo+y4qSrx1AZsLAGNqG1sOC85bR3T4+FdYiLJJ4/LpMqqd8dGnr7v6yDyh+QzIzdL0ID131IWHFCNqDl+nFd32zpf8k2lRJi7ZPE8vwGANK1Y7nH3aGHzrduoBDCjgQktFZiuStsTyV97VcUMftfUojTHpMh3TW9uUBRkp5dT8Gl/dWIjrx+aWMpccjrxoaOEfr80r2fQSfmmRe0bxIzpZNeQUDVN4na8te1MHDZ6o2hbLegiXubpadIdXQLJ9JbOJV3hJMOdP71YkZ9wCxFIrXS6C81TNJtQmBoLVR6Y6HQRJTKKfss4dCs2PIEET5JFfS6DcH+WWgJrPjNa87LJ2a6uQLILDDrp5vtTxLOOFZO2elaMQuOF45xlDnPvwrAcrwANR7ByHgCFntcySmcFGvuj4SzvliEXkJN5P4mnOJMIax0qW0Gx62mmSEPiGdgayqUC7FgnNB8jBZhBFuiebYIidjS1FECeK0092mYYDJeMvubIEINdWa/9r7FCmDnlpNJM1IAkMDzf0FoXqxmxCdHH5IiijMdZ7axGoUaSc8PgVIflkatg7J66gn8/nPKDk81HSjPGtx6OfadFWwb3FH2+Q2A8UO1MA97jV5AQr8wKoM05Rc2AU4uHQkTEYrJ8/t5N7Pj/3eqMHaVBRZi6tYodI11A4UQlvBj93+c/af76bve2oxh1UHSBfPTcJD3r5B+Prlj1dlC7TM1xjR1qkDhHLy/andTp76vCmFkRhxxxl4J0jF5LRcwqfOghZj7Nn+ToqTBKdJvZkhH92GdD3ZT8rUlWrQlmnJrGbOfwSkMVgdryb0e/lZoB7VdFEbaJ/QEB8Tg7REDdYKc3gTVwul+Vzjb4CJAflFLIUs4mMPJPPUL5/BzObUNrCh7T8C5TJJ0sxzAYK1CzNYHiXS70WabdWraRdIrp7Oijb2/LXQAOeY608w1vyenkNRxgkIPfiwfCec5xZxbqsg0WSY0CIEz8sOxcnMN2cLBSmgyDsgp1hRcA6JQ3LNxwtH7BRizz8Uvsl9sKqREKGO1ADo2DPKqud5jxfWPCboWjwAg09U09GMLLVlg8KoAFKPEPRoj7vcscY8ihbjxZfGsxgnQNO4mOkAuO4CmICWac39xE2LBuSMQKx7u7H7hXpx7eizv8PIDFGK6dKTTodHjpe/DzN6hJyS2N0RRLYWOtpRzbuOz0ByM70ixkK+gdQOFrrFuoBBkSTe7dfMC6bkNAIQa40yvHkOholCVDXdUUF1tbQmz+jVG+XtcH+n2KU4550O1DALtQfg/WSS9tImUpiGplHGe0hMF9bQcUpZe28Y53jLRGZzqPDh1i7A3m3LLLR3T4yXnOSeBVKor55P37PMzg9lRAUPx00mUiQ0GDMW10K+DUhBsVTQArvx+WtFuLmP2NKAHrEffJOm7lTSDai2Jo1zM7jOEQK2fGLAfMN9ZoVy+cMaBVitH1X+kFhxmaBTiv15itjpAR6Y5CGUlwsEHPrJ1Ajjl68ut6/C56FVhB4lCwZ7YUtZJ5rz2mfOaJOlC87clKxSMiWVB3AIk7hWhhikCcOQGbOMRgGesOq5p+a75a1NTK0W2Sn/B6Jxgjr9QTgaIWw4z00fUiLjzhua//40laANaEwoerrVHK1DtJtMhNhJmb8l+JyuipB6B4bF9yIo4ZcCX1+75v2XdQKFrrEuBwl133aU333xTq1atUlRUlMrLyzu0/ZcNFAJDDiV10PHXvE+J4ux4RH13T28dlf9zC8WNbPzsnZ3MEkZmADo+3kMK0KkD2xeiqGiQfrwAVXK6Kd08qzcD1l/WQ4EmRhEv/UEBWRLXfiAN6gGY2FRGg5qT+zNQDE2V3D5a0l4wvOWc6pI6HPvZr5OnnRGL9mHe7KaV2BbsRSG9pQwGZGgqzMGodAo0nT6I/O9HV6NOHtRDGthD6rcJ2vxInaxPzD43CQYhW9DHDwonYFtES06+vj2mX8TBZ6o5K/FNMLcAQmFmaZT0MwGAZgsH+ZZw0IFpjW+IezlZzMLdAtBNlTOTf1g49xlqXg1ytXDMUXK0FYvl6CCOVXNWoC2rNMdrD3v0udm3Da1IgJRGOezVLqH1sFqS4IyHIzX73h6O+f28kwdrSVE8+3WYuwmm9sIpAxgn/r2dScaADrSD9njZX2qMk1Y5KiN0+efOsr9vlpKjGK/SO/LQQ1g3UOga61KgcPvtt6tHjx7au3ev/vznP/9PA4VQugSvDxVzv+SOK4etHaihNeraYooS2Z4MozKg+I8PzucKYYdqKagSOJs5VIvztSGGnvGcZ3I0L/uGUsIXtyxkxi8xMPn9OOp1JQCZ26eQxhUqplrRwGDUO6Ht4il+P/HUlzZLN34E8zIgmZjuTeO5lzERTWdIrWkWQlmxmM1my+mB8JaY0Y4RA71NhXzTbBMl6f8kXSqyLh4S9He6WbYIOvpESWfLSa87jJL9kgAfq9W04FKwbRGz1jGtrNOa2aoegY+kVmg6Zqs5JW/tr4IBsamH8wTlHihmrJVT3+F4weoEhtIbhJZkohzGZqlIE/XKCUfYyosSDvlWhU6HtbZc6BfaYoE85rxLxLMaIMIS5aKYU545twmC1WijVlm7bVQrQGFXJTVG2spaaGh06i3cuhC2bUovlhk5aB92VsDiTcgOrRXaWiaNeZb3PMNUY/3sAO/8uUOky0bR76UjuoTrPmBcKjLaCK+flM6sOGooXD5K6huqxGaQvbmd3/O4rNYbUB2shXXcV80yfxfhHAmG9P7Z7RsXQ1k3UOga+1JCD08//bRuuOGGNoFCQ0ODGhoa/vP/yspK5ebm/teAQltW2eBUPmuPvbCRssiWbWhvGOJwrdpNUZecBMSG6bGEFJ5YI/3gI/QPuYmESH5Q0Dm0ZFENqu72zpb8fmnp/U1nj6uE0wjGJpVCMFgoR7i4WegBwoSA7XLz742ilkF/NafqawXoKBSNnSKEM8wTtPcTYlZ7lpprDwLN1iXYK0IFiYKKf1swFyfLESvGCNo7wWy3UE6PgpNEdchlQkgXXBBqhTlfK/YLFymKHvP/ODk1Fzxidj9EzNDt+hHmfhWbc5wt6QKznrUG4WxHiRm9rS9RL0pcTxZai+CfiQUIlplwCZbHJ57JYB0eIJKo4jnXnJtt5uQVz1BCqDhEhILeEs/6GAFyQhVqklrv09GSBQKFUpPmWN7A8tIm6a8bKZI0PYfU4Tl5Td/tCc9J2yqcegtVbsD8wB7oDcZl4qD3V0u/WQbbmJ9OtcVbJjrvkx2t7b49XpjFswe3fyxasBcGcHwWIswvDlHVNTseJuKuJYCWtkIV+6ulxfucJnGB3W37JVOB8pwhzce4T/bR9MqOS+uKqflw9RjCm4fLtEjdQKGrrAsJpY7bPffco1/84hdf+nE7AhIeX03cb0gqoYktZVDsM3u37fS/M7T17zvbEqKad3ULc0kXDaeZS1eAlNY6v31eBPNhldj/3s7/T4qRVE+IIEekO5aJ2PKZcoouJZn/W/MKhiBJOKNBIvfeJZznRuFgMgR78CeRAjdLgIJ0UdL4oJih2hLTh8Ts+++ikuMVwsk/ILIukoSDShKzabeYLfczn9l0vU/M37vN+VQJ9uJjc45VgsZ/w5xnvVDk54rUzRPMejsES2FTB63TbBQz8ClC1HmvYBMmm+v0qmm753vMfZosgECiqP44QwCHNJFm+phonBVo20SPjB4ixfL7csoq26qPgXa5OmY1ojHTaHM9ttpirggPWcAjUVzKZr5YaUCVqP4Y2PMh0DxynovVvwwWYGKC+XfwNVSYY515ZlPh8FXv8+6nxMD01XpIEa5oML1UMpq/W5+d3/Sz17Y6vVl+uxxnbQs3xUYwnlySL506oGmRs+D9RoaTYtma7ajgXTtoRI7byhFJSjATd0xp2un2Z1Na35+17eXomXrGU2Z+QjZNoK4wjaVaKvs8NcfRbUmwrllxrY9HpXWIOMdkEPr8umdy/K/ZN55R6CiTsLYYwdDGUposbSzl8yk96a0wK7f17dtjtjZBS+GOhXsZpIakkEK1ZD/I3YYPqt2kDLVV8vnLttNepR11SR2MSqMPDcU5g6UrR0tFLzE7v0/MfrPF7PoaUZinI7ZGpPodktPToU44zAwBOM4UwCLQdooZ/HBRwCjwETTKSX+sEMDiJ8LBjhJObpo53nA5WRLBHTIPCocbOHH6l2iCVGCW1grzVYoY/yQxmw4PcYwakQEwU4AXv3COq+VUafSJEIjMPs4TFS/r5WR1SDjZW831zRLU/hGGkvWMeD5WzGjbVNvr+LYAMekiIyZCgIaOYYFCjwAAIABJREFUjAJ1AthVCvATF7DsEtc+TACyk8RzeUVOdkSNAI4NQjB4wXDChcFgeNkB6hq0Vk3R70cU3BZrV2lYisOpUtiSrSyC8bDl2uu90m8+ky4bKc3Nbz1M0BE7aMTanZUevrOCiZgNUfxmGUxMWox0oulrEVyzpZtR6BrrMKNwxx13tDnrX7ZsmcaPH9/hk4mOjlZ0dBe3FztCyzcNTPx+lP5xkdCHA3vwkhyuzd9Ja+f8dGob3L1UumAYpU2DY5X/3kFcb2s5sdENpdJl80H2M3sT5/v1Z8wSbhh3+IBh/k5peRECxaGpZHNsKYMiTI5uiup3VUIlttZw5tXT+Ov2sv45r/PS76smy+TKC6RT0qSeDzqNkg7XRprFWr3oZJkuUgSHKXRnw75qGZREiBBDqvl/jWADAgV0lQKk1MgJNcwXAOgk4dyDwYlEFsiprV6RY0miFkKgPSacWqQ5z0jBXHjMMY8RM/bA4eigKMU8VU3LNyeqKVBpFOxKe9MqfQIs3SFAxXQ1rxp5lrheW9nSZ9YfLwBUo7hvbhFC+buc4kxTBHsQPHhtM9dcJyfd9D0B6nqa7a4Qv4G/CBYlsMR0rQB4tnlXnKTfZhBGuGAYYrtKt/TACkIMYzL5vU8IEjdvLSPleGJPaWpPAERkuPR/bwHYpvZimdKreRZVUnT7Qwh/2wDwmNSzeavnQBub1bRmQ5WblMvDcehL9qOxKMhqDphCZYQdiT2/ARCWkwAgyIzjGVw9Rjp9YPsqWX4d7ZFHHtF9992nwsJCjRgxQg888IBmzJjR5nYvvPCCzjvvPJ166ql69dVXO3TMDjMKxcXFKi4ubnWdvn37KibG8U7tZRSCrTPEjF6vX2FhlHRevtQnn0+aOIUhLxSbUOdpLrr7MqykDkHRuhJCG8+ZZilpMU775mDA4PXRD+LxNVJuAjRoXhKfP72edZKjpdsmokEIfrE8XmrDt9S1bUURAqWNpVRD21RG5kZSFKGXQ7WUqx2cQsz2k/0UbZo7om2VtM8PVZvSCogJFjsGpyoWisG9NR3Bl21Fkh6R08K4ynxm35g8AVimdfJxd4raBB7hZOtEDYhBAgRMEbqPIxlbVwsAF8on/VKECCQn/l8tAECUpNMFM5QkQkIparn3wp8E4IoUrEyFKJA0VmhHjlFosLVRAIXA4lV/FCDhBLNda/O2nYJNKhAZH15J8Rejyq9okCrc/H1zO2nKCZHSGYNotnTnUtKAK9047yrD6rkEM9g7QaryAIoHpUgXjyAe35qDl6QNJexrTCYZUysPOqWgPz9IyELiHZw3q3mo8UitrB4dwoEa6UCttOYQgmWJa7ppfNOwRUt2sJbsrfw0xI5jM1t/94PN70f8OSS19fW+7ozCiy++qAsvvFCPPPKIpk2bpkcffVRPPPGE1q9fr7y8UHVHsV27dmnatGnq37+/UlNTux4oHI79N4HCe+/4dPF5jeqZI8XHu/T5cr/GT3Tphxl+nTmYH/jBWukPKxHgRIVL9y5jJv7doR1rgtSWfbgbADIijRd8d2Xo2gRrDpFW2TeZugN9k4hfhgIvgVXJrC0tpKNb32SpT2LTbY95GUHl8DSYhzuXolm4YZyU2gYj8tNF0s5Kshn695Cy47hfm8ukp9dRFVKCffjD0dKcPod1m5qZBQx3C6GgLdPcQ6j2JwvafIZadoTBvR++THtB0Ndj1bSoUEtWIzI38gTQsP0NIgUISRYOs7UmR7UCMBzuMHZItHsOtM+E0LRAhDRmqWmtg0CrFM2rZoowkkuOVmKTCAkMEIBpumAeWnrDbRfSUOCgNfOJ595SRGCpuCYbZqgS4RoJ1uGyifRKeHM76XvJ0Sw3foRw8cLh0jF90CiUNQDUE6MIG966kFnv7Fzn3XtyDe/GlF4tT0RsllRJHcumMunhVYQ28hJ5p0/qJ2UnUFvlswN0pTx3SMeE1e21DSU0qsq22iIRAvi/EWgRhrXyUt23jGsprEH4uLWc8So5msnErRNb1zQdjn3dgcKkSZM0btw4/fGPf/zPZ8OGDdNpp52me+65J+Q2Xq9Xs2bN0sUXX6yFCxeqvLy8w0ChS8WMu3fvVmlpqXbv3i2v16tVq2jPMnDgQCUkdFbiUut2zLfCtL0oUvv3SXff4dWOtX7F7vProwbCBif1ZwaUFEUltLVmRn/JO7zst0xsH2Juj20olV7fhs6h0s1y51Jm/BeNcOKXIzNYvD4GqdQYnPhLG5mNjAvwEKHOa1JPlkCzxVueO5GXf0OptPQAIYBfLaE97vXjEGa21KTlzumhP5+dCzWYHkudhJZATXttZwXtt/MSTTe46xicj1lN3PqgmKUvMutvEuLDQpHiuEdQ6unCSWSISo0Joq5CKEARzFbsFEWEJgd97hdgxYoB22PfaeP758w12GZHsaI9dLIImUQIB9sgx9n+VjjXG+W07Q60I2WBXWoOaFziXQnsLSERIogRokA7SUwSLINETQuXnJLOkeKepgsBqxUutmS9W/muNQtT61qPMAE2c+SU614tmIfrz3PSDEcH7KTRx+89UJkfHC6Ij5R+f3Tz413ShuhQgjF4bDVMou0NEREGa3FJPiXZ7cTiW31bZgM7y4alSS+e7Py/uJYS7e1p5DSgB0svAzJu+lg6oa903rDDTzf/OlplZWWT/7cUgne73VqxYoVuueWWJp8fd9xx+uSTT1rc/y9/+UtlZGToe9/7nhYuXHhY59iljMLcuXP1zDPPNPv8ww8/1OzZs9vcvjPrKCTd7FZpHXRXa05s/k5i8SPSpeGpXaewveFDsgD6JuMMZ/duPgOvaJAufxfwsq8aNfSBGoon3T6VOGFL9vo2Bp1hqbAHj6yiBPR5Qx1w8cVBYoD9e8A8pMQ4rMK+KmZGA1OO/Fp3VaLy7meYiKw4aMirxoS+hg0lnO/uKrbdU4XAy1aLPP0QdQ8OCqeaLsfR+ATtvtZ8Xyxmx++Yf4eJWeyP1bRB0GWSNghKPEU4jk+EI6mXU3ehh6iSWCUcyulihn0kP5F1ApTYEs41gjmIFTUPJoh4fqLIXIgRuoOx6jggsCJG2x7aL1Ii+4i4f2tOea24N/eqae2FA6JxlAUL3xFAqiXbLnQe7a0DZEs+234dk9W8iFOobQI7ZbbHAruBtlY34cu0ohquo7MEh2/vIFNhoinONCjly+lNE5zW2VX2ZTIKOqtCijyCY3gqpb83V0rdfvvtuuOOO5p9vn//fuXk5Gjx4sWaOnXqfz6/++679cwzz2jTpk3Ntlm8eLHOPfdcrVq1Sunp6Zo7d+7/HqPw9NNP6+mnn+7KQ7RpgTqEtqh1STq2L0tX2wNHtb1OcrSD5qvd0lEvUWRFovtcVBjMQyjrlQDgeW0bVOHqQ4gl7/gEluSi4dLoTBaJFKqhTzFbGZJCfYXnNkBp3jqx5eNY8/up2TCgB+AqzCX9cRX53Y0+sjMW7COlamclgqgn11Jl8qbx1KG3A9awNOmhOc6+91Qi1hycYlpep0u+v+Ho/yQcjg1HfCRi4+fIKSPcIERrNoUy1M/gcRHfLxNOaaGo4NdfzNj7C6FbuXBYGcJRbhH0e2thgLZshFmslQhnOFTNmY/b2rnPeuHA+wZ9/pooWOSVU5bZVmicJ0DUzWb7fULbYO9XvriXtiSyrZvwunCus+XUTWjN+rfxvURGxCNqWlyqVhR7el5oDq6WIyy1ZvUZVqMRJ6eJVH+1Dugs6PpfAQlSx6n55QecScGkbDRIf/qC1tSHaplo7K2mcmZeIu/eVWM6hzFtzbrTGVu2PXv2NAE1bQn6XUE30+/3N/tMkqqqqnTBBRfo8ccfV3r6kdUY/9r2euisNtG1HmJr/ZMJFTy5Vvr+6C+/reuRIPKSOqqv9UrgOvqb8s6hVMqlJi763HqARWQY29xQQIW2lmYfdR7p7DdQfO+vQehUVAsrcUm+dGMBx5VgUn6yCEAwPJW/4zLbrv4YbH6/dMv9hCHsskM4cwknd7mYlQdao9pGyKWCPg9ezzqhljRVRoOqBLPsF07vZKHo7yqh9iKRbWFnxBFCyJctmI9jhCYg+PH5RDbENiEcTBTOv8HsM1qAljmCxQn1iGyGR2eaBQgR5pzrRdrsdCHOtEOprayZIApIRcgJ4VSK30KS0HR8R22DmI6AhPk7YeFs9oMEk+f1kRXRWjlir6/1DpPttbXFZEhZgeOhWoCABHP33WF0nMxJ5H0/UAOQuHQkPSA64xz+l+yryCi091zdbrfi4uL08ssv6/TTT//P59dff71WrVqljz/+uMn6q1at0tixYxUe7ow6Ph+1U8PCwrRp0yYNGNA+9evXEih0FkiQaKp00dvMhMNcOL+4CJzfDwKc39fNNpQQ6shN7Phg4vWhyD7+H2w/JIVQzvdHt1yE5UjMih39oiiQxGx8iHCUb4swRJpZqoTzPks40JbqHRyO/Uao721zIftXgnW4QAgvOzNEu0OEVzaoaaOlFwQoGiec60lqWdC5WgAgn1m2iQqVVmw4VYQdOsv+JhihAnWsXPZHogaCLTx1ULAk8XJElqeJ+/usCKm0VIQp2CxIWLxP+uMXsF2zc0l9drmkN7YRCrP6oiWFsGiRYaQmD+xBVtBSU454eBol0s8e7AB8v5+MCauDmtRTmtaLRm/D0xhjGhrRK7VnUlBUw3mkx7J4fIiOL86XTuzXXCjdnkZTX2X7OgMFCTFjQUGBHnnkkf98Nnz4cJ166qnNxIz19fXaunVrk89++tOfqqqqSg8++KAGDx6sqKj2lcH8n6rMeKTWmQDB2uBUaYnhWl/YSH2CYam8lCsP4gi/jvm8ramZ27LwMJpCHbrqy4l/2gF+9TwKBgVbrJh17hK0fqEQD34u4vOniOJLfxZU/Wlyyhe3x3YLRX6MoO0DbYnoO2HbFQe3YA60Teb8JqrlF3OfKAoULUdQ+KEAC3PktF+uFZT7ODV1xFuEUw3ugD4q6P/5IsTQ1gDhb+V6rO0V4ktbKdIrxKJF5jomikqUwwXAedKc91iFLuNdY7ZLlSOOnCCufVbANhe2cV7WnpX0QaZU9SQhvmoPKYnPbyDUd2OBdON4aX0Js/akaAR6/ZKkz6NgFGblSPkZUP9VHkJ73x0q5Zrx/+M90lmvw7AlRMJE7KwkY8hOQnrGE6bbXCZ9502YgGk51FwYnxWaccuKp8GcNb/fqVcSyr7OIOGbYDfeeKMuvPBCjR8/XlOmTNFjjz2m3bt368orGfkuuugi5eTk6J577lFMTIzy8/ObbN+jBzPb4M/bsq8Fo9AVAKHbus62lyNMzE0k/XTxPoSbeUloJfIS+XdRjfTsBpiItvLNrbWn0dRiIVQ8IBiEOhFq2G7+7RLx9psUOv3vRhHPtr0d9or8/eOE6LC1hlCBdpmoEWBz/v2SPhUO8FuC6g/u+1AkhJnugOVTwWL0E2EC2y9Cgq63oZYIMQP/t3DKcwSj0rcd5yo5QlE7YPgF61AuZvJTFFpcWS0Yi4iA5THzXX8BXsLksCGbzTaxQvNwvWCGQtkhAYQOt/vnqJtIJa7xkMmQEImI9jefSecPI/2xpfh9qCJjZfWkSQY7ZI8XEGBrtFS5pfuXc4xKtzT7Jao/psRIKdFkJdU3Eh6YO0I6bWDHQ3NfN/P7YWH8AkSNzyYjJTAD4+vOKEgUXLr33ntVWFio/Px8/e53v9PMmTMlSbNnz1bfvn1b1AYerpjxKw0UWgMIz64nFedI25YeiRVWExcMD5Ne3YJwsF87urC1x6rcDGpfxRnC71ZQd2F3JeGNlBhma71N9bVwFzqHKkPxhrukMweRljW1lRx0axUN0q4/dOyc3CKlso+YTQ9W82ZSEoPUZ3JKOVea/38i0hlHC0biaOHga4Vzt1UaY4VwMlloFqxIr1bk9T8vZtPj1XIPgmBbKESaoZzpWyK+b2fyGyW9KsST00RIYZRaD7lY3UW6nFRP+wgCyz0PE+BqQhvna7eLEcBlu5pqK34hwkbHCOBzBD2CWrXDES3esxRh4MwcaUbv9mmVqtyA4/z00GE8nx8naL+r89A06bwARuK/bQv2Us8l9zCywI5EX/Xv7YixD9SwNHhZkqNhba4cTcjG2jcBKPw37CsLFEKBhDP+RTggJ0HaUYmI7ztDpWvGgD6/bJv1IufTK15q9FMf4Jwh0o8nQFd21C74N3nVthLiu7vIYAjMGNhaRiXFQSmAkodXIRRsT9OqzrQdFe0DReX1pEq+v5tCVPnpDMDH96Ut9+OrybgYlc7f0RnNZ3hXv8esomc8y7+2kU555Sgp7zWcT7VwlJ2E05rYChG/76vmDnejKE1si/rUyZk5Rwhn/X3hZEvN+XVlJGuHOdfWpDV/EEyH/bmUiFLIWYLany2nvsTzZp1ZappauUYAiAI5GpD2mE3f7KrJ825J065uf1XAc16niVKVx+nD0GD6cBdk0SExOEuqvpGMo/J6p8PkjxcACKb2Quh4cX7zPgX/LfP5m4cI91dTrv1gLcDopU10hsyKI7Xy2rHtzw6rckun/UuKCafc9fgsylu3p+Tz/mqW7HjWf2QVIOHcIaFrOXQDha6xrwRQuP/hMH33onDFxrpaZRF8fhzojgrppFcoVDQ6A6d8wbDObbQi8QOOCGv9B+/1QWWe+E/in4NTEfddObr1JjKh7OM9ZCRsKZM+3EOJZQlEfec06MlF+6Tfr2SdHRW0rq3xUDzm5gnSKQNb1w28vYPtBppiKc+sQzF90fDWyzLPeRnH0i+Z5bn11Ie4sQD6tjWQ0tB4ZBUwX91CnYkDtbA4H+6RtlfwXXqsNKOOGffDgso+S8Tx/xtkjK1bECmc6Dj99ypGtmTvCYbD1iLYIuklkW46UxSsCtZw/EloP2xthTKRNWFrPhwjQiKdBYJ8In1yoNl/S0LLLYK1qJOUeQKVDP+wkqyfo3KpXXJcH8IInx1AvBhYs2DNIcSKSabq4jPrqDR4/rCmM9lA21VJr4ce0SzJ0YBev59x6MLhbacbH47VehAzdqQIk9tL6fUr5+PEbX2Fz4uk17YztmXEMp58vJdzv3Rk22PXr5dKj61hPI4JZxwqrWdsuGwkQKmzakMEWjdQ6Br7SgAFiRnA3BGUGm7L4dd5+GEG90boDPvnFuKXA3sARB5fQznTmwoAAaGs0ccspDOrkT23npnL4FSqIdrWzYFW7ZZOeZXjDk6BZTiuT+uZGvN3Sm/vJCXULm4vA8Z1Y9ELhKpHsb8agLGzkr/3mk5vEWHSWYOkebO/nBmU30/eeGwEZW89PmZF2ytQsksID0+V9D01ranQHmHeN9H2CwaitQngQsHa2EJOW0Rmw0zBQExtY/tQNl+wNVNEWOX3ItPBdprcK1gLl1nnx0KnEWiPSiocyDsQF8k78deNvDN9k3gXIsL4rT67nt/AsFR6ONw8sWkFxsM1n6kxMqt356cj1nmkknqc8MFa6dw3YNVs06kT+jV3yIdqpSl/w4m7BJjeWcmk5tg+AIFTgtpbrysmTbq9OomDtbyL6bFc88MrGYPm9OlagXM3UOga+0oAhRsL+OEXZDFL/W/G5es80hbjQN/YJj21js/7JjGwXDby65Ob3NAoXT6f8tZDU2nIMjK97SIwJXXM6oenAaj2G62GpQr/so4BrCtSJVuyDSVQyAVZ0iGTQ1koaaWg+1OEMPEekQ74bR1+n4TOsmqhawhlO8T5HS4b4RXOvTNaRoeyEnFfO0IUXScAhg3NNAgmQOL5nC4YIZvxMU+EQ44T7EyoYwXrENaXAGTKGmDoEqIAEWEu6foPCXmdN5S/ndnnpTNsSxkhgKm9mP3HRUrZfzQVVc2yvAiNzmkDmbUfk9d8PPIa8JwWSxpmRQMdMf9vBJViv6pW7eYejXuuGyh0tn0lgELFNV3T8ORI7fMiBphBKd+M2uUNjWgt7LVuLmXGPqKNol/H/d25V32SCBOU1kPDXj+ua2jYtszrk175HbUGyoRyv1zEsBuFIzpJdDxsyRl71Hmx9HJJ78rp9RAjZuQeSceLEsmNwklGC4HkG8LRHyfEk63VOfhCpFWGByy2BfNks/2sgH0sNNvMFhkSXwb2LZHTYyJSZKcsEmGLiWp6r20J6ZZCGR0VKla5OXZXsJCHa3urCJOUNfC+lNbTWM4vWJDrx1LK3bIePj9M48kDOtaZ8b9txbWMDe2pnNuSDX4SwJMRSwfebqDQufY/hpm/WhbYnOmrYF4fjt1qDVYfIhWrvcrqFUWEMiLDoSHTYqTXt8MO/HA8Md9QbM+7Z/G3yk2O+PH/IHxUWk/sNjm6eTjpmXXsv3ci4tR5ywFk3xkixQcM5jsqYDBaE6t6vIg6M+KgQjNipftXSJkF0i/yJa9pR1Iq6SHhGEeJSoaBL8j7gupOMssGUQDoZFH570hCrh4BUuoDlu2C9l8p0ghnicwDW17ZJ1I8N5nPZ6hlh54uYvk+OfUMPhCpmBkizXGecLwus165yNDIFILL01rZf2dYMCCbY5ZQ5lLngQSpYyGGFzcSr5/VW5qV2zWxdgltRE4CQDolmvd2fzVCvouGNw8hhrloLvdl2lNr0UZMMKmK7WVh6hspXldUQyroJe+gXxifhU7i0pEdmxxumAtzYkMP3da51s0ofINsa5k04XlCCQNTGIje3w3V+qPx7Z/Zl9cT839stfToalKmhqYi8LpoeOuhofpGYpetxTr9flTiuyspR7u3inTJRh9VMb1+p+VuSrT00mYGl6vGSOcMbr7vOg9dOg/VOjXv15WgRpeko/Okq3cjjGvN/i5y/KvMUihm+hIO90bBQrR0+V5Rb2Gq2q5G6Jf0ssikGK3mmQObBbV/uFjVAhPb/8AjZ5buE6DoIwE+pgug0R7zi+t8WQCY6YKxaK88JbiLZ3uO1yjYkk8lnTkHun1QStNqiEcSrrx7KT1Tqtw4ogo3/5b47f9iKrR9VxcXc3thEg73ODsryMKa3FPqeZh6oZVF0t+3ELo4WIuT31LGd32TpAePgtFo635PeA6RdVa8lBlLo6oeMdKl+dL3Rh6+8Lxbo9A11g0UvmQ7WMuM1uWiLvyw1LY7NNZ5Oq/Yit9khmwtp1/8K1sRPw1NpST1ye0r/S2J2XxGbNfTtfWN0mXvonMYncE9S47G6b+5XbrHiEvHZCLGuiS/9SY3Xp/008UMbBOzScm01TUXziONMVnE7mtEz4STRApjoH0gwMJYUe8geDK1TKQaxpslTlD6DWKm/G11bf+HI7GOCjtPECEc24raK0dfkCtA1FEh9ukWGRa2fXappJ+K+zJVLWsorpS0yvw7whzTJLpoVAYM1/lDERE/tBJl/5w8xHTDUtsHHPZWkQ64u4pZc2IUyyf7eXfPH0Yqb1uO2++Xvv8eGoIZvdEYdHV7aIlzfmM7jFtxHWPPw6t4tnmJlJb++ZSOja2rDiLMzIxjWbiXcu2XjZRO7H94zaVqPNL7uw5/+0DrBgpdY91A4Us0v18a9hSz49xEZshbyqTTBzGjnxxQV/dnixn8BvQg3WrBXulHE3CEnSXm3FACtflVvre7K0lB66xreGGj9JM3nVLBscIBeUURoFPN0p4JWbVQ5teafVVLuls4vgLhDGeq/bP1r5K9JmnP/7d33vFRVekbfyY9ISQQQhLSQ2gh9ACREgiCFCuwIqICspa1oay7+7OyoK7iCpZdFRZWBd1dXXtZFRWQpvTeOyGB9F4mmXp/fzwzTMpMMjOZcmdyvp/P/RAmk8zNzL3nvOc9z/u8oPahH5oGCLlgyeJgsPPkajR1bbwEZjiiQX3GPWjpf2H0oTD+3rwZ1MHM6gP8biOtlBUKluYpFJwoAWBUDwbEt/ZpeR9dqWHwabRx/v4im8CNiWXZZHYCSwetWdFf/wVLdasaZSG0ev7s1GTg5Szna3MqG4Dfb+EWXmQwdQtPbjcF045oCqXWURApF0Sg4BxEoOAGNDquUmb9j/t0aRE87h5AVT7AlfLxUuB8FbAznwYuAFdLz49m+ZInujIayamSv8L68KtM8T8HahYGG44eaH21XQaTLXNjlOCqO87Mz2wAsxJJ7Tzntmg+wbqCnwEcA4MtHfg+fA5mWTLB7Y3rwe2YGgDrQBHlACvPs7kmQa3jtprx/vjyLCteZvWlmNYSf9rKey3UYOVcp2GpcK8uzB7M6cfKH3NIEgN6Y1XW4WJWJYQFMHvw3E5m3+b0sz/t317qNMxOutOt1tmIQME5iEDBTeglrlys+bvWHeNN3s/QkrmHGc8EuXPX97yJEzrz+OIcV3sLh7KUS66NtSQJOPpa68/5Cizr6wxmGpQA1oLbCzeClsaN/7wKmDwcjEcu2CipP7iKngr79AenwEDGGINdBlfvxuZJ9QBeADMZE8HJ2NkVDbvAbpTGaos6UDSaAWYcJsC+v9Ue0aIt7Ctkmn54tPn77UKlqZNkjZpZwGIlXVDHx9NoLNVLu8vKFREoOIcOU/Wwr5Cq4QEyyfP6KKwPfu62rdGXLFk6iqYueTWmo7COLXknJwGvZ1OrIUl0lxwb5/hgaHMuNQ62lGEpFC0npOaNpxLBlXodWCmQBwYL68HJfxqA22Aq7wsDzYPqGx0rDb8nDawysDZuehUUH/qBJZ1nQR3ECLASQ2X4vwYmHcAlcNX+Bdg58wG03lTpAoDj4MrfnnnvGsNhpAz0RGhPQsnZQQLQtu37vPUMJMIM2oUGLV0bS5Qca7q3o9yvMTlVwBPbaKA0No5anPbu5QsEtuDVgcLfDwBvHeI+vAI0ARoXDzw0mLoAOe2teQp7CugEl9iZada3DzFDcGuf1t/PXl1Nos16DZ3xBnYHeoYBvdeyrW5cKBtDfXWeuoOHh9Ay1trStQZtU5vp4f+mmC0qhMrqs5XAsVKe630D7e9/0WKSepVWzEZOApgJdnA0l+X1RdO+CBJYimlPDJuGpv0jjFUHxqrzvGaPAAAgAElEQVSMsQDmwJTCLwIrBEaDjaHMfWTvgUJMheHQglkBX1C4ORG0wQ4ArZujYJvWwlqTKOP7rNSY9/WvUTvGOdFefpnT9P9fn2OZYHsdSHOqgHt+atororyB1T1dAoF7BgCLR7lGECkQAF6+9aDRUQOQX8uA4ZPT3OMfGgVcn2LbPn+NmqvdsEDgdDnLCuf1l5dBiz3YWjr2n5O0sc6t5p5spYreDD06cWK/f5DtjovVKpZBXqlh1uHRzRR+9e/GLn2LR5nvp/H3A5xEojuxlv2LsxxQfzeIaV+lxlTGVVIPPL+TZZHDotiU5o5+zmsWZk27a2dwEtx6aE/GOwesPjD2esgDMyDDwAAiANyuUIABSZ3h50YA+CMoXmwPVwCkLuB1pdYBL+7mSt3Yh6B3F5bIrthHMfDkZGalxsbJz02xLXR6YO56Xt9Zho6Uof70OOkSyJJBHwXwyCYKNW9Kbb3nSkdHbD04B68OFBqTW81I396U3X9OAn/cCtRrWTJ1poIpx7vTWb9vSeQkN3bmc3BNDmdG4JU9XAXd3s/2AUijY/o1yI/WzgMimRY1TuoVDWwRbctnV6rktkT/bm0P+h+fYpOsojoGhPuKGLwADAhXTgTGGJSDkkT73r4R1l8D5rrq2Yu7AgdHUAtmRiSYggLjoLHc8L1xYBVHW/4QReDWx1CYd7Uc9AdgyAd83/19mKUqqKMY0U/BVXRsJwryCurYKbV7MDOEc9OAsfFmfilYgbCviNoBd+udJn7K67RKxfHEGBAF+wEzegGLrwH6ya1TmJM5WsJeEO0N9ESg4Bw6TKDgKNQ6YPVhptyHRHHPe2gUV7CesG/4Uw5X3jmGjMC5Sq7eo0LY8OnBwW33crBEfi0w5ztuRSR2ZjnWC7sYTD0yxHLTLEcgSeyaGRFE8VmfrraXfm3NY0YiIojnvqsA2HiJnvmTHVBK1hg5Bg72VES05bewG8AemNpHa0Db7GBQtzAWwBSwAsKS7uDfJxicZyc0Fb1+dsbQHTSu5Wdzqgz46hzQoONkrNQA/zjC62REDEsUHxvGFburuVBpqogI9gOe/oUdZWf2ls8452xq1Rx/Suu5lfm3A7zfhkbREGpWH1OQby0aHce1Pu+JQMHRiCSWjQT4MoOwcFjbz5Ujk5N5AIy+H9rESX2AISPQpR0DVbcgYPk4k1jxUAm3bN48yIzM/41gDbsztCEKBQf+9nCmgn4VRl/9K7X8Oz49Q+3Ey+O4XeEIkWXjSdFdQUOJ4TCaJCkBLAX1CxPArYa2Boi23oog0LXSB9Q4aAxfp4IVF7ff3fbq+a7+5h+/tY/ln9FKgFrPviSRhuZHgb6chG7rC8zs1XaQcLiY5cnZ8e3rQ9Cc5tbLy7Ic97tbY08BrdDHxPEY3N19i5sNl2iUFhnMQ6dnEJfYmZbw1zTvY94Gf9gCvHusfeOXwDIioyBoQZ2GkX6cYatm4yWmgW0V/+0v4rbAoO4GQakNP9te211zqLT8ncZApaiOupU708xPBB+eZPAwKhbIjGl7YpEkOuFNTDQvvmuNH3NavoazA4jvQD8DfaPjJChe7Ao6R/4OtnXR1IImSpkAYs18P+4hZuXMeQk0aIF3jtKCua+V7omNaW2rqEZt0rNYYmc+Pz+lBlBqWb3w5TkGQ0Oi6IHw2DDPEUHnVgN3/0AdUUUDm0tVGWzL40KZPfzDcPs1D7sLmBFJ79b+bNv2y/SSsdfjwTheiK0H5+ARgULu/UxnuwK1Dlh/EbihHXaiews5uCR05uBzvMw+kV9zLlRS5R0ZzD3bn/O4L+soe2cjewqA+T9wRd01kJPeqXJgSHdgUQYbMzlaNHayjD70PTrxOFEOfH8BeHCIyYSqMTq97YPTp6fZNlsvscVutyDaz/r7cG/4noHs+2CtLuFKDXDr/5hCDgsEwgMoci2tB37Th90xJyS0PM86DTDza652Q/z57458BiXXp/D9vSm1abDRVtDQgLb1AW1RBuATcDsgHdb5K5wDSy2NwsfwfnwPipTMUN2QAjww2Ly51pES4PX93KPX6JgF+DmXE1tSGH/2qUw2BrOEVs/KlgYtrYrnfEeh6qREfpa2dFHcVwj8ms9+IiH+nFz/vAP4TW82YspOaDomXKrmvT4+3rUt062lXgMcLuF70DWIwdKSHRRhTzTTfrotyuuB7VdMGbed+cDnZzkmZcawQmluG71enI0IFJyDR2w9/Os4U9aOnhCNXK4Bxv6XUXZcKFd34YFUz9870PY9+/UXuQrPreGA2aDlXv2cfsCjQ4GhdnbyWbGPjo2VKk6mpyuAp7dzIH54iOMc30b2AE4uYJReUg88tJGDeVIYhZA78oEJiY55LSNl9ZwkCuror5BnsNN99xj3lMfFA2ld+VlEd+JqX6llAGYukDDHrL48NDoOdL9cARb+TCX9kCgGDGqd9Sus7iHAmutMpjtVKmB9DieZOg2Dnz5dWza4CfChYE2p5fPqNNyfNbbvNlbqNO4B0nz/fvWrpnbY/mATp+Ogd8JEAPYUc3QDu0Ray6A/ANF1gP6KYftCwX93F9Jo6JZUVhZZckPsHszAyM+H772fD4PFMXHcGrgptfUJeEsecMtX3FII8uO/V2oZ/H11jg3Cnspsu7ujsZfK8JimVTDl9by+jBmEnCpafNcZPrdqFbD2OAPPAZH8W58Y4frsZ7ES+POvTNePjeN7r1Dwb2psCx8VAvz7evtfp0jJDFxEEI+oEAZVM3sDC9JZseFpRnAC6/CIjIKztx70hkZJV2q4kp2/noPLkChGyguH2i96enk3cKQUGBTJFPzASOvbOlvC2Db5zYNM+6VHsuRvRm8Otr9e4eCWFMaJ7439wCND7bdMbu5P4AreP84VXkY023kfLOb/ixp1rStW8rkjY2jYNNpG8VODlpOLLYNbThUDwLFxLTMPKi1XmY07F1qDJNHfob8NKdzfb2ZJqUoLlF5gNcEFw/fSwC2DbNhv1WyvoZFOT3GauXLWttAYhIf23usqLVfM1/dkfwZL72W1ihO9UsvXW7KDVRHXJnKlPSrW/PV+oRL48BQDuk7+DG4WbaaQeU4/ZkCctZhpjFYPzP3e5LFQqTKJkn0VFN8uG+t8q+aLVcxuutLLolQJzPiG4s9rejAQSoswfdYio+AcRKDQjLJ6Bg5yTCU2prXU+7O/UJR3qZr7kjVqTmozejEzMzrWsyN/SaIQKsSP2YahUbZrAqxl9WG20u4SyCPYj5NFSji3feb2b7v7Z3uxRq/xyWletzHfUTxoK65wOpQL4/7LzFWwYYuhtJ5bed2DKZCcn84sU1vUqPmeu9r4yKiFaXxNPrARuLEnAxZ7q5bkyvM7gbXHmAX09WGAX6/lPfjbATSgMmZTRaDgHLwiUDhW2jSqFJj47gKVzgMimX0YEMkVuFx7K8iNahUnFePK7WAR8MyvVIyPjeNxS2rbmo2Nl5jlMZeJaM6xUgaCwX48Pj9LD4DZfVmm6QxToXoNB+I4F2mB5MSHJ5kBaa5BcBaSxG0qR5VmOkP460iOlgBLd7Jz56hYZj9tybwYM4cRQcyYPLkdmJLMz6v5vSQCBefgsYHC8r2GVUAIV885VRSjLUhvf2pf0D6qVNRpxIZSS1HeAPzjMMtKRzjJCdFV5NfSOa+tDNeMr+ngaexEWKPmZJ/Qmau+O/pxK8rcAH/vjzSHqjesnCpUpsEyoTO3WWb2bt/kkFvNzoi+CpO24A9b2Slxei/gll4Mvl05AeVWU+A4IdGk1/AWKhp4X9RqeC08tJGPZydQDJkVT4GtHDhbQb3Q6Fhua9pjOjb9K+pFygzCR2O1RXwodR+PD3fOZywCBefgEWJGc4yLp46gWMnUYW4NJ6MjJcDTmdzXFriHWjWNnfLruBq/UsMBY91xag6MPRyMq7eLVRyMWmsBLBes9fFfN7XpxLDmCK/V7ATur7a2r/vOlGa/6xj1CBMTaSZlazbIXNlgnYaVLDo9v6+TuMe9v4hZDB8Fq0LaSmNLElPBxqoHnZ4leWndmAofGWM503egCPj3Sf6MsXrhvWP8+8bFsfLo3oGeb5MOsDqmsI5BY+cABn+XqvneJHSmgNhdaPXAa/t4TuUN3H79/Cy/Fx7Ivih/HmWbFuGpTD6/WxCvrT9tBealsxpFZH49D4/NKBiRJK6MBkV2zLSpM9mVz4xNXCiDsVWHgQcG2Z6x+d95uugNi2baUaMDFm42lUL6+wDvn6AC/sHBTCs6czC5UsMGYTN7O0/b0Jzyesea9rRGrZqBs5+Ck65eAhb8wFXrzN78DJpnCmrVvI8mJdqWEr9cAwz7F79WGDITlSpO+gqwEuC17JYmQwBXrpvzmNXw8+HPPLaZToszDFmN9jZYciX5tRQ8juzRttfCB8cZRMnBqlmSWJXVNZBllMF+rAa6tQ9dVYeauV7kisgoOAePDxQEzmP6V8CFKqYQFWBWwEfBPflHhtIjwJ4BRKPjCrnQkHHYcInZoG5BzDjc2ociJXP+AzVqKq39fJg92mzwkmhtEt50CZj5jamsKyKItf6dA7jvv2AAFdSeMhi2xZ4C4JGfTd4ERmtblY6T8h1pwF+zHFdO2xidHnh8i6HxWk/bqh8qGvivLd4H7qSigRqgWkOJa0UD8PIeBgljYllF8dAQz+vyqNTwOvG0BluACBSchQdeCgJX8dV009ffngde2s2S0SFR3F/U6O1zqfP3pWI5xVCu2S8CeGokU7CtTda78mmYZAxYIoKoU3liG01x7h3I1H7z3zExCSh9yJBaradnwZ5CKtt7dKIHgqoN/4QqFTMfrspAtIeRPYA9d5r+X6cBnvmFe+HXJjp34vL1Af52reXvnypjZuqGntw+bPyetydAkCTggxOGVbqLtBV1GnpxdPLnlkKoP6/tgZHAjan0cXDWe11eD9y3gYF1Vhy1P44qYfaEa1zgWkRGQWA3Gh0HZKPWYEseB01XCBYbtNzPfucoJ4eRMZwgral+qVbxPFt73oky9qcID+Bg76sA/riNRkDz0jnJOaqzpDfzn5P0KdBLpiOvht/r5A88ngE8OdK+yenJbcwwafTs67A1j5mThM7cvnpsGKt8XEWVirbP9pbLVqt4P5nTAkgSMP5jkw1zlaHsWQIDlDvTgOfH2Odf4U2IjIJzEBkFN3C2AkgOsyxKkySm5aNCOJn99xSFm+Nl5ny2KReY9T+KzWINorcDxRTcPTSEaX1nrU6C/OhI+dAQ89/fcYX74uZc+awJOoP9WFdfpQIKDSZPVQYnvq2Xac/8hJ0TnCfx6j5u9dzYs6Uqv1bNv7+1gGl6L4owfRS0hD5fxW6JNxtcG1PsNAEDKJiUJGa1AnwZ3EUGM7s0s7dzu5WaIzzQtgyCMWiqUXOCK1ICd31P461x8TyyE/g7FQrgzWsZRIQ38k6Yksz32NuqRATyQgQKLuKO77gqjQuli9rOAuD+gSwVar5XrNTSUrpYaeqGdrmWPggPGSoGXOmGZompKUD1QqbuC2rpi3+llgLIc5VM8V9rp9VzjZrbCkOiLD+n8QT1xDZWWhi70RXUMS1sVM5PS2lZI9+8/vx4KR0gZ/TmBLYow/S9k2Wc2MbG2Sawa61RkRwZ/AEnLR9D2WRFg2mr55oeLOvs343vW2418MR24KaefG8mJrasjze6GBrpFgxsmmXfuf3vPK2ZpyRTdDk/ven3R8e2/tlIEnsfPP0Lf/66JGYc3BV812mA67/gvRwWwH99FOwN06crg5+QRiP04Gb3wgfTXHu+7kbufhHeTIffesit5gQSFQJ8cZbq/rvSHP96ewv5WldqgY9Ps19CkB8V6M+P5j56cySJ5Up3fMeU45DuFImNjQMGdrfudUuUwNFSrvjjOrNMr3MAU5WOXoW0R9nfoAXGfMTPwXi8dYj7vfcNBG7v13pwVK1iFqa03lShsSnX1N9+Rm+uzhqz9hjV3eGBVHyHBrAjXqg/WxHPT+f+ry2D09kKqt+N+9Z7CoFPznCCvTlV/qV+RmdS4/HEdv57QwrT+5+dMZRCSkz5b7jEnxsezfds4VDH7ZUv+ZXXgEZvaEMM+koADAZfHEtXvta2kL67AMxdTytzgPe6saY/Mhh4aLB8MkNKDcegW3rJYyEgJySJQfzvNvB+Hh1L86aksKb3p9h6cA4dPlB4chtrhosMhjY1ag7yd6axFNDeBk6t8WMOjUf6RrTtBCcZatztdYw7VAws/pWT15VaBh4aPVcwd6czQ9G3UYpWq2eFg6trnXV6TrLF9cykXKikA1uwHwVb1yayJa61A+iJMiA1vG3ltk7PjEhFA/D1edaTj4mjan1MHB0YbXkv/nOSh7HZU1k9980Bii9fzqIxmLVZBq2+/W6Bl2u4MrUniGstI3KyjB0Er+/ZduMle1AZ/Bn8ffgZ/JTDjMLM3tyGs8ZPQmvwifD34YRysYqVCbf1Aca7yIlR0BKNjk3f2srO3fEdXU2VWt77pfX8TId0Z7XS3elN5wYRKDgHrw0U6jS2r5if2MZWvxnRpsObxEF6iRE5wJX20Ci6A35zjjdgbCj/3sc2s9Xxbwc4v7GMJS7XcPJO6+a6wVypsX1l+e15ptMtlVd+e54mRhMSKbhsa7V9vJSfhbEb4v/Os7XvbX2YFbHGvW9nPoWl/j6cTEuUwF/3spx1Zm/uaTtjYhcIWqNGDRTVMTta0QA8uJFj0mhDUD4tpaVmpazeZGVerQZe2Anc1d/ylqQIFJyDV2kUvr/AVXNUCM1jLlYxbX1zqnWrj7+Oc/opuhUfBfDPyS0fDzC0591bxH8PlzDN9+cdbLX9/BjX14LHd+bhSqwJEuavp76ks8Fhr7yBWzs9w+lPcGe/piY6N6bysJZNucw4Nei4oi5SUu+x4RIbYS3LAmb1aXs7RCcBDYaKgMI6rsIOFDHdHupPoak7+n3UqpkxMmfAJHA/ksTAdEh3x2dx1xxhCavR2EkCtVd1GsPWqJnsQuPAODwQWJHt2HMSWIdXZRQ+P0MBW5GSvvpnK/h4WgTw1kT7hXUdiXMVtFo2ZhxSwoWAqDG1alNpWo0GeGUvA6vxBpX6+Hgg0YGLg09OM8NwXRL7MNjzWZwo42A8LMr1W0qVhi0dvWTaRvvjVmavbkih2HRsnGhS5k7OVrAvSaWh7PKdo8CRUlPjs/npzK46mk9PUzfkyE69IqPgHLwqo/CbPjwARq+h/kz3pto5wHZEenUF/jLW3WchX0IDmgoS11znXCfB2/q2/3f0N2MTbNy7NzaF2noZ+PcJbktMSnKcILFeS6dIhcJUSWGsmLhQxX4Hg7u7zt7aUfzrBCsTMqI9X+ew/TLw0yWu2I1tq8MDTVUuw1qpPGoPsxxwbQtcg1cFCo25f5C7z8C1HC5mmaU3aSo8AVuChMs1XNkH+1Fc+M15Vr/c3q99LY7XX+Rg3lYDp8as2Evdgq5RhUO9Fnj3GCeLF8bQp8Kec3pxF7c4bujJ7aO3J5m+V6dhaaK5UsrGnCyjcZJcqkTePcr238aOnrsKqGcKC+Bn93QmnT7dwXcXmHUaF09/FlsXRb8dyMPIngIGb55o4SxwDuJS8FAkCRjyL+73xYYyFf5jDvskPDiYKUNXZ1FUWqaQbfUNKFVyBePvC9RrOIHN7c9MkDfxrxMsL1RqKZysVjPd++4xVsGsGM8MQluf28MbqY0I8KXgcU8h+16Mi+fnP7N322ryJzN5GNl4CfjoFBtzTUqyTZOS9V8GQQD3nY0pbIDVI69PMLl1dvI3r9n45TLNmLSGTpJbL/O9yooHpiZT6JbezTnXdFEd98JbC4q6BzMzYxTWFSsZJMzuy5WxKzufHi8FVh8xGDWpmZXZX8TvJYWx3Hpuf/vfK3d2shTIExEoeDDfzaCxUH4tPQE0eq501DoO1jfZIKKzl7cPckCKCuEk8PvN3NNcMMB6Zf0tX9NkRgGuRPNqgOd2UlNy7wCq/duTCt9+mQO9pU59lQ20g57TzzmNkow8lcnDyPvHqR+YmEjVt7VVOrf1NfWnUOv43kUGUwxmb/39pCQe9vDlzYAe/PwUYFmrnw+vv2sTWs8cGDldQbGlvy+7XtYahJjVKkOJqMJxQUKpErhYTe+OBi3waz7w+n5mBq5LYsajb7N+ETf3avo7suLdl70LDaDYsHMAtVv7Cvn4nWmsVnLmNSzomHiVmLEjc7iYIjpXd977x2E6KBYpqa43GvD4+wB/HA48c431E6BKy2qLueu595vZA8iMYXtqWwKFtw9SkNUliGn0snoquUfEAPP7G0oV/2OwwzVY4h4q4YR0XRIwrz/36uVgwmMNp8poVywXB0hHOOjtLQRiQmxvaW4N35wDXj8ABPnyulLpuH0TGcxszG19GDS4Qvj51z287iYk8Pq0R9TZVil4rZrXhqdcz+1BiBmdg8goeAnN7V1dxQODTV8fKWGJ6ogYTvRdbAxaAv34s2fvad85ze3PCb9SZUjtH2WaOCLIVMNdvZCp2yoVcLIcuPsHYFQPrhR7d2Wg05hPT3M7R46rNUuZEnfhiJW/MxuL3dyraYbgUDGbU7VHJ2Itbx1sashVWs/sEMCtlbcmtnQQbQtzQUKpEqgyXN+FdcCc72hXnRXHLaqseO9zf3zvKEXsAscjAgULNHbEW3cMSI+kTa01g2C1iuIwWydKOZFTBZwqB6JDKJJbdZjeAXcPsJxyHdSdR3twxCouLLBpBqpvBPDhDS1/t7GJT1QIkP+7pp/t7d/SUdHowX+4hL4ckxJp+DKjl3yEdoL20Vo/EUdzcyonamMPjFWHGLzO6WfZtMuu1/kKqNPy+g0LoHZkbyHv58weQIALKzWKDH1XxsbZJri1lYlJrETJ+th5ryEHVq5cieXLl6OgoADp6el44403kJWVZfa5//znP/HBBx/g2LFjAICMjAy89NJLGDlypE2vKQIFC/R8B6jVAFHB/PdKLfcF7x9EY53mYq+tefRpjwph+v+p7Uxj3j/IsQOAq8ipBv55lKuRIiXFaiod8OyvnCSfyrR9gN10iYIwR67KtXqW+LX2/rYlNDO3rfHqeAoGjYKxx7fw9/QIpf1seYMIFAS2kxgGNLZzWTraOWPDjjtMXzdoWfo6s7djylDPVgD3/cReC2MNdufNF0XfXwDOVlL/U6HiFqVKRy+QG3oCi6+xzmXUFpLCKO72Zj7++GMsWrQIK1euxJgxY7B69WpMmzYNJ06cQGJiS6OgLVu2YM6cORg9ejSCgoLwyiuvYPLkyTh+/Dji4uKsfl2hUWiFBi3Vzbd9y/+PiGZKNCu+pdXo2QoGC0VKqpD/eZSPx3Ri45qnMz13j1AvAY/+zJXJ8BhmVhI6tz3ArT0GvLTb1OTpcg01CDel0jFzSrLtGQS9RBFg10AOepLESfyu/hRzOUNgppcoGHW1U6S7uFLD69bV5kwC+TL1c27R1Gq4LVejZmltchjFy7/PaLp4evsgkF9HjVB4IPDMLxw35vVn0ytnjYWeqFHIy8trcq6BgYEIDDQ/4WVmZmLYsGFYtWrV1cfS0tIwffp0LFu2rM2X1Ol06Nq1K9566y3MmzfP6lP1uoyCrdsErRHkx8lh22yWorVG7648ANaAX5/CoCI21POyCc3xUXDv1FbuSmNpW7GSx3M7OUBUqlj+1i3Y9tpzjY6lYBUNXNWXNwAHink8sc12AaU1+Ci8O0jYV8htJqMh0jZDaeJNqQYDplY8D3ZcAXYXstV0r66uPW85UqcBZn8L9I9g1c7YOPszT45oCOYo3p/KstDQAAYJT2+n/8eYOPMi2oeHmr5WaYEbe9rWnr0jkZDQVJSyZMkSLF26tMXz1Go19u/fjyeffLLJ45MnT8aOHTusei2lUgmNRoOIiIi2n9wIrwgUsj+m8j4imBNIXg2FOwvSudJszyrTR9F2kNCctG48XIkce7X7+zJV3yOU55cczp4I7VmpBvo1DVqOlQL/PcUB65oerq/6kAvGJloDIm2/DnKquYesN1gsX6xiZ7+vzzFdrJe4t65QUNT5wi5+rQBLGE+UMauTFkEPjweHyGeCcwUj/s00u3Fir9fSBOntQ/S1eGGM9bbeksH4qlYDHC0BHtxEW/DsBCA7nq3i3UFjbUF4YFMTrbYI9PPSIOGzYwDa84fVAoDZjII5SktLodPpEB3d1E87OjoahYWFVr3ik08+ibi4OEyaZMMHCC8JFDbNorq3rAGYt5715KNieXNq9e4+O+dQVk+TnJhOFCi9foDlZPcOdE5JWXtRKEwZF0cyINIzLKeNjoyOKmE8Uw68eZDBWIAPJ/gV+/ge/6Y3jwwrs2q39uFhZGse8NJYZsSaB3VTklmuKkkUyG27TKX5jT15DIlqf8Cq1gEPb+LrT0uW5/XcmM230fjK35dB1aLNzCTc0NO2zFa9Bui+yrBy9+fq/UIltzWPlLBC4rcDvK9aoaMTFhZm0zaJotkNJklSi8fM8corr+Cjjz7Cli1bEBRk24rKKwIFXx9mE7oG8aZ1lE+9nKlSsUOm0b/gbAVXIX/ZzWY7y7K4BSNwDyVK1uaHGNTtlQ00IrqtLxXuQ9s5oXYO4ISt0XNiLavn45LE1HB+LcvtrDE7as74VsrzmleUpIQD9wy0/Hxr6fMegymdnkFPpaE5EcCg57Xs9jfbKq/nGOHozFvjrQUfBfD3a+37PcH+QO2jpv8XK4HVh3m9iG0dQWRkJHx9fVtkD4qLi1tkGZqzYsUKvPTSS9i4cSMGDbK9v4FXTakKRccIEgC26V07lV9r9ezIFxdKbcawaNe3he5I1KoZALSWHajV0CWzTkPL5ioVkFvDVf+Hp4AHBgF/GmH/9dojlO6XRvKquc3mattrR20x7JrD99PXh8HOb39kRmRGL/t9IioauI2i1jGg2nqZPgaTk4HJSfTacGa5XnuJCgEWj3L3WQjkQkBAADIyMrBhwwbMmBHCP4oAAB5vSURBVDHj6uMbNmzALbfcYvHnli9fjr/85S/48ccfMXz4cLteu4NMq96Nnw/wxgTH/s5atSj/M1JYxwZOof48yhuAJTu40rsjDRhoRheQEg6sbLQNeLgY2FlAS+PeXR2/qrUlPS9JXK1nJzhnO8gempftfXZz+3/n7gLgxd3UGAX4AA06ZuC+PMvgIciPgYio7hB4Co8//jjmzp2L4cOHY9SoUVizZg1yc3PxwAMPAADmzZuHuLi4qxUQr7zyChYvXowPP/wQycnJV7MRoaGhCA21Xl/R4QMFOYoA3UGpElh5mD0RokKAjbnsUX//IA6mHbmTXIOWlSy1GgZQpfXMDvx1L/Cvk8DCISwPa+09GhzlPvdMAJjzLZ36/Ayr9r2FwP0buD0xvRdwd7r3pbenpvAwsreQWzKTk+zbkvF0TpZR09RRBb/ewOzZs1FWVobnn38eBQUFGDBgAL7//nskJbFRS25uLnx8TJHvypUroVarceuttzb5PZYqKyzRIX0UCuvoPBgZzL3QZXsoEpqS7N1q7YJaThLmqkBKlexiWKIEius5qJ4q5/f6dmWlgb1Ng1qjqI7nY0+wdqacq/tMFxta7S8CDhZxRZ7axTMCzT0FXFHrDN0ZF22hL8a0FJbyDot2fK+IBi11GdnxvHYcmaE6VQa8f4Llt6Niba9M6ghUq5hVqTEEuJvz6EEyqDsrKa5LouDS0vWbW01xZpIdravdhUt9FPAr2l/1MEb0epArR0uAl/dyZVhaz1XGF2dZwvPYUGBRhnwGnsoGCtfsTY8++jP/xugQ/k2v7+cK8neD2BzJODlEhgBPNHL1fO8oRXgje9jX494Sp8uBD04YDJOCmI7ffpkr2rn9Wy//KqwDbviCLnDhhvfkszO0bZ3Xn3v0rmj3mxHNw5No3DpYL9EbxNHOeADQ7z12kjRWRVypBf52gNferD7AX7PsK/E7UcZ+HFo99QYaHTtOvryH20GLMoCnRnquqZkzKKhjiWaowYa8TsPPJCyA5dujYlve11vzuHiqVlPfsWQHzdXGxTPguzOtY2ZjOjodMlC4LpkHwPKuf59gDf41sUA/J3Th21vITnXRnaiEP1dJt8bkcPPPf/84B1ijffQbB1j2uCDd9kF2QTrT5EVKtvHV6IGvz9M9ck4/4OEh5jvW/dYBSnZzBPkxk1PRwHM4XMIB/9lfgS/OAUtGcZVjjm5BwH+u50BWpWaA4augcLOsgX9fXKh3ZYVyq/n3BBnK7+77iYLVW/tYvn7awkfhnCABADbOMrSbVvBam7+eGZ+bU3mP2RvwJocBa6fQFdDPB7hcS4OtGb3YhlwuWgs50TcC+Gq66f9HS4Dl41sPplcd5vURFsCST4D316BIZlydFSTkVvOztdfeXatnINlNbKs4hQ659eBq7vsJOFrKNHuRkoYqClB9/fsM3oCN+e4C7VKLlUz//2Ro3RwZzIn0wcH2DbhHSzh4D4iUT8bkq7P8bEfG2J6azqumCM6RLoyupry+df/9UR8yI9SgpflRhcrkDeKoskFn4Uz9j16ST1ttb6WglsZb7W30Zol9hfz91SqOc0t3MuDLiuPWyOy+ret+PjtDa+hqNbdWGnSme0NsPTgWjwgU5vcH7hsEjDaTKvM0/rqHTnjGvhFp3VpfAW/J49bIiBg2VPH0v7+js6+QQWOQL411frzErNZtfYBZfdk0yxIqLfDgRuoJrjN0yhPXg8BTufdHZgfDArhwWX2E7p5z+7OaqK1txGoVPUPCArlY+L+t/JlHN4tAwdF4RKAAAImduY/9hNiHFHgw6y+ys169liug0+XAviIOlKNjgUXDTHbJAkFHIa+a2YX2GpEJMaNzcJpGIScnBy+88AJ+/vlnFBYWIjY2FnfddReeeeYZBATYlmM+Ph/oL1wGBV7AtBQeRr45R93G2DgRAHsaB4uoH7ouiVUdMTI2b5I7CWHyt+ruyDgtUDh16hT0ej1Wr16NXr164dixY7jvvvtQV1eHFStW2PS7vLlzn6Bjc3Mv179miZLBiegZYKJKxcZY4+Mta2UKaoHxn5j0Ig1a7o9/cAII8QOeHNk+t83GlCqBk+UUgspFTyTouDgtUJg6dSqmTp169f89e/bE6dOnsWrVKouBgkqlgkqluvr/6upqZ52ewA5KlcDnZ9le1haLaJWW/7rDtMkeQV1BLdAl0HvKwMrq+bkZzZYqDH0nJiayVPbmVHlbGTuL1/czVa3W8xpdfYQBwJg4mjLdM7Cp50j3EGCLoQFUkB+rdp7bwQqU621sAGWOT09TwGzsHvnyHlb1jI3jZ3XPQOdVqwgEreHSobuqqqrVPtjLli3Dc88958IzErTF9V/QYKhbEAepXQXA77dQcX/PQNZXm1Off3KaK9eIIIqNHtkE3JTKcs2hTvQgqGxg6+mwQIqktl8Bfsqhx8LERPPC0cs1rDfvHAB09qfh1N8OADN7A3f2A65N9GybX5WOqnKtnoZLtRo+tjmPQZROAub3957AyFr0EgWl4b5ctfv5sKRwchKv7+bGZH4+Tdsl9+8GfHyT487ndAXFecF+PAJ8WRY6PoHXYmtBQmUD/UUEAmfgMjHj+fPnMWzYMLz66qu49957zT7HXEYhISHB48sjPRlJolFLWQMV+/dtADJjTL4TWRb21j86CRwqoXNiWT17JegkrsbuGwi8MMY5A9vREjbIqlbzKK3nKg3gIP9yFnBXWtMsQ7ES+O8pDtK1GvpcfHaG55rZg34T9w2Ud7Dwcy4Q28m6BkpnK4ALVUyzd5Qmam1RrwEKlezRIQfqNMxYWKqCqVXzujU2HVuyg94rExLYT2R8Qse0ahZiRudgc6CwdOnSNlf9e/fubdKlKj8/H+PHj8f48ePxzjvvWP1a3uKj4C3UabjSsbV+PbeazpejYqlqduWeqzFgGRcPjIm1LjjZWwgoNQwS5DqRfngS+OocTWr8fYHzldxjT4vg6vPWPsAQN/aWEDiXtce4ddLJn4H6lRpmJFK7MLCd06/1UltvRQQKzsHmQKG0tBSlpaWtPic5ORlBQRyR8/PzMWHCBGRmZmLdunVNGla0hQgUbOd4KSfzbsHAznymlxek29+qVyBPzpQDZytpZazRA5+eATblMm0+JZlmXrHtGcPs4JfLnLTaW+LWGt+e5zU9LYXZrI7crKwxn55mNiQjumOX1opAwTnYfJtFRkYiMtK6WsUrV65gwoQJyMjIwNq1a20KElzB/85zRXZdkrzTygAFaOGBXM3/9xTT+DN7tdxXfm4nffHLGqjkrtcCr+xljf7CoXQ768gDibfQJ4KHkeEx9Bpx5XW87TLwrxP8WgEaiW24RBvtG3vSWnlyUvuut42XgAU/AFqJ+gq1ntf1a/upm1mWRa1MR3dpnNXXNa/zhy2s9sg2bG+YazAn8D6cFo/n5+cjOzsbiYmJWLFiBUpKSq5+LyYmxlkv2ya/+YaCvK6BXImdqeDKa24aDZ0cufJ2pIXtiP/QAjrQILoqUjJwuKMfB0pjk6JPGomr3jsKbL1s0BP0AAZ2F0GCt+KOvfW4UApEjTnJLZdZLTIunscIB6xux8UDR+ZT/e/rA/xwEfjyHKs1piR7RomnJDFg90SfjO8vAA9toiaiXsuATa1jT4hrE6k1GhXr7rMUOBuniRnXrVuHBQsWmP2etS/pjK0HvUTRWnkD8OdfqZAfHQuMjuO/7R1wT5dTvBcRxBXW6iNM/U9LMd98yVbqNWwRnFPFyX9ULPsktNYvQCCwFXuC3NxqBt3e1JTLXqpUpoxevRZ4aCO1OZOSGFyNiPGM90mpoTYp1J96nVf3MWCb3de2rS1J4lbo4Cjn9mYRWw/OwSMsnJ2lUWjQOl6s9u5R4OPTDESKlUBeDR+PCgGezmS3xvYOEM5stiPomFQ2sGTSR8Hjz7+yXfQtqVTSe7IWoKiOq3lXZh+e2g58fY56IaPnwpVa9iK4rQ9w9wDvFxvm1wK7C0zVRO8dY7fYEdHctpjVh31LHIkIFJyDB9/+7ccZivZ7BvIAmCb9MYeZilGxjnOYbG+QIElUyEcYvBH+d5519gsGsLOkwLF8epr2vmPi5LuX/tJumjLpJR6VKpaX/uMwqz/eyGY5rCeg1FDwaBR65tUAT2znfTg1mVsWQ6Kc+1ksy+JhZPVhvubImI4T5OdU8Zoy+pP4G9ql9+7K4NNZXSkFjsdjA4Wfc5l6l/O+39QUHnJDqwde3M2sR3kDV1y1GgrERsSwlfUNPZ17DkoNhW/TUuwrl5Rzm+F/HgHePGgyzimsY+labChXUXelUXwoJ14Zz8PIst00rLq+p3y8BaylTgO8f9xUOqqXeM0fKwVSw4GBkUB6N9dmSX432HWvJRdGx/EwsjMfGNK94xl7eQMetfVw7480F+kSyBVxTjUH3vnptDmV68Qhd17bB1yuBUb1cGzmozmLNgMHimgE0yWQorRAXwoy56e3XlaXU0Xv+/AAXgtvHuTj8/rLr/14tYqd8BoM+9N/2cVrdVIiMDGJZX2W+gl0JD4+xUl8SrJzNTbnK3n9jE/wDF2AwH7E1oNz8KiMwj0DaQtcqWJqUacHLlaxhCoy2Pv3/JzF48Pbfo4j+ONwVmtUNHD/8qNTQHQIUKVmAJESbtlN7nwl9R9VKj7/QiVLQNccAYZHAysnMRviTPYUsGJmVh+gRyvjQ1hgU03NB9OEDwhAW+9Vh03bG8VKVh35KBjs/d8I2nw7mtQuPAQCgX14VKBgLMPRS0B6JDAo0jGVBJYoqGX6sksQB7m8GmBuf+ca2ZwuB/p0ldcK2VHEdzZlKyoagLz7rW9GNDGJB0CNxYu7WUefFc8A0RnZpDPlLEsN8adS20/BLYTfb2Ed+dw09pBoa5UqggQyLh7oGc5rWwFg3XFqN25O5dG7q7vP0Hv4MQc4VMwSxqFRIpMiaB8eFSgY8VGYfAOcyeJfgY25nNQ0eqaRn/6FgqiFQx2jP1DrWDplFBZ+f5Gr7QUDmFb31hbb7fGhVyiAZ69x3LlYok8EUPYwP3elhjbU7x0z+OkncrvL3QPwoWLgZBk1JXIPSGI68TDSv5t8LbI9jWoVMHc9r1OlliXapyv4vW5BwF/Gyr9fiUC+iNu0Fd6ZYvr6xV2sDBgVyzSpI6sDJiYamicZSirPVQJ/P8B0/DOZzu22KGgdPx+DajsAuH8Q8OAQ957PhUpgzncUgAb4cvtt62V+PSWJDn2393Vups1RiCDBcQT7cfES4sfs174i4D8n2RJ+Zi8g0koHxU2XGMz17+adWU2BfYhb1UqecdIKNsAXmJPGryWJiuwhUWwvK25UeSGH1VhcKPDeFGai1Hoqyc9Wmno8TEz0jCBB4Fj8DWZORgZEmsq0WyO/lr4vdRpmI3bkM/CMCgGy4ykyvt7JFVAC+SMCBRmhUNAfXyCwRKAf9TlG0iKAx4aJoFLQFGuDWrWO2xSd/JlJiA0FugcDt/ZmNmJMXNu/Q+D9iEBBIPBg5K5LEMib5HDqF4zcUMkqHXdrbwTyQlwOAoFM+P4CcKXG+a+jl4AjJaZmTh2BcxVcOQtaJ7WLZwYJlQ3A8dKOdU27EpFREAjcxAfHga/P03Qq0JcVDIdKKJad1Qf4TW8gwUE+LJ+cZr8BPx92YvzbAbYqn96LhxwqOBxJRQPwzXnaOGslujKuPASM7EHR59QU2inLQXcisI8z5cCJMqBOy6qPP22jRbTA8YhAQdAEldazGwB5EqNjKRpT6ejieL6SBlRG4ylHekOodRSs6Qx2xho9raWPlQIJnfmaSfI2h7OJei0bEvn78KhSARL4HgCsDhBOrvLhRBnw+n76k2THA3FWlIXvKwK+u0CX007+DBJE4OccPMrCWeAc/nuKN1l4IBXPWy/Tw+E3vW173+s1XJW6UnVvbHA1OtbzB4mzFUCvLs4XJur0wA85NEByZUdFd3K8lAFDnwjXveZbB9me+bok6ya+jsZjP/PerTW0sr5Sy8eD/IDHM9ht19qW1Cot8Gs+MLQ7ELFSWDg7GrF2FGBXAVBaT2vsi1WM7rfkAUt3ACvGA7f2sTx5/XCRN3iXQJZ6PrwJmNmbgUZrvRvaw4cn6TwXamgd/OkZmszM7gvcmUZLZ0+sAnCVM6Gvj/ObfsmNdBd0Rf0xB1i+lxkitY79U/INk19GNPBaNoMzAXl0GPDQEN7HVWrg/7axBff0XrYvDAP9aIJWrXLOuXZ0RKAgwBsTTF9/fIrthbPigL4RbU+45Q3sMV+l4r7w5Rruf793DLh/ILBktONXrUOjGJTUqIFaNdCg4+sfK+Vg3T2Yam5L6CVaCBv/tjVHgAAflqaGi8yVwE6GdAf+MobXZqAv74MKFUsNb+gpGoE1p3H/jVgJ+HaG+85F0Dpi60HgMM5XUrk/No695l2xFaDSAvuL2LbZ2nbVX50F7lrPYKGTP/fsyxo4uF+fAizKECs/V7Azn/bCrtwOcCVavWsEohqdMNkyIrpHOgePyShodBzMY6xsIiRwPaldgIXDXPuagX5Ne95bw/TeQG1v7tXXaYA/buXKLzsemJBIEyOB4zlRBqw9Rl2JBLYNX3+Ruowbe7IxVHaCZ24bmcNZQYJOz2xag46izVf3sdx1UhJbmY+IEYGDwLF4RKCw5gjQPYQNmdK7cR96hh37WJ7A2QrRRc9V+PrwGlp9nfdMTnIm2I/VFQqw4qC0ntUHAyOBwd1pOyynz6GigVtbjipRdRQHioEZX/P9DPKj9fKFKmBvEQOGewYC0xzQsE4gMOIRgcLlWorsatTAhkuGm6KQHQQ9PcNQrASW7aYYsEsQ8O5R1rnPSwfu6Gd9G2aB/chpcvJmUsIpXjMyMZFBmpyaQ23Ioe5GrecE/OhmIDWcfTSmJAPj44FgK5X4zmJEDHD5d6b/f36G5a43Ch2EwEnI6Ba1zPOjmaocHccb1ZtaL/v7sFNbpYpBQ3kDqwgqD3B18Ewm0EtkGAReiL3X9YYc4GI1MC3Z8av9Tbm8DwN8eW/6KoCCOkPwoHPsazmK3/Rx9xkIvB2PCBQAqtHvTHPPa//9ADAsig1SHL367BoE3DeIX2t0VPRf08O7giGB/DlczCoXOa3ujZwsAx7YyGoVnZ5VOcfL+L1B3YHHhgJ3D3CMgdLL40xfVzYAN6UCExJcs+cvSSK7JZAnMhwW3I8kAdGrOGiGBQAl9VxlpIQzWLk7vWlpj6Pw96VngUDgbA4UARsvcXL1UbCsdEc+0+u39AJuSAEigt19liQxjB4EPuC5/nQJ+OwM6+1v6UXxqTMm2C5BwORkx/9ec+j09AK563s6ZU5KpLCzS5BrXr85eolbound3HseAnkgAgUzKBTcA6xRc/Uy5zuu9MfFA+PigPj2VMQIBI344SKzZZk9XGsprJOomtdLPOq1rAAx6oFya+QTKHTyp2GRkbRuwJ9GuO98HMkzv9BvwVfBhUmtmgHDu8eAm3rSl8FV5aMbcoBVh/n69VpWqZTW87qcmAi8nu0a4yqB/BA+Cm1gHEjl3jCnTkMFuULBZkNqHTCrrzAQkhv7CoEnt3MPPMCHepR9RUBcKCt5ZvcFxrrBw2H7ZW49RIW4/rU7Mo23GySJgcOg7jRocrW99qVq4FQ5x5FgP2Dxr0BkMK/JycnW+5S4E+Gj4BxERqENjKlZuTPwfaBEyYDGR0Hx1cLNwM09gQeHMH0ocD+9u3KVqNYzmHv/OLe2piTzMxrgphVbVjuDk/J6roxvTOXq3xPuGTnQeMtEoQBeynLfuSSFNW0M9vV0+4ODHVeYlZqQICq3vAERKHgJF+7lv2odDYTyamjDnBVPa1mBPAgPBK6JNf1/UHdaTnuiiO3GL4ECQy8DhQI4Wgo8v4sly7f3BRZfI5/tC4Ht2BokLP6VWi6lhp1JN+by8fRuwP2DWBor98ysJ7By5UosX74cBQUFSE9PxxtvvIGsLMsR5ueff47Fixfj/PnzSE1NxYsvvogZM2zzyxaBgpcR4Av8bYJnTjwdEU9O9f99Auv3JYlanrt/YGOeG3syOyLHCgqB8+gXAfTtSk1JjmEbY3Zf4PZ+zDKJMan9fPzxx1i0aBFWrlyJMWPGYPXq1Zg2bRpOnDiBxMTEFs/fuXMnZs+ejRdeeAEzZszAl19+idtuuw2//PILMjMzrX5dWWsUqqqq0KVLF+Tdz+oDQcdgm2G/PNqDJ9GOhkbH1aKYDORLnQZY8CMwsBswPgHIjKEFujOoVHHMdvUWVLUaSFgDVFZWGnQETniNqxqFnwC0Z1+lDsBk5OXlNdEoBAYGIjDQvLgsMzMTw4YNw6pVq64+lpaWhunTp2PZsmUtnj979mxUV1dj/fr1Vx+bOnUqunbtio8++sj6U5VkTF5engR6LYlDHOIQhzjEYdWRl5fntHmpvr5eiomJcch5hoaGtnhsyZIlZl9XpVJJvr6+0hdffNHk8UcffVQaN26c2Z9JSEiQXnvttSaPvfbaa1JiYqJNf7Osk4OxsbHIy8tD586dofDwpUp1dTUSEhJaRI+Cpoj3yTrE+2Qd4n1qG296jyRJQk1NDWJjY9t+sp0EBQXh4sWLUKvV7f5dkiS1mNssZRNKS0uh0+kQHR3d5PHo6GgUFhaa/ZnCwkKbnm8JWQcKPj4+iI/3rn6/YWFhHn8zugLxPlmHeJ+sQ7xPbeMt75GzthwaExQUhKAg97hQNQ8szAUb7Xm+OYQGVSAQCAQCmRMZGQlfX98W2YDi4uIWWQMjMTExNj3fEiJQEAgEAoFA5gQEBCAjIwMbNmxo8viGDRswevRosz8zatSoFs//6aefLD7fEr5Lly5datNPCOzG19cX2dnZ8POT9Y6P2xHvk3WI98k6xPvUNuI98gzCwsKwePFixMXFISgoCC+99BI2b96MtWvXokuXLpg3bx727NmDSZMmAQDi4uLw7LPPIjAwEJGRkXj33XfxzjvvYM2aNTZt68u6PFIgEAgEAoGJlStX4pVXXkFBQQEGDBiA119/HePGse1pdnY2kpOTsW7duqvP/+yzz/Dss8/iwoULVw2XZs6cadNrikBBIBAIBAKBRYRGQSAQCAQCgUVEoCAQCAQCgcAiIlAQCAQCgUBgEREoCAQCgUAgsIgIFFxMTk4O7rnnHqSkpCA4OBipqalYsmSJQ+xAvY0XX3wRo0ePRkhICLp06eLu05ENK1euREpKCoKCgpCRkYHt27e7+5Rkx7Zt23DTTTchNjYWCoUCX331lbtPSXYsW7YMI0aMQOfOnREVFYXp06fj9OnT7j4tgQwRgYKLOXXqFPR6PVavXo3jx4/j9ddfxz/+8Q88/fTT7j412aFWqzFr1iw8+OCD7j4V2WBsM/vMM8/g4MGDyMrKwrRp05Cbm+vuU5MVdXV1GDx4MN566y13n4ps2bp1Kx5++GHs2rULGzZsgFarxeTJk1FXV+fuUxPIDFEeKQOWL1+OVatW4cKFC+4+FVmybt06LFq0CJWVle4+Fbdja5tZAb3uv/zyS0yfPt3dpyJrSkpKEBUVha1bt16tyxcIAJFRkAVVVVWIiIhw92kIZI5arcb+/fsxefLkJo9PnjwZO3bscNNZCbyFqqoqABBjkaAFIlBwM+fPn8ebb76JBx54wN2nIpA59rSZFQisQZIkPP744xg7diwGDBjg7tMRyAwRKDiIpUuXQqFQtHrs27evyc/k5+dj6tSpmDVrFu699143nblrsed9EjTFEW1jBYLGPPLIIzhy5Ag++ugjd5+KQIaIDiAO4pFHHsHtt9/e6nOSk5Ovfp2fn48JEyZg1KhRWLNmjZPPTj7Y+j4JTNjTZlYgaIuFCxfim2++wbZt22xqFCToOIhAwUFERkYiMjLSqudeuXIFEyZMQEZGBtauXQsfn46T2LHlfRI0pXGb2RkzZlx9fMOGDbjlllvceGYCT0SSJCxcuBBffvkltmzZgpSUFHefkkCmiEDBxeTn5yM7OxuJiYlYsWIFSkpKrn4vJibGjWcmP3Jzc1FeXo7c3FzodDocOnQIANCrVy+Ehoa6+ezcw+OPP465c+di+PDhV7NRubm5QuPSjNraWpw7d+7q/y9evIhDhw4hIiICiYmJbjwz+fDwww/jww8/xNdff43OnTtfzVSFh4cjODjYzWcnkBWSwKWsXbtWAmD2EDRl/vz5Zt+nzZs3u/vU3Mrbb78tJSUlSQEBAdKwYcOkrVu3uvuUZMfmzZvNXjvz589396nJBkvj0Nq1a919agKZIXwUBAKBQCAQWKTjbI4LBAKBQCCwGREoCAQCgUAgsIgIFAQCgUAgEFhEBAoCgUAgEAgsIgIFgUAgEAgEFhGBgkAgEAgEAouIQEEgEAgEAoFFRKAgEAgEAoHAIiJQEAgEAoFAYBERKAgEAoFAILCICBQEAoFAIBBY5P8B1/TobO6eF2sAAAAASUVORK5CYII=", "text/plain": [ "Figure(PyObject <Figure size 640x480 with 2 Axes>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_contourf(Uf; cmap=\"jet\"); colorbar();\n", "plot_arrows(Uf; units=\"xy\", scale=10);\n", "axis(\"equal\");\n", "xlim(-1.5,1.5); ylim(-2.5,2.5);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "kernelspec": { "display_name": "Julia 1.3.1", "language": "julia", "name": "julia-1.3" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.3.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
texib/deeplearning_homework
Keras_LSTM2.ipynb
1
927508
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import keras" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 原始資料來源的 SQL，這是抽樣過的資料，當中也有一筆資料是修改過的，因為當天 Server 似乎出了一些問題，導至流量大幅下降\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "sql = \"\"\"\n", "SELECT \n", "date,count(distinct cookie_pta) as uv\n", "from\n", "TABLE_DATE_RANGE(pixinsight.article_visitor_log_1_100_, TIMESTAMP('2017-01-01'), CURRENT_TIMESTAMP())\n", "where venue = 'pixnet'\n", "group by date\n", "order by date\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " uv\n", "date \n", "2017-01-01 38633\n", "2017-01-02 39509\n", "2017-01-03 42734\n", "2017-01-04 43611\n", "2017-01-05 40036\n" ] } ], "source": [ "from os import environ\n", "# load and plot dataset\n", "import pandas as pd\n", "from pandas import read_csv\n", "from pandas import datetime\n", "from matplotlib import pyplot\n", "import matplotlib.dates as mdates\n", "\n", "%matplotlib notebook\n", "\n", "# %matplotlib inline \n", "\n", "# load dataset\n", "def parser(x):\n", " return datetime.strptime(x, '%Y%m%d')\n", "\n", "series = pd.read_gbq(sql,project_id=environ['PROJECT_ID'], verbose=False, private_key=environ['GOOGLE_KEY'])#,header=0, parse_dates=[0], index_col='date', squeeze=True, date_parser=parser)\n", "series['date'] = pd.to_datetime(series['date'],format='%Y%m%d')\n", "series.index = series['date']\n", "del series['date']\n", "\n", "# summarize first few rows\n", "print(series.head())\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 進行 scale to 0-1 ，方便作為 input 及 output (因為 sigmoid 介於 0~1 之間)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import scale,MinMaxScaler\n", "scaler = MinMaxScaler()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "x = series.values" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "x = x.reshape([x.shape[0],1])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n", " warnings.warn(msg, DataConversionWarning)\n" ] }, { "data": { "text/plain": [ "MinMaxScaler(copy=True, feature_range=(0, 1))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaler.fit(x)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "x_scaled = scaler.transform(x)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.figure()\n", "pyplot.plot(x_scaled)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 產生 x,y pair \n", "* 舉列來說假設將 Step Size 設為 4 天，故一筆 Training Data ，為連續 4 天的流量。再來利用這４天的資料來預測第 5 天的流量\n", "* 綠色的部是 Training Data(前4天的資料)，藍色的部份是需要被預測的部份。示意如下圖\n", "* <img align=\"left\" width=\"50%\" src=\"./imgs/sequence_uv.png\" />" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#往回看 30 天前的每一筆資料\n", "step_size = 15" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "原始資料長度:(312, 1)\n" ] } ], "source": [ "print(\"原始資料長度:{}\".format(x_scaled.shape))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "def window_stack(a, stepsize=1, width=3):\n", " return np.hstack( a[i:1+i-width or None:stepsize] for i in range(0,width) )\n", "\n", "import numpy as np\n", "train_x = window_stack(x_scaled, stepsize=1, width=step_size)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(297, 15)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 最後一筆資料要放棄，因為沒有未來的答案作驗證\n", "\n", "train_x = train_x[:-1]\n", "train_x.shape" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# 請注意千萬不將每一筆(Row) 當中的最後一天資料作為 Training Data 中的 Input Data\n", "train_y = np.array([i for i in x_scaled[step_size:]]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 確認產出來的 Training Data 沒有包含到 Testing Data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(297, 1)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_y.shape" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.80070082, 0.82187047, 0.89980667, 0.92100048, 0.83460609,\n", " 0.91906718, 0.88105365, 0.93615273, 0.89059932, 0.87539874,\n", " 0.88653939, 0.88124698, 0.81500725, 0.82450459, 0.84775254])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_x[0]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.82187047, 0.89980667, 0.92100048, 0.83460609, 0.91906718,\n", " 0.88105365, 0.93615273, 0.89059932, 0.87539874, 0.88653939,\n", " 0.88124698, 0.81500725, 0.82450459, 0.84775254, 0.87426293])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_x[1]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 0.87426293])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_y[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Design Graph" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# reshape input to be [samples, time steps, features]\n", "trainX = np.reshape(train_x, (train_x.shape[0], step_size, 1))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "lstm_1 (LSTM) (None, 4) 96 \n", "_________________________________________________________________\n", "dense_1 (Dense) (None, 1) 5 \n", "=================================================================\n", "Total params: 101\n", "Trainable params: 101\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "from keras import Sequential\n", "from keras.layers import LSTM,Dense\n", "# create and fit the LSTM network\n", "model = Sequential()\n", "# input_shape(step_size,feature_dim)\n", "model.add(LSTM(4, input_shape=(step_size,1), unroll=True))\n", "model.add(Dense(1))\n", "model.compile(loss='mean_squared_error', optimizer='adam',metrics=['accuracy'])\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 最後30 筆資料不要看" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "validation_size = 60" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 669us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 668us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 663us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 660us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 633us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 626us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 636us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 634us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 688us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 768us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 745us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 653us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 761us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 693us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 656us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 662us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 724us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 689us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 660us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 671us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 636us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 660us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 655us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 631us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 632us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 635us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 691us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 669us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 668us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 633us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 676us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 668us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 681us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 656us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 797us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 821us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 666us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 701us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 661us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 631us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 634us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 636us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 633us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 628us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 658us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 656us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 653us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "237/237 [==============================] - 0s 653us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 658us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 670us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 656us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 636us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 669us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 662us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 636us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 673us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 653us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 651us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 677us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 653us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 662us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 637us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 655us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 656us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 651us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 653us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 666us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "237/237 [==============================] - 0s 637us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 657us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 660us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 636us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 651us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 637us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 653us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 658us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 635us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 637us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 640us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 661us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 633us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 634us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 640us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 640us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 662us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 668us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 640us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 669us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 660us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 651us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 701us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 671us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 662us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 658us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 663us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 679us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 679us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 665us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 630us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 666us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 636us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 628us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 635us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 636us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 636us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 661us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 655us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 633us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 634us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 655us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 658us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 637us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 640us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 651us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 655us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 662us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 635us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 656us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 651us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 659us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 672us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "237/237 [==============================] - 0s 670us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 659us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 670us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 658us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 655us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 651us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 658us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 637us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 651us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 637us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 640us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 655us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 661us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 665us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 633us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 651us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 649us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 670us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 665us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 658us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 672us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 653us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 635us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 655us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 659us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 637us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 636us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 631us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 651us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 657us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 651us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 640us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 660us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 629us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 625us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 659us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 666us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 668us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 650us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 639us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 637us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "237/237 [==============================] - 0s 646us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 640us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 640us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 645us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 653us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 631us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 633us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 638us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 652us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 630us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 657us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 642us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 647us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 630us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 641us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 643us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 654us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 640us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 644us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 657us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n", "Train on 237 samples, validate on 60 samples\n", "Epoch 1/1\n", "237/237 [==============================] - 0s 648us/step - loss: 0.0061 - acc: 0.0042 - val_loss: 0.0012 - val_acc: 0.0000e+00\n" ] } ], "source": [ "val_loss = []\n", "loss = []\n", "for _ in range(400):\n", " history = model.fit(trainX[:-1*validation_size],\n", " train_y[:-1*validation_size],\n", " epochs=1,shuffle=False, \n", " validation_data=(trainX[-1*validation_size:],\n", " train_y[-1*validation_size:]))\n", " \n", " loss.append(history.history['loss'])\n", " val_loss.append(history.history['val_loss'])\n", " model.reset_states()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 看一下 Error Rate 曲線" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.figure()\n", "pyplot.plot(loss)\n", "pyplot.plot(val_loss)\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 看一下曲線擬合效果" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "predict_y = model.predict(trainX)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(297, 1)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_y.shape" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var ncells = cells.length;\n", " for (var i=0; i<ncells; i++) {\n", " var cell = cells[i];\n", " if (cell.cell_type === 'code'){\n", " for (var j=0; j<cell.output_area.outputs.length; j++) {\n", " var data = cell.output_area.outputs[j];\n", " if (data.data) {\n", " // IPython >= 3 moved mimebundle to data attribute of output\n", " data = data.data;\n", " }\n", " if (data['text/html'] == html_output) {\n", " return [cell, data, j];\n", " }\n", " }\n", " }\n", " }\n", "}\n", "\n", "// Register the function which deals with the matplotlib target/channel.\n", "// The kernel may be null if the page has been refreshed.\n", "if (IPython.notebook.kernel != null) {\n", " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", "}\n" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<img 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\" width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.figure()\n", "pyplot.plot(scaler.inverse_transform(predict_y))\n", "pyplot.plot(scaler.inverse_transform(train_y))\n", "\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 來預測最後 60 天資料預出來的結果" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "predict_y = model.predict(trainX[-1*validation_size:])" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "predict_y = scaler.inverse_transform(predict_y)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(60, 1)" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predict_y.shape" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support.' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('<div/>');\n", " this._root_extra_style(this.root)\n", " this.root.attr('style', 'display: inline-block');\n", "\n", " $(parent_element).append(this.root);\n", "\n", " this._init_header(this);\n", " this._init_canvas(this);\n", " this._init_toolbar(this);\n", "\n", " var fig = this;\n", "\n", " this.waiting = false;\n", "\n", " this.ws.onopen = function () {\n", " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", " fig.send_message(\"send_image_mode\", {});\n", " if (mpl.ratio != 1) {\n", " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", " }\n", " fig.send_message(\"refresh\", {});\n", " }\n", "\n", " this.imageObj.onload = function() {\n", " if (fig.image_mode == 'full') {\n", " // Full images could contain transparency (where diff images\n", " // almost always do), so we need to clear the canvas so that\n", " // there is no ghosting.\n", " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", " }\n", " fig.context.drawImage(fig.imageObj, 0, 0);\n", " };\n", "\n", " this.imageObj.onunload = function() {\n", " fig.ws.close();\n", " }\n", "\n", " this.ws.onmessage = this._make_on_message_function(this);\n", "\n", " this.ondownload = ondownload;\n", "}\n", "\n", "mpl.figure.prototype._init_header = function() {\n", " var titlebar = $(\n", " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n", " 'ui-helper-clearfix\"/>');\n", " var titletext = $(\n", " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n", " 'text-align: center; padding: 3px;\"/>');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('<div/>');\n", "\n", " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", "\n", " function canvas_keyboard_event(event) {\n", " return fig.key_event(event, event['data']);\n", " }\n", "\n", " canvas_div.keydown('key_press', canvas_keyboard_event);\n", " canvas_div.keyup('key_release', canvas_keyboard_event);\n", " this.canvas_div = canvas_div\n", " this._canvas_extra_style(canvas_div)\n", " this.root.append(canvas_div);\n", "\n", " var canvas = $('<canvas/>');\n", " canvas.addClass('mpl-canvas');\n", " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", "\n", " this.canvas = canvas[0];\n", " this.context = canvas[0].getContext(\"2d\");\n", "\n", " var backingStore = this.context.backingStorePixelRatio ||\n", "\tthis.context.webkitBackingStorePixelRatio ||\n", "\tthis.context.mozBackingStorePixelRatio ||\n", "\tthis.context.msBackingStorePixelRatio ||\n", "\tthis.context.oBackingStorePixelRatio ||\n", "\tthis.context.backingStorePixelRatio || 1;\n", "\n", " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", "\n", " var rubberband = $('<canvas/>');\n", " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", "\n", " var pass_mouse_events = true;\n", "\n", " canvas_div.resizable({\n", " start: function(event, ui) {\n", " pass_mouse_events = false;\n", " },\n", " resize: function(event, ui) {\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " stop: function(event, ui) {\n", " pass_mouse_events = true;\n", " fig.request_resize(ui.size.width, ui.size.height);\n", " },\n", " });\n", "\n", " function mouse_event_fn(event) {\n", " if (pass_mouse_events)\n", " return fig.mouse_event(event, event['data']);\n", " }\n", "\n", " rubberband.mousedown('button_press', mouse_event_fn);\n", " rubberband.mouseup('button_release', mouse_event_fn);\n", " // Throttle sequential mouse events to 1 every 20ms.\n", " rubberband.mousemove('motion_notify', mouse_event_fn);\n", "\n", " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", "\n", " canvas_div.on(\"wheel\", function (event) {\n", " event = event.originalEvent;\n", " event['data'] = 'scroll'\n", " if (event.deltaY < 0) {\n", " event.step = 1;\n", " } else {\n", " event.step = -1;\n", " }\n", " mouse_event_fn(event);\n", " });\n", "\n", " canvas_div.append(canvas);\n", " canvas_div.append(rubberband);\n", "\n", " this.rubberband = rubberband;\n", " this.rubberband_canvas = rubberband[0];\n", " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", " this.rubberband_context.strokeStyle = \"#000000\";\n", "\n", " this._resize_canvas = function(width, height) {\n", " // Keep the size of the canvas, canvas container, and rubber band\n", " // canvas in synch.\n", " canvas_div.css('width', width)\n", " canvas_div.css('height', height)\n", "\n", " canvas.attr('width', width * mpl.ratio);\n", " canvas.attr('height', height * mpl.ratio);\n", " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", "\n", " rubberband.attr('width', width);\n", " rubberband.attr('height', height);\n", " }\n", "\n", " // Set the figure to an initial 600x600px, this will subsequently be updated\n", " // upon first draw.\n", " this._resize_canvas(600, 600);\n", "\n", " // Disable right mouse context menu.\n", " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", " return false;\n", " });\n", "\n", " function set_focus () {\n", " canvas.focus();\n", " canvas_div.focus();\n", " }\n", "\n", " window.setTimeout(set_focus, 100);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('<button/>');\n", " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n", " 'ui-button-icon-only');\n", " button.attr('role', 'button');\n", " button.attr('aria-disabled', 'false');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", "\n", " var icon_img = $('<span/>');\n", " icon_img.addClass('ui-button-icon-primary ui-icon');\n", " icon_img.addClass(image);\n", " icon_img.addClass('ui-corner-all');\n", "\n", " var tooltip_span = $('<span/>');\n", " tooltip_span.addClass('ui-button-text');\n", " tooltip_span.html(tooltip);\n", "\n", " button.append(icon_img);\n", " button.append(tooltip_span);\n", "\n", " nav_element.append(button);\n", " }\n", "\n", " var fmt_picker_span = $('<span/>');\n", "\n", " var fmt_picker = $('<select/>');\n", " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n", " fmt_picker_span.append(fmt_picker);\n", " nav_element.append(fmt_picker_span);\n", " this.format_dropdown = fmt_picker[0];\n", "\n", " for (var ind in mpl.extensions) {\n", " var fmt = mpl.extensions[ind];\n", " var option = $(\n", " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n", " fmt_picker.append(option)\n", " }\n", "\n", " // Add hover states to the ui-buttons\n", " $( \".ui-button\" ).hover(\n", " function() { $(this).addClass(\"ui-state-hover\");},\n", " function() { $(this).removeClass(\"ui-state-hover\");}\n", " );\n", "\n", " var status_bar = $('<span class=\"mpl-message\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "}\n", "\n", "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n", " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n", " // which will in turn request a refresh of the image.\n", " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n", "}\n", "\n", "mpl.figure.prototype.send_message = function(type, properties) {\n", " properties['type'] = type;\n", " properties['figure_id'] = this.id;\n", " this.ws.send(JSON.stringify(properties));\n", "}\n", "\n", "mpl.figure.prototype.send_draw_message = function() {\n", " if (!this.waiting) {\n", " this.waiting = true;\n", " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n", " }\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " var format_dropdown = fig.format_dropdown;\n", " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n", " fig.ondownload(fig, format);\n", "}\n", "\n", "\n", "mpl.figure.prototype.handle_resize = function(fig, msg) {\n", " var size = msg['size'];\n", " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n", " fig._resize_canvas(size[0], size[1]);\n", " fig.send_message(\"refresh\", {});\n", " };\n", "}\n", "\n", "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n", " var x0 = msg['x0'] / mpl.ratio;\n", " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n", " var x1 = msg['x1'] / mpl.ratio;\n", " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n", " x0 = Math.floor(x0) + 0.5;\n", " y0 = Math.floor(y0) + 0.5;\n", " x1 = Math.floor(x1) + 0.5;\n", " y1 = Math.floor(y1) + 0.5;\n", " var min_x = Math.min(x0, x1);\n", " var min_y = Math.min(y0, y1);\n", " var width = Math.abs(x1 - x0);\n", " var height = Math.abs(y1 - y0);\n", "\n", " fig.rubberband_context.clearRect(\n", " 0, 0, fig.canvas.width, fig.canvas.height);\n", "\n", " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n", "}\n", "\n", "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n", " // Updates the figure title.\n", " fig.header.textContent = msg['label'];\n", "}\n", "\n", "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n", " var cursor = msg['cursor'];\n", " switch(cursor)\n", " {\n", " case 0:\n", " cursor = 'pointer';\n", " break;\n", " case 1:\n", " cursor = 'default';\n", " break;\n", " case 2:\n", " cursor = 'crosshair';\n", " break;\n", " case 3:\n", " cursor = 'move';\n", " break;\n", " }\n", " fig.rubberband_canvas.style.cursor = cursor;\n", "}\n", "\n", "mpl.figure.prototype.handle_message = function(fig, msg) {\n", " fig.message.textContent = msg['message'];\n", "}\n", "\n", "mpl.figure.prototype.handle_draw = function(fig, msg) {\n", " // Request the server to send over a new figure.\n", " fig.send_draw_message();\n", "}\n", "\n", "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n", " fig.image_mode = msg['mode'];\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Called whenever the canvas gets updated.\n", " this.send_message(\"ack\", {});\n", "}\n", "\n", "// A function to construct a web socket function for onmessage handling.\n", "// Called in the figure constructor.\n", "mpl.figure.prototype._make_on_message_function = function(fig) {\n", " return function socket_on_message(evt) {\n", " if (evt.data instanceof Blob) {\n", " /* FIXME: We get \"Resource interpreted as Image but\n", " * transferred with MIME type text/plain:\" errors on\n", " * Chrome. But how to set the MIME type? It doesn't seem\n", " * to be part of the websocket stream */\n", " evt.data.type = \"image/png\";\n", "\n", " /* Free the memory for the previous frames */\n", " if (fig.imageObj.src) {\n", " (window.URL || window.webkitURL).revokeObjectURL(\n", " fig.imageObj.src);\n", " }\n", "\n", " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n", " evt.data);\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n", " fig.imageObj.src = evt.data;\n", " fig.updated_canvas_event();\n", " fig.waiting = false;\n", " return;\n", " }\n", "\n", " var msg = JSON.parse(evt.data);\n", " var msg_type = msg['type'];\n", "\n", " // Call the \"handle_{type}\" callback, which takes\n", " // the figure and JSON message as its only arguments.\n", " try {\n", " var callback = fig[\"handle_\" + msg_type];\n", " } catch (e) {\n", " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n", " return;\n", " }\n", "\n", " if (callback) {\n", " try {\n", " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n", " callback(fig, msg);\n", " } catch (e) {\n", " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n", " }\n", " }\n", " };\n", "}\n", "\n", "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n", "mpl.findpos = function(e) {\n", " //this section is from http://www.quirksmode.org/js/events_properties.html\n", " var targ;\n", " if (!e)\n", " e = window.event;\n", " if (e.target)\n", " targ = e.target;\n", " else if (e.srcElement)\n", " targ = e.srcElement;\n", " if (targ.nodeType == 3) // defeat Safari bug\n", " targ = targ.parentNode;\n", "\n", " // jQuery normalizes the pageX and pageY\n", " // pageX,Y are the mouse positions relative to the document\n", " // offset() returns the position of the element relative to the document\n", " var x = e.pageX - $(targ).offset().left;\n", " var y = e.pageY - $(targ).offset().top;\n", "\n", " return {\"x\": x, \"y\": y};\n", "};\n", "\n", "/*\n", " * return a copy of an object with only non-object keys\n", " * we need this to avoid circular references\n", " * http://stackoverflow.com/a/24161582/3208463\n", " */\n", "function simpleKeys (original) {\n", " return Object.keys(original).reduce(function (obj, key) {\n", " if (typeof original[key] !== 'object')\n", " obj[key] = original[key]\n", " return obj;\n", " }, {});\n", "}\n", "\n", "mpl.figure.prototype.mouse_event = function(event, name) {\n", " var canvas_pos = mpl.findpos(event)\n", "\n", " if (name === 'button_press')\n", " {\n", " this.canvas.focus();\n", " this.canvas_div.focus();\n", " }\n", "\n", " var x = canvas_pos.x * mpl.ratio;\n", " var y = canvas_pos.y * mpl.ratio;\n", "\n", " this.send_message(name, {x: x, y: y, button: event.button,\n", " step: event.step,\n", " guiEvent: simpleKeys(event)});\n", "\n", " /* This prevents the web browser from automatically changing to\n", " * the text insertion cursor when the button is pressed. We want\n", " * to control all of the cursor setting manually through the\n", " * 'cursor' event from matplotlib */\n", " event.preventDefault();\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " // Handle any extra behaviour associated with a key event\n", "}\n", "\n", "mpl.figure.prototype.key_event = function(event, name) {\n", "\n", " // Prevent repeat events\n", " if (name == 'key_press')\n", " {\n", " if (event.which === this._key)\n", " return;\n", " else\n", " this._key = event.which;\n", " }\n", " if (name == 'key_release')\n", " this._key = null;\n", "\n", " var value = '';\n", " if (event.ctrlKey && event.which != 17)\n", " value += \"ctrl+\";\n", " if (event.altKey && event.which != 18)\n", " value += \"alt+\";\n", " if (event.shiftKey && event.which != 16)\n", " value += \"shift+\";\n", "\n", " value += 'k';\n", " value += event.which.toString();\n", "\n", " this._key_event_extra(event, name);\n", "\n", " this.send_message(name, {key: value,\n", " guiEvent: simpleKeys(event)});\n", " return false;\n", "}\n", "\n", "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n", " if (name == 'download') {\n", " this.handle_save(this, null);\n", " } else {\n", " this.send_message(\"toolbar_button\", {name: name});\n", " }\n", "};\n", "\n", "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n", " this.message.textContent = tooltip;\n", "};\n", "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n", "\n", "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n", "\n", "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n", " // Create a \"websocket\"-like object which calls the given IPython comm\n", " // object with the appropriate methods. Currently this is a non binary\n", " // socket, so there is still some room for performance tuning.\n", " var ws = {};\n", "\n", " ws.close = function() {\n", " comm.close()\n", " };\n", " ws.send = function(m) {\n", " //console.log('sending', m);\n", " comm.send(m);\n", " };\n", " // Register the callback with on_msg.\n", " comm.on_msg(function(msg) {\n", " //console.log('receiving', msg['content']['data'], msg);\n", " // Pass the mpl event to the overriden (by mpl) onmessage function.\n", " ws.onmessage(msg['content']['data'])\n", " });\n", " return ws;\n", "}\n", "\n", "mpl.mpl_figure_comm = function(comm, msg) {\n", " // This is the function which gets called when the mpl process\n", " // starts-up an IPython Comm through the \"matplotlib\" channel.\n", "\n", " var id = msg.content.data.id;\n", " // Get hold of the div created by the display call when the Comm\n", " // socket was opened in Python.\n", " var element = $(\"#\" + id);\n", " var ws_proxy = comm_websocket_adapter(comm)\n", "\n", " function ondownload(figure, format) {\n", " window.open(figure.imageObj.src);\n", " }\n", "\n", " var fig = new mpl.figure(id, ws_proxy,\n", " ondownload,\n", " element.get(0));\n", "\n", " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n", " // web socket which is closed, not our websocket->open comm proxy.\n", " ws_proxy.onopen();\n", "\n", " fig.parent_element = element.get(0);\n", " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n", " if (!fig.cell_info) {\n", " console.error(\"Failed to find cell for figure\", id, fig);\n", " return;\n", " }\n", "\n", " var output_index = fig.cell_info[2]\n", " var cell = fig.cell_info[0];\n", "\n", "};\n", "\n", "mpl.figure.prototype.handle_close = function(fig, msg) {\n", " var width = fig.canvas.width/mpl.ratio\n", " fig.root.unbind('remove')\n", "\n", " // Update the output cell to use the data from the current canvas.\n", " fig.push_to_output();\n", " var dataURL = fig.canvas.toDataURL();\n", " // Re-enable the keyboard manager in IPython - without this line, in FF,\n", " // the notebook keyboard shortcuts fail.\n", " IPython.keyboard_manager.enable()\n", " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n", " fig.close_ws(fig, msg);\n", "}\n", "\n", "mpl.figure.prototype.close_ws = function(fig, msg){\n", " fig.send_message('closing', msg);\n", " // fig.ws.close()\n", "}\n", "\n", "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n", " // Turn the data on the canvas into data in the output cell.\n", " var width = this.canvas.width/mpl.ratio\n", " var dataURL = this.canvas.toDataURL();\n", " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n", "}\n", "\n", "mpl.figure.prototype.updated_canvas_event = function() {\n", " // Tell IPython that the notebook contents must change.\n", " IPython.notebook.set_dirty(true);\n", " this.send_message(\"ack\", {});\n", " var fig = this;\n", " // Wait a second, then push the new image to the DOM so\n", " // that it is saved nicely (might be nice to debounce this).\n", " setTimeout(function () { fig.push_to_output() }, 1000);\n", "}\n", "\n", "mpl.figure.prototype._init_toolbar = function() {\n", " var fig = this;\n", "\n", " var nav_element = $('<div/>')\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items){\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) { continue; };\n", "\n", " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n", " button.click(method_name, toolbar_event);\n", " button.mouseover(tooltip, toolbar_mouse_event);\n", " nav_element.append(button);\n", " }\n", "\n", " // Add the status bar.\n", " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n", " nav_element.append(status_bar);\n", " this.message = status_bar[0];\n", "\n", " // Add the close button to the window.\n", " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n", " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n", " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", " buttongrp.append(button);\n", " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", " titlebar.prepend(buttongrp);\n", "}\n", "\n", "mpl.figure.prototype._root_extra_style = function(el){\n", " var fig = this\n", " el.on(\"remove\", function(){\n", "\tfig.close_ws(fig, {});\n", " });\n", "}\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(el){\n", " // this is important to make the div 'focusable\n", " el.attr('tabindex', 0)\n", " // reach out to IPython and tell the keyboard manager to turn it's self\n", " // off when our div gets focus\n", "\n", " // location in version 3\n", " if (IPython.notebook.keyboard_manager) {\n", " IPython.notebook.keyboard_manager.register_events(el);\n", " }\n", " else {\n", " // location in version 2\n", " IPython.keyboard_manager.register_events(el);\n", " }\n", "\n", "}\n", "\n", "mpl.figure.prototype._key_event_extra = function(event, name) {\n", " var manager = IPython.notebook.keyboard_manager;\n", " if (!manager)\n", " manager = IPython.keyboard_manager;\n", "\n", " // Check for shift+enter\n", " if (event.shiftKey && event.which == 13) {\n", " this.canvas_div.blur();\n", " event.shiftKey = false;\n", " // Send a \"J\" for go to next cell\n", " event.which = 74;\n", " event.keyCode = 74;\n", " manager.command_mode();\n", " manager.handle_keydown(event);\n", " }\n", "}\n", "\n", "mpl.figure.prototype.handle_save = function(fig, msg) {\n", " fig.ondownload(fig, null);\n", "}\n", "\n", "\n", "mpl.find_output_cell = function(html_output) {\n", " // Return the cell and output element which can be found *uniquely* in the notebook.\n", " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", " // IPython event is triggered only after the cells have been serialised, which for\n", " // our purposes (turning an active figure into a static one), is too late.\n", " var cells = IPython.notebook.get_cells();\n", " var 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width=\"640\">" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pyplot.figure()\n", "pyplot.plot(x[-1*(validation_size+1):-1])\n", "pyplot.plot(predict_y)\n", "\n", "\n", "pyplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 心得觀察\n", "* LSTM 可以學習到 Period Pattern 是沒有問題的，但是似乎對於大幅的震盪以目前的 Model 來說無法完全的 Catch 到，但是還是有學到漲幅的趨勢預測\n", "* 至於 LSTM 要如何調整震盪的幅度有兩個想法可以實驗看看\n", " * 直接 Modified Training，將震盪幅度加大\n", " * 修改 Loss Function ，把平方改為 2.x 次方不知道是否有效果" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
matthijsvk/multimodalSR
code/Experiments/Lasagne_examples/examples/ResNets/resnet50/ImageNet Pretrained Network (ResNet-50).ipynb
1
26308
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "\n", "This example demonstraites how to convert Caffe pretrained ResNet-50 model from https://github.com/KaimingHe/deep-residual-networks (firstly described in http://arxiv.org/pdf/1512.03385v1.pdf) into Theano/Lasagne format.\n", "\n", "We will create a set of Lasagne layers corresponding to the Caffe model specification (prototxt), then copy the parameters from the caffemodel file into our model (like <a href=\"https://github.com/Lasagne/Recipes/blob/master/examples/Using%20a%20Caffe%20Pretrained%20Network%20-%20CIFAR10.ipynb\">here</a>).\n", "\n", "This notebook produce *resnet50.pkl* file, which contains dictionary with following foelds:\n", " * values: numpy array with parameters of the model\n", " * synset_words: labels of classes\n", " * mean_image: mean image which should be subtracted from each input image\n", "\n", "This file can be used for initialization of weights of the model created by *modelzoo/resnet50.py*.\n", "\n", "## License\n", "Same as in parent project https://github.com/KaimingHe/deep-residual-networks/blob/master/LICENSE\n", "\n", "# Requirements\n", "\n", "## Download the required files\n", "\n", "<a href=\"https://onedrive.live.com/?authkey=%21AAFW2-FVoxeVRck&id=4006CBB8476FF777%2117887&cid=4006CBB8476FF777\">Here</a> you can find folder with caffe/proto files, we need followings to be stored in ./:\n", " * *ResNet-50-deploy.prototxt* contains architecture of ResNet-50 in proto format\n", " * *ResNet-50-model.caffemodel* is proto serialization of model parameters\n", " * *ResNet_mean.binaryproto* contains mean values\n", " \n", "## Imports\n", "We need caffe to load weights and compare results" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import caffe\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need a lot of building blocks from Lasagne to build network" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import lasagne\n", "from lasagne.utils import floatX\n", "from lasagne.layers import InputLayer\n", "from lasagne.layers import Conv2DLayer as ConvLayer # can be replaced with dnn layers\n", "from lasagne.layers import BatchNormLayer\n", "from lasagne.layers import Pool2DLayer as PoolLayer\n", "from lasagne.layers import NonlinearityLayer\n", "from lasagne.layers import ElemwiseSumLayer\n", "from lasagne.layers import DenseLayer\n", "from lasagne.nonlinearities import rectify, softmax" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Helper modules, some of them will help us to download images and plot them" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['figure.figsize'] = 8, 6\n", "import io\n", "import urllib\n", "import skimage.transform\n", "from IPython.display import Image\n", "import pickle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Build Lasagne model\n", "\n", "## BatchNormalization issue in caffe\n", "\n", "Caffe doesn't have correct BN layer as described in https://arxiv.org/pdf/1502.03167.pdf:\n", " * it can collect datasets mean ($\\hat{\\mu}$) and variance ($\\hat{\\sigma}^2$)\n", " * it can't fit $\\gamma$ and $\\beta$ parameters to scale and shift standardized distribution of feature in following formula: $\\hat{x}_i = \\dfrac{x_i - \\hat{\\mu}_i}{\\sqrt{\\hat{\\sigma}_i^2 + \\epsilon}}\\cdot\\gamma + \\beta$\n", "\n", "To fix this issue, <a href=\"https://github.com/KaimingHe/deep-residual-networks\">here</a> authors use such BN layer followed by Scale layer, which can fit scale and shift parameters, but can't standardize data:\n", "\n", "<pre>\n", "layer {\n", "\tbottom: \"res2a_branch1\"\n", "\ttop: \"res2a_branch1\"\n", "\tname: \"bn2a_branch1\"\n", "\ttype: \"BatchNorm\"\n", "\tbatch_norm_param {\n", "\t\tuse_global_stats: true\n", "\t}\n", "}\n", "\n", "layer {\n", "\tbottom: \"res2a_branch1\"\n", "\ttop: \"res2a_branch1\"\n", "\tname: \"scale2a_branch1\"\n", "\ttype: \"Scale\"\n", "\tscale_param {\n", "\t\tbias_term: true\n", "\t}\n", "}\n", "</pre>\n", "\n", "In Lasagne we have correct BN layer, so we do not need use such a trick.\n", "\n", "## Replicated blocks\n", "\n", "### Simple blocks\n", "\n", "ResNet contains a lot of similar replicated blocks, lets call them *simple blocks*, which have one of two architectures:\n", " * Convolution $\\rightarrow$ BN $\\rightarrow$ Nonlinearity\n", " * Convolution $\\rightarrow$ BN\n", " \n", "http://ethereon.github.io/netscope/#/gist/2f702ea9e05900300462102a33caff9c" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image(filename='images/head.png', width='40%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can increase, decrease or keep same dimensionality of data using such blocks. In ResNet-50 only several transformation are used.\n", "\n", "#### Keep shape with 1x1 convolution\n", "We can apply nonlinearity transformation from (None, 64, 56, 56) to (None, 64, 56, 56) if we apply simple block with following parameters (look at the origin of a network after first pool layer):\n", " * num_filters: same as parent has\n", " * filter_size: 1\n", " * stride: 1\n", " * pad: 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image(filename='images/conv1x1.png', width='40%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Keep shape with 3x3 convolution\n", "Also we can apply nonlinearity transformation from (None, 64, 56, 56) to (None, 64, 56, 56) if we apply simple block with following parameters (look at the middle of any residual blocks):\n", " * num_filters: same as parent has\n", " * filter_size: 3x3\n", " * stride: 1\n", " * pad: 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image(filename='images/conv3x3.png', width='40%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Increase shape using number of filters\n", "We can nonlinearly increase shape from (None, 64, 56, 56) to (None, 256, 56, 56) if we apply simple block with following parameters (look at the last simple block of any risidual block):\n", " * num_filters: four times greater then parent has\n", " * filter_size: 1x1\n", " * stride: 1\n", " * pad: 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image(filename='images/increase_fn.png', width='40%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Increase shape using number of filters\n", "We can nonlinearly decrease shape from (None, 256, 56, 56) to (None, 64, 56, 56) if we apply simple block with following parameters (look at the first simple block of any risidual block without left branch):\n", " * num_filters: four times less then parent has\n", " * filter_size: 1x1\n", " * stride: 1\n", " * pad: 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image(filename='images/decrease_fn.png', width='40%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Increase shape using number of filters\n", "We can also nonlinearly decrease shape from (None, 256, 56, 56) to (None, 128, 28, 28) if we apply simple block with following parameters (look at the first simple block of any risidual block with left branch):\n", " * num_filters: two times less then parent has\n", " * filter_size: 1x1\n", " * stride: 2\n", " * pad: 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image(filename='images/decrease_fnstride.png', width='40%')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following function creates simple block" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def build_simple_block(incoming_layer, names,\n", " num_filters, filter_size, stride, pad, \n", " use_bias=False, nonlin=rectify):\n", " \"\"\"Creates stacked Lasagne layers ConvLayer -> BN -> (ReLu)\n", " \n", " Parameters:\n", " ----------\n", " incoming_layer : instance of Lasagne layer\n", " Parent layer\n", " \n", " names : list of string\n", " Names of the layers in block\n", " \n", " num_filters : int\n", " Number of filters in convolution layer\n", " \n", " filter_size : int\n", " Size of filters in convolution layer\n", " \n", " stride : int\n", " Stride of convolution layer\n", " \n", " pad : int\n", " Padding of convolution layer\n", " \n", " use_bias : bool\n", " Whether to use bias in conlovution layer\n", " \n", " nonlin : function\n", " Nonlinearity type of Nonlinearity layer\n", " \n", " Returns\n", " -------\n", " tuple: (net, last_layer_name)\n", " net : dict\n", " Dictionary with stacked layers\n", " last_layer_name : string\n", " Last layer name\n", " \"\"\"\n", " net = []\n", " net.append((\n", " names[0], \n", " ConvLayer(incoming_layer, num_filters, filter_size, pad, stride, \n", " flip_filters=False, nonlinearity=None) if use_bias \n", " else ConvLayer(incoming_layer, num_filters, filter_size, stride, pad, b=None, \n", " flip_filters=False, nonlinearity=None)\n", " ))\n", " \n", " net.append((\n", " names[1], \n", " BatchNormLayer(net[-1][1])\n", " ))\n", " if nonlin is not None:\n", " net.append((\n", " names[2], \n", " NonlinearityLayer(net[-1][1], nonlinearity=nonlin)\n", " ))\n", " \n", " return dict(net), net[-1][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Residual blocks\n", "\n", "ResNet also contains several **residual blockes** built from simple blocks, each of them have two branches; left branch sometimes contains simple block, sometimes not. Each block ends with Elementwise sum layer followed by ReLu nonlinearity. \n", "\n", "http://ethereon.github.io/netscope/#/gist/410e7e48fa1e5a368ee7bca5eb3bf0ca" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image(filename='images/left_branch.png', width='40%')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Image(filename='images/no_left_branch.png', width='40%')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "simple_block_name_pattern = ['res%s_branch%i%s', 'bn%s_branch%i%s', 'res%s_branch%i%s_relu']\n", "\n", "def build_residual_block(incoming_layer, ratio_n_filter=1.0, ratio_size=1.0, has_left_branch=False, \n", " upscale_factor=4, ix=''):\n", " \"\"\"Creates two-branch residual block\n", " \n", " Parameters:\n", " ----------\n", " incoming_layer : instance of Lasagne layer\n", " Parent layer\n", " \n", " ratio_n_filter : float\n", " Scale factor of filter bank at the input of residual block\n", " \n", " ratio_size : float\n", " Scale factor of filter size\n", " \n", " has_left_branch : bool\n", " if True, then left branch contains simple block\n", " \n", " upscale_factor : float\n", " Scale factor of filter bank at the output of residual block\n", " \n", " ix : int\n", " Id of residual block\n", " \n", " Returns\n", " -------\n", " tuple: (net, last_layer_name)\n", " net : dict\n", " Dictionary with stacked layers\n", " last_layer_name : string\n", " Last layer name\n", " \"\"\"\n", " net = {}\n", " \n", " # right branch\n", " net_tmp, last_layer_name = build_simple_block(\n", " incoming_layer, map(lambda s: s % (ix, 2, 'a'), simple_block_name_pattern),\n", " int(lasagne.layers.get_output_shape(incoming_layer)[1]*ratio_n_filter), 1, int(1.0/ratio_size), 0)\n", " net.update(net_tmp)\n", " \n", " net_tmp, last_layer_name = build_simple_block(\n", " net[last_layer_name], map(lambda s: s % (ix, 2, 'b'), simple_block_name_pattern),\n", " lasagne.layers.get_output_shape(net[last_layer_name])[1], 3, 1, 1)\n", " net.update(net_tmp)\n", " \n", " net_tmp, last_layer_name = build_simple_block(\n", " net[last_layer_name], map(lambda s: s % (ix, 2, 'c'), simple_block_name_pattern),\n", " lasagne.layers.get_output_shape(net[last_layer_name])[1]*upscale_factor, 1, 1, 0,\n", " nonlin=None)\n", " net.update(net_tmp)\n", " \n", " right_tail = net[last_layer_name]\n", " left_tail = incoming_layer\n", " \n", " # left branch\n", " if has_left_branch:\n", " net_tmp, last_layer_name = build_simple_block(\n", " incoming_layer, map(lambda s: s % (ix, 1, ''), simple_block_name_pattern),\n", " int(lasagne.layers.get_output_shape(incoming_layer)[1]*4*ratio_n_filter), 1, int(1.0/ratio_size), 0,\n", " nonlin=None)\n", " net.update(net_tmp)\n", " left_tail = net[last_layer_name]\n", " \n", " net['res%s' % ix] = ElemwiseSumLayer([left_tail, right_tail], coeffs=1)\n", " net['res%s_relu' % ix] = NonlinearityLayer(net['res%s' % ix], nonlinearity=rectify)\n", " \n", " return net, 'res%s_relu' % ix" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### Gathering everighting together\n", "\n", "Create head of the network (everithing before first residual block)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "net = {}\n", "net['input'] = InputLayer((None, 3, 224, 224))\n", "sub_net, parent_layer_name = build_simple_block(\n", " net['input'], ['conv1', 'bn_conv1', 'conv1_relu'],\n", " 64, 7, 3, 2, use_bias=True)\n", "net.update(sub_net)\n", "net['pool1'] = PoolLayer(net[parent_layer_name], pool_size=3, stride=2, pad=0, mode='max', ignore_border=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create four groups of residual blocks" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "block_size = list('abc')\n", "parent_layer_name = 'pool1'\n", "for c in block_size:\n", " if c == 'a':\n", " sub_net, parent_layer_name = build_residual_block(net[parent_layer_name], 1, 1, True, 4, ix='2%s' % c)\n", " else:\n", " sub_net, parent_layer_name = build_residual_block(net[parent_layer_name], 1.0/4, 1, False, 4, ix='2%s' % c)\n", " net.update(sub_net)\n", " \n", "block_size = list('abcd')\n", "for c in block_size:\n", " if c == 'a':\n", " sub_net, parent_layer_name = build_residual_block(net[parent_layer_name], 1.0/2, 1.0/2, True, 4, ix='3%s' % c)\n", " else:\n", " sub_net, parent_layer_name = build_residual_block(net[parent_layer_name], 1.0/4, 1, False, 4, ix='3%s' % c)\n", " net.update(sub_net)\n", " \n", "block_size = list('abcdef')\n", "for c in block_size:\n", " if c == 'a':\n", " sub_net, parent_layer_name = build_residual_block(net[parent_layer_name], 1.0/2, 1.0/2, True, 4, ix='4%s' % c)\n", " else:\n", " sub_net, parent_layer_name = build_residual_block(net[parent_layer_name], 1.0/4, 1, False, 4, ix='4%s' % c)\n", " net.update(sub_net)\n", " \n", "block_size = list('abc')\n", "for c in block_size:\n", " if c == 'a':\n", " sub_net, parent_layer_name = build_residual_block(net[parent_layer_name], 1.0/2, 1.0/2, True, 4, ix='5%s' % c)\n", " else:\n", " sub_net, parent_layer_name = build_residual_block(net[parent_layer_name], 1.0/4, 1, False, 4, ix='5%s' % c)\n", " net.update(sub_net)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Create tail of the network (everighting after last resudual block)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "net['pool5'] = PoolLayer(net[parent_layer_name], pool_size=7, stride=1, pad=0, \n", " mode='average_exc_pad', ignore_border=False)\n", "net['fc1000'] = DenseLayer(net['pool5'], num_units=1000, nonlinearity=None)\n", "net['prob'] = NonlinearityLayer(net['fc1000'], nonlinearity=softmax)\n", "\n", "print 'Total number of layers:', len(lasagne.layers.get_all_layers(net['prob']))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Transfer weights from caffe to lasagne\n", "\n", "## Load pretrained caffe model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "net_caffe = caffe.Net('./ResNet-50-deploy.prototxt', './ResNet-50-model.caffemodel', caffe.TEST)\n", "layers_caffe = dict(zip(list(net_caffe._layer_names), net_caffe.layers))\n", "print 'Number of layers: %i' % len(layers_caffe.keys())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Copy weights\n", "\n", "There is one more issue with BN layer: caffa stores variance $\\sigma^2$, but lasagne stores inverted standard deviation $\\dfrac{1}{\\sigma}$, so we need make simple transfommation to handle it.\n", "\n", "Other issue reffers to weights ofthe dense layer, in caffe it is transposed, we should handle it too." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for name, layer in net.items(): \n", " if name not in layers_caffe:\n", " print name, type(layer).__name__\n", " continue\n", " if isinstance(layer, BatchNormLayer):\n", " layer_bn_caffe = layers_caffe[name]\n", " layer_scale_caffe = layers_caffe['scale' + name[2:]]\n", " layer.gamma.set_value(layer_scale_caffe.blobs[0].data)\n", " layer.beta.set_value(layer_scale_caffe.blobs[1].data)\n", " layer.mean.set_value(layer_bn_caffe.blobs[0].data)\n", " layer.inv_std.set_value(1/np.sqrt(layer_bn_caffe.blobs[1].data) + 1e-4)\n", " continue\n", " if isinstance(layer, DenseLayer):\n", " layer.W.set_value(layers_caffe[name].blobs[0].data.T)\n", " layer.b.set_value(layers_caffe[name].blobs[1].data)\n", " continue\n", " if len(layers_caffe[name].blobs) > 0:\n", " layer.W.set_value(layers_caffe[name].blobs[0].data)\n", " if len(layers_caffe[name].blobs) > 1:\n", " layer.b.set_value(layers_caffe[name].blobs[1].data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Testing\n", "\n", "Read ImageNet synset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('./imagenet_classes.txt', 'r') as f:\n", " classes = map(lambda s: s.strip(), f.readlines())" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Download some image urls for recognition" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "index = urllib.urlopen('http://www.image-net.org/challenges/LSVRC/2012/ori_urls/indexval.html').read()\n", "image_urls = index.split('<br>')\n", "np.random.seed(23)\n", "np.random.shuffle(image_urls)\n", "image_urls = image_urls[:100]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Load mean values" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "blob = caffe.proto.caffe_pb2.BlobProto()\n", "data = open('./ResNet_mean.binaryproto', 'rb').read()\n", "blob.ParseFromString(data)\n", "mean_values = np.array(caffe.io.blobproto_to_array(blob))[0]" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Image loader" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def prep_image(url, fname=None):\n", " if fname is None:\n", " ext = url.split('.')[-1]\n", " im = plt.imread(io.BytesIO(urllib.urlopen(url).read()), ext)\n", " else:\n", " ext = fname.split('.')[-1]\n", " im = plt.imread(fname, ext)\n", " h, w, _ = im.shape\n", " if h < w:\n", " im = skimage.transform.resize(im, (256, w*256/h), preserve_range=True)\n", " else:\n", " im = skimage.transform.resize(im, (h*256/w, 256), preserve_range=True)\n", " h, w, _ = im.shape\n", " im = im[h//2-112:h//2+112, w//2-112:w//2+112]\n", " rawim = np.copy(im).astype('uint8')\n", " im = np.swapaxes(np.swapaxes(im, 1, 2), 0, 1)\n", " im = im[::-1, :, :]\n", " im = im - mean_values\n", " return rawim, floatX(im[np.newaxis])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "Lets take five images and compare prediction of Lasagne with Caffe" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "source": [ "n = 5\n", "m = 5\n", "i = 0\n", "for url in image_urls:\n", " print url\n", " try:\n", " rawim, im = prep_image(url)\n", " except:\n", " print 'Failed to download'\n", " continue\n", "\n", " prob_lasangne = np.array(lasagne.layers.get_output(net['prob'], im, deterministic=True).eval())[0]\n", " prob_caffe = net_caffe.forward_all(data=im)['prob'][0]\n", "\n", " \n", " print 'Lasagne:'\n", " res = sorted(zip(classes, prob_lasangne), key=lambda t: t[1], reverse=True)[:n]\n", " for c, p in res:\n", " print ' ', c, p\n", " \n", " print 'Caffe:'\n", " res = sorted(zip(classes, prob_caffe), key=lambda t: t[1], reverse=True)[:n]\n", " for c, p in res:\n", " print ' ', c, p\n", " \n", " plt.figure()\n", " plt.imshow(rawim.astype('uint8'))\n", " plt.axis('off')\n", " plt.show()\n", " \n", " i += 1\n", " if i == m:\n", " break\n", " \n", " print '\\n\\n'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model = {\n", " 'values': lasagne.layers.get_all_param_values(net['prob']),\n", " 'synset_words': classes,\n", " 'mean_image': mean_values\n", "}\n", "\n", "pickle.dump(model, open('./resnet50.pkl', 'wb'), protocol=-1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
qxcv/joint-regressor
keras/left-right-confusion.ipynb
1
3682
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook contains some HDF5-manipulation code to write a new dataset `leftright` to a set of training samples. The `leftright` dataset should contain the joint coordinates for the left *or* right arm present in the sample; this means that both left arms and right arms will have a nonzero `leftright` entry.\n", "\n", "The aim here is to get a regressor which regresses any arms in an image, regardless of whether they're left or right. Presumably I'll still need the left/right classifier to do its thing as well if I'm to have any hope of integrating this into a real pipeline." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import h5py\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "h5_paths = [\n", " '../cache/train-patches-mpii/samples-000001.h5',\n", " '../cache/train-patches-mpii/negatives.h5',\n", " '../cache/val-patches-mpii/samples-000001.h5',\n", " '../cache/val-patches-mpii/negatives.h5'\n", "]\n", "\n", "def add_leftright(path, overwrite=False):\n", " with h5py.File(path, 'r+') as fp:\n", " # Overwrite check\n", " if 'leftright' in fp.keys():\n", " print('Warning: \"{}\" already has a leftright dataset'.format(path))\n", " if not overwrite:\n", " print('Skipping')\n", " return\n", " print('Overwriting it')\n", " del fp['leftright']\n", " \n", " # We'll save these old values for some assertions later\n", " old_l = fp['left'][:]\n", " old_r = fp['right'][:]\n", " \n", " # Now we can write the dataset\n", " leftright = np.array(fp['left'][:])\n", " class_nums = np.argmax(fp['class'], axis=1)\n", " left_mask = class_nums == 1\n", " right_mask = class_nums == 2\n", " # Make sure we don't have any real annotations in there already\n", " assert (leftright[right_mask] == 0).all()\n", " # I'm doing [:] because there's a bug in h5py's selections.py;\n", " # specifically, FancySelection.__getitem__ relies on a count\n", " # variable which is zero when the selection vector is empty\n", " # (or something). I should probably report this to the devs.\n", " leftright[right_mask] = fp['right'][:][right_mask, :]\n", " fp['leftright'] = leftright\n", " \n", " # Make sure that our new leftright dataset corresponds to the\n", " # old left dataset and old right dataset\n", " new_lr = fp['leftright'][:]\n", " assert np.all(new_lr[right_mask] == old_r[right_mask])\n", " assert np.all(new_lr[left_mask] == old_l[left_mask])\n", "\n", "for path in h5_paths:\n", " print('Adding leftright to {}'.format(path))\n", " add_leftright(path)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
bgruening/EDeN
examples/parameter_exploration.ipynb
1
166939
{ "metadata": { "name": "", "signature": "sha256:bc9cdb7d96f2e7c05541997f965a01a2c2e89eadbf2934b8afe9e654044ef192" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "from eden import graph\n", "from eden.util import eden_io\n", "from eden.converter.graph import gspan" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.linear_model import SGDClassifier\n", "from sklearn.grid_search import RandomizedSearchCV\n", "from sklearn import cross_validation\n", "\n", "from scipy.stats import randint\n", "from scipy.stats import uniform\n", "\n", "import numpy as np\n", "from scipy import stats\n", "\n", "import time" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "input_data_url='http://www.bioinf.uni-freiburg.de/~costa/bursi.gspan'\n", "input_target_url='http://www.bioinf.uni-freiburg.de/~costa/bursi.target'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "y=eden_io.load_target(input_target_url)\n", "print('Target size:%d' % y.shape[0])\n", "print('Target classes:%d' % len(set(y)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Target size:4337\n", "Target classes:2\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "%%time\n", "#quick parameter analysis\n", "clf = SGDClassifier()\n", "results = []\n", "for max_radius in range(2,8):\n", " for max_distance in [0,int(max_radius/2),max_radius, max_radius*2]:\n", " t0 = time.clock()\n", " \n", " vec=graph.Vectorizer(r=max_radius,d=max_distance)\n", " g_it=gspan.gspan_to_eden(input_data_url, 'url')\n", " X=vec.transform(g_it, n_jobs=-1)\n", "\n", " scores = cross_validation.cross_val_score(clf, X, y,cv=10, scoring='roc_auc', n_jobs=-1)\n", " \n", " #results \n", " perf=np.mean(scores)\n", " std=np.std(scores)\n", " dt=time.clock() - t0\n", " err=1/(1-perf)\n", " result={'perf':perf, 'std':std, 'dt':dt, 'err':err, 'r':max_radius, 'd':max_distance}\n", " results.append(result)\n", " print('r=%d d=%d AUCROC=%.4f (+- %.4f) runtime=%.1f sec' % (max_radius, max_distance, perf,std,dt))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=0 AUCROC=0.8834 (+- 0.0139) runtime=14.1 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=1 AUCROC=0.8931 (+- 0.0135) runtime=15.9 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=2 AUCROC=0.8972 (+- 0.0137) runtime=17.8 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=4 AUCROC=0.8992 (+- 0.0157) runtime=23.0 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=0 AUCROC=0.9000 (+- 0.0150) runtime=14.8 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=1 AUCROC=0.9014 (+- 0.0155) runtime=17.4 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=3 AUCROC=0.9050 (+- 0.0160) runtime=22.9 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=6 AUCROC=0.9059 (+- 0.0162) runtime=28.1 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=0 AUCROC=0.9027 (+- 0.0173) runtime=16.1 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=2 AUCROC=0.9051 (+- 0.0150) runtime=22.5 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=4 AUCROC=0.9085 (+- 0.0134) runtime=28.1 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=8 AUCROC=0.9089 (+- 0.0150) runtime=33.2 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=0 AUCROC=0.9063 (+- 0.0129) runtime=16.5 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=2 AUCROC=0.9062 (+- 0.0141) runtime=25.0 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=5 AUCROC=0.9092 (+- 0.0128) runtime=32.9 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=10 AUCROC=0.9087 (+- 0.0126) runtime=37.8 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=0 AUCROC=0.9058 (+- 0.0126) runtime=18.0 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=3 AUCROC=0.9080 (+- 0.0139) runtime=29.3 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=6 AUCROC=0.9088 (+- 0.0122) runtime=38.4 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=12 AUCROC=0.9073 (+- 0.0123) runtime=42.9 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=0 AUCROC=0.9058 (+- 0.0135) runtime=18.3 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=3 AUCROC=0.9094 (+- 0.0125) runtime=31.3 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=7 AUCROC=0.9092 (+- 0.0133) runtime=44.3 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=14 AUCROC=0.9070 (+- 0.0134) runtime=48.9 sec\n", "CPU times: user 9min 18s, sys: 1min 19s, total: 10min 37s\n", "Wall time: 16min 49s\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "#plot\n", "plt.figure(figsize=(10,10))\n", "plt.grid(True)\n", "for result in results:\n", " label='r:%d d:%d \\np:%.3f'%(result['r'],result['d'],result['perf'])\n", " x2=result['dt']\n", " y2=result['err']\n", " plt.annotate(label,xy = (x2, y2), xytext = (-20, -25), textcoords = 'offset points') \n", "plt.scatter([result['dt'] for result in results],[result['err'] for result in results])\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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X6bpz5w42bNiAGTNm1Nu+srIShYWFAIC0tDS5Ol0tpfpq3ubNm+Hq6orBgwdj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"text": [ "<matplotlib.figure.Figure at 0x1217728d0>" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "#optimal parameter analysis\n", "clf = SGDClassifier()\n", "param_dist = {\"n_iter\": randint(5, 100),\n", " \"power_t\": uniform(0.1),\n", " \"alpha\": uniform(1e-08,1e-03),\n", " \"eta0\" : uniform(1e-03,10),\n", " \"penalty\": [\"l1\", \"l2\", \"elasticnet\"],\n", " \"learning_rate\": [\"invscaling\", \"constant\",\"optimal\"]}\n", "\n", "results = []\n", "for max_radius in range(2,8):\n", " for max_distance in [0,int(max_radius/2),max_radius, max_radius*2]:\n", " t0 = time.clock()\n", " \n", " #feature creation\n", " vec=graph.Vectorizer(r=max_radius,d=max_distance)\n", " g_it=gspan.gspan_to_eden(input_data_url,'url')\n", " X=vec.transform(g_it, n_jobs=-1)\n", "\n", " \n", " #parameter optimisation\n", " n_iter_search = 20\n", " random_search = RandomizedSearchCV(clf,param_distributions=param_dist,n_iter=n_iter_search,cv=3,scoring='roc_auc', n_jobs=-1)\n", " random_search.fit(X, y)\n", " optclf = SGDClassifier(**random_search.best_params_)\n", " scores = cross_validation.cross_val_score(optclf, X, y,cv=10, scoring='roc_auc')\n", " \n", " #performance results \n", " dt=time.clock() - t0\n", " \n", " perf=np.mean(scores)\n", " std=np.std(scores)\n", " err=1/(1-perf)\n", " result={'perf':perf, 'std':std, 'dt':dt, 'err':err, 'r':max_radius, 'd':max_distance}\n", " results.append(result)\n", " print('r=%d d=%d AUCROC=%.4f (+- %.4f) runtime=%.1f sec' % (max_radius, max_distance, perf,std,dt))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=0 AUCROC=0.8957 (+- 0.0144) runtime=18.1 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=1 AUCROC=0.9059 (+- 0.0122) runtime=20.5 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=2 AUCROC=0.9070 (+- 0.0122) runtime=36.2 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=4 AUCROC=0.8966 (+- 0.0117) runtime=30.4 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=0 AUCROC=0.9057 (+- 0.0120) runtime=17.1 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=1 AUCROC=0.9112 (+- 0.0111) runtime=23.4 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=3 AUCROC=0.9135 (+- 0.0109) runtime=39.3 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=6 AUCROC=0.9127 (+- 0.0117) runtime=46.0 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=0 AUCROC=0.8889 (+- 0.0159) runtime=19.6 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=2 AUCROC=0.9057 (+- 0.0123) runtime=38.8 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=4 AUCROC=0.9112 (+- 0.0113) runtime=49.5 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=8 AUCROC=0.9101 (+- 0.0125) runtime=90.6 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=0 AUCROC=0.9005 (+- 0.0122) runtime=21.0 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=2 AUCROC=0.9123 (+- 0.0129) runtime=49.4 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=5 AUCROC=0.9134 (+- 0.0105) runtime=65.5 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=10 AUCROC=0.9102 (+- 0.0144) runtime=115.8 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=0 AUCROC=0.9126 (+- 0.0122) runtime=23.3 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=3 AUCROC=0.9147 (+- 0.0124) runtime=69.4 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=6 AUCROC=0.9097 (+- 0.0119) runtime=106.8 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=12 AUCROC=0.8902 (+- 0.0133) runtime=124.8 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=0 AUCROC=0.9126 (+- 0.0122) runtime=24.2 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=3 AUCROC=0.9130 (+- 0.0111) runtime=74.0 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=7 AUCROC=0.9067 (+- 0.0120) runtime=134.7 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=14 AUCROC=0.9116 (+- 0.0119) runtime=115.1 sec\n" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "#plot\n", "plt.figure(figsize=(10,10))\n", "plt.grid(True)\n", "for result in results:\n", " label='r:%d d:%d \\np:%.3f'%(result['r'],result['d'],result['perf'])\n", " x2=result['dt']\n", " y2=result['err']\n", " plt.annotate(label,xy = (x2, y2), xytext = (-20, -25), textcoords = 'offset points') \n", "plt.scatter([result['dt'] for result in results],[result['err'] for result in results])\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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YFRUV8jpW2sLS0hLr16/XdDMkrV+/HkZGRrhw4QIWLlyIRYsWKe1TUFCAsLAw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"text": [ "<matplotlib.figure.Figure at 0x113998510>" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "#optimal parameter analysis\n", "clf = SGDClassifier()\n", "param_dist = {\"n_iter\": randint(5, 100),\n", " \"power_t\": uniform(0.1),\n", " \"alpha\": uniform(1e-08,1e-03),\n", " \"eta0\" : uniform(1e-03,10),\n", " \"penalty\": [\"l1\", \"l2\", \"elasticnet\"],\n", " \"learning_rate\": [\"invscaling\", \"constant\",\"optimal\"]}\n", "\n", "results = []\n", "for max_radius in range(2,8):\n", " for max_distance in [0,int(max_radius/2),max_radius, max_radius*2]:\n", " t0 = time.clock()\n", " \n", " #feature creation\n", " vec=graph.Vectorizer(r=max_radius,d=max_distance)\n", " g_it=gspan.gspan_to_eden(input_data_url, 'url')\n", " X=vec.transform(g_it, n_jobs=-1)\n", "\n", " \n", " #parameter optimisation\n", " n_iter_search = 50\n", " random_search = RandomizedSearchCV(clf,param_distributions=param_dist,n_iter=n_iter_search,cv=3,scoring='roc_auc', n_jobs=-1)\n", " random_search.fit(X, y)\n", " optclf = SGDClassifier(**random_search.best_params_)\n", " scores = cross_validation.cross_val_score(optclf, X, y,cv=10, scoring='roc_auc')\n", " \n", " #performance results \n", " dt=time.clock() - t0\n", " \n", " perf=np.mean(scores)\n", " std=np.std(scores)\n", " err=1/(1-perf)\n", " result={'perf':perf, 'std':std, 'dt':dt, 'err':err, 'r':max_radius, 'd':max_distance}\n", " results.append(result)\n", " print('r=%d d=%d AUCROC=%.4f (+- %.4f) runtime=%.1f sec' % (max_radius, max_distance, perf,std,dt))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=0 AUCROC=0.8876 (+- 0.0142) runtime=18.3 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=1 AUCROC=0.9002 (+- 0.0122) runtime=20.7 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=2 AUCROC=0.9048 (+- 0.0123) runtime=40.5 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=2 d=4 AUCROC=0.9045 (+- 0.0121) runtime=52.3 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=0 AUCROC=0.9096 (+- 0.0115) runtime=21.5 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=1 AUCROC=0.9002 (+- 0.0126) runtime=27.2 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=3 AUCROC=0.9176 (+- 0.0125) runtime=71.8 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=3 d=6 AUCROC=0.9019 (+- 0.0126) runtime=87.9 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=0 AUCROC=0.9151 (+- 0.0114) runtime=23.2 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=2 AUCROC=0.9146 (+- 0.0118) runtime=73.7 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=4 AUCROC=0.9155 (+- 0.0114) runtime=82.8 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=4 d=8 AUCROC=0.9168 (+- 0.0128) runtime=131.4 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=0 AUCROC=0.9125 (+- 0.0108) runtime=22.8 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=2 AUCROC=0.9141 (+- 0.0124) runtime=68.9 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=5 AUCROC=0.9127 (+- 0.0111) runtime=200.7 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=5 d=10 AUCROC=0.9153 (+- 0.0125) runtime=144.5 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=0 AUCROC=0.9090 (+- 0.0112) runtime=25.9 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=3 AUCROC=0.9051 (+- 0.0122) runtime=90.4 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=6 AUCROC=0.9170 (+- 0.0113) runtime=143.9 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=6 d=12 AUCROC=0.9148 (+- 0.0118) runtime=179.2 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=0 AUCROC=0.9097 (+- 0.0118) runtime=28.4 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=3 AUCROC=0.9139 (+- 0.0113) runtime=91.7 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=7 AUCROC=0.9063 (+- 0.0120) runtime=173.5 sec\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "r=7 d=14 AUCROC=0.9134 (+- 0.0127) runtime=201.3 sec\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "#plot\n", "plt.figure(figsize=(10,10))\n", "plt.grid(True)\n", "for result in results:\n", " label='r:%d d:%d \\np:%.3f'%(result['r'],result['d'],result['perf'])\n", " x2=result['dt']\n", " y2=result['err']\n", " plt.annotate(label,xy = (x2, y2), xytext = (-20, -25), textcoords = 'offset points') \n", "plt.scatter([result['dt'] for result in results],[result['err'] for result in results])\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": 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UVK3zFi5cKFasWCGEEGL58uUiJiZGz1pTQysvLxfPPTdK2NsHipYtJwlb2/Zi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\nIiIiImJm/x8jVD111aBkbAAAAABJRU5ErkJggg==\n", "text": [ "<matplotlib.figure.Figure at 0x113999550>" ] } ], "prompt_number": 11 } ], "metadata": {} } ] }
gpl-3.0
KartikKannapur/Programming_Challenges
HackerRank/Intro_to_Statistics/Day_02.ipynb
2
2966
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 2: Basic Probability Puzzles #1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Objective \n", "In this challenge, we practice calculating probability.\n", "\n", "### Task \n", "In a single toss of 2 fair (evenly-weighted) dice, find the probability of that their sum will be at most 9." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer : 5/6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 2: Basic Probability Puzzles #2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Objective \n", "In this challenge, we practice calculating probability.\n", "\n", "### Task \n", "For a single toss of 2 fair (evenly-weighted) dice, find the probability that the values rolled by each die will be different and their sum is 6." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer : 1/9" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 2: Basic Probability Puzzles #3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Objective \n", "In this challenge, we practice calculating probability.\n", "\n", "### Task \n", "There are 3 urns: XX, YY and ZZ.\n", "\n", "Urn XX contains 4 red balls and 3 black balls.\n", "Urn YY contains 5 red balls and 4 black balls.\n", "Urn ZZ contains 4 red balls and 4 black balls.\n", "One ball is drawn from each urn. What is the probability that the 33 balls drawn consist of 2 red balls and 1 black ball?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Answer : 17/42" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 2: Basic Probability Puzzles #4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Objective \n", "In this challenge, we practice calculating probability.\n", "\n", "### Task \n", "Bag1 contains 4 red balls and 5 black balls. \n", "Bag2 contains 3 red balls and 7 black balls. \n", "One ball is drawn from the Bag1, and 2 balls are drawn from Bag2. Find the probability that 2 balls are black and 1 ball is red." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Answer : 7/15" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
vadim-ivlev/STUDY
APIS/algo/Untitled1.ipynb
1
1322
{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.2.2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "IOPub data rate exceeded.\n", "The notebook server will temporarily stop sending output\n", "to the client in order to avoid crashing it.\n", "To change this limit, set the config variable\n", "`--NotebookApp.iopub_data_rate_limit`.\n" ] } ], "source": [ "import plotly\n", "print(plotly.__version__) # version >1.9.4 required\n", "plotly.offline.init_notebook_mode() # run at the start of every notebook" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
sdpython/actuariat_python
_doc/notebooks/sessions/seance5_approche_fonctionnelle_correction.ipynb
1
16234
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Donn\u00e9es, approches fonctionnelles - correction\n", "\n", "Correction de l'approche fonctionnelle. Elle s'appuie principalement sur des it\u00e9rateurs et le module [cytoolz](https://pypi.python.org/pypi/cytoolz)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { "text/html": [ "<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n", "<script>\n", "function repeat_indent_string(n){\n", " var a = \"\" ;\n", " for ( ; n > 0 ; --n) {\n", " a += \" \";\n", " }\n", " return a;\n", "}\n", "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item) {\n", " var anchors = document.getElementsByClassName(\"section\");\n", " if (anchors.length == 0) {\n", " anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n", " }\n", " var i,t;\n", " var text_menu = begin;\n", " var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n", " var ind = \"\";\n", " var memo_level = 1;\n", " var href;\n", " var tags = [];\n", " var main_item = 0;\n", " for (i = 0; i <= llast; i++) {\n", " tags.push(\"h\" + i);\n", " }\n", "\n", " for (i = 0; i < anchors.length; i++) {\n", " text_memo += \"**\" + anchors[i].id + \"--\\n\";\n", "\n", " var child = null;\n", " for(t = 0; t < tags.length; t++) {\n", " var r = anchors[i].getElementsByTagName(tags[t]);\n", " if (r.length > 0) {\n", "child = r[0];\n", "break;\n", " }\n", " }\n", " if (child == null){\n", " text_memo += \"null\\n\";\n", " continue;\n", " }\n", " if (anchors[i].hasAttribute(\"id\")) {\n", " // when converted in RST\n", " href = anchors[i].id;\n", " text_memo += \"#1-\" + href;\n", " // passer \u00e0 child suivant (le chercher)\n", " }\n", " else if (child.hasAttribute(\"id\")) {\n", " // in a notebook\n", " href = child.id;\n", " text_memo += \"#2-\" + href;\n", " }\n", " else {\n", " text_memo += \"#3-\" + \"*\" + \"\\n\";\n", " continue;\n", " }\n", " var title = child.textContent;\n", " var level = parseInt(child.tagName.substring(1,2));\n", "\n", " text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n", "\n", " if ((level < lfirst) || (level > llast)) {\n", " continue ;\n", " }\n", " if (title.endsWith('\u00b6')) {\n", " title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\").replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\")\n", " }\n", "\n", " if (title.length == 0) {\n", " continue;\n", " }\n", "\n", " while (level < memo_level) {\n", " text_menu += \"</ul>\\n\";\n", " memo_level -= 1;\n", " }\n", " if (level == lfirst) {\n", " main_item += 1;\n", " }\n", " if (keep_item != -1 && main_item != keep_item + 1) {\n", " // alert(main_item + \" - \" + level + \" - \" + keep_item);\n", " continue;\n", " }\n", " while (level > memo_level) {\n", " text_menu += \"<ul>\\n\";\n", " memo_level += 1;\n", " }\n", " text_menu += repeat_indent_string(level-2) + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", " }\n", " while (1 < memo_level) {\n", " text_menu += \"</ul>\\n\";\n", " memo_level -= 1;\n", " }\n", " text_menu += send;\n", " //text_menu += \"\\n\" + text_memo;\n", " return text_menu;\n", "};\n", "var update_menu = function() {\n", " var sbegin = \"\";\n", " var sformat = '<li><a href=\"#__HREF__\">__TITLE__</a></li>';\n", " var send = \"\";\n", " var keep_item = -1;\n", " var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item);\n", " var menu = document.getElementById(\"my_id_menu_nb\");\n", " menu.innerHTML=text_menu;\n", "};\n", "window.setTimeout(update_menu,2000);\n", " </script>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')\n", "import pyensae\n", "from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le notebook utilisera des donn\u00e9es issues d'une table de mortalit\u00e9 extraite de [table de mortalit\u00e9 de 1960 \u00e0 2010](http://www.data-publica.com/opendata/7098--population-et-conditions-sociales-table-de-mortalite-de-1960-a-2010) qu'on r\u00e9cup\u00e8re \u00e0 l'aide de la fonction [table_mortalite_euro_stat](http://www.xavierdupre.fr/app/actuariat_python/helpsphinx/actuariat_python/data/population.html#actuariat_python.data.population.table_mortalite_euro_stat)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercice 1 : application aux grandes bases de donn\u00e9es\n", "\n", "Imaginons qu'on a une base de donn\u00e9es de 10 milliards de lignes. On doit lui appliquer deux traitements : ``f1``, ``f2``. On a deux options possibles :\n", "\n", "* Appliquer la fonction ``f1`` sur tous les \u00e9l\u00e9ments, puis appliquer ``f2`` sur tous les \u00e9l\u00e9ments transform\u00e9s par ``f1``.\n", "* Application la combinaison des g\u00e9n\u00e9rateurs ``f1``, ``f2`` sur chaque ligne de la base de donn\u00e9es.\n", "\n", "Que se passe-t-il si on a fait une erreur d'impl\u00e9mentation dans la fonction ``f2`` ?" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Exercice 2 : cytoolz\n", "\n", "La note d'un candidat \u00e0 un concours de patinage artistique fait la moyenne de trois moyennes parmi cinq, les deux extr\u00eames n'\u00e9tant pas prises en compte. Il faut calculer cette somme pour un ensemble de candidats avec [cytoolz](https://pypi.python.org/pypi/cytoolz)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>juge</th>\n", " <th>nom</th>\n", " <th>note</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>A</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>A</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>A</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>A</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>A</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1</td>\n", " <td>B</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>2</td>\n", " <td>B</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>3</td>\n", " <td>B</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>4</td>\n", " <td>B</td>\n", " <td>9</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1</td>\n", " <td>B</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>2</td>\n", " <td>C</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>3</td>\n", " <td>C</td>\n", " <td>10</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>4</td>\n", " <td>C</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>5</td>\n", " <td>C</td>\n", " <td>8</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>5</td>\n", " <td>C</td>\n", " <td>8</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " juge nom note\n", "0 1 A 8\n", "1 2 A 9\n", "2 3 A 7\n", "3 4 A 4\n", "4 5 A 5\n", "5 1 B 7\n", "6 2 B 4\n", "7 3 B 7\n", "8 4 B 9\n", "9 1 B 10\n", "10 2 C 0\n", "11 3 C 10\n", "12 4 C 8\n", "13 5 C 8\n", "14 5 C 8" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notes = [dict(nom=\"A\", juge=1, note=8),\n", " dict(nom=\"A\", juge=2, note=9),\n", " dict(nom=\"A\", juge=3, note=7),\n", " dict(nom=\"A\", juge=4, note=4),\n", " dict(nom=\"A\", juge=5, note=5),\n", " dict(nom=\"B\", juge=1, note=7),\n", " dict(nom=\"B\", juge=2, note=4),\n", " dict(nom=\"B\", juge=3, note=7),\n", " dict(nom=\"B\", juge=4, note=9),\n", " dict(nom=\"B\", juge=1, note=10),\n", " dict(nom=\"C\", juge=2, note=0),\n", " dict(nom=\"C\", juge=3, note=10),\n", " dict(nom=\"C\", juge=4, note=8),\n", " dict(nom=\"C\", juge=5, note=8), \n", " dict(nom=\"C\", juge=5, note=8), \n", " ]\n", "\n", "import pandas\n", "pandas.DataFrame(notes)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import cytoolz.itertoolz as itz\n", "import cytoolz.dicttoolz as dtz\n", "from functools import reduce\n", "from operator import add" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'A': 6.666666666666667, 'B': 7.666666666666667, 'C': 8.0}" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gr = itz.groupby(lambda d: d[\"nom\"], notes)\n", "\n", "def select_note(key_value):\n", " key, value = key_value\n", " return key, map(lambda d: d[\"note\"], value)\n", "\n", "gr_notes = dtz.itemmap(select_note, gr)\n", "\n", "def enleve_extreme(key_value):\n", " key, value = key_value\n", " return key, itz.take(3, itz.drop(1,sorted(value)))\n", "\n", "def moyenne(key_value):\n", " key, value = key_value\n", " return key, reduce(add, value)/3\n", "\n", "no_ext = dtz.itemmap( enleve_extreme, gr_notes)\n", "\n", "moy = dtz.itemmap( moyenne, no_ext)\n", "moy" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
ppham27/MLaPP-solutions
chap12/8.ipynb
1
65266
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Latent Semantic Indexing\n", "\n", "Here, we apply the technique *Latent Semantic Indexing* to capture the similarity of words. We are given a list of words and their frequencies in 9 documents, found on [GitHub](https://github.com/ppham27/MLaPP-solutions/blob/master/chap12/lsiDocuments.pdf)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>000</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>100</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1913</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1977</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2001</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>500</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>\"</th>\n", " <td>0</td>\n", " <td>27</td>\n", " <td>13</td>\n", " <td>23</td>\n", " <td>19</td>\n", " <td>48</td>\n", " <td>56</td>\n", " <td>35</td>\n", " <td>22</td>\n", " </tr>\n", " <tr>\n", " <th>(</th>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>7</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>)</th>\n", " <td>4</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>5</td>\n", " <td>8</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>*</th>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>,</th>\n", " <td>34</td>\n", " <td>30</td>\n", " <td>27</td>\n", " <td>83</td>\n", " <td>26</td>\n", " <td>74</td>\n", " <td>39</td>\n", " <td>49</td>\n", " <td>38</td>\n", " </tr>\n", " <tr>\n", " <th>-</th>\n", " <td>6</td>\n", " <td>0</td>\n", " <td>12</td>\n", " <td>2</td>\n", " <td>20</td>\n", " <td>5</td>\n", " <td>15</td>\n", " <td>16</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>.</th>\n", " <td>38</td>\n", " <td>41</td>\n", " <td>39</td>\n", " <td>41</td>\n", " <td>25</td>\n", " <td>36</td>\n", " <td>30</td>\n", " <td>31</td>\n", " <td>38</td>\n", " </tr>\n", " <tr>\n", " <th>:</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>;</th>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>3</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>?</th>\n", " <td>0</td>\n", " <td>4</td>\n", " <td>7</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>[</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>]</th>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8\n", "000 2 0 4 0 0 0 0 0 0\n", "100 0 0 2 0 0 0 0 0 0\n", "1913 0 0 0 3 0 0 0 0 0\n", "1977 0 0 0 0 0 0 2 0 0\n", "2001 0 0 0 0 0 0 0 2 0\n", "4 0 0 2 0 0 0 0 0 0\n", "5 0 0 2 0 0 0 0 0 0\n", "500 2 0 0 0 0 0 0 0 0\n", "\" 0 27 13 23 19 48 56 35 22\n", "( 4 2 3 7 4 0 5 8 4\n", ") 4 2 3 6 4 0 5 8 4\n", "* 2 0 0 0 0 0 0 0 2\n", ", 34 30 27 83 26 74 39 49 38\n", "- 6 0 12 2 20 5 15 16 7\n", ". 38 41 39 41 25 36 30 31 38\n", ": 0 0 4 0 3 0 3 3 2\n", "; 0 2 0 0 3 0 3 3 0\n", "? 0 4 7 0 0 3 0 0 4\n", "[ 0 0 0 0 0 2 0 0 0\n", "] 0 0 0 0 0 2 0 0 0" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from sklearn import preprocessing\n", "\n", "plt.rcParams['font.size'] = 16\n", "\n", "words_list = list()\n", "with open('lsiWords.txt') as f:\n", " for line in f:\n", " words_list.append(line.strip())\n", "words = pd.Series(words_list, name=\"words\")\n", "word_frequencies = pd.read_csv('lsiMatrix.txt', sep=' ', index_col=False,\n", " header=None, names=words)\n", "word_frequencies.T.head(20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now as per part (a), we compute the SVD and use the first two singular values. Recall the model is that\n", "\n", "\\begin{equation}\n", "\\mathbf{x} \\sim \\mathcal{N}\\left(W\\mathbf{z},\\Psi\\right),\n", "\\end{equation}\n", "\n", "where $\\Psi$ is diagonal. If the SVD is $X = UDV^\\intercal,$ $W$ will be the first two columns of $V$." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = word_frequencies.as_matrix().astype(np.float64)\n", "U, D, V = np.linalg.svd(X.T) # in matlab the matrix is read in as its transpose" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this way, we let $Z = UD$, so $X = ZV^\\intercal$. Now, let $\\tilde{Z}$ be the approximation from using 2 singular values, so $\\tilde{X} = \\tilde{Z}W^\\intercal$, so $\\tilde{Z} = \\tilde{U}\\tilde{D}$. For some reason, the textbook chooses not to scale by $\\tilde{D}$, so we just have $\\tilde{U}$. Recall that all the variables are messed up because we used the tranpose." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[-0.29579069, -0.51925267],\n", " [-0.3133317 , -0.33578154],\n", " [-0.27364718, -0.45865789],\n", " [-0.34873574, 0.47093077],\n", " [-0.22977276, 0.04358919],\n", " [-0.44143778, 0.28243982],\n", " [-0.3498604 , 0.22347862],\n", " [-0.35250449, 0.17379354],\n", " [-0.35143587, -0.1538357 ]])" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Z = V.T[:,:2]\n", "Z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's plot these results." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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0LwRSSmneFLb5APC9lNL/HeUxkxFVzmOPPcaxxx7LJZdcwtKlS9l+++059thj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\nVeIwjSRJqiyTEZXGMeDWMM7FM8bFM8a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"text/plain": [ "<matplotlib.figure.Figure at 0x108230390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "def plot_latent_variables(Z, ax=None):\n", " if ax == None:\n", " ax = plt.gca()\n", " ax.plot(Z[:,0], Z[:,1], 'o', markerfacecolor='none')\n", " for i in range(len(Z)):\n", " ax.text(Z[i,0] + 0.005, Z[i,1], i, \n", " verticalalignment='center')\n", " ax.set_xlabel('$z_1$')\n", " ax.set_ylabel('$z_2$')\n", " ax.set_title('PCA with $L = 2$ for Alien Documents')\n", " ax.grid(True)\n", "plot_latent_variables(Z)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I, respectfully, disagree with the book for this reason. The optimal latent representation $Z = XW$ (observations are rows here), should be chosen such that\n", "\n", "\\begin{equation}\n", "J(W,Z) = \\frac{1}{N}\\left\\lVert X - ZW^\\intercal\\right\\rVert^2\n", "\\end{equation}\n", "\n", "is minimized, where $W$ is orthonormal." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "U, D, V = np.linalg.svd(X)\n", "V = V.T # python implementation of SVD factors X = UDV (note that V is not tranposed)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By section 12.2.3 of the book, $W$ is the first $2$ columns of $V$. Thus, our actual plot should be below." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VHqlIbdN5vug8XxER6U6XlxQREQmIim8NCL0vE3K+kLOB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\n/QCjyx4/bQ1XmQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1096e8390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "W = V[:,:2]\n", "Z = np.dot(X, W)\n", "plt.figure(figsize=(8,8))\n", "ax = plt.gca();\n", "plot_latent_variables(Z, ax=ax)\n", "ax.set_aspect('equal')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this is very similar with the $y$-axis flipped. That part does not actually matter. What matters is the scaling by eigenvalues for computing. Before that scaling the proximity of points may not mean much if the eigenvalue is actually very large.\n", "\n", "Now, the second part asks us to see if we can properly identify documents related to abductions by using a document with the single word *abducted* as a probe." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.],\n", " [ 6.],\n", " [ 3.]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "probe_document = np.zeros_like(words, dtype=np.float64)\n", "abducted_idx = (words=='abducted').as_matrix()\n", "probe_document[abducted_idx] = 1\n", "X[0:3,abducted_idx]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that despite the first document being about abductions, it doesn't contain the word *abducted*.\n", "\n", "Let's look at the latent variable representation. We'll use cosine similarity to account for the difference in magnitude." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[(0, 0.59127529086736064),\n", " (2, 0.57730256865377172),\n", " (1, 0.45835648369851389),\n", " (8, 0.31235652085315346),\n", " (4, 0.15438034299529768),\n", " (7, 0.076686002588720159),\n", " (6, 0.039651047230294334),\n", " (5, 0.039384780001176511),\n", " (3, -0.13308361596893792)]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from scipy.spatial import distance\n", "z = np.dot(probe_document, W)\n", "similarities = list(map(lambda i : (i, 1 - distance.cosine(z,Z[i,:])), range(len(Z))))\n", "similarities.sort(key=lambda similarity_tuple : -similarity_tuple[1])\n", "similarities" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indeed, we find the three alien abduction documents, $0$, $2$, and $1$ are most similar to our probe." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
changshuaiwei/Udc-ML
multi-digit/9_cnn_softmax _deeper_reg _2.ipynb
1
482013
{ "cells": [ { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import tensorflow as tf\n", "import os\n", "import sys\n", "from six.moves import cPickle as pickle\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Read the training data" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#pickle_file = 'train.pickle'\n", "'''\n", "with open(pickle_file, 'rb') as f:\n", " save = pickle.load(f)\n", " train_X_1 = save['data']\n", " train_outcome_1 = save['outcome']\n", " del save # hint to help gc free up memory\n", "'''\n", " \n", "pickle_file = 'train2.pickle'\n", "\n", "with open(pickle_file, 'rb') as f:\n", " save = pickle.load(f)\n", " train_X_0 = save['data']\n", " train_outcome_0 = save['outcome']\n", " del save # hint to help gc free up memory\n", " \n", "'''\n", "pickle_file = 'test.pickle'\n", "\n", "with open(pickle_file, 'rb') as f:\n", " save = pickle.load(f)\n", " test_X_1 = save['data']\n", " test_outcome_1 = save['outcome']\n", " del save # hint to help gc free up memory\n", " \n", "'''\n", "\n", "pickle_file = 'test2.pickle'\n", "\n", "with open(pickle_file, 'rb') as f:\n", " save = pickle.load(f)\n", " test_X_0 = save['data']\n", " test_outcome_0 = save['outcome']\n", " del save # hint to help gc free up memory" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#reformat the label\n", "#for each digit, add a 'end_digit' as '10'\n", "#for each label, add a digit size\n", "#each of them is a one-hot coding\n", "\n", "def label_reformat(label, max_size = 5):\n", " digit_size = np.asarray([len(x) for x in label])\n", " digit_size[digit_size > max_size]= max_size\n", " digit_size = ((np.arange(max_size)+1) == digit_size[:,None]).astype(np.float32)\n", " \n", " digits = {}\n", " end_digit = 10.0\n", " for i in range(max_size):\n", " digit_coding = np.asarray( [x[i] if len(x)>i else end_digit for x in label])\n", " digit_coding = (np.arange(end_digit+1) == digit_coding[:,None]).astype(np.float32)\n", " digits['digit_'+ str(i)] = digit_coding\n", " \n", " return digit_size, digits " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# sample a smaller data" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#train_X_0 = np.vstack((train_X_1 ,train_X_2 ))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(33402, 64, 64, 3)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_X_0.shape" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#train_X_0 = np.vstack((train_X_1 ,train_X_2 ))\n", "\n", "image_size = train_X_0.shape[1]\n", "num_channels = train_X_0.shape[3]\n", "batch_size = 100\n", "val_size = 50\n", "test_size = 50\n", "\n", "\n", "#train_label = train_outcome_1['label'] + train_outcome_2['label']\n", "train_label = train_outcome_0['label'][:400]\n", "train_digit_size, train_digits = label_reformat(train_label)\n", "train_X = train_X_0[:400]\n", "\n", "\n", "val_label = test_outcome_0['label']\n", "val_digit_size, val_digits = label_reformat(val_label)\n", "val_X = test_X_0\n", "\n", "val_size = val_X.shape[0]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(400, 5)\n", "(400, 11)\n", "(400, 64, 64, 3)\n" ] } ], "source": [ "print train_digit_size.shape\n", "print train_digits['digit_0'].shape\n", "print train_X.shape" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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zksLPub0ZECaIMKED4LRqn2VcAtC6udExvgBmjnzfhAcD41LyUvZzyRmwRRfI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\nRYEn+AW6ZOcE4OfPBjaiZIGqJzX4lu1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mqJkR1FMCGlZJCcCByzMAt7RBYyoDirr/sufE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\n5dA3QDxR4cMWynkjkDr8RGFlNVtS+F1xmoV7bw1OeNeQOIZNImuJRM7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IatMSDqZxbax4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j+T4Lglb6b4Guo1w3oTgcbhtmN7CeYKhQhjGDaVftenwZ6/6yMPsq90RZwHmtXel20P3i6BMu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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fda8014e8d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", "[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n" ] } ], "source": [ "plt.imshow(train_X[0,:,:,:])\n", "plt.show()\n", "print train_digits['digit_0'][0]\n", "print train_digits['digit_1'][0]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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9BDPyfbuk9MuvzcPkkvuwGWJRRbryWYRIfJuiPie1Mijp3MET1TfsmJHhIzYA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"text/plain": [ "<matplotlib.figure.Figure at 0x7fdaa9d0c050>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", "[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" ] } ], "source": [ "plt.imshow(val_X[1,:,:,:])\n", "plt.show()\n", "print val_digits['digit_0'][1]\n", "print val_digits['digit_1'][1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# start tensorflow session" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def next_batch(X, y_dsize, y_ds, batch_size=50, replace = True):\n", " idx = np.random.choice(X.shape[0],batch_size, replace = replace)\n", " batch_x = X[idx,:,:,:]\n", " batch_y_dsize = y_dsize[idx,:]\n", " batch_y_d1 = y_ds['digit_0'][idx,:]\n", " batch_y_d2 = y_ds['digit_1'][idx,:]\n", " batch_y_d3 = y_ds['digit_2'][idx,:]\n", " batch_y_d4 = y_ds['digit_3'][idx,:]\n", " batch_y_d5 = y_ds['digit_4'][idx,:]\n", " \n", " return batch_x, batch_y_dsize, batch_y_d1, batch_y_d2, batch_y_d3, batch_y_d4, batch_y_d5\n" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reg = 1e-4\n", "\n", "graph = tf.Graph()\n", "with graph.as_default():\n", " \n", " def weight_variable(shape):\n", " initial = tf.truncated_normal(shape, stddev=0.01)\n", " return tf.Variable(initial)\n", "\n", " def bias_variable(shape):\n", " initial = tf.constant(1.0, shape=shape)\n", " return tf.Variable(initial)\n", "\n", " def conv2d(x, W):\n", " conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n", " return conv\n", "\n", " def max_pool_2x2(x):\n", " return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n", " \n", " def max_pool_2x2_same(x):\n", " return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1], padding='SAME')\n", " \n", " x_image = tf.placeholder(tf.float32, shape=(batch_size, image_size, image_size, num_channels))\n", "\n", " y_d1 = tf.placeholder(tf.float32, shape=(batch_size, 11))\n", " y_d2 = tf.placeholder(tf.float32, shape=(batch_size, 11))\n", " y_d3 = tf.placeholder(tf.float32, shape=(batch_size, 11))\n", " y_d4 = tf.placeholder(tf.float32, shape=(batch_size, 11))\n", " y_d5 = tf.placeholder(tf.float32, shape=(batch_size, 11))\n", "\n", " y_dsize = tf.placeholder(tf.float32, shape=(batch_size, 5))\n", " \n", " val_x_image = tf.placeholder(tf.float32, shape=(val_size, image_size, image_size, num_channels))\n", "\n", " val_y_d1 = tf.placeholder(tf.float32, shape=(val_size, 11))\n", " val_y_d2 = tf.placeholder(tf.float32, shape=(val_size, 11))\n", " val_y_d3 = tf.placeholder(tf.float32, shape=(val_size, 11))\n", " val_y_d4 = tf.placeholder(tf.float32, shape=(val_size, 11))\n", " val_y_d5 = tf.placeholder(tf.float32, shape=(val_size, 11))\n", "\n", " val_y_dsize = tf.placeholder(tf.float32, shape=(val_size, 5))\n", "\n", " test_x_image = tf.placeholder(tf.float32, shape=(test_size, image_size, image_size, num_channels))\n", "\n", " test_y_d1 = tf.placeholder(tf.float32, shape=(test_size, 11))\n", " test_y_d2 = tf.placeholder(tf.float32, shape=(test_size, 11))\n", " test_y_d3 = tf.placeholder(tf.float32, shape=(test_size, 11))\n", " test_y_d4 = tf.placeholder(tf.float32, shape=(test_size, 11))\n", " test_y_d5 = tf.placeholder(tf.float32, shape=(test_size, 11))\n", "\n", " test_y_dsize = tf.placeholder(tf.float32, shape=(test_size, 5))\n", " \n", " \n", " W_conv1 = weight_variable([5, 5, num_channels, 32])\n", " b_conv1 = bias_variable([32])\n", "\n", " h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)\n", " h_pool1 = max_pool_2x2(h_conv1)\n", " \n", " \n", " W_conv2 = weight_variable([5, 5, 32, 64])\n", " b_conv2 = bias_variable([64])\n", "\n", " h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)\n", " h_pool2 = max_pool_2x2_same(h_conv2)\n", " \n", " #W_fc1 = weight_variable([16 * 16 * 64, 1024])\n", " #b_fc1 = bias_variable([1024])\n", "\n", " #h_pool2_flat = tf.reshape(h_pool2, [-1, 16*16*64])\n", " #h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)\n", " \n", " W_conv3 = weight_variable([5, 5, 64, 128])\n", " b_conv3 = bias_variable([128])\n", "\n", " h_conv3 = tf.nn.relu(conv2d(h_pool2, W_conv3) + b_conv3)\n", " h_pool3 = max_pool_2x2(h_conv3)\n", " \n", " \n", " W_conv4 = weight_variable([5, 5, 128, 160])\n", " b_conv4 = bias_variable([160])\n", "\n", " h_conv4 = tf.nn.relu(conv2d(h_pool3, W_conv4) + b_conv4)\n", " h_pool4 = max_pool_2x2_same(h_conv4)\n", " \n", " \n", " W_conv5 = weight_variable([5, 5, 160, 180])\n", " b_conv5 = bias_variable([180])\n", "\n", " h_conv5 = tf.nn.relu(conv2d(h_pool4, W_conv5) + b_conv5)\n", " h_pool5 = max_pool_2x2_same(h_conv5)\n", " \n", " \n", " W_conv6 = weight_variable([5, 5, 180, 180])\n", " b_conv6 = bias_variable([180])\n", "\n", " h_conv6 = tf.nn.relu(conv2d(h_pool5, W_conv6) + b_conv6)\n", " h_pool6 = max_pool_2x2_same(h_conv6)\n", " \n", " \n", "\n", " W_fc1 = weight_variable([16 * 16 * 180, 1024])\n", " b_fc1 = bias_variable([1024])\n", "\n", " h_pool6_flat = tf.reshape(h_pool6, [-1, 16*16*180])\n", " z_fc1 = tf.matmul(h_pool6_flat, W_fc1) + b_fc1\n", " h_fc1 = tf.nn.relu(z_fc1)\n", " \n", " keep_prob = tf.placeholder(tf.float32)\n", " h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)\n", " \n", " \n", " #first digit\n", " W_fc2_d1 = weight_variable([1024, 11])\n", " b_fc2_d1 = bias_variable([11])\n", "\n", " y_conv_d1 = tf.matmul(h_fc1_drop, W_fc2_d1) + b_fc2_d1\n", "\n", " #second digit\n", " W_fc2_d2 = weight_variable([1024, 11])\n", " b_fc2_d2 = bias_variable([11])\n", "\n", " y_conv_d2 = tf.matmul(h_fc1_drop, W_fc2_d2) + b_fc2_d2\n", "\n", " #third digit\n", " W_fc2_d3 = weight_variable([1024, 11])\n", " b_fc2_d3 = bias_variable([11])\n", "\n", " y_conv_d3 = tf.matmul(h_fc1_drop, W_fc2_d3) + b_fc2_d3\n", "\n", " #fourth digit\n", " W_fc2_d4 = weight_variable([1024, 11])\n", " b_fc2_d4 = bias_variable([11])\n", "\n", " y_conv_d4 = tf.matmul(h_fc1_drop, W_fc2_d4) + b_fc2_d4\n", "\n", " #fifth digit\n", " W_fc2_d5 = weight_variable([1024, 11])\n", " b_fc2_d5 = bias_variable([11])\n", "\n", " y_conv_d5 = tf.matmul(h_fc1_drop, W_fc2_d5) + b_fc2_d5\n", "\n", " #digit size\n", " W_fc2_dsize = weight_variable([1024, 5])\n", " b_fc2_dsize = bias_variable([5])\n", "\n", " y_conv_dsize = tf.matmul(h_fc1_drop, W_fc2_dsize) + b_fc2_dsize\n", "\n", " \n", " cross_entropy = ( tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_d1, y_d1)) \n", " + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_d2, y_d2))\n", " + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_d3, y_d3))\n", " + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_d4, y_d4))\n", " + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_d5, y_d5))\n", " + tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y_conv_dsize, y_dsize))\n", " ) + reg *(tf.nn.l2_loss(W_conv1) + tf.nn.l2_loss(W_conv2) \n", " + tf.nn.l2_loss(W_conv3) + tf.nn.l2_loss(W_conv4) \n", " + tf.nn.l2_loss(W_fc1)\n", " + tf.nn.l2_loss(W_fc2_d1) + tf.nn.l2_loss(W_fc2_d2) \n", " + tf.nn.l2_loss(W_fc2_d3) + tf.nn.l2_loss(W_fc2_d4) \n", " + tf.nn.l2_loss(W_fc2_d5) + tf.nn.l2_loss(W_fc2_dsize) \n", " ) \n", "\n", " train_step = tf.train.AdamOptimizer(1e-2,epsilon=0.1).minimize(cross_entropy)\n", " #train_step = tf.train.tf.train.RMSPropOptimizer(1e-4).minimize(cross_entropy)\n", " \n", " #let's just check the first digit\n", " correct_prediction = tf.equal(tf.argmax(y_conv_d1,1), tf.argmax(y_d1,1))\n", " accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Train model on a small data, see whether it overfit \n", "if overfit, then good. If not, check bugs." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initialized\n", "step 0, training accuracy 0.25\n", "step 0, val accuracy 0.3\n", "step 20, training accuracy 0.35\n", "step 20, val accuracy 0.24\n", "step 40, training accuracy 0.25\n", "step 40, val accuracy 0.31\n", "step 60, training accuracy 0.33\n", "step 60, val accuracy 0.35\n", "step 80, training accuracy 0.3\n", "step 80, val accuracy 0.28\n", "step 100, training accuracy 0.33\n", "step 100, val accuracy 0.26\n", "step 120, training accuracy 0.32\n", "step 120, val accuracy 0.25\n", "step 140, training accuracy 0.24\n", "step 140, val accuracy 0.2\n", "step 160, training accuracy 0.25\n", "step 160, val accuracy 0.31\n", "step 180, training accuracy 0.25\n", "step 180, val accuracy 0.26\n", "step 200, training accuracy 0.26\n", "step 200, val accuracy 0.35\n", "step 220, training accuracy 0.29\n", "step 220, val accuracy 0.31\n", "step 240, training accuracy 0.29\n", "step 240, val accuracy 0.21\n", "step 260, training accuracy 0.41\n", "step 260, val accuracy 0.3\n", "step 280, training accuracy 0.32\n", "step 280, val accuracy 0.31\n", "step 300, training accuracy 0.32\n", "step 300, val accuracy 0.32\n", "step 320, training accuracy 0.28\n", "step 320, val accuracy 0.25\n", "step 340, training accuracy 0.26\n", "step 340, val accuracy 0.24\n", "step 360, training accuracy 0.35\n", "step 360, val accuracy 0.38\n", "step 380, training accuracy 0.27\n", "step 380, val accuracy 0.31\n", "step 400, training accuracy 0.26\n", "step 400, val accuracy 0.35\n", "step 420, training accuracy 0.25\n", "step 420, val accuracy 0.25\n", "step 440, training accuracy 0.32\n", "step 440, val accuracy 0.22\n", "step 460, training accuracy 0.38\n", "step 460, val accuracy 0.28\n", "step 480, training accuracy 0.38\n", "step 480, val accuracy 0.23\n", "step 500, training accuracy 0.3\n", "step 500, val accuracy 0.32\n", "step 520, training accuracy 0.28\n", "step 520, val accuracy 0.29\n", "step 540, training accuracy 0.3\n", "step 540, val accuracy 0.33\n", "step 560, training accuracy 0.27\n", "step 560, val accuracy 0.33\n", "step 580, training accuracy 0.3\n", "step 580, val accuracy 0.26\n", "step 600, training accuracy 0.35\n", "step 600, val accuracy 0.27\n", "step 620, training accuracy 0.22\n", "step 620, val accuracy 0.22\n", "step 640, training accuracy 0.25\n", "step 640, val accuracy 0.35\n", "step 660, training accuracy 0.29\n", "step 660, val accuracy 0.28\n", "step 680, training accuracy 0.26\n", "step 680, val accuracy 0.29\n", "step 700, training accuracy 0.22\n", "step 700, val accuracy 0.23\n", "step 720, training accuracy 0.3\n", "step 720, val accuracy 0.28\n", "step 740, training accuracy 0.3\n", "step 740, val accuracy 0.29\n", "step 760, training accuracy 0.29\n", "step 760, val accuracy 0.29\n", "step 780, training accuracy 0.34\n", "step 780, val accuracy 0.32\n", "step 800, training accuracy 0.32\n", "step 800, val accuracy 0.36\n", "step 820, training accuracy 0.24\n", "step 820, val accuracy 0.25\n", "step 840, training accuracy 0.37\n", "step 840, val accuracy 0.31\n", "step 860, training accuracy 0.18\n", "step 860, val accuracy 0.31\n", "step 880, training accuracy 0.27\n", "step 880, val accuracy 0.33\n", "step 900, training accuracy 0.34\n", "step 900, val accuracy 0.32\n", "step 920, training accuracy 0.28\n", "step 920, val accuracy 0.29\n", "step 940, training accuracy 0.33\n", "step 940, val accuracy 0.29\n", "step 960, training accuracy 0.32\n", "step 960, val accuracy 0.34\n", "step 980, training accuracy 0.38\n", "step 980, val accuracy 0.25\n", "step 1000, training accuracy 0.31\n", "step 1000, val accuracy 0.2\n", "step 1020, training accuracy 0.23\n", "step 1020, val accuracy 0.25\n", "step 1040, training accuracy 0.32\n", "step 1040, val accuracy 0.32\n", "step 1060, training accuracy 0.28\n", "step 1060, val accuracy 0.18\n", "step 1080, training accuracy 0.28\n", "step 1080, val accuracy 0.28\n", "step 1100, training accuracy 0.27\n", "step 1100, val accuracy 0.34\n", "step 1120, training accuracy 0.13\n", "step 1120, val accuracy 0.17\n", "step 1140, training accuracy 0.31\n", "step 1140, val accuracy 0.19\n", "step 1160, training accuracy 0.26\n", "step 1160, val accuracy 0.25\n", "step 1180, training accuracy 0.42\n", "step 1180, val accuracy 0.26\n", "step 1200, training accuracy 0.27\n", "step 1200, val accuracy 0.23\n", "step 1220, training accuracy 0.18\n", "step 1220, val accuracy 0.1\n", "step 1240, training accuracy 0.24\n", "step 1240, val accuracy 0.21\n", "step 1260, training accuracy 0.25\n", "step 1260, val accuracy 0.3\n", "step 1280, training accuracy 0.29\n", "step 1280, val accuracy 0.24\n", "step 1300, training accuracy 0.31\n", "step 1300, val accuracy 0.33\n", "step 1320, training accuracy 0.34\n", "step 1320, val accuracy 0.25\n", "step 1340, training accuracy 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training accuracy 0.27\n", "step 1620, val accuracy 0.35\n", "step 1640, training accuracy 0.31\n", "step 1640, val accuracy 0.26\n", "step 1660, training accuracy 0.31\n", "step 1660, val accuracy 0.27\n", "step 1680, training accuracy 0.28\n", "step 1680, val accuracy 0.33\n", "step 1700, training accuracy 0.3\n", "step 1700, val accuracy 0.28\n", "step 1720, training accuracy 0.28\n", "step 1720, val accuracy 0.33\n", "step 1740, training accuracy 0.25\n", "step 1740, val accuracy 0.32\n", "step 1760, training accuracy 0.28\n", "step 1760, val accuracy 0.41\n", "step 1780, training accuracy 0.33\n", "step 1780, val accuracy 0.2\n", "step 1800, training accuracy 0.29\n", "step 1800, val accuracy 0.29\n", "step 1820, training accuracy 0.33\n", "step 1820, val accuracy 0.27\n", "step 1840, training accuracy 0.29\n", "step 1840, val accuracy 0.31\n", "step 1860, training accuracy 0.32\n", "step 1860, val accuracy 0.29\n", "step 1880, training accuracy 0.34\n", "step 1880, val accuracy 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accuracy 0.22\n", "step 2440, val accuracy 0.28\n", "step 2460, training accuracy 0.37\n", "step 2460, val accuracy 0.32\n", "step 2480, training accuracy 0.29\n", "step 2480, val accuracy 0.29\n", "step 2500, training accuracy 0.3\n", "step 2500, val accuracy 0.18\n", "step 2520, training accuracy 0.36\n", "step 2520, val accuracy 0.35\n", "step 2540, training accuracy 0.29\n", "step 2540, val accuracy 0.24\n", "step 2560, training accuracy 0.3\n", "step 2560, val accuracy 0.27\n", "step 2580, training accuracy 0.31\n", "step 2580, val accuracy 0.25\n", "step 2600, training accuracy 0.31\n", "step 2600, val accuracy 0.32\n", "step 2620, training accuracy 0.28\n", "step 2620, val accuracy 0.31\n", "step 2640, training accuracy 0.31\n", "step 2640, val accuracy 0.32\n", "step 2660, training accuracy 0.29\n", "step 2660, val accuracy 0.26\n", "step 2680, training accuracy 0.29\n", "step 2680, val accuracy 0.29\n", "step 2700, training accuracy 0.32\n", "step 2700, val accuracy 0.29\n", 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accuracy 0.26\n", "step 3000, training accuracy 0.31\n", "step 3000, val accuracy 0.3\n", "step 3020, training accuracy 0.24\n", "step 3020, val accuracy 0.33\n", "step 3040, training accuracy 0.33\n", "step 3040, val accuracy 0.23\n", "step 3060, training accuracy 0.33\n", "step 3060, val accuracy 0.33\n", "step 3080, training accuracy 0.29\n", "step 3080, val accuracy 0.3\n", "step 3100, training accuracy 0.27\n", "step 3100, val accuracy 0.25\n", "step 3120, training accuracy 0.26\n", "step 3120, val accuracy 0.23\n", "step 3140, training accuracy 0.27\n", "step 3140, val accuracy 0.31\n", "step 3160, training accuracy 0.34\n", "step 3160, val accuracy 0.25\n", "step 3180, training accuracy 0.32\n", "step 3180, val accuracy 0.26\n", "step 3200, training accuracy 0.26\n", "step 3200, val accuracy 0.29\n", "step 3220, training accuracy 0.3\n", "step 3220, val accuracy 0.27\n", "step 3240, training accuracy 0.32\n", "step 3240, val accuracy 0.36\n", "step 3260, training accuracy 0.36\n", "step 3260, val accuracy 0.25\n", "step 3280, training accuracy 0.26\n", "step 3280, val accuracy 0.28\n", "step 3300, training accuracy 0.28\n", "step 3300, val accuracy 0.25\n", "step 3320, training accuracy 0.24\n", "step 3320, val accuracy 0.31\n", "step 3340, training accuracy 0.23\n", "step 3340, val accuracy 0.28\n", "step 3360, training accuracy 0.42\n", "step 3360, val accuracy 0.27\n", "step 3380, training accuracy 0.25\n", "step 3380, val accuracy 0.35\n", "step 3400, training accuracy 0.19\n", "step 3400, val accuracy 0.28\n", "step 3420, training accuracy 0.31\n", "step 3420, val accuracy 0.33\n", "step 3440, training accuracy 0.31\n", "step 3440, val accuracy 0.36\n", "step 3460, training accuracy 0.33\n", "step 3460, val accuracy 0.24\n", "step 3480, training accuracy 0.39\n", "step 3480, val accuracy 0.28\n", "step 3500, training accuracy 0.24\n", "step 3500, val accuracy 0.27\n", "step 3520, training accuracy 0.32\n", "step 3520, val accuracy 0.27\n", "step 3540, training accuracy 0.35\n", "step 3540, val accuracy 0.26\n", "step 3560, training accuracy 0.27\n", "step 3560, val accuracy 0.34\n", "step 3580, training accuracy 0.27\n", "step 3580, val accuracy 0.22\n", "step 3600, training accuracy 0.31\n", "step 3600, val accuracy 0.28\n", "step 3620, training accuracy 0.35\n", "step 3620, val accuracy 0.33\n", "step 3640, training accuracy 0.3\n", "step 3640, val accuracy 0.25\n", "step 3660, training accuracy 0.3\n", "step 3660, val accuracy 0.25\n", "step 3680, training accuracy 0.29\n", "step 3680, val accuracy 0.36\n", "step 3700, training accuracy 0.23\n", "step 3700, val accuracy 0.21\n", "step 3720, training accuracy 0.29\n", "step 3720, val accuracy 0.34\n", "step 3740, training accuracy 0.27\n", "step 3740, val accuracy 0.26\n", "step 3760, training accuracy 0.25\n", "step 3760, val accuracy 0.31\n", "step 3780, training accuracy 0.23\n", "step 3780, val accuracy 0.3\n", "step 3800, training accuracy 0.31\n", "step 3800, val accuracy 0.28\n", "step 3820, training accuracy 0.31\n", "step 3820, val accuracy 0.3\n", "step 3840, training accuracy 0.29\n", "step 3840, val accuracy 0.29\n", "step 3860, training accuracy 0.29\n", "step 3860, val accuracy 0.3\n", "step 3880, training accuracy 0.31\n", "step 3880, val accuracy 0.28\n", "step 3900, training accuracy 0.28\n", "step 3900, val accuracy 0.29\n", "step 3920, training accuracy 0.35\n", "step 3920, val accuracy 0.26\n", "step 3940, training accuracy 0.22\n", "step 3940, val accuracy 0.3\n", "step 3960, training accuracy 0.29\n", "step 3960, val accuracy 0.26\n", "step 3980, training accuracy 0.3\n", "step 3980, val accuracy 0.25\n", "step 4000, training accuracy 0.4\n", "step 4000, val accuracy 0.25\n", "step 4020, training accuracy 0.29\n", "step 4020, val accuracy 0.31\n", "step 4040, training accuracy 0.28\n", "step 4040, val accuracy 0.29\n", "step 4060, training accuracy 0.37\n", "step 4060, val accuracy 0.33\n", "step 4080, training accuracy 0.26\n", "step 4080, val accuracy 0.22\n", "step 4100, training accuracy 0.35\n", "step 4100, val accuracy 0.32\n", "step 4120, training accuracy 0.35\n", "step 4120, val accuracy 0.27\n", "step 4140, training accuracy 0.22\n", "step 4140, val accuracy 0.28\n", "step 4160, training accuracy 0.31\n", "step 4160, val accuracy 0.18\n", "step 4180, training accuracy 0.32\n", "step 4180, val accuracy 0.33\n", "step 4200, training accuracy 0.41\n", "step 4200, val accuracy 0.38\n", "step 4220, training accuracy 0.35\n", "step 4220, val accuracy 0.33\n", "step 4240, training accuracy 0.29\n", "step 4240, val accuracy 0.26\n", "step 4260, training accuracy 0.31\n", "step 4260, val accuracy 0.32\n", "step 4280, training accuracy 0.31\n", "step 4280, val accuracy 0.31\n", "step 4300, training accuracy 0.29\n", "step 4300, val accuracy 0.21\n", "step 4320, training accuracy 0.3\n", "step 4320, val accuracy 0.24\n", "step 4340, training accuracy 0.34\n", "step 4340, val accuracy 0.23\n", "step 4360, training accuracy 0.25\n", "step 4360, val accuracy 0.27\n", "step 4380, training accuracy 0.35\n", "step 4380, val accuracy 0.34\n", "step 4400, training accuracy 0.33\n", "step 4400, val accuracy 0.26\n", "step 4420, training accuracy 0.32\n", "step 4420, val accuracy 0.19\n", "step 4440, training accuracy 0.35\n", "step 4440, val accuracy 0.25\n", "step 4460, training accuracy 0.38\n", "step 4460, val accuracy 0.25\n", "step 4480, training accuracy 0.26\n", "step 4480, val accuracy 0.34\n", "step 4500, training accuracy 0.29\n", "step 4500, val accuracy 0.27\n", "step 4520, training accuracy 0.28\n", "step 4520, val accuracy 0.21\n", "step 4540, training accuracy 0.3\n", "step 4540, val accuracy 0.24\n", "step 4560, training accuracy 0.29\n", "step 4560, val accuracy 0.3\n", "step 4580, training accuracy 0.23\n", "step 4580, val accuracy 0.28\n", "step 4600, training accuracy 0.39\n", "step 4600, val accuracy 0.23\n", "step 4620, training accuracy 0.36\n", "step 4620, val accuracy 0.33\n", "step 4640, training accuracy 0.29\n", "step 4640, val accuracy 0.3\n", "step 4660, training accuracy 0.39\n", "step 4660, val accuracy 0.29\n", "step 4680, training accuracy 0.29\n", "step 4680, val accuracy 0.23\n", "step 4700, training accuracy 0.34\n", "step 4700, val accuracy 0.25\n", "step 4720, training accuracy 0.31\n", "step 4720, val accuracy 0.29\n", "step 4740, training accuracy 0.37\n", "step 4740, val accuracy 0.22\n", "step 4760, training accuracy 0.26\n", "step 4760, val accuracy 0.25\n", "step 4780, training accuracy 0.39\n", "step 4780, val accuracy 0.27\n", "step 4800, training accuracy 0.2\n", "step 4800, val accuracy 0.33\n", "step 4820, training accuracy 0.38\n", "step 4820, val accuracy 0.29\n", "step 4840, training accuracy 0.28\n", "step 4840, val accuracy 0.2\n", "step 4860, training accuracy 0.32\n", "step 4860, val accuracy 0.28\n", "step 4880, training accuracy 0.35\n", "step 4880, val accuracy 0.32\n", "step 4900, training accuracy 0.26\n", "step 4900, val accuracy 0.31\n", "step 4920, training accuracy 0.29\n", "step 4920, val accuracy 0.25\n", "step 4940, training accuracy 0.33\n", "step 4940, val accuracy 0.26\n", "step 4960, training accuracy 0.29\n", "step 4960, val accuracy 0.27\n", "step 4980, training accuracy 0.29\n", "step 4980, val accuracy 0.21\n" ] } ], "source": [ "num_steps = 5000\n", "summary_frequency = 20\n", "\n", "BNs_train, BNs_test, acc_train, acc_test = [], [], [], []\n", "\n", "with tf.Session(graph=graph) as session:\n", "\n", " tf.initialize_all_variables().run()\n", " print('Initialized')\n", "\n", " for i in range(num_steps):\n", " (batch_x, batch_y_dsize, \n", " batch_y_d1, batch_y_d2, \n", " batch_y_d3, batch_y_d4, batch_y_d5) = next_batch(train_X, \n", " train_digit_size, \n", " train_digits, batch_size)\n", " feed_dict={\n", " x_image: batch_x, y_dsize: batch_y_dsize,\n", " y_d1: batch_y_d1, y_d2: batch_y_d2, y_d3: batch_y_d3,\n", " y_d4: batch_y_d4, y_d5: batch_y_d5,\n", " keep_prob: 0.5}\n", " \n", " session.run(train_step,feed_dict=feed_dict)\n", " \n", " if i%summary_frequency == 0:\n", " res_train = session.run([accuracy,z_fc1],feed_dict=feed_dict)\n", " print(\"step %d, training accuracy %g\"%(i, res_train[0]))\n", " \n", " acc_train.append(res_train[0])\n", " BNs_train.append(np.mean(res_train[1],axis=0).flatten()[:10])\n", " \n", " (batch_x, batch_y_dsize, batch_y_d1,\n", " batch_y_d2, batch_y_d3, batch_y_d4, batch_y_d5) = next_batch(val_X, \n", " val_digit_size, \n", " val_digits, \n", " batch_size, replace = False)\n", " feed_dict={x_image: batch_x, \n", " y_dsize: batch_y_dsize,y_d1: batch_y_d1, \n", " y_d2: batch_y_d2, y_d3: batch_y_d3,y_d4: batch_y_d4, \n", " y_d5: batch_y_d5, keep_prob: 1}\n", " \n", " res = session.run([accuracy,z_fc1],feed_dict=feed_dict)\n", "\n", " acc_test.append(res[0])\n", "\n", " # record the first 10 mean value of BN2 over the entire test set\n", " BNs_test.append(np.mean(res[1],axis=0).flatten()[:10])\n", " print(\"step %d, val accuracy %g\"%(i, res[0]))\n", " \n", "BNs_train, BNs_test, acc_train, acc_test = ( np.array(BNs_train), \n", " np.array(BNs_test), \n", " np.array(acc_train), \n", " np.array(acc_test) )\n", "\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HnJv528+gXUQoNNMlObgcrNU6oV6/o/QP9c9wSeRgMduCk6Ymf96drNZW/3vf\nvskvS7Ir7eDEObfeOfdJYBlwNfDP+MawecCZZlY+NUVMZGZ5+Kqci5xz24PJ07Hu0XR3+wNFwUl2\nHbTBSZ/fUfqG+ma4JHKw6OuD4WEYHJzpkky9zk5YsQIefHDyywrO2cp6554JD8LmnOsGrgWuNbPV\nwMeBbwA/NLP7nHPvmOAim4FhYEnS9CX4LsrJyoFTgJPM7IrItDzAzGwAeLNz7uHRVnbBBRdQUVGR\nMG3dunWsW7dugsWO6ez0vxWcZFdwJ3iwtTkJgpP+YWVOJDv6InFuby/MmTOzZZlqTU3+8+7ePfll\nKThJ7cYbb+TGG29MmNY+zWM6ZDJCbJRzbgvwNTP7JvB2xm/AmmoZg2ZWDbwRuAN8lBH5P1UT0w7g\n+KRpn8ePTvteYNdY67vssstYs2bNRIs5JgUnU+OgzZyoWkeyLD44mZfqyWdxnPM/eRkPJDGzgmtk\nNrpOB+fshobJLyubhochP+lJdUE1lqVRTzCReVNJdcO+ceNG1q5dm9kCM5CV3dM5N+ycuy2DrEng\nUuCTZnaumR2L74VTClwHYGaXRMZUwXkvxv8AjUCfc26zc27aa16DcTgUnGTXwdogVtU6km3xwcl4\nbroJjj56asszldra/O9sBie5lDl55hmoqBjZDuYzn4FPfSq9ZfzsZ3D66dkv23SaVOYkW5xzf4g0\nrv0evjrnaeAtzrmmyCxLgcNmqnzjic+cOJd5tCqJDvrMiap1JEsmEpw88wzs2nXgnqsO9szJrl2+\nKnv9enjXu2LTa2ognGb3j+3b/XIOZDmT2HPO/dw5d7hzrsQ592rn3Ia41853zv3TGO/9rnMuu3U1\nExAEJwMDs6vF/FQ7WMc5ibY5UbWOZMlEgpOGBh+Y9B+gu1+2gpPBwVjWO5cyJ8F3WF2dOL2tLZY1\nGk93d2yfOFDlTHByIAuCExi7auen63/KuX86d8xlbdoExcX+juaEE7JUwGn2+tfDLbdMfjkHY7VO\n2IVp7/Nn1+nKnPz2t/CWt0zLqmSGTCQ4CS7EB2rQn61qnbY24OU3UfTpM6YsONm0yVehTeQRHMH5\nLjk4aW9P/zljXV2JwclHPnLgDRKq4CQL0g1ONu3bxON1j4+5rGee8Xc0558Pzz/vszEHkuBJwhs2\njD/veA7Gap32vnYcvrXadLU5eewxnyKWg1N8F+J0Mydw4B5X2cqchELAisfpX/YI9Y1Tc6Nw772+\nimUi46hRrY+vAAAgAElEQVTEZ07ix3KZTHCyYcPIYCfXKTjJgvioOBjUJ5XugW4auxvHXFZ9PVRV\nwbvfPf7yclFwEDaO/THTcjBmToIqHZi+ap3aWr+PZmPQKsk98RehdI6VIEtwoB5XWQ1O5tcC0Bbe\nPSVV8kFAkEnmpLER9uzxfzsXC07SaXfS1QVDQ/4H/D5yoD3dXcFJFnR2xsYWGCtz0j3Y7X8GRs+n\nNjTAsmWwMDL2bnNzFguagfe/Hx56KP35g7uybAQn09XmZNs2XxWVfLL+3vfg4ouzu67W3li0OV3V\nOnV1/oSWrZPvcHiYN//6zWxq2JTxMrZvhzPOUButbIgPTsbbnn19sRueAzU4Ga1a56674Lzz0l9O\nKARU1Pl/KmqnpFFsEJzEZ9fH09MDhYWJ7+/u9hmy0Z7SXlcHr3tdbD3BPEG7ot5eBSezUmcnHHqo\n/3vM4CQSlDT1NI06T309LF/usycALS3ZKuXEOQd/+tPEqmiCu7JsBidTfRL9xz98VVTy00n/8Adf\nT5tuC/l0BD11YHqqdZzzmROY2AlyLC29Ldy34z7+tvtvGS/jmWfgkUf8SVUmZyLBSXz1woHa5iS4\nyCbvz488Arfdlv5yfHASOTgq6rIenLS2+qfVw8SOvd5efz1ZsiQWnMQHFqmCjKeegscfj/XQCYKT\nYN/o60u/MW2uUHCSBZ2dPpgoLR0/cwKMWbUTZE5yITjp7/fR+kRafWczczJd1TpBWeMbxQ0P+4xK\nY6MPXrJluqt1WltjF6FsBSfNPT6dN14V5ViC73Qm9++DxUSCk/h9/GDLnIRCflq61Zf1LR1QElnY\n/NqsN4qNb+Mx0cxJaSmsXRtbRnxgkSrICMoebJMgOAn2B2VOZqnOTigvh8rK9DInY53Ug8zJ/Pm+\nx850n7yv3XQtVz51JTByB09HtjIng4P+p6Bg+oKT+Dun3btjKdE778zeukK9IfIsj+KC4mmp1onP\nTGQtc9Ljd8rxgpPLLoP/+Z/UrwUBU3K15ZVXwq9+NdkSpu+SSyZ2tz2dLn/ycn733O/42tfGfo7M\neMFJZ38n77/5/TR1N2U9OPn+I9/n7pq7J7+gCRitzUkwzlS6GaFdbT5rMidvDvkLsl+tU10NZWX+\n74lmTkpL4ZRT0s+cJAcnwTYInrk0MKDMyayUdnAyTubEOX+BXL7cD11cWTn9bU5+telX3PTCTcDI\n1GA64hvbTSZtHJxkFy6cmczJli3+92tfm+XgpC/E/OL5FBcUT0u1TlClAxNrlDeWlt5IcNIzdnDy\nv/8LX/kKbN488rXRMic33AC//302SpmeG26A22+fvvVNxK+f/TW3vvgnfvpT+MtfRp9vvODk8brH\n+eOLf+TpfU/T0BAbtj4bx9V1z1zHPVvvmfyCJqC93Vd5DAwkjtUSnHvTbSi7t9NH7qcecipzFk1N\n5mTNGpg7d+INYktLfRfkxkb/ncYHFqmCkyCwCjJH8efuYBtNJKuUCxScZEFXl4+QFyyYXOaktdUf\ncMuW+f8XLpz+zElNSw0d/f7oziQ4aWjwgRpMLnsSH5xMdd14quCkpsY3Svv85+G55xIv8pMR6g1R\nWVxJUX7RtFTrxJd7OjMnra2+DY9z8C//MvKkOFpw0tCQnSrBdHV35+5jJ0K9Ifa3dzA4OHZKfrzg\nZEO9bzTWOdBJfT0cFhlrOxvHVUd/Bz1D01s/1Nbmn0oMiYHIRIOTfX21WLiA0w45DSrqpiQ4WbvW\nnw8nWq1TUgKLF/v/m5oSv//xqnX6+mLt5Pr6YvvE8PCB1c5o1gUn//3fsS9rQ/0GfvTEjyb0/qf3\nPc1/Pf5fCdPSzZx09ScGJz9d/1PW74kNQBHsYMuX+99VVekFJy0tcMEFsW5jj9c9zs+f+nl6HyhO\na28rzT3N0eAkPjWYrvp6eMUr/N+TucgEF6/pzJzEp3Vravydy9ln+55Yo2VPXmp+iYseuijtdYX6\nQlSWVE5btU5tbSzYHesE2dMDX/zi+CevH/8Ynq4ZPzjZuBE48Td88Ae/54EH4Mwz4b3v9ds1WB8k\n7t/OwZ55f6SudOrqWR58EC69NPZ/TgcnfSGaI1fadIKTwkLYNHgjd2y5I+H16gZfN9DZ3xnNzJaW\njjyuLr/cN6pM5aWX4KIUu3lHf8eYvQ8z9Y9/wE9/mvq19vZYgDVWcHL11fCOd/gh4J96auRymodq\nKR44jCMqj2CgeDf1DZm1fP/d7+DWWxOnBY1hMwlOgmqdIDhpbPSfOT/fV3OPlzmJz9L09SWev5Pb\nrnzxi7k7kuysC05uusnfCQNc//T1fP/R70/o/bduvpVvP/RtBocHo9PSCU6GwkMMOj+iWmN3I845\nvv3gt7n5hZuj8wTBSXAxSTc4uf9+fyAHLbVvev4mfvj4Dyf0ucBnTYBJZ05OOsn/nY3MSVXVzFXr\nrF7tn/B64om+d0kq1z99Pd979HsMhYfSWleoL5I5KSialmqdujp42cv832OdIJ9+2rcP2bhx9Hn6\n+uBb34Innxu/QWx1NeSfejW3DH2cr11cR2mp7/kVdEtPFZy0tMDwyVfRdtQvstpDKtDZCR/+cOJF\nL1eDk7AL09bXRqjXH4tjtRcIjs/KSthY8L9cU31NwutBcNI10BVt0zZ3buJxNTwM3/zm6FVqN93k\nu9bHB6/9Q/0MDA/QM5j9A/TGG+G//mvk9L4+n10eK3MS7OdXXeUHsrz3Xt/NOFmbq6M8vIKVFSsJ\n5w1Q1zqBkdLiXHklXHhh4rSgrUg2MieNjf77r6jwP+O1OUkOTuKzafHv/ctf/DGfqto1F8y64ARi\nX1BNaw2dA530Dqbf4jPUG2IoPMTOtp3RaekEJ9G7i3Aejd2N1HfW0z3YTVtf7KwTRL8TDU6CRo/B\nTtk12BUNgCZiS7NvaJFpcDIw4FOQwbD72cycTGVdaWOjv1NJzpysWuX/Xrly9C6vwYk//nscS6jX\nZ06ms1rniCP85xvrBBnst2O1cXruOd9IOdTnd8q2vjYGhlMPYVxdDcUVXfQM9rD96K9wxx3+GAn2\nqVQNYhsagIJeKOyYkoDhu9/16wjKMDzs6+NzMTjp7O8k7MJ0DaafOamshB5rTggaG7sb2dPhR/IK\nqnWWLRuZOXnpJf//aN9/kPGKryYMzhNTEZw0NqZupxEEacmZk+Hh2N/B71AI1q3zx2+qZXUX1FJp\nK1k5fyUADb2Z1d22tfntt21bbFrQGHbVKv87k8zJokX+/yBzUlHhO0okB6rxY9dMJHMSDBGRi/s/\nzNLgJPiCgovxWOOOJAu6ggbvhcTgZLQRXYPGsHQcRmN3I1ta/Pvb+2NnnWB02KIi/39VVXoNYoMT\nRjQ4Geiif7ifzoH0j4hnn4VbH/FnoP7hfvqH+kcNTvbv940dk93w5O2w/ClWrPBlTyc4uavmLp7c\n8+SI6fHBSdDaPB2hkK96SA5m/rz1zzy86+ER83d3+58TToC93bu48qmr6O31wUh8cJKqzYlzLhqc\nxI9fMmb5IpmTdKt1urt9j5Kh9BIzI9TW+rvM+MAgZbkixR8rGA7uBjuGWijI8w80b+pOfexUV0PB\n3E6OWXAMt2y+hfu230dZWawMqTIn9fXAnF4o6sh6u5OXXvKPkQ+ec/Jo7aPc+eJ9QOYn52uvTbwg\nTdTAAPzgB6nbiQTnmT43seCkz1oSgpPqev+lFeYXjqjWic+CBN9tSwtcteEq6jsTG2AEDcTjg/Tk\n4CTswvzoiR/RNTD5lteNjb58ycdxsB2Sg5P4i258cFJZ6YODVNWV/cW1LCpcyYoKn4bpyqtLq2fi\njh3+uw/sL/srrHgsITsTNIbNy/PHXmN/Lb/c+MvxF06sQWxhoQ9GguBk/vzEzMlVV/njO37smo6O\nxM86VuYk+M5zdRTyWRmctLdD72Avde3+SJvIeA3BSSOoAgmH/c5QVhbLnKS6y49mTkJHsL+rMfr+\n5OAkyJpA+g1igwtnsFOm02U52VVXwV1/r4n+3znQOWpwcuutvpFjcgT//fVfg1P/l2XLfEoynQvM\nhQ9eyBVPXTFienyDWEi/auemm+Df/m1kpuNr93+N/3j4P0bM3xS5tr7iFTC46g987p7P8vizu3HO\nV+uAv7jX1Y38Xmvba6MjvsaP/DqWaIPYgqK0gpObbvJVKS+8kNbiE/T2+s+3cuX4qeWJBCfdroVj\nFhwDpN7Hoo1h53Ry7ivOZXXVav700p8SLhKpgpNo5mQKgpN77vEn+3/7N5/9ufjRS/jvv/vhf7u7\nY8+mSZdz8NnP+pR+pp56ylcH/OlPI18Lgt3h/E7yC8JpVetUzA8zkB9KyJpWN1RTWVzJMQuOIdTb\nSWtr6sxJ8N02hfr57N2f5U+bY4VyLr3Mya62XXzt/q9x/477J7AVUmts9OtNDhaC7ZBcrRMfYHZ0\n+GC+oyMWnCQH5gPDAwyXNrCsdAXzi+dTmj8P5qfXnfhXv4LPfS72f+sJ34X/9+OEdmlBY1jwx96O\n0pv49F2fZjg8PO7yg2odiJ1Hk6t1+vr8/vfLXya2VRwvcxIEJ+FwrApXmZMc0tYG20Pbow9gG+3u\nL5XgpBFkPoIqhyBzMjiY+kIazZy0HUFzT1M085JcrRM0hgWffQiFxh+hNFXmBCYWnNTWQnjBFg4p\n8lfkjv6OURvEpmpA6pxjX28dlIRYvjz94KShs4HeoZG3K/GZk/j/xxOcZOPL1jPYw4tNL7KxYSNh\nl7gxgzKedBJQ4gOMm5/xt0DxmZPe3pFZrKAXBCQOrjaWoEFsUX56bU6CE14mzxEJArRsByf9eS0c\nt+g4IPU+Fpz0BuikvLCcE5acQE1LTUKXyunOnNTV+e0QDG5Y39GQkO2a6Ak66Fk3mYepBZ8xVWPr\n6P5kjpef3D1u5qSwEArntYGF6R3qjZ5vNtRvYO3ytcwrmkdjm98BUjWIjQYnXf4Lic+67t8f23dS\nBSfBuoLzTtCbazKCm4bkoCLYDkuX+sahowUnQRBTWZm6K+/Olj1gjsPm+SqdQ8tWpj2EfU2Nrw7s\n7fVZ3eHiRuYuauHRR3354hvDgj/2usOthF04rZuYoFoHYufR5Gqd4Niuro6d61avHr/NSbBdtm9P\nve1yyawMTtrbE6tlghPso4/6hoFjCXauIPMRHLRBcAKpv+z4zMmQG+LJvb4qo71v9MxJVZUPTOLv\nmu6quYvvPvxdLn704mi5R7Q5iZwkJhJ01daFYcFWiltPAfyJJzlz0j/Uzy83/pKGxsFoecFvszsf\nbGLA9UFJiKoqf1A1jbP6geEBmnqaUrb5mWjmxDnHDc/cwFMbhhPKBr6HVdiF6RzoZGvL1oT3JQQn\nxf6Le2TfXcyfH1v3Sn/+GlG1U13v70phZLXOw7seZlvrtmjZrnv6Otr72mnra4tW64Q6+rk/7ibz\nkUdid6jgt/t9vuaBhlAbf3jhD2NvhCRBeYNqnbGCkyC1O1pw0tfnGxcedxwMFTZzbNXowUl1NZTN\nG6JvuJfyonJWLVjFlpYtCXewQeDb0hLLSNXXQ36xD072789uI6PaWv89BoNiNXTV09Yf+84mmtoO\n9q+NG/0xOhQe4vqnr6elNcwPfgBf+e4evnP9GIOTEDs+/vKXkZmb+IvYiad00NMzenantxeKi8Hm\nxqLn4Hupbqhm7bK1lBWW0drpN35yg9jhYdi0CQ45JNaeqLM/trNs2QKUtDDv1NvGzJwE57hgHJxM\nhcPjByfz5/vG6gkX2Hl7mHPsvXR0xM7Bo2VOXqz3H+TIBf7gPqJyZdqjxAZVXKFQ5Jia64OToSH/\nXcY3hgW//j58gdK5YQyqdZp7mhk6+raU1TrBOX/DBti71wenhx+eGJyYwbauZ3iudUP0/2D7BWWc\nP3/k9erZZ+HJkTXt027WBic1LTUUu0ron8e+Tr/DfPKT8M53jn0hDPWFKMovimZO0g1OQt2xzAn4\nO5o8yxtRrZOcOYHEC8Yn7/wkl66/lG8/9G1uev6mhMdoZ5o5cQ52te6GOX00bnolkDo4uWXzLXzy\nzk/y9/DPgFjEftFF8MHP+oO9oDxEXl56mZN9Xb6ydKzMyYIF/vd4XVyf2f8MH73tozzf7p/3En+S\nqa6vjraRCNqIBIIynnACUOK/uG3DD3D8yd2Y+deCFPKI4KShmteueC0FeQUjMiefuOMT0W7qNS01\nnH/7+Xz+ns8D+MxJQRFbtvfzoQ/F3vPxjyc+aPDhh2Of+8491/KBP35gQnel1dX+InTooeM3yhuv\nQWzQGPatZzkoaaVqziGUF5aPGpycuNYXvLywnNULV7OnYw/F87oTMidBpjEoV7RaJ3+I+qbs9mRK\nCE7yB2jpa6J9IPPMSbB/dXbC1q1w3/b7OO/287jilqe58EL4+fpf8J9b38Pg8OiNhRob/QWjrQ2e\neCLxtfhgd9WJY7c76euLVAOUxPaNxu5GOvs72dOxhxOXnEh5UTn7Qp0UFPjtEN/mJGgM++Y3Q6+N\nzJzU1ICt+T86znovtXWxaonk4CRbmZO2tlgbq+Sgoq3Nb7Py8hTByWmXE37nuWkFJ7ua9gNwxEJ/\nN3h41aFYxZ5xg5Nw2H/fwTobW/uhuIP+vBZe/Wrfw+gf/4g1hgVf1v48X6B02jf29vrv8/fP/571\nh7+bhtb2aLXO/Pl+PwjORU1Nvnpw+XL/emen/6x5eX773NH1ba6p+xLgryfBPrRhg98Pjjxy5L5/\n4YW+2n6mzcrgpK3NV8vMG1wFXUvYvLuRjg5/ENbV+QaIqTjnCPWGWLNsDfu69tHR35F2cNIYimVO\nAAbDg7xs0cui1Trxo8MGguAkuGB09Hewr2sfV73tKg6b5xvWphoBdKLBSVsbdBdHMkFbRmZOgizG\nnTU+//z8wv+AebEDubYWugt8KG+RC3w6wUlDp49uUmVOenr83WBwpzte5iQ4IQ4X+4M/Pj1b3VDN\nSUtP4oj5R0QbCAYaG30AVFbmA6ul7iTCef0cdWYspRE8Nym+HYtzjg31Gzhl+SlUFlcmXEycc+zt\n3Ettu/9ygt+/fe63ANFB2Lr7+6L1yf39PhW8JZbQ4847/d1QXh7UdPpyBxm7dNx5p7/gzJkz+Wqd\n6mo/zsKrz2iHvGEKBheyeO7iUYOTl6/xKysrLGNVVeQsvWBrQnASNGoM1llfD+F8vy/sbc6gHmsM\nQcPgsjJgrr8w9Q53QZ5PR0w0OEnYv6pjQe++tjZKS+FNbw/BnF4e2/zSqMtobPSZqGXLRlbtxAe7\nC5aOH5wUF0M4KTgJ9rvD5x9OeWE5Da2dnH663xfiq3WCu+g3vYlogJMcnJQfVgsWZue+WLlGDU4m\nmTmJP2+kypyUl8cuvgnByaKXCBe2pwxOkm9uWjp8mZct8vUnS+YuJq+8adxqnd27YzdroRDsavTn\nm67hVn5yaZhNm+BHP4o1hgVf3oH89DIng4M+MCstjQWo9W5TtFqnosKfL2prY+1S7rnH70PB9ggG\nBS0pgbbhvdT3+5PK0qWxLHx1tR8eP1UP05de8uehmR5NdlYGJ0HmpKR7NXQvZmt9I5siT39/z3v8\nQG1bt458X89gD4PhQT+iIH4ZwcETjBAL8Iebw3ztmj8zOBRr39DUnpg5ATh1+akMDA/QN9RHS4vf\nMZMbxFLWwIbdz0bXB7CqalX0whBcMAsLMw9OamuBhVsosDnM7z8RGJk5GRwe5M9b/8yXT/syNjQX\n3vyvCcHJ0mP9iTBcGAtOmppi7WXau/q5/PZHE9Yb9AhIlTkJ6l2DutfxgpPgZJ5X1sJJJyVlTiKp\n7bXL17KhIfERy42NsfEECspCdG97JTSvpuuQ2NXCLNZjxzlf1bK9ZRehvhBrl62lsqQy4WLS3t9O\n31BfLDhpqyXP8jhuoa8KWVCygOKCYnoHfIPYmhpfBxwOx04KzvkL1tvf7k9uO/t9uUcLTp54IrFq\norER1q/374fJ99aproaXvxzKl0Rm6Kny+2DSEPZBY9hjjvcXt/Ki8mhwMjivJtZou3eIkpffn7DO\n+n1DOPO3zPtaRwYng8OD/GXbX6KNPes760cEmwB//3tiINbVBa2d3fQufoy5c4HyuJ2juC3h86fj\nhcYX2Lx3L1VV/s6zujrW/qips43KSsgv9cv96/OjP9K7sdFfMN72thTBSW8IcwWRMo491kk0OClM\nCk4iz45ZUbGCYiunvbeTc87xrycHJ0cf7T8Lpf5OKLlap3iJX1ZDe0u0eikITobCQwwMD0TPO8GD\nITMVH5wkBxVB9QaMDE7yFtXg8vsJdfb773PhZroLdqfMnLR0dsNQEQsX5AOweO5iwsWN7K33+9b6\nPetTVovHV7uGQlDX4gs77IY59hXtfOITvoxBlQ74Y294TnrBSfCdlJTE2iO2Fm+grS2xWqe21jfi\nX7LEH3PLl8e2R9BBo7gYOlwDXeEW8spaWLjQvzdoDHvymmH6DvszraHYdWpgwN8kdXRM70jNqczq\n4CSvbRV0L2ZPqJHqar9DXHedP3BTDUYUXIBOOzQWnMRnThYs8HdnV6y/hh81nM3Vd8dGf23u6AZn\n0LWUvMhJ59RDTvXl6WuPdgdbujS2vqoq4LU/5Ptb3w3E2skcU3VM9MJQW+vvjI84Iuh652LP8Bnn\n2SeBujpg0QscNX8Vb3htGebyRwQnT+x+gvb+dj584ocp/vt34fib2NXYHG18duLp/uQ1nNdL/1A/\nixf7O4DghPqt3/6RL218A3X7Ymebhq6xMycTCk4idxmLD2/hyCNjd7ZBY9i1y9ZyyrJT2NSwKaFR\nbHxwYiUhOvdXMq/5TWzu+nvC8les8CeEJ5/02YhbHvF3xCcsOWFE5iQIumrbanHOUdtey/Ly5Vx9\nztWsqFjByvkrE8Y5qamJZUza2nymbNs2f5d21llQVtVBk/NnxaA6Md7goB+B9bzzYtP+/Gf/+21v\n878nmzl56SVf9ZU3188w1FmVMnMSNIY9fFUkOCksZ0HJAhaWLqS3NBbMh5bcwZOrz4Sqmmi7k4bm\n2H7Q2D6ysA/ufJCzfnsWv3vud4RdmPfc9B7W3bIuYZ6eHjj9dH8cB+rqgLW/4Ad7zyCvqDsxOCkJ\nYTax4OTc287lrq6LWLYs9uTYIHPS2tNOZSWEC32a48ndo7eYDfa9M8/0N0PxVWqhvhAF3ZHUUlF6\nmZPBwmZsoIyFpQujmZOCvAKWlS2jaW85bk5nNFiNb3OyebP/bquqgFL//cZ3B66pgfA8f3y74hb2\n7vXTg+AE/HE2HZmToHoDEoOT5tAg4Yrtfp6eTv99vvMT/PAfF6YMTp59sQcG50bHElk8dzEub4i6\nRn/CevdN72bdLetGjBVVU+Mb4oLfZ/aEYoVt6W3hkkvgmGPgLW+Jvae8nGh7tvGCkyBLXVoa68kZ\nXlJNV1esWqe7298ArFwZC4LiMyednT44KSoZohufJSxatjUh69LRAXlHPcwTh5/N1vLYkzZ37PBt\nkCAxizsTZmVw0tzTQktvC8ONPjhp6fXByUkn+R3p0ENTN+YMLkArK1aytGwpW5q3JAQnBQVQvbmZ\nyvd9C4Bnd8ZyhKGubhiYy5LFeRSHF1FWWMaxC48FfIQcHJBLlsTWV1gIBRWNNA3toKWnhZqWGpaV\nLWNe0bzohaG21qfH583zB2DfUB9hFybP8iaUObFDqnnloWtYttTIG5w3orfOnVvuZHn5ck5ctIbu\n594IwLbujdFqpd7CWvLN34WE+kIJAwgB7Gmrh7wwz26OnSXGypwE3enmzvX/j9fmJAgcFx7WwvLl\ncY11I41h1y73mZPkRrHxwclwYQj6Knn5yqUj7v6CgdjuiIwMvq/DpykWli4ckTmJ/1zNPc3Utdex\nsmIlr1v5Omq/XMuCkgVYuIhh8/nhLVsS78hqamKp9lNPhTmH+bTesrJlKTMnL77oT2p33hkbCfPO\nO+G002KfLZ3gJLgLS9U7rLXVZ/IGCvx2GWxPHZxUV0eqOJf477m8qBzw2b6u4i2xgLcsMizl8g20\ntPjlD7rYftDcOTJzEgSz/3rfv/Kz9T/jyb1PsqdjT8IF5OmnfVAcn56vrQWWP0WYMPsGt0J53Isl\nIRYunFhwsrt9N82Du1m+3F8cNmyODXTW3tfOggXQMeAvcls6xg9OggbX8dm+uqYQg82+sdNwQZrB\nSUEL9Ma+l7r2Og6bdxj5efnUbi0jv6SLo4/274lvc9Lc7MuxcCEjqnUGB31WL6i2paQlmq1NDk6C\nm6LJtjlpbIxViaSq1kmVOanr2AV5PuvW1usH8csra2ZHaMeI3jrPPAN/29BDRWkpxcV+2uK5/kDZ\nG2piYHiAfV37eGDnA9z8YmwEb/DH6jHH+HNTKAQNHXHBSY/PTtTUJAYnZWVE27OlmzkpLY3rybnc\n70NBtQ74NmArVsSCkyBz4pzvXVVWBvkV+8H8sZG/ZEs06xKcawbm+WNw96pvRr+z5PPQTJqVwUkL\nfqv37l5N0dBieqyRf/wj9kUnj8z6fOPzQOwCWFlSyaqqVX6E2U5/IAV3+N+8/5u+i3I4n+37Yjti\nqLsbBudy9NEwZ2Axq6pWMb/YH2Xt/e3Ri3hwMQkUlPt1VjdUs6VlSzRFHl+tEzT06+qK3fEcOu/Q\ntIOTHbWDsORZXnnIKf7uqX9eLHOy+Dn6Drub27bcxjnHnENLi0HoKPIG5tFg1dHgJBSuiwZbod5Q\nwtDLAE29/o8Xt8WijOhFPC5zsnmzj9yDap2gXnW8zElTl99OZYuaWbYsdqJ/bHs1hXmFHL/4eNYs\nWxPdloHgAhF2YQby2qC3klOPr6K1tzV60attq2XJig5qa2Pp99aeEIX5hZQUlFBZXElTV4i774a7\n74YX6mIXv7r2Omrba6MDPQX6u4uhoJ+5c2OZk5e/3FchPfL8FtZv6It2fXXLqskPl3DOqnNGBCc7\nQzu59vG7sdV38/J33c0nfvhXbr9ziHvvjVXpQOoGsTt2xLZrKORT+8PDqS+CwYBWQW+OnmZ/EWzq\nbu6TCbsAACAASURBVCIcjp3Iqqvh5JOhezCWOQFYXbWa9nyfOQmHYWief0P+YdU0N0e+r4LYfhDq\nSQxOnnoKHtnQSFFeCR39HXzlr19h6dxl9A71JjQqD4K6+LtvH5z4F+q6a6C8nnzmAFBUEUp4YOdz\nz43ddT/oYdZJfTQ46Z7nl12QV0D7gK/Wae9rx8IF7LenUz7aYHPTZvY3DbN4cawqNz442b43RF7v\nIkoLShnKG1mtE3ZhXmj0g98EwclAfgv0VLGodFE0c1KVv5K774aa58qhKLYDxFfrtLT4wKSigmjm\nJKjW2bULhvI76HWRlZc2R4/5joGxMyf9Q/1sboqNjb5vH9FjJDlD55zvJQL+u1uyxN/sjZc52Y9/\nFsnevthtfke/D06suJ3a9lrfW6bP79vO+Yd5LljSw+LK0uh7guBkf1djtKH+gjnL+PwdX6G1KzGL\ntGpVrK1G/Dl2tIzR3LJwtPow3cxJSYm/LhRYAVRthaL2aLUO+GM0PnMSBCfg96O5cyFvXuw8ZAtr\noo1pt2zxA302uxoqOBRng1z44IXRzzd3rs/EKziZZosWQUe+T/+17zqKY1csxpU2sm2bSxmcbG7a\nzAlXnsCmhk3RzEllcSVHVR7FztDOaArNDJ7c8yS/3PRLLv6niykaWsTultiO2N7TjQ3O5dBDobjz\n5bzqkFdRUez3tPY+H5wUFcWe6BvImxsJTuqrqWmpGRGcxDf0iw9Ojqw8Mu3g5Pn9L+Dy+1m7bK3v\nvtzrg5POLgcffy186Bx2hHbw/pe/35/0XR4Lh9bQVrqB2lp/EqnvqeWkpf6hOqG+kcFJaMD/sWVn\nimqdSOaku9vXo/7pT7HMSV6eP/GOF5xs3e2305wKnzlpafGNTK+7+0XyQ8dRmF/IgpIFHL3gaB6v\niz3drLExsk/0d4A5KksrOfWEKobCQ9E7w7N/dzbPlv+IlhbfnRZig6mZGZXFlbxUG+Kcc+Ccc+CK\nG+qjvYNq22ujmZN4vZ1FkN/PGWf4k0BNjf/sh60I892GV3J78484xbdNZqCqmorek3jZopextXVr\nQrXUB/74AS5vPge37hxeOOkc9p/5Ft71nd/S0wPvfndsfUGbk/gs9Wte4x/2FozZcIwfVy1l1U4Q\nnLT0tmBDpYSaSqL74MUXO1av9g/VCxraBXfeZYW+RfOqqlW0WA2dXc43KKwKghOfOYmOcRLRPdQR\nHRX49tt9Bum6mxvpbzyMC078AUvLlnLyvssBP2ZJIBiSOz442VrXAQv9xWtr6xYK5jewKM8fR0UV\noeiFJnho5U03jfz8gf1dPk3eP6eBZcsiXdCXV1OWX8nRC46me9BX67T3t7MsfBrh/F5eak5sFNvZ\n38krrnoFzQtvY/HiWFVufLanIRTikKpK5hXPo3uow6f544LGe7bew4lXncjejr3R4KQvrwXXvZBF\npf572RWqZeNDKzjnHOhsKWfY+qPPBEsOTqqq/LE2pyIxc1JTA1TEWt2XLmqJBSf9HSwq9SnS7oHu\nhN46zjl+8+xvOP7K49nU4DN/X/4y0WPkggsSt+u99/pt/9xzseAk1fgkQcNQgJ751bxw+on8bfff\naAzHrqRdgz44CRe2Ud9ZT/Fc/5m7u30A9MQTcNrruplbODI46XKNbNvnv4jWX/+c5oG9fPWa2IMU\ng2duBftMc28jeV2HRj93Knkl/txSnF8yocxJe187a5b6JgQs2xSt1gmsXAmvepXfTscfnxiclJUR\nrb5cGD4eV1kTrdapqfHHek3rFg4vOgX32Lf4xcZf0DvYy5YtPvhavVrVOtNu6VLozWukvLCcga65\nvPK4xVAwAEUd0YvBwoWx+t/93f5ktLV1a0LmZEHJAtr62qIto4fDw3z+ns9z8tKT+fTaT1Oen5jy\n7uzvJj9cSlUVLHniN/zP2f9DRZE/yoJqncWLiXZfjSr21QcbGjZQ01LD6io/SNriuYvp6O9g156+\nlJmTI+cfSXNPc1ojEm7vqcZcHictPcnfqffNI9TT4VPTRR1wxzXs/EwLbzryTdGT/urytYSXVPPc\nc3DIEV209rbGgpPeEBUV/mQXXOg6hv0bd+xOzJzMyZsTzZx0dfk0ck1N4kBEqZ6gmmxLrf9uhgpb\noj2e9u2DhtY2+kNV0bvhtx71Vu7eejfOueh4CosXx6rsfvfLSpaU+25SQdVOXXsdg8X+QJ8zx18I\n2vr9YGrgG7h2DYY4+2z/8LS61nqOWXAMJQUl7AjtYE/HnujzOwJd7UVQEAtOgpPeyhPrGLBO6kpv\niwbLnfM2MLf9FFZXraZvqI/d7bujy9nWuo3l2y7kfbsaaPhqA6csO42zLrid1lbfEyRQXu4zAsGd\nWWen3z5bt8ayBkHKPzk46e31d56Vlf4EXDhU5ffXuYvpHerl4h91U1wMn/qUz8asXesvwPmWT3GB\nz5uvrlpNr2uj15ro7HSwcAvl+VUMLdxEc0s41o04UNRBU5P/3r/0JXjrW+GdH2yE7sW8rONL7L5g\nN3s3+EzYI5tiKYdUmZNnGv3FsaqkiprWGvIq6ql0x5DnCpgzL5Y5efFFH7zdNsZDkYNsX7i4hUXL\n+qmqAjtkA4fkrWF+8Xx6wj44aetr48SKM4CR7U4auxsZDA/C/J0sXuyrbxcujGVO2tqgcyjEsYdX\nMq/I3ygkP/BtZ2gnYRfmqfqnosFJD75aZ0HR4miD2HBoJb/8JVx3jb/rCYKO4Jjq7fUX7aBnYH5Z\nYuZkyxYoWuzrcUoKSpi3ODE4WVrmI6v4zMmwG6a9v51trdsIuzBf+PMXCLsw9fXwvvfBV7/qqx/j\nH8kQbPMnn4xlM1O1FYmv1hmc659vdttLt9GWt4WSYV8n3j3UQXNbPy6/n7AL01foq9y6umL7Rem8\nHubOmRtdbmVJJXnkw9xGbrnPfxF/vOw1WLiI9c/4bdLX57NwQeaktdXfdBX2rqB0TumoDYGDTgKH\nlayeUOakra+N/7fiVBgoheUbEqp1wAcnS5bEGuAmByfhufWYy2dJ/2sYmr8lmjkJsj81LTWsLFsF\n9WsJuzANXQ3U1Pjz0OrVypxMu2XLwJU2Mn+Oj5SPWe5/F1U1cqyvlUjInAR3z7VttYR6Q5TOKaUw\nv5CKogra+tqiz9X5xcZfUN1QzRVnX0F+Xj4LSxbTPtwYvfvr6u9mDnOpqoLWFiPP8phX5PemoFon\nuUoHYGhOCAvP4b7t99E92M3yolX09///7L15eJxXeff/eWbfd400Y1mSN8lrvMhZnD0he2KSNEAK\nKSSEFChLXwjQlMBLQ9uUfUsLDW9ZArQQGiiLswdCFhJCsOPsjhTHtmRrRh7NaDSafX1+f5w5zzOj\nGTk2b0l/1y+/+7p8ObFG8yznnPt8z/f+3vetI/3p+RkGB/VTRitzslhFwv37hXBROplpwy5Cymqc\nFqcW1klm58mqzaNccg12RCqSXNwnLxsF3wSP7koRXiWc18bejYBgTgwGIRCW7zGvil/cP9UOToZ8\nQ9TVOtV6VVuYExO6IBY6+4BISxfT2knwYFI4gNliSqPJ9+6FTHmeRtHDweZ+vn1kO5OZSZ5LPKfV\nUwiHWzQrLj8hh6i+liqmNKdbN4v3eMYZ4rmytbRWgM1v91M2zrJhA5x2GpQtcfymKIO+QR5+5Qlq\njVoHc5Kbs6GYS6xZozdcGx4G/8pmW4TepxhcPyXCa9ZxzMlRjTUbS41RLsN0Oku6lCbxwlpO3dRH\nn6uPP1tzKY9M3Y/N2V4aXzJyCyt9Tky8OjhpTctMFVPY0cEJgG9Jgl//WswraIKTSha31Y3SRNta\nOnFwnJenUmBPc0bozTTMWQ5kXyYWA0+wHZwkEvDZzwpG4Z//GUrGBA7C7NoFat3ESzvFQN//O7GZ\n5PMiLNjT0w5OXimIsNhFqy4SYTF3DHs1ilX1Y3TpzMn4OOA7wF1PPcWL03tR1fa+JUBbzxl7aBpF\nEXqtUGUrPpuPEnP4fCrz5XnWRJZCcpiHxtrBiUb/u+Lamm/VSd17L2BLs2VNOzhpDetI1nFXbJcG\nTvJqEgpB/JYwU9kppvNxmBtk6VKIBJrgpAk6nE4BxOQ1JTjBkcTYsGl+ZHwcelYJYe2anjXYAile\nfFH4j7liJzhREOOdKqSYyEzQ4+jh8YOP8/1nvk8qJQq9nX9ZmnS2xO+amvPYfJwddwpKb+fOdnAy\nly+0ZQ61hnWqNvEOdozvIGcbZwmiRlPNOM9USkdyOYPwT7mcPpfrhgIOs86cGBQDAVsPOBPc9XAc\nGiYuOy+I0+jl5YPCz7/yClpbCzln5usJbPUwIUdo0bBO1SQuGrF0ghPJ8M7kZ2iojXbmpJwh5Ahi\nTm2GyC58vnbmRNZfMgqpnwZO5uebB2ZHHGu1D3d5DWXXy7g9DXI5AcKXD5eYmJtgpX8YsmIdxbNx\njTkZHhbP+8f29PrvsNclOMGZwInwCkM94u91JyQ0FXZXcJKZIF1KE7CLTdpn85EpZzRw8oXHv8BV\nG65i29JtACzxhcGRYN8+8T35ah5LE5xIVsZoMOK2uLWwzkJwoqoqVeMc1sTJ2onnb989zN//vb4x\n4EwwNKTn8ktR2nL/cqAzxplIiE1odFSkDt5zDxT9O1npEMf0UAgoe5jNz1MwykpTUS23X3bwPXet\noJn2zO3Cs1TsdCOhEaxGq8ZCtDJQRaO4j6lEXjTyq1dIFpKsCKwQP68VtWtMTrb3l1jY3l3aKd85\nhVt+fwv79kG+Lq6ZKurMyT33AJZ5KHs0ivKMwTNwWVzsGNuhZR309enMScAeIOgQnjpVSGnphEUE\nG/SmNzVPdDWdOXEa/ajmHCuGq4LtcMcwFaP0uwa567lHATo0J5lZwZzIQk0gnJ45MgZ1MzSMxD13\n8suxJqV86ASGfEOYDWbGkuOcdx6cdolwurXkoMaybB/ZTr6a72hy+N8FTpKFJG6jACepSXFSfe/H\nYpx8Mvz1XwtgsGqVAMlSbwKwIrACIybofYbdk2Iwzu//cwAmq7t47DEILxHgxKAYwDrP44+LtP6/\n+Rtxb4l8gj5XmJ07Ra+hSt6OoezjyT1ik3rmGcEOXXBBOzhJGHfRhwiLjSXHqDtjWMpRLHU/BocO\nTp59OQ1/vZL8X4yy7pur+Ng/7WfFivbTuwQFAAZfjGQhScN1CEdmC16rl4qSweHP0VAbrFzqhfgW\nnoq3l53WTtjuWBs4kWGd+x9ogH2O5ZGAACeVee3UK02CpJ3xnRo4ydUFc+IxhYVmChUyA7jdenhN\ngg4J/KW4VYKTujWFrTxIuS5CQOPj4FwywVLPUnocPVj8SR5/XPiPF/bO0+tsBydRt1h8qaIAJxes\nvIAr113Jp37zKZIplWAQPvLcGViv+nN27BCh8KVf6Sfm+iXLlgnmS7KZLhfcZ/yglpEViwmwKJMG\nSmbxDl5KvkQ59HsGrZsE+2Gd17JuAOYQk12CE0WBCu3gBKDXFQZngonZGM5GBKPBQI/bR92c4YEH\n4P77xe+uXq2Dk5yawKmGCdqDi4Z1ZI2THmWETDmjZempqhCtX/3OOqv+eRX/tuvfOsI6XpsXb/ZE\nWP5rFNu8BswWsiiggxMQ765mi2EuR7Dlh1GNJWoOcUKbmgLvsr2oqKwNj0BWjNnewzEOHxZ+aHhY\nsNgHDnR9pNfEXnfgpK8PcCYwlYVXWNEn/v7op/T0nGBQOPFKRQcnk5lJTWcA4LV5KdVKzOXKuN0C\ndW6NbtW+Y1lYTHRJjRVreWxGAU5KJX2z9dl8bWGdVstWsqhKndKL5wBgVIxMPr2c3/62HZxs3NgZ\n1lnmF/VUFoKTqSnhwH/wAxGvvPpaIYbd0rdVe3bKHjKleUqmprfMRtrASTgMJw6vgJIHtW8X1l6R\nqRN1R/Hb/RpbI0GeqqpUzeI+akqeyUm9OuxynwBRhWqhjTk5mrDOgbkDjKXGhEjVnsZn9TFbnMXn\nb2CxCPGqYpvHUPNo42A1WTl/xfnsGN+h1bbZsKFF7GzzE7Q3wUlR7/A6V04zPi6qCLtcoleGnAul\ntPg7smyOcBiMvhjlZBRDdgDVKX5/YVhnLmlFNVQYGlI1ULxqlagFQmoV1sQp3Hngdm741Q2sUa+g\nPLUGo8HIysBKfvn4GI88AntnhNNV5geF9gFY17OOId+QVjBP2kJwIjelyUm9PopsCLcYOAkExDvx\nWgQ42f2gGLulG0WZ/i9+UWhyDAZxQpeZOgA2k43jfKfB8F28eHgcVIXRvhNw15bx0vwu7r4brrhS\nTICgPQTWeT7+cbFeP/5x8R2JfIJl4TC7dwuBrKJArzNCbD7G1JTY2CwWOOssvdt0tSq0CSPurQwH\nh8mUM9StSYzFCKaaH6UFnLx4IAmGOu5dNwFwyw/2USi0t7SIZWPYDWKjV51xLb1fTazBafKhWjNY\n3GJjXBbxYc4tJ5Y/0PY+tU3MrTMnrSLuF/ZmQWngty0e1mllToolFZtdJVtLQSGEx9DiSDKDuN26\nMLk1rANojGIoJELTVWMaU25I++zYGBgDQtAddAQJD6Z46in46U+hrM5zeK84dctsHTnPU4WUprW6\nbst1HJw/SMr0NHXvyzyXeI7ysl/ww12/5H13v48GDczH/Rcf+IDQhMRiOjhJGJ5i97RYqB/9qNiQ\n3/EOcc8FQxymj8NqtIK5yJB7BKfJI1i3lpeVqol1ks+Lcfb5oFDNd4CTiDuMwZ0AV5x+r9iwQy4v\nvr4M3/kO3HSTCF2GQjo4KRoSuI1hgo7gosyJLF3vb4iQvKwS++yzgp348Y4ZMuUMP9nzE80HKuYS\n5XoZn83HSPpDYM7zmd/dhNksDm2Dg53XadUrOp2CwTUVo1izzX5pFj1OY+gR/70hOgwlH2bFynMH\nxJySmhP4n9WdvO7AiWRO1FwTnEQDGBQD2Ya+ibeWjV/InMjTstsiYOvhTAaHp0yxVtRYFWiCE5cO\nTsqNAnajU+vXIjcAr827aFhHnubNM8djw4NPXQ4NM7t3Q9AmhGihwQTBYHdBLMB0NkG1XtUyT+R1\nTz4ZPvvVOZKO34KpzOkrR/Vnb4IT3DGsqg9q9g5w4nIaMM5sgaW/o+zZQ7+nH5PBJGp+NDd6CU7m\ny/OoxmZ8y5JnfFyvDqsxJ9XFmZNu4KRcE+88lo2xYweY3WlWBlfSUBtkynNEIoKSNrvn8Ts8bfHT\nS4Yv4cmpJ3n0qcOsXCkcXrqYRkHBa/NiN9uxm+ykCjo4kRlIBoN410VVB6qZw+LvUH8aVVVRXXFm\nJyOk9jU9SCFANe9qu//ZhNBi1JUyy1eoRPvruFyQNoxDaphhdTsPHXiIudIcV7i+rKVMLvMM88jz\n41x5JZx52QTUTazuj2iVdBVF4ZJVl7BjfEdbiq38eRtzYqhRqai81NRr+v1ozJ6q6pRuW1inkCJk\nD5FMwl0/t+OoDrA3LTyYwaASDAmNU7aS1U7r0s6IXALLHuTF9G7IDBBw21liGKXWs5NzzoEtJwrP\n3OfqxeQU1Ze/+lUx/qqqksgnWDMYJpsVotU1a2BVXxTFHefHP4ZHf1dmzfHTuHqF85+ZgZf2CzHs\nlj49LAag5KIYKn5U66y20ew7JF7OWYMiVd7VF8NqbW/uF8/GiZjWQs1C0RQTYSJVoTC1EmvDC7Y5\nTC6xMfpsXvrsg2QaU1r4EfSwjuKJaeMSjcJUTKXWqGnibr9dByey4Zu0WDbGgHegmTl0CKMtT1Wt\nQCGIS2kFJ0txufSUbhkiWQhOgsFm6qqialWsp9NZYjGo2CcZ9A0SsodIl1Js3iyKVZqc8zx6b1Pn\nURWC2IFmI714Lk4sG2PQN8jpg6fjsXior7iTfeYdWI1W1jpPZ2rblTwVfwpP4jwMI3dz/Il1cSDM\nVwiHweFsMG8eJ5aNcfevcvzoR4JJk6GNeTUGs6sYDZ4FwKrAMC6zACdYxRgE7UESlXbmxO8XYKpV\ncwIQdoWx+BMYvDGGm7Fhr81L78Acd90l9GayvYTfD7NplbJJSASC9sXBSaacBlXBVRGK81hG+JQ7\n7xSAYsPJApU+fOBhUjmx0MuI+/davQz4luJ66n9zy+9v4fnE8/h87eBE6gpNJn1cXS7BLBkLEQzz\ngxgaFlKq7gSLjnG8Vi8rIz2Agt8UZayJjoeHRfjNbhdzf3r6yAUc/1T2ugUnpVQYkwm8HoOWeiet\nFUC0aU5K+ob0w2+LFfLw7zK4QvqpW1qvMwz2WfaMCQ9fUfM4zM6Ofjle6xHASXOTP2FDAPvsiTSm\n19PXJzaYyf1WjDUvfSvFfbeCE6NipMfRg1G18sV/O8jGWzdy86M3t133D5k7OfveHtR3nA11M+es\nF0dvjwcMVQ+5qgAnXoM4QSwEJwC+3IkwsoNf527RQEZrzQ8JTlrfrdEuwImkpSWIKtaK2qkhn4dD\nh46sOZHXmJqP8/AjDWrmNCsDIi6RKuihHcU2T6/P03YCuHjVxaioPHLo15oIOl1K47V5RUgBCDqC\nJAtJHZy01DFxuaCk6EA1ebA57rY0c6U5GoYSUy9F2bur6UEyA1pRNBCM3FzKCgiQVTvv/dQvb4Y4\n8mNYsiNcuPyNAHzytE8y6BsgmxWMV35ihKpvD1/6Emw6YxJDvp+TTjC2vZuLhy9mMjPZlnYsT1XS\nyUxMgOH9G+HEf+bpp0WmmN2uj9nXv97sN0RnWCfsDmqpn8u9IqUe4LtPf5eRfxFHrmwl2xbWATh/\n2XYwlfl96fuQGsbphGH3Fuh7RmhKmllbva5ebN55IYK9tPkKyxmqjSpbhsXk+/WvRWhhIBDBsyTG\nRz4CdzjO5JlzI7zlD2FY/yMSCXj4JZFqeurKzawMrNQ0Eep8FEPZT90qmJN8HmIpsXFvPzsMJS8X\nXhln48Z2cBLLxbBWoxgKERLFmGiDoQ6QmrZjqnvBmsHQrA7rtXlZHhxEVRptWhWdOYlpAvhoFGLR\nW1n1tRHSZRH28dv8eCzdmZNYNsb2YZErPu/aRcMu46dBHKp4R15TGGr2rsyJrB80OSkAt9erg6bK\njJi3z4+Lz2aYYNA72MYO1Bo1aoYCSsmPUbVSqBbIFHP85Ps9WA12nj38LA21wYB3AIvRwqmR82Fk\nB8+Wd3D2srP5/pu/CYY6PHUt87+8ibIpSaXnCZTjb4WP9uENFTH6YtSN4lTyd7eMc9JJ8Pa36+9g\nrhGDbISBwmVQN7G2d1jo+KzzWurucb3HMV3qDk4WMidhh2BOXJE4S33CgfhsPjy94sV/5jP6wdXv\nh3Q+i2osE7QKcLKYIHa2OItS8WIsiBDYm69JUC4LZvf88+HdHxEHtWqjyu7sfc00Yn0ODQzAsunr\nWe5fzs2P3kxfn55Zlywk8X7Wq1VKlqEdlwsKxhhKLkq5ZMRVXcHhmqjvFAjAodIYI6ERfD5F9CpS\nokzMxujra+4BBli7VvRNi0TgG9/o+mh/UnvdgZPeXhWcCbLTYaG0V+goJtWNOcmUM0zMTWgb0rM7\nBXPylX/N8MGP6ScdaTLs8uKBZtEqJY/L2glOfDYfqbwQXC3GnJx/up/Mbd8n/YNbueEG8bOdO6Ex\nH8a/RNy309nc9Io5XBYXiqKgFMI87fw8e5J7eGFG1ERIJsFoK3DDwx/g9MHT+flb7uSO835PwC28\nlaKAw+impM6DO07QIhapBA4yHgywbvZG+I+7+Obpd3LbpbeJd9BSLVWewg/O6u820JdnbEzP1Fnq\nEVUwW5kTeZ0jaU7kNQ7NxagZ51FRWRUQKzaliWJVqoZ5loTamZMeZw8Be4D9sxOaVqM1ZAdoAjc5\nL3KVnHb6dTpFHFl+Pr5P/D1XTmubUGE6SmZC6Ey8DGrF0aB5Wq01wUm9zJKN46R7fslMfobJzCQ3\nfXCYT//1ME+/52luOPUGzeHkcmBIbEL1HMTmT3G4NMEJw4N88Yvt70bWm5Hl86GL5uRgjUZgD6z/\nEU8/rfeFkuDkhz8UVWGrVeHQ7XYwmKrEc3GWBcWYGY1w0soRLbTxq32/4pX0KxSrRaE5sbaDk3V9\nqyA5QlnJQHIEhwMuPK0XbBlWDdcpVosoKIQcIdaPzvOf/6lnr8lxWN4bZlmzA8ToKERdUXxLY/zk\nF3kMS5/kfZuvx28NQGAviQS8cEBk252wuh+byaaFHWpzEdSin7oprT27ahLI7cI3uFgWitKzLKZV\ngJUWy8YwFqLYqlGR3ZAap9c0TCIBxpoPbBnxB7G2z94irvfspD4WcoNXzXmNyYhEoDHwIAfm98GK\n+4F25qQVnJRrZWaLs2yNbqXX2Uveu4uGtelQCkHsDbFAA4ZBbezlWHTTnAQCYjOSm2sxPgTA2P4c\nGCsky3ER1mnqKlRV1e57eb8HY8MhwEkhR63gxFwN8VRclAqWQvATfNthyR94fv5Rtg9vZ3RwNXdd\n+BI/v+5W7vn2CYQcIf59z7dQzrsB7GkytmepefUTxYHcOGedpRdnA5irxen3Rbn7H6+DW59maY8P\nr62dOdkQ3kAs1y6IXRScOMNYAwkswRgRd5M5sXrBOsfu3XDddfpn/X6oNEPVPc6mIHYRzUm6lMZY\n8dPICbZ7MpXghhtEg8BLLgFrMAYNAz2s4ZninW3VYX02HzfeCHf+wsIZg2ewd3YvP/85fOpT4ruf\nO/wc+WqePUlRT0b6CruzRkFJoM5HRSNBtUcIptEzdYaDwxowddQixLJxLTwMcMcdgt2580644oqu\nj/YntdcdODFYi2AuMnswrDEkrwZOrEaxkbyUfAm/zU+pBK+8IMDJ+q1zKI5O5kSCk/Ep8b11Qx6P\nTQcnUijqtXlJ5cREXIw5ufwCP435PpRCmKuuEgVyfvxjUHNhbEFBYUt6OJ0T4KRWg/p8mIZ9BpPB\npIVRUimwnfNZ4rk4t158K5euuZg3nbq57bpui4eaIQ+eg/Q6xCLtxpwM9Hrg5Yt4x7aLWeoVG1bA\nHtAzX0Lievub6kSbwYk/3Azr5OL0ufo0B9HKnEg7kuZEXiNVPqz1A5HMSbKQFMyJqUSDGgO9hUaj\n3AAAIABJREFUbk3HIi1ojlK1xXVw0hKyAzSatrWLqKzYaHdVaBgL2ucnxsTf6WJaF0zmIngawjGP\n9A1yzz16y/vJSaAmwjqlWol8PU2lUeFfd/4rKiqnrx3BZoONfRtFVlfT4WSzUJ8SN7wrvouJzASr\nwgNaTydpMoNCjjl0hnX2J5rVI/t/z57JhLZBh0IClDzR7Lxw+LDu0KeyUzTUBiN9AnSdeqqIWe+d\n3Uu9UdeK28VzcaE5WcCcuFzAWLMyXGoYhwNCbvFw2UqWYq2I3WzHa/VSNcy3xdDl+gw7wxrbtXUr\nRNwREsU4kc3P0KDBdcf/BX3uPnCkmJmBffEUNAz0egXTORwcxqCaKaeDqAW/AJly2JtFyjxWN8t6\nIsRzYn7s2aMzTvFsnEYmiosIsaxgTpY6hkVdjbwXjFWhhUBsbFddItbFXY/p4CRZSGJoiPGXYDYa\nRSsUx8bvA7RpTlrDOnKOLXEvYTQ6Sjmwk5q5uTEWgyhlL2aDGR+DWCxCh2MxWjAbzF3DOhpTLDfX\nuSEA9k5mCS47hIqqMSfVRpVsJasd2sJeD1QFOMlVc1BxUZ4LajoRKQRfbbwQGgbqap1LhkWDn4u2\nLefS7WYuOM/Ixasu5ranb8NsMEPdxFRjFyXXODRMBO1BMsaxNv9YqpWYLc5y3VuiZDNGmFmH3w8+\nh2ROMtiNLpb7lzM5P4nVpmrgJBBYHJykyymSxRlN2CuZ7U2b2ss8+P1AU0/W5z6y5iRdTGOu+8nN\n2aDswd2X4GuisTsXXSTmgKkSpj93GWP1u7E762RKeljH4xGZORF3hHg2zsCALoaV7Szk+pC+om4T\n67uRiQhwgp98Q/jM4WHRCmU4MKw9i6USZa4ea+sJtGyZaH9x8cWwYkXXR/uT2usOnKRLQv1Xy4Q1\noBB2hrXy0yBimoqig5M1PaJgRF2t47f5ee45qOea1V1LGb04WxfmZLacIB4XpySvw4nHI2KDrWGd\n2YIOTp47/Bwrb1lJtpzVvnfNMh+jo0In0tMjTox33QXke1AdelgHRLE3p8UpmtTNR2B+CecE36U5\nwanZNIXNn+NjJ3+MVcFVXd+R196c4cFxbZF2AydLlwqnKktAQydzMjcHB5IJsUHYl+IN5XngAbj5\na3Fy8Qg2k6BHWpkTKRA9IjhpXkNFxTkkRBMr/GIFpQopli4Fq0c40JVLPaiqEJ9Js1Qi4I6xZUvz\n+0rtzEnQEdQ0J61l+UHoWwBNY7R3jxMDJtIlnTnp90W48NQlgtJet5y5OX3DP3AAnTmplbVn+Zcn\n/wWgTRsB7SmCmX0rsTQ87Izt7FrcDYT4NGAPtIUSDAbB+Eih93Sh+TNFpbHiLm2DrvX+nufOHEY1\niTjazY/czI8rV+P3ozWSO25gEEURFWhHgiOU62VemHlBCyPFsrGuYR2nExgT4SqSa7Ba0dLp58vz\nFKtFUUujuSG3Wis4Of54wdps2gRRd5RirciD+x/EYrSwLryOkCOIxZckkYDJVBJz3a+F69aG1uJW\n+8nnDNRzfioGHZzYvM2Qh8VJ1B0llhXOWt30HU7/9lladdjSTISAOcqh+UPsnd3LKr8IZaWnxY6R\nqk1iVIw4zA5WDDgxlUM89rze0jpVTGHPrdXeFYAjOAv+/RgrfgiNafon+S4cnjLJP1/D7U88xEnn\nCHAScUfYGtlKtff35E1N8FMIUSopRN1R3PXlbQDPbXWTrWT52Z6fcd7P14F1noMHWw5jcnOdE3Nq\nfyxLdI247wHvgJ5iX0hp4xMJuFErTvKVPMVaHiouqpkguUqOsDOM3SzWd20+BAdP5rjwJu0g02pv\nHBHz4s+8n4HEBl7O7yJvG8OUXc5IcC1Vz3gbOJGC+m0bIrz3vcJfBwLgt3tQbCKs4zH7GPQNUqqV\nsA89w02zKzjI4yKMVxF+stW0JAPQ/J7P5tOAQqu1gpOot4egPUipVtI6NLdaupTG2vCLAoG5MOdc\nmiAa1f15PBfHUY/inLqEgpJEWbJTOwjJQp3ynqZz0221q+Sak1mF0lcUm5lM1bTItHQa/GTKaVwu\nGFqdJlVMaX7G74f8dIS6vR2c/E/b6w6cyAqP5Hu0RbmtfxtPTj2pLTijsRlfTwqnORIcEYgeAUB2\n7QJDTcyCudJcW6aHtNZsmttvB8x5fE4nitKeqiwmv5iI4bA4Eb+SfoXx1DjpUhqP1YPRYOQ//kNv\nZjY6KvQHLkNYq7wqwYkM64yPA/d9GdMPf0VjZlg7bU1kx1GNFd609k2LviO/oznDHbMMBnRwUiiI\nE6R0Etdf39lNdaHmRFURZfwLITxWNyPr83zrW7BmyyzpWJB7dzTBSQtzImn7Iwli21rKD78ICMbA\naXaSKqZ497vhB3eI8RwZEs/TGtppZKLYQnHtBJIudjInUnMiM58kiDC69PFOpSA9q+AyClAWz8bx\n2/zs+C87X/mSid9c/Rv+98XX4nCIAlMgsj/6I3pYJ11K4zQ7mSnM4LfpdVaktYKT6biBqGELTxx6\nQhMcdrOoO9qW9gp6CfupKcAlfuYorYCRHdoGPed7BIIvEx0VJ99fT/2CA8b7BDhphonWRAd48EFR\nBlw6uNuf1ztlxrOCOVkoiLVYwDx9KpFf3YczcTaKsgCcNJmTxcCJUTHit/t573vhoYcE2JGbyI7x\nHRzXexwWo4WQI4TZkyKRgMPzKZyGoPY9Hz/t47zV8FNR8C/rp6LkcHkFpRWKins2KAairijxbJx1\n68Cw4kF2zz3EoxMiLTwXj9LriLBnZg+VeoW1feIdJCbFgeVQbgKfzafVeAlbBxmbntBqHqUKKQwz\n68W7ao7RoboIg9R/ez2Apn/yWD3UGjVStt9D6CW+cf89HG4Cy6g7yjWb3gmmIveWb8JitEDFSbEI\nP7vyZ2wuflTzCyB0J9lylkcmHmEs/SKc8Wnm51vASSGF0+SGoviHg4ksgSGRb7/Es6Qti02Oz5KQ\nh3rRQbaSpUoRl9WFqSI+15o+n0yC5Z7b+M83d+moClw6cil3ve0u/u2v3sXFW0Z5JrGLjGkcw+wI\n/fZhCLaDE41xckf54hdFp3C3W8wng12Edbw2r3YP+fPfziz7mDW+iM+vLsqcSIu4dEGs1uOmxTRw\noiosCQTbyg8stHQpjQ2/yPrKh7EFE/zqV/Dd7+rP4jVEKOwXdaKU4MtkyhkUFG19yGetq/U2bYsE\nJwuZk6KxqWNJibCO0yj88gMPwPa3ij1Qhq78ftj/bBTsc6zf1Nnn7H/KXnfgRGv6lteZk+0j26k2\nqtz/yv3a52RIYr48j8/m09C+3yYQ8IZ1RlwWF5lyhtniLFajVTslgDh9OcwOlgwn+N73G2ApEHAJ\npN4KTrxWL7maQOY9PS0dbTMTzBb1dNWREb0OhaS1+/16OEo6ofmSACdjY2ArrOKMdatJ7ouSq+TI\nlrOaOGxh3Y1WC7r0BbEirId1ZDNE6SRCITTmQdpC5gRgMjkD+TBum5O6Mc+73gV9QxmW9ni56ZPt\nzInVCkND4vckc+J0dhHEtnQBNi8Rehq/3a/Ffn0+WL5aONCBXg9eb3ta3HwsgtGnMwsdzEkzrJPI\nJ7QNWAIixa4zZfI7JSiLZUW8etMmoSM4eenJ+BxuNm/WtQs7d8K6EUE3FaoFMqWMBhaHg8PapiZN\nOpzZWcFcDbtG+dW+X9FQG12ZExDOtZU5Ab2E/cQEor+MYmR97RpYcT8ev6i9kLeJB9p44S6Mlir7\nC89SMh3GFRSaqx5HDw6zgzPPFIzZgHcAq9HKj57/EXaTHZvJpjMnCzQnAG6XQu6Z83A6hOs5Fuak\nx9mDQTHgdouQknxOgCennmQ0MqqNneIU3XPnKin8Vh2chJ1hVrk3k8tBZd7fHE+x+fj7dLYn4hbv\nz2RSsfeLDeCbu74pxmEiSr83KuqIAFsGBHMS3y+Q7sTcRNuJd6RvgKpjgkcFtiFVTFGeGcSKWxuj\nZ2Z2olTc8OQHMKgmbS7K97Ov8Rug2RfKHcOIFb/NT79rCB69kUwjJp5bUSgWYXNkM41sT1fmZCw1\nJoDMSV+D8PNtzEnAHoS6BZNiYXo2i7M3htvixmVxtW3AUlg72OehUXYQzwjn0OtzsTQkPtc6N1Mp\nCJtWMBIa6ZgTIGo+XbTqIpwOA5dsGeWFmRc4zLPUE8OEDSMQGqOnR88+k+8t4orgcMAbRIIVHqsH\nxS7COn67V7uHql/0nchVM3h8Vepq/YjgpJU5KdfLWm0SaRo4KYQI+IwacOsmik0X0zgUP6oKTiVM\npjYjMs1W6c/SY4ty+KATaz2I6p0gU8rgtro1xk8+a+uzQ0tYp9mBXhO+KzEMGCnN9lAsgssk/PJJ\nJ0HZIDYfec9+P5ST4nkN3vYDzf+kve7AyVhqDFQFikFtUQ75hlgfXt9WG6I1DdZj9WibuWRORkd1\nym/hqVta2BlmaH2CZ14QaDTkEeAkFNKLYMmS1x6vitWq6wRkXZXW9GRpEhCsjAhwsjO2k3c+tg0s\nWbIlnTlZtQqOPx4mXtAndao+ibnhatuIO+67pZrPit4oRqMAJ7Kwlew43M38dj/FWpFyray93+ms\nKDvutTu1InFzpTlOP9FLNt3OnLTm8LcyJwvByWxxVjiThpGS+wVBg1u9bbFfucF5bZ62csyNBiRe\niVKyxLR0266C2GZYZ3VQb2gIoNp15kR+Z9gtFv/4rB4KazUprKzXBXOyYa1gThL5BCoqF668kKA9\n2NV5y+F4+WXBRG0Mj2r9iBYDmTIs0WrFjV/hAfVvxdxzxQk7ezk5cClY8hTCD4tnNDR73vTvIrj6\nBWoIp2wIjzOZmey4nqy9cmDuAJv6NhFxCa3GwiJs0mRoSQLPxZiTYq1IpV7hvB+cx0MHHiKRT7Rt\nHtLk6Q/QwYkjiGpL8eSToNpSWjuC1nvI56E8J8a7rKRFX6tgTmN7ZLgoU85Q9YgN4OcviRrrtXSE\n5T1ijK1GKxuHxMFlYlwAksnMpNaaAuC4wUFMoQn+8z/F/yfzSSrpIH5zRFvvu+K7cGQ2Q8nHSvMZ\n2rqX7+fFogAnBe9T4I5hq0ZQFEWEQh//GL3mFYQcIWw2XVuVy7XXvnBb3OQqOcZT47xn9D0o6ZXw\nrm3csXQJn3zwk6QKKXqcQRQF6gUXpUYOkz+uzeduzMnyJUJzMjUnnEPI42LDCsH8hczt4CTYPgyL\n2mhklFqjRkadop4Yxl0ZBmsWxX1Y+0w8G9f6ZbWax+oBSxbscwSdPgL2AE6zE1f6ZBy1JZTI4PQJ\nGnYxcGIymDQgJsextbkkSHByGPI9+Hy0VZVeaOlSGpdJzLU+V1hn7+Wz5OJEXFGmp8FeGaTummCu\nNNc2h0AHTHJdV+oV9qf3YzaYO5iTdOMQHmMvqAbm58FjFocnVVW1e5TP6PejVYmdbjJ5qqpyzc+v\n4Zs7v9nxPK+Vve7AyfjsOJZ6EBomTQgGsH14O3e/fLcWz1sITiQCdxj8PP+82GxkCfuFp25pYWeY\n0EACLGJn7fEKcPLmN4tmZjt3CtpQpUFPVCjuYrkmczI30SHSlBYIwE9+ApeeE6ZcL3P1z6/m6eQT\nEHqJXEUHJyMj4j5T+8WkjufizCsTeBnsOJ23Wq9PBycD/ohoKlbS2Z4jORn5HtKltPZ+Z8sJjCXB\nnOQr4l1kyhn6Qz5O3GpGUY0UqwKc2Gw6ONE2ME9np9x0KY3PEoRcLyllD16bF6PB2JbSJx2ox+oh\nGtVLkWcygu6sU9HYkA5BrCNIsVYU2Sn+ZZgMJu2zsleGx+JnerpZH8Tp58EDD3Lv3nt5y9q3dLyX\n0VEBLn7/exGi2rRegBMZO+9x9nDHm+/gxlNv7PhdyYrJeiQnD+mB4cXAiQQJ0lRVJbnya7xs/TGT\nk2ALx1jiiXLC0Hoo+plzCUHMrGEMBQOvFHfiXLlLAHmg7htnIjPRNYwkmaXRyChRd5SD8wcpVAtd\nmRP5LF3BSQtzArA7vpsH9j3At5761qLgxGF2aE58NKozJ1VzSpTTt6dYGmoPk7lczc7DxWaWVSnN\nHXfAyrU62yNPqU9PP03FMI9h3/lUG1VMihmKQa0OxsrAStwuIy4XZA6LsM7B+YNax3GAId8gim+S\nb31b5cndBUr1EnY1xPKeqLbed8V2EaqJ+//w6q/x1Qu+2vZ+Xpj/HWT6RbHB4x7TqnqWSkDNxo3L\nf87XLvgadnt7/6SFzEmqmGL/3H42hDfgu3sHPP4xhk3ncPOjN/Pr/b8m5Azyve+Bx+7mrAuyWIIx\nbVN0mB1YjVaShaQOTvpdUHVqPch6/U6OXy8cxPzBPw6cbOjdoDXOJDmCOiPmV1LVqc9YVtzXQj/m\ntrhpmOcxOUVYR1EUfnjFDznu5R9iKPvBNofNI3zQwjonTosTu8lOxBXRGAvJgC0M7dhsYAjtg7ll\neL0cOaxTTOM2i7m2LNynrXkQNUqmc9MMBCLU66BkBqg6JsmUM21zCESKvYKirev96f3U1Tpbo1s7\nwMnO5IOssp8AiDnitfip1CsUa0XtHluZEzmfJPD56Z6f8r1nvscD+x7oeJ7Xyl534OSV2Ve0VLvW\nxbJ9eDvJQpInDj2h/UxqTlrBycykn1qtCU6aBdQWAxE9jh7qtgShqFgMYb9YDO99Lxx3HLzvfXox\nN39E7L7yJKUVfVuE4bjiCljR20xXnhGaC3wT5Ks5nGan1iNhdBTI6cxJwTxByNQ9FCAtEtTBScSl\ng5PWeheLmXwP6WJayyLJqaL/hNOsMyeZUgav1StaezfsWvl6u13vGSE3ML9f75wrTYrMyEYpqwXt\nPXVjTjxWT1soLZlsfycNtcFcaa4jrAOiNX2vs7ctXFUzp6FmQa3YNafrt/k5MHeA46PH864t7+p4\nLzIU961vib9HN4qwjhxvv83PWcvO6sqcyOJKEpycuGolHqunTXC40CRzIpmh5xPPU7ZPkDdN8MpE\nCWtQnIgHBxWIbSVl2cVcaY6ZQoI3LD+bl5IvUet/BFdxDYZ8hJKzCU66hJFkM8qt0a1E3VEtdNqN\nOZHgRNbZkEzFQuYE4DcHBFtw98t3C+rb0Z2yi7qjWIwW1oeFjiPoCFIxZMBQRXElWbpgV9R0GCUd\nSG/fDjWjHtaRG7JsA9B4TGhBAuYIqAbWD4qfy/Hq6QEqLlAVao1aW1hnwDtAlSLL1iX5i78Uk/Ad\nbw4yGBCho9niLPvn9jNkFpPk3E3rOHVAxK3ku6g2KrBbzKuM9zHKyQiqqgvVVwfWc9ayszrASavm\nxGVx8fT00zTUBsPBYdzVVfDwp3hP33fYEtnCK+lXCNqDvP3tsDTsZv2WrMiEarJTiqJojOJ8eR63\nxc2SqAGqDmbLTXFoyMVQWLzvfU/pwPlYwInNZGNDuFlkJzXM/MQKaBh4ZU4XjQm2IdLxux6rh4Yp\nh8E5q4HWN468kR7zIPWCqENjc3dnTkAcKFuZTwkQuolilZAomOj1irluMpg6mBPpW3wWMdfWD0ba\nRK0zBdFTZ2Vvs2TD9CAl2wSZcqZtDoFgdMLOsAYgZEjntIHT2sGJM8FTiSc4KbBd+12vVffLyUIS\nj9WD2djUUfrRqsTGsjFylRwfvu/DAB3s62tprztwUq1XcSud4OSEJSfQ4+jhp3t+qv0sOVulWCu2\nhXXGnvZjMglwIfvrLAwJSAs7w8wUEpz+BrEhS82JySSKXP3hD/DoA2Lye8Ni8rdqThb73tbvB7h2\n07XiFOCdpFDLY1VcHDokwMnQEARcLiy4OTgXo+acpM++uN4EYEmP8GiGsh+72d4GTkwmfWPpZq3M\nidksFkvVInoZOc2COVFVVdCWtiY4qdu7MieT1d285Y634PYJwWJal5mQLqYxVPwayJCgKGTX6w3M\nl+cxG8xYjdY2cJJKoZ0UpHizoTY6mJPW99wq9K0a01Dyk88rmtMN2AMoKHz9oq+3xYmljYyI93b7\n7SLc1uNvZ066gdtW83iEZsZohN6wgS2RLUfUDUXcESp1nRm6c7xZaEVR+elv9qJ4YkRcEfGuY6NM\nqbs0cd3b1r8NFZW4/w7MyVHU5DDzlpcWzQ7SmJPoKBFXRHOaCwWx0MmcGBQDLotLByemdnBiNphJ\nl9I8ceiJrsyJfFYphgUdWGKfxeBMEXIsAk6KusOG9pL7ckN+6MBDGDDAgTMY8WzGhfj31YMBLEaL\nlo4ZDgOqAUtDbCitp175zj5804Roeghc/ZagEC1n4/zuoOiAt8Y7itmsa66ANkEkL1+EW+1HpUEt\nHeXwYR2cyIy5IzInFjeTGZF9MxIa0dZxT8jINy76Rtu7c1uEPiWWjRF16Zu1BP/y0OZygRkHhYbw\nX0t6XFqI45lHBrX7SyaPHpyAYOEcRjfk+pjYZ8GcX8azh5/Vfi6Zk4Um31fdeahtDFwuqGS9YMtg\ndR4ZnLSGChcL61TqFeru/ZAcweMRwK1bIbb5sqjBJNf3CWuEqFWWKJD+fk1/kwmbHqRgFmGdhcwJ\noM0ZEGJYl8XFcb3HifYBlbwAJ6vuBuDU8MXa7wXsul9OFVP6GoHmIVKhzylE9Dc/cjPJQpIr113Z\nIap/Le11B04APKZOcGI0GLluy3X8685/ZX96v2hal5V1DzxcvuZy/umML/P1L4S57DLhBFrDOt20\nIbJ+ytuuadKILalrp5wCV18Nt35VTH5XaA5VVbX6H5OZyUUZGWmrQ6u5+eyb+cJ5X2DAO4ApOEGp\nkaPSLJU+MiJS7DZsAGs5yv5kHLwTLPUcmTkJh4xQdmEqiUVqt+vgxO9vz/dfaK3MCUAgVAdHEo8x\njNMimJNCtUBdreOz+XA6Qam3MyfbtsHN/1Tnq/uu444X7yBtFlU+28BJKU0t58fZEIt6MebEY/WI\nE19oITjRmRPZC6h1DFszZsLOcBtzUjGkoegnlxPfFQrBuza/i9suu43jlxzf9b3I1NdiUbBZVlMT\nnOSn2+5/MfN4RM+Rvj6RFvzpMz/N35/594t+fmF8esf4Do7zitP4tu3jGLzCuUejcPX5o6TrUzwy\n8QgAl62+DKvRSk0pUnhlFHVmhAP1xyjVSl0B0WWrL+Pz53yetT1ribqjGmN1NGEdQBPAFqvtzMlv\nJ3/LJcOX0Ovspa7WFwUnnzjtE3zmDZ/R/l8bO+cMDUu6DWi23gMVF0bFqAG41vRnGS564tATDHiG\noG7lrYEvc2r97wgGwWZTuPXiWzWWTIrEbYpYz616ARkKi6ye5PpPiEnY6xbgZCo7xace+hSb+zbz\noXes4v/8H73LrHw3IE7Nn//IcaIbOEA2wtjYsYMT+Xevs1cbg2AQTuw/ke9e+l3tedxWkdkTz8bb\nNmspFJdrC8Bp0QdzIOLi9MHT+dC6z1I6cBwPPST+/ViYE4APnfQhPn7cNwCFffugN/1nfHv3tzVw\nFcvGFmVOAOqW2Tbmwelsln+wzWE+Ajj59Jmf5qPbPqr9/2JhnX3pfWCoYysMa6UPepw9HeBE+owL\nzgjwzW/Cyl69A7B8DoB1AxHhVzOD1A1F9s7u7dCcQFOo3QwFjiXHGA4O0+sSLQQS+QSXXw6br7yT\nE5acQMTTq/1ewKH75VQh1bYm3vhGuOUWGPBHeXjiYb70uy/x8VM/zilLT2ljX19re12CE7+lE5yA\ncHIhR4gP3fchUaOjqIcFAvYAqTs/zHxG4UtfEp/3Wr26IHZR5mQGf7h7jPNzn4NGQUxAu19k/VTq\nFbb1byNZSGppqYuZ0WDkxtNuJGAPMOgbxBAQ4KSQFt5XdrwdHhY1T/YkXhLdToNHBieyv46tIja4\nVubkSCEdaGdOALx9KVBU/BadOZGnEBnWUWrtzInFAoFz/43d00+hoHCwJtJcFjInpXRAKxInQVFr\nFctWBxoMNrUm1SY4qVsJ2EQtkG6p4K0ni4XMSQnBnORy+olwc2Qz79j4jiO+G1lDYHRUUNcgnJRB\nMXTdyFtNxpKbUgdOHzydC1dduOjnW8FJIp/giUNP8L/OeCc+m4/TLn+BdCVBxCUc4t9dJ27sR8//\nSGveeFzvcQCU949Caph0XaSUdtOc+O1+PnbKxzAohraN7EhhnYXgJFsWRdgcZoc2ZoVqgeOjx3Px\nKnECXAycnL3sbM5Zfo72/5rj9e9DVRptYwmtzJ+CxxzQNpCFIt6oO0q5XmZ1jyi1706diXv6Qq01\nwjs3v1Mr/CfBid3QCU6C9iAOs4OJuQlOODOp/VvEFaFQLfBU/Cm+ftHXGRk2cM017c9mNVlF/Zae\ndXzswzZOaeqNlFyU8XEdiHQDJx2C2OYcGwmNoCiKNgZSknPNpmvY1CdKhLosLqayU+Sr+TaGQtb/\naV1bHpvu14aiLhxmB1++4gYGB4xaqQEJ4o/W1oXX8eaRvwBg/34Yzf1vfDYf198nwmvxXPyIzAnQ\nwZxQFmEdo034427g5MJVF3LKwCkd37cwrCNZRl9ND8MuLOYJuh9cPejn3e/uPDTI9R/1hsUcyjQZ\n+uRYV3ASdelC9/HZcUaCI9q6SOQTBHrKvKzex/bh7W31p4LOxZmTvj744AcF8Hni0BMs9S7lb075\nG6LuKJV6RTu8vdb2ugQnPXYxmAsXi9Pi5Cvnf4Vfjv2Sg5b7UC0CnHzlsx62b4evfQ0++UldE6GF\ndRZhOMLOMLlKTqPuFxb96e2FT90gFpDVO6dNupP6TwJEM61Xo/ulDXoHwTtBWc0xnxSVaKXmY2QE\nijNRIZoFRnqPDpw46jo4KRaPDpzYzXasRqs2oV29TRW/XWdO5ClEMidqtZ05mSvNceOvb+TaTdey\ntmctewtdwEkpzdy0nwF/J3NSrpfJV/Md4AREOm4q1ayR4RE0Zrcieh6rB5PBpNXWaG1oWFDbmZOj\nzkJoAScmgwmDYmA6N43P5usaCmo1CU6inf64q7VWib37ZUHzXrzqYoaDwzw6+SgqquYoh3xDBOwB\nnoo/1SZuVVBgehOk9KJwi6UuS2vdMLoBLgkMWkODbcxJS1gHRKhIVhRdDJwsNM3xBkWjO6t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pUXcuHK7jVYFgUnzWyIoxUpvppp4GQRINAKTkC0tYDuGUYgNg6pU+nrW/y6Jyw5gXOWn9N1TEdC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\nBPwYcPxHILuea7LrS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"text/plain": [ "<matplotlib.figure.Figure at 0x7fdac79d1e90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.plot(range(0,len(acc_train)*summary_frequency,summary_frequency),acc_train, label='Training')\n", "ax.plot(range(0,len(acc_test)*summary_frequency,summary_frequency),acc_test, label='Validation')\n", "ax.set_xlabel('Training steps')\n", "ax.set_ylabel('Accuracy')\n", "ax.set_ylim([0,1])\n", "ax.set_title('No Batch normalization Accuracy')\n", "ax.legend(loc=4)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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/+18pOFx0kSyrrJS8ZzdoY8dKYQ6QHqQdO0oenT5djNOHHybWpK5d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bjMpZvZrdwf368bY773RG9a1b54witA2nhganIduzC48Zw/dVjI0+ffjBNHEiN7hNm7iOvPYa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\nwSP/HxjfUJSRhcr4AAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fdac778f750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(5, 2, figsize=(6,12))\n", "fig.tight_layout()\n", "\n", "for i, ax in enumerate(axes):\n", " ax[0].set_title(\"training BN\")\n", " ax[1].set_title(\"validation BN\")\n", " ax[0].plot(BNs_train[:,i])\n", " ax[1].plot(BNs_test[:,i])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
nkmk/python-snippets
notebook/float_to_hex.ipynb
1
7653
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import struct\n", "import sys" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "f_max = sys.float_info.max" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.7976931348623157e+308\n" ] } ], "source": [ "print(f_max)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b'\\x7f\\xef\\xff\\xff\\xff\\xff\\xff\\xff'\n" ] } ], "source": [ "print(struct.pack('>d', f_max))" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'bytes'>\n" ] } ], "source": [ "print(type(struct.pack('>d', f_max)))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b'\\xff\\xff\\xff\\xff\\xff\\xff\\xef\\x7f'\n" ] } ], "source": [ "print(struct.pack('<d', f_max))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(9218868437227405311,)\n" ] } ], "source": [ "print(struct.unpack('>Q', struct.pack('>d', f_max)))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'tuple'>\n" ] } ], "source": [ "print(type(struct.unpack('>Q', struct.pack('>d', f_max))))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9218868437227405311\n" ] } ], "source": [ "print(struct.unpack('>Q', struct.pack('>d', f_max))[0])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'int'>\n" ] } ], "source": [ "print(type(struct.unpack('>Q', struct.pack('>d', f_max))[0]))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.7976931348623157e+308\n" ] } ], "source": [ "print(struct.unpack('>d', struct.pack('>d', f_max))[0])" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0x7fefffffffffffff\n" ] } ], "source": [ "print(hex(struct.unpack('>Q', struct.pack('>d', f_max))[0]))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'str'>\n" ] } ], "source": [ "print(type(hex(struct.unpack('>Q', struct.pack('>d', f_max))[0])))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def double_to_hex(f):\n", " return hex(struct.unpack('>Q', struct.pack('>d', f))[0])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0x7fefffffffffffff\n" ] } ], "source": [ "print(double_to_hex(f_max))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0x404518f5c28f5c29\n" ] } ], "source": [ "print(double_to_hex(42.195))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0x7ff0000000000000\n" ] } ], "source": [ "print(double_to_hex(1e500))" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0x0\n" ] } ], "source": [ "print(double_to_hex(1e-500))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9218868437227405311\n" ] } ], "source": [ "print(int(double_to_hex(f_max), 16))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0b111111111101111111111111111111111111111111111111111111111111111\n" ] } ], "source": [ "print(bin(int(double_to_hex(f_max), 16)))" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0o777577777777777777777\n" ] } ], "source": [ "print(oct(int(double_to_hex(f_max), 16)))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "def double_to_bin(f):\n", " return bin(struct.unpack('>Q', struct.pack('>d', f))[0])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def double_to_oct(f):\n", " return oct(struct.unpack('>Q', struct.pack('>d', f))[0])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0b111111111101111111111111111111111111111111111111111111111111111\n" ] } ], "source": [ "print(double_to_bin(f_max))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0o777577777777777777777\n" ] } ], "source": [ "print(double_to_oct(f_max))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "def float_to_hex(f):\n", " return hex(struct.unpack('>I', struct.pack('>f', f))[0])" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0x4228c7ae\n" ] } ], "source": [ "print(float_to_hex(42.195))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
materialsinnovation/mks-tutorial
index.ipynb
1
59360
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pymks\n", "import dask\n", "import matplotlib" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==================================================================================== test session starts =====================================================================================\n", "platform linux -- Python 3.6.1, pytest-3.1.3, py-1.4.33, pluggy-0.4.0\n", "rootdir: /home/wd15/git/pymks-project/pymks, inifile: setup.cfg\n", "plugins: cov-2.5.1, nbval-0.5\n", "collected 102 items\n", "\n", "../pymks-project/pymks/pymks/filter.py .\n", "../pymks-project/pymks/pymks/mks_homogenization_model.py ...\n", "../pymks-project/pymks/pymks/mks_localization_model.py .....\n", "../pymks-project/pymks/pymks/mks_structure_analysis.py ....\n", "../pymks-project/pymks/pymks/stats.py ........\n", "../pymks-project/pymks/pymks/bases/fourier.py ..\n", "../pymks-project/pymks/pymks/bases/gsh.py ..\n", "../pymks-project/pymks/pymks/bases/legendre.py ..\n", "../pymks-project/pymks/pymks/bases/primitive.py .\n", "../pymks-project/pymks/pymks/datasets/__init__.py ....\n", "../pymks-project/pymks/pymks/datasets/cahn_hilliard_simulation.py .\n", "../pymks-project/pymks/pymks/datasets/elastic_FE_simulation.py ...\n", "../pymks-project/pymks/pymks/datasets/microstructure_generator.py .\n", "../pymks-project/pymks/pymks/datasets/spherical_microstructure_generator.py ...\n", "../pymks-project/pymks/pymks/tests/test_basis_functions.py ...\n", "../pymks-project/pymks/pymks/tests/test_datasets.py ...\n", "../pymks-project/pymks/pymks/tests/test_elastic_FE_simulation.py .\n", "../pymks-project/pymks/pymks/tests/test_filter.py .\n", "../pymks-project/pymks/pymks/tests/test_homogenization.py .............\n", "../pymks-project/pymks/pymks/tests/test_localization.py .......\n", "../pymks-project/pymks/pymks/tests/test_microstructure_generator.py ........\n", "../pymks-project/pymks/pymks/tests/test_stats.py ................\n", "../pymks-project/pymks/pymks/tests/test_structure_analysis.py .........." ] }, { "name": "stderr", "output_type": "stream", "text": [ "Coverage.py warning: Module pymks was previously imported, but not measured. (module-not-measured)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "----------- coverage: platform linux, python 3.6.1-final-0 -----------\n", "Name Stmts Miss Cover Missing\n", "-----------------------------------------------------------------------------------------------------------------------\n", "/home/wd15/git/pymks-project/pymks/pymks/__init__.py 24 24 0% 1-45\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/__init__.py 7 7 0% 1-9\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/abstract.py 31 16 48% 1-6, 25, 29-32, 34, 36-38, 45-53, 69-85\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/fftmodule.py 68 61 10% 33-133, 146-149, 153-167, 179-180, 193-194, 206-207\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/fourier.py 17 2 88% 48, 67\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/gsh.py 60 29 52% 8-56, 76, 83, 87, 89-127, 156-173, 182-185, 202-203\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/gsh_functions/__init__.py 7 7 0% 1-8\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/gsh_functions/gsh_cub_tri_L0_16.py 6492 6492 0% 1-7074\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/gsh_functions/gsh_hex_tri_L0_16.py 6282 5587 11% 1-4, 561, 587-588, 595-596, 603-605, 608-609, 612-614, 617-618, 626-627, 630-632, 635-636, 639-641, 650-657, 660-662, 665-671, 674-677, 690-691, 694-700, 703-706, 709-715, 723-727, 730-733, 736-739, 742-744, 754-757, 760-766, 769-770, 773-779, 788-795, 798-799, 802-808, 811-814, 827-835, 838-845, 848-856, 859-867, 880-887, 890-897, 900-903, 906-913, 926-933, 936-944, 947-955, 958-965, 979-982, 985-994, 997-1000, 1003-1013, 1023-1030, 1033-1034, 1037-1046, 1049-1053, 1067-1069, 1072-1080, 1083-1087, 1090-1097, 1107-1115, 1118-1121, 1124-1127, 1130-1133, 1147-1151, 1154-1161, 1164-1166, 1169-1177, 1187-1195, 1198-1199, 1202-1211, 1214-1218, 1231-1232, 1235-1245, 1248-1251, 1254-1263, 1278-1287, 1290-1300, 1303-1311, 1314-1324, 1339-1348, 1351-1359, 1362-1371, 1374-1378, 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"/home/wd15/git/pymks-project/pymks/pymks/bases/gsh_functions/gsh_tri_tri_L0_13.py 21321 17650 17% 1-4, 3664, 3683, 3689-3690, 3693, 3696-3697, 3700, 3703-3704, 3707, 3710-3711, 3714, 3717-3718, 3721-3722, 3725-3726, 3729-3730, 3733-3734, 3737-3738, 3741-3742, 3745-3746, 3749-3750, 3753-3754, 3757-3758, 3761-3762, 3765-3766, 3769-3770, 3773-3774, 3777-3778, 3781-3782, 3785-3786, 3789-3790, 3793-3794, 3797-3798, 3801-3802, 3805-3806, 3809-3810, 3813-3814, 3817-3819, 3822-3823, 3826-3827, 3830-3831, 3834-3835, 3838-3839, 3842-3844, 3847-3848, 3851-3852, 3855-3856, 3859-3860, 3863-3864, 3867-3868, 3871-3872, 3875-3876, 3879-3880, 3883-3885, 3888-3890, 3893-3895, 3898-3899, 3902-3903, 3906-3907, 3910-3911, 3914-3915, 3918-3919, 3922-3924, 3927-3928, 3931-3932, 3935-3936, 3939-3940, 3943-3945, 3948-3949, 3952-3954, 3957-3958, 3961-3962, 3965-3966, 3969-3970, 3973-3974, 3977-3978, 3981-3982, 3985-3986, 3989-3990, 3993-3995, 3998-3999, 4002-4003, 4006-4007, 4010-4011, 4014-4015, 4018-4020, 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28486-28487, 28490-28496, 28499-28500, 28503-28509, 28512-28513, 28516-28522, 28525-28526, 28529-28533, 28536-28537, 28540-28544, 28547-28548, 28551-28557, 28560-28561, 28564-28570, 28573-28574, 28577-28583, 28586-28587, 28590-28595, 28598-28599, 28602-28607, 28610-28611, 28614-28624, 28631-28646\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/imag_ffts.py 7 5 29% 1-11, 22\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/legendre.py 11 1 91% 51\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/primitive.py 14 2 86% 112, 124\n", "/home/wd15/git/pymks-project/pymks/pymks/bases/real_ffts.py 7 5 29% 1-11, 22\n", "/home/wd15/git/pymks-project/pymks/pymks/datasets/__init__.py 80 7 91% 287-288, 290, 294, 298, 357-367\n", "/home/wd15/git/pymks-project/pymks/pymks/datasets/base_microstructure_generator.py 30 1 97% 56\n", "/home/wd15/git/pymks-project/pymks/pymks/datasets/cahn_hilliard_simulation.py 33 1 97% 91\n", "/home/wd15/git/pymks-project/pymks/pymks/datasets/elastic_FE_simulation.py 228 7 97% 5-6, 13-14, 108, 177, 182\n", "/home/wd15/git/pymks-project/pymks/pymks/datasets/microstructure_generator.py 32 1 97% 36\n", "/home/wd15/git/pymks-project/pymks/pymks/datasets/spherical_microstructure_generator.py 31 0 100%\n", "/home/wd15/git/pymks-project/pymks/pymks/filter.py 55 13 76% 22, 40, 53, 65, 69, 72, 80, 82, 88, 112-147, 162, 179\n", "/home/wd15/git/pymks-project/pymks/pymks/mks_homogenization_model.py 82 31 62% 5-66, 114, 117, 124, 134, 138, 145, 149, 156, 160, 166, 170, 176, 180, 187, 261, 267, 315, 324-351, 359\n", "/home/wd15/git/pymks-project/pymks/pymks/mks_localization_model.py 46 10 78% 62, 86, 114-115, 128, 135, 142, 180, 223-258\n", "/home/wd15/git/pymks-project/pymks/pymks/mks_structure_analysis.py 74 19 74% 3-49, 101, 104-107, 111, 118, 123, 130, 163, 198, 232, 239, 249, 258, 277, 283\n", "/home/wd15/git/pymks-project/pymks/pymks/stats.py 81 18 78% 10, 51, 60, 134, 186, 203, 213, 254, 269, 289, 310, 319, 348, 393, 412, 417, 432-433\n", "/home/wd15/git/pymks-project/pymks/pymks/tools.py 434 385 11% 3-6, 27-32, 42-47, 58-62, 72-76, 86-90, 105-117, 126-130, 143-147, 159-164, 176-194, 208-211, 223-226, 237-240, 252-255, 267-268, 280-283, 297-334, 351-387, 402-439, 450-457, 479-497, 518-534, 546-570, 582-606, 618-647, 660-675, 686-689, 700-705, 716-719, 730-734, 745-785, 794-800, 811-812, 852-882\n", "-----------------------------------------------------------------------------------------------------------------------\n", "TOTAL 35554 30381 15%\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "================================================================================ 102 passed in 75.54 seconds =================================================================================\n" ] } ], "source": [ "# NBVAL_IGNORE_OUTPUT\n", "\n", "pymks.test()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
lnls-fac/scripts
experiments/sirius-va-errors.ipynb
1
8941
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sirius\n", "import numpy\n", "import pyaccel\n", "import matplotlib.pyplot as plt\n", "\n", "PREFIX = 'XVA-'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 151 }, { "cell_type": "code", "collapsed": false, "input": [ "si = sirius.si.create_accelerator()\n", "pyaccel.tracking.set6dtracking(si)\n", "print(si)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "energy : 3000000000.0 eV\n", "harmonic_number: 864\n", "cavity_on : True\n", "radiation_on : True\n", "vchamber_on : False\n", "lattice size : 3704\n", "lattice length : 518.3959999999935 m\n" ] } ], "prompt_number": 152 }, { "cell_type": "code", "collapsed": false, "input": [ "r, *_ = pyaccel.tracking.ringpass(si, nr_turns=1000, particles=[0.0,0,0,0,0,0], turn_by_turn=False)\n", "print(r)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[ -7.24139464e-08 4.22229662e-09 0.00000000e+00 0.00000000e+00\n", " -4.92032551e-04 -3.17445506e-02]\n" ] } ], "prompt_number": 156 }, { "cell_type": "code", "collapsed": false, "input": [ "help(pyaccel.tracking.ringpass)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Help on function ringpass in module pyaccel.tracking:\n", "\n", "ringpass(accelerator, particles, nr_turns=1, turn_by_turn=None, element_offset=0)\n", " Track particle(s) along a ring.\n", " \n", " Accepts one or multiple particles initial positions. In the latter case,\n", " a list of particles or a numpy 2D array (with particle as firts index)\n", " should be given as input; tracked particles positions at the end of\n", " the ring are output variables, as well as information on whether particles\n", " have been lost along the tracking and where they were lost.\n", " \n", " Keyword arguments: (accelerator, particles, nr_turns,\n", " turn_by_turn, elment_offset)\n", " \n", " accelerator -- Accelerator object\n", " particles -- initial 6D particle(s) position(s).\n", " Few examples\n", " ex.1: particles = [rx,px,ry,py,de,dl]\n", " ex.2: particles = [[0.001,0,0,0,0,0],[0.002,0,0,0,0,0]]\n", " ex.3: particles = numpy.zeros((Np,6))\n", " nr_turns -- number of turns around ring to track each particle.\n", " turn_by_turn -- parameter indicating what turn by turn positions are to\n", " be returned. If None ringpass returns particles\n", " positions only at the end of the ring, at the last turn.\n", " If turn_by_turn is 'closed' ringpass returns positions\n", " at the end of the ring for every turn. If it is 'open'\n", " than positions are returned at the beginning of every\n", " turn.\n", " \n", " element_offset -- element offset (default 0) for tracking. tracking will\n", " start at the element with index 'element_offset'\n", " \n", " Returns: (particles_out, lost_flag, lost_turn, lost_element, lost_plane)\n", " \n", " particles_out -- 6D position for each particle at end of ring. The structure\n", " of 'particles_out' depends on inputs 'particles' and\n", " 'turn_by_turn'. If 'turn_by_turn' is None then only\n", " tracked positions at the end 'nr_turns' are returned. There\n", " are still two possibilities for the structure of\n", " particles_out, depending on 'particles':\n", " \n", " (1) if 'particles' is a single particle defined as a python\n", " list of coordinates, 'particles_out' will also be a\n", " simple list:\n", " ex.:particles = [rx1,px1,ry1,py1,de1,dl1]\n", " turn_by_turn = False\n", " particles_out=numpy.array([rx2,px2,ry2,py2,de2,dl2])\n", " \n", " (2) if 'particles' is either a python list of particles or a\n", " numpy matrix then 'particles_out' will be a matrix\n", " (numpy array of arrays) whose first index selects the\n", " coordinate rx, px, ry, py, de or dl, in this order, and\n", " the second index selects a particular particle.\n", " ex.: particles = [[rx1,px1,ry1,py1,de1,dl1],\n", " [rx2,px2,ry2,py2,de2,dl2]]\n", " turn_by_turn = False\n", " particles_out = numpy.array(\n", " [ [rx3,px3,ry3,py3,de3,dl3],\n", " [rx4,px4,ry4,py4,de4,dl4]\n", " ])\n", " \n", " 'turn_by_turn' can also be either 'close' or 'open'. In\n", " either case 'particles_out' will have tracked positions at\n", " the entrances of the elements. The difference is that for\n", " 'closed' it will have an additional tracked position at the\n", " exit of the last element, thus closing the data, in case\n", " the line is a ring. The format of 'particles_out' is ...\n", " \n", " (3) a numpy matrix, when 'particles' is a single particle\n", " defined as a python list. The first index of\n", " 'particles_out' runs through coordinates rx, px, ry, py,\n", " de or dl and the second index runs through the turn\n", " number\n", " \n", " (4) a numpy rank-3 tensor, when 'particles' is the initial\n", " positions of many particles. The first index now runs\n", " through particles, the second through coordinates and\n", " the third through turn number.\n", " \n", " lost_flag -- a general flag indicating whether there has been particle\n", " loss.\n", " lost_turn -- list of turn index where each particle was lost.\n", " lost_element -- list of element index where each particle was lost\n", " If the particle survived the tracking through the ring its\n", " corresponding element in this list is set to None. When\n", " there is only one particle defined as a python list (not as\n", " a numpy matrix with one column) 'lost_element' returns a\n", " single number.\n", " lost_plane -- list of strings representing on what plane each particle\n", " was lost while being tracked. If the particle is not lost\n", " then its corresponding element in the list is set to None.\n", " If it is lost in the horizontal or vertical plane it is set\n", " to string 'x' or 'y', correspondingly. If tracking is\n", " performed with a single particle described as a python list\n", " then 'lost_plane' returns a single string\n", "\n" ] } ], "prompt_number": 149 } ], "metadata": {} } ] }
mit
YufeiZhang/Principles-of-Programming-Python-3
Lectures/Lecture_2/Jupyter_notebook_sheets/functions.ipynb
1
15779
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 align=\"center\">Functions</h1>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scope rules" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = 0\n", "m = 0\n", "n = 0\n", "x = 0\n", "\n", "print('PRINT 0: a =', a,\n", " ' m =', m, ' n =', n,\n", " ' x =', x)\n", "\n", "def f_1():\n", " m = 1\n", " global n\n", " n = 1\n", " x = 1\n", " y = 1\n", " z = 1\n", " print('PRINT 1: a =', a,\n", " ' m =', m, ' n =', n,\n", " ' x =', x, ' y =', y, ' z =', z)\n", " \n", " def f_2():\n", " global m\n", " m = 2\n", " # Cannot write:\n", " # nonlocal n\n", " global n\n", " n = 2\n", " global p\n", " p = 2\n", " x = 2\n", " nonlocal y\n", " y = 2\n", " # Cannot write:\n", " # nonlocal u\n", " print('PRINT 2: a =', a,\n", " ' m =', m, ' n =', n, ' p =', p,\n", " ' x =', x, ' y =', y, ' z =', z)\n", "\n", " def f_3():\n", " nonlocal x\n", " x = 3\n", " nonlocal y\n", " y = 3\n", " nonlocal z\n", " z = 3\n", " print('PRINT 3: a =', a,\n", " ' m =', m, ' n =', n, ' p =', p,\n", " ' x =', x, ' y =', y, ' z =', z)\n", "\n", " f_3()\n", " print('PRINT 4: a =', a,\n", " ' m =', m, ' n =', n, ' p =', p,\n", " ' x =', x, ' y =', y, ' z =', z)\n", "\n", " f_2()\n", " print('PRINT 5: a =', a,\n", " ' m =', m, ' n =', n, ' p =', p,\n", " ' x =', x, ' y =', y, ' z =', z)\n", "\n", "f_1()\n", "print('PRINT 6: a =', a,\n", " ' m =', m, ' n =', n, ' p =', p,\n", " ' x =', x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = 0\n", "\n", "def f():\n", " print(x)\n", " x = 1\n", "\n", "f()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f():\n", " m = 0\n", " class C:\n", " m = 1\n", " def g(self):\n", " print(m)\n", " C().g()\n", "f()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "i = 1\n", "bad_increment = lambda x: x + i\n", "i = 0\n", "print(bad_increment(2))\n", "\n", "i = 1\n", "good_increment = lambda x, i = i: x + i\n", "i = 0\n", "print(good_increment(2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Closures (factory functions)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def v1_multiply_by(m):\n", " def multiply(n):\n", " return n * m\n", " return multiply\n", "\n", "multiply_by_7 = v1_multiply_by(7)\n", "print(multiply_by_7(4))\n", "\n", "def v2_multiply_by(m):\n", " return lambda n: n * m\n", "\n", "multiply_by_7 = v2_multiply_by(7)\n", "print(multiply_by_7(4))\n", "\n", "def multiplications_between_0_and_9():\n", " multiply_by = []\n", " for m in range(10):\n", " # If \"lambda n, m = m: n * m\" is replaced by \"lambda n, m: n * m\"\n", " # then all mulplications are by 9\n", " multiply_by.append(lambda n, m = m: n * m)\n", " return multiply_by\n", "\n", "multiply_by = multiplications_between_0_and_9()\n", "multiply_by_7 = multiply_by[7]\n", "print(multiply_by_7(4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function states" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from random import randrange\n", "\n", "def randomly_odd_or_even_random_digit():\n", " odd = randrange(2)\n", " if odd:\n", " def random_odd_or_random_even_digit():\n", " return randrange(1, 10, 2)\n", " else:\n", " def random_odd_or_random_even_digit():\n", " return randrange(0, 10, 2)\n", " random_odd_or_random_even_digit.odd = odd\n", " return random_odd_or_random_even_digit\n", "\n", "for i in range(10):\n", " random_odd_or_random_even_digit = randomly_odd_or_even_random_digit()\n", " if random_odd_or_random_even_digit.odd:\n", " print('Will be a random odd digit.... ', random_odd_or_random_even_digit())\n", " else:\n", " print('Will be a random even digit... ', random_odd_or_random_even_digit())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function parameters:\n", "- first parameters without default values, if any,\n", "- then parameters with default values, if any,\n", "- then, possibly,\n", " - either a starred parameter to\n", " - gather values and assign them to parameters of the first and second type beyond the longest initial segment of those that are otherwise assigned an argument, if any, provided none of those parameters is assigned a keyword argument,\n", " - and to store an arbitray number of positional arguments beyond those that have been assigned to a parameter, if any,\n", " - or only a star,\n", "- if a starred parameter or only a star is present, then parameters for required keyword arguments (so called \"keyword-only arguments\"), if any, with or without defaults (actually the defaults make the associated keyword-only arguments not truly required and these parameters could be part of the second group),\n", "- then a double starred parameter to store an arbitray number of keyword arguments, if any." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function arguments:\n", "\n", "- positional arguments precede keyword arguments and double starred ones, and\n", "- starred arguments precede double starred ones." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f1(a, b, c = 3, d = 4, e = 5, f = 6):\n", " print(a, b, c, d, e, f)\n", "\n", "f1(11, 12, 13, 14, 15, 16)\n", "f1(11, 12, 13, *(14, 15, 16))\n", "f1(11, *(12, 13, 14), **{'f': 16, 'e': 15})\n", "f1(11, 12, 13, e = 15)\n", "f1(11, c = 13, b = 12, e = 15)\n", "f1(11, c = 13, *(12,), e = 15)\n", "f1(11, *(12, 13), e = 15)\n", "f1(11, e = 15, *(12, 13))\n", "f1(11, f = 16, e = 15, b = 12, c = 13)\n", "f1(11, f = 16, **{'e': 15, 'b': 12, 'c': 13})\n", "f1(11, *(12, 13), e = 15, **{'f': 16, 'd': 14})\n", "f1(11, e = 15, *(12,), **{'f': 16, 'd': 14})\n", "f1(11, f = 16, *(12, 13), e = 15, **{'d': 14})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f2(*x):\n", " print(x)\n", "\n", "f2()\n", "f2(11)\n", "f2(11, 12, *(13, 14, 15))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f3(*x, a, b = -2, c):\n", " print(x, a, b, c)\n", "\n", "f3(c = 23, a = 21)\n", "f3(11, 12, a = 21, **{'b': 22, 'c': 23})\n", "f3(11, *(12, 13), c = 23, a = 21)\n", "f3(11, 12, 13, c = 23, *(14, 15), **{'a': 21})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f4(*, a, b = -2, c):\n", " print(a, b, c)\n", "\n", "f4(c = 23, a = 21)\n", "f4(**{'a': 21, 'b': 22, 'c': 23})\n", "f4(c = 23, **{'a': 21})\n", "f4(a = 21, **{'c': 23, 'b': 22})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f5(**x):\n", " print(x)\n", "\n", "f5()\n", "f5(a = 11, b = 12)\n", "f5(**{'a': 11, 'b': 12, 'c': 13})\n", "f5(a = 11, c = 12, e = 15, **{'b': 13, 'd': 14})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f6(a, b, c, d = 4, e = 5, *x, m, n = -2, o, **z):\n", " print(a, b, c, d, e, x, m, n, o, z)\n", "\n", "# Cannot replace \"*(12,)\" by \"*(12, 21)\"\n", "f6(11, t = 40, e = 15, *(12,), o = 33, c = 13, m = 31, u = 41,\n", " **{'v': 42, 'w': 43}) \n", "# Cannot replace \"*(13, 14)\" by \"*(13, 14, 21)\"\n", "f6(11, 12, u = 41, m = 31, t = 40, e = 15, *(13, 14), o = 33,\n", " **{'v': 42, 'w': 43}) \n", "f6(11, u = 41, o = 33, *(12, 13, 14, 15, 21, 22), n = 32, t = 40, m = 31,\n", " **{'v': 42, 'w': 43}) \n", "f6(11, 12, 13, n = 32, t = 40, *(14, 15, 21, 22, 23), o = 33, u = 41, m = 31,\n", " **{'v': 42, 'w': 43})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function annotations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def f(w: str, a: int, b: int = -2, x: float = -3.) -> int:\n", " if w == 'incorrect_return_type':\n", " return '0'\n", " return 0\n", "\n", "from inspect import signature\n", "\n", "def type_check(function, *args, **kwargs):\n", " '''Assumes that \"function\" has nothing but variables possibly with defaults\n", " as arguments and has type annotations for all arguments and the returned value.\n", " Checks whether a combination of positional and default arguments is correct,\n", " and in case it is whether those arguments are of the appropriate types,\n", " and in case they are whether the returned value is of the appropriate type.\n", " '''\n", " good_arguments = True\n", " argument_type_errors = ''\n", " parameters = list(reversed(function.__code__.co_varnames))\n", " if len(args) > len(parameters):\n", " print('Incorrect sequence of arguments')\n", " return\n", " for argument in args:\n", " parameter = parameters.pop()\n", " if not isinstance(argument, function.__annotations__[parameter]):\n", " argument_type_errors += ('{} should be of type {}\\n'\n", " .format(parameter, function.__annotations__[parameter]))\n", " good_arguments = False\n", " for argument in kwargs:\n", " if not argument in parameters:\n", " print('Incorrect sequence of arguments')\n", " return\n", " if not isinstance(kwargs[argument], function.__annotations__[argument]):\n", " argument_type_errors += ('{} should be of type {}\\n'\n", " .format(argument, function.__annotations__[argument]))\n", " good_arguments = False\n", " parameters.remove(argument)\n", " # Make sure that all parameters left are given a default value.\n", " if any([parameter for parameter in parameters\n", " if signature(function).parameters[parameter].default is\n", " signature(function).parameters[parameter].empty]):\n", " print('Incorrect sequence of arguments')\n", " return\n", " if good_arguments:\n", " if isinstance(function(*args, **kwargs), function.__annotations__['return']):\n", " print('All good')\n", " else:\n", " (print('The returned value should be of type {}'\n", " .format(function.__annotations__['return'])))\n", " else:\n", " print(argument_type_errors, end = '')\n", "\n", "for args, kwargs in [(('0', 1, 2, 3.), {}),\n", " (('0', 1, 2), {'x': 3.}),\n", " (('0', 1), {'b': 2, 'x': 3.}),\n", " (('0',), {'x': 3., 'a': 1, 'b': 2}),\n", " ((), {'x': 3., 'w': '0', 'a': 1}),\n", " (('0', 1, 2), {}),\n", " (('0',), {}),\n", " (('0'), {'x': 3.}),\n", " (('0', 1, 2, 3., 4), {}),\n", " (('incorrect_return_type', 1, 2, 3.), {'x' : 3}),\n", " (('incorrect_return_type', 1, 2), {'y': 3}),\n", " (('0', 1), {'x': 3, 'c': 2}),\n", " ((), {'a': 1, 'b': 2,'x': 3}),\n", " ((0, 1, 2, 3.), {}),\n", " (('0', 1., 2, 3), {'w': 'incorrect_return_type'}),\n", " (('incorrect_return_type', 1, 2), {'x': 3}),\n", " ((0, 1), {'b': 2., 'x': 3.}),\n", " ((0,), {'x': 3, 'a': 1., 'b': 2.}),\n", " ((), {'x': 3, 'w': 0, 'a': 1.}),\n", " (('incorrect_return_type', 1, 2, 3.), {})]:\n", " print('Testing {}, {}:'.format(args, kwargs))\n", " type_check(f, *args, **kwargs)\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mutable versus immutable default values" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def append_one_v1(L = []):\n", " L.append(1)\n", " return L\n", "\n", "def append_one_v2(L = None):\n", " if L == None:\n", " L = []\n", " L.append(1)\n", " return L\n", "\n", "for i in range(5):\n", " print(append_one_v1([0]))\n", "print()\n", "for i in range(5):\n", " print(append_one_v1())\n", "print()\n", "for i in range(5):\n", " print(append_one_v2([0]))\n", "print()\n", "for i in range(5):\n", " print(append_one_v2())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "_nothing = object()\n", "\n", "def f(x = _nothing):\n", " if x is _nothing:\n", " print('Nothing')\n", " else:\n", " print('Something')\n", "\n", "f(0), f(1), f([]), f([1]), f(None)\n", "print()\n", "f()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
grfiv/predict-blood-donations
Prediction of leaderboard score (R).ipynb
1
261083
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prediction of leaderboard score (R)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>model</th><th scope=col>leaderboard_score</th><th scope=col>accuracy</th><th scope=col>logloss</th><th scope=col>AUC</th><th scope=col>f1</th><th scope=col>mu</th><th scope=col>std</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>19</th><td>bagged_nolearn </td><td>0.4313</td><td>0.7813</td><td>7.5554</td><td>0.5857</td><td>0.3152</td><td>NA</td><td>NA</td></tr>\n", "\t<tr><th scope=row>2</th><td>ensemble of averages </td><td>0.437</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n", "\t<tr><th scope=row>16</th><td>voting_ensemble_softWgtd </td><td>0.4396</td><td>0.7865</td><td>7.3755</td><td>0.6065</td><td>0.3692</td><td>0.76</td><td>0.0013</td></tr>\n", "\t<tr><th scope=row>20</th><td>LogisticRegression </td><td>0.4411</td><td>0.7708</td><td>7.9152</td><td>0.554</td><td>0.2235</td><td>0.7601</td><td>0.0013</td></tr>\n", "\t<tr><th scope=row>22</th><td>bagged_logit </td><td>0.4442</td><td>0.783</td><td>7.4954</td><td>0.5794</td><td>0.2938</td><td>0.76</td><td>0.0015</td></tr>\n", "\t<tr><th scope=row>3</th><td>GradientBoostingClassifier </td><td>0.4452</td><td>0.7934</td><td>7.1356</td><td>0.6309</td><td>0.4251</td><td>0.7538</td><td>0.0047</td></tr>\n", "\t<tr><th scope=row>21</th><td>LogisticRegressionCV </td><td>0.4457</td><td>0.783</td><td>7.4954</td><td>0.5794</td><td>0.2938</td><td>0.7602</td><td>0.0012</td></tr>\n", "\t<tr><th scope=row>25</th><td>bagged_scikit_nn </td><td>0.4465</td><td>0.7986</td><td>6.9558</td><td>0.674</td><td>0.5085</td><td>0.7463</td><td>0.0065</td></tr>\n", "\t<tr><th scope=row>17</th><td>bagged_gbc </td><td>0.4527</td><td>0.7899</td><td>7.2556</td><td>0.6137</td><td>0.3858</td><td>0.7573</td><td>0.0037</td></tr>\n", "\t<tr><th scope=row>1</th><td>nolearn </td><td>0.4566</td><td>0.8056</td><td>6.7159</td><td>0.6711</td><td>0.5044</td><td>NA</td><td>NA</td></tr>\n", "\t<tr><th scope=row>4</th><td>ExtraTreesClassifier </td><td>0.4729</td><td>0.776</td><td>7.7353</td><td>0.5996</td><td>0.3582</td><td>0.7526</td><td>0.0061</td></tr>\n", "\t<tr><th scope=row>26</th><td>blending_ensemble </td><td>0.4834</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr>\n", "\t<tr><th scope=row>5</th><td>XGBClassifier </td><td>0.4851</td><td>0.7743</td><td>7.7953</td><td>0.6034</td><td>0.3689</td><td>0.7474</td><td>0.0065</td></tr>\n", "\t<tr><th scope=row>13</th><td>BaggingClassifier </td><td>0.4885</td><td>0.7743</td><td>7.7952</td><td>0.5985</td><td>0.3564</td><td>0.7523</td><td>0.0051</td></tr>\n", "\t<tr><th scope=row>24</th><td>scikit_nn </td><td>0.502</td><td>0.7969</td><td>7.0157</td><td>0.6803</td><td>0.5185</td><td>0.7387</td><td>0.0116</td></tr>\n", "\t<tr><th scope=row>18</th><td>boosted_svc </td><td>0.5334</td><td>0.7691</td><td>7.9751</td><td>0.5206</td><td>0.0828</td><td>0.7602</td><td>0.0012</td></tr>\n", "\t<tr><th scope=row>11</th><td>SVC </td><td>0.5336</td><td>0.7552</td><td>8.4548</td><td>0.5264</td><td>0.1455</td><td>0.742</td><td>0.0079</td></tr>\n", "\t<tr><th scope=row>6</th><td>SGDClassifier </td><td>0.567</td><td>0.7274</td><td>9.4143</td><td>0.5528</td><td>0.2765</td><td>0.6339</td><td>0.069</td></tr>\n", "\t<tr><th scope=row>7</th><td>cosine_similarity </td><td>0.5732</td><td>0.7906</td><td>7.2333</td><td>0.6452</td><td>0.4595</td><td>NA</td><td>NA</td></tr>\n", "\t<tr><th scope=row>23</th><td>boosted_logit </td><td>0.5891</td><td>0.7708</td><td>7.9152</td><td>0.5788</td><td>0.3053</td><td>0.7589</td><td>0.002</td></tr>\n", "\t<tr><th scope=row>12</th><td>KMeans </td><td>0.6289</td><td>0.6892</td><td>10.7335</td><td>0.5425</td><td>0.2869</td><td>NA</td><td>NA</td></tr>\n", "\t<tr><th scope=row>8</th><td>AdaBoostClassifier </td><td>0.6642</td><td>0.783</td><td>7.4954</td><td>0.6191</td><td>0.4019</td><td>0.7574</td><td>0.0036</td></tr>\n", "\t<tr><th scope=row>9</th><td>KNeighborsClassifier </td><td>1.187</td><td>0.7778</td><td>7.6753</td><td>0.6206</td><td>0.4074</td><td>0.7387</td><td>0.0086</td></tr>\n", "\t<tr><th scope=row>10</th><td>RandomForestClassifier </td><td>1.7907</td><td>0.7344</td><td>9.1744</td><td>0.5722</td><td>0.32</td><td>0.6954</td><td>0.0149</td></tr>\n", "\t<tr><th scope=row>14</th><td>voting_ensemble_hard </td><td>NA</td><td>0.7882</td><td>7.3155</td><td>0.6076</td><td>0.3711</td><td>0.7594</td><td>0.0017</td></tr>\n", "\t<tr><th scope=row>15</th><td>voting_ensemble_hardWgtd </td><td>NA</td><td>0.7813</td><td>7.5554</td><td>0.5733</td><td>0.2759</td><td>0.76</td><td>0.0012</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllllll}\n", " & model & leaderboard_score & accuracy & logloss & AUC & f1 & mu & std\\\\\n", "\\hline\n", "\t19 & bagged_nolearn & 0.4313 & 0.7813 & 7.5554 & 0.5857 & 0.3152 & NA & NA\\\\\n", "\t2 & ensemble of averages & 0.437 & NA & NA & NA & NA & NA & NA\\\\\n", "\t16 & voting_ensemble_softWgtd & 0.4396 & 0.7865 & 7.3755 & 0.6065 & 0.3692 & 0.76 & 0.0013\\\\\n", "\t20 & LogisticRegression & 0.4411 & 0.7708 & 7.9152 & 0.554 & 0.2235 & 0.7601 & 0.0013\\\\\n", "\t22 & bagged_logit & 0.4442 & 0.783 & 7.4954 & 0.5794 & 0.2938 & 0.76 & 0.0015\\\\\n", "\t3 & GradientBoostingClassifier & 0.4452 & 0.7934 & 7.1356 & 0.6309 & 0.4251 & 0.7538 & 0.0047\\\\\n", "\t21 & LogisticRegressionCV & 0.4457 & 0.783 & 7.4954 & 0.5794 & 0.2938 & 0.7602 & 0.0012\\\\\n", "\t25 & bagged_scikit_nn & 0.4465 & 0.7986 & 6.9558 & 0.674 & 0.5085 & 0.7463 & 0.0065\\\\\n", "\t17 & bagged_gbc & 0.4527 & 0.7899 & 7.2556 & 0.6137 & 0.3858 & 0.7573 & 0.0037\\\\\n", "\t1 & nolearn & 0.4566 & 0.8056 & 6.7159 & 0.6711 & 0.5044 & NA & NA\\\\\n", "\t4 & ExtraTreesClassifier & 0.4729 & 0.776 & 7.7353 & 0.5996 & 0.3582 & 0.7526 & 0.0061\\\\\n", "\t26 & blending_ensemble & 0.4834 & NA & NA & NA & NA & NA & NA\\\\\n", "\t5 & XGBClassifier & 0.4851 & 0.7743 & 7.7953 & 0.6034 & 0.3689 & 0.7474 & 0.0065\\\\\n", "\t13 & BaggingClassifier & 0.4885 & 0.7743 & 7.7952 & 0.5985 & 0.3564 & 0.7523 & 0.0051\\\\\n", "\t24 & scikit_nn & 0.502 & 0.7969 & 7.0157 & 0.6803 & 0.5185 & 0.7387 & 0.0116\\\\\n", "\t18 & boosted_svc & 0.5334 & 0.7691 & 7.9751 & 0.5206 & 0.0828 & 0.7602 & 0.0012\\\\\n", "\t11 & SVC & 0.5336 & 0.7552 & 8.4548 & 0.5264 & 0.1455 & 0.742 & 0.0079\\\\\n", "\t6 & SGDClassifier & 0.567 & 0.7274 & 9.4143 & 0.5528 & 0.2765 & 0.6339 & 0.069\\\\\n", "\t7 & cosine_similarity & 0.5732 & 0.7906 & 7.2333 & 0.6452 & 0.4595 & NA & NA\\\\\n", "\t23 & boosted_logit & 0.5891 & 0.7708 & 7.9152 & 0.5788 & 0.3053 & 0.7589 & 0.002\\\\\n", "\t12 & KMeans & 0.6289 & 0.6892 & 10.7335 & 0.5425 & 0.2869 & NA & NA\\\\\n", "\t8 & AdaBoostClassifier & 0.6642 & 0.783 & 7.4954 & 0.6191 & 0.4019 & 0.7574 & 0.0036\\\\\n", "\t9 & KNeighborsClassifier & 1.187 & 0.7778 & 7.6753 & 0.6206 & 0.4074 & 0.7387 & 0.0086\\\\\n", "\t10 & RandomForestClassifier & 1.7907 & 0.7344 & 9.1744 & 0.5722 & 0.32 & 0.6954 & 0.0149\\\\\n", "\t14 & voting_ensemble_hard & NA & 0.7882 & 7.3155 & 0.6076 & 0.3711 & 0.7594 & 0.0017\\\\\n", "\t15 & voting_ensemble_hardWgtd & NA & 0.7813 & 7.5554 & 0.5733 & 0.2759 & 0.76 & 0.0012\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " model leaderboard_score accuracy logloss AUC f1\n", "19 bagged_nolearn 0.4313 0.7813 7.5554 0.5857 0.3152\n", "2 ensemble of averages 0.4370 NA NA NA NA\n", "16 voting_ensemble_softWgtd 0.4396 0.7865 7.3755 0.6065 0.3692\n", "20 LogisticRegression 0.4411 0.7708 7.9152 0.5540 0.2235\n", "22 bagged_logit 0.4442 0.7830 7.4954 0.5794 0.2938\n", "3 GradientBoostingClassifier 0.4452 0.7934 7.1356 0.6309 0.4251\n", "21 LogisticRegressionCV 0.4457 0.7830 7.4954 0.5794 0.2938\n", "25 bagged_scikit_nn 0.4465 0.7986 6.9558 0.6740 0.5085\n", "17 bagged_gbc 0.4527 0.7899 7.2556 0.6137 0.3858\n", "1 nolearn 0.4566 0.8056 6.7159 0.6711 0.5044\n", "4 ExtraTreesClassifier 0.4729 0.7760 7.7353 0.5996 0.3582\n", "26 blending_ensemble 0.4834 NA NA NA NA\n", "5 XGBClassifier 0.4851 0.7743 7.7953 0.6034 0.3689\n", "13 BaggingClassifier 0.4885 0.7743 7.7952 0.5985 0.3564\n", "24 scikit_nn 0.5020 0.7969 7.0157 0.6803 0.5185\n", "18 boosted_svc 0.5334 0.7691 7.9751 0.5206 0.0828\n", "11 SVC 0.5336 0.7552 8.4548 0.5264 0.1455\n", "6 SGDClassifier 0.5670 0.7274 9.4143 0.5528 0.2765\n", "7 cosine_similarity 0.5732 0.7906 7.2333 0.6452 0.4595\n", "23 boosted_logit 0.5891 0.7708 7.9152 0.5788 0.3053\n", "12 KMeans 0.6289 0.6892 10.7335 0.5425 0.2869\n", "8 AdaBoostClassifier 0.6642 0.7830 7.4954 0.6191 0.4019\n", "9 KNeighborsClassifier 1.1870 0.7778 7.6753 0.6206 0.4074\n", "10 RandomForestClassifier 1.7907 0.7344 9.1744 0.5722 0.3200\n", "14 voting_ensemble_hard NA 0.7882 7.3155 0.6076 0.3711\n", "15 voting_ensemble_hardWgtd NA 0.7813 7.5554 0.5733 0.2759\n", " mu std\n", "19 NA NA\n", "2 NA NA\n", "16 0.7600 0.0013\n", "20 0.7601 0.0013\n", "22 0.7600 0.0015\n", "3 0.7538 0.0047\n", "21 0.7602 0.0012\n", "25 0.7463 0.0065\n", "17 0.7573 0.0037\n", "1 NA NA\n", "4 0.7526 0.0061\n", "26 NA NA\n", "5 0.7474 0.0065\n", "13 0.7523 0.0051\n", "24 0.7387 0.0116\n", "18 0.7602 0.0012\n", "11 0.7420 0.0079\n", "6 0.6339 0.0690\n", "7 NA NA\n", "23 0.7589 0.0020\n", "12 NA NA\n", "8 0.7574 0.0036\n", "9 0.7387 0.0086\n", "10 0.6954 0.0149\n", "14 0.7594 0.0017\n", "15 0.7600 0.0012" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "score_data = read.csv('../input/scores.csv',stringsAsFactors=FALSE)\n", "score_data[with(score_data, order(leaderboard_score)), ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model using all variables" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Start: AIC=-60.18\n", "leaderboard_score ~ accuracy + logloss + AUC + f1 + mu + std\n", "\n", " Df Sum of Sq RSS AIC\n", "- AUC 1 0.000051 0.29209 -62.179\n", "- f1 1 0.004574 0.29662 -61.902\n", "- accuracy 1 0.022137 0.31418 -60.867\n", "- logloss 1 0.022258 0.31430 -60.860\n", "<none> 0.29204 -60.182\n", "- mu 1 0.107786 0.39983 -56.528\n", "- std 1 0.254788 0.54683 -50.892\n", "\n", "Step: AIC=-62.18\n", "leaderboard_score ~ accuracy + logloss + f1 + mu + std\n", "\n", " Df Sum of Sq RSS AIC\n", "- accuracy 1 0.02267 0.31476 -62.834\n", "- logloss 1 0.02282 0.31492 -62.825\n", "- f1 1 0.03171 0.32380 -62.324\n", "<none> 0.29209 -62.179\n", "+ AUC 1 0.00005 0.29204 -60.182\n", "- mu 1 0.15368 0.44577 -56.570\n", "- std 1 0.32770 0.61980 -50.637\n", "\n", "Step: AIC=-62.83\n", "leaderboard_score ~ logloss + f1 + mu + std\n", "\n", " Df Sum of Sq RSS AIC\n", "- logloss 1 0.02081 0.33557 -63.681\n", "- f1 1 0.02103 0.33579 -63.669\n", "<none> 0.31476 -62.834\n", "+ accuracy 1 0.02267 0.29209 -62.179\n", "+ AUC 1 0.00058 0.31418 -60.867\n", "- mu 1 0.30384 0.61860 -52.672\n", "- std 1 0.62665 0.94141 -45.114\n", "\n", "Step: AIC=-63.68\n", "leaderboard_score ~ f1 + mu + std\n", "\n", " Df Sum of Sq RSS AIC\n", "- f1 1 0.00139 0.33695 -65.607\n", "<none> 0.33557 -63.681\n", "+ logloss 1 0.02081 0.31476 -62.834\n", "+ accuracy 1 0.02065 0.31492 -62.825\n", "+ AUC 1 0.01626 0.31931 -62.575\n", "- std 1 1.33496 1.67052 -36.790\n", "- mu 1 1.61961 1.95517 -33.958\n", "\n", "Step: AIC=-65.61\n", "leaderboard_score ~ mu + std\n", "\n", " Df Sum of Sq RSS AIC\n", "<none> 0.33695 -65.607\n", "+ f1 1 0.00139 0.33557 -63.681\n", "+ logloss 1 0.00116 0.33579 -63.669\n", "+ accuracy 1 0.00115 0.33581 -63.669\n", "+ AUC 1 0.00017 0.33678 -63.616\n", "- std 1 1.33570 1.67265 -38.767\n", "- mu 1 1.61917 1.95612 -35.949\n" ] }, { "data": { "text/plain": [ "\n", "Call:\n", "lm(formula = leaderboard_score ~ mu + std, data = score_data, \n", " na.action = na.omit)\n", "\n", "Residuals:\n", " Min 1Q Median 3Q Max \n", "-0.18728 -0.05472 -0.03539 0.02082 0.42898 \n", "\n", "Coefficients:\n", " Estimate Std. Error t value Pr(>|t|) \n", "(Intercept) 25.722 2.962 8.685 3.09e-07 ***\n", "mu -33.089 3.897 -8.490 4.11e-07 ***\n", "std -60.589 7.857 -7.711 1.35e-06 ***\n", "---\n", "Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n", "\n", "Residual standard error: 0.1499 on 15 degrees of freedom\n", " (8 observations deleted due to missingness)\n", "Multiple R-squared: 0.8311,\tAdjusted R-squared: 0.8086 \n", "F-statistic: 36.91 on 2 and 15 DF, p-value: 1.61e-06\n" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm.fit = lm(leaderboard_score ~ accuracy + logloss + AUC + f1 + mu + std, \n", " data = score_data, \n", " na.action = na.omit)\n", "\n", "slm.fit = step(lm.fit, direction = \"both\")\n", "summary(slm.fit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot Predicted Scores vs Actual Leaderboard Scores" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>models</th><th scope=col>scores</th><th scope=col>predictions</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>GradientBoostingClassifier </td><td>0.4452</td><td>0.4947</td></tr>\n", "\t<tr><th scope=row>2</th><td>ExtraTreesClassifier </td><td>0.4729</td><td>0.4496</td></tr>\n", "\t<tr><th scope=row>3</th><td>XGBClassifier </td><td>0.4851</td><td>0.5974</td></tr>\n", "\t<tr><th scope=row>4</th><td>SGDClassifier </td><td>0.567</td><td>0.5662</td></tr>\n", "\t<tr><th scope=row>5</th><td>AdaBoostClassifier </td><td>0.6642</td><td>0.4422</td></tr>\n", "\t<tr><th scope=row>6</th><td>SVC </td><td>0.5336</td><td>0.6912</td></tr>\n", "\t<tr><th scope=row>7</th><td>BaggingClassifier </td><td>0.4885</td><td>0.5201</td></tr>\n", "\t<tr><th scope=row>8</th><td>voting_ensemble_hard </td><td>NA</td><td>0.4911</td></tr>\n", "\t<tr><th scope=row>9</th><td>voting_ensemble_hardWgtd </td><td>NA</td><td>0.5016</td></tr>\n", "\t<tr><th scope=row>10</th><td>voting_ensemble_softWgtd </td><td>0.4396</td><td>0.4955</td></tr>\n", "\t<tr><th scope=row>11</th><td>bagged_gbc </td><td>0.4527</td><td>0.4394</td></tr>\n", "\t<tr><th scope=row>12</th><td>boosted_svc </td><td>0.5334</td><td>0.495</td></tr>\n", "\t<tr><th scope=row>13</th><td>LogisticRegression </td><td>0.4411</td><td>0.4922</td></tr>\n", "\t<tr><th scope=row>14</th><td>LogisticRegressionCV </td><td>0.4457</td><td>0.495</td></tr>\n", "\t<tr><th scope=row>15</th><td>bagged_logit </td><td>0.4442</td><td>0.4834</td></tr>\n", "\t<tr><th scope=row>16</th><td>boosted_logit </td><td>0.5891</td><td>0.4895</td></tr>\n", "\t<tr><th scope=row>17</th><td>scikit_nn </td><td>0.502</td><td>0.5763</td></tr>\n", "\t<tr><th scope=row>18</th><td>bagged_scikit_nn </td><td>0.4465</td><td>0.6338</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|lll}\n", " & models & scores & predictions\\\\\n", "\\hline\n", "\t1 & GradientBoostingClassifier & 0.4452 & 0.4947\\\\\n", "\t2 & ExtraTreesClassifier & 0.4729 & 0.4496\\\\\n", "\t3 & XGBClassifier & 0.4851 & 0.5974\\\\\n", "\t4 & SGDClassifier & 0.567 & 0.5662\\\\\n", "\t5 & AdaBoostClassifier & 0.6642 & 0.4422\\\\\n", "\t6 & SVC & 0.5336 & 0.6912\\\\\n", "\t7 & BaggingClassifier & 0.4885 & 0.5201\\\\\n", "\t8 & voting_ensemble_hard & NA & 0.4911\\\\\n", "\t9 & voting_ensemble_hardWgtd & NA & 0.5016\\\\\n", "\t10 & voting_ensemble_softWgtd & 0.4396 & 0.4955\\\\\n", "\t11 & bagged_gbc & 0.4527 & 0.4394\\\\\n", "\t12 & boosted_svc & 0.5334 & 0.495\\\\\n", "\t13 & LogisticRegression & 0.4411 & 0.4922\\\\\n", "\t14 & LogisticRegressionCV & 0.4457 & 0.495\\\\\n", "\t15 & bagged_logit & 0.4442 & 0.4834\\\\\n", "\t16 & boosted_logit & 0.5891 & 0.4895\\\\\n", "\t17 & scikit_nn & 0.502 & 0.5763\\\\\n", "\t18 & bagged_scikit_nn & 0.4465 & 0.6338\\\\\n", "\\end{tabular}\n" ], "text/plain": [ " models scores predictions\n", "1 GradientBoostingClassifier 0.4452 0.4947\n", "2 ExtraTreesClassifier 0.4729 0.4496\n", "3 XGBClassifier 0.4851 0.5974\n", "4 SGDClassifier 0.5670 0.5662\n", "5 AdaBoostClassifier 0.6642 0.4422\n", "6 SVC 0.5336 0.6912\n", "7 BaggingClassifier 0.4885 0.5201\n", "8 voting_ensemble_hard NA 0.4911\n", "9 voting_ensemble_hardWgtd NA 0.5016\n", "10 voting_ensemble_softWgtd 0.4396 0.4955\n", "11 bagged_gbc 0.4527 0.4394\n", "12 boosted_svc 0.5334 0.4950\n", "13 LogisticRegression 0.4411 0.4922\n", "14 LogisticRegressionCV 0.4457 0.4950\n", "15 bagged_logit 0.4442 0.4834\n", "16 boosted_logit 0.5891 0.4895\n", "17 scikit_nn 0.5020 0.5763\n", "18 bagged_scikit_nn 0.4465 0.6338" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictions = c()\n", "models = c()\n", "scores = c()\n", "for (i in 1:nrow(score_data)) {\n", " if (is.na(score_data[i,'std'])) {next}\n", " if (score_data[i,'model']=='RandomForestClassifier ') {next} # a far outlier\n", " if (score_data[i,'model']=='KNeighborsClassifier ') {next} # a far outlier\n", " \n", "# print(paste0(\"|\",score_data[i,'model'],\"|\"))\n", " \n", " \n", " models = c(models, score_data[i,'model'])\n", " scores = c(scores, score_data[i,'leaderboard_score'])\n", " \n", " accuracy = score_data[i,'accuracy']\n", " logloss = score_data[i,'logloss']\n", " AUC = score_data[i,'AUC']\n", " f1 = score_data[i,'f1']\n", " mu = score_data[i,'mu']\n", " std = score_data[i,'std']\n", " predictions = c(predictions, round(predict(object=slm.fit,\n", " newdata = data.frame(accuracy,logloss,AUC,f1,mu,std)),4))\n", "}\n", "pred_v_act = data.frame(models,scores,predictions)\n", "pred_v_act" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/pdf": 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", 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clip-rule=\"nonzero\">\n", "<path style=\"fill:none;stroke-width:0.75;stroke-linecap:round;stroke-linejoin:round;stroke:rgb(0%,0%,0%);stroke-opacity:1;stroke-miterlimit:10;\" d=\"M 59.039062 432.53125 L 473.761719 16.019531 \"/>\n", "</g>\n", "</g>\n", "</svg>\n" ], "text/plain": [ "Plot with title “Predicted Score v Leaderboard”" ] }, "metadata": { "image/svg+xml": { "isolated": true } }, "output_type": "display_data" } ], "source": [ "#par(pin=c(6,6))\n", "library(car)\n", "plot(pred_v_act[,'predictions'], pred_v_act[,'scores'], main=\"Predicted Score v Leaderboard\", \n", " ylab=\"Leaderboard (worse ->)\", xlab=\"Predicted Score\", pch=19)#, xlim=c(0.25,1.8),ylim=c(0.25,1.8))\n", "text(pred_v_act[,'predictions'], pred_v_act[,'scores'], labels=models, cex= 0.6)\n", "abline(coef=c(0,1))\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "|model | leaderboard_score| \n", "|:--------------------------|-----------------:| \n", "|bagged_nolearn | 0.4313| \n", "|ensemble of averages | 0.4370| \n", "|voting_ensemble_softWgtd | 0.4396| \n", "|LogisticRegression | 0.4411| \n", "|bagged_logit | 0.4442| \n", "|GradientBoostingClassifier | 0.4452| \n", "|LogisticRegressionCV | 0.4457| \n", "|bagged_scikit_nn | 0.4465| \n", "|bagged_gbc | 0.4527| \n", "|nolearn | 0.4566| \n", "|ExtraTreesClassifier | 0.4729| \n", "|blending_ensemble | 0.4834| \n", "|XGBClassifier | 0.4851| \n", "|BaggingClassifier | 0.4885| \n", "|scikit_nn | 0.5020| \n", "|boosted_svc | 0.5334| \n", "|SVC | 0.5336| \n", "|SGDClassifier | 0.5670| \n", "|cosine_similarity | 0.5732| \n", "|boosted_logit | 0.5891| \n", "|KMeans | 0.6289| \n", "|AdaBoostClassifier | 0.6642| \n", "|KNeighborsClassifier | 1.1870| \n", "|RandomForestClassifier | 1.7907| \n", "|voting_ensemble_hard | NA| \n", "|voting_ensemble_hardWgtd | NA| \n" ] }, { "data": { "text/plain": [ "NULL" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "score_data = score_data[with(score_data, order(leaderboard_score)), ]\n", "\n", "library(knitr)\n", "foo = kable(score_data, format = \"markdown\", digits = 4)\n", "foof = ''\n", "for (i in 1:length(foo)) {\n", " subs = substr(foo[i],5,52)\n", " foof = cat(foof,cat(subs,'\\n'))\n", "}\n", "foof" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "R", "language": "R", "name": "ir" }, "language_info": { "codemirror_mode": "r", "file_extension": ".r", "mimetype": "text/x-r-source", "name": "R", "pygments_lexer": "r", "version": "3.2.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
DigitalSlideArchive/HistomicsTK
docs/examples/segmentation_masks_to_annotations.ipynb
1
58643
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Converting masks back to annotations\n", "\n", "**Overview:**\n", "\n", "![masks_to_annotations](https://user-images.githubusercontent.com/22067552/80078415-e2de0100-851c-11ea-81ce-3b2d74ee6246.png)\n", "\n", "Most segmentation algorithms produce outputs in an image format. Visualizing these outputs in HistomicsUI requires conversion from mask images to an annotation document containing (x,y) coordinates in the whole-slide image coordinate frame. This notebook demonstrates this conversion process in two steps:\n", "\n", "- Converting a mask image into contours (coordinates in the mask frame)\n", "\n", "- Placing contours data into a format following the annotation document schema that can be pushed to DSA for visualization in HistomicsUI.\n", "\n", "This notebook is based on work described in Amgad et al, 2019:\n", "\n", "_Mohamed Amgad, Habiba Elfandy, Hagar Hussein, ..., Jonathan Beezley, Deepak R Chittajallu, David Manthey, David A Gutman, Lee A D Cooper, Structured crowdsourcing enables convolutional segmentation of histology images, Bioinformatics, 2019, btz083_\n", "\n", "**Where to look?**\n", "\n", "```\n", "|_ histomicstk/\n", " |_annotations_and_masks/\n", " | |_masks_to_annotations_handler.py\n", " |_tests/\n", " |_test_masks_to_annotations_handler.py\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "CWD = os.getcwd()\n", "import girder_client\n", "from pandas import read_csv\n", "from imageio import imread\n", "from histomicstk.annotations_and_masks.masks_to_annotations_handler import (\n", " get_contours_from_mask,\n", " get_single_annotation_document_from_contours,\n", " get_annotation_documents_from_contours)\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = 7, 7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Connect girder client and set parameters\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": "{'_accessLevel': 2,\n '_id': '59bc677892ca9a0017c2e855',\n '_modelType': 'user',\n 'admin': True,\n 'created': '2017-09-15T23:51:20.203000+00:00',\n 'email': '[email protected]',\n 'emailVerified': False,\n 'firstName': 'Mohamed',\n 'groupInvites': [],\n 'groups': ['59f7713a92ca9a0017a29765',\n '5c607488e62914004d0ff4a6',\n '5e44a2e0ddda5f8398785304',\n '5e76b3f3ddda5f83982beb9a'],\n 'lastName': 'Tageldin',\n 'login': 'kheffah',\n 'otp': False,\n 'public': True,\n 'size': 0,\n 'status': 'enabled'}" }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# APIURL = 'http://demo.kitware.com/histomicstk/api/v1/'\n", "# SAMPLE_SLIDE_ID = '5bbdee92e629140048d01b5d'\n", "APIURL = 'http://candygram.neurology.emory.edu:8080/api/v1/'\n", "SAMPLE_SLIDE_ID = '5d586d76bd4404c6b1f286ae'\n", "\n", "# Connect to girder client\n", "gc = girder_client.GirderClient(apiUrl=APIURL)\n", "gc.authenticate(interactive=True)\n", "# gc.authenticate(apiKey='kri19nTIGOkWH01TbzRqfohaaDWb6kPecRqGmemb')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's inspect the ground truth codes file\n", "\n", "This contains the ground truth codes and information dataframe. This is a dataframe that is indexed by the annotation group name and has the following columns:\n", "\n", "- ``group``: group name of annotation (string), eg. \"mostly_tumor\"\n", "- ``GT_code``: int, desired ground truth code (in the mask) Pixels of this value belong to corresponding group (class)\n", "- ``color``: str, rgb format. eg. rgb(255,0,0).\n", "\n", "**NOTE:**\n", "\n", "Zero pixels have special meaning and do not encode specific ground truth class. Instead, they simply mean 'Outside ROI' and should be ignored during model training or evaluation." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# read GTCodes dataframe\n", "GTCODE_PATH = os.path.join(\n", " CWD, '..', '..', 'tests', 'test_files', 'sample_GTcodes.csv')\n", "GTCodes_df = read_csv(GTCODE_PATH)\n", "GTCodes_df.index = GTCodes_df.loc[:, 'group']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": " group overlay_order \\\ngroup \nroi roi 0 \nevaluation_roi evaluation_roi 0 \nmostly_tumor mostly_tumor 1 \nmostly_stroma mostly_stroma 2 \nmostly_lymphocytic_infiltrate mostly_lymphocytic_infiltrate 1 \n\n GT_code is_roi is_background_class \\\ngroup \nroi 254 1 0 \nevaluation_roi 253 1 0 \nmostly_tumor 1 0 0 \nmostly_stroma 2 0 1 \nmostly_lymphocytic_infiltrate 3 0 0 \n\n color comments \ngroup \nroi rgb(200,0,150) NaN \nevaluation_roi rgb(255,0,0) NaN \nmostly_tumor rgb(255,0,0) core class \nmostly_stroma rgb(255,125,0) core class \nmostly_lymphocytic_infiltrate rgb(0,0,255) core class ", "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>group</th>\n <th>overlay_order</th>\n <th>GT_code</th>\n <th>is_roi</th>\n <th>is_background_class</th>\n <th>color</th>\n <th>comments</th>\n </tr>\n <tr>\n <th>group</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>roi</th>\n <td>roi</td>\n <td>0</td>\n <td>254</td>\n <td>1</td>\n <td>0</td>\n <td>rgb(200,0,150)</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>evaluation_roi</th>\n <td>evaluation_roi</td>\n <td>0</td>\n <td>253</td>\n <td>1</td>\n <td>0</td>\n <td>rgb(255,0,0)</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>mostly_tumor</th>\n <td>mostly_tumor</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>rgb(255,0,0)</td>\n <td>core class</td>\n </tr>\n <tr>\n <th>mostly_stroma</th>\n <td>mostly_stroma</td>\n <td>2</td>\n <td>2</td>\n <td>0</td>\n <td>1</td>\n <td>rgb(255,125,0)</td>\n <td>core class</td>\n </tr>\n <tr>\n <th>mostly_lymphocytic_infiltrate</th>\n <td>mostly_lymphocytic_infiltrate</td>\n <td>1</td>\n <td>3</td>\n <td>0</td>\n <td>0</td>\n <td>rgb(0,0,255)</td>\n <td>core class</td>\n </tr>\n </tbody>\n</table>\n</div>" }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "GTCodes_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read and visualize mask" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# read mask\n", "X_OFFSET = 59206\n", "Y_OFFSET = 33505\n", "MASKNAME = \"TCGA-A2-A0YE-01Z-00-DX1.8A2E3094-5755-42BC-969D-7F0A2ECA0F39\" + \\\n", " \"_left-%d_top-%d_mag-BASE.png\" % (X_OFFSET, Y_OFFSET)\n", "MASKPATH = os.path.join(CWD, '..', '..', 'tests', 'test_files', 'annotations_and_masks', MASKNAME)\n", "MASK = imread(MASKPATH)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": "<Figure size 504x504 with 1 Axes>", "image/png": 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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(7,7))\n", "plt.imshow(MASK)\n", "plt.title(MASKNAME[:23])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2. Get contours from mask" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function ``get_contours_from_mask()`` generates contours from a mask image. There are many parameters that can be set but most have defaults set for the most common use cases. The only required parameters you must provide are ``MASK`` and ``GTCodes_df``, but you may want to consider setting the following parameters based on your specific needs: ``get_roi_contour``, ``roi_group``, ``discard_nonenclosed_background``, ``background_group``, that control behaviour regarding region of interest (ROI) boundary and background pixel class (e.g. stroma)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Parse ground truth mask and gets countours for annotations.\n", "\n", " Parameters\n", " -----------\n", " MASK : nd array\n", " ground truth mask (m,n) where pixel values encode group membership.\n", " GTCodes_df : pandas Dataframe\n", " the ground truth codes and information dataframe.\n", " This is a dataframe that is indexed by the annotation group name and\n", " has the following columns.\n", "\n", " group: str\n", " group name of annotation, eg. mostly_tumor.\n", " GT_code: int\n", " desired ground truth code (in the mask). Pixels of this value\n", " belong to corresponding group (class).\n", " color: str\n", " rgb format. eg. rgb(255,0,0).\n", " groups_to_get : None\n", " if None (default) then all groups (ground truth labels) will be\n", " extracted. Otherwise pass a list fo strings like ['mostly_tumor',].\n", " MIN_SIZE : int\n", " minimum bounding box size of contour\n", " MAX_SIZE : None\n", " if not None, int. Maximum bounding box size of contour. Sometimes\n", " very large contours cause segmentation faults that originate from\n", " opencv and are not caught by python, causing the python process\n", " to unexpectedly hault. If you would like to set a maximum size to\n", " defend against this, a suggested maximum would be 15000.\n", " get_roi_contour : bool\n", " whether to get contour for boundary of region of interest (ROI). This\n", " is most relevant when dealing with multiple ROIs per slide and with\n", " rotated rectangular or polygonal ROIs.\n", " roi_group : str\n", " name of roi group in the GT_Codes dataframe (eg roi)\n", " discard_nonenclosed_background : bool\n", " If a background group contour is NOT fully enclosed, discard it.\n", " This is a purely aesthetic method, makes sure that the background group\n", " contours (eg stroma) are discarded by default to avoid cluttering the\n", " field when posted to DSA for viewing online. The only exception is\n", " if they are enclosed within something else (eg tumor), in which case\n", " they are kept since they represent holes. This is related to\n", " https://github.com/DigitalSlideArchive/HistomicsTK/issues/675\n", " WARNING - This is a bit slower since the contours will have to be\n", " converted to shapely polygons. It is not noticeable for hundreds of\n", " contours, but you will notice the speed difference if you are parsing\n", " thousands of contours. Default, for this reason, is False.\n", " background_group : str\n", " name of background group in the GT_codes dataframe (eg mostly_stroma)\n", " verbose : bool\n", " Print progress to screen?\n", " monitorPrefix : str\n", " text to prepend to printed statements\n", "\n", " Returns\n", " --------\n", " pandas DataFrame\n", " contours extracted from input mask. The following columns are output.\n", "\n", " group : str\n", " annotation group (ground truth label).\n", " color : str\n", " annotation color if it were to be posted to DSA.\n", " is_roi : bool\n", " whether this annotation is a region of interest boundary\n", " ymin : int\n", " minimun y coordinate\n", " ymax : int\n", " maximum y coordinate\n", " xmin : int\n", " minimum x coordinate\n", " xmax : int\n", " maximum x coordinate\n", " has_holes : bool\n", " whether this contour has holes\n", " touches_edge-top : bool\n", " whether this contour touches top mask edge\n", " touches_edge-bottom : bool\n", " whether this contour touches bottom mask edge\n", " touches_edge-left : bool\n", " whether this contour touches left mask edge\n", " touches_edge-right : bool\n", " whether this contour touches right mask edge\n", " coords_x : str\n", " vertix x coordinates comma-separated values\n", " coords_y\n", " vertix y coordinated comma-separated values\n", "\n", " \n" ] } ], "source": [ "print(get_contours_from_mask.__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extract contours" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TCGA-A2-A0YE: getting contours: non-roi: roi: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: evaluation_roi: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: mostly_tumor: getting contours\n", "TCGA-A2-A0YE: getting contours: non-roi: mostly_tumor: adding contours\n", "TCGA-A2-A0YE: getting contours: non-roi: mostly_stroma: getting contours\n", "TCGA-A2-A0YE: getting contours: non-roi: mostly_stroma: adding contours\n", "TCGA-A2-A0YE: getting contours: non-roi: nest 1 of 11: TOO SIMPLE (1 coordinates) -- IGNORED\n", "TCGA-A2-A0YE: getting contours: non-roi: nest 2 of 11: TOO SIMPLE (2 coordinates) -- IGNORED\n", "TCGA-A2-A0YE: getting contours: non-roi: nest 3 of 11: TOO SIMPLE (1 coordinates) -- IGNORED\n", "TCGA-A2-A0YE: getting contours: non-roi: nest 4 of 11: TOO SIMPLE (1 coordinates) -- IGNORED\n", "TCGA-A2-A0YE: getting contours: non-roi: nest 5 of 11: TOO SMALL (10 x 18 pixels) -- IGNORED\n", "TCGA-A2-A0YE: getting contours: non-roi: nest 6 of 11: TOO SIMPLE (1 coordinates) -- IGNORED\n", "TCGA-A2-A0YE: getting contours: non-roi: nest 8 of 11: TOO SIMPLE (1 coordinates) -- IGNORED\n", "TCGA-A2-A0YE: getting contours: non-roi: nest 9 of 11: TOO SIMPLE (1 coordinates) -- IGNORED\n", "TCGA-A2-A0YE: getting contours: non-roi: mostly_lymphocytic_infiltrate: getting contours\n", "TCGA-A2-A0YE: getting contours: non-roi: mostly_lymphocytic_infiltrate: adding contours\n", "TCGA-A2-A0YE: getting contours: non-roi: nest 5 of 14: TOO SMALL (23 x 74 pixels) -- IGNORED\n", "TCGA-A2-A0YE: getting contours: non-roi: necrosis_or_debris: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: glandular_secretions: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: mostly_blood: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: exclude: getting contours\n", "TCGA-A2-A0YE: getting contours: non-roi: exclude: adding contours\n", "TCGA-A2-A0YE: getting contours: non-roi: metaplasia_NOS: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: mostly_fat: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: mostly_plasma_cells: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: other_immune_infiltrate: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: mostly_mucoid_material: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: normal_acinus_or_duct: getting contours\n", "TCGA-A2-A0YE: getting contours: non-roi: normal_acinus_or_duct: adding contours\n", "TCGA-A2-A0YE: getting contours: non-roi: lymphatics: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: undetermined: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: nerve: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: skin_adnexia: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: blood_vessel: getting contours\n", "TCGA-A2-A0YE: getting contours: non-roi: blood_vessel: adding contours\n", "TCGA-A2-A0YE: getting contours: non-roi: angioinvasion: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: mostly_dcis: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: non-roi: other: NO OBJECTS!!\n", "TCGA-A2-A0YE: getting contours: discarding backgrnd: discarded 3 contours\n", "TCGA-A2-A0YE: getting contours: roi: roi: getting contours\n", "TCGA-A2-A0YE: getting contours: roi: roi: adding contours\n" ] } ], "source": [ "# Let's extract all contours from a mask, including ROI boundary. We will\n", "# be discarding any stromal contours that are not fully enclosed within a \n", "# non-stromal contour since we already know that stroma is the background\n", "# group. This is so things look uncluttered when posted to DSA.\n", "groups_to_get = None\n", "contours_df = get_contours_from_mask(\n", " MASK=MASK, GTCodes_df=GTCodes_df, groups_to_get=groups_to_get,\n", " get_roi_contour=True, roi_group='roi',\n", " discard_nonenclosed_background=True,\n", " background_group='mostly_stroma',\n", " MIN_SIZE=30, MAX_SIZE=None, verbose=True,\n", " monitorPrefix=MASKNAME[:12] + \": getting contours\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's inspect the contours dataframe\n", "\n", "The columns that really matter here are ``group``, ``color``, ``coords_x``, and ``coords_y``." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": " group color ymin ymax xmin xmax has_holes \\\n0 roi rgb(200,0,150) 0.0 4593.0 0.0 4541.0 0.0 \n1 mostly_tumor rgb(255,0,0) 4269.0 4560.0 1639.0 2039.0 1.0 \n2 mostly_tumor rgb(255,0,0) 3764.0 4282.0 1607.0 2187.0 0.0 \n3 mostly_tumor rgb(255,0,0) 3712.0 4051.0 1201.0 1411.0 0.0 \n4 mostly_tumor rgb(255,0,0) 3356.0 3748.0 3108.0 3540.0 0.0 \n\n touches_edge-top touches_edge-left touches_edge-bottom \\\n0 1.0 1.0 1.0 \n1 0.0 0.0 0.0 \n2 0.0 0.0 0.0 \n3 0.0 0.0 0.0 \n4 0.0 0.0 0.0 \n\n touches_edge-right coords_x \\\n0 1.0 2835,2834,2833,2832,2831,2830,2829,2827,2826,2... \n1 0.0 1673,1672,1668,1667,1662,1661,1659,1658,1658,1... \n2 0.0 1770,1769,1768,1767,1765,1764,1762,1761,1760,1... \n3 0.0 1214,1213,1211,1210,1208,1207,1206,1205,1203,1... \n4 0.0 3342,3341,3337,3336,3332,3331,3328,3327,3326,3... \n\n coords_y \n0 0,1,1,2,2,3,3,5,5,6,6,8,8,9,9,10,10,12,12,13,1... \n1 4269,4270,4270,4271,4271,4272,4272,4273,4274,4... \n2 3764,3765,3765,3766,3766,3767,3767,3768,3768,3... \n3 3712,3713,3713,3714,3714,3715,3715,3716,3716,3... \n4 3356,3357,3357,3358,3358,3359,3359,3360,3360,3... ", "text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>group</th>\n <th>color</th>\n <th>ymin</th>\n <th>ymax</th>\n <th>xmin</th>\n <th>xmax</th>\n <th>has_holes</th>\n <th>touches_edge-top</th>\n <th>touches_edge-left</th>\n <th>touches_edge-bottom</th>\n <th>touches_edge-right</th>\n <th>coords_x</th>\n <th>coords_y</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>roi</td>\n <td>rgb(200,0,150)</td>\n <td>0.0</td>\n <td>4593.0</td>\n <td>0.0</td>\n <td>4541.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>2835,2834,2833,2832,2831,2830,2829,2827,2826,2...</td>\n <td>0,1,1,2,2,3,3,5,5,6,6,8,8,9,9,10,10,12,12,13,1...</td>\n </tr>\n <tr>\n <th>1</th>\n <td>mostly_tumor</td>\n <td>rgb(255,0,0)</td>\n <td>4269.0</td>\n <td>4560.0</td>\n <td>1639.0</td>\n <td>2039.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1673,1672,1668,1667,1662,1661,1659,1658,1658,1...</td>\n <td>4269,4270,4270,4271,4271,4272,4272,4273,4274,4...</td>\n </tr>\n <tr>\n <th>2</th>\n <td>mostly_tumor</td>\n <td>rgb(255,0,0)</td>\n <td>3764.0</td>\n <td>4282.0</td>\n <td>1607.0</td>\n <td>2187.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1770,1769,1768,1767,1765,1764,1762,1761,1760,1...</td>\n <td>3764,3765,3765,3766,3766,3767,3767,3768,3768,3...</td>\n </tr>\n <tr>\n <th>3</th>\n <td>mostly_tumor</td>\n <td>rgb(255,0,0)</td>\n <td>3712.0</td>\n <td>4051.0</td>\n <td>1201.0</td>\n <td>1411.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1214,1213,1211,1210,1208,1207,1206,1205,1203,1...</td>\n <td>3712,3713,3713,3714,3714,3715,3715,3716,3716,3...</td>\n </tr>\n <tr>\n <th>4</th>\n <td>mostly_tumor</td>\n <td>rgb(255,0,0)</td>\n <td>3356.0</td>\n <td>3748.0</td>\n <td>3108.0</td>\n <td>3540.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>3342,3341,3337,3336,3332,3331,3328,3327,3326,3...</td>\n <td>3356,3357,3357,3358,3358,3359,3359,3360,3360,3...</td>\n </tr>\n </tbody>\n</table>\n</div>" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "contours_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Get annotation documents from contours\n", "\n", "This method ``get_annotation_documents_from_contours()`` generates formatted annotation documents from contours that can be posted to the DSA server." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Given dataframe of contours, get list of annotation documents.\n", "\n", " This method parses a dataframe of contours to a list of dictionaries, each\n", " of which represents and large_image style annotation. This is a wrapper\n", " that extends the functionality of the method\n", " get_single_annotation_document_from_contours(), whose docstring should\n", " be referenced for implementation details and further explanation.\n", "\n", " Parameters\n", " -----------\n", " contours_df : pandas DataFrame\n", " WARNING - This is modified inside the function, so pass a copy.\n", " This dataframe includes data on contours extracted from input mask\n", " using get_contours_from_mask(). If you have contours using some other\n", " method, just make sure the dataframe follows the same schema as the\n", " output from get_contours_from_mask(). You may find a sample dataframe\n", " in thie repo at ./tests/test_files/annotations_and_masks/sample_contours_df.tsv\n", " The following columns are relevant for this method.\n", "\n", " group : str\n", " annotation group (ground truth label).\n", " color : str\n", " annotation color if it were to be posted to DSA.\n", " coords_x : str\n", " vertix x coordinates comma-separated values\n", " coords_y\n", " vertix y coordinated comma-separated values\n", " separate_docs_by_group : bool\n", " if set to True, you get one or more annotation documents (dicts)\n", " for each group (eg tumor) independently.\n", " annots_per_doc : int\n", " maximum number of annotation elements (polygons) per dict. The smaller\n", " this number, the more numerous the annotation documents, but the more\n", " seamless it is to post this data to the DSA server or to view using the\n", " HistomicsTK interface since you will be loading smaller chunks of data\n", " at a time.\n", " annprops : dict\n", " properties of annotation elements. Contains the following keys\n", " F, X_OFFSET, Y_OFFSET, opacity, lineWidth. Refer to\n", " get_single_annotation_document_from_contours() for details.\n", " docnamePrefix : str\n", " test to prepend to annotation document name\n", " verbose : bool\n", " Print progress to screen?\n", " monitorPrefix : str\n", " text to prepend to printed statements\n", "\n", " Returns\n", " --------\n", " list of dicts\n", " DSA-style annotation document.\n", "\n", " \n" ] } ], "source": [ "print(get_annotation_documents_from_contours.__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned in the docs, this function wraps ``get_single_annotation_document_from_contours()``" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Given dataframe of contours, get annotation document.\n", "\n", " This uses the large_image annotation schema to create an annotation\n", " document that maybe posted to DSA for viewing using something like:\n", " resp = gc.post(\"/annotation?itemId=\" + slide_id, json=annotation_doc)\n", " The annotation schema can be found at:\n", " github.com/girder/large_image/blob/master/docs/annotations.md .\n", "\n", " Parameters\n", " -----------\n", " contours_df_slice : pandas DataFrame\n", " The following columns are of relevance and must be contained.\n", "\n", " group : str\n", " annotation group (ground truth label).\n", " color : str\n", " annotation color if it were to be posted to DSA.\n", " coords_x : str\n", " vertix x coordinates comma-separated values\n", " coords_y\n", " vertix y coordinated comma-separated values\n", " docname : str\n", " annotation document name\n", " F : float\n", " how much smaller is the mask where the contours come from is relative\n", " to the slide scan magnification. For example, if the mask is at 10x\n", " whereas the slide scan magnification is 20x, then F would be 2.0.\n", " X_OFFSET : int\n", " x offset to add to contours at BASE (SCAN) magnification\n", " Y_OFFSET : int\n", " y offset to add to contours at BASE (SCAN) magnification\n", " opacity : float\n", " opacity of annotation elements (in the range [0, 1])\n", " lineWidth : float\n", " width of boarders of annotation elements\n", " verbose : bool\n", " Print progress to screen?\n", " monitorPrefix : str\n", " text to prepend to printed statements\n", "\n", " Returns\n", " --------\n", " dict\n", " DSA-style annotation document ready to be post for viewing.\n", "\n", " \n" ] } ], "source": [ "print(get_single_annotation_document_from_contours.__doc__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's get a list of annotation documents (each is a dictionary). For the purpose of this tutorial, \n", "we separate the documents by group (i.e. each document is composed of polygons from the same\n", "style/group). You could decide to allow heterogeneous groups in the same annotation document by\n", "setting ``separate_docs_by_group`` to ``False``. We place 10 polygons in each document for this demo\n", "for illustration purposes. Realistically you would want each document to contain several hundred depending on their complexity. Placing too many polygons in each document can lead to performance issues when rendering in HistomicsUI." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get annotation documents" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 1 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 2 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 3 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 4 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 5 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 6 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 7 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 8 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 9 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 10 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 11 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 12 of 13\n", "TCGA-A2-A0YE: annotation docs: mostly_lymphocytic_infiltrate: doc 1 of 1: contour 13 of 13\n", "TCGA-A2-A0YE: annotation docs: exclude: doc 1 of 1: contour 1 of 1\n", "TCGA-A2-A0YE: annotation docs: blood_vessel: doc 1 of 1: contour 1 of 3\n", "TCGA-A2-A0YE: annotation docs: blood_vessel: doc 1 of 1: contour 2 of 3\n", "TCGA-A2-A0YE: annotation docs: blood_vessel: doc 1 of 1: contour 3 of 3\n", "TCGA-A2-A0YE: annotation docs: roi: doc 1 of 1: contour 1 of 1\n", "TCGA-A2-A0YE: annotation docs: normal_acinus_or_duct: doc 1 of 1: contour 1 of 2\n", "TCGA-A2-A0YE: annotation docs: normal_acinus_or_duct: doc 1 of 1: contour 2 of 2\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 1 of 2: contour 1 of 10\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 1 of 2: contour 2 of 10\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 1 of 2: contour 3 of 10\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 1 of 2: contour 4 of 10\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 1 of 2: contour 5 of 10\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 1 of 2: contour 6 of 10\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 1 of 2: contour 7 of 10\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 1 of 2: contour 8 of 10\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 1 of 2: contour 9 of 10\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 1 of 2: contour 10 of 10\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 1 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 2 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 3 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 4 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 5 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 6 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 7 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 8 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 9 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 10 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 11 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 12 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 13 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 14 of 15\n", "TCGA-A2-A0YE: annotation docs: mostly_tumor: doc 2 of 2: contour 15 of 15\n" ] } ], "source": [ "# get list of annotation documents\n", "annprops = {\n", " 'X_OFFSET': X_OFFSET,\n", " 'Y_OFFSET': Y_OFFSET,\n", " 'opacity': 0.2,\n", " 'lineWidth': 4.0,\n", "}\n", "annotation_docs = get_annotation_documents_from_contours(\n", " contours_df.copy(), separate_docs_by_group=True, annots_per_doc=10,\n", " docnamePrefix='demo', annprops=annprops,\n", " verbose=True, monitorPrefix=MASKNAME[:12] + \": annotation docs\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's examine one of the documents. \n", "\n", "Limit display to the first two elements (polygons) and cap the vertices for clarity." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "ann_doc = annotation_docs[0].copy()\n", "ann_doc['elements'] = ann_doc['elements'][:2]\n", "for i in range(2):\n", " ann_doc['elements'][i]['points'] = ann_doc['elements'][i]['points'][:5]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": "{'name': 'demo_mostly_lymphocytic_infiltrate-0',\n 'description': '',\n 'elements': [{'group': 'mostly_lymphocytic_infiltrate',\n 'type': 'polyline',\n 'lineColor': 'rgb(0,0,255)',\n 'lineWidth': 4.0,\n 'closed': True,\n 'points': [[61974.0, 37427.0, 0.0],\n [61975.0, 37428.0, 0.0],\n [61975.0, 37429.0, 0.0],\n [61976.0, 37430.0, 0.0],\n [61976.0, 37431.0, 0.0]],\n 'label': {'value': 'mostly_lymphocytic_infiltrate'},\n 'fillColor': 'rgba(0,0,255,0.2)'},\n {'group': 'mostly_lymphocytic_infiltrate',\n 'type': 'polyline',\n 'lineColor': 'rgb(0,0,255)',\n 'lineWidth': 4.0,\n 'closed': True,\n 'points': [[60531.0, 37045.0, 0.0],\n [60528.0, 37048.0, 0.0],\n [60527.0, 37048.0, 0.0],\n [60522.0, 37053.0, 0.0],\n [60522.0, 37054.0, 0.0]],\n 'label': {'value': 'mostly_lymphocytic_infiltrate'},\n 'fillColor': 'rgba(0,0,255,0.2)'}]}" }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ann_doc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Post the annotation to the correct item/slide in DSA" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# deleting existing annotations in target slide (if any)\n", "existing_annotations = gc.get('/annotation/item/' + SAMPLE_SLIDE_ID)\n", "for ann in existing_annotations:\n", " gc.delete('/annotation/%s' % ann['_id'])\n", "\n", "# post the annotation documents you created \n", "for annotation_doc in annotation_docs:\n", " resp = gc.post(\n", " \"/annotation?itemId=\" + SAMPLE_SLIDE_ID, json=annotation_doc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now you can go to HistomicsUI and confirm that the posted annotations make\n", "sense and correspond to tissue boundaries and expected labels." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
cogeorg/black_rhino
examples/firesales_SA/.ipynb_checkpoints/sys_plot-checkpoint.ipynb
2
6143
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This is for the distribution moments. It will write a csv \n" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[array([ 1.62450000e+11, 2.43947695e+11, 3.15150000e+11]), array([ 1.18400000e+11, 2.33234735e+11, 3.14150000e+11]), array([ 1.08448674e+11, 2.31521482e+11, 3.14150000e+11]), array([ 1.06465405e+11, 2.31102857e+11, 3.14150000e+11]), array([ 1.06450340e+11, 2.30998812e+11, 3.14150000e+11]), array([ 1.06450340e+11, 2.30998812e+11, 3.14150000e+11]), array([ 1.05497623e+11, 2.30958177e+11, 3.14150000e+11]), array([ 1.05497133e+11, 2.30948071e+11, 3.14150000e+11])]\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib as mpl\n", "mpl.rcParams.update({'font.size': 15, 'font.family':'serif'})\n", "from matplotlib.patches import Rectangle\n", "import matplotlib.pyplot as plt\n", "\n", "df = pd.read_csv('test100.csv')\n", "\n", "keys = np.unique(df['current_step'])\n", "\n", "for key in keys:\n", " if key == 0:\n", " I0 = np.argwhere(df['current_step']==key).squeeze() \n", " if key == 1:\n", " I1 = np.argwhere(df['current_step']==key).squeeze()\n", " if key == 2:\n", " I2 = np.argwhere(df['current_step']==key).squeeze() \n", " if key == 3:\n", " I3 = np.argwhere(df['current_step']==key).squeeze() \n", " if key == 4:\n", " I4 = np.argwhere(df['current_step']==key).squeeze() \n", " if key == 5: \n", " I5 = np.argwhere(df['current_step']==key).squeeze() \n", " if key == 6: \n", " I6 = np.argwhere(df['current_step']==key).squeeze()\n", " if key == 7:\n", " I7 = np.argwhere(df['current_step']==key).squeeze()\n", "\n", "period1 = df['system_equity'][I1]\n", "period2 = df['system_equity'][I2]\n", "period3 = df['system_equity'][I3]\n", "period4 = df['system_equity'][I4]\n", "period5 = df['system_equity'][I5]\n", "period6 = df['system_equity'][I5]\n", "period7 = df['system_equity'][I6]\n", "period8 = df['system_equity'][I7]\n", "\n", "list = [period1, period2, period3, period4, period5, period6, period7, period8]\n", "\n", "\n", "def calc_percentile(array):\n", " p5 = np.percentile(array, 5) \n", " p95 = np.percentile(array, 95)\n", " pm = np.mean(array) \n", " return p5, pm, p95\n", "\n", "output = []\n", "for i in list:\n", " vfunc = np.array(np.array(calc_percentile(i)))\n", " output.append(vfunc)\n", "\n", "print output\n", "\n", "df = pd.DataFrame(output)\n", "df.to_csv(\"1000_moments.csv\", index=False, header=False)\n", "\n", "# fig = plt.figure()\n", "# ax1 = fig.add_subplot(121)\n", "\n", "# for key in keys: \n", "# I = np.argwhere(df['current_step']==key).squeeze()\n", "\n", "# ax1.plot(df['current_step'][I], df['system_equity'][I], label=\"system_Equity\")\n", "\n", "# plt.show()\n", "\n", "\n", "# ax1 = fig.add_subplot(121)\n", "# ax1.plot(df['current_step'][I], df['system_Equity'][I], label=\"system_Equity\")\n", "# ax1.set_xlabel('$p$')\n", "# ax1.set_ylabel('$x$')\n", "\n", "# nrows=2, ncols=2,\n", "# ax[0,0] top left\n", "# ax[0,1] top right\n", "# ax[row, col] \n", "\n", " #fig = plt.figure()\n", "# ax[0].plot(df['current_step'][I], df['system_TAS'][I], label=\"system_TAS %s\"%key)\n", " \n", "# ax1 = fig.add_subplot(121)\n", "# ax1.plot(p, data_sorted)\n", "# ax1.set_xlabel('$p$')\n", "# ax1.set_ylabel('$x$')\n", "\n", "# ax2 = fig.add_subplot(122)\n", "# ax2.plot(data_sorted, p)\n", "# ax2.set_xlabel('$x$')\n", "# ax2.set_ylabel('$p$')\n", "\n", "# plt.show() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is for the actual values per shock simulated:\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 128, "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('test1000.csv')\n", "\n", "new_df = df[[\"system_equity\", \"shock\"]].copy()\n", "numpyMatrix = new_df.as_matrix()\n", "\n", "df1 = df.pivot(index='current_step', columns='shock', values='system_equity')\n", "# output = []\n", "# for key in keys2:\n", "# I = np.argwhere(df['shock']==key).squeeze() \n", "# output.append(df['system_equity'][I]) \n", "# print df['system_equity'][I]\n", "\n", "# shock_array = np.array(df['system_equity'][I])\n", "# df = pd.DataFrame(output)\n", "df1.to_csv(\"1000_hihishocks.csv\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df3 = pd.read_csv('1000_plot.csv')\n", "\n", "df4 = df3.loc[ : , '5%': ]\n", "\n", "col_names=[]\n", "for i in df4.columns:\n", " col_names.append(str(i))\n", " \n", "# rows = [df4.loc[ : , :label ] for label in col_names]\n", "print \"hello\"\n", "# print rows\n", "\n", "# df.plot(x=\"current_step\", y = (df3.loc[ : , '5%': ]))\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
shreekumar3d/python-workshop
12-Functions.ipynb
1
2581
{ "metadata": { "name": "12-Functions" }, "nbformat": 2, "worksheets": [ { "cells": [ { "cell_type": "markdown", "source": [ "Till now, we have used various functions - built-in functions, as well as methods inside objects.", "", "It is time to write our own now. Functions in python exist for the same reason that they exist in other languages. Functions provide modularity and reduce code duplication. ", "", "A basic function in Python looks like: " ] }, { "cell_type": "code", "collapsed": true, "input": [ "def fnname(parameter_list):", " stmt1", " stmt2", " ...", " stmtN" ], "language": "python", "outputs": [] }, { "cell_type": "markdown", "source": [ "Functions may take any number of arguments (including None). This is specified in the comma separated parameter list. The \"return\" statement is used to exit the function, and optionally return a value as a result of the function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def factorial(n):", " if n>0:", " return n*factorial(n-1)", " return 1", "", "# function usage", "print factorial(5)" ], "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "120" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "source": [ "Functions can call themselves - called recursion -- like above !" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def sum2(x, y):", " return x + y", "", "print sum2(5,6)" ], "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "11" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "source": [ "Functions can take default arguments:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def sum2(x, y=100):", " return x+y", "", "print sum2(5)" ], "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "105" ] } ], "prompt_number": 3 } ] } ] }
apache-2.0
obulpathi/datascience
scikit/titanic/notebooks/Section 1-3 - Parameter Tuning.ipynb
1
15924
{ "metadata": { "name": "", "signature": "sha256:97cf3b8f8bdd8bd6614708d69d91224fa13a903567258328444e2a64eff21cdb" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Section 1-3 - Parameter Tuning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In previous sections, we took the approach of using Scikit-learn as a black box. We now review how to tune the parameters of the model to make more accurate predictions." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Pandas - Extracting data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import numpy as np\n", "\n", "df = pd.read_csv('../data/train.csv')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Pandas - Cleaning data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df = df.drop(['Name', 'Ticket', 'Cabin'], axis=1)\n", "\n", "age_mean = df['Age'].mean()\n", "df['Age'] = df['Age'].fillna(age_mean)\n", "\n", "from scipy.stats import mode\n", "\n", "mode_embarked = mode(df['Embarked'])[0][0]\n", "df['Embarked'] = df['Embarked'].fillna(mode_embarked)\n", "\n", "df['Gender'] = df['Sex'].map({'female': 0, 'male': 1}).astype(int)\n", "\n", "pd.get_dummies(df['Embarked'], prefix='Embarked').head(10)\n", "df = pd.concat([df, pd.get_dummies(df['Embarked'], prefix='Embarked')], axis=1)\n", "\n", "df = df.drop(['Sex', 'Embarked'], axis=1)\n", "\n", "cols = df.columns.tolist()\n", "cols = [cols[1]] + cols[0:1] + cols[2:]\n", "\n", "df = df[cols]\n", "\n", "train_data = df.values" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Scikit-learn - Training the model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The documentation for the Random Forest Classifier details the different input parameters of the model. These input parameters include the number of trees, and the number of branches each tree has. It is unclear, off-the-bat, which values would be optimal. \n", "\n", "http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "GridSearchCV allows us to test the desired range of input parameters, and review the performance of each set of values on a cross-validation basis. Here we review the number of features considered at each step a branch is made (max_features: 50% or 100% of features) and the maximum number of branches (max_depth: 5 levels or no limitations). " ] }, { "cell_type": "code", "collapsed": false, "input": [ "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.grid_search import GridSearchCV\n", "\n", "parameter_grid = {\n", " 'max_features': [0.5, 1.],\n", " 'max_depth': [5., None]\n", "}\n", "\n", "grid_search = GridSearchCV(RandomForestClassifier(n_estimators = 100), parameter_grid,\n", " cv=5, verbose=3)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "grid_search.fit(train_data[0:,2:], train_data[0:,0])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n", "[GridSearchCV] max_features=0.5, max_depth=5.0 .................................\n", "[GridSearchCV] ........ max_features=0.5, max_depth=5.0, score=0.843575 - 0.2s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] max_features=0.5, max_depth=5.0 .................................\n", "[GridSearchCV] ........ max_features=0.5, max_depth=5.0, score=0.797753 - 0.2s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] max_features=0.5, max_depth=5.0 .................................\n", "[GridSearchCV] ........ max_features=0.5, max_depth=5.0, score=0.797753 - 0.1s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] max_features=0.5, max_depth=5.0 .................................\n", "[GridSearchCV] ........ max_features=0.5, max_depth=5.0, score=0.870787 - 0.1s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] max_features=0.5, max_depth=5.0 .................................\n", "[GridSearchCV] ........ max_features=0.5, max_depth=5.0, score=0.831461 - 0.1s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] max_features=1.0, max_depth=5.0 .................................\n", "[GridSearchCV] ........ max_features=1.0, max_depth=5.0, score=0.854749 - 0.2s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] max_features=1.0, max_depth=5.0 .................................\n", "[GridSearchCV] ........ max_features=1.0, max_depth=5.0, score=0.797753 - 0.2s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] max_features=1.0, max_depth=5.0 .................................\n", "[GridSearchCV] ........ max_features=1.0, max_depth=5.0, score=0.786517 - 0.2s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] max_features=1.0, max_depth=5.0 .................................\n", "[GridSearchCV] ........ max_features=1.0, max_depth=5.0, score=0.842697 - 0.2s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] max_features=1.0, max_depth=5.0 .................................\n", "[GridSearchCV] ........ max_features=1.0, max_depth=5.0, score=0.831461 - 0.2s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] max_features=0.5, max_depth=None ................................\n", "[GridSearchCV] ....... max_features=0.5, max_depth=None, score=0.832402 - 0.2s" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "[GridSearchCV] max_features=0.5, max_depth=None ................................\n", "[GridSearchCV] ....... max_features=0.5, max_depth=None, score=0.803371 - 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Making predictions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "df_test = pd.read_csv('../data/test.csv')\n", "\n", "df_test = df_test.drop(['Name', 'Ticket', 'Cabin'], axis=1)\n", "\n", "df_test['Age'] = df_test['Age'].fillna(age_mean)\n", "\n", "fare_means = df.pivot_table('Fare', index='Pclass', aggfunc='mean')\n", "df_test['Fare'] = df_test[['Fare', 'Pclass']].apply(lambda x:\n", " fare_means[x['Pclass']] if pd.isnull(x['Fare'])\n", " else x['Fare'], axis=1)\n", "\n", "df_test['Gender'] = df_test['Sex'].map({'female': 0, 'male': 1}).astype(int)\n", "df_test = pd.concat([df_test, pd.get_dummies(df_test['Embarked'], prefix='Embarked')],\n", " axis=1)\n", "\n", "df_test = df_test.drop(['Sex', 'Embarked'], axis=1)\n", "\n", "test_data = df_test.values\n", "\n", "output = model.predict(test_data[:,1:])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "/Users/savarin/anaconda/lib/python2.7/site-packages/pandas/core/index.py:496: FutureWarning: scalar indexers for index type Int64Index should be integers and not floating point\n", " type(self).__name__),FutureWarning)\n" ] } ], "prompt_number": 8 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Pandas - Preparing for submission" ] }, { "cell_type": "code", "collapsed": false, "input": [ "result = np.c_[test_data[:,0].astype(int), output.astype(int)]\n", "\n", "\n", "df_result = pd.DataFrame(result[:,0:2], columns=['PassengerId', 'Survived'])\n", "df_result.to_csv('../results/titanic_1-3.csv', index=False)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 } ], "metadata": {} } ] }
apache-2.0
LiamConnell/deep-algotrading
notebooks/TF-FIN-3-regression_with_policy_training.ipynb
1
45354
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pylab inline\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "import numpy as np\n", "import numpy.random as rng\n", "import pandas_datareader.data as web\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def get_prices(symbol):\n", " start, end = '2007-05-02', '2016-04-11'\n", " data = web.DataReader(symbol, 'google', start, end)\n", " data=pd.DataFrame(data)\n", " prices=data['Close']\n", " prices=prices.astype(float)\n", " return prices\n", "\n", "def get_returns(prices):\n", " return ((prices-prices.shift(-1))/prices)[:-1]\n", " \n", "def get_data(list):\n", " l = []\n", " for symbol in list:\n", " rets = get_returns(get_prices(symbol))\n", " l.append(rets)\n", " return np.array(l).T\n", "\n", "def sort_data(rets):\n", " ins = []\n", " outs = []\n", " for i in range(len(rets)-100):\n", " ins.append(rets[i:i+100].tolist())\n", " outs.append(rets[i+100])\n", " return np.array(ins), np.array(outs)\n", " \n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "symbol_list = ['C', 'GS']\n", "rets = get_data(symbol_list)\n", "ins, outs = sort_data(rets)\n", "ins = ins.transpose([0,2,1]).reshape([-1, len(symbol_list) * 100])\n", "div = int(.8 * ins.shape[0])\n", "train_ins, train_outs = ins[:div], outs[:div]\n", "test_ins, test_outs = ins[div:], outs[div:]\n", "\n", "#normalize inputs (this is new but not specific to PG; you should always normalize inputs)\n", "train_ins, test_ins = train_ins/np.std(ins), test_ins/np.std(ins)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess = tf.InteractiveSession()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "positions = tf.constant([-1,0,1]) #long, neutral or short\n", "num_positions = 3\n", "\n", "x = tf.placeholder(tf.float32, [None, len(symbol_list) * 100])\n", "y_ = tf.placeholder(tf.float32, [None, len(symbol_list)])\n", "\n", "W = tf.Variable(tf.random_normal([len(symbol_list) * 100, num_positions * len(symbol_list)]))\n", "b = tf.Variable(tf.random_normal([num_positions * len(symbol_list)]))\n", "\n", "# we define our model: y = W*x + b\n", "y = tf.matmul(x, W) + b # y is tensor of shape [num_inputs, num_positions * len(symbol_list)]\n", "# a row of y will look like [prob_symbol_1_short, prob_symbol_1_neutral, prob_symbol_1_long, prob_symbol_2_short, ...]\n", "# note that they are not really probabilities because I did not perform a softmax yet\n", "\n", "\n", "# loop through symbols, taking the buckets for one symbol at a time\n", "pos = {}\n", "symbol_returns = {}\n", "relevant_target_column = {}\n", "for i in range(len(symbol_list)):\n", " # ISOLATE the buckets relevant to the symbol and get a softmax as well\n", " symbol_probs = y[:,i*num_positions:(i+1)*num_positions]\n", " symbol_probs_softmax = tf.nn.softmax(symbol_probs) # softmax[i, j] = exp(logits[i, j]) / sum(exp(logits[i]))\n", " # SAMPLE probability to chose our policy's action\n", " sample = tf.multinomial(tf.log(symbol_probs_softmax), 1)\n", " pos[i] = tf.reshape(sample, [-1]) - 1 # choose(-1,0,1)\n", " # GET RETURNS by multiplying the policy (position taken) by the target return for that day\n", " symbol_returns[i] = tf.multiply(tf.cast(pos[i], float32), y_[:,i])\n", " # isolate the output probability the selected policy (for use in calculating gradient)\n", " # see https://github.com/tensorflow/tensorflow/issues/206 for TF discussion including my solution\n", " sample_mask = tf.reshape(tf.one_hot(sample, 3), [-1,3])\n", " relevant_target_column[i] = tf.reduce_sum(symbol_probs_softmax * sample_mask,1)\n", " \n", "\n", "# calculate the PERFORMANCE METRICS for the data chosen\n", "daily_returns_by_symbol = tf.concat(axis=1, values=[tf.reshape(t, [-1,1]) for t in symbol_returns.values()]) \n", "daily_returns = tf.reduce_sum(daily_returns_by_symbol,1)/2\n", "total_return = tf.reduce_prod(daily_returns + 1)\n", "ann_vol = tf.multiply(\n", " tf.sqrt(tf.reduce_mean(tf.pow((daily_returns - tf.reduce_mean(daily_returns)),2))) ,\n", " np.sqrt(252)\n", " )\n", "sharpe = total_return / ann_vol\n", "\n", "# since we only train the sampled classes, we will combine them so that we can feed them into cross entropy\n", "training_target_cols = tf.concat(axis=1, values=[tf.reshape(t, [-1,1]) for t in relevant_target_column.values()])\n", "# we want to either push the gradient toward our selection or away from it. We use these ones to find the direction\n", "# of the gradient, which we will then multiply by our fitness function\n", "ones = tf.ones_like(training_target_cols)\n", "\n", "# this isnt actually a gradient, but karpathy sort of calls it one. Since it's a tensor it sort of is a gradient anyway\n", "gradient = tf.nn.sigmoid_cross_entropy_with_logits(labels=training_target_cols, logits=ones) ####should this be a prob???\n", "\n", "# COST\n", "# how should we do this step? it depends how we want to group our results. Choose your own adventure here by uncommenting a cost fn\n", "# this is the most obvious: we push each weight to what works or not. Try it out...we're gonna be RICH!!!! oh, wait...\n", "#cost = tf.multiply(gradient , daily_returns_by_symbol)\n", "# this takes the overall daily return and pushes the weights so that the overall day wins. Again, it overfits enormously\n", "cost = tf.multiply(gradient , tf.reshape(daily_returns,[-1,1]))\n", "# this multiplies every gradient by the overall return. If the strategy won for the past ten years, we do more of it and vice versa\n", "#cost = tf.multiply(gradient , total_return)\n", "\n", "# minimize the cost (push the weights where we want them to go)\n", "optimizer = tf.train.GradientDescentOptimizer(0.1).minimize(cost)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 1000 cost= 23840.125000000 total return= 23839.125000000\n", "Epoch: 2000 cost= 154422.281250000 total return= 154421.281250000\n", "Epoch: 3000 cost= 421916.937500000 total return= 421915.937500000\n", "Epoch: 4000 cost= 504377.406250000 total return= 504376.406250000\n", "Epoch: 5000 cost= 854653.875000000 total return= 854652.875000000\n" ] } ], "source": [ "init = tf.global_variables_initializer()\n", "sess.run(init)\n", "for epoch in range(5000):\n", " sess.run(optimizer, feed_dict={x: train_ins, y_: train_outs})#.reshape(1,-1).T})\n", " if (epoch+1)%1000== 0:\n", " c,t = sess.run([cost, total_return], feed_dict={x: train_ins, y_: train_outs})#.reshape(1,-1).T})\n", " print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\", \"{:.9f}\".format(t), \"total return=\", \"{:.9f}\".format(t-1))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# in sample results\n", "d, t = sess.run([daily_returns, gradient], feed_dict={x: train_ins, y_: train_outs})" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10647ad30>]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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k0i3nHM1tURpaIjS0tNHUGiUSdeysa6KhuS0ob43Q3BqhJRIlGnUU5Gbz8IoK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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x111fe8748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# equity curve\n", "plot(np.cumprod(d+1))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "#out of sample results\n", "d, t = sess.run([daily_returns, gradient], feed_dict={x: test_ins, y_: test_outs})" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10821a4e0>]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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hTUuXj1mFmeH9C6Zk93uPeCTLvRMIhvjZC3s42idGEZk5JmtBYjOkHrla6+eB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\n7vRFCUWOx0FNqxGUPNLUxaz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"text/plain": [ "<matplotlib.figure.Figure at 0x106463a20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#out of sample results\n", "plot(np.cumprod(d+1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }
mit