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apagac/cfme_tests | notebooks/MultiProcessing.ipynb | 5 | 10521 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Multiprocessing in Python 3\n",
"\n",
" ### Threads vs Processes\n",
" ### Thread/Process execution, timing\n",
" ### Direct Thread/Process Instantiation\n",
" ### Thread/Process Pools\n",
" ### Iteration with complex function signatures\n",
" ### Storing/Fetching data with Queues"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Threads vs Processes\n",
"\n",
"* Thread\n",
" * Is bound to processor that python process running on\n",
" * Is controlled by Global Interpreter Lock (GIL)\n",
" * Single python bytecode executed at a time by any thread\n",
" \n",
"* Process\n",
" * Uses multiple processors\n",
" * Concurrency between threads and processes (local and remote)\n",
" * Ignores GIL\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from os import getpid, getppid\n",
"from time import sleep\n",
"\n",
"def printer(val, wait=0):\n",
" sleep(wait)\n",
" print('Pid: {}, PPid: {}, Value: {}'\n",
" .format(getpid(), getppid(), val))\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Process Instantiation\n",
"\n",
"Let's start with most basic example of spawning new process to run a function"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting demo...\n",
"Pid: 18625, PPid: 18613, Value: hello demo\n"
]
}
],
"source": [
"from multiprocessing import Process\n",
"\n",
"print('Starting demo...')\n",
"p = Process(target=printer, args=('hello demo',))\n",
"p.start()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Process timing\n",
"\n",
"- Use printer's delay to see process timing\n",
"- Track multiple process objects\n",
"- Execute code in main process while chile process is running\n",
"- Use Process.join() to wait for processes to finish"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pid: 18628, PPid: 18613, Value: immediate\n",
"Not waiting for proccesses to finish...\n",
"Pid: 18629, PPid: 18613, Value: delayed\n",
"Pid: 18632, PPid: 18613, Value: eternity\n",
"After processes...\n"
]
}
],
"source": [
"proc_list = []\n",
"for values in [('immediate', 0), ('delayed', 2), ('eternity', 5)]:\n",
" p = Process(target=printer, args=values)\n",
" proc_list.append(p)\n",
" p.start() # start execution of printer\n",
"\n",
"print('Not waiting for proccesses to finish...')\n",
" \n",
"[p.join() for p in proc_list]\n",
"\n",
"print('After processes...')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Process Pool\n",
"\n",
"- Worker processes instead of direct instantiation\n",
"- Context manager to handle starting/joining child processes\n",
"- Pool.map() works like default python `map(f, args)` function\n",
"- Pool.map() Does not unpack args"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pid: 18640, PPid: 18613, Value: Its\n",
"Pid: 18641, PPid: 18613, Value: ('A', 5)\n",
"Pid: 18642, PPid: 18613, Value: Race\n"
]
}
],
"source": [
"from multiprocessing.pool import Pool\n",
"\n",
"with Pool(3) as pool:\n",
" pool.map(printer, ['Its', ('A', 5), 'Race'])\n",
" # each worker process executes one function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Process + args/kwargs iteration with starmap"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pid: 18652, PPid: 18613, Value: Its\n",
"Pid: 18652, PPid: 18613, Value: Race\n",
"Pid: 18653, PPid: 18613, Value: A\n"
]
}
],
"source": [
"with Pool(2) as pool:\n",
" pool.starmap(printer, [('Its',), ('A', 2), ('Race',)])\n",
" # one worker will execute 2 functions, one worker will execute the 'slow' function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Starmap is the bomb"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def pretend_delete_method(provider, vm_name):\n",
" print('Pretend delete: {} on {}. (Pid: {})'\n",
" .format(vm_name, provider, getpid())) \n",
" \n",
"# Assuming we fetched a list of vm names on providers we want to cleanup...\n",
"example_provider_vm_lists = dict(\n",
" vmware=['test_vm_1', 'test_vm_2'],\n",
" rhv=['test_vm_3', 'test_vm_4'],\n",
" osp=['test_vm_5', 'test_vm_6'],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pretend delete: test_vm_1 on vmware. (Pid: 18613)Pretend delete: test_vm_2 on vmware. (Pid: 18613)\n",
"Pretend delete: test_vm_3 on rhv. (Pid: 18613)\n",
"Pretend delete: test_vm_4 on rhv. (Pid: 18613)\n",
"Pretend delete: test_vm_5 on osp. (Pid: 18613)\n",
"Pretend delete: test_vm_6 on osp. (Pid: 18613)\n",
"\n"
]
}
],
"source": [
"# don't hate me for nested comprehension here - building tuples of provider+name\n",
"from multiprocessing.pool import ThreadPool\n",
"\n",
"# Threadpool instead of process pool, same interface\n",
"with ThreadPool(6) as pool:\n",
" pool.starmap(\n",
" pretend_delete_method, \n",
" [(key, vm) \n",
" for key, vms \n",
" in example_provider_vm_lists.items() \n",
" for vm in vms]\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Locking\n",
"\n",
"- semaphore-type object that can be acquired and released\n",
"- When acquired, only thread that has the lock can run\n",
"- Necessary when using shared objects"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Printing is thread safe, but will sometimes print separate messages on the same line (above)\n",
"# Use a lock around print\n",
"from multiprocessing import Lock\n",
"\n",
"lock = Lock()\n",
"def safe_printing_method(provider, vm_name):\n",
" with lock:\n",
" print('Pretend delete: {} on {}. (Pid: {})'\n",
" .format(vm_name, provider, getpid()))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pretend delete: test_vm_1 on vmware. (Pid: 18613)\n",
"Pretend delete: test_vm_2 on vmware. (Pid: 18613)\n",
"Pretend delete: test_vm_3 on rhv. (Pid: 18613)\n",
"Pretend delete: test_vm_4 on rhv. (Pid: 18613)\n",
"Pretend delete: test_vm_5 on osp. (Pid: 18613)\n",
"Pretend delete: test_vm_6 on osp. (Pid: 18613)\n"
]
}
],
"source": [
"with ThreadPool(6) as pool:\n",
" pool.starmap(\n",
" safe_printing_method, \n",
" [(key, vm) for key, vms in example_provider_vm_lists.items() for vm in vms])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Queues\n",
"\n",
"- Store data/objects in child thread/processes and retrieve in parent\n",
"- FIFO stack with put, get, and empty methods\n",
"\n",
"- multiprocessing.Queue\n",
" - cannot be pickled and thus can't be passed to Pool methods\n",
" - can deadlock with improper join use\n",
"- multiprocessing.Manager.Queue\n",
" - is proxy, can be pickled\n",
" - can be shared between processes"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Results are in: [None, True, None, None, None, True]\n",
"Failures are in: ['test_vm_1', 'test_vm_4', 'test_vm_3', 'test_vm_5']\n"
]
}
],
"source": [
"from multiprocessing import Manager\n",
"from random import randint\n",
"\n",
"# Create instance of manager\n",
"manager = Manager()\n",
"\n",
"def multiple_output_method(provider, vm_name, fail_queue):\n",
" # random success of called method\n",
" if randint(0, 1):\n",
" return True\n",
" else:\n",
" # Store our failure vm on the queue\n",
" fail_queue.put(vm_name)\n",
" return None\n",
"\n",
"# Create queue object to give to child processes\n",
"queue_for_failures = manager.Queue()\n",
"with Pool(2) as pool:\n",
" results = pool.starmap(\n",
" multiple_output_method, \n",
" [(key, vm, queue_for_failures)\n",
" for key, vms\n",
" in example_provider_vm_lists.items()\n",
" for vm in vms]\n",
" )\n",
"\n",
"print('Results are in: {}'.format(results))\n",
"\n",
"failed_vms = []\n",
"# get items from the queue while its not empty\n",
"while not queue_for_failures.empty():\n",
" failed_vms.append(queue_for_failures.get())\n",
" \n",
"print('Failures are in: {}'.format(failed_vms))"
]
}
],
"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
}
| gpl-2.0 |
mwaskom/seaborn | doc/tutorial/axis_grids.ipynb | 1 | 20207 | {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
".. _grid_tutorial:\n",
"\n",
".. currentmodule:: seaborn"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Building structured multi-plot grids\n",
"====================================\n",
"\n",
".. raw:: html\n",
"\n",
" <div class=col-md-9>\n"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"When exploring multi-dimensional data, a useful approach is to draw multiple instances of the same plot on different subsets of your dataset. This technique is sometimes called either \"lattice\" or \"trellis\" plotting, and it is related to the idea of `\"small multiples\" <https://en.wikipedia.org/wiki/Small_multiple>`_. It allows a viewer to quickly extract a large amount of information about a complex dataset. Matplotlib offers good support for making figures with multiple axes; seaborn builds on top of this to directly link the structure of the plot to the structure of your dataset.\n",
"\n",
"The :doc:`figure-level <function_overview>` functions are built on top of the objects discussed in this chapter of the tutorial. In most cases, you will want to work with those functions. They take care of some important bookkeeping that synchronizes the multiple plots in each grid. This chapter explains how the underlying objects work, which may be useful for advanced applications."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide"
]
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide"
]
},
"outputs": [],
"source": [
"sns.set_theme(style=\"ticks\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"hide"
]
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"np.random.seed(sum(map(ord, \"axis_grids\")))"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
".. _facet_grid:\n",
"\n",
"Conditional small multiples\n",
"---------------------------\n",
"\n",
"The :class:`FacetGrid` class is useful when you want to visualize the distribution of a variable or the relationship between multiple variables separately within subsets of your dataset. A :class:`FacetGrid` can be drawn with up to three dimensions: ``row``, ``col``, and ``hue``. The first two have obvious correspondence with the resulting array of axes; think of the hue variable as a third dimension along a depth axis, where different levels are plotted with different colors.\n",
"\n",
"Each of :func:`relplot`, :func:`displot`, :func:`catplot`, and :func:`lmplot` use this object internally, and they return the object when they are finished so that it can be used for further tweaking.\n",
"\n",
"The class is used by initializing a :class:`FacetGrid` object with a dataframe and the names of the variables that will form the row, column, or hue dimensions of the grid. These variables should be categorical or discrete, and then the data at each level of the variable will be used for a facet along that axis. For example, say we wanted to examine differences between lunch and dinner in the ``tips`` dataset:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tips = sns.load_dataset(\"tips\")\n",
"g = sns.FacetGrid(tips, col=\"time\")"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Initializing the grid like this sets up the matplotlib figure and axes, but doesn't draw anything on them.\n",
"\n",
"The main approach for visualizing data on this grid is with the :meth:`FacetGrid.map` method. Provide it with a plotting function and the name(s) of variable(s) in the dataframe to plot. Let's look at the distribution of tips in each of these subsets, using a histogram:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.FacetGrid(tips, col=\"time\")\n",
"g.map(sns.histplot, \"tip\")"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"This function will draw the figure and annotate the axes, hopefully producing a finished plot in one step. To make a relational plot, just pass multiple variable names. You can also provide keyword arguments, which will be passed to the plotting function:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.FacetGrid(tips, col=\"sex\", hue=\"smoker\")\n",
"g.map(sns.scatterplot, \"total_bill\", \"tip\", alpha=.7)\n",
"g.add_legend()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"There are several options for controlling the look of the grid that can be passed to the class constructor."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.FacetGrid(tips, row=\"smoker\", col=\"time\", margin_titles=True)\n",
"g.map(sns.regplot, \"size\", \"total_bill\", color=\".3\", fit_reg=False, x_jitter=.1)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Note that ``margin_titles`` isn't formally supported by the matplotlib API, and may not work well in all cases. In particular, it currently can't be used with a legend that lies outside of the plot.\n",
"\n",
"The size of the figure is set by providing the height of *each* facet, along with the aspect ratio:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.FacetGrid(tips, col=\"day\", height=4, aspect=.5)\n",
"g.map(sns.barplot, \"sex\", \"total_bill\", order=[\"Male\", \"Female\"])"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The default ordering of the facets is derived from the information in the DataFrame. If the variable used to define facets has a categorical type, then the order of the categories is used. Otherwise, the facets will be in the order of appearance of the category levels. It is possible, however, to specify an ordering of any facet dimension with the appropriate ``*_order`` parameter:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ordered_days = tips.day.value_counts().index\n",
"g = sns.FacetGrid(tips, row=\"day\", row_order=ordered_days,\n",
" height=1.7, aspect=4,)\n",
"g.map(sns.kdeplot, \"total_bill\")"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Any seaborn color palette (i.e., something that can be passed to :func:`color_palette()` can be provided. You can also use a dictionary that maps the names of values in the ``hue`` variable to valid matplotlib colors:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pal = dict(Lunch=\"seagreen\", Dinner=\".7\")\n",
"g = sns.FacetGrid(tips, hue=\"time\", palette=pal, height=5)\n",
"g.map(sns.scatterplot, \"total_bill\", \"tip\", s=100, alpha=.5)\n",
"g.add_legend()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"If you have many levels of one variable, you can plot it along the columns but \"wrap\" them so that they span multiple rows. When doing this, you cannot use a ``row`` variable."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"attend = sns.load_dataset(\"attention\").query(\"subject <= 12\")\n",
"g = sns.FacetGrid(attend, col=\"subject\", col_wrap=4, height=2, ylim=(0, 10))\n",
"g.map(sns.pointplot, \"solutions\", \"score\", order=[1, 2, 3], color=\".3\", ci=None)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Once you've drawn a plot using :meth:`FacetGrid.map` (which can be called multiple times), you may want to adjust some aspects of the plot. There are also a number of methods on the :class:`FacetGrid` object for manipulating the figure at a higher level of abstraction. The most general is :meth:`FacetGrid.set`, and there are other more specialized methods like :meth:`FacetGrid.set_axis_labels`, which respects the fact that interior facets do not have axis labels. For example:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with sns.axes_style(\"white\"):\n",
" g = sns.FacetGrid(tips, row=\"sex\", col=\"smoker\", margin_titles=True, height=2.5)\n",
"g.map(sns.scatterplot, \"total_bill\", \"tip\", color=\"#334488\")\n",
"g.set_axis_labels(\"Total bill (US Dollars)\", \"Tip\")\n",
"g.set(xticks=[10, 30, 50], yticks=[2, 6, 10])\n",
"g.figure.subplots_adjust(wspace=.02, hspace=.02)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"For even more customization, you can work directly with the underling matplotlib ``Figure`` and ``Axes`` objects, which are stored as member attributes at ``figure`` and ``axes_dict``, respectively. When making a figure without row or column faceting, you can also use the ``ax`` attribute to directly access the single axes."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.FacetGrid(tips, col=\"smoker\", margin_titles=True, height=4)\n",
"g.map(plt.scatter, \"total_bill\", \"tip\", color=\"#338844\", edgecolor=\"white\", s=50, lw=1)\n",
"for ax in g.axes_dict.values():\n",
" ax.axline((0, 0), slope=.2, c=\".2\", ls=\"--\", zorder=0)\n",
"g.set(xlim=(0, 60), ylim=(0, 14))"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
".. _custom_map_func:\n",
"\n",
"Using custom functions\n",
"----------------------\n",
"\n",
"You're not limited to existing matplotlib and seaborn functions when using :class:`FacetGrid`. However, to work properly, any function you use must follow a few rules:\n",
"\n",
"1. It must plot onto the \"currently active\" matplotlib ``Axes``. This will be true of functions in the ``matplotlib.pyplot`` namespace, and you can call :func:`matplotlib.pyplot.gca` to get a reference to the current ``Axes`` if you want to work directly with its methods.\n",
"2. It must accept the data that it plots in positional arguments. Internally, :class:`FacetGrid` will pass a ``Series`` of data for each of the named positional arguments passed to :meth:`FacetGrid.map`.\n",
"3. It must be able to accept ``color`` and ``label`` keyword arguments, and, ideally, it will do something useful with them. In most cases, it's easiest to catch a generic dictionary of ``**kwargs`` and pass it along to the underlying plotting function.\n",
"\n",
"Let's look at minimal example of a function you can plot with. This function will just take a single vector of data for each facet:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from scipy import stats\n",
"def quantile_plot(x, **kwargs):\n",
" quantiles, xr = stats.probplot(x, fit=False)\n",
" plt.scatter(xr, quantiles, **kwargs)\n",
" \n",
"g = sns.FacetGrid(tips, col=\"sex\", height=4)\n",
"g.map(quantile_plot, \"total_bill\")"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"If we want to make a bivariate plot, you should write the function so that it accepts the x-axis variable first and the y-axis variable second:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def qqplot(x, y, **kwargs):\n",
" _, xr = stats.probplot(x, fit=False)\n",
" _, yr = stats.probplot(y, fit=False)\n",
" plt.scatter(xr, yr, **kwargs)\n",
" \n",
"g = sns.FacetGrid(tips, col=\"smoker\", height=4)\n",
"g.map(qqplot, \"total_bill\", \"tip\")"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Because :func:`matplotlib.pyplot.scatter` accepts ``color`` and ``label`` keyword arguments and does the right thing with them, we can add a hue facet without any difficulty:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.FacetGrid(tips, hue=\"time\", col=\"sex\", height=4)\n",
"g.map(qqplot, \"total_bill\", \"tip\")\n",
"g.add_legend()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Sometimes, though, you'll want to map a function that doesn't work the way you expect with the ``color`` and ``label`` keyword arguments. In this case, you'll want to explicitly catch them and handle them in the logic of your custom function. For example, this approach will allow use to map :func:`matplotlib.pyplot.hexbin`, which otherwise does not play well with the :class:`FacetGrid` API:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def hexbin(x, y, color, **kwargs):\n",
" cmap = sns.light_palette(color, as_cmap=True)\n",
" plt.hexbin(x, y, gridsize=15, cmap=cmap, **kwargs)\n",
"\n",
"with sns.axes_style(\"dark\"):\n",
" g = sns.FacetGrid(tips, hue=\"time\", col=\"time\", height=4)\n",
"g.map(hexbin, \"total_bill\", \"tip\", extent=[0, 50, 0, 10]);"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
".. _pair_grid:\n",
"\n",
"Plotting pairwise data relationships\n",
"------------------------------------\n",
"\n",
":class:`PairGrid` also allows you to quickly draw a grid of small subplots using the same plot type to visualize data in each. In a :class:`PairGrid`, each row and column is assigned to a different variable, so the resulting plot shows each pairwise relationship in the dataset. This style of plot is sometimes called a \"scatterplot matrix\", as this is the most common way to show each relationship, but :class:`PairGrid` is not limited to scatterplots.\n",
"\n",
"It's important to understand the differences between a :class:`FacetGrid` and a :class:`PairGrid`. In the former, each facet shows the same relationship conditioned on different levels of other variables. In the latter, each plot shows a different relationship (although the upper and lower triangles will have mirrored plots). Using :class:`PairGrid` can give you a very quick, very high-level summary of interesting relationships in your dataset.\n",
"\n",
"The basic usage of the class is very similar to :class:`FacetGrid`. First you initialize the grid, then you pass plotting function to a ``map`` method and it will be called on each subplot. There is also a companion function, :func:`pairplot` that trades off some flexibility for faster plotting.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"iris = sns.load_dataset(\"iris\")\n",
"g = sns.PairGrid(iris)\n",
"g.map(sns.scatterplot)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"It's possible to plot a different function on the diagonal to show the univariate distribution of the variable in each column. Note that the axis ticks won't correspond to the count or density axis of this plot, though."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.PairGrid(iris)\n",
"g.map_diag(sns.histplot)\n",
"g.map_offdiag(sns.scatterplot)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"A very common way to use this plot colors the observations by a separate categorical variable. For example, the iris dataset has four measurements for each of three different species of iris flowers so you can see how they differ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.PairGrid(iris, hue=\"species\")\n",
"g.map_diag(sns.histplot)\n",
"g.map_offdiag(sns.scatterplot)\n",
"g.add_legend()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"By default every numeric column in the dataset is used, but you can focus on particular relationships if you want."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.PairGrid(iris, vars=[\"sepal_length\", \"sepal_width\"], hue=\"species\")\n",
"g.map(sns.scatterplot)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"It's also possible to use a different function in the upper and lower triangles to emphasize different aspects of the relationship."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.PairGrid(iris)\n",
"g.map_upper(sns.scatterplot)\n",
"g.map_lower(sns.kdeplot)\n",
"g.map_diag(sns.kdeplot, lw=3, legend=False)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"The square grid with identity relationships on the diagonal is actually just a special case, and you can plot with different variables in the rows and columns."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.PairGrid(tips, y_vars=[\"tip\"], x_vars=[\"total_bill\", \"size\"], height=4)\n",
"g.map(sns.regplot, color=\".3\")\n",
"g.set(ylim=(-1, 11), yticks=[0, 5, 10])"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Of course, the aesthetic attributes are configurable. For instance, you can use a different palette (say, to show an ordering of the ``hue`` variable) and pass keyword arguments into the plotting functions."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.PairGrid(tips, hue=\"size\", palette=\"GnBu_d\")\n",
"g.map(plt.scatter, s=50, edgecolor=\"white\")\n",
"g.add_legend()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
":class:`PairGrid` is flexible, but to take a quick look at a dataset, it can be easier to use :func:`pairplot`. This function uses scatterplots and histograms by default, although a few other kinds will be added (currently, you can also plot regression plots on the off-diagonals and KDEs on the diagonal)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sns.pairplot(iris, hue=\"species\", height=2.5)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"You can also control the aesthetics of the plot with keyword arguments, and it returns the :class:`PairGrid` instance for further tweaking."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"g = sns.pairplot(iris, hue=\"species\", palette=\"Set2\", diag_kind=\"kde\", height=2.5)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
".. raw:: html\n",
"\n",
" </div>"
]
}
],
"metadata": {
"celltoolbar": "Tags",
"kernelspec": {
"display_name": "seaborn-py38-latest",
"language": "python",
"name": "seaborn-py38-latest"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
arasdar/DL | impl-dl/test-multiple-gpus.ipynb | 1 | 15527 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "InternalError",
"evalue": "Dst tensor is not initialized.\n\t [[Node: _arg_Placeholder_2_0_0/_3 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device_incarnation=1, tensor_name=\"edge_24__arg_Placeholder_2_0_0\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"]()]]\n\t [[Node: MatMul_29/_5 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_20_MatMul_29\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mInternalError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/anaconda3/envs/arasdar-DL-env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1322\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1323\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1324\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/arasdar-DL-env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1301\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1302\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/arasdar-DL-env/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[0;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[1;32m 472\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 473\u001b[0;31m c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[1;32m 474\u001b[0m \u001b[0;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mInternalError\u001b[0m: Dst tensor is not initialized.\n\t [[Node: _arg_Placeholder_2_0_0/_3 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device_incarnation=1, tensor_name=\"edge_24__arg_Placeholder_2_0_0\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"]()]]\n\t [[Node: MatMul_29/_5 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_20_MatMul_29\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mInternalError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-66ef2ec05aa7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConfigProto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlog_device_placement\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog_device_placement\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;31m# Run the op.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 64\u001b[0m \u001b[0mt2_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/arasdar-DL-env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 887\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 888\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 889\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 890\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 891\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/arasdar-DL-env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1118\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1119\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1120\u001b[0;31m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m 1121\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1122\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/arasdar-DL-env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1315\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1316\u001b[0m return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1317\u001b[0;31m options, run_metadata)\n\u001b[0m\u001b[1;32m 1318\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1319\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/envs/arasdar-DL-env/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1334\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1335\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1336\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1337\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1338\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mInternalError\u001b[0m: Dst tensor is not initialized.\n\t [[Node: _arg_Placeholder_2_0_0/_3 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device_incarnation=1, tensor_name=\"edge_24__arg_Placeholder_2_0_0\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"]()]]\n\t [[Node: MatMul_29/_5 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_20_MatMul_29\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]"
]
}
],
"source": [
"from __future__ import print_function\n",
"'''\n",
"Basic Multi GPU computation example using TensorFlow library.\n",
"Author: Aymeric Damien\n",
"Project: https://github.com/aymericdamien/TensorFlow-Examples/\n",
"'''\n",
"\n",
"'''\n",
"This tutorial requires your machine to have 2 GPUs\n",
"\"/cpu:0\": The CPU of your machine.\n",
"\"/gpu:0\": The first GPU of your machine\n",
"\"/gpu:1\": The second GPU of your machine\n",
"'''\n",
"\n",
"\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"import datetime\n",
"\n",
"# Processing Units logs\n",
"log_device_placement = True\n",
"\n",
"# Num of multiplications to perform\n",
"n = 10\n",
"\n",
"'''\n",
"Example: compute A^n + B^n on 2 GPUs\n",
"Results on 8 cores with 2 GTX-980:\n",
" * Single GPU computation time: 0:00:11.277449\n",
" * Multi GPU computation time: 0:00:07.131701\n",
"'''\n",
"# Create random large matrix\n",
"A = np.random.rand(10000, 10000).astype('float32')\n",
"B = np.random.rand(10000, 10000).astype('float32')\n",
"\n",
"# Create a graph to store results\n",
"c1 = []\n",
"c2 = []\n",
"\n",
"def matpow(M, n):\n",
" if n < 1: #Abstract cases where n < 1\n",
" return M\n",
" else:\n",
" return tf.matmul(M, matpow(M, n-1))\n",
"\n",
"'''\n",
"Single GPU computing\n",
"'''\n",
"with tf.device('/gpu:0'):\n",
" a = tf.placeholder(tf.float32, [10000, 10000])\n",
" b = tf.placeholder(tf.float32, [10000, 10000])\n",
" # Compute A^n and B^n and store results in c1\n",
" c1.append(matpow(a, n))\n",
" c1.append(matpow(b, n))\n",
"\n",
"with tf.device('/cpu:0'):\n",
" sum = tf.add_n(c1) #Addition of all elements in c1, i.e. A^n + B^n\n",
"\n",
"t1_1 = datetime.datetime.now()\n",
"with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n",
" # Run the op.\n",
" sess.run(sum, {a:A, b:B})\n",
"t2_1 = datetime.datetime.now()\n",
"\n",
"\n",
"'''\n",
"Multi GPU computing\n",
"'''\n",
"# GPU:0 computes A^n\n",
"with tf.device('/gpu:0'):\n",
" # Compute A^n and store result in c2\n",
" a = tf.placeholder(tf.float32, [10000, 10000])\n",
" c2.append(matpow(a, n))\n",
"\n",
"# GPU:1 computes B^n\n",
"with tf.device('/gpu:1'):\n",
" # Compute B^n and store result in c2\n",
" b = tf.placeholder(tf.float32, [10000, 10000])\n",
" c2.append(matpow(b, n))\n",
"\n",
"with tf.device('/cpu:0'):\n",
" sum = tf.add_n(c2) #Addition of all elements in c2, i.e. A^n + B^n\n",
"\n",
"t1_2 = datetime.datetime.now()\n",
"with tf.Session(config=tf.ConfigProto(log_device_placement=log_device_placement)) as sess:\n",
" # Run the op.\n",
" sess.run(sum, {a:A, b:B})\n",
"t2_2 = datetime.datetime.now()\n",
"\n",
"\n",
"print(\"Single GPU computation time: \" + str(t2_1-t1_1))\n",
"print(\"Multi GPU computation time: \" + str(t2_2-t1_2))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 44. 56.]\n",
" [ 98. 128.]]\n"
]
}
],
"source": [
"# Creates a graph.\n",
"c = []\n",
"for d in ['/device:GPU:0', '/device:GPU:1']:\n",
" with tf.device(d):\n",
" a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3])\n",
" b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2])\n",
" c.append(tf.matmul(a, b))\n",
"with tf.device('/cpu:0'):\n",
" sum = tf.add_n(c)\n",
"# Creates a session with log_device_placement set to True.\n",
"sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n",
"# Runs the op.\n",
"print(sess.run(sum))"
]
},
{
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| unlicense |
dedert/Brand2Vec | Preprocessing.ipynb | 1 | 11194 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### 2016.12.11 최종 버전"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from preprocess import util, preprocess\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"1. 데이터 불러와서 meta 와 join\n",
"2. 문장 단위로 형태소 분석"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_path = \"E:/dataset/Amazon/\"\n",
"save_path = \"E:/dataset/MasterThesis/FINAL/preprocess_data/\"\n",
"category_list = [\"Electronics\",\"Beauty\",\"Clothing_Shoes_and_Jewelry\"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Start extract sentences of Electronics\n",
"Completed extract sentences of Electronics, time : 488.42\n",
"Start extract samples from data\n",
"Completed extract samples of Electronics, time : 3.74\n",
"check shape ----------\n",
"Electronics shape after sampling : 250000, 10\n",
"Start pos tag of sentences in Electronics\n",
"Completed pos-tagging and save of Electronics, time : 1799.74\n",
"Start preprocessing in Electronics\n",
"Completed preprocess and save of Electronics, time : 88.07\n",
"Start extract sentences of Beauty\n",
"Completed extract sentences of Beauty, time : 202.66\n",
"Start extract samples from data\n",
"Completed extract samples of Beauty, time : 7.05\n",
"check shape ----------\n",
"Beauty shape after sampling : 202181, 10\n",
"Start pos tag of sentences in Beauty\n",
"Completed pos-tagging and save of Beauty, time : 792.45\n",
"Start preprocessing in Beauty\n",
"Completed preprocess and save of Beauty, time : 39.41\n",
"Start extract sentences of Clothing_Shoes_and_Jewelry\n",
"Completed extract sentences of Clothing_Shoes_and_Jewelry, time : 124.45\n",
"Start extract samples from data\n",
"Completed extract samples of Clothing_Shoes_and_Jewelry, time : 3.58\n",
"check shape ----------\n",
"Clothing_Shoes_and_Jewelry shape after sampling : 178026, 10\n",
"Start pos tag of sentences in Clothing_Shoes_and_Jewelry\n",
"Completed pos-tagging and save of Clothing_Shoes_and_Jewelry, time : 676.39\n",
"Start preprocessing in Clothing_Shoes_and_Jewelry\n",
"Completed preprocess and save of Clothing_Shoes_and_Jewelry, time : 31.93\n",
"Wall time: 1h 11min 37s\n"
]
}
],
"source": [
"%%time\n",
"pre = preprocess.Preprocess(data_path, save_path, category_list)\n",
"pre.preprocess()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"category = \"Electronics\"\n",
"data = pd.read_csv(save_path + \"preprocess_complete_\" + category + \".csv\")"
]
},
{
"cell_type": "code",
"execution_count": 15,
"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>reviewTime</th>\n",
" <th>asin</th>\n",
" <th>reviewerID</th>\n",
" <th>overall</th>\n",
" <th>helpful</th>\n",
" <th>reviewText</th>\n",
" <th>title</th>\n",
" <th>brand</th>\n",
" <th>reviewSentence</th>\n",
" <th>sent_length</th>\n",
" <th>reviewSentence_tagged</th>\n",
" <th>preprocessed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2013-07-21</td>\n",
" <td>B00CM0XHNS</td>\n",
" <td>A372YX80GGM7DR</td>\n",
" <td>5.0</td>\n",
" <td>576</td>\n",
" <td>Ok, so I didn't buy this on Amazon, as I didn'...</td>\n",
" <td>Ultimate Ears BOOM Wireless Bluetooth Speaker ...</td>\n",
" <td>Logitech</td>\n",
" <td>[\"Ok, so I didn't buy this on Amazon, as I did...</td>\n",
" <td>58</td>\n",
" <td>[[('Ok', 'NNP'), (',', ','), ('so', 'IN'), ('I...</td>\n",
" <td>[['ok', 'so', 'i', 'did', \"n't\", 'buy', 'this'...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2013-05-19</td>\n",
" <td>B00BQ5RY1G</td>\n",
" <td>A1BG2Z071TYO7P</td>\n",
" <td>2.0</td>\n",
" <td>522</td>\n",
" <td>I received a Harmony Ultimate from Logitech be...</td>\n",
" <td>Logitech Harmony Ultimate Remote with Customiz...</td>\n",
" <td>Logitech</td>\n",
" <td>['I received a Harmony Ultimate from Logitech ...</td>\n",
" <td>27</td>\n",
" <td>[[('I', 'PRP'), ('received', 'VBD'), ('a', 'DT...</td>\n",
" <td>[['i', 'received', 'a', 'harmony', 'ultimate',...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2013-12-16</td>\n",
" <td>B00EZ9XG62</td>\n",
" <td>AELAESM03451</td>\n",
" <td>1.0</td>\n",
" <td>290</td>\n",
" <td>This review is for the iPad Air keyboard. I ha...</td>\n",
" <td>Logitech Ultrathin Keyboard Cover for iPad Air...</td>\n",
" <td>Logitech</td>\n",
" <td>['This review is for the iPad Air keyboard.', ...</td>\n",
" <td>23</td>\n",
" <td>[[('This', 'DT'), ('review', 'NN'), ('is', 'VB...</td>\n",
" <td>[['this', 'review', 'is', 'for', 'the', 'ipad'...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2013-01-21</td>\n",
" <td>B0099SMFVQ</td>\n",
" <td>A36CMGR5ELUM34</td>\n",
" <td>5.0</td>\n",
" <td>283</td>\n",
" <td>Design: Very well put together. Elegant and th...</td>\n",
" <td>Logitech Bluetooth Illuminated Keyboard K810 f...</td>\n",
" <td>Logitech</td>\n",
" <td>['Design: Very well put together.', 'Elegant a...</td>\n",
" <td>28</td>\n",
" <td>[[('Design', 'NN'), (':', ':'), ('Very', 'RB')...</td>\n",
" <td>[['design', 'very', 'well', 'put', 'together']...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2013-07-29</td>\n",
" <td>B00CM0XHNS</td>\n",
" <td>A9TETE58A7JR3</td>\n",
" <td>3.0</td>\n",
" <td>260</td>\n",
" <td>So, I've been testing a few bluetooth speakers...</td>\n",
" <td>Ultimate Ears BOOM Wireless Bluetooth Speaker ...</td>\n",
" <td>Logitech</td>\n",
" <td>[\"So, I've been testing a few bluetooth speake...</td>\n",
" <td>57</td>\n",
" <td>[[('So', 'RB'), (',', ','), ('I', 'PRP'), (\"'v...</td>\n",
" <td>[['so', 'i', 'been', 'testing', 'a', 'few', 'b...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" reviewTime asin reviewerID overall helpful \\\n",
"0 2013-07-21 B00CM0XHNS A372YX80GGM7DR 5.0 576 \n",
"1 2013-05-19 B00BQ5RY1G A1BG2Z071TYO7P 2.0 522 \n",
"2 2013-12-16 B00EZ9XG62 AELAESM03451 1.0 290 \n",
"3 2013-01-21 B0099SMFVQ A36CMGR5ELUM34 5.0 283 \n",
"4 2013-07-29 B00CM0XHNS A9TETE58A7JR3 3.0 260 \n",
"\n",
" reviewText \\\n",
"0 Ok, so I didn't buy this on Amazon, as I didn'... \n",
"1 I received a Harmony Ultimate from Logitech be... \n",
"2 This review is for the iPad Air keyboard. I ha... \n",
"3 Design: Very well put together. Elegant and th... \n",
"4 So, I've been testing a few bluetooth speakers... \n",
"\n",
" title brand \\\n",
"0 Ultimate Ears BOOM Wireless Bluetooth Speaker ... Logitech \n",
"1 Logitech Harmony Ultimate Remote with Customiz... Logitech \n",
"2 Logitech Ultrathin Keyboard Cover for iPad Air... Logitech \n",
"3 Logitech Bluetooth Illuminated Keyboard K810 f... Logitech \n",
"4 Ultimate Ears BOOM Wireless Bluetooth Speaker ... Logitech \n",
"\n",
" reviewSentence sent_length \\\n",
"0 [\"Ok, so I didn't buy this on Amazon, as I did... 58 \n",
"1 ['I received a Harmony Ultimate from Logitech ... 27 \n",
"2 ['This review is for the iPad Air keyboard.', ... 23 \n",
"3 ['Design: Very well put together.', 'Elegant a... 28 \n",
"4 [\"So, I've been testing a few bluetooth speake... 57 \n",
"\n",
" reviewSentence_tagged \\\n",
"0 [[('Ok', 'NNP'), (',', ','), ('so', 'IN'), ('I... \n",
"1 [[('I', 'PRP'), ('received', 'VBD'), ('a', 'DT... \n",
"2 [[('This', 'DT'), ('review', 'NN'), ('is', 'VB... \n",
"3 [[('Design', 'NN'), (':', ':'), ('Very', 'RB')... \n",
"4 [[('So', 'RB'), (',', ','), ('I', 'PRP'), (\"'v... \n",
"\n",
" preprocessed \n",
"0 [['ok', 'so', 'i', 'did', \"n't\", 'buy', 'this'... \n",
"1 [['i', 'received', 'a', 'harmony', 'ultimate',... \n",
"2 [['this', 'review', 'is', 'for', 'the', 'ipad'... \n",
"3 [['design', 'very', 'well', 'put', 'together']... \n",
"4 [['so', 'i', 'been', 'testing', 'a', 'few', 'b... "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": 1
}
| mit |
darienmt/intro-to-tensorflow | LeNet-Lab.ipynb | 1 | 19754 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# LeNet Lab\n",
"\n",
"Source: Yan LeCun"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Load Data\n",
"\n",
"Load the MNIST data, which comes pre-loaded with TensorFlow.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting ./datasets/train-images-idx3-ubyte.gz\n",
"Extracting ./datasets/train-labels-idx1-ubyte.gz\n",
"Extracting ./datasets/t10k-images-idx3-ubyte.gz\n",
"Extracting ./datasets/t10k-labels-idx1-ubyte.gz\n",
"\n",
"Image Shape: (28, 28, 1)\n",
"\n",
"Training Set: 55000 samples\n",
"Validation Set: 5000 samples\n",
"Test Set: 10000 samples\n"
]
}
],
"source": [
"from tensorflow.examples.tutorials.mnist import input_data\n",
"\n",
"mnist = input_data.read_data_sets(\"./datasets/\", reshape=False)\n",
"X_train, y_train = mnist.train.images, mnist.train.labels\n",
"X_validation, y_validation = mnist.validation.images, mnist.validation.labels\n",
"X_test, y_test = mnist.test.images, mnist.test.labels\n",
"\n",
"assert(len(X_train) == len(y_train))\n",
"assert(len(X_validation) == len(y_validation))\n",
"assert(len(X_test) == len(y_test))\n",
"\n",
"print()\n",
"print(\"Image Shape: {}\".format(X_train[0].shape))\n",
"print()\n",
"print(\"Training Set: {} samples\".format(len(X_train)))\n",
"print(\"Validation Set: {} samples\".format(len(X_validation)))\n",
"print(\"Test Set: {} samples\".format(len(X_test)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"The MNIST data that TensorFlow pre-loads comes as 28x28x1 images.\n",
"\n",
"However, the LeNet architecture only accepts 32x32xC images, where C is the number of color channels.\n",
"\n",
"In order to reformat the MNIST data into a shape that LeNet will accept, we pad the data with two rows of zeros on the top and bottom, and two columns of zeros on the left and right (28+2+2 = 32).\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Updated Image Shape: (32, 32, 1)\n"
]
}
],
"source": [
"import numpy as np\n",
"\n",
"# Pad images with 0s\n",
"X_train = np.pad(X_train, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n",
"X_validation = np.pad(X_validation, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n",
"X_test = np.pad(X_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n",
" \n",
"print(\"Updated Image Shape: {}\".format(X_train[0].shape))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Visualize Data\n",
"\n",
"View a sample from the dataset.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"8\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAFsAAABZCAYAAABR/liSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrQIwi2MUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f746ceead30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import random\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"index = random.randint(0, len(X_train))\n",
"image = X_train[index].squeeze()\n",
"\n",
"plt.figure(figsize=(1,1))\n",
"plt.imshow(image, cmap=\"gray\")\n",
"print(y_train[index])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Preprocess Data\n",
"\n",
"Shuffle the training data.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.utils import shuffle\n",
"\n",
"X_train, y_train = shuffle(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Setup TensorFlow\n",
"The `EPOCH` and `BATCH_SIZE` values affect the training speed and model accuracy.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"\n",
"EPOCHS = 10\n",
"BATCH_SIZE = 128"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## TODO: Implement LeNet-5\n",
"Implement the [LeNet-5](http://yann.lecun.com/exdb/lenet/) neural network architecture.\n",
"\n",
"This is the only cell you need to edit.\n",
"### Input\n",
"The LeNet architecture accepts a 32x32xC image as input, where C is the number of color channels. Since MNIST images are grayscale, C is 1 in this case.\n",
"\n",
"### Architecture\n",
"**Layer 1: Convolutional.** The output shape should be 28x28x6.\n",
"\n",
"**Activation.** Your choice of activation function.\n",
"\n",
"**Pooling.** The output shape should be 14x14x6.\n",
"\n",
"**Layer 2: Convolutional.** The output shape should be 10x10x16.\n",
"\n",
"**Activation.** Your choice of activation function.\n",
"\n",
"**Pooling.** The output shape should be 5x5x16.\n",
"\n",
"**Flatten.** Flatten the output shape of the final pooling layer such that it's 1D instead of 3D. The easiest way to do is by using `tf.contrib.layers.flatten`, which is already imported for you.\n",
"\n",
"**Layer 3: Fully Connected.** This should have 120 outputs.\n",
"\n",
"**Activation.** Your choice of activation function.\n",
"\n",
"**Layer 4: Fully Connected.** This should have 84 outputs.\n",
"\n",
"**Activation.** Your choice of activation function.\n",
"\n",
"**Layer 5: Fully Connected (Logits).** This should have 10 outputs.\n",
"\n",
"### Output\n",
"Return the result of the 2nd fully connected layer."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from tensorflow.contrib.layers import flatten\n",
"\n",
"def LeNet(x): \n",
" # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer\n",
" mu = 0\n",
" sigma = 0.1\n",
" \n",
" # TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6.\n",
" conv1_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma))\n",
" conv1_b = tf.Variable(tf.zeros(6))\n",
" conv1 = tf.nn.conv2d(x, conv1_W, strides=[1, 1, 1, 1], padding='VALID') + conv1_b\n",
" \n",
" # TODO: Activation.\n",
" conv1 = tf.nn.relu(conv1)\n",
"\n",
" # TODO: Pooling. Input = 28x28x6. Output = 14x14x6.\n",
" conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
"\n",
" # TODO: Layer 2: Convolutional. Output = 10x10x16.\n",
" conv2_W = tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma))\n",
" conv2_b = tf.Variable(tf.zeros(16))\n",
" conv2 = tf.nn.conv2d(conv1, conv2_W, strides=[1, 1, 1, 1], padding='VALID') + conv2_b\n",
" \n",
" # TODO: Activation.\n",
" conv2 = tf.nn.relu(conv2)\n",
"\n",
" # TODO: Pooling. Input = 10x10x16. Output = 5x5x16.\n",
" conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n",
"\n",
" # TODO: Flatten. Input = 5x5x16. Output = 400.\n",
" fc0 = flatten(conv2)\n",
" \n",
" # TODO: Layer 3: Fully Connected. Input = 400. Output = 120.\n",
" fc1_W = tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma))\n",
" fc1_b = tf.Variable(tf.zeros(120))\n",
" fc1 = tf.matmul(fc0, fc1_W) + fc1_b \n",
" \n",
" # TODO: Activation.\n",
" fc1 = tf.nn.relu(fc1)\n",
"\n",
" # TODO: Layer 4: Fully Connected. Input = 120. Output = 84.\n",
" fc2_W = tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma))\n",
" fc2_b = tf.Variable(tf.zeros(84))\n",
" fc2 = tf.matmul(fc1, fc2_W) + fc2_b\n",
" \n",
" # TODO: Activation.\n",
" fc2 = tf.nn.relu(fc2)\n",
"\n",
" # TODO: Layer 5: Fully Connected. Input = 84. Output = 10.\n",
" fc3_W = tf.Variable(tf.truncated_normal(shape=(84, 10), mean = mu, stddev = sigma))\n",
" fc3_b = tf.Variable(tf.zeros(10))\n",
" logits = tf.matmul(fc2, fc3_W) + fc3_b\n",
" \n",
" return logits"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Features and Labels\n",
"Train LeNet to classify [MNIST](http://yann.lecun.com/exdb/mnist/) data.\n",
"\n",
"`x` is a placeholder for a batch of input images.\n",
"`y` is a placeholder for a batch of output labels.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"x = tf.placeholder(tf.float32, (None, 32, 32, 1))\n",
"y = tf.placeholder(tf.int32, (None))\n",
"one_hot_y = tf.one_hot(y, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Training Pipeline\n",
"Create a training pipeline that uses the model to classify MNIST data.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"rate = 0.001\n",
"\n",
"logits = LeNet(x)\n",
"cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits)\n",
"loss_operation = tf.reduce_mean(cross_entropy)\n",
"optimizer = tf.train.AdamOptimizer(learning_rate = rate)\n",
"training_operation = optimizer.minimize(loss_operation)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Model Evaluation\n",
"Evaluate how well the loss and accuracy of the model for a given dataset.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1))\n",
"accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
"saver = tf.train.Saver()\n",
"\n",
"def evaluate(X_data, y_data):\n",
" num_examples = len(X_data)\n",
" total_accuracy = 0\n",
" sess = tf.get_default_session()\n",
" for offset in range(0, num_examples, BATCH_SIZE):\n",
" batch_x, batch_y = X_data[offset:offset+BATCH_SIZE], y_data[offset:offset+BATCH_SIZE]\n",
" accuracy = sess.run(accuracy_operation, feed_dict={x: batch_x, y: batch_y})\n",
" total_accuracy += (accuracy * len(batch_x))\n",
" return total_accuracy / num_examples"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Train the Model\n",
"Run the training data through the training pipeline to train the model.\n",
"\n",
"Before each epoch, shuffle the training set.\n",
"\n",
"After each epoch, measure the loss and accuracy of the validation set.\n",
"\n",
"Save the model after training.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training...\n",
"\n",
"EPOCH 1 ...\n",
"Validation Accuracy = 0.972\n",
"\n",
"EPOCH 2 ...\n",
"Validation Accuracy = 0.982\n",
"\n",
"EPOCH 3 ...\n",
"Validation Accuracy = 0.986\n",
"\n",
"EPOCH 4 ...\n",
"Validation Accuracy = 0.984\n",
"\n",
"EPOCH 5 ...\n",
"Validation Accuracy = 0.986\n",
"\n",
"EPOCH 6 ...\n",
"Validation Accuracy = 0.990\n",
"\n",
"EPOCH 7 ...\n",
"Validation Accuracy = 0.987\n",
"\n",
"EPOCH 8 ...\n",
"Validation Accuracy = 0.988\n",
"\n",
"EPOCH 9 ...\n",
"Validation Accuracy = 0.987\n",
"\n",
"EPOCH 10 ...\n",
"Validation Accuracy = 0.989\n",
"\n",
"Model saved\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" num_examples = len(X_train)\n",
" \n",
" print(\"Training...\")\n",
" print()\n",
" for i in range(EPOCHS):\n",
" X_train, y_train = shuffle(X_train, y_train)\n",
" for offset in range(0, num_examples, BATCH_SIZE):\n",
" end = offset + BATCH_SIZE\n",
" batch_x, batch_y = X_train[offset:end], y_train[offset:end]\n",
" sess.run(training_operation, feed_dict={x: batch_x, y: batch_y})\n",
" \n",
" validation_accuracy = evaluate(X_validation, y_validation)\n",
" print(\"EPOCH {} ...\".format(i+1))\n",
" print(\"Validation Accuracy = {:.3f}\".format(validation_accuracy))\n",
" print()\n",
" \n",
" saver.save(sess, './models/lenet')\n",
" print(\"Model saved\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Evaluate the Model\n",
"Once you are completely satisfied with your model, evaluate the performance of the model on the test set.\n",
"\n",
"Be sure to only do this once!\n",
"\n",
"If you were to measure the performance of your trained model on the test set, then improve your model, and then measure the performance of your model on the test set again, that would invalidate your test results. You wouldn't get a true measure of how well your model would perform against real data.\n",
"\n",
"You do not need to modify this section."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Accuracy = 0.990\n"
]
}
],
"source": [
"with tf.Session() as sess:\n",
" saver.restore(sess, tf.train.latest_checkpoint('./models'))\n",
"\n",
" test_accuracy = evaluate(X_test, y_test)\n",
" print(\"Test Accuracy = {:.3f}\".format(test_accuracy))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.5.2"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
TenninYan/Perceptron | ch11/discrete_distribution.ipynb | 2 | 34310 | {
"metadata": {
"name": "",
"signature": "sha256:811dd7d28362bf77f853df38158c1376025bea92f401d98b14bfba804b2cad28"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"# \u30dd\u30a2\u30bd\u30f3\u5206\u5e03\u306b\u5f93\u3046\u4e71\u6570\u3092\u7d2f\u7a4d\u5206\u5e03\u95a2\u6570\u304b\u3089\u751f\u6210\n",
"import numpy as np\n",
"from scipy.stats import uniform, poisson\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# \u30dd\u30a2\u30bd\u30f3\u5206\u5e03\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\n",
"lam = 4\n",
"\n",
"# \u3053\u306e\u5024\u307e\u3067\u78ba\u7387\u5024\u3092\u8a08\u7b97\n",
"K = 20\n",
"\n",
"# \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u6570\n",
"N = 10000\n",
"\n",
"rv = poisson(mu=lam)\n",
"\n",
"# cdf\u306e\u8868\u3092\u8a08\u7b97\n",
"t = np.arange(K)\n",
"prob = rv.cdf(t)\n",
"\n",
"X = []\n",
"for i in range(N):\n",
" u = uniform.rvs(loc=0, scale=1, size=1)\n",
" # prob < u\u306fcdf\u304cu\u3088\u308a\u5c0f\u3055\u3044\u3068\u304dTRUE\u3092\u8fd4\u3059\n",
" # TRUE\u306f1\u3068\u89e3\u91c8\u3055\u308c\u308b\u306e\u3067sum()\u3067TRUE\u306e\u6570\u3092\u30ab\u30a6\u30f3\u30c8\u3057\u3066\n",
" # \u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u6c42\u3081\u3066\u3044\u308b\n",
" X.append(np.sum(prob < u))\n",
"\n",
"# \u30dd\u30a2\u30bd\u30f3\u5206\u5e03\u306b\u5f93\u3046\u4e71\u6570\u306e\u5206\u5e03\u3092\u63cf\u753b\n",
"# hist()\u306enormed=True\u306f\u30d0\u30fc\u306e\u7a4d\u5206\u304c1\u306b\u306a\u308b\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570\u306b\u306a\u308b\u305f\u3081\u96e2\u6563\u5206\u5e03\u3067\u306f\u4f7f\u3048\u306a\u3044\n",
"# \u96e2\u6563\u5206\u5e03\u3067\u306f\u30d0\u30fc\u306e\u9ad8\u3055\u306e\u5408\u8a08\u304c1\u306b\u306a\u308b\u78ba\u7387\u8cea\u91cf\u95a2\u6570\u306b\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\n",
"# http://stackoverflow.com/questions/3866520/plotting-histograms-whose-bar-heights-sum-to-1-in-matplotlib\n",
"plt.figure(1)\n",
"nbins = np.arange(-0.5, K, 1.0)\n",
"weights = np.ones_like(X) / float(len(X))\n",
"plt.hist(X, nbins, weights=weights)\n",
"plt.plot(t, rv.pmf(t), 'ro-', lw=1)\n",
"plt.xlim((0, K))\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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dtMikjkSCi38d2V+ahSNBLrTZg3Fub2HNzevXN1s3PLw7p0597aXeRIKT/xzZ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\n9vDbAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x23479a10>"
]
}
],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# \u4e8c\u9805\u5206\u5e03\u306b\u5f93\u3046\u4e71\u6570\u3092\u7d2f\u7a4d\u5206\u5e03\u95a2\u6570\u304b\u3089\u751f\u6210\n",
"import numpy as np\n",
"from scipy.stats import uniform, binom\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# \u4e8c\u9805\u5206\u5e03\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\n",
"n = 20\n",
"p = 0.5\n",
"\n",
"# \u3053\u306e\u5024\u307e\u3067\u78ba\u7387\u5024\u3092\u8a08\u7b97\n",
"K = 40\n",
"\n",
"# \u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u6570\n",
"N = 10000\n",
"\n",
"rv = binom(n=n, p=p)\n",
"\n",
"# cdf\u306e\u8868\u3092\u8a08\u7b97\n",
"t = np.arange(K)\n",
"prob = rv.cdf(t)\n",
"\n",
"X = []\n",
"for i in range(N):\n",
" u = uniform.rvs(loc=0, scale=1, size=1)\n",
" # prob < u\u306fcdf\u304cu\u3088\u308a\u5c0f\u3055\u3044\u3068\u304dTRUE\u3092\u8fd4\u3059\n",
" # TRUE\u306f1\u3068\u89e3\u91c8\u3055\u308c\u308b\u306e\u3067sum()\u3067TRUE\u306e\u6570\u3092\u30ab\u30a6\u30f3\u30c8\u3057\u3066\n",
" # \u30a4\u30f3\u30c7\u30c3\u30af\u30b9\u3092\u6c42\u3081\u3066\u3044\u308b\n",
" X.append(np.sum(prob < u))\n",
"\n",
"# \u4e8c\u9805\u5206\u5e03\u306b\u5f93\u3046\u4e71\u6570\u306e\u5206\u5e03\u3092\u63cf\u753b\n",
"# hist()\u306enormed=True\u306f\u30d0\u30fc\u306e\u7a4d\u5206\u304c1\u306b\u306a\u308b\u78ba\u7387\u5bc6\u5ea6\u95a2\u6570\u306b\u306a\u308b\u305f\u3081\u96e2\u6563\u5206\u5e03\u3067\u306f\u4f7f\u3048\u306a\u3044\n",
"# \u96e2\u6563\u5206\u5e03\u3067\u306f\u30d0\u30fc\u306e\u9ad8\u3055\u306e\u5408\u8a08\u304c1\u306b\u306a\u308b\u78ba\u7387\u8cea\u91cf\u95a2\u6570\u306b\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\n",
"# http://stackoverflow.com/questions/3866520/plotting-histograms-whose-bar-heights-sum-to-1-in-matplotlib\n",
"plt.figure(1)\n",
"nbins = np.arange(-0.5, K, 1.0)\n",
"weights = np.ones_like(X) / float(len(X))\n",
"plt.hist(X, nbins, weights=weights)\n",
"plt.plot(t, rv.pmf(t), 'ro-', lw=1)\n",
"\uff50\uff4c\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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pU0ydOlXrkhxq0Or9dPnyZe69914eeeQR7rnnHkD7c3W1pocffthe\nUyCcq6smTJjAkiVL+Nvf/qb5uepb06FDhzCZTPbl/j5XBw8eZPfu3ZSXl3Px4kU6Ojp45JFHPDpP\nmg/pnDp1yv7zf//3fzN37lzNagnE7w6cP3+ezs5OAM6dO0dlZaWm56i37OxsXnvtNQBee+01e5Bo\nSev3k1KK3NxcUlJSWLdunX25ludqoJq0Pleff/65fWjkwoUL7N27l3nz5ml6rgaqqb293b6Nv8/V\nxo0bsVgsNDc3s2vXLr73ve/x+uuve3aefPFp8lA88sgjau7cueqmm25SP/jBD1R7e7um9ZSXl6vE\nxEQVFxenNm7cqGktSil1/PhxlZaWptLS0lRqaqpmNeXk5Kjp06crvV6vDAaD+uMf/6i++OILdeed\nd2p2WWbfmv7t3/5N8/fTgQMHlE6nU2lpaQ6X8Gl5rpzVVF5ervm5+vjjj9W8efNUWlqamjt3rvrn\nf/5npZTS9FwNVJPW5+qqqqoq+1U6npwnTee0FUII4T+aD+kIIYTwDwl8IYQIERL4QggRIiTwhRAi\nREjgCyFEiJDAF0KIEPH/AWk8vgGnjFRzAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x29c87750>"
]
}
],
"prompt_number": 56
}
],
"metadata": {}
}
]
} | mit |
AllenDowney/ModSimPy | notebooks/jump2.ipynb | 1 | 23352 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Modeling and Simulation in Python\n",
"\n",
"Bungee dunk example, taking into account the mass of the bungee cord\n",
"\n",
"Copyright 2019 Allen Downey\n",
"\n",
"License: [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Configure Jupyter so figures appear in the notebook\n",
"%matplotlib inline\n",
"\n",
"# Configure Jupyter to display the assigned value after an assignment\n",
"%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'\n",
"\n",
"# import functions from the modsim.py module\n",
"from modsim import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bungee jumping"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the previous case study, we simulated a bungee jump with a model that took into account gravity, air resistance, and the spring force of the bungee cord, but we ignored the weight of the cord.\n",
"\n",
"It is tempting to say that the weight of the cord doesn't matter, because it falls along with the jumper. But that intuition is incorrect, as explained by [Heck, Uylings, and Kędzierska](http://iopscience.iop.org/article/10.1088/0031-9120/45/1/007). As the cord falls, it transfers energy to the jumper. They derive a differential equation that relates the acceleration of the jumper to position and velocity:\n",
"\n",
"$a = g + \\frac{\\mu v^2/2}{\\mu(L+y) + 2L}$ \n",
"\n",
"where $a$ is the net acceleration of the number, $g$ is acceleration due to gravity, $v$ is the velocity of the jumper, $y$ is the position of the jumper relative to the starting point (usually negative), $L$ is the length of the cord, and $\\mu$ is the mass ratio of the cord and jumper.\n",
"\n",
"If you don't believe this model is correct, [this video might convince you](https://www.youtube.com/watch?v=X-QFAB0gEtE).\n",
"\n",
"Following the example in Chapter 21, we'll model the jump with the following modeling assumptions:\n",
"\n",
"1. Initially the bungee cord hangs from a crane with the attachment point 80 m above a cup of tea.\n",
"\n",
"2. Until the cord is fully extended, it applies a force to the jumper as explained above.\n",
"\n",
"3. After the cord is fully extended, it obeys [Hooke's Law](https://en.wikipedia.org/wiki/Hooke%27s_law); that is, it applies a force to the jumper proportional to the extension of the cord beyond its resting length.\n",
"\n",
"4. The jumper is subject to drag force proportional to the square of their velocity, in the opposite of their direction of motion.\n",
"\n",
"First I'll create a `Param` object to contain the quantities we'll need:\n",
"\n",
"1. Let's assume that the jumper's mass is 75 kg and the cord's mass is also 75 kg, so `mu=1`.\n",
"\n",
"2. The jumpers's frontal area is 1 square meter, and terminal velocity is 60 m/s. I'll use these values to back out the coefficient of drag.\n",
"\n",
"3. The length of the bungee cord is `L = 25 m`.\n",
"\n",
"4. The spring constant of the cord is `k = 40 N / m` when the cord is stretched, and 0 when it's compressed.\n",
"\n",
"I adopt the coordinate system and most of the variable names from [Heck, Uylings, and Kędzierska](http://iopscience.iop.org/article/10.1088/0031-9120/45/1/007).\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"m = UNITS.meter\n",
"s = UNITS.second\n",
"kg = UNITS.kilogram\n",
"N = UNITS.newton"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"params = Params(v_init = 0 * m / s,\n",
" g = 9.8 * m/s**2,\n",
" M = 75 * kg, # mass of jumper\n",
" m_cord = 75 * kg, # mass of cord\n",
" area = 1 * m**2, # frontal area of jumper\n",
" rho = 1.2 * kg/m**3, # density of air\n",
" v_term = 60 * m / s, # terminal velocity of jumper\n",
" L = 25 * m, # length of cord\n",
" k = 40 * N / m) # spring constant of cord"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now here's a version of `make_system` that takes a `Params` object as a parameter.\n",
"\n",
"`make_system` uses the given value of `v_term` to compute the drag coefficient `C_d`.\n",
"\n",
"It also computes `mu` and the initial `State` object."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def make_system(params):\n",
" \"\"\"Makes a System object for the given params.\n",
" \n",
" params: Params object\n",
" \n",
" returns: System object\n",
" \"\"\"\n",
" M, m_cord = params.M, params.m_cord\n",
" g, rho, area = params.g, params.rho, params.area\n",
" v_init, v_term = params.v_init, params.v_term\n",
" \n",
" # back out the coefficient of drag\n",
" C_d = 2 * M * g / (rho * area * v_term**2)\n",
" \n",
" mu = m_cord / M\n",
" init = State(y=0*m, v=v_init)\n",
" t_end = 10 * s\n",
"\n",
" return System(params, C_d=C_d, mu=mu,\n",
" init=init, t_end=t_end)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's make a `System`"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"system = make_system(params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`drag_force` computes drag as a function of velocity:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def drag_force(v, system):\n",
" \"\"\"Computes drag force in the opposite direction of `v`.\n",
" \n",
" v: velocity\n",
" \n",
" returns: drag force in N\n",
" \"\"\"\n",
" rho, C_d, area = system.rho, system.C_d, system.area\n",
"\n",
" f_drag = -np.sign(v) * rho * v**2 * C_d * area / 2\n",
" return f_drag"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's drag force at 20 m/s."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"drag_force(20 * m/s, system)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following function computes the acceleration of the jumper due to tension in the cord.\n",
"\n",
"$a_{cord} = \\frac{\\mu v^2/2}{\\mu(L+y) + 2L}$ "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def cord_acc(y, v, system):\n",
" \"\"\"Computes the force of the bungee cord on the jumper:\n",
" \n",
" y: height of the jumper\n",
" v: velocity of the jumpter\n",
" \n",
" returns: acceleration in m/s\n",
" \"\"\"\n",
" L, mu = system.L, system.mu\n",
" \n",
" a_cord = -v**2 / 2 / (2*L/mu + (L+y))\n",
" return a_cord"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's acceleration due to tension in the cord if we're going 20 m/s after falling 20 m."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"y = -20 * m\n",
"v = -20 * m/s\n",
"cord_acc(y, v, system)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now here's the slope function:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def slope_func1(state, t, system):\n",
" \"\"\"Compute derivatives of the state.\n",
" \n",
" state: position, velocity\n",
" t: time\n",
" system: System object containing g, rho,\n",
" C_d, area, and mass\n",
" \n",
" returns: derivatives of y and v\n",
" \"\"\"\n",
" y, v = state\n",
" M, g = system.M, system.g\n",
" \n",
" a_drag = drag_force(v, system) / M\n",
" a_cord = cord_acc(y, v, system)\n",
" dvdt = -g + a_cord + a_drag\n",
" \n",
" return v, dvdt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As always, let's test the slope function with the initial params."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"slope_func1(system.init, 0, system)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll need an event function to stop the simulation when we get to the end of the cord."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def event_func(state, t, system):\n",
" \"\"\"Run until y=-L.\n",
" \n",
" state: position, velocity\n",
" t: time\n",
" system: System object containing g, rho,\n",
" C_d, area, and mass\n",
" \n",
" returns: difference between y and -L\n",
" \"\"\"\n",
" y, v = state \n",
" return y + system.L"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can test it with the initial conditions."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"event_func(system.init, 0, system)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then run the simulation."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"results, details = run_ode_solver(system, slope_func1, events=event_func)\n",
"details.message"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's how long it takes to drop 25 meters."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"t_final = get_last_label(results)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's the plot of position as a function of time."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def plot_position(results, **options):\n",
" plot(results.y, **options)\n",
" decorate(xlabel='Time (s)',\n",
" ylabel='Position (m)')\n",
" \n",
"plot_position(results)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use `min` to find the lowest point:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"min(results.y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's velocity as a function of time:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"def plot_velocity(results):\n",
" plot(results.v, color='C1', label='v')\n",
" \n",
" decorate(xlabel='Time (s)',\n",
" ylabel='Velocity (m/s)')\n",
" \n",
"plot_velocity(results)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Velocity when we reach the end of the cord."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"min(results.v)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although we compute acceleration inside the slope function, we don't get acceleration as a result from `run_ode_solver`.\n",
"\n",
"We can approximate it by computing the numerical derivative of `v`:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"a = gradient(results.v)\n",
"plot(a)\n",
"decorate(xlabel='Time (s)',\n",
" ylabel='Acceleration (m/$s^2$)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The maximum downward acceleration, as a factor of `g`"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"max_acceleration = max(abs(a)) * m/s**2 / params.g"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using Equation (1) from [Heck, Uylings, and Kędzierska](http://iopscience.iop.org/article/10.1088/0031-9120/45/1/007), we can compute the peak acceleration due to interaction with the cord, neglecting drag."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def max_acceleration(system):\n",
" mu = system.mu\n",
" return 1 + mu * (4+mu) / 8\n",
"\n",
"max_acceleration(system)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you set `C_d=0`, the simulated acceleration approaches the theoretical result, although you might have to reduce `max_step` to get a good numerical estimate."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sweeping cord weight\n",
"\n",
"Now let's see how velocity at the crossover point depends on the weight of the cord."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"def sweep_m_cord(m_cord_array, params):\n",
" sweep = SweepSeries()\n",
"\n",
" for m_cord in m_cord_array:\n",
" system = make_system(Params(params, m_cord=m_cord))\n",
" results, details = run_ode_solver(system, slope_func1, events=event_func)\n",
" min_velocity = min(results.v) * m/s\n",
" sweep[m_cord.magnitude] = min_velocity\n",
" \n",
" return sweep"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"m_cord_array = linspace(1, 201, 21) * kg\n",
"sweep = sweep_m_cord(m_cord_array, params)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's what it looks like. As expected, a heavier cord gets the jumper going faster.\n",
"\n",
"There's a hitch near 25 kg that seems to be due to numerical error."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"plot(sweep)\n",
"\n",
"decorate(xlabel='Mass of cord (kg)',\n",
" ylabel='Fastest downward velocity (m/s)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Phase 2\n",
"\n",
"Once the jumper falls past the length of the cord, acceleration due to energy transfer from the cord stops abruptly. As the cord stretches, it starts to exert a spring force. So let's simulate this second phase."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`spring_force` computes the force of the cord on the jumper:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def spring_force(y, system):\n",
" \"\"\"Computes the force of the bungee cord on the jumper:\n",
" \n",
" y: height of the jumper\n",
" \n",
" Uses these variables from system:\n",
" y_attach: height of the attachment point\n",
" L: resting length of the cord\n",
" k: spring constant of the cord\n",
" \n",
" returns: force in N\n",
" \"\"\"\n",
" L, k = system.L, system.k\n",
" \n",
" distance_fallen = -y\n",
" extension = distance_fallen - L\n",
" f_spring = k * extension\n",
" return f_spring"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The spring force is 0 until the cord is fully extended. When it is extended 1 m, the spring force is 40 N. "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"spring_force(-25*m, system)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"spring_force(-26*m, system)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The slope function for Phase 2 includes the spring force, and drops the acceleration due to the cord."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def slope_func2(state, t, system):\n",
" \"\"\"Compute derivatives of the state.\n",
" \n",
" state: position, velocity\n",
" t: time\n",
" system: System object containing g, rho,\n",
" C_d, area, and mass\n",
" \n",
" returns: derivatives of y and v\n",
" \"\"\"\n",
" y, v = state\n",
" M, g = system.M, system.g\n",
" \n",
" a_drag = drag_force(v, system) / M\n",
" a_spring = spring_force(y, system) / M\n",
" dvdt = -g + a_drag + a_spring\n",
" \n",
" return v, dvdt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I'll run Phase 1 again so we can get the final state."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"system1 = make_system(params)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"event_func.direction=-1\n",
"results1, details1 = run_ode_solver(system1, slope_func1, events=event_func)\n",
"print(details1.message)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now I need the final time, position, and velocity from Phase 1."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"t_final = get_last_label(results1)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"init2 = results1.row[t_final]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And that gives me the starting conditions for Phase 2."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
"system2 = System(system1, t_0=t_final, init=init2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's how we run Phase 2, setting the direction of the event function so it doesn't stop the simulation immediately. "
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"event_func.direction=+1\n",
"results2, details2 = run_ode_solver(system2, slope_func2, events=event_func)\n",
"print(details2.message)\n",
"t_final = get_last_label(results2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the results on the same axes."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"plot_position(results1, label='Phase 1')\n",
"plot_position(results2, label='Phase 2')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And get the lowest position from Phase 2."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"min(results2.y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To see how big the effect of the cord is, I'll collect the previous code in a function."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"def simulate_system2(params):\n",
" \n",
" system1 = make_system(params)\n",
" event_func.direction=-1\n",
" results1, details1 = run_ode_solver(system1, slope_func1, events=event_func)\n",
"\n",
" t_final = get_last_label(results1)\n",
" init2 = results1.row[t_final]\n",
" \n",
" system2 = System(system1, t_0=t_final, init=init2)\n",
" results2, details2 = run_ode_solver(system2, slope_func2, events=event_func)\n",
" t_final = get_last_label(results2)\n",
" return TimeFrame(pd.concat([results1, results2]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can run both phases and get the results in a single `TimeFrame`."
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"results = simulate_system2(params);"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"plot_position(results)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"params_no_cord = Params(params, m_cord=1*kg)\n",
"results_no_cord = simulate_system2(params_no_cord);"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"plot_position(results, label='m_cord = 75 kg')\n",
"plot_position(results_no_cord, label='m_cord = 1 kg')\n",
"\n",
"savefig('figs/jump.png')"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"min(results_no_cord.y)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"diff = min(results.y) - min(results_no_cord.y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The difference is more than 2 meters, which could certainly be the difference between a successful bungee dunk and a bad day."
]
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
tetsuyasu/jupyter_tfbook | Chapter03/Double layer network example.ipynb | 1 | 29505 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[DNE-01]** モジュールをインポートして、乱数のシードを設定します。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from numpy.random import multivariate_normal, permutation\n",
"import pandas as pd\n",
"from pandas import DataFrame, Series\n",
"\n",
"np.random.seed(20160615)\n",
"tf.set_random_seed(20160615)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[DNE-02]** トレーニングセットのデータを生成します。"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"def generate_datablock(n, mu, var, t):\n",
" data = multivariate_normal(mu, np.eye(2)*var, n)\n",
" df = DataFrame(data, columns=['x1','x2'])\n",
" df['t'] = t\n",
" return df\n",
"\n",
"df0 = generate_datablock(30, [-7,-7], 18, 1)\n",
"df1 = generate_datablock(30, [-7,7], 18, 0)\n",
"df2 = generate_datablock(30, [7,-7], 18, 0)\n",
"df3 = generate_datablock(30, [7,7], 18, 1)\n",
"\n",
"df = pd.concat([df0, df1, df2, df3], ignore_index=True)\n",
"train_set = df.reindex(permutation(df.index)).reset_index(drop=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[DNE-03]** (x1, x2) と t を別々に集めたものをNumPyのarrayオブジェクトとして取り出しておきます。"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_x = train_set[['x1','x2']].as_matrix()\n",
"train_t = train_set['t'].as_matrix().reshape([len(train_set), 1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[DNE-04]** 二層ネットワークによる二項分類器のモデルを定義します。"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"num_units1 = 2\n",
"num_units2 = 2\n",
"\n",
"x = tf.placeholder(tf.float32, [None, 2])\n",
"\n",
"w1 = tf.Variable(tf.truncated_normal([2, num_units1]))\n",
"b1 = tf.Variable(tf.zeros([num_units1]))\n",
"hidden1 = tf.nn.tanh(tf.matmul(x, w1) + b1)\n",
"\n",
"w2 = tf.Variable(tf.truncated_normal([num_units1, num_units2]))\n",
"b2 = tf.Variable(tf.zeros([num_units2]))\n",
"hidden2 = tf.nn.tanh(tf.matmul(hidden1, w2) + b2)\n",
"\n",
"w0 = tf.Variable(tf.zeros([num_units2, 1]))\n",
"b0 = tf.Variable(tf.zeros([1]))\n",
"p = tf.nn.sigmoid(tf.matmul(hidden2, w0) + b0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[DNE-05]** 誤差関数 loss、トレーニングアルゴリズム train_step、正解率 accuracy を定義します。"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t = tf.placeholder(tf.float32, [None, 1])\n",
"loss = -tf.reduce_sum(t*tf.log(p) + (1-t)*tf.log(1-p))\n",
"train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss)\n",
"correct_prediction = tf.equal(tf.sign(p-0.5), tf.sign(t-0.5))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[DNE-06]** セッションを用意して、Variableを初期化します。"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"sess = tf.InteractiveSession()\n",
"sess.run(tf.global_variables_initializer())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[DNE-07]** パラメーターの最適化を2000回繰り返します。"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Step: 100, Loss: 83.176933, Accuracy: 0.508333\n",
"Step: 200, Loss: 83.176178, Accuracy: 0.508333\n",
"Step: 300, Loss: 83.174591, Accuracy: 0.508333\n",
"Step: 400, Loss: 83.171082, Accuracy: 0.500000\n",
"Step: 500, Loss: 83.162636, Accuracy: 0.508333\n",
"Step: 600, Loss: 83.140877, Accuracy: 0.516667\n",
"Step: 700, Loss: 83.075996, Accuracy: 0.541667\n",
"Step: 800, Loss: 82.822495, Accuracy: 0.541667\n",
"Step: 900, Loss: 81.475693, Accuracy: 0.625000\n",
"Step: 1000, Loss: 75.140419, Accuracy: 0.658333\n",
"Step: 1100, Loss: 59.051060, Accuracy: 0.866667\n",
"Step: 1200, Loss: 46.646378, Accuracy: 0.900000\n",
"Step: 1300, Loss: 41.770844, Accuracy: 0.900000\n",
"Step: 1400, Loss: 39.639244, Accuracy: 0.900000\n",
"Step: 1500, Loss: 38.510742, Accuracy: 0.900000\n",
"Step: 1600, Loss: 37.788445, Accuracy: 0.900000\n",
"Step: 1700, Loss: 37.159111, Accuracy: 0.900000\n",
"Step: 1800, Loss: 36.648502, Accuracy: 0.900000\n",
"Step: 1900, Loss: 36.529396, Accuracy: 0.891667\n",
"Step: 2000, Loss: 36.352604, Accuracy: 0.891667\n"
]
}
],
"source": [
"i = 0\n",
"for _ in range(2000):\n",
" i += 1\n",
" sess.run(train_step, feed_dict={x:train_x, t:train_t})\n",
" if i % 100 == 0:\n",
" loss_val, acc_val = sess.run(\n",
" [loss, accuracy], feed_dict={x:train_x, t:train_t})\n",
" print ('Step: %d, Loss: %f, Accuracy: %f'\n",
" % (i, loss_val, acc_val))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**[DNE-08]** 得られた確率を色の濃淡で図示します。"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7efe10359c50>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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M9567iGwE8E0ASQD/oKp/afdcVS1VH9RKgzipqqj1mJuKDaeVJrMdO6U5/Pr5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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efe123b1d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"train_set1 = train_set[train_set['t']==1]\n",
"train_set2 = train_set[train_set['t']==0]\n",
"\n",
"fig = plt.figure(figsize=(6,6))\n",
"subplot = fig.add_subplot(1,1,1)\n",
"subplot.set_ylim([-15,15])\n",
"subplot.set_xlim([-15,15])\n",
"subplot.scatter(train_set1.x1, train_set1.x2, marker='x')\n",
"subplot.scatter(train_set2.x1, train_set2.x2, marker='o')\n",
"\n",
"locations = []\n",
"for x2 in np.linspace(-15,15,100):\n",
" for x1 in np.linspace(-15,15,100):\n",
" locations.append((x1,x2))\n",
"p_vals = sess.run(p, feed_dict={x:locations})\n",
"p_vals = p_vals.reshape((100,100))\n",
"subplot.imshow(p_vals, origin='lower', extent=(-15,15,-15,15),\n",
" cmap=plt.cm.gray_r, alpha=0.5)"
]
}
],
"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": 1
}
| apache-2.0 |
echohenry2006/tvb-library | tvb/simulator/doc/tutorials/Tutorial_Modeling_The_Impact_Of_Structural_Lesions/Tutorial_Modeling_The_Impact_Of_Structural_Lesions_Part_III.ipynb | 4 | 1631491 | null | gpl-2.0 |
georgetown-analytics/classroom-occupancy | exploration/exploration at 03-25-2017/Exploratory.ipynb | 2 | 233783 | {
"cells": [
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from datetime import datetime"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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g37/YjqOXAgd/xiZj1Idb8MWWM9h1Ngv/HEnDzjNZuOUz28DW0RRr9szkH/bj+13nEZea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\nRx60h62sKQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x117074cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Read CO2 data\n",
"CO2 = pd.read_csv('CO2.csv', header = 0, \n",
" names = ['Date_Time','CO2','TimeFix'])\n",
"\n",
"#plot the CO2 feature\n",
"CO2.plot.line('Date_Time','CO2',figsize=(20,5))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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SMSftKgG1AWn1ty6E0BHIiDFWNDw+E/hP4FHgMmBqw9dpDac8CtwTQriFPYuVDwFmNntw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\nIiIiIiIiIiIt4v8B/qUDcIN4il4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107c1fdd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Read bluetooth data \n",
"Bluetooth = pd.read_csv('Bluetooth.csv', delimiter = \";\", header = 0, \n",
" names = ['Date_Time','Location','Bluetooth','TimeFix'])\n",
"\n",
"#plot the Bluetooth feature\n",
"Bluetooth.plot.line('Date_Time','Bluetooth',figsize=(20,5))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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QJUmSJqSIZ59HkiRJ9WUQJUmSJiQbQEmSJI0/BlGSJGlSMJiSJElqPIMoSZIkSZIk1YVB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\nmZmZmZlNCp+IMjMzMzMzMzOzSfF/VESxl0nydV0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1164e88d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Read light date\n",
"Light = pd.read_csv('Light.csv', delimiter = \";\", header = 0, names = ['Date_Time','Location','Light','TimeFix'])\n",
"\n",
"#plot Light feature\n",
"Light.plot.line('Date_Time','Light',figsize=(20,5))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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BLGsq+uiwq7zxexTOXc7HqC92oP//tth+wTVWVKLDKScbZ+Vhq+7aEpuGLzfb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\n68EGPI6yWsiifuVMnMoe55TaEZnTxldsfSjKkJNamcY4ikOAs1Tnb56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"text/plain": [
"<matplotlib.figure.Figure at 0x117313dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Read sound date\n",
"Sound = pd.read_csv('Sound.csv', header = 0, \n",
" names = ['Date_Time','Sound','TimeFix'])\n",
"\n",
"#Plot sound feature\n",
"Sound.plot.line('Date_Time','Sound',figsize=(20,5))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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1/u9W6bOiqlbHm4dQAIKPIAoAAABAn7a/pHXY0Nxd542TJJXXOrQjv1wHS6r1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\noT9hZpr+k9DedtZobTh4XLUNPas9z9m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iiChJkiRJkiSNABNRkiRJkiRJ6op+E1ERMSMi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\nSQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11851d390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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0yPpmNtBi6Jt/D3F7r3wPApd7JxVXZ7mriJKK3+S6y/Xs8y4aUJ0B4Fm9T1HD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\neEBrXTyg5sK36gihJCk40KI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gPrq5o1cQKlPr2pHM/Xs/3rymLQCLnurvPveW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\niIiIFIr/B+uGdoUIp0AEAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x117673048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Read temperature and humidity data\n",
"Temp_humidity = pd.read_csv('Temp_humidity.csv', delimiter = \";\", header = 0, \n",
" names = ['Date_Time','Location','Temp','Humidity','Time fix'])\n",
"\n",
"#Plot temperature data\n",
"Temp_humidity.plot.line('Date_Time','Temp',figsize=(20,5))\n",
"plt.show()\n",
"\n",
"#Plot humidity data\n",
"Temp_humidity.plot.line('Date_Time','Humidity',figsize=(20,5))\n",
"plt.show()"
]
}
],
"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 |
astro4dev/OAD-Data-Science-Toolkit | Teaching Materials/Machine Learning/ml-training-intro/notebooks/04 - Preprocessing.ipynb | 2 | 4844 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"% matplotlib inline\n",
"plt.rcParams[\"figure.dpi\"] = 200"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import load_boston\n",
"boston = load_boston()\n",
"from sklearn.model_selection import train_test_split\n",
"X, y = boston.data, boston.target\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" X, y, random_state=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(boston.DESCR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"fig, axes = plt.subplots(3, 5, figsize=(20, 10))\n",
"for i, ax in enumerate(axes.ravel()):\n",
" if i > 12:\n",
" ax.set_visible(False)\n",
" continue\n",
" ax.plot(X[:, i], y, 'o', alpha=.5)\n",
" ax.set_title(\"{}: {}\".format(i, boston.feature_names[i]))\n",
" ax.set_ylabel(\"MEDV\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.boxplot(X)\n",
"plt.xticks(np.arange(1, X.shape[1] + 1),\n",
" boston.feature_names, rotation=30, ha=\"right\")\n",
"plt.ylabel(\"MEDV\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"scaler = StandardScaler()\n",
"X_train_scaled = scaler.fit_transform(X_train)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import cross_val_score"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsRegressor\n",
"scores = cross_val_score(KNeighborsRegressor(),\n",
" X_train, y_train, cv=10)\n",
"np.mean(scores), np.std(scores)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.neighbors import KNeighborsRegressor\n",
"scores = cross_val_score(KNeighborsRegressor(),\n",
" X_train_scaled, y_train, cv=10)\n",
"np.mean(scores), np.std(scores)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Categorical Variables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"df = pd.DataFrame({'salary': [103, 89, 142, 54, 63, 219],\n",
" 'boro': ['Manhatten', 'Queens', 'Manhatten', 'Brooklyn', 'Brooklyn', 'Bronx']})\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pd.get_dummies(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame({'salary': [103, 89, 142, 54, 63, 219],\n",
" 'boro': [0, 1, 0, 2, 2, 3]})\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pd.get_dummies(df, columns=['boro'])"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Exercise\n",
"Apply dummy encoding and scaling to the \"adult\" dataset consisting of income data from the census.\n",
"\n",
"Bonus: visualize the data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = pd.read_csv(\"adult.csv\", index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# %load solutions/load_adult.py"
]
}
],
"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
}
| gpl-3.0 |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/labs/samples/contrib/mnist/04_Reusable_and_Pre-build_Components_as_Pipeline.ipynb | 1 | 27845 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Copyright 2019 Google Inc. All Rights Reserved.\n",
"#\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",
"# http://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.\n",
"# =============================================================================="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Composing a pipeline from reusable, pre-built, and lightweight components\n",
"\n",
"This tutorial describes how to build a Kubeflow pipeline from reusable, pre-built, and lightweight components. The following provides a summary of the steps involved in creating and using a reusable component:\n",
"\n",
"- Write the program that contains your component’s logic. The program must use files and command-line arguments to pass data to and from the component.\n",
"- Containerize the program.\n",
"- Write a component specification in YAML format that describes the component for the Kubeflow Pipelines system.\n",
"- Use the Kubeflow Pipelines SDK to load your component, use it in a pipeline and run that pipeline.\n",
"\n",
"Then, we will compose a pipeline from a reusable component, a pre-built component, and a lightweight component. The pipeline will perform the following steps:\n",
"- Train an MNIST model and export it to Google Cloud Storage.\n",
"- Deploy the exported TensorFlow model on AI Platform Prediction service.\n",
"- Test the deployment by calling the endpoint with test data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: Ensure that you have Docker installed, if you want to build the image locally, by running the following command:\n",
" \n",
"`which docker`\n",
" \n",
"The result should be something like:\n",
"\n",
"`/usr/bin/docker`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import kfp\n",
"import kfp.gcp as gcp\n",
"import kfp.dsl as dsl\n",
"import kfp.compiler as compiler\n",
"import kfp.components as comp\n",
"import datetime\n",
"\n",
"import kubernetes as k8s"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"parameter"
]
},
"outputs": [],
"source": [
"# Required Parameters\n",
"PROJECT_ID='<ADD GCP PROJECT HERE>'\n",
"GCS_BUCKET='gs://<ADD STORAGE LOCATION HERE>'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create client\n",
"\n",
"If you run this notebook **outside** of a Kubeflow cluster, run the following command:\n",
"- `host`: The URL of your Kubeflow Pipelines instance, for example \"https://`<your-deployment>`.endpoints.`<your-project>`.cloud.goog/pipeline\"\n",
"- `client_id`: The client ID used by Identity-Aware Proxy\n",
"- `other_client_id`: The client ID used to obtain the auth codes and refresh tokens.\n",
"- `other_client_secret`: The client secret used to obtain the auth codes and refresh tokens.\n",
"\n",
"```python\n",
"client = kfp.Client(host, client_id, other_client_id, other_client_secret)\n",
"```\n",
"\n",
"If you run this notebook **within** a Kubeflow cluster, run the following command:\n",
"```python\n",
"client = kfp.Client()\n",
"```\n",
"\n",
"You'll need to create OAuth client ID credentials of type `Other` to get `other_client_id` and `other_client_secret`. Learn more about [creating OAuth credentials](\n",
"https://cloud.google.com/iap/docs/authentication-howto#authenticating_from_a_desktop_app)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Optional Parameters, but required for running outside Kubeflow cluster\n",
"\n",
"# The host for 'AI Platform Pipelines' ends with 'pipelines.googleusercontent.com'\n",
"# The host for pipeline endpoint of 'full Kubeflow deployment' ends with '/pipeline'\n",
"# Examples are:\n",
"# https://7c021d0340d296aa-dot-us-central2.pipelines.googleusercontent.com\n",
"# https://kubeflow.endpoints.kubeflow-pipeline.cloud.goog/pipeline\n",
"HOST = '<ADD HOST NAME TO TALK TO KUBEFLOW PIPELINE HERE>'\n",
"\n",
"# For 'full Kubeflow deployment' on GCP, the endpoint is usually protected through IAP, therefore the following \n",
"# will be needed to access the endpoint.\n",
"CLIENT_ID = '<ADD OAuth CLIENT ID USED BY IAP HERE>'\n",
"OTHER_CLIENT_ID = '<ADD OAuth CLIENT ID USED TO OBTAIN AUTH CODES HERE>'\n",
"OTHER_CLIENT_SECRET = '<ADD OAuth CLIENT SECRET USED TO OBTAIN AUTH CODES HERE>'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# This is to ensure the proper access token is present to reach the end point for 'AI Platform Pipelines'\n",
"# If you are not working with 'AI Platform Pipelines', this step is not necessary\n",
"! gcloud auth print-access-token"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Create kfp client\n",
"in_cluster = True\n",
"try:\n",
" k8s.config.load_incluster_config()\n",
"except:\n",
" in_cluster = False\n",
" pass\n",
"\n",
"if in_cluster:\n",
" client = kfp.Client()\n",
"else:\n",
" if HOST.endswith('googleusercontent.com'):\n",
" CLIENT_ID = None\n",
" OTHER_CLIENT_ID = None\n",
" OTHER_CLIENT_SECRET = None\n",
"\n",
" client = kfp.Client(host=HOST, \n",
" client_id=CLIENT_ID,\n",
" other_client_id=OTHER_CLIENT_ID, \n",
" other_client_secret=OTHER_CLIENT_SECRET)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build reusable components"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Writing the program code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following cell creates a file `app.py` that contains a Python script. The script downloads MNIST dataset, trains a Neural Network based classification model, writes the training log and exports the trained model to Google Cloud Storage.\n",
"\n",
"Your component can create outputs that the downstream components can use as inputs. Each output must be a string and the container image must write each output to a separate local text file. For example, if a training component needs to output the path of the trained model, the component writes the path into a local file, such as `/output.txt`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"# Create folders if they don't exist.\n",
"mkdir -p tmp/reuse_components_pipeline/mnist_training\n",
"\n",
"# Create the Python file that lists GCS blobs.\n",
"cat > ./tmp/reuse_components_pipeline/mnist_training/app.py <<HERE\n",
"import argparse\n",
"from datetime import datetime\n",
"import tensorflow as tf\n",
"\n",
"parser = argparse.ArgumentParser()\n",
"parser.add_argument(\n",
" '--model_path', type=str, required=True, help='Name of the model file.')\n",
"parser.add_argument(\n",
" '--bucket', type=str, required=True, help='GCS bucket name.')\n",
"args = parser.parse_args()\n",
"\n",
"bucket=args.bucket\n",
"model_path=args.model_path\n",
"\n",
"model = tf.keras.models.Sequential([\n",
" tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
" tf.keras.layers.Dense(512, activation=tf.nn.relu),\n",
" tf.keras.layers.Dropout(0.2),\n",
" tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n",
"])\n",
"\n",
"model.compile(optimizer='adam',\n",
" loss='sparse_categorical_crossentropy',\n",
" metrics=['accuracy'])\n",
"\n",
"print(model.summary()) \n",
"\n",
"mnist = tf.keras.datasets.mnist\n",
"(x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
"x_train, x_test = x_train / 255.0, x_test / 255.0\n",
"\n",
"callbacks = [\n",
" tf.keras.callbacks.TensorBoard(log_dir=bucket + '/logs/' + datetime.now().date().__str__()),\n",
" # Interrupt training if val_loss stops improving for over 2 epochs\n",
" tf.keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'),\n",
"]\n",
"\n",
"model.fit(x_train, y_train, batch_size=32, epochs=5, callbacks=callbacks,\n",
" validation_data=(x_test, y_test))\n",
"\n",
"from tensorflow import gfile\n",
"\n",
"gcs_path = bucket + \"/\" + model_path\n",
"# The export require the folder is new\n",
"if gfile.Exists(gcs_path):\n",
" gfile.DeleteRecursively(gcs_path)\n",
"tf.keras.experimental.export_saved_model(model, gcs_path)\n",
"\n",
"with open('/output.txt', 'w') as f:\n",
" f.write(gcs_path)\n",
"HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create a Docker container\n",
"Create your own container image that includes your program. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating a Dockerfile"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now create a container that runs the script. Start by creating a Dockerfile. A Dockerfile contains the instructions to assemble a Docker image. The `FROM` statement specifies the Base Image from which you are building. `WORKDIR` sets the working directory. When you assemble the Docker image, `COPY` copies the required files and directories (for example, `app.py`) to the file system of the container. `RUN` executes a command (for example, install the dependencies) and commits the results. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash\n",
"\n",
"# Create Dockerfile.\n",
"# AI platform only support tensorflow 1.14\n",
"cat > ./tmp/reuse_components_pipeline/mnist_training/Dockerfile <<EOF\n",
"FROM tensorflow/tensorflow:1.14.0-py3\n",
"WORKDIR /app\n",
"COPY . /app\n",
"EOF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Build docker image"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have created our Dockerfile for creating our Docker image. Then we need to build the image and push to a registry to host the image. There are three possible options:\n",
"- Use the `kfp.containers.build_image_from_working_dir` to build the image and push to the Container Registry (GCR). This requires [kaniko](https://cloud.google.com/blog/products/gcp/introducing-kaniko-build-container-images-in-kubernetes-and-google-container-builder-even-without-root-access), which will be auto-installed with 'full Kubeflow deployment' but not 'AI Platform Pipelines'.\n",
"- Use [Cloud Build](https://cloud.google.com/cloud-build), which would require the setup of GCP project and enablement of corresponding API. If you are working with GCP 'AI Platform Pipelines' with GCP project running, it is recommended to use Cloud Build.\n",
"- Use [Docker](https://www.docker.com/get-started) installed locally and push to e.g. GCR."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**:\n",
"If you run this notebook **within Kubeflow cluster**, **with Kubeflow version >= 0.7** and exploring **kaniko option**, you need to ensure that valid credentials are created within your notebook's namespace.\n",
"- With Kubeflow version >= 0.7, the credential is supposed to be copied automatically while creating notebook through `Configurations`, which doesn't work properly at the time of creating this notebook. \n",
"- You can also add credentials to the new namespace by either [copying credentials from an existing Kubeflow namespace, or by creating a new service account](https://www.kubeflow.org/docs/gke/authentication/#kubeflow-v0-6-and-before-gcp-service-account-key-as-secret).\n",
"- The following cell demonstrates how to copy the default secret to your own namespace.\n",
"\n",
"```bash\n",
"%%bash\n",
"\n",
"NAMESPACE=<your notebook name space>\n",
"SOURCE=kubeflow\n",
"NAME=user-gcp-sa\n",
"SECRET=$(kubectl get secrets \\${NAME} -n \\${SOURCE} -o jsonpath=\"{.data.\\${NAME}\\.json}\" | base64 -D)\n",
"kubectl create -n \\${NAMESPACE} secret generic \\${NAME} --from-literal=\"\\${NAME}.json=\\${SECRET}\"\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"IMAGE_NAME=\"mnist_training_kf_pipeline\"\n",
"TAG=\"latest\" # \"v_$(date +%Y%m%d_%H%M%S)\"\n",
"\n",
"GCR_IMAGE=\"gcr.io/{PROJECT_ID}/{IMAGE_NAME}:{TAG}\".format(\n",
" PROJECT_ID=PROJECT_ID,\n",
" IMAGE_NAME=IMAGE_NAME,\n",
" TAG=TAG\n",
")\n",
"\n",
"APP_FOLDER='./tmp/reuse_components_pipeline/mnist_training/'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# In the following, for the purpose of demonstration\n",
"# Cloud Build is choosen for 'AI Platform Pipelines'\n",
"# kaniko is choosen for 'full Kubeflow deployment'\n",
"\n",
"if HOST.endswith('googleusercontent.com'):\n",
" # kaniko is not pre-installed with 'AI Platform Pipelines'\n",
" import subprocess\n",
" # ! gcloud builds submit --tag ${IMAGE_NAME} ${APP_FOLDER}\n",
" cmd = ['gcloud', 'builds', 'submit', '--tag', GCR_IMAGE, APP_FOLDER]\n",
" build_log = (subprocess.run(cmd, stdout=subprocess.PIPE).stdout[:-1].decode('utf-8'))\n",
" print(build_log)\n",
" \n",
"else:\n",
" if kfp.__version__ <= '0.1.36':\n",
" # kfp with version 0.1.36+ introduce broken change that will make the following code not working\n",
" import subprocess\n",
" \n",
" builder = kfp.containers._container_builder.ContainerBuilder(\n",
" gcs_staging=GCS_BUCKET + \"/kfp_container_build_staging\"\n",
" )\n",
"\n",
" kfp.containers.build_image_from_working_dir(\n",
" image_name=GCR_IMAGE,\n",
" working_dir=APP_FOLDER,\n",
" builder=builder\n",
" )\n",
" else:\n",
" raise(\"Please build the docker image use either [Docker] or [Cloud Build]\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### If you want to use docker to build the image\n",
"Run the following in a cell\n",
"```bash\n",
"%%bash -s \"{PROJECT_ID}\"\n",
"\n",
"IMAGE_NAME=\"mnist_training_kf_pipeline\"\n",
"TAG=\"latest\" # \"v_$(date +%Y%m%d_%H%M%S)\"\n",
"\n",
"# Create script to build docker image and push it.\n",
"cat > ./tmp/components/mnist_training/build_image.sh <<HERE\n",
"PROJECT_ID=\"${1}\"\n",
"IMAGE_NAME=\"${IMAGE_NAME}\"\n",
"TAG=\"${TAG}\"\n",
"GCR_IMAGE=\"gcr.io/\\${PROJECT_ID}/\\${IMAGE_NAME}:\\${TAG}\"\n",
"docker build -t \\${IMAGE_NAME} .\n",
"docker tag \\${IMAGE_NAME} \\${GCR_IMAGE}\n",
"docker push \\${GCR_IMAGE}\n",
"docker image rm \\${IMAGE_NAME}\n",
"docker image rm \\${GCR_IMAGE}\n",
"HERE\n",
"\n",
"cd tmp/components/mnist_training\n",
"bash build_image.sh\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"image_name = GCR_IMAGE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Writing your component definition file\n",
"To create a component from your containerized program, you must write a component specification in YAML that describes the component for the Kubeflow Pipelines system.\n",
"\n",
"For the complete definition of a Kubeflow Pipelines component, see the [component specification](https://www.kubeflow.org/docs/pipelines/reference/component-spec/). However, for this tutorial you don’t need to know the full schema of the component specification. The notebook provides enough information to complete the tutorial.\n",
"\n",
"Start writing the component definition (component.yaml) by specifying your container image in the component’s implementation section:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%bash -s \"{image_name}\"\n",
"\n",
"GCR_IMAGE=\"${1}\"\n",
"echo ${GCR_IMAGE}\n",
"\n",
"# Create Yaml\n",
"# the image uri should be changed according to the above docker image push output\n",
"\n",
"cat > mnist_pipeline_component.yaml <<HERE\n",
"name: Mnist training\n",
"description: Train a mnist model and save to GCS\n",
"inputs:\n",
" - name: model_path\n",
" description: 'Path of the tf model.'\n",
" type: String\n",
" - name: bucket\n",
" description: 'GCS bucket name.'\n",
" type: String\n",
"outputs:\n",
" - name: gcs_model_path\n",
" description: 'Trained model path.'\n",
" type: GCSPath\n",
"implementation:\n",
" container:\n",
" image: ${GCR_IMAGE}\n",
" command: [\n",
" python, /app/app.py,\n",
" --model_path, {inputValue: model_path},\n",
" --bucket, {inputValue: bucket},\n",
" ]\n",
" fileOutputs:\n",
" gcs_model_path: /output.txt\n",
"HERE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"mnist_train_op = kfp.components.load_component_from_file(os.path.join('./', 'mnist_pipeline_component.yaml')) "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mnist_train_op.component_spec"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Define deployment operation on AI Platform"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mlengine_deploy_op = comp.load_component_from_url(\n",
" 'https://raw.githubusercontent.com/kubeflow/pipelines/1.4.0/components/gcp/ml_engine/deploy/component.yaml')\n",
"\n",
"def deploy(\n",
" project_id,\n",
" model_uri,\n",
" model_id,\n",
" runtime_version,\n",
" python_version):\n",
" \n",
" return mlengine_deploy_op(\n",
" model_uri=model_uri,\n",
" project_id=project_id, \n",
" model_id=model_id, \n",
" runtime_version=runtime_version, \n",
" python_version=python_version,\n",
" replace_existing_version=True, \n",
" set_default=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Kubeflow serving deployment component as an option. **Note that, the deployed Endppoint URI is not availabe as output of this component.**\n",
"```python\n",
"kubeflow_deploy_op = comp.load_component_from_url(\n",
" 'https://raw.githubusercontent.com/kubeflow/pipelines/1.4.0/components/gcp/ml_engine/deploy/component.yaml')\n",
"\n",
"def deploy_kubeflow(\n",
" model_dir,\n",
" tf_server_name):\n",
" return kubeflow_deploy_op(\n",
" model_dir=model_dir,\n",
" server_name=tf_server_name,\n",
" cluster_name='kubeflow', \n",
" namespace='kubeflow',\n",
" pvc_name='', \n",
" service_type='ClusterIP')\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a lightweight component for testing the deployment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def deployment_test(project_id: str, model_name: str, version: str) -> str:\n",
"\n",
" model_name = model_name.split(\"/\")[-1]\n",
" version = version.split(\"/\")[-1]\n",
" \n",
" import googleapiclient.discovery\n",
" \n",
" def predict(project, model, data, version=None):\n",
" \"\"\"Run predictions on a list of instances.\n",
"\n",
" Args:\n",
" project: (str), project where the Cloud ML Engine Model is deployed.\n",
" model: (str), model name.\n",
" data: ([[any]]), list of input instances, where each input instance is a\n",
" list of attributes.\n",
" version: str, version of the model to target.\n",
"\n",
" Returns:\n",
" Mapping[str: any]: dictionary of prediction results defined by the model.\n",
" \"\"\"\n",
"\n",
" service = googleapiclient.discovery.build('ml', 'v1')\n",
" name = 'projects/{}/models/{}'.format(project, model)\n",
"\n",
" if version is not None:\n",
" name += '/versions/{}'.format(version)\n",
"\n",
" response = service.projects().predict(\n",
" name=name, body={\n",
" 'instances': data\n",
" }).execute()\n",
"\n",
" if 'error' in response:\n",
" raise RuntimeError(response['error'])\n",
"\n",
" return response['predictions']\n",
"\n",
" import tensorflow as tf\n",
" import json\n",
" \n",
" mnist = tf.keras.datasets.mnist\n",
" (x_train, y_train),(x_test, y_test) = mnist.load_data()\n",
" x_train, x_test = x_train / 255.0, x_test / 255.0\n",
"\n",
" result = predict(\n",
" project=project_id,\n",
" model=model_name,\n",
" data=x_test[0:2].tolist(),\n",
" version=version)\n",
" print(result)\n",
" \n",
" return json.dumps(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # Test the function with already deployed version\n",
"# deployment_test(\n",
"# project_id=PROJECT_ID,\n",
"# model_name=\"mnist\",\n",
"# version='ver_bb1ebd2a06ab7f321ad3db6b3b3d83e6' # previous deployed version for testing\n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"deployment_test_op = comp.func_to_container_op(\n",
" func=deployment_test, \n",
" base_image=\"tensorflow/tensorflow:1.15.0-py3\",\n",
" packages_to_install=[\"google-api-python-client==1.7.8\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create your workflow as a Python function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define your pipeline as a Python function. ` @kfp.dsl.pipeline` is a required decoration, and must include `name` and `description` properties. Then compile the pipeline function. After the compilation is completed, a pipeline file is created."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define the pipeline\n",
"@dsl.pipeline(\n",
" name='Mnist pipeline',\n",
" description='A toy pipeline that performs mnist model training.'\n",
")\n",
"def mnist_reuse_component_deploy_pipeline(\n",
" project_id: str = PROJECT_ID,\n",
" model_path: str = 'mnist_model', \n",
" bucket: str = GCS_BUCKET\n",
"):\n",
" train_task = mnist_train_op(\n",
" model_path=model_path, \n",
" bucket=bucket\n",
" ).apply(gcp.use_gcp_secret('user-gcp-sa'))\n",
" \n",
" deploy_task = deploy(\n",
" project_id=project_id,\n",
" model_uri=train_task.outputs['gcs_model_path'],\n",
" model_id=\"mnist\", \n",
" runtime_version=\"1.14\",\n",
" python_version=\"3.5\"\n",
" ).apply(gcp.use_gcp_secret('user-gcp-sa')) \n",
" \n",
" deploy_test_task = deployment_test_op(\n",
" project_id=project_id,\n",
" model_name=deploy_task.outputs[\"model_name\"], \n",
" version=deploy_task.outputs[\"version_name\"],\n",
" ).apply(gcp.use_gcp_secret('user-gcp-sa'))\n",
" \n",
" return True"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Submit a pipeline run"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pipeline_func = mnist_reuse_component_deploy_pipeline"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"experiment_name = 'minist_kubeflow'\n",
"\n",
"arguments = {\"model_path\":\"mnist_model\",\n",
" \"bucket\":GCS_BUCKET}\n",
"\n",
"run_name = pipeline_func.__name__ + ' run'\n",
"\n",
"# Submit pipeline directly from pipeline function\n",
"run_result = client.create_run_from_pipeline_func(pipeline_func, \n",
" experiment_name=experiment_name, \n",
" run_name=run_name, \n",
" arguments=arguments)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**As an alternative, you can compile the pipeline into a package.** The compiled pipeline can be easily shared and reused by others to run the pipeline.\n",
"\n",
"```python\n",
"pipeline_filename = pipeline_func.__name__ + '.pipeline.zip'\n",
"compiler.Compiler().compile(pipeline_func, pipeline_filename)\n",
"\n",
"experiment = client.create_experiment('python-functions-mnist')\n",
"\n",
"run_result = client.run_pipeline(\n",
" experiment_id=experiment.id, \n",
" job_name=run_name, \n",
" pipeline_package_path=pipeline_filename, \n",
" params=arguments)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "virtualPython35",
"language": "python",
"name": "virtualpython35"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
theideasmith/theideasmith.github.io | _notebooks/Untitled.ipynb | 2 | 1665 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One of the big questions that remained unanswered during my childhood was how enciphered messages can be deciphered. I knew that some bit of statistics was involved to somehow *match up the frequencies of words* but had not the formal language to actually concretize this idea. \n",
"Now I've been able to reduce the problem decryption, assuming the encryption cipher is a bijection between two sets of symbols of equal cardinality, to:\n",
"$$\\arg\\min_\\mathbf{F} \\bigg[\\ln \\mathcal{L} \\propto \\mathbf{X}_R\\cdot \\mathbf{F}\\cdot\\ln\\mathbf{p}_R\\bigg]$$ subject to $\\mathbf{F}$ living in the symmetric group of $\\mathbf{I}$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"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": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
babraham123/script-runner | notebooks/bubble_sort.ipynb | 1 | 8824 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'nb_description': 'An example implementation of bubble sort',\n",
" 'nb_display_name': 'Bubble Sort',\n",
" 'nb_filename': 'bubble_sort.ipynb',\n",
" 'params': [{'description': '',\n",
" 'display_name': 'Test num',\n",
" 'input_type': 'integer',\n",
" 'name': 'user_id'},\n",
" {'description': '',\n",
" 'display_name': 'Test str',\n",
" 'input_type': 'string',\n",
" 'name': 'username'}]}"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"{\n",
" \"nb_display_name\": \"Bubble Sort\",\n",
" \"nb_description\": \"An example implementation of bubble sort\",\n",
" \"nb_filename\": \"bubble_sort.ipynb\",\n",
" \"params\":[\n",
" {\n",
" \"name\":\"user_id\",\n",
" \"display_name\":\"Test num\",\n",
" \"description\":\"\",\n",
" \"input_type\":\"integer\"\n",
" },\n",
" {\n",
" \"name\":\"username\",\n",
" \"display_name\":\"Test str\",\n",
" \"description\":\"\",\n",
" \"input_type\":\"string\"\n",
" }\n",
" ]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Sebastian Raschka](http://www.sebastianraschka.com) \n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Last updated: 10/06/2014\n"
]
}
],
"source": [
"import time\n",
"print('Last updated: %s' %time.strftime('%d/%m/%Y'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sorting Algorithms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Overview"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import platform\n",
"import multiprocessing\n",
"\n",
"def print_sysinfo():\n",
" \n",
" print('\\nPython version :', platform.python_version())\n",
" print('compiler :', platform.python_compiler())\n",
"\n",
" print('\\nsystem :', platform.system())\n",
" print('release :', platform.release())\n",
" print('machine :', platform.machine())\n",
" print('processor :', platform.processor())\n",
" print('CPU count :', multiprocessing.cpu_count())\n",
" print('interpreter :', platform.architecture()[0])\n",
" print('\\n\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bubble sort"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#Overview)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quick note about Bubble sort"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I don't want to get into the details about sorting algorithms here, but there is a great report \n",
"[\"Sorting in the Presence of Branch Prediction and Caches - Fast Sorting on Modern Computers\"](https://www.cs.tcd.ie/publications/tech-reports/reports.05/TCD-CS-2005-57.pdf) written by Paul Biggar and David Gregg, where they describe and analyze elementary sorting algorithms in very nice detail (see chapter 4). \n",
"\n",
"And for a quick reference, [this website](http://www.sorting-algorithms.com/bubble-sort) has a nice animation of this algorithm."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A long story short: The \"worst-case\" complexity of the Bubble sort algorithm (i.e., \"Big-O\") \n",
" $\\Rightarrow \\pmb O(n^2)$"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Python version : 3.4.0\n",
"compiler : GCC 4.2.1 (Apple Inc. build 5577)\n",
"\n",
"system : Darwin\n",
"release : 13.2.0\n",
"machine : x86_64\n",
"processor : i386\n",
"CPU count : 4\n",
"interpreter : 64bit\n",
"\n",
"\n",
"\n"
]
}
],
"source": [
"print_sysinfo()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bubble sort implemented in (C)Python"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def python_bubblesort(a_list):\n",
" \"\"\" Bubblesort in Python for list objects (sorts in place).\"\"\"\n",
" length = len(a_list)\n",
" for i in range(length):\n",
" for j in range(1, length):\n",
" if a_list[j] < a_list[j-1]:\n",
" a_list[j-1], a_list[j] = a_list[j], a_list[j-1]\n",
" return a_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Below is a improved version that quits early if no further swap is needed."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def python_bubblesort_improved(a_list):\n",
" \"\"\" Bubblesort in Python for list objects (sorts in place).\"\"\"\n",
" length = len(a_list)\n",
" swapped = 1\n",
" for i in range(length):\n",
" if swapped: \n",
" swapped = 0\n",
" for ele in range(length-i-1):\n",
" if a_list[ele] > a_list[ele + 1]:\n",
" temp = a_list[ele + 1]\n",
" a_list[ele + 1] = a_list[ele]\n",
" a_list[ele] = temp\n",
" swapped = 1\n",
" return a_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Verifying that all implementations work correctly"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bubblesort works correctly\n"
]
}
],
"source": [
"import random\n",
"import copy\n",
"random.seed(4354353)\n",
"\n",
"l = [random.randint(1,1000) for num in range(1, 1000)]\n",
"l_sorted = sorted(l)\n",
"for f in [python_bubblesort, python_bubblesort_improved]:\n",
" assert(l_sorted == f(copy.copy(l)))\n",
"print('Bubblesort works correctly')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Performance comparison"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1000 loops, best of 3: 1.36 ms per loop\n",
"100000 loops, best of 3: 17.9 µs per loop\n"
]
}
],
"source": [
"# small list\n",
"\n",
"l_small = [random.randint(1,100) for num in range(1, 100)]\n",
"l_small_cp = copy.copy(l_small)\n",
"\n",
"%timeit python_bubblesort(l_small)\n",
"%timeit python_bubblesort_improved(l_small_cp)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1 loops, best of 3: 15.6 s per loop\n",
"1 loops, best of 3: 2.07 ms per loop\n"
]
}
],
"source": [
"# larger list\n",
"\n",
"l_small = [random.randint(1,10000) for num in range(1, 10000)]\n",
"l_small_cp = copy.copy(l_small)\n",
"\n",
"%timeit python_bubblesort(l_small)\n",
"%timeit python_bubblesort_improved(l_small_cp)"
]
}
],
"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 |
statsmodels/statsmodels.github.io | v0.13.1/examples/notebooks/generated/statespace_arma_0.ipynb | 2 | 261758 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Autoregressive Moving Average (ARMA): Sunspots data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook replicates the existing ARMA notebook using the `statsmodels.tsa.statespace.SARIMAX` class rather than the `statsmodels.tsa.ARMA` class."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:57.122208Z",
"iopub.status.busy": "2021-11-12T23:37:57.121765Z",
"iopub.status.idle": "2021-11-12T23:37:57.501761Z",
"shell.execute_reply": "2021-11-12T23:37:57.502687Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:57.506603Z",
"iopub.status.busy": "2021-11-12T23:37:57.505529Z",
"iopub.status.idle": "2021-11-12T23:37:58.316958Z",
"shell.execute_reply": "2021-11-12T23:37:58.316510Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy import stats\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import statsmodels.api as sm"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:58.322230Z",
"iopub.status.busy": "2021-11-12T23:37:58.321746Z",
"iopub.status.idle": "2021-11-12T23:37:58.323860Z",
"shell.execute_reply": "2021-11-12T23:37:58.323480Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"from statsmodels.graphics.api import qqplot"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sunspots Data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:58.328955Z",
"iopub.status.busy": "2021-11-12T23:37:58.328127Z",
"iopub.status.idle": "2021-11-12T23:37:58.331485Z",
"shell.execute_reply": "2021-11-12T23:37:58.331086Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"::\n",
"\n",
" Number of Observations - 309 (Annual 1700 - 2008)\n",
" Number of Variables - 1\n",
" Variable name definitions::\n",
"\n",
" SUNACTIVITY - Number of sunspots for each year\n",
"\n",
" The data file contains a 'YEAR' variable that is not returned by load.\n",
"\n"
]
}
],
"source": [
"print(sm.datasets.sunspots.NOTE)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:58.335600Z",
"iopub.status.busy": "2021-11-12T23:37:58.333592Z",
"iopub.status.idle": "2021-11-12T23:37:58.343588Z",
"shell.execute_reply": "2021-11-12T23:37:58.343127Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"dta = sm.datasets.sunspots.load_pandas().data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:58.352767Z",
"iopub.status.busy": "2021-11-12T23:37:58.352295Z",
"iopub.status.idle": "2021-11-12T23:37:58.354365Z",
"shell.execute_reply": "2021-11-12T23:37:58.353966Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"dta.index = pd.Index(pd.date_range(\"1700\", end=\"2009\", freq=\"A-DEC\"))\n",
"del dta[\"YEAR\"]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:58.358341Z",
"iopub.status.busy": "2021-11-12T23:37:58.357894Z",
"iopub.status.idle": "2021-11-12T23:37:58.793158Z",
"shell.execute_reply": "2021-11-12T23:37:58.793584Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dta.plot(figsize=(12,4));"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:58.818989Z",
"iopub.status.busy": "2021-11-12T23:37:58.812274Z",
"iopub.status.idle": "2021-11-12T23:37:59.218589Z",
"shell.execute_reply": "2021-11-12T23:37:59.219227Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/runner/work/statsmodels/statsmodels/statsmodels/graphics/tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
" warnings.warn(\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax1 = fig.add_subplot(211)\n",
"fig = sm.graphics.tsa.plot_acf(dta.values.squeeze(), lags=40, ax=ax1)\n",
"ax2 = fig.add_subplot(212)\n",
"fig = sm.graphics.tsa.plot_pacf(dta, lags=40, ax=ax2)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:59.225379Z",
"iopub.status.busy": "2021-11-12T23:37:59.223310Z",
"iopub.status.idle": "2021-11-12T23:37:59.458706Z",
"shell.execute_reply": "2021-11-12T23:37:59.458257Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"intercept 14.793947\n",
"ar.L1 1.390659\n",
"ar.L2 -0.688568\n",
"sigma2 274.761105\n",
"dtype: float64\n"
]
}
],
"source": [
"arma_mod20 = sm.tsa.statespace.SARIMAX(dta, order=(2,0,0), trend='c').fit(disp=False)\n",
"print(arma_mod20.params)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:59.465578Z",
"iopub.status.busy": "2021-11-12T23:37:59.465083Z",
"iopub.status.idle": "2021-11-12T23:37:59.774931Z",
"shell.execute_reply": "2021-11-12T23:37:59.775328Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"arma_mod30 = sm.tsa.statespace.SARIMAX(dta, order=(3,0,0), trend='c').fit(disp=False)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:59.780161Z",
"iopub.status.busy": "2021-11-12T23:37:59.779093Z",
"iopub.status.idle": "2021-11-12T23:37:59.783353Z",
"shell.execute_reply": "2021-11-12T23:37:59.783702Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2622.636338141521 2637.5697032491116 2628.606725986767\n"
]
}
],
"source": [
"print(arma_mod20.aic, arma_mod20.bic, arma_mod20.hqic)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:59.788272Z",
"iopub.status.busy": "2021-11-12T23:37:59.787821Z",
"iopub.status.idle": "2021-11-12T23:37:59.791316Z",
"shell.execute_reply": "2021-11-12T23:37:59.791972Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"intercept 16.762205\n",
"ar.L1 1.300810\n",
"ar.L2 -0.508122\n",
"ar.L3 -0.129612\n",
"sigma2 270.102651\n",
"dtype: float64\n"
]
}
],
"source": [
"print(arma_mod30.params)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:59.794870Z",
"iopub.status.busy": "2021-11-12T23:37:59.794425Z",
"iopub.status.idle": "2021-11-12T23:37:59.799771Z",
"shell.execute_reply": "2021-11-12T23:37:59.800433Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2619.4036296635304 2638.0703360480193 2626.866614470088\n"
]
}
],
"source": [
"print(arma_mod30.aic, arma_mod30.bic, arma_mod30.hqic)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Does our model obey the theory?"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:59.805579Z",
"iopub.status.busy": "2021-11-12T23:37:59.802581Z",
"iopub.status.idle": "2021-11-12T23:37:59.809540Z",
"shell.execute_reply": "2021-11-12T23:37:59.810200Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"1.9564844805409447"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sm.stats.durbin_watson(arma_mod30.resid)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:59.821767Z",
"iopub.status.busy": "2021-11-12T23:37:59.812225Z",
"iopub.status.idle": "2021-11-12T23:37:59.976115Z",
"shell.execute_reply": "2021-11-12T23:37:59.975678Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,4))\n",
"ax = fig.add_subplot(111)\n",
"ax = plt.plot(arma_mod30.resid)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:59.980168Z",
"iopub.status.busy": "2021-11-12T23:37:59.979723Z",
"iopub.status.idle": "2021-11-12T23:37:59.982066Z",
"shell.execute_reply": "2021-11-12T23:37:59.981667Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"resid = arma_mod30.resid"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:37:59.987766Z",
"iopub.status.busy": "2021-11-12T23:37:59.987304Z",
"iopub.status.idle": "2021-11-12T23:37:59.990129Z",
"shell.execute_reply": "2021-11-12T23:37:59.990526Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"NormaltestResult(statistic=49.847006530010574, pvalue=1.4992016872414017e-11)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats.normaltest(resid)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:38:00.005621Z",
"iopub.status.busy": "2021-11-12T23:37:59.995619Z",
"iopub.status.idle": "2021-11-12T23:38:00.168166Z",
"shell.execute_reply": "2021-11-12T23:38:00.167745Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,4))\n",
"ax = fig.add_subplot(111)\n",
"fig = qqplot(resid, line='q', ax=ax, fit=True)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:38:00.190696Z",
"iopub.status.busy": "2021-11-12T23:38:00.187323Z",
"iopub.status.idle": "2021-11-12T23:38:00.472868Z",
"shell.execute_reply": "2021-11-12T23:38:00.473600Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/runner/work/statsmodels/statsmodels/statsmodels/graphics/tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
" warnings.warn(\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x576 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(12,8))\n",
"ax1 = fig.add_subplot(211)\n",
"fig = sm.graphics.tsa.plot_acf(resid, lags=40, ax=ax1)\n",
"ax2 = fig.add_subplot(212)\n",
"fig = sm.graphics.tsa.plot_pacf(resid, lags=40, ax=ax2)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:38:00.477002Z",
"iopub.status.busy": "2021-11-12T23:38:00.476045Z",
"iopub.status.idle": "2021-11-12T23:38:00.487113Z",
"shell.execute_reply": "2021-11-12T23:38:00.487783Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" AC Q Prob(>Q)\n",
"lag \n",
"1 0.009176 0.026273 8.712350e-01\n",
"2 0.041820 0.573727 7.506142e-01\n",
"3 -0.001342 0.574292 9.022915e-01\n",
"4 0.136064 6.407488 1.707135e-01\n",
"5 0.092433 9.108334 1.048203e-01\n",
"6 0.091919 11.788018 6.686843e-02\n",
"7 0.068735 13.291374 6.531942e-02\n",
"8 -0.015021 13.363411 9.994250e-02\n",
"9 0.187599 24.636915 3.400198e-03\n",
"10 0.213724 39.317881 2.233182e-05\n",
"11 0.201092 52.358270 2.347759e-07\n",
"12 0.117192 56.802110 8.581666e-08\n",
"13 -0.014051 56.866210 1.895534e-07\n",
"14 0.015394 56.943403 4.001105e-07\n",
"15 -0.024986 57.147464 7.747084e-07\n",
"16 0.080892 59.293626 6.880520e-07\n",
"17 0.041120 59.850085 1.112486e-06\n",
"18 -0.052030 60.744064 1.550379e-06\n",
"19 0.062500 62.038494 1.833802e-06\n",
"20 -0.010292 62.073718 3.385223e-06\n",
"21 0.074467 63.924062 3.196544e-06\n",
"22 0.124962 69.152771 8.984833e-07\n",
"23 0.093170 72.069532 5.802915e-07\n",
"24 -0.082149 74.345042 4.715786e-07\n"
]
}
],
"source": [
"r,q,p = sm.tsa.acf(resid, fft=True, qstat=True)\n",
"data = np.c_[r[1:], q, p]\n",
"index = pd.Index(range(1,q.shape[0]+1), name=\"lag\")\n",
"table = pd.DataFrame(data, columns=[\"AC\", \"Q\", \"Prob(>Q)\"], index=index)\n",
"print(table)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* This indicates a lack of fit."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* In-sample dynamic prediction. How good does our model do?"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:38:00.491079Z",
"iopub.status.busy": "2021-11-12T23:38:00.490095Z",
"iopub.status.idle": "2021-11-12T23:38:00.507141Z",
"shell.execute_reply": "2021-11-12T23:38:00.507809Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"predict_sunspots = arma_mod30.predict(start='1990', end='2012', dynamic=True)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:38:00.510947Z",
"iopub.status.busy": "2021-11-12T23:38:00.510006Z",
"iopub.status.idle": "2021-11-12T23:38:00.730279Z",
"shell.execute_reply": "2021-11-12T23:38:00.730990Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(figsize=(12, 8))\n",
"dta.loc['1950':].plot(ax=ax)\n",
"predict_sunspots.plot(ax=ax, style='r');"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:38:00.734379Z",
"iopub.status.busy": "2021-11-12T23:38:00.733415Z",
"iopub.status.idle": "2021-11-12T23:38:00.737571Z",
"shell.execute_reply": "2021-11-12T23:38:00.738262Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"def mean_forecast_err(y, yhat):\n",
" return y.sub(yhat).mean()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-11-12T23:38:00.741494Z",
"iopub.status.busy": "2021-11-12T23:38:00.740551Z",
"iopub.status.idle": "2021-11-12T23:38:00.749026Z",
"shell.execute_reply": "2021-11-12T23:38:00.749704Z"
},
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"5.63554983393475"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mean_forecast_err(dta.SUNACTIVITY, predict_sunspots)"
]
}
],
"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": "3.8.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| bsd-3-clause |
rsignell-usgs/notebook | UGRID/plot_mesh-Copy1.ipynb | 1 | 175701 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# plot a ugrid mesh"
]
},
{
"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 matplotlib.tri as tri\n",
"import netCDF4"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#url = 'http://www.smast.umassd.edu:8080/thredds/dodsC/fvcom/mwra/fvcom'\n",
"url = 'http://geoport.whoi.edu/thredds/dodsC/usgs/vault0/models/tides/vdatum_fl_sab/adcirc54.ncml'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ncv = netCDF4.Dataset(url).variables\n",
"lon = ncv['lon'][:]\n",
"lat = ncv['lat'][:]\n",
"nv = ncv['ele'][:]-1\n",
"triang = tri.Triangulation(lon,lat,triangles=nv)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import cartopy.crs as ccrs\n",
"import matplotlib.pyplot as plt\n",
"from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
"\n",
"\n",
"def make_map(projection=ccrs.PlateCarree()):\n",
" fig, ax = plt.subplots(figsize=(8, 6),\n",
" subplot_kw=dict(projection=projection))\n",
" ax.coastlines(resolution='10m')\n",
" gl = ax.gridlines(draw_labels=True)\n",
" gl.xlabels_top = gl.ylabels_right = False\n",
" gl.xformatter = LONGITUDE_FORMATTER\n",
" gl.yformatter = LATITUDE_FORMATTER\n",
" return fig, ax"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fe7d82ae890>,\n",
" <matplotlib.lines.Line2D at 0x7fe7d81a1810>]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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Wr6fkOHs2zwUwXGDUKKo369UDxo83pEILSQ6WWtKChReET2w2vA+gLIDMYJbx9+J2SC8G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9YHDxJUT5wgEHTtqqpTv/ceGX337py4x4/z2K+/5j1fucLtx46xbxYL+dy+fWqyN2xIwL17V4hK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\nB1W4qVChQoWKYof/D5Z/hrOh9WvLAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7d82ae850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))\n",
"#fig, ax = make_map(projection=geodetic)\n",
"fig, ax = make_map()\n",
"\n",
"kw = dict(marker='.', linestyle='-', alpha=0.85, color='darkgray')\n",
"kw = dict(linestyle='-',color='red')\n",
"ax.triplot(triang, **kw) # or lon, lat, triangules;\n",
"#ax.set_extent([-84, -78, 25, 32])"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Yr1TiUXIyGnXsiKbNm6Np06bw9PTM08/h8uXLWLp0KaZPn/5Oz42JiUH9+vWR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\nWkFtMlj282HQRj4FkHs0ghiRN6/UCgqSnu3by2OPPCLTAXkIkFKA+IE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IAAAU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\ngQ+yOfiIuQ984AMf+MAH2Rz+D64QwOvkOgjmAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe7d8117ad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import mplleaflet\n",
"#geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))\n",
"#fig, ax = make_map(projection=geodetic)\n",
"fig, ax = make_map()\n",
"\n",
"kw = dict(marker='.', linestyle='-', alpha=0.85, color='darkgray')\n",
"kw = dict(linestyle='-',color='red')\n",
"ax.triplot(triang, **kw) # or lon, lat, triangules;\n",
"ax.set_extent([-87.5, -82.5, 29.4, 31])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from cartopy.io.img_tiles import MapQuestOpenAerial, MapQuestOSM, OSM\n",
"geodetic = ccrs.Geodetic(globe=ccrs.Globe(datum='WGS84'))\n",
"\n",
"fig = plt.figure(figsize=(12,8))\n",
"tiler = MapQuestOpenAerial()\n",
"ax = plt.axes(projection=tiler.crs)\n",
"\n",
"bbox=[-71, -69.3, 42, 42.8]\n",
"#ax = plt.axes(projection=ccrs.PlateCarree())\n",
"ax.set_extent(bbox)\n",
"ax.add_image(tiler, 8)\n",
"\n",
"#ax.coastlines()\n",
"kw = dict(marker='.', linestyle='-', alpha=0.85, color='darkgray', transform=geodetic)\n",
"ax.triplot(triang, **kw) # or lon, lat, triangules\n",
"#ax.set_extent()\n",
"gl = ax.gridlines(draw_labels=True)\n",
"gl.xlabels_top = False\n",
"gl.ylabels_right = False\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"gist_id": "",
"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 |
arasuarun/shogun | doc/ipython-notebooks/pca/pca_notebook.ipynb | 20 | 52817 | {
"metadata": {
"name": "",
"signature": "sha256:4ef16dd9d0c6a3a9522876944bea4b94db5864ca5749514f5675287b76c83fce"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Principal Component Analysis in Shogun"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"By Abhijeet Kislay (GitHub ID: <a href='https://github.com/kislayabhi'>kislayabhi</a>)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook is about finding Principal Components (<a href=\"http://en.wikipedia.org/wiki/Principal_component_analysis\">PCA</a>) of data (<a href=\"http://en.wikipedia.org/wiki/Unsupervised_learning\">unsupervised</a>) in Shogun. Its <a href=\"http://en.wikipedia.org/wiki/Dimensionality_reduction\">dimensional reduction</a> capabilities are further utilised to show its application in <a href=\"http://en.wikipedia.org/wiki/Data_compression\">data compression</a>, image processing and <a href=\"http://en.wikipedia.org/wiki/Facial_recognition_system\">face recognition</a>. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"%matplotlib inline\n",
"# import all shogun classes\n",
"from modshogun import *"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Some Formal Background (Skip if you just want code examples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension.\n",
"\n",
"In machine learning problems data is often high dimensional - images, bag-of-word descriptions etc. In such cases we cannot expect the training data to densely populate the space, meaning that there will be large parts in which little is known about the data. Hence it is expected that only a small number of directions are relevant for describing the data to a reasonable accuracy.\n",
"\n",
"The data vectors may be very high dimensional, they will therefore typically lie closer to a much lower dimensional 'manifold'.\n",
"Here we concentrate on linear dimensional reduction techniques. In this approach a high dimensional datapoint $\\mathbf{x}$ is 'projected down' to a lower dimensional vector $\\mathbf{y}$ by:\n",
"$$\\mathbf{y}=\\mathbf{F}\\mathbf{x}+\\text{const}.$$\n",
"where the matrix $\\mathbf{F}\\in\\mathbb{R}^{\\text{M}\\times \\text{D}}$, with $\\text{M}<\\text{D}$. Here $\\text{M}=\\dim(\\mathbf{y})$ and $\\text{D}=\\dim(\\mathbf{x})$.\n",
"\n",
"From the above scenario, we assume that\n",
"\n",
"* The number of principal components to use is $\\text{M}$.\n",
"* The dimension of each data point is $\\text{D}$.\n",
"* The number of data points is $\\text{N}$.\n",
"\n",
"We express the approximation for datapoint $\\mathbf{x}^n$ as:$$\\mathbf{x}^n \\approx \\mathbf{c} + \\sum\\limits_{i=1}^{\\text{M}}y_i^n \\mathbf{b}^i \\equiv \\tilde{\\mathbf{x}}^n.$$\n",
"* Here the vector $\\mathbf{c}$ is a constant and defines a point in the lower dimensional space.\n",
"* The $\\mathbf{b}^i$ define vectors in the lower dimensional space (also known as 'principal component coefficients' or 'loadings').\n",
"* The $y_i^n$ are the low dimensional co-ordinates of the data.\n",
"\n",
"Our motive is to find the reconstruction $\\tilde{\\mathbf{x}}^n$ given the lower dimensional representation $\\mathbf{y}^n$(which has components $y_i^n,i = 1,...,\\text{M})$. For a data space of dimension $\\dim(\\mathbf{x})=\\text{D}$, we hope to accurately describe the data using only a small number $(\\text{M}\\ll \\text{D})$ of coordinates of $\\mathbf{y}$.\n",
"To determine the best lower dimensional representation it is convenient to use the square distance error between $\\mathbf{x}$ and its reconstruction $\\tilde{\\mathbf{x}}$:$$\\text{E}(\\mathbf{B},\\mathbf{Y},\\mathbf{c})=\\sum\\limits_{n=1}^{\\text{N}}\\sum\\limits_{i=1}^{\\text{D}}[x_i^n - \\tilde{x}_i^n]^2.$$\n",
"* Here the basis vectors are defined as $\\mathbf{B} = [\\mathbf{b}^1,...,\\mathbf{b}^\\text{M}]$ (defining $[\\text{B}]_{i,j} = b_i^j$).\n",
"* Corresponding low dimensional coordinates are defined as $\\mathbf{Y} = [\\mathbf{y}^1,...,\\mathbf{y}^\\text{N}].$\n",
"* Also, $x_i^n$ and $\\tilde{x}_i^n$ represents the coordinates of the data points for the original and the reconstructed data respectively.\n",
"* The bias $\\mathbf{c}$ is given by the mean of the data $\\sum_n\\mathbf{x}^n/\\text{N}$.\n",
"\n",
"Therefore, for simplification purposes we centre our data, so as to set $\\mathbf{c}$ to zero. Now we concentrate on finding the optimal basis $\\mathbf{B}$( which has the components $\\mathbf{b}^i, i=1,...,\\text{M} $).\n"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Deriving the optimal linear reconstruction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To find the best basis vectors $\\mathbf{B}$ and corresponding low dimensional coordinates $\\mathbf{Y}$, we may minimize the sum of squared differences between each vector $\\mathbf{x}$ and its reconstruction $\\tilde{\\mathbf{x}}$:\n",
"\n",
"$\\text{E}(\\mathbf{B},\\mathbf{Y}) = \\sum\\limits_{n=1}^{\\text{N}}\\sum\\limits_{i=1}^{\\text{D}}\\left[x_i^n - \\sum\\limits_{j=1}^{\\text{M}}y_j^nb_i^j\\right]^2 = \\text{trace} \\left( (\\mathbf{X}-\\mathbf{B}\\mathbf{Y})^T(\\mathbf{X}-\\mathbf{B}\\mathbf{Y}) \\right)$\n",
"\n",
"where $\\mathbf{X} = [\\mathbf{x}^1,...,\\mathbf{x}^\\text{N}].$\n",
"Considering the above equation under the orthonormality constraint $\\mathbf{B}^T\\mathbf{B} = \\mathbf{I}$ (i.e the basis vectors are mutually orthogonal and of unit length), we differentiate it w.r.t $y_k^n$. The squared error $\\text{E}(\\mathbf{B},\\mathbf{Y})$ therefore has zero derivative when: \n",
"\n",
"$y_k^n = \\sum_i b_i^kx_i^n$\n",
"\n",
"By substituting this solution in the above equation, the objective becomes\n",
"\n",
"$\\text{E}(\\mathbf{B}) = (\\text{N}-1)\\left[\\text{trace}(\\mathbf{S}) - \\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right)\\right],$\n",
"\n",
"where $\\mathbf{S}$ is the sample covariance matrix of the data.\n",
"To minimise equation under the constraint $\\mathbf{B}^T\\mathbf{B} = \\mathbf{I}$, we use a set of Lagrange Multipliers $\\mathbf{L}$, so that the objective is to minimize: \n",
"\n",
"$-\\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right)+\\text{trace}\\left(\\mathbf{L}\\left(\\mathbf{B}^T\\mathbf{B} - \\mathbf{I}\\right)\\right).$\n",
"\n",
"Since the constraint is symmetric, we can assume that $\\mathbf{L}$ is also symmetric. Differentiating with respect to $\\mathbf{B}$ and equating to zero we obtain that at the optimum \n",
"\n",
"$\\mathbf{S}\\mathbf{B} = \\mathbf{B}\\mathbf{L}$.\n",
"\n",
"This is a form of eigen-equation so that a solution is given by taking $\\mathbf{L}$ to be diagonal and $\\mathbf{B}$ as the matrix whose columns are the corresponding eigenvectors of $\\mathbf{S}$. In this case,\n",
"\n",
"$\\text{trace}\\left(\\mathbf{S}\\mathbf{B}\\mathbf{B}^T\\right) =\\text{trace}(\\mathbf{L}),$\n",
"\n",
"which is the sum of the eigenvalues corresponding to the eigenvectors forming $\\mathbf{B}$. Since we wish to minimise $\\text{E}(\\mathbf{B})$, we take the eigenvectors with the largest corresponding eigenvalues.\n",
"Whilst the solution to this eigen-problem is unique, this only serves to define the solution subspace since one may rotate and scale $\\mathbf{B}$ and $\\mathbf{Y}$ such that the value of the squared loss is exactly the same. The justification for choosing the non-rotated eigen solution is given by the additional requirement that the principal components corresponds to directions of maximal variance."
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Maximum variance criterion"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We aim to find that single direction $\\mathbf{b}$ such that, when the data is projected onto this direction, the variance of this projection is maximal amongst all possible such projections.\n",
"The projection of a datapoint onto a direction $\\mathbf{b}$ is $\\mathbf{b}^T\\mathbf{x}^n$ for a unit length vector $\\mathbf{b}$. Hence the sum of squared projections is: $$\\sum\\limits_{n}\\left(\\mathbf{b}^T\\mathbf{x}^n\\right)^2 = \\mathbf{b}^T\\left[\\sum\\limits_{n}\\mathbf{x}^n(\\mathbf{x}^n)^T\\right]\\mathbf{b} = (\\text{N}-1)\\mathbf{b}^T\\mathbf{S}\\mathbf{b} = \\lambda(\\text{N} - 1)$$ \n",
"which ignoring constants, is simply the negative of the equation for a single retained eigenvector $\\mathbf{b}$(with $\\mathbf{S}\\mathbf{b} = \\lambda\\mathbf{b}$). Hence the optimal single $\\text{b}$ which maximises the projection variance is given by the eigenvector corresponding to the largest eigenvalues of $\\mathbf{S}.$ The second largest eigenvector corresponds to the next orthogonal optimal direction and so on. This explains why, despite the squared loss equation being invariant with respect to arbitrary rotation of the basis vectors, the ones given by the eigen-decomposition have the additional property that they correspond to directions of maximal variance. These maximal variance directions found by PCA are called the $\\text{principal} $ $\\text{directions}.$\n",
"\n",
"There are two eigenvalue methods through which shogun can perform PCA namely\n",
"* Eigenvalue Decomposition Method.\n",
"* Singular Value Decomposition.\n"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"EVD vs SVD"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The EVD viewpoint requires that one compute the eigenvalues and eigenvectors of the covariance matrix, which is the product of $\\mathbf{X}\\mathbf{X}^\\text{T}$, where $\\mathbf{X}$ is the data matrix. Since the covariance matrix is symmetric, the matrix is diagonalizable, and the eigenvectors can be normalized such that they are orthonormal:\n",
"\n",
"$\\mathbf{S}=\\frac{1}{\\text{N}-1}\\mathbf{X}\\mathbf{X}^\\text{T},$\n",
"\n",
"where the $\\text{D}\\times\\text{N}$ matrix $\\mathbf{X}$ contains all the data vectors: $\\mathbf{X}=[\\mathbf{x}^1,...,\\mathbf{x}^\\text{N}].$\n",
"Writing the $\\text{D}\\times\\text{N}$ matrix of eigenvectors as $\\mathbf{E}$ and the eigenvalues as an $\\text{N}\\times\\text{N}$ diagonal matrix $\\mathbf{\\Lambda}$, the eigen-decomposition of the covariance $\\mathbf{S}$ is\n",
"\n",
"$\\mathbf{X}\\mathbf{X}^\\text{T}\\mathbf{E}=\\mathbf{E}\\mathbf{\\Lambda}\\Longrightarrow\\mathbf{X}^\\text{T}\\mathbf{X}\\mathbf{X}^\\text{T}\\mathbf{E}=\\mathbf{X}^\\text{T}\\mathbf{E}\\mathbf{\\Lambda}\\Longrightarrow\\mathbf{X}^\\text{T}\\mathbf{X}\\tilde{\\mathbf{E}}=\\tilde{\\mathbf{E}}\\mathbf{\\Lambda},$\n",
"\n",
"where we defined $\\tilde{\\mathbf{E}}=\\mathbf{X}^\\text{T}\\mathbf{E}$. The final expression above represents the eigenvector equation for $\\mathbf{X}^\\text{T}\\mathbf{X}.$ This is a matrix of dimensions $\\text{N}\\times\\text{N}$ so that calculating the eigen-decomposition takes $\\mathcal{O}(\\text{N}^3)$ operations, compared with $\\mathcal{O}(\\text{D}^3)$ operations in the original high-dimensional space. We then can therefore calculate the eigenvectors $\\tilde{\\mathbf{E}}$ and eigenvalues $\\mathbf{\\Lambda}$ of this matrix more easily. Once found, we use the fact that the eigenvalues of $\\mathbf{S}$ are given by the diagonal entries of $\\mathbf{\\Lambda}$ and the eigenvectors by\n",
"\n",
"$\\mathbf{E}=\\mathbf{X}\\tilde{\\mathbf{E}}\\mathbf{\\Lambda}^{-1}$\n",
"\n",
"\n",
"\n",
"\n",
"* On the other hand, applying SVD to the data matrix $\\mathbf{X}$ follows like:\n",
"\n",
"$\\mathbf{X}=\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}$\n",
"\n",
"where $\\mathbf{U}^\\text{T}\\mathbf{U}=\\mathbf{I}_\\text{D}$ and $\\mathbf{V}^\\text{T}\\mathbf{V}=\\mathbf{I}_\\text{N}$ and $\\mathbf{\\Sigma}$ is a diagonal matrix of the (positive) singular values. We assume that the decomposition has ordered the singular values so that the upper left diagonal element of $\\mathbf{\\Sigma}$ contains the largest singular value.\n",
"\n",
"Attempting to construct the covariance matrix $(\\mathbf{X}\\mathbf{X}^\\text{T})$from this decomposition gives:\n",
"\n",
"$\\mathbf{X}\\mathbf{X}^\\text{T} = \\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)\\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)^\\text{T}$\n",
"\n",
"$\\mathbf{X}\\mathbf{X}^\\text{T} = \\left(\\mathbf{U}\\mathbf{\\Sigma}\\mathbf{V}^\\text{T}\\right)\\left(\\mathbf{V}\\mathbf{\\Sigma}\\mathbf{U}^\\text{T}\\right)$\n",
"\n",
"and since $\\mathbf{V}$ is an orthogonal matrix $\\left(\\mathbf{V}^\\text{T}\\mathbf{V}=\\mathbf{I}\\right),$\n",
"\n",
"$\\mathbf{X}\\mathbf{X}^\\text{T}=\\left(\\mathbf{U}\\mathbf{\\Sigma}^\\mathbf{2}\\mathbf{U}^\\text{T}\\right)$\n",
"\n",
"Since it is in the form of an eigen-decomposition, the PCA solution given by performing the SVD decomposition of $\\mathbf{X}$, for which the eigenvectors are then given by $\\mathbf{U}$, and corresponding eigenvalues by the square of the singular values.\n",
"\n"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"[CPCA](http://www.shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1CPCA.html) Class Reference (Shogun) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"CPCA class of Shogun inherits from the [CPreprocessor](http://www.shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1CPreprocessor.html) class. Preprocessors are transformation functions that doesn't change the domain of the input features. Specifically, CPCA performs principal component analysis on the input vectors and keeps only the specified number of eigenvectors. On preprocessing, the stored covariance matrix is used to project vectors into eigenspace.\n",
"\n",
"Performance of PCA depends on the algorithm used according to the situation in hand.\n",
"Our PCA preprocessor class provides 3 method options to compute the transformation matrix:\n",
"\n",
"* $\\text{PCA(EVD)}$ sets $\\text{PCAmethod == EVD}$ : Eigen Value Decomposition of Covariance Matrix $(\\mathbf{XX^T}).$\n",
"The covariance matrix $\\mathbf{XX^T}$ is first formed internally and then\n",
"its eigenvectors and eigenvalues are computed using QR decomposition of the matrix.\n",
"The time complexity of this method is $\\mathcal{O}(D^3)$ and should be used when $\\text{N > D.}$\n",
"\n",
"\n",
"* $\\text{PCA(SVD)}$ sets $\\text{PCAmethod == SVD}$ : Singular Value Decomposition of feature matrix $\\mathbf{X}$.\n",
"The transpose of feature matrix, $\\mathbf{X^T}$, is decomposed using SVD. $\\mathbf{X^T = UDV^T}.$\n",
"The matrix V in this decomposition contains the required eigenvectors and\n",
"the diagonal entries of the diagonal matrix D correspond to the non-negative\n",
"eigenvalues.The time complexity of this method is $\\mathcal{O}(DN^2)$ and should be used when $\\text{N < D.}$\n",
"\n",
"\n",
"* $\\text{PCA(AUTO)}$ sets $\\text{PCAmethod == AUTO}$ : This mode automagically chooses one of the above modes for the user based on whether $\\text{N>D}$ (chooses $\\text{EVD}$) or $\\text{N<D}$ (chooses $\\text{SVD}$)"
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"PCA on 2D data"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 1: Get some data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will generate the toy data by adding orthogonal noise to a set of points lying on an arbitrary 2d line. We expect PCA to recover this line, which is a one-dimensional linear sub-space."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#number of data points.\n",
"n=100\n",
"\n",
"#generate a random 2d line(y1 = mx1 + c)\n",
"m = random.randint(1,10)\n",
"c = random.randint(1,10)\n",
"x1 = random.random_integers(-20,20,n)\n",
"y1=m*x1+c\n",
"\n",
"#generate the noise.\n",
"noise=random.random_sample([n]) * random.random_integers(-35,35,n)\n",
"\n",
"#make the noise orthogonal to the line y=mx+c and add it.\n",
"x=x1 + noise*m/sqrt(1+square(m))\n",
"y=y1 + noise/sqrt(1+square(m))\n",
"\n",
"twoD_obsmatrix=array([x,y])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#to visualise the data we must plot it.\n",
"\n",
"rcParams['figure.figsize'] = 7, 7 \n",
"figure,axis=subplots(1,1)\n",
"xlim(-50,50)\n",
"ylim(-50,50)\n",
"axis.plot(twoD_obsmatrix[0,:],twoD_obsmatrix[1,:],'o',color='green',markersize=6)\n",
"\n",
"#the line from which we generated the data is plotted in red\n",
"axis.plot(x1[:],y1[:],linewidth=0.3,color='red')\n",
"title('One-Dimensional sub-space with noise')\n",
"xlabel(\"x axis\")\n",
"_=ylabel(\"y axis\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 2: Subtract the mean."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For PCA to work properly, we must subtract the mean from each of the data dimensions. The mean subtracted is the average across each dimension. So, all the $x$ values have $\\bar{x}$ subtracted, and all the $y$ values have $\\bar{y}$ subtracted from them, where:$$\\bar{\\mathbf{x}} = \\frac{\\sum\\limits_{i=1}^{n}x_i}{n}$$ $\\bar{\\mathbf{x}}$ denotes the mean of the $x_i^{'s}$"
]
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"Shogun's way of doing things :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Preprocessor PCA performs principial component analysis on input feature vectors/matrices. It provides an interface to set the target dimension by $\\text{set_target_dim method}.$ When the $\\text{init()}$ method in $\\text{PCA}$ is called with proper\n",
"feature matrix $\\text{X}$ (with say $\\text{N}$ number of vectors and $\\text{D}$ feature dimension), a transformation matrix is computed and stored internally.It inherenty also centralizes the data by subtracting the mean from it."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#convert the observation matrix into dense feature matrix.\n",
"train_features = RealFeatures(twoD_obsmatrix)\n",
"\n",
"#PCA(EVD) is choosen since N=100 and D=2 (N>D).\n",
"#However we can also use PCA(AUTO) as it will automagically choose the appropriate method. \n",
"preprocessor = PCA(EVD)\n",
"\n",
"#since we are projecting down the 2d data, the target dim is 1. But here the exhaustive method is detailed by\n",
"#setting the target dimension to 2 to visualize both the eigen vectors.\n",
"#However, in future examples we will get rid of this step by implementing it directly.\n",
"preprocessor.set_target_dim(2)\n",
"\n",
"#Centralise the data by subtracting its mean from it.\n",
"preprocessor.init(train_features)\n",
"\n",
"#get the mean for the respective dimensions.\n",
"mean_datapoints=preprocessor.get_mean()\n",
"mean_x=mean_datapoints[0]\n",
"mean_y=mean_datapoints[1]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 3: Calculate the covariance matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To understand the relationship between 2 dimension we define $\\text{covariance}$. It is a measure to find out how much the dimensions vary from the mean $with$ $respect$ $to$ $each$ $other.$$$cov(X,Y)=\\frac{\\sum\\limits_{i=1}^{n}(X_i-\\bar{X})(Y_i-\\bar{Y})}{n-1}$$\n",
"A useful way to get all the possible covariance values between all the different dimensions is to calculate them all and put them in a matrix.\n",
"\n",
"Example: For a 3d dataset with usual dimensions of $x,y$ and $z$, the covariance matrix has 3 rows and 3 columns, and the values are this:\n",
"$$\\mathbf{S} = \\quad\\begin{pmatrix}cov(x,x)&cov(x,y)&cov(x,z)\\\\cov(y,x)&cov(y,y)&cov(y,z)\\\\cov(z,x)&cov(z,y)&cov(z,z)\\end{pmatrix}$$\n",
"\n",
"\n"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 4: Calculate the eigenvectors and eigenvalues of the covariance matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the eigenvectors $e^1,....e^M$ of the covariance matrix $\\mathbf{S}$."
]
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"Shogun's way of doing things :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Step 3 and Step 4 are directly implemented by the PCA preprocessor of Shogun toolbar. The transformation matrix is essentially a $\\text{D}$$\\times$$\\text{M}$ matrix, the columns of which correspond to the eigenvectors of the covariance matrix $(\\text{X}\\text{X}^\\text{T})$ having top $\\text{M}$ eigenvalues."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Get the eigenvectors(We will get two of these since we set the target to 2). \n",
"E = preprocessor.get_transformation_matrix()\n",
"\n",
"#Get all the eigenvalues returned by PCA.\n",
"eig_value=preprocessor.get_eigenvalues()\n",
"\n",
"e1 = E[:,0]\n",
"e2 = E[:,1]\n",
"eig_value1 = eig_value[0]\n",
"eig_value2 = eig_value[1]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 5: Choosing components and forming a feature vector."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets visualize the eigenvectors and decide upon which to choose as the $principle$ $component$ of the data set."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#find out the M eigenvectors corresponding to top M number of eigenvalues and store it in E\n",
"#Here M=1\n",
"\n",
"#slope of e1 & e2\n",
"m1=e1[1]/e1[0]\n",
"m2=e2[1]/e2[0]\n",
"\n",
"#generate the two lines\n",
"x1=range(-50,50)\n",
"x2=x1\n",
"y1=multiply(m1,x1)\n",
"y2=multiply(m2,x2)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#plot the data along with those two eigenvectors\n",
"figure, axis = subplots(1,1)\n",
"xlim(-50, 50)\n",
"ylim(-50, 50)\n",
"axis.plot(x[:], y[:],'o',color='green', markersize=5, label=\"green\")\n",
"axis.plot(x1[:], y1[:], linewidth=0.7, color='black')\n",
"axis.plot(x2[:], y2[:], linewidth=0.7, color='blue')\n",
"p1 = Rectangle((0, 0), 1, 1, fc=\"black\")\n",
"p2 = Rectangle((0, 0), 1, 1, fc=\"blue\")\n",
"legend([p1,p2],[\"1st eigenvector\",\"2nd eigenvector\"],loc='center left', bbox_to_anchor=(1, 0.5))\n",
"title('Eigenvectors selection')\n",
"xlabel(\"x axis\")\n",
"_=ylabel(\"y axis\")"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the above figure, the blue line is a good fit of the data. It shows the most significant relationship between the data dimensions.\n",
"It turns out that the eigenvector with the $highest$ eigenvalue is the $principle$ $component$ of the data set.\n",
"Form the matrix $\\mathbf{E}=[\\mathbf{e}^1,...,\\mathbf{e}^M].$\n",
"Here $\\text{M}$ represents the target dimension of our final projection"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#The eigenvector corresponding to higher eigenvalue(i.e eig_value2) is choosen (i.e e2).\n",
"#E is the feature vector.\n",
"E=e2"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 6: Projecting the data to its Principal Components."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the final step in PCA. Once we have choosen the components(eigenvectors) that we wish to keep in our data and formed a feature vector, we simply take the vector and multiply it on the left of the original dataset.\n",
"The lower dimensional representation of each data point $\\mathbf{x}^n$ is given by \n",
"\n",
"$\\mathbf{y}^n=\\mathbf{E}^T(\\mathbf{x}^n-\\mathbf{m})$\n",
"\n",
"Here the $\\mathbf{E}^T$ is the matrix with the eigenvectors in rows, with the most significant eigenvector at the top. The mean adjusted data, with data items in each column, with each row holding a seperate dimension is multiplied to it."
]
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"Shogun's way of doing things :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Step 6 can be performed by shogun's PCA preprocessor as follows:\n",
"\n",
"The transformation matrix that we got after $\\text{init()}$ is used to transform all $\\text{D-dim}$ feature matrices (with $\\text{D}$ feature dimensions) supplied, via $\\text{apply_to_feature_matrix methods}$.This transformation outputs the $\\text{M-Dim}$ approximation of all these input vectors and matrices (where $\\text{M}$ $\\leq$ $\\text{min(D,N)}$)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#transform all 2-dimensional feature matrices to target-dimensional approximations.\n",
"yn=preprocessor.apply_to_feature_matrix(train_features)\n",
"\n",
"#Since, here we are manually trying to find the eigenvector corresponding to the top eigenvalue.\n",
"#The 2nd row of yn is choosen as it corresponds to the required eigenvector e2.\n",
"yn1=yn[1,:]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Step 5 and Step 6 can be applied directly with Shogun's PCA preprocessor (from next example). It has been done manually here to show the exhaustive nature of Principal Component Analysis."
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 7: Form the approximate reconstruction of the original data $\\mathbf{x}^n$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The approximate reconstruction of the original datapoint $\\mathbf{x}^n$ is given by : $\\tilde{\\mathbf{x}}^n\\approx\\text{m}+\\mathbf{E}\\mathbf{y}^n$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x_new=(yn1 * E[0]) + tile(mean_x,[n,1]).T[0]\n",
"y_new=(yn1 * E[1]) + tile(mean_y,[n,1]).T[0]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The new data is plotted below"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"figure, axis = subplots(1,1)\n",
"xlim(-50, 50)\n",
"ylim(-50, 50)\n",
"\n",
"axis.plot(x[:], y[:],'o',color='green', markersize=5, label=\"green\")\n",
"axis.plot(x_new, y_new, 'o', color='blue', markersize=5, label=\"red\")\n",
"title('PCA Projection of 2D data into 1D subspace')\n",
"xlabel(\"x axis\")\n",
"ylabel(\"y axis\")\n",
"\n",
"#add some legend for information\n",
"p1 = Rectangle((0, 0), 1, 1, fc=\"r\")\n",
"p2 = Rectangle((0, 0), 1, 1, fc=\"g\")\n",
"p3 = Rectangle((0, 0), 1, 1, fc=\"b\")\n",
"legend([p1,p2,p3],[\"normal projection\",\"2d data\",\"1d projection\"],loc='center left', bbox_to_anchor=(1, 0.5))\n",
"\n",
"#plot the projections in red:\n",
"for i in range(n):\n",
" axis.plot([x[i],x_new[i]],[y[i],y_new[i]] , color='red')"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"PCA on a 3d data."
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step1: Get some data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We generate points from a plane and then add random noise orthogonal to it. The general equation of a plane is: $$\\text{a}\\mathbf{x}+\\text{b}\\mathbf{y}+\\text{c}\\mathbf{z}+\\text{d}=0$$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rcParams['figure.figsize'] = 8,8 \n",
"#number of points\n",
"n=100\n",
"\n",
"#generate the data\n",
"a=random.randint(1,20)\n",
"b=random.randint(1,20)\n",
"c=random.randint(1,20)\n",
"d=random.randint(1,20)\n",
"\n",
"x1=random.random_integers(-20,20,n)\n",
"y1=random.random_integers(-20,20,n)\n",
"z1=-(a*x1+b*y1+d)/c\n",
"\n",
"#generate the noise\n",
"noise=random.random_sample([n])*random.random_integers(-30,30,n)\n",
"\n",
"#the normal unit vector is [a,b,c]/magnitude\n",
"magnitude=sqrt(square(a)+square(b)+square(c))\n",
"normal_vec=array([a,b,c]/magnitude)\n",
"\n",
"#add the noise orthogonally\n",
"x=x1+noise*normal_vec[0]\n",
"y=y1+noise*normal_vec[1]\n",
"z=z1+noise*normal_vec[2]\n",
"threeD_obsmatrix=array([x,y,z])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#to visualize the data, we must plot it.\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"\n",
"fig = pyplot.figure()\n",
"ax=fig.add_subplot(111, projection='3d')\n",
"\n",
"#plot the noisy data generated by distorting a plane\n",
"ax.scatter(x, y, z,marker='o', color='g')\n",
"\n",
"ax.set_xlabel('x label')\n",
"ax.set_ylabel('y label')\n",
"ax.set_zlabel('z label')\n",
"legend([p2],[\"3d data\"],loc='center left', bbox_to_anchor=(1, 0.5))\n",
"title('Two dimensional subspace with noise')\n",
"xx, yy = meshgrid(range(-30,30), range(-30,30))\n",
"zz=-(a * xx + b * yy + d) / c"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 2: Subtract the mean."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#convert the observation matrix into dense feature matrix.\n",
"train_features = RealFeatures(threeD_obsmatrix)\n",
"\n",
"#PCA(EVD) is choosen since N=100 and D=3 (N>D).\n",
"#However we can also use PCA(AUTO) as it will automagically choose the appropriate method. \n",
"preprocessor = PCA(EVD)\n",
"\n",
"#If we set the target dimension to 2, Shogun would automagically preserve the required 2 eigenvectors(out of 3) according to their\n",
"#eigenvalues.\n",
"preprocessor.set_target_dim(2)\n",
"preprocessor.init(train_features)\n",
"\n",
"#get the mean for the respective dimensions.\n",
"mean_datapoints=preprocessor.get_mean()\n",
"mean_x=mean_datapoints[0]\n",
"mean_y=mean_datapoints[1]\n",
"mean_z=mean_datapoints[2]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 3 & Step 4: Calculate the eigenvectors of the covariance matrix"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#get the required eigenvectors corresponding to top 2 eigenvalues.\n",
"E = preprocessor.get_transformation_matrix()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Steps 5: Choosing components and forming a feature vector."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we performed PCA for a target $\\dim = 2$ for the $3 \\dim$ data, we are directly given \n",
"the two required eigenvectors in $\\mathbf{E}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"E is automagically filled by setting target dimension = M. This is different from the 2d data example where we implemented this step manually."
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 6: Projecting the data to its Principal Components."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#This can be performed by shogun's PCA preprocessor as follows:\n",
"yn=preprocessor.apply_to_feature_matrix(train_features)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 7: Form the approximate reconstruction of the original data $\\mathbf{x}^n$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The approximate reconstruction of the original datapoint $\\mathbf{x}^n$ is given by : $\\tilde{\\mathbf{x}}^n\\approx\\text{m}+\\mathbf{E}\\mathbf{y}^n$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"new_data=dot(E,yn)\n",
"\n",
"x_new=new_data[0,:]+tile(mean_x,[n,1]).T[0]\n",
"y_new=new_data[1,:]+tile(mean_y,[n,1]).T[0]\n",
"z_new=new_data[2,:]+tile(mean_z,[n,1]).T[0]"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#all the above points lie on the same plane. To make it more clear we will plot the projection also.\n",
"\n",
"fig=pyplot.figure()\n",
"ax=fig.add_subplot(111, projection='3d')\n",
"ax.scatter(x, y, z,marker='o', color='g')\n",
"ax.set_xlabel('x label')\n",
"ax.set_ylabel('y label')\n",
"ax.set_zlabel('z label')\n",
"legend([p1,p2,p3],[\"normal projection\",\"3d data\",\"2d projection\"],loc='center left', bbox_to_anchor=(1, 0.5))\n",
"title('PCA Projection of 3D data into 2D subspace')\n",
"\n",
"for i in range(100):\n",
" ax.scatter(x_new[i], y_new[i], z_new[i],marker='o', color='b')\n",
" ax.plot([x[i],x_new[i]],[y[i],y_new[i]],[z[i],z_new[i]],color='r') "
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"PCA Performance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Uptill now, we were using the EigenValue Decomposition method to compute the transformation matrix$\\text{(N>D)}$ but for the next example $\\text{(N<D)}$ we will be using Singular Value Decomposition."
]
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Practical Example : Eigenfaces"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The problem with the image representation we are given is its high dimensionality. Two-dimensional $\\text{p} \\times \\text{q}$ grayscale images span a $\\text{m=pq}$ dimensional vector space, so an image with $\\text{100}\\times\\text{100}$ pixels lies in a $\\text{10,000}$ dimensional image space already. \n",
"\n",
"The question is, are all dimensions really useful for us?\n",
" \n",
"$\\text{Eigenfaces}$ are based on the dimensional reduction approach of $\\text{Principal Component Analysis(PCA)}$. The basic idea is to treat each image as a vector in a high dimensional space. Then, $\\text{PCA}$ is applied to the set of images to produce a new reduced subspace that captures most of the variability between the input images. The $\\text{Pricipal Component Vectors}$(eigenvectors of the sample covariance matrix) are called the $\\text{Eigenfaces}$. Every input image can be represented as a linear combination of these eigenfaces by projecting the image onto the new eigenfaces space. Thus, we can perform the identfication process by matching in this reduced space. An input image is transformed into the $\\text{eigenspace,}$ and the nearest face is identified using a $\\text{Nearest Neighbour approach.}$"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 1: Get some data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here data means those Images which will be used for training purposes."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rcParams['figure.figsize'] = 10, 10 \n",
"import os\n",
"def get_imlist(path):\n",
" \"\"\" Returns a list of filenames for all jpg images in a directory\"\"\"\n",
" return [os.path.join(path,f) for f in os.listdir(path) if f.endswith('.pgm')]\n",
"\n",
"#set path of the training images\n",
"path_train='../../../data/att_dataset/training/'\n",
"#set no. of rows that the images will be resized.\n",
"k1=100\n",
"#set no. of columns that the images will be resized.\n",
"k2=100\n",
"\n",
"filenames = get_imlist(path_train)\n",
"filenames = array(filenames)\n",
"\n",
"#n is total number of images that has to be analysed.\n",
"n=len(filenames)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets have a look on the data:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# we will be using this often to visualize the images out there.\n",
"def showfig(image):\n",
" imgplot=imshow(image, cmap='gray')\n",
" imgplot.axes.get_xaxis().set_visible(False)\n",
" imgplot.axes.get_yaxis().set_visible(False)\n",
" \n",
"import Image\n",
"from scipy import misc\n",
"\n",
"# to get a hang of the data, lets see some part of the dataset images.\n",
"fig = pyplot.figure()\n",
"title('The Training Dataset')\n",
"\n",
"for i in range(49):\n",
" fig.add_subplot(7,7,i)\n",
" train_img=array(Image.open(filenames[i]).convert('L'))\n",
" train_img=misc.imresize(train_img, [k1,k2])\n",
" showfig(train_img)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Represent every image $I_i$ as a vector $\\Gamma_i$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#To form the observation matrix obs_matrix.\n",
"#read the 1st image.\n",
"train_img = array(Image.open(filenames[0]).convert('L'))\n",
"\n",
"#resize it to k1 rows and k2 columns\n",
"train_img=misc.imresize(train_img, [k1,k2])\n",
"\n",
"#since Realfeatures accepts only data of float64 datatype, we do a type conversion\n",
"train_img=array(train_img, dtype='double')\n",
"\n",
"#flatten it to make it a row vector.\n",
"train_img=train_img.flatten()\n",
"\n",
"# repeat the above for all images and stack all those vectors together in a matrix\n",
"for i in range(1,n):\n",
" temp=array(Image.open(filenames[i]).convert('L')) \n",
" temp=misc.imresize(temp, [k1,k2])\n",
" temp=array(temp, dtype='double')\n",
" temp=temp.flatten()\n",
" train_img=vstack([train_img,temp])\n",
"\n",
"#form the observation matrix \n",
"obs_matrix=train_img.T"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 2: Subtract the mean"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is very important that the face images $I_1,I_2,...,I_M$ are $centered$ and of the $same$ size"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We observe here that the no. of $\\dim$ for each image is far greater than no. of training images. This calls for the use of $\\text{SVD}$.\n",
"\n",
"Setting the $\\text{PCA}$ in the $\\text{AUTO}$ mode does this automagically according to the situation."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"train_features = RealFeatures(obs_matrix)\n",
"preprocessor=PCA(AUTO)\n",
"\n",
"preprocessor.set_target_dim(100)\n",
"preprocessor.init(train_features)\n",
"\n",
"mean=preprocessor.get_mean()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 3 & Step 4: Calculate the eigenvectors and eigenvalues of the covariance matrix."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#get the required eigenvectors corresponding to top 100 eigenvalues\n",
"E = preprocessor.get_transformation_matrix()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#lets see how these eigenfaces/eigenvectors look like:\n",
"fig1 = pyplot.figure()\n",
"title('Top 20 Eigenfaces')\n",
"\n",
"for i in range(20):\n",
" a = fig1.add_subplot(5,4,i+1)\n",
" eigen_faces=E[:,i].reshape([k1,k2])\n",
" showfig(eigen_faces)\n",
" \n"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These 20 eigenfaces are not sufficient for a good image reconstruction. Having more eigenvectors gives us the most flexibility in the number of faces we can reconstruct. Though we are adding vectors with low variance, they are in directions of change nonetheless, and an external image that is not in our database could in fact need these eigenvectors to get even relatively close to it. But at the same time we must also keep in mind that adding excessive eigenvectors results in addition of little or no variance, slowing down the process.\n",
"\n",
"Clearly a tradeoff is required.\n",
"\n",
"We here set for M=100."
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 5: Choosing components and forming a feature vector."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we set target $\\dim = 100$ for this $n \\dim$ data, we are directly given the $100$ required eigenvectors in $\\mathbf{E}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"E is automagically filled. This is different from the 2d data example where we implemented this step manually."
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 6: Projecting the data to its Principal Components."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The lower dimensional representation of each data point $\\mathbf{x}^n$ is given by $$\\mathbf{y}^n=\\mathbf{E}^T(\\mathbf{x}^n-\\mathbf{m})$$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#we perform the required dot product.\n",
"yn=preprocessor.apply_to_feature_matrix(train_features)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Step 7: Form the approximate reconstruction of the original image $I_n$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The approximate reconstruction of the original datapoint $\\mathbf{x}^n$ is given by : $\\mathbf{x}^n\\approx\\text{m}+\\mathbf{E}\\mathbf{y}^n$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"re=tile(mean,[n,1]).T[0] + dot(E,yn)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#lets plot the reconstructed images.\n",
"fig2 = pyplot.figure()\n",
"title('Reconstructed Images from 100 eigenfaces')\n",
"for i in range(1,50):\n",
" re1 = re[:,i].reshape([k1,k2])\n",
" fig2.add_subplot(7,7,i)\n",
" showfig(re1)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Recognition part."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In our face recognition process using the Eigenfaces approach, in order to recognize an unseen image, we proceed with the same preprocessing steps as applied to the training images.\n",
"Test images are represented in terms of eigenface coefficients by projecting them into face space$\\text{(eigenspace)}$ calculated during training. Test sample is recognized by measuring the similarity distance between the test sample and all samples in the training. The similarity measure is a metric of distance calculated between two vectors. Traditional Eigenface approach utilizes $\\text{Euclidean distance}$."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#set path of the training images\n",
"path_train='../../../data/att_dataset/testing/'\n",
"test_files=get_imlist(path_train)\n",
"test_img=array(Image.open(test_files[0]).convert('L'))\n",
"\n",
"rcParams.update({'figure.figsize': (3, 3)})\n",
"#we plot the test image , for which we have to identify a good match from the training images we already have\n",
"fig = pyplot.figure()\n",
"title('The Test Image')\n",
"showfig(test_img)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#We flatten out our test image just the way we have done for the other images\n",
"test_img=misc.imresize(test_img, [k1,k2])\n",
"test_img=array(test_img, dtype='double')\n",
"test_img=test_img.flatten()\n",
"\n",
"#We centralise the test image by subtracting the mean from it.\n",
"test_f=test_img-mean"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we have to project our training image as well as the test image on the PCA subspace."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Eigenfaces method then performs face recognition by:\n",
"1. Projecting all training samples into the PCA subspace.\n",
"2. Projecting the query image into the PCA subspace.\n",
"3. Finding the nearest neighbour between the projected training images and the projected query image."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#We have already projected our training images into pca subspace as yn.\n",
"train_proj = yn\n",
"\n",
"#Projecting our test image into pca subspace\n",
"test_proj = dot(E.T, test_f)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"Shogun's way of doing things:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Shogun uses [CEuclideanDistance](http://www.shogun-toolbox.org/doc/en/3.0.0/classshogun_1_1CEuclideanDistance.html) class to compute the familiar Euclidean distance for real valued features. It computes the square root of the sum of squared disparity between the corresponding feature dimensions of two data points.\n",
"\n",
"$\\mathbf{d(x,x')=}$$\\sqrt{\\mathbf{\\sum\\limits_{i=0}^{n}}|\\mathbf{x_i}-\\mathbf{x'_i}|^2}$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#To get Eucledian Distance as the distance measure use EuclideanDistance.\n",
"workfeat = RealFeatures(mat(train_proj))\n",
"testfeat = RealFeatures(mat(test_proj).T)\n",
"RaRb=EuclideanDistance(testfeat, workfeat)\n",
"\n",
"#The distance between one test image w.r.t all the training is stacked in matrix d.\n",
"d=empty([n,1])\n",
"for i in range(n):\n",
" d[i]= RaRb.distance(0,i)\n",
" \n",
"#The one having the minimum distance is found out\n",
"min_distance_index = d.argmin()\n",
"iden=array(Image.open(filenames[min_distance_index]))\n",
"title('Identified Image')\n",
"showfig(iden)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"References:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[1] David Barber. Bayesian Reasoning and Machine Learning.\n",
"\n",
"[2] Lindsay I Smith. A tutorial on Principal Component Analysis.\n",
"\n",
"[3] Philipp Wanger. Face Recognition with GNU Octave/MATLAB."
]
}
],
"metadata": {}
}
]
}
| gpl-3.0 |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_decoding_time_generalization.ipynb | 3 | 2897 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Decoding sensor space data with Generalization Across Time\n\n\nThis example runs the analysis computed in:\n\nJean-Remi King, Alexandre Gramfort, Aaron Schurger, Lionel Naccache\nand Stanislas Dehaene, \"Two distinct dynamic modes subtend the detection of\nunexpected sounds\", PLOS ONE, 2013,\nhttp://www.ncbi.nlm.nih.gov/pubmed/24475052\n\nThe idea is to learn at one time instant and assess if the decoder\ncan predict accurately over time.\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# Authors: Jean-Remi King <[email protected]>\n# Alexandre Gramfort <[email protected]>\n# Denis Engemann <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport mne\nfrom mne.datasets import spm_face\nfrom mne.decoding import GeneralizationAcrossTime\n\nprint(__doc__)\n\n# Preprocess data\ndata_path = spm_face.data_path()\n# Load and filter data, set up epochs\nraw_fname = data_path + '/MEG/spm/SPM_CTF_MEG_example_faces%d_3D_raw.fif'\n\nraw = mne.io.Raw(raw_fname % 1, preload=True) # Take first run\n\npicks = mne.pick_types(raw.info, meg=True, exclude='bads')\nraw.filter(1, 45, method='iir')\n\nevents = mne.find_events(raw, stim_channel='UPPT001')\nevent_id = {\"faces\": 1, \"scrambled\": 2}\ntmin, tmax = -0.1, 0.5\n\ndecim = 4 # decimate to make the example faster to run\nepochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,\n picks=picks, baseline=None, preload=True,\n reject=dict(mag=1.5e-12), decim=decim, verbose=False)\n\n# Define decoder. The decision function is employed to use cross-validation\ngat = GeneralizationAcrossTime(predict_mode='cross-validation', n_jobs=1)\n\n# fit and score\ngat.fit(epochs)\ngat.score(epochs)\ngat.plot(vmin=0.1, vmax=0.9,\n title=\"Generalization Across Time (faces vs. scrambled)\")\ngat.plot_diagonal() # plot decoding across time (correspond to GAT diagonal)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
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"file_extension": ".py",
"version": "2.7.11",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | bsd-3-clause |
UrbanCCD-UChicago/sustainableSystems | src/notebooks/ShapefilesToTimeseries.ipynb | 1 | 309 | {
"metadata": {
"name": "ShapefilesToTimeseries"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
dkirkby/astroml-study | Chapter8/Overfitting_Demo_Ridge_Lasso.ipynb | 1 | 324869 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Overfitting demo\n",
"\n",
"## Create a dataset based on a true sinusoidal relationship\n",
"Let's look at a synthetic dataset consisting of 30 points drawn from the sinusoid $y = \\sin(4x)$:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-04-07 10:38:20,885 [INFO] graphlab.cython.cy_server, 176: GraphLab Create v1.8.5 started. Logging: /tmp/graphlab_server_1460050696.log\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"This non-commercial license of GraphLab Create is assigned to [email protected] and will expire on February 15, 2017. For commercial licensing options, visit https://dato.com/buy/.\n"
]
}
],
"source": [
"import graphlab\n",
"import math\n",
"import random\n",
"import numpy\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create random values for x in interval [0,1)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"random.seed(98103)\n",
"n = 30\n",
"x = graphlab.SArray([random.random() for i in range(n)]).sort()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Compute y"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y = x.apply(lambda x: math.sin(4*x))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add random Gaussian noise to y"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"random.seed(1)\n",
"e = graphlab.SArray([random.gauss(0,1.0/3.0) for i in range(n)])\n",
"y = y + e"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Put data into an SFrame to manipulate later"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <tr>\n",
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" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Y</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.0395789449501</td>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.648655851372</td>\n",
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" <tr>\n",
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" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.259900980166</td>\n",
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" <tr>\n",
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"[30 rows x 2 columns]<br/>Note: Only the head of the SFrame is printed.<br/>You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tX1\tfloat\n",
"\tY\tfloat\n",
"\n",
"Rows: 30\n",
"\n",
"Data:\n",
"+-----------------+----------------+\n",
"| X1 | Y |\n",
"+-----------------+----------------+\n",
"| 0.0395789449501 | 0.587050191026 |\n",
"| 0.0415680996791 | 0.648655851372 |\n",
"| 0.0724319480801 | 0.307803309485 |\n",
"| 0.150289044622 | 0.310748447417 |\n",
"| 0.161334144502 | 0.237409625496 |\n",
"| 0.191956312795 | 0.705017157224 |\n",
"| 0.232833917145 | 0.461716676992 |\n",
"| 0.259900980166 | 0.383260507851 |\n",
"| 0.380145814869 | 1.06517691429 |\n",
"| 0.432444723508 | 1.03184706949 |\n",
"+-----------------+----------------+\n",
"[30 rows x 2 columns]\n",
"Note: Only the head of the SFrame is printed.\n",
"You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns."
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = graphlab.SFrame({'X1':x,'Y':y})\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create a function to plot the data, since we'll do it many times"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x120e1ce90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def plot_data(data): \n",
" plt.plot(data['X1'],data['Y'],'k.')\n",
" plt.xlabel('x')\n",
" plt.ylabel('y')\n",
"\n",
"plot_data(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define some useful polynomial regression functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define a function to create our features for a polynomial regression model of any degree:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def polynomial_features(data, deg):\n",
" data_copy=data.copy()\n",
" for i in range(1,deg):\n",
" data_copy['X'+str(i+1)]=data_copy['X'+str(i)]*data_copy['X1']\n",
" return data_copy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define a function to fit a polynomial linear regression model of degree \"deg\" to the data in \"data\":"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def polynomial_regression(data, deg):\n",
" model = graphlab.linear_regression.create(polynomial_features(data,deg), \n",
" target='Y', l2_penalty=0.,l1_penalty=0.,\n",
" validation_set=None,verbose=False)\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define function to plot data and predictions made, since we are going to use it many times."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_poly_predictions(data, model):\n",
" plot_data(data)\n",
"\n",
" # Get the degree of the polynomial\n",
" deg = len(model.coefficients['value'])-1\n",
" \n",
" # Create 200 points in the x axis and compute the predicted value for each point\n",
" x_pred = graphlab.SFrame({'X1':[i/200.0 for i in range(200)]})\n",
" y_pred = model.predict(polynomial_features(x_pred,deg))\n",
" \n",
" # plot predictions\n",
" plt.plot(x_pred['X1'], y_pred, 'g-', label='degree ' + str(deg) + ' fit')\n",
" plt.legend(loc='upper left')\n",
" plt.axis([0,1,-1.5,2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a function that prints the polynomial coefficients in a pretty way :)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def print_coefficients(model): \n",
" # Get the degree of the polynomial\n",
" deg = len(model.coefficients['value'])-1\n",
"\n",
" # Get learned parameters as a list\n",
" w = list(model.coefficients['value'])\n",
"\n",
" # Numpy has a nifty function to print out polynomials in a pretty way\n",
" # (We'll use it, but it needs the parameters in the reverse order)\n",
" print 'Learned polynomial for degree ' + str(deg) + ':'\n",
" w.reverse()\n",
" print numpy.poly1d(w)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fit a degree-2 polynomial"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fit our degree-2 polynomial to the data generated above:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"model = polynomial_regression(data, deg=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Inspect learned parameters"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Learned polynomial for degree 2:\n",
" 2\n",
"-5.129 x + 4.147 x + 0.07471\n"
]
}
],
"source": [
"print_coefficients(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Form and plot our predictions along a grid of x values:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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j3wzaVG/jdUlFJpy7sEREQpLP5yM5ORmfz4eZ8dRVT/FG5ze4efzNzN4y2+vy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\nICIiEhAFiIiIBEQBIiIiAfn/frJ+U6mtnw4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x12b677350>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_poly_predictions(data,model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Fit a degree-4 polynomial"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Learned polynomial for degree 4:\n",
" 4 3 2\n",
"23.87 x - 53.82 x + 35.23 x - 6.828 x + 0.7755\n"
]
},
{
"data": {
"image/png": 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6VouIhIf/6vxfVClfhR4TevDJrZ/QsW5Hr0sKmOTkZJKTk0tkWV6PgcQDw51z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\nFwWIiIj4RQEiIiJ++f+NJk2XA+gw5wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10211f690>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model = polynomial_regression(data, deg=4)\n",
"print_coefficients(model)\n",
"plot_poly_predictions(data,model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fit a degree-16 polynomial"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Learned polynomial for degree 16:\n",
" 16 15 14 13\n",
"2.583e+06 x - 1.092e+07 x + 1.443e+07 x + 1.873e+06 x \n",
" 12 11 10 9\n",
" - 2.095e+07 x + 1.295e+07 x + 9.366e+06 x - 1.232e+07 x\n",
" 8 7 6 5 4\n",
" - 2.544e+06 x + 1.181e+07 x - 9.325e+06 x + 3.887e+06 x - 9.666e+05 x\n",
" 3 2\n",
" + 1.441e+05 x - 1.215e+04 x + 506.6 x - 7.325\n"
]
}
],
"source": [
"model = polynomial_regression(data, deg=16)\n",
"print_coefficients(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Woah!!!! Those coefficients are *crazy*! On the order of 10^6."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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mP8XwW4cXqMfkDYF64STle5dPxg9pP4TzzvNM3jzZ6tBs72zaWYqFFbPdPnL2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\n6mSGPgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a0d6d10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_poly_predictions(data,model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Above: Fit looks pretty wild, too. Here's a clear example of how overfitting is associated with very large magnitude estimated coefficients."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" # "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" # "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Ridge Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ridge regression aims to avoid overfitting by adding a cost to the RSS term of standard least squares that depends on the 2-norm of the coefficients $\\|w\\|$. The result is penalizing fits with large coefficients. The strength of this penalty, and thus the fit vs. model complexity balance, is controled by a parameter lambda (here called \"L2_penalty\")."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define our function to solve the ridge objective for a polynomial regression model of any degree:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def polynomial_ridge_regression(data, deg, l2_penalty):\n",
" model = graphlab.linear_regression.create(polynomial_features(data,deg), \n",
" target='Y', l2_penalty=l2_penalty,\n",
" validation_set=None,verbose=False)\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Perform a ridge fit of a degree-16 polynomial using a *very* small penalty strength"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Learned polynomial for degree 16:\n",
" 16 15 14 13\n",
"2.583e+06 x - 1.092e+07 x + 1.443e+07 x + 1.873e+06 x \n",
" 12 11 10 9\n",
" - 2.095e+07 x + 1.295e+07 x + 9.366e+06 x - 1.232e+07 x\n",
" 8 7 6 5 4\n",
" - 2.544e+06 x + 1.181e+07 x - 9.325e+06 x + 3.887e+06 x - 9.666e+05 x\n",
" 3 2\n",
" + 1.441e+05 x - 1.215e+04 x + 506.6 x - 7.325\n"
]
}
],
"source": [
"model = polynomial_ridge_regression(data, deg=16, l2_penalty=1e-25)\n",
"print_coefficients(model)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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mP8XwW4cXqMfkDYF64STle5dPxg9pP4TzzvNM3jzZ6tBs72zaWYqFFbPdPnL2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\n6mSGPgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119f4efd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_poly_predictions(data,model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Perform a ridge fit of a degree-16 polynomial using a very large penalty strength"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Learned polynomial for degree 16:\n",
" 16 15 14 13 12 11\n",
"-0.301 x - 0.2802 x - 0.2604 x - 0.2413 x - 0.2229 x - 0.205 x \n",
" 10 9 8 7 6 5\n",
" - 0.1874 x - 0.1699 x - 0.1524 x - 0.1344 x - 0.1156 x - 0.09534 x\n",
" 4 3 2\n",
" - 0.07304 x - 0.04842 x - 0.02284 x - 0.002257 x + 0.6416\n"
]
}
],
"source": [
"model = polynomial_ridge_regression(data, deg=16, l2_penalty=100)\n",
"print_coefficients(model)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11a0d6850>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_poly_predictions(data,model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Let's look at fits for a sequence of increasing lambda values"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"lambda = 1.00e-25\n",
"Learned polynomial for degree 16:\n",
" 16 15 14 13\n",
"2.583e+06 x - 1.092e+07 x + 1.443e+07 x + 1.873e+06 x \n",
" 12 11 10 9\n",
" - 2.095e+07 x + 1.295e+07 x + 9.366e+06 x - 1.232e+07 x\n",
" 8 7 6 5 4\n",
" - 2.544e+06 x + 1.181e+07 x - 9.325e+06 x + 3.887e+06 x - 9.666e+05 x\n",
" 3 2\n",
" + 1.441e+05 x - 1.215e+04 x + 506.6 x - 7.325\n",
"\n",
"\n",
"lambda = 1.00e-10\n",
"Learned polynomial for degree 16:\n",
" 16 15 14 13\n",
"4.975e+04 x - 7.821e+04 x - 2.265e+04 x + 3.949e+04 x \n",
" 12 11 10 9 8\n",
" + 4.366e+04 x + 3080 x - 3.333e+04 x - 2.785e+04 x + 1.032e+04 x\n",
" 7 6 5 4 3 2\n",
" + 2.962e+04 x - 1441 x - 2.597e+04 x + 1.839e+04 x - 5596 x + 866.1 x - 65.19 x + 2.159\n",
"\n",
"\n",
"lambda = 1.00e-06\n",
"Learned polynomial for degree 16:\n",
" 16 15 14 13 12 11\n",
"329.1 x - 356.4 x - 264.2 x + 33.8 x + 224.7 x + 210.8 x \n",
" 10 9 8 7 6 5 4\n",
" + 49.62 x - 122.4 x - 178 x - 79.13 x + 84.89 x + 144.9 x + 5.123 x\n",
" 3 2\n",
" - 156.9 x + 88.21 x - 14.82 x + 1.059\n",
"\n",
"\n",
"lambda = 1.00e-03\n",
"Learned polynomial for degree 16:\n",
" 16 15 14 13 12 11\n",
"6.364 x - 1.596 x - 4.807 x - 4.778 x - 2.776 x + 0.1238 x \n",
" 10 9 8 7 6 5\n",
" + 2.977 x + 4.926 x + 5.203 x + 3.248 x - 0.9291 x - 6.011 x\n",
" 4 3 2\n",
" - 8.395 x - 2.655 x + 9.861 x - 2.225 x + 0.5636\n",
"\n",
"\n",
"lambda = 1.00e+00\n",
"Learned polynomial for degree 16:\n",
" 16 15 14 13 12 11\n",
"1.311 x + 0.8888 x + 0.516 x + 0.1902 x - 0.09031 x - 0.3261 x \n",
" 10 9 8 7 6 5\n",
" - 0.5165 x - 0.6592 x - 0.7496 x - 0.7801 x - 0.7389 x - 0.6094 x\n",
" 4 3 2\n",
" - 0.3729 x - 0.02616 x + 0.3598 x + 0.5091 x + 0.499\n",
"\n",
"\n",
"lambda = 1.00e+02\n",
"Learned polynomial for degree 16:\n",
" 16 15 14 13 12 11\n",
"-0.301 x - 0.2802 x - 0.2604 x - 0.2413 x - 0.2229 x - 0.205 x \n",
" 10 9 8 7 6 5\n",
" - 0.1874 x - 0.1699 x - 0.1524 x - 0.1344 x - 0.1156 x - 0.09534 x\n",
" 4 3 2\n",
" - 0.07304 x - 0.04842 x - 0.02284 x - 0.002257 x + 0.6416\n",
"\n",
"\n"
]
},
{
"data": {
"image/png": 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9nA6nUIpiv1NB+Ou42l/YnpEdRjLkqyE2yNBPrAYSQlwuFx8t+IgvJ37J9ddf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\nLGo1kHbANlXdpaqpwAzgphxlbgKmAaj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"text/plain": [
"<matplotlib.figure.Figure at 0x11a06b990>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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PH164ferUqdStW5cWLVowYMAAbrjhBm655RaPztODDz7IE088QaNGjXjuuec8qsOYYBFI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\nL132EiPfGknKrhQ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SOWlzeH/D+8y5Zo5X6y1QlTsSOt0CWQ2cLyLxwH5gHPCrYmXmAncC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\n5gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a4952d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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Eoi8eQDTwZY7XjwGPFvKZHUB4PsuKPwwzl5e+e0nbT2mvrlRXiesqLfaf2K+3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\nGK9YAjHGGOOV/wcFvv2A11wugwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a0d6250>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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SCkG/Y5HIqApLRESiTglEREQiEngCMbOXzGybmS0qZPnFZrbLzD4LP34T7RhFROR45aER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\nO1exaNui0OPrRSz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wd08Lv34IcHf/YyHr0jgQEZESqAjjQAoL/hOgrZklmFl1YCAwOXph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\nRAlEREQi8v8Bt59n0x5NvjQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a588fd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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iB+1Cnwe3EqAj8IKZ3dOIsTV76kAViR8z4/Zht/Pd/t/llMdP4YviL8IOKeXV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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11b029b50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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SGkTPYosWLeKCCy5g69at9O/fn6effpqePXuGHZaItBLqwpKcoN+xSHLUhSUiIhmnBCIi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\nKUogIiKSFCUQERFJyv8HrqYLcmNEiuMAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a93ba10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for l2_penalty in [1e-25, 1e-10, 1e-6, 1e-3, 1e0,1e2]:\n",
" model = polynomial_ridge_regression(data, deg=16, l2_penalty=l2_penalty)\n",
" print 'lambda = %.2e' % l2_penalty\n",
" print_coefficients(model)\n",
" print '\\n'\n",
" plt.figure()\n",
" plot_poly_predictions(data,model)\n",
" plt.title('Ridge, lambda = %.2e' % l2_penalty)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Perform a ridge fit of a degree-16 polynomial using a \"good\" penalty strength"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will learn about cross validation later in this course as a way to select a good value of the tuning parameter (penalty strength) lambda. Here, we consider \"leave one out\" (LOO) cross validation, which one can show approximates average mean square error (MSE). As a result, choosing lambda to minimize the LOO error is equivalent to choosing lambda to minimize an approximation to average MSE."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# LOO cross validation -- return the average MSE\n",
"def loo(data, deg, l2_penalty_values):\n",
" # Create polynomial features\n",
" polynomial_features(data, deg)\n",
" \n",
" # Create as many folds for cross validatation as number of data points\n",
" num_folds = len(data)\n",
" folds = graphlab.cross_validation.KFold(data,num_folds)\n",
" \n",
" # for each value of l2_penalty, fit a model for each fold and compute average MSE\n",
" l2_penalty_mse = []\n",
" min_mse = None\n",
" best_l2_penalty = None\n",
" for l2_penalty in l2_penalty_values:\n",
" next_mse = 0.0\n",
" for train_set, validation_set in folds:\n",
" # train model\n",
" model = graphlab.linear_regression.create(train_set,target='Y', \n",
" l2_penalty=l2_penalty,\n",
" validation_set=None,verbose=False)\n",
" \n",
" # predict on validation set \n",
" y_test_predicted = model.predict(validation_set)\n",
" # compute squared error\n",
" next_mse += ((y_test_predicted-validation_set['Y'])**2).sum()\n",
" \n",
" # save squared error in list of MSE for each l2_penalty\n",
" next_mse = next_mse/num_folds\n",
" l2_penalty_mse.append(next_mse)\n",
" if min_mse is None or next_mse < min_mse:\n",
" min_mse = next_mse\n",
" best_l2_penalty = l2_penalty\n",
" \n",
" return l2_penalty_mse,best_l2_penalty"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Run LOO cross validation for \"num\" values of lambda, on a log scale"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"l2_penalty_values = numpy.logspace(-4, 10, num=10)\n",
"l2_penalty_mse,best_l2_penalty = loo(data, 16, l2_penalty_values)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot results of estimating LOO for each value of lambda"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11a8a05d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(l2_penalty_values,l2_penalty_mse,'k-')\n",
"plt.xlabel('$\\L2_penalty$')\n",
"plt.ylabel('LOO cross validation error')\n",
"plt.xscale('log')\n",
"plt.yscale('log')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Find the value of lambda, $\\lambda_{\\mathrm{CV}}$, that minimizes the LOO cross validation error, and plot resulting fit"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.12915496650148839"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_l2_penalty"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Learned polynomial for degree 16:\n",
" 16 15 14 13 12 11\n",
"1.345 x + 1.141 x + 0.9069 x + 0.6447 x + 0.3569 x + 0.04947 x \n",
" 10 9 8 7 6 5\n",
" - 0.2683 x - 0.5821 x - 0.8701 x - 1.099 x - 1.216 x - 1.145 x\n",
" 4 3 2\n",
" - 0.7837 x - 0.07406 x + 0.7614 x + 0.7703 x + 0.3918\n"
]
}
],
"source": [
"model = polynomial_ridge_regression(data, deg=16, l2_penalty=best_l2_penalty)\n",
"print_coefficients(model)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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CpObJK8hj/cH1xUGxct9Kvt7/Na1jWzOwzUCSWicxIGEAfeP7Et8ovlbuUVRFVU9yPHTi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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1166e9a10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_poly_predictions(data,model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Lasso Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lasso regression jointly shrinks coefficients to avoid overfitting, and implicitly performs feature selection by setting some coefficients exactly to 0 for sufficiently large penalty strength lambda (here called \"L1_penalty\"). In particular, lasso takes the RSS term of standard least squares and adds a 1-norm cost of the coefficients $\\|w\\|$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define our function to solve the lasso objective for a polynomial regression model of any degree:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def polynomial_lasso_regression(data, deg, l1_penalty):\n",
" model = graphlab.linear_regression.create(polynomial_features(data,deg), \n",
" target='Y', l2_penalty=0.,\n",
" l1_penalty=l1_penalty,\n",
" validation_set=None, \n",
" solver='fista', verbose=False,\n",
" max_iterations=3000, convergence_threshold=1e-10)\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore the lasso solution as a function of a few different penalty strengths"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We refer to lambda in the lasso case below as \"l1_penalty\""
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"l1_penalty = 1.000000e-04\n",
"number of nonzeros = 17\n",
"Learned polynomial for degree 16:\n",
" 16 15 14 13 12 11\n",
"29.02 x + 1.35 x - 12.72 x - 16.93 x - 13.82 x - 6.698 x \n",
" 10 9 8 7 6 5\n",
" + 1.407 x + 8.939 x + 12.88 x + 11.44 x + 3.759 x - 8.062 x\n",
" 4 3 2\n",
" - 16.28 x - 7.682 x + 17.86 x - 4.384 x + 0.685\n",
"\n",
"\n",
"l1_penalty = 1.000000e-02\n",
"number of nonzeros = 14\n",
"Learned polynomial for degree 16:\n",
" 16 15 11 10 9 8\n",
"-1.181 x - 0.0003731 x + 0.08711 x + 0.7386 x + 3.828 x + 0.4759 x\n",
" 7 6 5 4 3 2\n",
" + 0.1281 x + 0.002859 x - 0.6152 x - 10.11 x - 0.0002888 x + 6.686 x - 1.28 x + 0.5057\n",
"\n",
"\n",
"l1_penalty = 1.000000e-01\n",
"number of nonzeros = 5\n",
"Learned polynomial for degree 16:\n",
" 16 6 5\n",
"2.21 x - 1.002 x - 2.962 x + 1.216 x + 0.3473\n",
"\n",
"\n",
"l1_penalty = 1.000000e+01\n",
"number of nonzeros = 2\n",
"Learned polynomial for degree 16:\n",
" 9\n",
"-1.526 x + 0.5755\n",
"\n",
"\n"
]
},
{
"data": {
"image/png": 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Th/lg1Qe8vfRtykWU44mOT9CvZT8bKryEm71lNkOmD6FLTBf+0+0/VCtXzeuQ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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a8ac510>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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eHvi0zmgDFssJJA14xDk32H//IOBCz0L8BPL/nHNDyzC/KnklemnyXT7Lv1/O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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11ad2a8d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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OfpGSVmxdwVX/vgoz4/FzHqdHqx5Rh1SnHEoCibrxsROwJub9WmBwKeWGmNk8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\nteg7fEaXsiIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a2e77d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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Pu3Xr5jNnzqw2jvr6Pxap78LPTlzH3EZxJXpN7O64rqE5hI+v24Vsc+fOpaSkhFtvvRWA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\nEYmLEoiIiMTl/wP+YkygwtB3TQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11a28f450>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for l1_penalty in [0.0001, 0.01, 0.1, 10]:\n",
" model = polynomial_lasso_regression(data, deg=16, l1_penalty=l1_penalty)\n",
" print 'l1_penalty = %e' % l1_penalty\n",
" print 'number of nonzeros = %d' % (model.coefficients['value']).nnz()\n",
" print_coefficients(model)\n",
" print '\\n'\n",
" plt.figure()\n",
" plot_poly_predictions(data,model)\n",
" plt.title('LASSO, lambda = %.2e, # nonzeros = %d' % (l1_penalty, (model.coefficients['value']).nnz()))"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"Above: We see that as lambda increases, we get sparser and sparser solutions. However, even for our non-sparse case for lambda=0.0001, the fit of our high-order polynomial is not too wild. This is because, like in ridge, coefficients included in the lasso solution are shrunk relative to those of the least squares (unregularized) solution. This leads to better behavior even without sparsity. Of course, as lambda goes to 0, the amount of this shrinkage decreases and the lasso solution approaches the (wild) least squares solution."
]
},
{
"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": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
mne-tools/mne-tools.github.io | stable/_downloads/709b65f447b790ec915e9d00176f0746/virtual_evoked.ipynb | 1 | 3696 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import mne\nfrom mne.datasets import sample\n\nprint(__doc__)\n\n# read the evoked\ndata_path = sample.data_path()\nmeg_path = data_path / 'MEG' / 'sample'\nfname = meg_path / 'sample_audvis-ave.fif'\nevoked = mne.read_evokeds(fname, condition='Left Auditory', baseline=(None, 0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's call remap gradiometers to magnometers, and plot\nthe original and remapped topomaps of the magnetometers.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# go from grad + mag to mag and plot original mag\nvirt_evoked = evoked.as_type('mag')\nevoked.plot_topomap(ch_type='mag', title='mag (original)', time_unit='s')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# plot interpolated grad + mag\nvirt_evoked.plot_topomap(ch_type='mag', time_unit='s',\n title='mag (interpolated from mag + grad)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we remap magnometers to gradiometers, and plot\nthe original and remapped topomaps of the gradiometers\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# go from grad + mag to grad and plot original grad\nvirt_evoked = evoked.as_type('grad')\nevoked.plot_topomap(ch_type='grad', title='grad (original)', time_unit='s')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# plot interpolated grad + mag\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": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
gfeiden/Notebook | Projects/ngc2516_spots/ngc2516_vs_pleiades.ipynb | 1 | 279001 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NGC 2516 vs the Pleiades\n",
"\n",
"These two clusters have similar ages, but do their CMDs show a similar morphology for low-mass stars?"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import NGC 2516 low-mass star data."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ngc2516 = np.genfromtxt('data/ngc2516_Christophe_v3.dat') # data for this study from J&J (2012)\n",
"irwin07 = np.genfromtxt('data/irwin2007.phot') # data from Irwin+ (2007)\n",
"jeffr01 = np.genfromtxt('data/jeff_2001.tsv', delimiter=';', comments='#') # data from Jeffries+ (2001)\n",
"jeffr01 = np.array([star for star in jeffr01 if star[9] == 1]) # extract candidate members"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jackson et al. (2009) recommend a small correction to I-band magnitudes from Irwin et al. (2007) to place them on the same photometric scale as Jeffries et al. (2001), which they deem to be \"better calibrated.\" Jackson & Jeffries (2012) suggest that the tabulated data (on Vizier) has been transformed to the \"better calibrated\" system. Key to understanding their results, however, is to also transform $(V-I_C)$ and then calculate a correction to $V$-band magnitudes, as well."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"irwinVI = (irwin07[:, 7] - irwin07[:, 8])*(1.0 - 0.153) + 0.300\n",
"irwin07[:, 8] = (1.0 - 0.0076)*irwin07[:, 8] + 0.080\n",
"irwin07[:, 7] = irwinVI + irwin07[:, 8]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"~~Note that it is not immediately clear whether this correction should be applied to photometric data cataloged by Jackson & Jeffries (2012).~~ Reading through Irwin et al. (2007) and Jackson & Jeffries (2012), it appears that the transformations are largely performed to transform the Irwin+ photometric system (Johnson $I$-band) into Cousins $I_C$ magnitudes. There may be reasons related to a \"better calibration,\" but the issue is to first and foremost put them in the same photometric system. Why that involves altering the $V$-band magnitudes is not abundantly clear.\n",
"\n",
"Now data for the Pleiades."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pleiades_s07 = np.genfromtxt('../pleiades_colors/data/Stauffer_Pleiades_litPhot.txt', usecols=(2, 3, 5, 6, 8, 9, 13, 14, 15))\n",
"pleiades_k14 = np.genfromtxt('../pleiades_colors/data/Kamai_Pleiades_cmd.dat', usecols=(0, 1, 2, 3, 4, 5))\n",
"iso_emp_k14 = np.genfromtxt('../pleiades_colors/data/Kamai_Pleiades_emp.iso') # empirical Pleiades isochrone"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adopt literature values for reddening, neglecting differential reddening across the Pleiades."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pl_dis = 5.61\n",
"pl_ebv = 0.034\n",
"pl_evi = 1.25*pl_ebv\n",
"pl_evk = 2.78*pl_ebv\n",
"pl_eik = pl_evk - pl_evi\n",
"pl_av = 3.12*pl_ebv\n",
"\n",
"ng_dis = 7.95\n",
"ng_ebv = 0.12\n",
"ng_evi = 1.25*ng_ebv\n",
"ng_evk = 2.78*ng_ebv\n",
"ng_eik = ng_evk - ng_evi\n",
"ng_av = 3.12*ng_ebv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Overlay the CMDs for each cluster, corrected for reddening and distance."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1085b4a10>]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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OddCuX/uhnXOOyPLP49KkBjEA8riiVDQMiRCyj1p5/WrXdpcv1v9+zbbnXwMg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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105bc6150>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(12., 8.), sharex=True, sharey=True)\n",
"\n",
"for axis in ax:\n",
" axis.grid(True)\n",
" axis.tick_params(which='major', axis='both', length=15., labelsize=16.)\n",
" axis.set_ylim(12., 5.)\n",
" axis.set_xlim(0.5, 3.0)\n",
" axis.set_xlabel('$(V - I_C)$', fontsize=20.)\n",
"\n",
"ax[0].set_ylabel('$M_V$', fontsize=20.)\n",
"\n",
"ax[0].plot(jeffr01[:,5] - ng_evi, jeffr01[:,3] - ng_av - ng_dis, \n",
" 'o', markersize=4.0, c='#555555', alpha=0.2)\n",
"ax[0].plot(irwin07[:, 7] - irwin07[:, 8] - ng_evi, irwin07[:, 7] - ng_av - ng_dis, \n",
" 'o', c='#1e90ff', markersize=4.0, alpha=0.6)\n",
"ax[0].plot(ngc2516[:, 1] - ngc2516[:, 2] - ng_evi, ngc2516[:, 1] - ng_av - ng_dis, \n",
" 'o', c='#555555', markersize=4.0, alpha=0.8)\n",
"ax[0].plot(iso_emp_k14[:, 2] - pl_evi, iso_emp_k14[:, 0] - pl_av - pl_dis, \n",
" dashes=(20., 5.), lw=3, c='#b22222')\n",
"\n",
"ax[1].plot(irwin07[:, 7] - irwin07[:, 8] - ng_evi, irwin07[:, 7] - ng_av - ng_dis, \n",
" 'o', c='#1e90ff', markersize=4.0, alpha=0.6)\n",
"ax[1].plot(ngc2516[:, 1] - ngc2516[:, 2] - ng_evi, ngc2516[:, 1] - ng_av - ng_dis, \n",
" 'o', c='#555555', markersize=4.0, alpha=0.6)\n",
"ax[1].plot(iso_emp_k14[:, 2] - pl_evi, iso_emp_k14[:, 0] - pl_av - pl_dis, \n",
" dashes=(20., 5.), lw=3, c='#b22222')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While the Stauffer et al (2007) and Jackson et al. (2009) samples lie a bit redward of the median sequence in the Jeffries et al. (2001), the former two samples compare well against the empirical cluster sequence (shown as a red dashed line; Kamai et al. 2014) from the Pleiades in a $M_V/(V-I_C)$ CMD. \n",
"\n",
"What about $M_V/(V-K)$ and $M_V/(I_C-K)$ CMDs?"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x107b2bc50>]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8MSYxxrzPGHO+MeaXxpjbgN8D72HLp32JlEbiXMqWk/SvpNa2hIpH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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x105bc0ad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(12., 8.), sharey=True)\n",
"\n",
"for axis in ax:\n",
" axis.grid(True)\n",
" axis.tick_params(which='major', axis='both', length=15., labelsize=16.)\n",
" axis.set_ylim(12., 5.)\n",
" \n",
"ax[0].set_xlim(1.0, 6.0)\n",
"ax[0].set_xlabel('$(V - K)$', fontsize=20.)\n",
"ax[0].set_ylabel('$M_V$', fontsize=20.)\n",
"\n",
"# include K_CIT --> K_2mass correction for NGC 2516\n",
"ax[0].plot(ngc2516[:, 1] - ngc2516[:, 3] - 0.024 - ng_evk, ngc2516[:, 1] - ng_av - ng_dis, \n",
" 'o', c='#555555', markersize=4.0, alpha=0.6)\n",
"ax[0].plot(iso_emp_k14[:, 3] - pl_evk, iso_emp_k14[:, 0] - pl_av - pl_dis, \n",
" dashes=(20., 5.), lw=3, c='#b22222')\n",
"\n",
"ax[1].set_xlim(0.5, 3.0)\n",
"ax[1].set_xlabel('$(I_C - K)$', fontsize=20.)\n",
"\n",
"ax[1].plot(ngc2516[:, 2] - ngc2516[:, 3] - 0.024 - ng_eik, ngc2516[:, 1] - ng_av - ng_dis, \n",
" 'o', c='#555555', markersize=4.0, alpha=0.6)\n",
"ax[1].plot(iso_emp_k14[:, 3] - iso_emp_k14[:, 2] - pl_eik, iso_emp_k14[:, 0] - pl_av - pl_dis, \n",
" dashes=(20., 5.), lw=3, c='#b22222')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While data in the $M_V/(V-I_C)$ CMD appears to be bluer for early M-dwarf stars and redder for later M-dwarf stars, we find that M-dwarfs in NGC 2516 appear to be generally bluer than low-mass stars in the Pleiades. \n",
"\n",
"An interesting implication is that empirical isochornes based on the Pleiades or NGC 2516 may not reliably fit other clusters. Something is different between the two. Is it magnetic activity, or perhaps chemical composition?\n",
"\n",
"---\n",
"\n",
"$(B-V)/(V-I_C)$ color-color diagram using data from Jeffries et al. (2001) for NGC 2516."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x108275e10>]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wzj/bee6rAO4A+GcA/sJpFnrUjcK33bNCsegZpViEw2FF13V/sVj0bm9v7+Zy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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x107a25790>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1, figsize=(6., 8.))\n",
"\n",
"ax.grid(True)\n",
"ax.tick_params(which='major', axis='both', length=15., labelsize=16.)\n",
"ax.set_xlim(-0.5, 2.0)\n",
"ax.set_ylim( 0.0, 2.5)\n",
"ax.set_ylabel('$(V - I_C)$', fontsize=20.)\n",
"ax.set_xlabel('$(B - V)$', fontsize=20.)\n",
"\n",
"ax.plot(jeffr01[:,4] - ng_ebv, jeffr01[:,5] - ng_evi, \n",
" 'o', markersize=4.0, c='#555555', alpha=0.2)\n",
"ax.plot(iso_emp_k14[:, 1] - pl_ebv, iso_emp_k14[:, 2] - pl_evi, \n",
" dashes=(20., 5.), lw=3, c='#b22222')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The empirical isochrone from Kamai et al. (2014) agrees well with photometric data for NGC 2516, with both corrected for differential extinction. There may be some small disagreements at various locations along the sequence, but the morphology of the empirical isochrone is broadly consistent with NGC 2516. However, stars in NGC 2516 appear to have bluer $(B-V)$ colors for $(V-I_C) > 1.8$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Exploring a transformation of the Irwin+ data from Johnson $(V-I)$ to Cousins $(V-I_C)$ from Bessell (1979). This is ignoring issues related to photometric calibrations, that Jackson & Jeffries posit is important. I'm not claiming JJ are wrong, just wondering how using a more standard photometric transformation would affect the resulting CMDs.\n",
"\n",
"__WAIT__: The Irwin et al. (2007) data file suggests the I band photometry is quoted in terms of Cousins I, not Johnson... unlike what is stated in their paper. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tmp_data = np.genfromtxt('data/irwin2007.phot') # re-load Irwin et al. (2007) data into new array"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, applying transformation from Bessell (1979), which states that\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"old_vmi = tmp_data[:, 7] - tmp_data[:, 8]\n",
"new_vmi = old_vmi*0.835 - 0.130"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x108eb94d0>]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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M7qPZuaAtSqAtzKdnhEYZGZG/s7Y2/Fa7PanJMgUAeCqTIXsF4bJ+TXn/sTGOe9AoMgAA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\nzErlQmNmZqX6f23pwRCvALuNAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1085d8710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 1, figsize=(6., 8.))\n",
"\n",
"ax.grid(True)\n",
"ax.tick_params(which='major', axis='both', length=15., labelsize=16.)\n",
"ax.set_xlim(2.0, 3.0)\n",
"ax.set_ylim(22., 16.)\n",
"ax.set_xlabel('$(V - I_C)$', fontsize=20.)\n",
"ax.set_ylabel('$M_V$', fontsize=20.)\n",
"\n",
"ax.plot(jeffr01[:, 5], jeffr01[:, 3], 'o', markersize=4.0, c='#555555', alpha=0.2)\n",
"ax.plot(old_vmi, tmp_data[:, 7], 'o', c='#b22222', alpha=0.6)"
]
},
{
"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": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ltiao/project-euler | problem-1-multiples-of-3-and-5.ipynb | 1 | 1884 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we list all the natural numbers below 10 that are multiples of 3 or 5, we get 3, 5, 6 and 9. The sum of these multiples is 23.\n",
"\n",
"Find the sum of all the multiples of 3 or 5 below 1000."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from six.moves import range"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Version 1: The obvious way"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"233168"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(filter(lambda x: x%3==0 or x%5==0, range(1000)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Version 2: If you prefer list comprehensions"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"233168"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum(x for x in range(1000) if x%3==0 or x%5==0)"
]
}
],
"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
}
| unlicense |
Diyago/Machine-Learning-scripts | DEEP LEARNING/Pytorch from scratch/TODO/Autoencoders/linear-autoencoder/Simple_Autoencoder_Solution.ipynb | 1 | 44209 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Simple Autoencoder\n",
"\n",
"We'll start off by building a simple autoencoder to compress the MNIST dataset. With autoencoders, we pass input data through an encoder that makes a compressed representation of the input. Then, this representation is passed through a decoder to reconstruct the input data. Generally the encoder and decoder will be built with neural networks, then trained on example data.\n",
"\n",
"<img src='notebook_ims/autoencoder_1.png' />\n",
"\n",
"### Compressed Representation\n",
"\n",
"A compressed representation can be great for saving and sharing any kind of data in a way that is more efficient than storing raw data. In practice, the compressed representation often holds key information about an input image and we can use it for denoising images or oher kinds of reconstruction and transformation!\n",
"\n",
"<img src='notebook_ims/denoising.png' width=60%/>\n",
"\n",
"In this notebook, we'll be build a simple network architecture for the encoder and decoder. Let's get started by importing our libraries and getting the dataset."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import torch\n",
"import numpy as np\n",
"from torchvision import datasets\n",
"import torchvision.transforms as transforms\n",
"\n",
"# convert data to torch.FloatTensor\n",
"transform = transforms.ToTensor()\n",
"\n",
"# load the training and test datasets\n",
"train_data = datasets.MNIST(root='data', train=True,\n",
" download=True, transform=transform)\n",
"test_data = datasets.MNIST(root='data', train=False,\n",
" download=True, transform=transform)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create training and test dataloaders\n",
"\n",
"# number of subprocesses to use for data loading\n",
"num_workers = 0\n",
"# how many samples per batch to load\n",
"batch_size = 20\n",
"\n",
"# prepare data loaders\n",
"train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, num_workers=num_workers)\n",
"test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, num_workers=num_workers)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize the Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x11bedad30>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAATUAAAEyCAYAAACbGke8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKAa3AAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
" \n",
"# obtain one batch of training images\n",
"dataiter = iter(train_loader)\n",
"images, labels = dataiter.next()\n",
"images = images.numpy()\n",
"\n",
"# get one image from the batch\n",
"img = np.squeeze(images[0])\n",
"\n",
"fig = plt.figure(figsize = (5,5)) \n",
"ax = fig.add_subplot(111)\n",
"ax.imshow(img, cmap='gray')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Linear Autoencoder\n",
"\n",
"We'll train an autoencoder with these images by flattening them into 784 length vectors. The images from this dataset are already normalized such that the values are between 0 and 1. Let's start by building a simple autoencoder. The encoder and decoder should be made of **one linear layer**. The units that connect the encoder and decoder will be the _compressed representation_.\n",
"\n",
"Since the images are normalized between 0 and 1, we need to use a **sigmoid activation on the output layer** to get values that match this input value range.\n",
"\n",
"<img src='notebook_ims/simple_autoencoder.png' width=50% />\n",
"\n",
"\n",
"#### TODO: Build the graph for the autoencoder in the cell below. \n",
"> The input images will be flattened into 784 length vectors. The targets are the same as the inputs. \n",
"> The encoder and decoder will be made of two linear layers, each.\n",
"> The depth dimensions should change as follows: 784 inputs > **encoding_dim** > 784 outputs.\n",
"> All layers will have ReLu activations applied except for the final output layer, which has a sigmoid activation.\n",
"\n",
"**The compressed representation should be a vector with dimension `encoding_dim=32`.**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Autoencoder(\n",
" (fc1): Linear(in_features=784, out_features=32, bias=True)\n",
" (fc2): Linear(in_features=32, out_features=784, bias=True)\n",
")\n"
]
}
],
"source": [
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"\n",
"# define the NN architecture\n",
"class Autoencoder(nn.Module):\n",
" def __init__(self, encoding_dim):\n",
" super(Autoencoder, self).__init__()\n",
" ## encoder ##\n",
" # linear layer (784 -> encoding_dim)\n",
" self.fc1 = nn.Linear(28 * 28, encoding_dim)\n",
" \n",
" ## decoder ##\n",
" # linear layer (encoding_dim -> input size)\n",
" self.fc2 = nn.Linear(encoding_dim, 28*28)\n",
" \n",
"\n",
" def forward(self, x):\n",
" # add layer, with relu activation function\n",
" x = F.relu(self.fc1(x))\n",
" # output layer (sigmoid for scaling from 0 to 1)\n",
" x = F.sigmoid(self.fc2(x))\n",
" return x\n",
"\n",
"# initialize the NN\n",
"encoding_dim = 32\n",
"model = Autoencoder(encoding_dim)\n",
"print(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"## Training\n",
"\n",
"Here I'll write a bit of code to train the network. I'm not too interested in validation here, so I'll just monitor the training loss and the test loss afterwards. \n",
"\n",
"We are not concerned with labels in this case, just images, which we can get from the `train_loader`. Because we're comparing pixel values in input and output images, it will be best to use a loss that is meant for a regression task. Regression is all about comparing _quantities_ rather than probabilistic values. So, in this case, I'll use `MSELoss`. And compare output images and input images as follows:\n",
"```\n",
"loss = criterion(outputs, images)\n",
"```\n",
"\n",
"Otherwise, this is pretty straightfoward training with PyTorch. We flatten our images, pass them into the autoencoder, and record the training loss as we go."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# specify loss function\n",
"criterion = nn.MSELoss()\n",
"\n",
"# specify loss function\n",
"optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 1 \tTraining Loss: 0.636266\n",
"Epoch: 2 \tTraining Loss: 0.300271\n",
"Epoch: 3 \tTraining Loss: 0.258592\n",
"Epoch: 4 \tTraining Loss: 0.249542\n",
"Epoch: 5 \tTraining Loss: 0.245499\n",
"Epoch: 6 \tTraining Loss: 0.243327\n",
"Epoch: 7 \tTraining Loss: 0.241832\n",
"Epoch: 8 \tTraining Loss: 0.240754\n",
"Epoch: 9 \tTraining Loss: 0.239949\n",
"Epoch: 10 \tTraining Loss: 0.239308\n",
"Epoch: 11 \tTraining Loss: 0.238760\n",
"Epoch: 12 \tTraining Loss: 0.238279\n",
"Epoch: 13 \tTraining Loss: 0.237813\n",
"Epoch: 14 \tTraining Loss: 0.237417\n",
"Epoch: 15 \tTraining Loss: 0.237069\n",
"Epoch: 16 \tTraining Loss: 0.236747\n",
"Epoch: 17 \tTraining Loss: 0.236448\n",
"Epoch: 18 \tTraining Loss: 0.236169\n",
"Epoch: 19 \tTraining Loss: 0.235894\n",
"Epoch: 20 \tTraining Loss: 0.235611\n"
]
}
],
"source": [
"# number of epochs to train the model\n",
"n_epochs = 20\n",
"\n",
"for epoch in range(1, n_epochs+1):\n",
" # monitor training loss\n",
" train_loss = 0.0\n",
" \n",
" ###################\n",
" # train the model #\n",
" ###################\n",
" for data in train_loader:\n",
" # _ stands in for labels, here\n",
" images, _ = data\n",
" # flatten images\n",
" images = images.view(images.size(0), -1)\n",
" # clear the gradients of all optimized variables\n",
" optimizer.zero_grad()\n",
" # forward pass: compute predicted outputs by passing inputs to the model\n",
" outputs = model(images)\n",
" # calculate the loss\n",
" loss = criterion(outputs, images)\n",
" # backward pass: compute gradient of the loss with respect to model parameters\n",
" loss.backward()\n",
" # perform a single optimization step (parameter update)\n",
" optimizer.step()\n",
" # update running training loss\n",
" train_loss += loss.item()*images.size(0)\n",
" \n",
" # print avg training statistics \n",
" train_loss = train_loss/len(train_loader)\n",
" print('Epoch: {} \\tTraining Loss: {:.6f}'.format(\n",
" epoch, \n",
" train_loss\n",
" ))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Checking out the results\n",
"\n",
"Below I've plotted some of the test images along with their reconstructions. For the most part these look pretty good except for some blurriness in some parts."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1800x288 with 20 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# obtain one batch of test images\n",
"dataiter = iter(test_loader)\n",
"images, labels = dataiter.next()\n",
"\n",
"images_flatten = images.view(images.size(0), -1)\n",
"# get sample outputs\n",
"output = model(images_flatten)\n",
"# prep images for display\n",
"images = images.numpy()\n",
"\n",
"# output is resized into a batch of images\n",
"output = output.view(batch_size, 1, 28, 28)\n",
"# use detach when it's an output that requires_grad\n",
"output = output.detach().numpy()\n",
"\n",
"# plot the first ten input images and then reconstructed images\n",
"fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(25,4))\n",
"\n",
"# input images on top row, reconstructions on bottom\n",
"for images, row in zip([images, output], axes):\n",
" for img, ax in zip(images, row):\n",
" ax.imshow(np.squeeze(img), cmap='gray')\n",
" ax.get_xaxis().set_visible(False)\n",
" ax.get_yaxis().set_visible(False)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Up Next\n",
"\n",
"We're dealing with images here, so we can (usually) get better performance using convolution layers. So, next we'll build a better autoencoder with convolutional layers."
]
}
],
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
mrmeswani/Robotics | RoboND-Rover-Project/sample_code/Practice.ipynb | 1 | 130932 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.image as mpimg\n",
"import matplotlib.pyplot as plt\n",
"# Uncomment the next line for use in a Jupyter notebook\n",
"# This enables the interactive matplotlib window\n",
"%matplotlib notebook\n",
"image = mpimg.imread('example_grid1.jpg')\n",
"rock_image = mpimg.imread('example_rock1.jpg')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"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 (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>"
]
},
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"output_type": "display_data"
},
{
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O0Jbs3yrwufjQGzx23oddYo5e4/aI9/hIn4Wfpm2VdUvehe/gWWGNhd1lEgAI7ww3hz5LBFAj53Hw8eLY5/IpiKc1V7sfq7+IC5RXjyGHi9sd3x55che1NFH5ePtu28fhoQyisPnIVIAI9s7DQGMbMYPfjBMRABL9SNXB3L3jQD2KXdLm6ary/58GXkls095eCIb1RW7pwTwi77qGzVaEsC+FeQmCXdORJ7apuWdIvK97dw9XYyh+/zpvoD/SkT+W5gDqIs7vkREfrd9T7eBeWG7DYzKvz/V3n92RR5JAEkAG3NBNy2qaSSASyWQBHC1ZSEBXA4SSABXbSOnwnUNCpFodQ0KIhJNAkgXcAXv+ZQHVSKnizX879UtodP73yIiPygiOsFTlT1dvPHL8IJtBK0qIG4E/RcVuWsI4Hvf9kbZPZPmSnnFzeKyCn3hwoUVBRDdlF2Kyeo8sOmKqoeV99w55cbLeWoaf6Su+DlfkfvZRrdKYswdrEqQfQNxsm+gW1jdw5FrAYkAutUiRUHzhgqgd5lhGnROYKS2+c7F8LKRssZpKhZiiXMA0YWN7mKcI4gYIOa5NEV2Zu/lVJ7cSB7dK4iXfQPTbPNw9BsWn5+D6r+D7lssMw2HLvFl+pcKY5R/dIsagfYdU0mZK9VTT7AiVQnLyddV/TtSIcyeIoUMVT+8xrxhOrw9e7UtwgCV9QgDrw7ZXD8MmyOfFgZdwJgmfe5V7EjZ83WutqwiVRLTnFPVc6qkty2fHos7V+8i5R6xQMz72G1OOfNYYh3FuuaxwPRZm5Qr4whb/G7JNpr+pZ0DGIX1GEZl4m3KsMSwubrXZUtRu4vlhHWcCqD1BKXayec5BBoC+P/9238jF84n4qUGbEYYEYIrV64s5lOpWxXn2WFnbCTOzyHr6nwiV42mB91W+A0jCphmbByQkGJnFs2Xw8pqZEoJoF17t0RXg9vVwSG26NJLJGK6kteSC85cujjXUtOLacM4LA/o2sd70ai9yyXhyxI7zYgY43NcAKHfxXRY+nUwgt83+7SFQJher2B6YuQJW0SWsJLYoo5cxfFYdXU6+izC1t+3MGjDUWeHg58SqcBv9CGUWJ+jeqdxoN1GBDAa5OC3kexHHbcnI8H017BY8BtRB23244mtTwPW0a4BG+bjZJq7d+XAtilH2iJynyPMSHwj2+7zjcjucnHVEMBo4OLtISJO0TeiPiIiSF1tU/MMgIwIb0SYoyk3+G3f5uXqfA0dyeGscSgB/JKX/Nd6SRdwDagMu0CABLBdPUsCuFxQgfWDBDBuLUgAV3cBsI4qR05JAFftiARwScFwcKa4WFtMAricR42DZ7MkEkAqgKflsg0B/KPffK3ccbvuKZ1WS+LEYL2HbljdSiQa0aGKg2oOTt73io3Gjasw9Ts2kT8aEeKIGxUf3AIgGnVh5dFGJVIDMZ3RRPnc6A8LANOMrtAoTTiSXSqtaRsdG1VGCiCqi7bVCXa6eO2NA7fwib4R5TsaHXs8UamwaxwNe6XE3rewfrW14YX2YEqsvoPbCUUVANUYe44E3+cpGu0Ph6vb3xheXSqJfStS7UoVNVKNvY1gvF6l1/gje1EVG/MXKUGIB+IUqXOe4HnV0is0XeqKxySvfMabPeTqusbr8x3ZNuKSw7ZLAcSFJvo+1gOLL5dGTA/Wea9ieoxySp6Fw7pWsjlfj0t2W6NoRW19Li+R7WPYLnW1hHO2/YIPRPmOcLY2yNoC3254BTD6tr+Xa4usbKK02T0lgM/7Wj0JlgpgH1tnmJMINATw7b/+Grntgm4JmOYMIRnyxq5uX9uTTd3BtopWO2trfMyNhHPvtLJ4Aqdx63s4j9B3otioYgXDxkvT4ytKriPScJY+yxv+6+fKGRboskQYo0ZR73W5yex7vhNQF7D9vPsn6sQtH9iA+k4bOzDvxsb3PGGLSJFviDWtnhBERBnLvkS0fRlbfDYwSAOGJU7Y8UWdPHauOfITEQ8kgN5laLaH2EZkqU/HkiNnXdji4CdS1hBDHBx5pSVqEHP5wLyibfr5Wt4e1iGAvu7atj9YT/21/1vbBMsLzv/sk78cEfJ1Xf+2sDnXJKYLyUrkBo/qF9qvb6/igctyH1dsI7Ec8BoJfF8CGBFdb0sRAfTtTY4c+2kfffKhae8aEHj7MP0xwjxHGj3+EYmP+oYu0lcihBifD0sCSAUwasNr7pEAths/Y2cR7Z1HAhhvOkwCmHAhAVxuih15CEqDJuzkSQBXXX/RQDU36FNSEpE9EsDVbaNIAGtows0b9rGwEfSnEt2FC/hse4IGdmZGhNQtjK6h1T3ZUlVSZcbUQPtX3TA4qXz1FIx0QgWOzvVvf5oILgRAJQnVDP2Od4nllEN9zxRMH59+H+PFgrEFF3avJO/bc3Rj4sjWr9DVeHUVsKUblVE/YjeygXv8IbY4qkdcbNNre45KkV8li2Ewz5gvve/dz+iqRSJgNmP2oEoyKpJYXhYWbW5178a0WTiSBs0LvufVgOQ6Xm4gjdMccNWxXasCaGFwQJDDFuPrq6SgreE1vo9KMm5W7hcQme3av5Fq6TGJ0omKHtpOzhXq52tpnCUCGDV4XolBezAF0Cu4kSJncWsdNwy8Aoh1zGwI3XsWhydZXnHDNPuBUBQHKoAYV6RAYzsckT1vOyVMo/LTb2D59bVbPxiOFEXMv307136jkqpx+bLoo5LpN7rswdvOOieB4JZYUT/hbThyx+eIuG9XkfTbtc8D5wBSATwteVwsArFtYHC+X0QUsGHypCFKjJFB30Bi54odrN23RiN3UgN2ELqti1Ug7LSwY/cE0ROIXOOc6wxynYtVVj9fxDoaTKfvxPXYPO9+1/dwixPcqNvIFLqtkaznXCKYhqhjwPeiVdxYzr5csXPFjZntHVyBnRtUYNlYWtBG9Bg5w9MwXD3FY7mq2L6BLkHsUDEv2DgfHFxd2JSVtWKLG4pjOrvcVtiY4xQLI+QWj3cpYWeo8dvABYkvxo2kD4kqko2STeQ6KF9ffJpzNoVtiF37QYPGdVL1W47tbc6nHyxGRA7trG9eI9Lh27KIhGGHj2WSI8FICDypiTr8aMCCNoKY+/YvIg1RGUZ2W+pU9J2cTXkS6UlmlAaPBQ6m+oT36Y0Ita8zAkd+RsQxsh1PwCIb8W3jie+6DfJ9HDXlcWV/X770pbpbHOcAlmyWz2MESADhTFXfaOYUBxzJRvOL9DkJ4HJBDzZqJIDLObYkgIn4kQCmxtm3NySAy5XCpYEJdm8kgJtDd+gCPl1ZNwTwT/7dbyzOAtYG2S/KwJGuEptIGYzkbhz9RKN+a/SMFGi8eGarzxqmDZVKfd+7Z/yIPOdKixSwqOHFtCABjJSB0kgx6vD0nu49hyP8SMVBwnnpUjr/G8sHMdRVv7byF90X6G6IVFKMz68KxTT5b6M6jGWF6hwqt4YprjzHstTn3g2r+djaSuc+o3KjamN0njOqTrlRfVRe0+lkxZWZyme0mNKg6ituoo2djlem0D2FCi0eO4jYIcZYF6PrSLFHm8T6FNmqVygiZcrfw7+9WoP1HEkNfhvVPswTxruqMJ103Vsdwn99OqNyjYgEKna55jTCzttyZAO+bkbxR21XnzYmIjqrrvOTK9l9/qP6WOpScu9gXqOy9PYQkTYMU7LF6LlPey7MrB34Y7nau7l6gvaUI6R4P8KpRGT9O119ibqAv/Sl36zJ5j6AJaPl8xABEsBgR3gSwOVZsySAywn5JICpDSEBTLoDCeBqn0ICuFo3SACvP+uiAng6jBdHwdk2MNiwRfOrvFLh5y3lRmA4yvbzxrBDwbleGhcultC/own7fsK3qTWopOBoGRcq2HVuUj2qnZi3VYVi1QxzruOcSmrx4lFwuflCet+eWdrx+Dqcn6PqD27JY/PX7Hu5MsFJ3rm8eCXQ4ozy6NVADeuPHIzmVOo9dBlbue7unl0UhTWyGp+5VPE0FBzVR2oAjvYRW50iFNmGKWqKK87BtO9oeuw9nANpeKE9671obiraFtot2j4uivHzhnCCupZ5l616QpdTOdBmonpg6czN5UOc0e4iBXC1HLRupfqF+fK2a/mw9Pt5al3NZJfCGSkwUbuCOHoMPHZeYSopgL6Mcm2sJ6WYdnwH81sz5yyKA8uhpG6hqunbidK7OfuL7kf59vdQAcQ2Qq+9KhjViYjs+nulPOUUzpzq5+9zGxguAulq1/o8awjgO9/8erl4m14mN5cZWjQ/xyoI/tt1jXPhsJNAN1nkhrWOXztc63Sx0UASaQtNfNqihtU6EmtUvbtOG8Rcp5TrDHzjon9bHHmCcdI9owcU4WpedK8hEbCCNYyQdOTSr++YOxhJbUTgsSHznb3H1Ocd8x2R2IjA5zbtRQKIi2PG462QECBh8zalcaH9YaOPcS/TvDzyDAcMEZHT981G9Rs4cNLvICHFPOk7froF2qd3h+NeiEh2La9RufpFPFGd6FL0Sh2vf+47wVxnFtU7TD+WCe4DaN9DO0cSg+QsV19zbsGuvEZx5Tr8XP3BcirVQU+YI9uNGnicbhA992nrcufmOhCMA8kuho/qPhJ4/zzqA/p0YF0E0BNtrPNdB/Xl7MMTuqhNxjAlBTDKX460R2G5CIQEsKaORGFJAFsXcERMsHHDxitHNLGBIQFc3fjbGkYSwKWqSQK4quiTAC49CSSAdV0bEi8c5EQDHr1HAliH780ami7g05VMQwDf8aZfkvPnzp1QVaJRb7RfVi4JTUWDybYYDkd8qDR6NQO3zfArbi19OJkeCQZ+G/OCi068KwLdleim9CpDVyOjz/BIOwyL8wt946Rbj+CiB1RBotWzpnx6d1hOuTX8o2/kFtLgKDYaUft7uFVJToHQ+36knGvA4ykGqdrj9jeaDlSV/OhclTC0ObSNSAHUU1m86uKnLmD57e3tNWnyHbfeQ7XKq22RAhipwBovKoumAKKNRqoSDlwwbb5sLF25aQo5d1WkwESuL6x/3l7NHiwM1vnkDh4tkosYeBe9BorqFw7YMN+5POUUIK/OdCmA+B17L1LHT7q7T9Yar7h1Nfk5BRDrQ1Q+vk56DLrqKw58Ef8c7t5mvD3kbLBvV5drm1fut3uCRtjiwB/bv5xd5NKVU1cRL/8ueiqwTLzSqmlRBfD5X/+tGoyLQPoaB8OtINAQwHf/9r9erALGChFVpK75Lb6BLP0dNTqRrI7psM4aCYteW2XDTiE3v8XuI4FAUohqlcUbuZl9A4f5jSo/dsC+Y06lstxEV/OB6TcXo7px/dwzv08gzmdE7LpchV0rqXPkzPKPJM2ufSPa5eLRd5C0YgNp6Ue7wH3hcCUuEkD/PcUssi0sw1WVd3m6h6VB40T7QlyiTc7xvYiU6/eQACIB0nThgEf/tm9geSsRxPK2/FhcuPp7XQLYRQZymPq67TuzqB1G8rBqU4kA6reQANogC8k8Dm6ijtaTtq50loigJw+IRdR2WtqR9HnPQh9y5rHz5My3u5hHJFsYj/9uV5n7+ZW5gYfZYkQ+/WAKv9dFAKO85QiVH5yuvNvuA+jJp8bVlbbIbjHeXB8QpTt3L7IBEsCYuVEBPB2jJQEcL08k8Q2WNnQkgMsVwUiWsIMjAVySM7/Iw3cofloBCeCyASMBXFXGc2SQBDBDBmBzZz8QOjH4IQE8HXO4Sd4mATxdQTQE8I/f8itybi+trIwUQK+SWMPkRyXRSKxWBveKAqbHExAjIaaI+REtphMVIVNx/EjcNxoYn6kNHqNIUcB0+vQjRt69ubOzVKm8qmfvoXvs4sWLizKL1C8ksF3p0Ei8KwoJnn07pwRiPmylsVdHvKvTpwdd9/Y9VEdQQbN9AHMuYBzB59SBSN1ZVfRGK+5EI3K4IATjNvuIjvgzm/H2pemP8p3D3uwclUjcYzEqH6/sdikoaLeYZl+v8G90a3YREyxvv5doyf5mszRjC9Uaf/KLPse6kWsWMQ7f1ng7z7U9GC5qY3x7iUqwzyvauGpfCggAACAASURBVE8bKvbeJvTvSFnLKXe5sF5hj9q3yBYi1c8rboYT1n1spyxNnpx5BTCnsHl7jep6VJc03PFksjCRqM/p8lh02Tk+K6l+0fNSf4nxcxEIF4Hk2rm+9xdnAdscwC43kTVeEQHMkYOo4fGVJCebW6ebI5x2XzvGaLsQbIDQdWdho84g13hrxx419kiysNGwRqzrG75x0nln5urFzZFzHb41rJp/O6tYCZh9W5/jXLFo/iY25OheR1daVIY5BTBqhK0c8V/ETe/nCCC6lO39HAH0HTiWZW5uoifwllfdkgenBVgniO5WS5uGMwLo56xaGjAs2h/m2zf+aDv6DN/LkRf7XtRpdtVt7IxLHRzW9S4CiMQBcVasEA8N5+f3YhqOj1NnjXF4JVWfa50xN7mmC9MZ1d1cfe3C1mMTufb1/UgVt3hzgy3vesy5jK2MS+2wx99j2EUWo2fLujEODwNAbPD96AxrX35d87KxHndd59oe/47+ffVgeZ64L1PEDZ+VSDfap9m0j7uLzGpYf4IUts++v9BtYJ77Nd+kr3EOYFSIvFdEgAQw2Ag6qnQkgLE6rFh5JTNqcP0cPxLA1TltHg/rhBA3EsDldAQSwKUCiFjUKoARafNExhNHnFeKZJYEcHW6jNVXTyK7iCoJYJGzrASgC7gOLx96sQ2MuYAjCRoleRwF4UgpcgdrQxKtZMX4UN3KuZRLWdT3/ORvrHyaDu+C9JUw5yqwcDqKxTDoOsGRuGGSW4DiG2jMG6Y5507BEaSpWkjCTq6gTMdo4XuWNjw+TdMVuXVQUYxcbIitVz4iTCPVArH1nQi6jDwpwvSg69VPrNf3DmDE7zvMqEyQ8FuaECNPfFGptPhQlTYsFHtr5Le20vxTjN/bOqYNXYJRJxIpXv5oxZICEdX/HKnQNBwdHTdJwUFApOhrHBa3KtbeNrw6id80Fdu3Dzhn0GMYudc0DKpskWsyh090H/d39Bhh2uxd3CsSVcZIcUQSFrUF+ByxHcB+l3gfyzWyk1Ibi89xQ3ksk4iIYp3BOEp26OsFpjnCttZuBc6A71I7T4WVm5PYB2Nvt104cSNouoD72FRXmMUcwL2zu00437D4l3MNa47Y2H3fiEVkyr6P/2KD7a8tbu9S0ffRpeRHqdEWLdiBRCTFz0uJGgZshCLXWA4D+3aONOYK0IiEjzfqjDWMERJUldANlmugkVybiy3qfLHccmmOOr6o8c6RIn3fVmTnSFhko0joSjaOhCaySb3nv+1X4mIcfq7iMm+z3vVX05yzW59ftE20DawHniRbHDjXNSKZJxO8OgbvQ65LgxvflvQlgBFJ8DaJpAhtseTKtjLH/OcGrJ7MGrZ+UBqlDe0+suMccVy0haPRytxAX7+7yHxfY/T56CKz14IA+nYJB6rYdkZ9UO4eWq0n4DnSl+vjcrgpGb+eP84BvHUJ4BeLyPeJyDNE5HEi8hIReX3GWP4PEfk2EflbIvKPIMwdIvJTIvJiHdiKyGtF5HtE5HKF0ZEAulEakoPIzRQREySMnsz5EVyO+Op7JIAnLTfqsEgAV0+RIQFMHS0JoMiQBHClESEBrGADt2DQW9UF/BUi8oUi8o6WuOUI4FeLyI+KyN0i8r84AvhrLXn8dp3/LCKvEpG3i8jLKspxsQ3M+XNpI9tohNw1xyQapUaytY8XR8W4+MATLO+yQbeNVW59PyJhONEczxjG0X7UeUYT8/FILa+wREpJTgHsUlX8yLNrZF1RxougmAevaOS+rfnAEbcvq67J+1H6/Whe/87Zl1evLM2mCGGnn3Nx2fdwpTiS/Cg93gYjRcDbOJJ3e2bvofqKrmpd9BPZg3/fwkSLeKK8+E4vqgeRaq7fQWxzNoZ5t2P50FUfTbfAsszVH8Qdw6BiHZE8VOdzHX5JvVlnyoa3nZJ3JPc8whmVMyzjYp3KuIBL+a9pT66VO7mERzRY9uqqpTtSrnNKXtOOBBn2SqAfVNRiqGS89tflDcG4NC2qAH75X/tv9DYXgdQCfROF1z0OIgL4BBH5fRH5r0Tk37TkzxTAzxKR94jIF4jIH7Z5eYGIvEFEnigiH+2ZPxLAQAEkAVxOZiYBjPdmIwFMLQwJYMIhR0yQRJQIj+/gcSDgyUl2EEMCuICRBLAnC7iFg92qCuBKXQ8IoPo03iQivywiPyki73cE8FtE5JUicjtEpDPKdW3714nI6zJluiMi+r/9zovIh/UkkAvn01Fw+ItG3tFIq8t+bMKwxhVt9aGNHM4n8yuncpXYKwSWVtynDRWYSMXyc6IsH9EcuVzjjOoO5iOaE+ZH8r5R10niUX77KlbY2fjrXBlFaq23gWjki6NznHNoKqPPK4bX+HEOWm6ep7cNe8++gTaFcUQKE+bT5yenWPtwuXKwdPl8mQ3gYoHVhSujFXXVY+RtwXDGdODq4UgdQoUW5yJGC2U0/agy5tSmVRVueUqHvp8rByNJ+G/OJjV+VEKwvFe/nVy/iIvF6RdoRDaBdttHXfX2gBh6PEtKnaWzixRiHNFc3Ughm85mnXMAPea1qpaVcVeb75/VEN/ITjy5jrAteQBO1Pk2kX36F8tPCSufz3XmAGI+cm2z3ecikFt3DuAKnwgI4A+KyHNb9U8VQk8Af0hEdAOgz3SV7RMi8iMi8jOZCqruZH2+8nvnm18vt11QLrjaMUeVo8+GyFhhcOUb3vfkx575xRZdBMWeoQsRyRuSMKxYEeGM3NCapqix8UQAOxLskPB9u47yjUS1pmHtImZRPJ6U+k4xWl2HeY06d2yckQz77/tFI4o3KhyRCxGxxPexw49WcuK3u4hf1AljmiIMc+WHK379Yhvc1Fvft2+cObOzuM650vF7uW1gujomLB/v9o2wyXWu0WAw2UZCKSIjK41cEzYFxjoe4Y02p9fYhlg6cHAQ5aOLAPr6qu+jCzhq9yKMkfR1kRRLs2/bELdSfc1937fZB4eHi6iiRSpof7596tv2nCBTHatda8mftyWfJv12NECPsPXpXPkbVgF31XNvw11thi/Ptmr0hbUJl2uTI3tXAvhFX/WN+hpdwFUo31yBvQtYF4aoy/fzwZXblwDeLyI/LCI/m8liqACSAKZVw/rLKZC+U8KKSgKYhHgSwHQkHAngSccM1h8SwGXr3EWQcMBGApgwIwFcDrZIAB+bCuB/LyL/a7uy11oKZSe60vdDIvLpIrKuC9jzwsVG0BfOJwUwN7LGkXpJFcPneOyaxeFHaxi+axJsbkSHalLUoHrlCrfTwAnyRgC9WmWjbPu+dz16VUj/xgUXmO8upQT3qsuNUTB/ua0rcp1KlE67h+WOapR+IypvHAljniIXBsYdqYz6vZwCaHEbqdK/0Q7QNebzlxu9IxnBMB636O+cOmTpiI6CQ/tDPEej4cqJGJhHS1eUJ68SeIywPnStLI/sBO9FSou3BzumDesRDoiwzqCtRUoK5hnzjYpvXwUwUr98nUK7jVzAXpHx70eqky83jKMrfFTfc2mydjpqS/CECz9ojTCPiGVJH+nKR8mmcnWyqx76fER1vqQAnug72gMA+qQnlzbEP8J2ZvJ4CVD3PCrXSG1WAvjsF32Dvk0FsBLjmym4VwDvbFf3Yhp/XUT+WbvS909ExBaBPFNE/qgN+HwReeM6i0D+4I2/uDgLGF0ZOeISVZqoY9Rwub3qcnF4AphrxLwC58mlzweGt5WOUePnyYrvXK1BjzpHbJiwE8S04X3faPkGv4t4aTpqt42JCA3ei677kCVssKK96rAsoornT1lBouDLNTd40Hi7yKAnjZG9Yv71eWRTEUnxdu7VZL+CfEl2JysbmNt7mMdocOTtNhrEWNp9/fH1RtOOnUuuXka2qPeMAEYdoMczKvuoE0XShO8gkcZ6Gn1Hw0ZkJIpb8xGt4s51EtH3cvUE40AXPtpRzqYiwlwKO5kuV5aX0oQY1nSIfXG1OKvJmSNOpXxYmUbtefbeGuTMK9clAriOC7irrfS4KwH8Ky9uNv0gAawx4JsgrK64eGqbjneKyPeKyFtE5CER+WCQPu8C1iC6Dcy9IvIdsA2Mrgiu3gaGBHCJOAlgwiIiEGiXEVnUeySAyw23cV4gdtwkgKv1De0tIkZ2jwRwiZsnRUZ0SABXe08SwJuA7VzHJNyqq4C/pCV8HppXi8grehJA3Qj6p91G0N+9zkbQv/eGfyV2EkjUyXv52cLkRoJ4H903VhnRPdNndIdpiip05Db2CmA0CvWuXBtJYv5QBYquI4USFQVPlLpGjbu7u+EKvhxemO+cWphTfDSPikl0OgF2tH7vwKiD9uqadeiYV0yfPsc84T6OWNaaPu+O1zgjZQTLAVfGGka4wjopV+kUjsie7H6kACIxjkgd5suw02+j+mNq0/Hx0eK+P8rO46zxGhZYPpHLHesr5hvdtzjQ0W9jPa1pryeTpDhh3UZs0QaiuX85BTBKg3frRlM1FkRoMllE4es+qveW9pp9O6PBEZaJb0OjPOYUVSx3Px8ZccY2ZqVc4fSJXNuKbVKuftfYANahqD7VKoC+bvo4sdzt2o7FzNVpH8c6xAHLw/cv1uZh37GOAujbwi47UQXwOV/9cv0kFcAag2XYBQLNHMDf+eV/LrYRdM6wrdHwDUap8uOcnRzu2CB1zXvJvZ+bk4INCV77TYF9o+HJij7H/fAQo4gc6/OInOXSb9+L0mVpi3DGTgtdy9gZ4ypZix/jQpKDrsSok4s6FG8vpTRFRKG2PpbOVPVEwQgnNs7RNTbiOWJYKsMIW28vFgduBI3fjjo4jaOvm9J38Lk6Gt2PSEek9loejADWlGGEEb6PdqbXXUQo1+F3TRWI0prbBgYJmX/vBKmA1bDR4DjnAo46ef1W1+bG2TKFRJbacp/GrsEphtV0RYOwyM71Xm7KTdTOavKjssOwNW2rt6tFvXc4Yf3DQSa+Hw1g/HvYriABzGEblUHURmrdx7Zcw3Aj6MfGIpCatvNahyUBDLYDIAFc3aIl10FZ44eNGwngstlHXJBokwCexIgEcPUcdhLApUX4AQEJYNrrkQSQBPC0hLAhgOoCPrd3tokrUj5yI110I6EShiOlPkdL1aotfTLdpTKUDryPRu9+Ujl26NEoLjeRv4tM+b3gMN7lvLHpQl2MFtjgiDSnWER4e3drNGLNlROmM7eSFdOv6dK0o3u2S2nx+cgpvl120WWHNXnNfSOyGVQDYntZ7o0XqWyoqGhckbteyyRyZ1tZoapcUrAi9aXLnvSZXwSC38hhomXvMe/CL4e5t8euOt/VxiCGfdvAKFyXUtqFfa5NKC3yivLkNx/uUvV8mqIyiO55QhalP2q7PMnP1fmussrZSQkrX17DVq3FOhZhhe95FzDW7+h6vKUntKZfhFGk4ub2r/TTgDROEkASwK4+r8+zhgDqIpBoDmBUEZU8YWeGbkN0Q1rHEXW82ID4innajiDqBJDcaPw4X6QEUi49pcY+OjsY5wn5RtTSEW0orN+yfOH8tmifMJ9/LBNPfHMdJjaKmo+uvPpGzE5+wXewg7U0Yz663HWlTh4b7ejacM3No8S84iCmz8ClD9mxehClbTism4lkru9Sh4KYdc3ximw7Grh01ZGaxZRow9iGGEY5F1s0xzdH1CJSEXW014KM9BkQRd/Jpce3i+u0Tf782Zr2NMpPSDKhPcq1aTl8171fIqc5ApjLPxLAqN+KsI+mIpjtRuHRBWw45tohe15z0pYSwC/7ur+pn+YcwFJl4fMQARLAgmGQAJIA9mk7ujparzIsyQ8JIHa+JIBLRXhdmyMBXEWOBLCPJd26Yepa0Fs3n9cr5QsXcGkRiCUgt0ijD1HKyeBdkn/fjGPcpg6gyoCuSa9OafjcghH7/jpuR69G5FQ/VEZwDzlTb3BjX6/+2N+l9OdG9yXs8duokGHe8NumUinG2Ll7+8Dvotrm1ZEofbiwqK8CiCpqbjUpprlmJF5So1AlWFUcl81Xrv5g/vtOQse4olXCXrUoqdld+TMXcM6OcgoSlpulp0atKilJuXliNSpbl/rct11CrLvS5NsKH7/HBu0Iw47GeiT88teVB30WtYWRQon3fP3pakM0/pw6l3sPzxM328v1HZbT0uIlxKSp54EL2CuBvp6Uytzn5+j4ePFK3/pR07+qAvilL9UzIagAlsqGz2MESABbH1aJQJEAxhsGa8NGApjfTsaqXckFTAI4yG7cXGq8uwYJnrySAC4dkySAab/TaDqCr7clG4wGSSSAfVA7XRgqgKfDr5oARg1qNEqzZEVz4bwShg34OioAxhfNi0NlR4lc1zYw0WRbPxLsM5rLzVvqyp9/B0kDbg0SqSfYYFk8+i9uHVCaaxeNsnPuS1Qs8L1cmjH9Hk/cViKnXkSqRM70I4z72Nw6thelwduHV9js7/l8eVoF2iiWZZRv32l55QLLGRWFXDq8OlLTpESLQEpllVMyc2XfR6H0ac6pVN4O7L3S3MculauEV9e7fZ9FCmBEUtQFXFLwcu1tl8rmiTOGxbYgSlMtdtF57BZHrlxxfmzOXlbutwn17Zu1TVFbUFI+fT6naxw313UalK8fqgA+/+u/VW9zDmCpEvJ5iED1HMDcRNg+nXGpYdI41lHacCIykp+okcKONmoovGvFwvRZZYYYRK63Ugesebf3cJUsxoVuWBy5GtHG56jOIUYRwcgRJH2vqwP2ZRp1LrlGNup8+9TTqEPJkQ67X+rgbRBj368t7z7x+85lOl2uhvULlaJONSJqWN6RC6uLjEZYRx1fN/kYnoimRA5yhDMiINgm5Ah6jvAjhiVyk2t3umwtl55awpOz+Sie0iAFVwH3aW8jO4tIMsaFmxUjKfIEyeKuXTyRGwhH5LJ0zw+WF21OsGdjqTyxTchhtFJm7Td8WPSY+DL27UjObvX+lf2r8qK/8Z0kgH06DYYhAbQOuGuuBwng6n5kJID9Wg4SwCVOJIDrnAFx0s5IABMmXQMQEkASwH4tNENFCFTvA1iaiO5H9zllJlccpRFu9F5pNOYnX2Oaoi0AIoKYc+nWmFUpb35kjWpm9H0c3aKLNacE4b6BUboj9VTDdZWhVwdL5R2piV37a0WNf4SF7yyjkTUqpnbtVVJ7r2arIMUo2qMvwhjzoyeBRGmKlCtUcFHFxr0jLQ2oCnqsSuqUPfeqLdoa5mE4HK1k09fFnArllRlfzvheH1XSEwV8B8s411ZECmDJprrSvA5569OWlMpPQNnKtZV4H6fMoN11Xau3AUl+bnGLxRFtRdTlVehqp3JtaM7jEimAem8ER+Z5Eok2gu2fVwBRybNwiIu5gH0diFQ9xCpXZ3yZqAL4lS//Lr1NF3CfysMwJxCoPgmk2ADBJ3zYEgHS5+sQLa/aedKSm9fnyapvCPB5V16ifEU4lciRJ9dIUroaDR9v1FnrPX/eqU93RAA1TRZ/5OJBMqLXJfuI0uYb74gUrRKndI5vzU+/EZFkJAc5t3uf73jC4W0wsi2dAxiRdQvrzzQ2UqrvGNnTuU82/8k6Wjzb1+9niHYUdfJRnfBuZixDvw9gHwKo3/AdczS1wdJi9uHtNerc7R0cTGGacm1BqQ73IXSl+t3HjjCM2UYu3ui+3wam9M3cMW1dtpFrC/2gAcs5sv8cCYyId9T+eVvLkT1LB35vy62W9m199D1PdD0B9O2jLQLx+YzaQPt+zVGoSgBf+I3foa+SAJYMnc9DBEgAW1i6RqRRZ14b3scRdSgkgCdX5UWN+jqDBBLAZOgkgOkYLV8fSQBjt3VpIOvJvK+vOdLuyWE0gEISSgJ4sv8mAeRJIKfltQ0B/O1ferXYPoClSeU5FS/XUKzTsNZmyh9/1NW4aHq6Dln3I0H7u3ZxiuERjYpz+cN04Xs58ljC1r+He1/5UXFuEYIqBF2qnn82qzgawt6NVopb+lC103tYDqqQ2YhZry0+zIt/PyLz2NHYdWkfQFQ+9R10D3UpEWibpYUmHltvH74MI2Vh98yZ0F3nJ+97DNC2vDq30lk7Y87ZCtpzSSX29dc+4etSri3C8FFd8wQwp1r6+z6urlNWur5b2771UQAtznFmFXBk43qvZjBl6ZhMV6cu4B6rvp307VjUvqIK3zx3W3NpGnE3gwg/PYGoS6XFqRKaxmiFLmJkcSE+qMhru+PTpO/4euNtsWSzXgHMCQ16nwSQBLC2LfHhGwL45l/8OTm3t9c8w44t6lBycjY22l2jOd8B9+kMipnMzHvJVbaujjfX2EYNW1e6kADmwvmOqEtVjEbAFj5ymUTf7CKAOaLqXWnoJrYOBF0h6ILKYeltwKfV8oWuTpzfhq4xbKgjF3auHLoaVk0PNsS5jqXUmEedXe5ehL9XhA8ODprXc27MCCOc6+QJjXdhYbn0qeca3nek+I0cxrVnqmInirbhO9ecXfn2yNc7fK+LDHo7rR0UFtuxTIDc4BoJi10rAYzul+Lwn+5ql7XMS30DxhfVVyR9OL9V708nk5XkYFqwf8KBCW7tFaXND3BtoIrlHc25xoGln5IRzZ/Eb+P0jihNpfYB+1R/rX9zGxgSwHXbFHuPBNAhSAK4uuqOBDDe5LlL2YgqZakDJgFctbuIXCNGnhxHpCdHEEkAEzJdJI8EMM0zJgE8LcW4vu9zI+jT4dsQwDf9wj9eUQBxtOIbUZS5/WjMT2jX531WbGLjXaOqLN4DDHINV5calWsM/ciz1Cn1GdFFSp7dy7mUvCoRdWx9Rv2oDnizyak1ZzpciBqHV6OiA9Cj9CO23n1qdqTk09Qi3OcQ8482Z3Gia9jsT+/lVIvofrSyMWdbWA8iG+hTfhEB9CTTXMCaNlMoMZ14jKBdo5ri09/VyfdtVnIrHSP7MpzRzktlgvH0IYAWPloV6ut5VBfNpi1sTv0tEai++NWGKw0kcgpgF0Huak+i9Kl6FrUXURur91ABxPpo9R6VNQ1/fHTUfNa+4duBaLFTtIioa2ER2i2mQ7+L7l1PACObyal+2DZFamCEbW5gg3jYe5ev7Mtzv+ab9E8uAqmtSAzfINAQwN96zavk3N7ZRaXzRAf/znXWUWXrS+Y80aotm2j6Mna6uYY/6qxzDXuOIOWUoGjloobNNZK+gvuwkbsut/VIrmP3BLAPAfDnEHfZhj47ODxcwGrxRy5LLB99HuEbEUANZ+RHba60Abaloc+JGDl7jbD3Nhq5A0uDDn2nK4x/ZnnNzYmy9GNdnBwfh0es9a2bpbrj656vd57AmZ37QSaSaF+nakhiHwKInXhEjHLE0BOlmvlzN4owRtub5No5bHNy9hDeDzZR9jhGbTrardZHnL+7UsfaOYA4ELSV7urqxVXvFsYP8CxvWJb4DVuhi+5nTA/O74vsBbHDecbRfEhv211TB/pOK9A4lQB+0Vd9IwlgLWFg+AUCJIAtFKXOmgQwAUUCGLceJIAJFxLAfO/SZ8B12r6JBHB1iyESwNNa1M39Pl3ApyufxSpgVAC7ovSj3hwxsjhqjwGqUSYW6YQRaZfCFqkD67jFSnnW57jnXqTMRBh3ua0iVaKWdHR1DrnOyY+scbSrefCTq4/bCdxIBPTaq2j+efR9HDn773o1psvdE6kvpc4YFdg+ilBJxS7Zdc5u8X7XFAHM4wq2GXU1p7J5JScidStpdYbsyxUfRxigutdn0nxfV1qXOlejAEb11NJcqwDWhu/6dq6NLhFA/55XYv0gLywz2ETZ21FkP6im2TUqgN5DsLO93USDCiC6fe0avRPR/nlazujexXSYpwLbpmgRmbffyF4xnSW3L7ZTURmW2iV8hwogF4Gcjv61LmDdBoYEMEHZpwKSAC4bZxLApDiQAC7rT8llFpHCnAvYEwwSwG7NgwRwaV0kgKelBzf/+7eqAvjFIvJ9IvIMEXmciLxERF7fwr0lIn9PRF4oIk/ROXoi8iYR+QER+SgUyR0i8lMi8mKdiy8irxWR7xGRyxXFdkIBxHdzqkwuTBS+dAxQboRVkQftgRfBcwQO75cWpvQhgaX04RxAVK+wA/Nx4Fy4nGJUkw9PaG0bmFIZ43NUAFEdiOZl6fPxlprvyePjonl0lhecaI0KUu57ujDFvoHxWnyRKuHn21kecwpwpABivnz5+FNWfNqjQUOXmm7pw/cim8J04gT7BRZQmIhL15SGCJtIodRwfhFIlwIY5Smyoz6kL6c82Tf8vNLI5nNENVf/fZnXthOlfR9LbQralG837W+s412Kq/+Wtwf/7srfbg5g9B3Exk6w8YQsh//Z3d0meVG7mVPQsC7lFpqgAmhzAFEBxDRHAw38Ng58c+17VMdK7XpOJY5sTRXA53z1yxUqLgLpU3luojBfISJfKCLvaIkbEkAtzNeIyP8pIn8sIreLyE/qcaMi8kzIw6+15PHbRUR73VeJyNtF5GUV+WwI4Ftf909l72yqdL7z9n97t2OuY1g0SB1nLkYNWt9JsJjH3ObDpcoWVao+jbrPc0Qioo6tRv7vcmnmGs5Sx20brOY6j8hu+uCBOBsB9HaTI1oaTt03mKfom9gg255fvvHGOHJln+tE/f2cbeQa9VLn3pWers44Ii65PER2MZ8tj83r6uRL6cvZgW4KvFIXZ/FJGzmb60NE+7iGK9q8EyeBaN4sHbn2LCKctS7d2vCWpxKRw+c5ApiLAwdN+D1sv3w6cB9AX8+jqR54LxoI6rewjM0FHKUBv+ddx+jC1XBaJ3OLOWzxEg5Y0IaiPTWx3nkyqM+w78KBWVc/1NUu5uq+vcNVwI8NF7DaIhLAqC37AhH5AxH5NBH5oIh8loi8R0T0/h+2L7xARN4gIk90SmFX20gC6NDpQ3hIAFdBIwFczvnMVbYSwepDytHuulSXFfskATxRJJ4okwCe3IORBDBtqI1KHwlgzTDrxoS9VV3AK+S+BwH8MhH5DRG5KCKXRORbROSVrTpocY1Fyu6KLwAAIABJREFURI8K+DoReV0G/h0R0f/td15EPvw7v/zPQwWwpO71KeJohIkVyXdk6yiAeDRRNGru0/mW1EAc0ek3ItdjyYXg0+G/mVtwEakPfsRp+e4aUaIa1Bcn70rzeTiRpzbiPkTa0pBzAWO+sUPqcrfmbNJvXxQptPi9UvpRtdBvhupbcOYsxuuPwCstlEHlsyt9WIf8qQo5xS2yh17125W3V2WiOEpE1r+DW2tE5VZKJ+Y5atP6qDBRHSypvj5dJZvy4WuUPwvrp3l01VffpuXqhLcNbW8jHLXsUXGzemHbtni1zb6H2z01ylr7wSj/+g1U+uwavQi48COqlxo9TlXxYfS7uKempQMHClj/I7KMbULODhAPSwPW3Vz/iwrg8772FRo9XcClRuAmfl5SAHXC09tE5D+ISLPpj4j8kIjoDpCf6fL1CRH5ERH5mUx+f7R9vvL4bf/6/14sAinh5CtxF0nUZxGh6zLsKHzUaa006pDoro44apDs1VJl8419iVSuQ2RL2Oeee6KEc1IwnbonXFenFHVQ6LbKETLsIPBoMCyLqJONBge+UYzesw4F83KtiI3FE3XupW/Ull+OmORIU4lARM+V9Ecde+TC6kp/rn54F7Dv+Eo2FabZnSeN5d1FUhBPHLj4qRe+jcCOXdPft0PvIo598lWyl3XaEK+IlAhg1I6V0h6VeR/b8e1Hjsz7PGh6Sm7k0tnd3i7t/PhcnY5w8TtalLCN3N2YjhLOJftQFzAJYAmlm/95FwHUuX26uEPdul/Sqn9dBPB+EflhEfnZTLZDBZAE8OSiBU8MSQBTx+g7R9+AkgD2b3BIAONV975jJAGssCkXtERSSACXdLM0wCMB7G+HNyrkY9kFrOTvF9qVwM8TkQcB1HVdwL5cmjmAv/sr/6K3ApiTqHOqBd7vUgttZNTlVsmpbqOxer/zv1wjuI6iEikcpXj6VoaSqujjiRQ0v3gE4zT3UM3IU10rXQoskkJNHx6yjiS6pMzmwkYqS2lled9ODdUej200Yb+rfGq+ad/K7ZHZx14idTXCGBf+dCkwPk19bdovwCrVcf1OpNZ0qWnoArZ0lgYjWLbeRv3gzn87UgBL+So971OmPsz1UAC9zfe1W8wfDvL65Ctqp3ybsvJ3G2nOxiNlLddvZOtsu5I5et5FBGvCR21Il537vqWErSqAX/rSb9ZgdAGXwLqJn0cKoJG/p4nIc0VElT382SIQXRX8R+2D54vIG9dZBKJzAG0fwBJOuUYpV9FqyIaGLUn5UXzDUZqwm2vcfAeAcfTt1HEbEX2/qwPWdKyz4q+2wY+w6Gq8tgKiXOq4+swBXFGyXOPtG7UcEcylIwpfa4O1DWuuDnSRoq6BEdoLxu1d2RHxiMrT25/vEFdsz53b2lVP9JlfyVhqD3Ir8Lswx017a9qHrvodkSfEM8LW3skRQIwzss+cPeRsuZbw1w4Im/SC+7xEVmrsHPPUVeYle+nz3FSdHOZR24vxdg1YoaM4kZQSXn46TJSXlbYwmAOcs6k+uPgwJIC37irgcyLy1LZA3yki3ysibxGRh9oVvOr2/XwR+UoR+TgUvD5PJ2WL6DYw94rId8A2MLoiuHobGBLAuPqtqGdwbisJ4BKvE+S6fVRD6DxBKnW2JICrAxASwJOOoJwS3kfxKoWJBiUlsqjPSQD70RwSwH44kQDeugRQ5/Mp4fO/V4uILtT484wJqBr42+0z3Qj6p91G0N+9zkbQug9gXwVQG7+cyuQbznVGsF45KxEJxSJySXQpYV0LTXLvoQKYBtq2k1Q8jyk6mqhUrWsVwCi+LpXKnxJQckdY/KWR8Qpm7b6Pubj7KD4lbEs4RmpAFy4lJbXPqL1GAbTv4VFWSKRLSiDanx+MWFrtG11zZGrynavLpQUBuW/0sQPLS0lNj9KG0wQ8tl1qoH4zRwAjm+6TDyzvGttdJ2xpTlRfT01XvpYtX78URqpYZxvqFgF5e/c2rn9HynVUp6x8a/Og7/VRABERDN/V/vRD8WSoK/vqAtbZYHQBr4vhpr/XeRJIBE6uEnglqIs8+HhLRDEacXuXRKkhxuddbq5cPiJS4TsLzEftFhHrGGLfxnwRt2tY+xAtDJP73opSCu74vqTLx1tqLEvPI8KmZGtdG7kWBDDqtHKDKSQoJbKSs8s+ZR6lqUS2vJ2WCGBk17mBTq581ulIcwQwt/rZ0pSzRSTaiFvJppC8XIsBXqmdKBHAPnWnmCc4CaSUHv88anP8vSgPpXT7ld7WNkd1qSmTIOGlb/RpBzBMSQQo9XslbEkAb10FsFS2N+o5CaBrzEgAV02PBDCPBz4pKYAkgEu0SACvX/NOArjENqf8kgBeP/u70TGX7P1Gp+dW+141AfQZLI1icqOq0ntRh5kbTeoikC73TJfS1ZUOfOYXQ6BiEKk0tUrKOoZTsyK7ib/DtZIb3dbmw/bXqslPX1so2UTXCB7nX3k1J1KY+yjaten2mKBCgHHlVL9SmOi9XAOZwypSLaK6ZXmJFgREuJSw6vwGnGZSUpAsXeheNzVI/9U6g8d82bPc/Dy0DW8nuXalRkmqqSd9wuJG0EUlz7m7+4TPkac+afNtTO57pb0MLZ4Vz0PmyNGozuj7kd2W8o9x5coY71evTM5MP8hhyzmAVABr650PTwLY4c4gAaxfzUwC2L9KkgAusSIB7G83XSFJAJfo+EHcSnu+xmCYBPDa2Oi1jIUK4OnQXJwF3HcRSJ9KgEpNzoWYUwmsUywpBivp6IFBnxFbFE2f9Pu06t/rqAClPHdls8+76+wDWKsA5raI6JO+KH/Re6WRevTczwGMlJ1G3Qi20UBlLbpuFIXMmbumMEVKRGmOkMcjp1J1YesX/mCcXdiiSpq7jpSUnOKCKlyXIu/LAP/273Upcn6er6ULFUDcXzDCtgsDX+Ze3cI2MFKsejRZ4WKU0nvRKRr+HcSx5EWI6tK13gbGf6PrOLucfeXqQO5+tF9pVGa+Pahpe3J9QFcdKZUvPqcCSAWwxl6isNUEMNeY5Br10C0FqpuvoBEBLFX6wyPbGSc+xsm/b3/3ceFYmK4J3FFFX4fw1E4SL333BDENLKDUGecWT+QM73gyWTxaB4N1DbqUDz9w6UMALS3o7s+5/iO77TNNoDRQwOfRBtglG8a9H6OBisc7IgR4/qonRbUr8HPl63EoDdh8Oowo2nu5Oq/5MxyRAEZ1r5YA1thgHzuvbQ8aku0i7sJVn+WIb0SG7N61JoAnBjoZdy4OIrquS3VK343stlSXuog0DlTsOidydA0IatpMHgVHAtinHekKQwLYolOq/CSA/UyNBHDZBZMAxluqRJZEAngSFRLAJSY1QgIJYL+2+rEQii7g05ViQwDf8kuv7r0PYJeKEI1e8GSPPqObtRq9wOXaRej6pON0sMZv9+3k+n67ZoRZCutH1KgAWLpVCULlzN7BxrlP49s3f7lwpWPaSnlFZQdH7t5mfKejf+c6Ipzw7THIvjc7Fpnp8dwig+FUBuNH0nXbqg0mTxI5fly6N7os43Npe9CpPCijue4BL7J/aU/G28fN9WjngXRv/wE5f063CRWZHp2XuX1jsi2DwTDBOpjKaHQ1XY+vigyScntwmDbIGA4vynBwV5uePTmcJZX9eP6IyNbl5nr7zLEMr+w1194u7F50v3ZaAW7ZgfH5DbB9uUZu39IikC7bOG39ra0b67RTtd9IBlD3q01XbXiR7qM961Ibh55MDq5FNJ1xbM2XJ1TNM0xl5mJYaYMy71hcV67sy5e99BUaA4+Cu+6l+dj8QEMAf+u1/2RtAmiwlNy0Xe4UbNRrd8tv+q/xuDjnDitWbQeEHVxfM4jczOvEU/s9DV8iQFGZ5d7zpK+LAPZNqw9X20HcCAKIaYxs26fZ7DbnmoywGWoDPzvTPBoOjmUw/mQbrKU8x49fEEAZPSwHs3c3z8+eP5Bzu5/eXH/io1M5OErv3X5326kND2Qg28297cFFGQ13m+vBfFdmk9SrHB3ty3R+Jd0fHchonL65u6t9iT4/J1cPzjbX0+muDEaJOA63D2U+vtRcH88vy87RxZWsRfnXAKchJkYAc21IdDashkWyjit/o1XAkev7euQlsoNrea+2baute429nGIfwH55bQcp/QI3oWrtazZbTlWp+ExV0C1gfTkCaBHOWrK3QgA7vqbxJQLIjaCrCoWBVxAgAexpELUNDAlgT2DX6FBIAEkAkZyRAC7rGglgv3aHBLAfTjd7KLqAT1dC1QQQiVBO9cMk+f3XbLSGSpJ3vdVmKZocj3H4EWttI5lUkGlVsqJRcimftSPrXFlYQrvc9bkw0Qg/V1YeEP1eLU4ah3f7l3DocyRTV977uIBLafB5NxssqUYY72gwFpknF/BcJjIcJReuSHIMzY/ukcHknnRr9JBcuOcjzeX9D7xHjg+S6/WD77skH/zwf2iut86m96ezy3JwNdnrQM7I4+77tOb6yZ/22fK4e5/UXO/sbMlsnlzA0/llmbVnI1x5NH17a/s+GQ3ua66Pjs7KrFV9RmemIuPkAp7MLsn2cXI14y/nDrYwtdjiooPIFrvu6TfVvszGtD3yp35oenKrYUt5OZH5a3ijdtCpn65t29bxuNRmsT4f9Qpgbb4HUtee1+ZZw4/nJ/ORdQVXKoAaP08C4SKQdewS36meA+g3RO5q1LVhjSq/J4749xGs6D1N5nDeVdMRntJtUUts8Hsl4rdux1jbsJYIYYRRqWP0ZVSLk5VNrnxKRLqvjfh4+pZJLn6PfVcHhLa4gudsSwbt/DwZHMlg6+Hmc0NpV7VPlADenu6N7pfJ1rua67e89V/K1UdT57IzvleOjh5srs+cS+/vnDmW8Si5fa8cHsjW+FxzvT04L7Npml91cHAgx5PkApbG/bzVXF48/8Tm3yd/2rPkCU/4gvRY7pDLBylNMzmS4U4ijoPRoYyPU/pyeEY2Wmu3uTmAJXKGLmDDHY8Mixbp1OSlNh8J7P6/deKvfWcdAlj7jXpy1h8jC1mbplN2B70SODK/LoQuEUAfcdeZxSSAJIC9DLEjEAlgTwRriQ0JYE9gW3JOAkgCmLMYEsD+damWCJEA9se2NiQJYC1i9eHpAq7HDN9IJ4G87p/2XgRSqwAiccIRec59XLs5rmYm+kajUGT2G1xHDaxtWEvKR0kZ7VOspTT554i/j7+GgNWoJH3yUVtWpXxH38wRcgybc6mXvqfP0W67yhbVwPlse+ECHg4mMtxOizlGg7Sqdzi5W0aztChjuPUXMtg1BfCfydXL7aKN7cfJwdWPL8KkC11UknI2PisL1W9yuC2zaVocMpjNpRX9ZLQ9kPE4LUa5/xNJ6XvqU75InvoZz2mu984+WQ6Ok+I4nR/IYOswxTE8lMFxWgSSw6hUD/rYB+7ZFimN0Tf8RtBdW/J0tQddZV+yC5+3aiVsDZmqtm2rDd9V1rmyrB08j7pkr8xHavMxG6zxkT7GCmFOSwBLKaQCSAWw0iRPBK9WAHMfzJEtC+8by1zHUFuRNf4+c8IsHTm39GmB7PN+Hwz6xBOF6dPRdrmAc7hr413qINEVt46i4PMTEdVcGvoSj3Xsqm9ZaNy4EMGu8V+0UUvzcHRWhvNEvAaDmYy2Hmqux6N2W5fZXTKeaxUVGY4/LG/6nX/QXH/y0f8kt+0lF+/88JwcHd7fXJ9vp+PpPrqTFIVcOtL40vXWUGRrnIjcYDCS6TQFOjoWOW7Dnz+ftpe57dzT5Z67n9lc33XHZ8uZvbQlzHw4l+PZo8318eyyjGd3noCpRIxqt3rCjd772LkmCO0QSbe/PpH4ihu1hK42/Do2W4ttRXYXQUvl6+OszfcoOKatlM7afM9vwBxAmaa61rUC2HuJm/6pkNnVbWC4CrhkG3yeR4AE8AZaBwlgP7BJAEkA0VJIAPvVGw1VS4T6x7wMSQLYEzUSwJ5ArR+MLuD1sdM3GwL45te8qrcLGD9XUv26kpZrRNYZ9fZVgU4HVd3buYY4cmFZzLUNa808Q407SlOEN95T12aX61jjRfVrnfLzdlRDACPsroV7PSptVI/0OabbsFUszOWF/yJGSwVwR2SelLyhbsw8Tos4RsPkhh1O75St2fnmerD1QXn9r/2d5nq8c0ku7qXVw0dXtmXWKnJn01oPOTxYukbmjQs43Z9otO32Z6oS7qZPy96FLdk9m1y5+1fMRfyX5ML5z2nuPf7ez5dzt6UVwZPZRA6nSQGcDw9lOEuLQGp+tSTlqJUna+p5TgFssHSu1XW9ArX1tVYJq8HUwtZiu843at+pxWkdBbC23bkRCuB8Fq9mDtaGLCF1ttmlHnIfQLqAa+uiD08CeFoEM++TAPYHlgSQBLDLWkgA+9clEsB+WJEA9sPpZg9FBfB0JdQQwDf94s/J3tm063/pVzuPLLcIJLcFQ+2EYU1vNLLWUWdObbsW89RKOEV7E5ZcwLUKgWGYy6f/XkntK42ivQJm2Nt3UP0q4YPPc/OyauZrdSmrkepTmz6LI6dO2n1URM2W8Z7GY2k9lpkMp6YAzmXYKoC2DYwqgKP2+XjrQ/L77/yJJtmP7L9Phq2StzXbld3dNGtoMksngTzysMhtSTgU3ehlq50DqId52NxA3W1p1k422j6j+/6l8Dvbdzf/bg2fLHffmeYAPuGJz5TtrTTX75H9SzKRtA3M2b2RzCat7FgDaGXY3CIQHw3agK/jp/FW5JJbq2xd7/CntfO+xVJqJ7rKpc831lEA+8SLYTpVuNrIMuFzCmDnqSC2equN0x8Vh59SBfD5PAruGpXWZkZzKgWwD2TXu7Hok4bTNkjX6hvXoxOqSVstwVynQ4nKu0TOajtGJNclUmv4+IFFyc0cDUSupfte0zUdHctwmjZ01h32BqPWBTxP7G4wuUNGk/R8tPV++Y23/o/t9SdlqyVvo+kFGcpyVW4Tkz5rCeLxlsi87UXm0+W1DERG7ZGrw63l9WiUCODj7num3HNX2gfwnns+Vz7+8UT6ts5uy/5ROrP4zO5ABvOWaWYMsWQP9lqXDeBG0H3tvbbdWWdQWLtjQWlqQtfgum++fbgSDrk2oWuwWoqzT3vbFceg3XQ/1172takuzIZby/0wrT3Z2kp7Yfrz6w8PU/3a29sT26dW8dG9NPV3331pesTHP/7xxeKjixcvypX9NJd3f39f7rgjrdC6enDQHF2qv0uXLsntt6cpFJ+8ZOeA64r89PziHbfLJz+ZdgbQtD3ySApz7ty5RvTQVcAv/Lpv1ls8C3jdCrLh75EAXicDqG3sr1MyVqIlAVzCQQJIAoiVgwRwtQUiAUx4aDtBAngjeqf1vnGruoC/WES+T0SeoYNtEXmJiLweINB8/ZiIfKsOBETkbSLynSLypxBGhxQ/JSIvbs+Oeq2IfI+IpHOa+v1IAPvhVB2KBDDeGy7CpVYB9BP8feFECkHppI4+cfgwmO5aNUjjmo73ZThdulAHw0vNJ0atZDc4vkNG0zQ1Y7T1AfnNt/5Iut5+QMatqjec3LbYN3A02l8kcTBN28scDg5k3q5GnE7GMmuvZ/MjkVGKZLCle/qlV8fjpFQ854tfJocHSdk4e/YpcnSY4tPXZ8OkiMyHBzLUVSY9fzhVoArvm3Q/vNoBVW2bULu9VVcx1NYxi6tPfS0pgn1Uegwz0hVKlb/a/NlWK1pv8RhH/eyVK1cWC+ZU9bOpNqrG3Xtv2iZJv3f5cupqzVtw9uzZhUKoKuJhW9ee9KQnyXve854mrCp+u+3qq8tXrsj2drvoqrVxDft7b/+DJuzOzs7ie//pz94nP/7jP97c/8AHH5Rze0np/2Sq8lQAK+3lUx38K0TkC0XkHSKixM0TwO8XkR8UkVeIyJ+JiJa8Lsn7bD3FqU38r7Xk8dtVIRaRV4nI20XkZRWZIwGsAKsmaK5B6nR9rNHR1aSptsPSuEuNe64jL813xPdqG+8+q5l9uktu6K4Orw8G62A7HV8GAjiUwSB1KAsCOFnOARyN3y+/+W+TC3gABHA8ub1ZQay/4TAd7TYaDGTQrh6eDI9kNk2TAKeT4WJFsJ49LOPUlAyGE7EFi9Np2nfwZX/9b8tffCx1TvP542RrJ+0DqFuyjNo5h4dHl2Uo7eRBKFDDOod5bl6wReFdf4M1CEGfMjuNDdbUuy7b6lvHutzDfWw9VxbrTEvxtt6H4OXKNsLx+LCdvxA8rG0rctiPzyS7VZeudwGrfdogU7+nLlz9qevV3L4PP/yw3Hlnmhdrz++++2559NG0Ql7j3d5NdenKwVVRl7D+lEQaXkoun/flX9Xcv+18cvtevjyRYTs148yZkezvp7qt3unW4yx6hPhkoueHi+1mSAK4ToW8Sd7RckQCqOrfR0XklSLyD9s0agHrdv9KCH9eRD5LRHRIoZN0/rAN8wIReYOI6GGe+n6fHwlgH5TWCEMCuLqdaVfDXduokwAuFUASwLhykgAmXEpknASQBHCN7u2meeVWdQEjgJ4APkVE3icinyci6eyn9Htr+7e6eXX7byWIuAmXjht0SP91IvK6TAnp5mFpA7H00xncH153H8DrYQW1ZOB6pOFaxFmTj9rOat301aRp3W/0naB9rdLSV33QcDkVpK86qJhcy7JaUQDnYxkMk3pgqyAHx0sFcLD9Z/LGt6hTQGS8/YiM25ZvSxeBtFx7MGxnf8yGMm9X56qbdj5PCuBskia56284mstwOymA89GRyCCFOTpKLt3nPfflMhpqUyRyfHSvyDAdSaderXHbguwfXJJx+x4Sjei6+U57wgMeJ4lzMXPzMtdRAGvt91rZY+13c+G9vUYKYETe+uyiUMprzsbtfpcC2Lf+d9elVgKrAFMVuy6vg3921NYZzYt3w6p6Zwqg5scUPl2IYYNPvbZFIx/72MealN52222N21Z/6lp+5EqaKqHhXvTir2muz5wZLncBOJ5Lu95DrqatP1OYnVS5B6PhQgFUEfzcuRS3KpaPPnpFtDq1YikVwApbudmCegL47HbO3+NFJFlW+v2CtqEi8vUi8kMi8k0i8pkuM58QEZ0o9DOZTP5o+3zlMQngtTeJUiOLX7Sw13sPr5o0rYsIdgB95g+t8x2Lt8st5uPNucly5K/PogCMs0/H69M01zl7szQHcDAbi5gL1+YATu6S4Sw1+sOtP5Vf/a2/3Vxvn7kq2+3WLtuzPbFjTefz5AKeTkSmR+17w0OZtXtezGdjGar/SDuRrZmMdpKrbT6aLo6fGrfbwOxsf4Y88xk6lhQZzJ8oD19KndKZc+dlNki91dFsfzEX0fKmeObIYFTWOQK4Ul7XeWpEg8Eax4+tY7t939H0dM0BzNm+P6u9po70TVvtIKjUDvjnsyFqFP1ShYQZ7S/agL3Btp1WoPXciNzVq2mlu7ppzS2sblr76WIQaxe0vr///e9vHp0/n1bCq5v3Xe9Kms1P/uT/LkYydW7gI5eSG3lneygHR2nu7e7uWK5eTXVwZyfNexyOx3J4uGSDe+fT9zUflj7dF12rRGOyiciSAPYzk5syVF8C+Iuty/8bOgigHgr6wyLys5mcUgG8QSZQ06GQANYVCgkgCWCdxZRD19TXcmynD0ECWIchCaCkFWQb9qMLeFngfVzA3jyu+xzA2oa1Nvw69l47gl3nG7l8dOXvRqSrNi+15ZHLQ5cKUJtvjKvvHCbvUotwyMVrYbtcTLV50Dj1KDWxo97mIxkMkkowlHZ17vHtMpy3qwR3/kR+rVUAx2cPZbtdKLkl2zI0/W6e9h2bHovMjtN7O9u6CCTlYD4dyWiU3MDqxh1vp/Cz4VQmrUvs3N4Tmnsf/MBAXviC/665Hm19ujz4UIrj7N5tcjhL6dTFIyOLvAUpp6iWsFX8cmW5Opu0nwVfK7vt+lrtwp+autHYQc/FL5jX3Dslt25jH6CCllS7qE6g+ovPccP6PurcwfHqKuAuVdniU5erXVu54Mb0eo1q4Kz1vdoWL409g9JsSp/uvffhD3+4yY66ik0Z1EUgr3992rjjoYeTcqhk5L5702KPj338YT3Tsbk+ms7knnvSIipdRHLpUpqqcccdF5t9AfVnK4N1EYm5pC9fWa7qb8KcbVfiS1KHNb/Tg0ZBpALYr1m4KUPlFoHoAhCd56c/JWrq3vWLQHSr/j9qwzxfRN54sy0CqW2Ia8OvU6LrdNa13yk1oNHzvg1+bVpOE762PPrOASoRgto0l1xduXzkbAE7d+y0ctfWaNekez48FmlX3Q50DuAguXCH7dq+ga7wnSeX7WDnvfIbb9PNAUTGuwfLOYDzgYzaYfCwnQzYeIfb7WVGg8sLAijzoWyNW2J4dku2dxIznI8mctweTzAapW0uHrh/T57xeeps0GPqniyzeZpufDwbyHzYvjc8lrGyzevwW5lKsEb8tXa7xieq3caluuHTjBueR4OPPmQb7bUpa3dCUh87t/cMIyRbGJ8nWRY+In3qQrVv6zWGmQ4T0YmInH4bz9i293Tenl3jc9ueCd/TcI9cTfPzdH6fhfergfX5Rz7yEXnve9/bhH344UfFOPlwOJDpNA1NjDceQVU4t7cl03GaT6sk0tp2/YZtJq3zBSftAGqqy3p1sDUei13r33fec09zX4mhbVA9n05ksLWV8ntEArhO3f1Uv6MTf57aJuKdIvK9IvIWHUzo4FtEtKX/gXae35+328D858E2MNpafwdsA6Mrgm+qbWBqG+La8OsUJAlgf9Rqy6PUyflOITWgpxfySQBVMUodEgngjZnTd63rBglgKjcSwOU2OCSA3X3V6XuO/n3htQz5JS3h83G+ulX5bCPob2s3gv5dEfkuEfmP8ILu2PrTbiPo777ZNoKubSRrw69TKNeCcJS+SwVwiVBOqbAQ16I8Sq7grsnxfd7VtGo+cpPKo7OfizYymMtgsRH00gUsg9QBDCczC23TAAAgAElEQVQXZaSLQ1oF8C1/8HcTwTtzebHwQ4+EG5s7uJ07vz0ay7DZP17k6qUHFgqgHjN65kyKb+/8rmyfbVcbjicyab8znaT3tsZ/WW6/7VnN9Wz6JLnjzqc11w8+fEXGu+2+gtMj2dINpTt+6wwITkS3xgChth2pDV8q2+h5CQufBlxY1KXkoQqHx5ihImdxmevQ7HmhKoEyiGHQ3i1P6saMVD38Hr6H30B1Dq8x/KNHyaDtnqp3FoeqZ3at9zEMKor6Pr6HCqCGO5qlaRa6AMTK5cEHH2zuffRj6V/9bW+JoLJnK3QPDpcTE8btgqyzZ3fk0qNJWVTv79V2s3ZdBGIriXWl8KNX2v0+R6PFquHj9vi7w/192TqTFNDjw0PZ2m2vr16VrXYD6WNdrDIapVUgk+YjdAGvUyH5TuNafuR6rgKubVhrw69ThteCcJS+SwK4RIgEMLaWOQngApjOek8CeGI/PyR9JICJ7JEAlnqlx97zW1UBvFlKoiWAr5Zze/2PdKpJfO1E6Zq4l2HboVbPl9chmTeCNM7a/dp6ZuOGBKvN91Cuz5wwzOxgWlfe+m5tPmajduVET5TXsvP5bXJGJQadc/fg/XLb+d3m+lyr0l2+dL/snmndQfNPyh+/S2eJ6F59H5KtcToYfji4X3bbnSp0e4hGOTjWI6fS9UzFgnZbtdGOyFarEo7P6HWq8zvbf6mZ56e/Ry/f3fw7Hj5Vxlu6p7xuTXG7TNvtY2ZyLM3cxeY3lTNbyxMVmu/NZoutMvRvm4Olc6Bsu41I2dKwuYHCaLzcv7A0mCjV7cgO9N7RrN0rsZ1cbzZjc7dwEZGVtd8qyMLqv0hGLL/2HOfQYbx4nKDGbadO6PuolhlWkZp2NNe5acl28T2cT2ffwbl3eo3Kmv8eztk7mMwX5arvYHz2Ht6z7UtyizK8UncsyWAxLq+AGgaWL8XWFnTYv4qt2Zxe233F1bBtDfnEPyW1NvIa6D28P5udPNGk5G0o2S8mNHkkmjtUAHMFyfudCJAA9jSQWgLRM9qVYLNmHdnN9avN93BeR5y6cjvP4GEbJV9PpOa2u3LPj6xDAK9eHTRng+rv6r4eq5Za8+mkXVU4PZA7b08E6+DgAblyOa1GvHL5/fKBP9dTJEXuuU8njev6MN3bL60ovPyoyKQtBvUmt9uUye13jWX3fHIp6SRymac9COdyj8zmieztnnl6ujfT1cBpAvpktitHbXyT2WFavdysYp7JuD1DDkkRHqOFBNAWyuT2TMzZ2tFx/miwPsXjO+aokx1sL/dbw+eWJv3Xu0OR6HliiuTFrg0XJICafiNeSkpsgUBTnu1KVSRnFtbfs29cOZ4tCBkSK0wPEj271rxFCyYiN+3+wVFIMiPCqbiY+xNdxL7c0EXdbo23MiBAcm1lglMyIowQw8aO24UWfepqbqAQ2VvObudBW0gC2KfG9g9z8/WY/dN+M4QkAexZCrVEqGe0K8FIAFdRIwEkAVSLIAFcqnMkgHpmdur2SQCpAJIArsM0lu80BPC3XvNqOXf2FnYBD+pUpxqZ3aC6EQRwKqv7X52uaK/N27X5XlcBzJG9KBez1sVVk8PaMtf5eTW/2vg17tF4Z7GYQ9WJs7utf9byNzmUra1k2w8/+GG56+52H7DjB+Rdf6wnQ4o8/r5ded/7khp4x53JVXp2byR7e+n6ws6TZKvdNHCwNZHjWVIJdf+xSbvH4NbOfbK19WnN/ake+9a43+5ebP0ylV31NTf39aD64XZKk3pmj6+s1j1VuUytQVWmCd8qWrVYjbdPngxRcgWj6uevowUVx4OEl3dTWlh/X8MqGcNFCNH2IxrG7ht5825TU/3UVYqLOMx1WqMAqlKLSh66YjXN+jc+t/rtF4ygIme4WH04brc/0b89CfNli8qpVwBR1cO6Nt5NJ2tYXLnyw4Uduk1KH2UPv9N1HbV7udOBcvZMBbAv2uuHIwFcH7umf0iLQP6v60YAaxv79bJz/Tvr9dJV91YNCaqL+caFHiwOFSt/s09+54OTpHg8bJfdlT+xCFFthzfABby9vSUPPPBAk8bBYCh33q4L+9UFnFyeR1evyvZW28TND2U3eYNla3wkB4fpvQvnB/Lud/9+ur+V5uYdTy/L3l57FNzR7TKX5LI9nl2R43kigMPxQM6dv7O5vv3OT5fz59IcwIOD1PlOJ7fL0TQRzslkKNN5StNM5//p2cHNRtBT2ZbWjdxuIqw4d7lNrUA8CfMrSLEz3z9s9x2EVdi5+W0WrxKpiLzpfSNcqKa1p3OdIEiR69Tu6ZyyyNWr94xkebKn+df3LA0YFucAKllGAugJmSdeizl+w62V/fO83eN7+o7NkdN0YZl4MrVCwoa6Yr0dEAyXZ9vqO3hf48Sj1vxKXCSAuAfq7vm0Eh2JM5JWXBFsYbyb39uZ/d3YLbjz8b4P459FWHncVvE+OVeZLuAc4uvdJwFcDzd7iwTwdPhd07f7EKJr+sHrEBkJYH9QSQCXpIMEcKncqQWRAJIAlloSLgJJp6/wtz4CJIDrY3fN39wkAujzGil9EcAzPSdjjdMn6l3Zde74aoWx6eWXatTW1o4MWsHg4KBV2OZDOXsmrQy+7fyePProw831o48+IHdcTPdFDmQuSdW7cCEpdpcuPSS7Z9sTP+Z3yqw9Ik4VwOk8HS81mR+JLaaeTMcymSQlbzJNCuBsdk5ms/ZEBhnITJICOB8cyHSQvjebH8homtQadG9auSmJwZWvuB9czZYdx+1SR1yooHHhYoYm7bAVyJUrVxYKkqpttupTFyTYogS716iFw+QCRmWx69ryiKtrcZEIuoYnaa822W1d/Kj04UKSyURPx0gx64kTdjKb/mv37bu4M44+Mx9IbjKMdZT63iJei6w9xiw6ec6+o0oeLmJBlRevrbwtrP6N7+FCDAyLcZw5p4taVxfH2ApedKlD8lcusa5HC3r0u7mFSEt8T1ILPO1H442mEuC9QTCNhApgrtTWu08CuB5u9laaA/gL//i6uYBPl7yeb1vP2TP4Op31Ou/0TM4i2HxQ79qs/UZt+Gud74jkIvlTgtf1m+vz9ozNmrxUE8C6aaU1SVmW9+iy7LZ7uAwGI7n0SDoK7uyuVkuR3Z1d+Yu/SCt8p0dTuffedn7eRDfiTUzhkUv3y7m9RNTm7RY8V648KufOp1Wt+w8NxDaWHoyPZafljeOd5ZYVR8czOTpKW29MjpLreDo7I7N5KoupzGXeEsDJfF+OJ2kj28l0X7aHyY1sZEzdlkascN4cEi/cUgRde+iaRZfnQ4+k7+UIILpykXghyUSy6OfFaTib1+bnqaHdINnT9KDbUsOhS9PqDbo9jUAoITrTbvar/1q8iJG+Z8QppWl1mgsSOSWAi8ejAbi+l8eUIWFEYomGO7IzBcEdjEeY2RzO44OrK+kZtfVRnyPxa2xyPl/M/UQs9H7kAm7csy0ZR1evlRlOMcC0lwgdhtVvlNq1qL3wLuAuAqjfIwFcq1mseokEsAquE4FJAHviV2owekbTGYwEUIQEkAQQVR4lPySAJIAkgCe7DrqA6QI+Le8gAeyJIAlgT6AywWqUv0bly/xmqhCYhFGRpFoFUGbXf2x5cPyQbLc7M6uKs38luX7vvuu+pLxNB3L/J9Jij6c97TPlAx/4UHP9lKc8Rd797nc117fddl5uvyO5YR99NG0OrUqOKTHj2QWZTJLL9uj4SrNApIl7fiCzdp+y2XQuk1lygR4fpXwfHemCkOT2PTjYl/3DFPf+1U/Klf3kij482peHHkhptu+pEqPuV/3pv6YG6nW0HxyqdKjMoTvVBkYnFz4kVcwWTaNGpqJUZCbq5jTFCtWqK4dpAQ269vTvro2gNf5huzpa1S9TiHC1KC7yMBvMKYCXL19eYDedzmVnZ7kyuWuFq6qDltfReLyyCKTJVI+fps2nG1/D/A1nuPmzKnypvu7s7CwwQAVU7+vPLwKx+L3L9tJ+WrRU84tUvZy71Zdx9J3SKmDsD3LXVABrSnC9sNe/lV4vXbfKW2kO4C/+nOxdp21gbgwQ138V8I0ggNUk5QaAey3ynSN/qPYh6VOS53/4/Cu+6muqc17LGYf1h41Up0mP87R5V7rY+bDt91pOIbrbxuPvfXwT7ytf+ROyu5fm5ynReMlLXrLoVAftiuXjdm7k2TNn5MpBmus3lnMyaqdKj0YzGbSneMxkeYavwj1st0GZzlvSMV1ufKzO5eGoBWRwvCDgOofw4oU0d1DPONXf7u7uwh2spA/durjpsIGFbjR0TWJ5RbW7dDqckrwluVzOkdPvGr6GfTMnrD3pBEmmt/0uEqbuXSM6eC40brtizzXeBUEHwqbz3KJ5kvpdnxZ0P2OaB8MlAVxhwJEPWMFo7+N8TXS1WxzD0WhB7rbHOkBI9nN8PJGtrTR9QMveb/at4eyeP/Fj6eZfrTpKYvXnXe5N2cFERSRyOJcSY8O5hd5l21Vho7bYf/tEG+UaGRLA6iax+gUSwGrIVl4gAeyJ37UgQqVPkQAmhEgAEw4kgAkHEkASQGs7SQCXvQhdwHQBlzhF6Xm7EfSrZGucFiCoUZkrwyobTua2UayFxcnaRpJsdKv/mlvH79OUW6llcUQjNxwJr8r7cTZLhCp67t0DPj36pdJKLhy9+nziKBTdS/aOqQeYV33H3GqK6R13pP3ibJ8w/B5Okka3iJaDlSsqGdHEdT/R2vKg9628ERdslK3s1fVnE92PJ8uTDM6cTYsTNE6beP+CF/21xXL+RmeCYd1CFbLt8OYi6xyZ7BWjFYUpYBgmeJUq0Gmer2xxONfFFqs/TeNSPbWD4jQMuMhhv8LInnfG52WxIW2zYXq7mleXdrQu4GGz2CPFORyk1cPqVpy1CZo3YU0BnMigXXSl6bcwtoAAbUFtJbfgwNyb1p6oTZq7WBelLDr8ga5Y7oeyljGuWu33Vgo1HHVvBN2EadUndOXiohO7f+7cOTnbelS03qlrV39Wt/3qaFRJLQ6/UhXbw6gNsudHx4r5EjCMz/BYuNfd0mJT3vDbtum6KoALBXN6uFD1NC5rh/Q9zbv+rK3B/Ot9bP9sYYtX53sWd03xXpOwmr+utv+kK3hpx5bHZKOpMcM+0ZevJTgSHhDDdoU5zwK+JiW8eZGQAJ7odJfL+7HBylX8HMm0RpYEUIQEMG5YSADTvLFEOEkA1f1LAhgrvjdD10wCeDOUwmoa6AI+XZk0BPAdb/4VObOTRv44ZwWPNsJJvagameKDahOqgjgfpqT64cg2CuvnwixH4kszyE3IRZhwHy4/QvbH/UTzfrw6FxVBbkSHYb2ioAqIfV87AxtF33nnnYuO4cEHHxQ99kh/58+nOWH6i7Zr0PumLuj8IssL7r9l5aPpjU410A4a9+6y8NZx63umAOgk/9supDTpd3faPez074OjNMl+sVhgPpDDo6RGffXXfOPCzdeM/kHgglOnmrCNKrbGbjk3pQK4kqjliQrzhdo5F5yKaPe1i7TrZjsQ3OTNGePWQPd9MfVO9wtZ6oyr6kKrAEpSwjTS1fl5rS4z0JM+0rVqkhNTEe2oODdHK6o/atfe9jU+XBCyMj8McIrUraiuoRIZ1U9/b3XLlYSX9zj47+g7qABa2nQunCmAes/2sLNv4nF5Wresjvr9AfGUC3u3Sz3SMJPp6v5010MBnB5fXSiAPn+ad2yPNG84r9Hy0aXAb7IC6O2ypADqYiGdgqvbf/ax88daGBLA05VoQwD/9O1vlQstmcBVa5GrUCd7W4XGSctY6XF1HZ5tGXUo+A19/olPpH3PugzfZxlPBsPVgzlocKKy/w4SWTzGyRp0S1uJaNZ2QBqvEqm77rqrSbaWg5K9plEH9626Va2RffjhtBpTO1RchWkdij4zN6ymHzsrfablh9srGOFUfIzoaRi7r9jghrqG795ecusqIT1uFx8odsNRmsytaTs8bo8Sa9mdxm8E8Cv/6jcs53kB+dNu2DoD7DBmNou/wvZzgw+MAst01O6BV/GJ6qDDdsGFvjhrcgpErZkLqXO/UrRL8peujROtdJYLIrhMyggCnHC1LTndgnXrfoSLX4vBUPdNAya6qMea5kFaDGCrYdHucwQKF4ogycF91pZ7wE1E3Y+5NkHv9xls5ZR6y6ulW/MWtSGRuw4XHuAm1GrbVu8wT1YvvZscj4XD9ERehCivmDddmFMiySuLK8AoDGfMl7mAB8PhcjHH8bJN0PQiwfPnPWP737eCRGUV9Qd947tW4bDccva0ms56F3BXv+IxSLba5I4E8FoV8obFQwJ4YuXWUvkgAUwkkQTw+rQKJIDL4760QyUBXKqzSMKi6Se5KSkkgNenrqZBznJ0SgJ4/XCuiZkKYA1aJ8M2BPCP3/ZmOd9O3sVRBrp67VonNJuCpm5AdJ2gq9BG7DYS1ngtbh2BRqNsfW7xWVgceeMcDIwPZ43kRr9YYXG/Mb+DvIaztOnI3EbnjzzyyKIBQBdOpCx5FQGVTwuPYRaur8nRiuvIigvVR3WzWprNBYyqrV6jooAuW7sf7VeGSizGp2WNSoR/V0kyurAu7qU5XY3rZ5I6NLWTo/Z62kpZ5y9clMNJUgW//Pl/NXRpqkBl/HxlgcRNeGLKOtVwONVpFytO3iaa2SDhsjgbLlAA7XuoDC7SAK0irBFpHi/GO6AM4olV8zbCVcVrJENpVbhGYUofaOx6eLW5xrbC0oE2hfFpm+CPjkMCqHFZHW2OaYM5AV1KEH5Dw+U66eg+KoDRNyIFEL0FOHVG82/1ROuutZe2DyK+p3UNTzJBhS9qywzbHAFclEsbMFIMsR3GtgmnhqA7vjHFwWCRD00vKn3oRfCej2gKgMUX5QXrUa6sP1VqYGQDvt6vtvXLStZ3EYjvj2JPWPoqaBdUANdpgPmONATwc5/26TKZpDlaaoDWaGCjaIap96zDV3egVX5d/fXkJz+5ieNzPudzmn/1b3MP5lwrWAa5RhZXz1natGFZEKELeyfSrOnETgkbTJyfg42epgXnLCIR0k4L9+7CuY++sdS/Md+WR0yT3ltxxaiOf+7cojMwV6u9aysJ9R1bBWxzAfVfm2ekZMtcxLgpr8ZnnRLmETsfu695s4YH5y/pfXQ1Wx5sPmDjZj5IU1Gms+XcpwsXLixcwFeups3ubrt4h1w5SBsUv/BFL13M+1NuYc3mDFZJzIB05Dr2W60+JwJoPyWCieaq67f5wRGHTfZxdXSQ2WjuFJzutTq7fq5HweFkyvTNaXtAsEK/2CdvMF4SwPlo4Q5WIjgcp/KMpjx4Aoj1EUmIvu87OhwoHbdn6a4O+k4C4O0iR5Ii++ljUz4MqnTYVjRF1/rotR2w9sYIINb93ObIJVvOpRfrLpJg75rHNNi1xYlxoIsedyjAdtiXM9qDL7OIkPpyyokDhsmnkgBaGrCvwnStkvZ6AojzQNEGctM32jAkgKUKw+chAiSAbS+H5M6QIgFMLmD7kQBe21aEBHA5CCIBXGLRx8pIAPugdG3DeKLqy+Ak2SUBvLYlEAz6rvcHPoXx6/D8R0Xkb4iIng31URH5JyLy92BuvGoCPyYi3yoieh7U20TkO0XkT3umuyGAT3vSfXLc7uyOblgbNSIR0ufm0lBFyUYsShRstGgjRQ2HI3lMU+Re1nuorGl4/duUK/3XOgp0Pd5//8cX76HK5ePyI0j8Xm6EbKNRVblQGY2USFz9bPv24SjcHxfllUNpV1SaImI443tPf/rT5dnPfnaTFcsrlpmGtbTpc1MLdXGJ5dEUCQ2LpzMgXlau+p4pF83CjpYwo5vcrpvVnYdphfJovLUgj3ffe99i8cjH708LW86dv00ebBexfOmXfaVMgwUMqgCa+2/hWpEhCmE9zTyeGO9fxgZ9Dupb749UBhwc6yKZpLyl7C/32mv+0sUewal4jUoanFSyXDCjAdKL49F0ofw1otQ8LczRM0KW+dXIUoRzSZ6AZr+y9tvDwUgG9kfzfhuHqoijtAgEsfPKtj1Hj4JXgvQdnEKCyrzubWd1Auuw2UQfRa+kPA3RD96zHLW+YJ1f5nsZwe7uzsIbYJ4T9F7g4pFEIHp+PBNMT+WIppyUFMB2NWkTq+3pqNfR/ZVTe9wpJX4qgOLetZrZ205j925/Qix7HCjcaCUwpyhjUdwIBTCwESqAp6s2N+XbPyQi3ysi3yQi/15EnikirxKRvysi/1ub4u8XkR8UkVeIyJ+JyI+rB1ZEPlt33uiRq4YAPvlx96zMbzMChw22VUp1ReZcob6R1b8vXVquTkcCZXEoaUDXo7k+LSySH722TkJXzNr2IweH+8tjiraTWw1XxmKjovnAFXo4h1HD4bw/nI+B7p6S20bTjnMfrRw0/UbqND7cdkXD7IwHiw2fNW+2qapuA4P5NuJnJFPjtO9pvIahfs/c8k996lMXq4qf+MQnNkl6whOesHBPKS4W3/333y8PPJDOoH3b294mDz30UHOt33jWs57VXBsJvXjxonz0ozo2EfnQhz4kz3nW5zbX463txXY19z7u8Yuy+sjHPt48v/2Ou+T+T6bzZb/nb/2dxRzAZt5fu6xbOxokftYRDNZYoZtTmKIOSO9Nh+08vB6VaN0go2NlWAuH92J7lQUp1I2Wc7OcbXVws1l0ImTzRWB9KbG3raE2A7avjJI+I29DmS9WU89Emk2ilbi3sy2bv9NH0ua1yV2sR8bZZtF6bz5NcwDth1M90kbQ7ZYxsIkuus9wILUkgLiRMdgDzCMudf7YSfrNdy2t2KFHLmxPNpBYNdjCABgHeupGt+9vb48Xdcy+iwNqzTPGi98MeFDR1PR7qyRkuekwlpFea523sEj0dIxn2Pj7jZ1BvfT1J6pnObUSM9MVBpW1TyUB9HmNFEDEGNaMLOyhtBE09jkeyw43MAlgsWbcegF+VUS0t/ybkPTX6m4hrSqoNVt73leKyD9sw6gh6DtKCH++R5ZJANstJqKFHySAJIA96tDaQUgAl0RjVQEkAUwErd60SADrMat5o0sF9OSUBLAG2fXCPpZXAasC+G0i8nwR+Y8i8l+IyG+0quC/EJGniMj7ROTzRORdAN9b27+/J4BUl2imZZrpp7v2fvgJd94uu7tnmhu4QCCajKvqEhIjC6+jYasc5urQf7FSoFsH95wzVUzVOFwpp+nRe6iaWdyqRJpipZPlLT5bkKDvoLvS0qz/2n1VtCxuS2dub0NNC7q7ceUbqpmGoblZFUPDUcPh6jm/EvJg/8piAYdiaWF1UQe6vu2+5UkxM+z/f/bePNiyLL3q+96UY01ZQ9bcXV09TwQKCzW0QLIQxpIaC4sQIPgHKcJMwjb4DywpTEQLiGAwlhFISNiOQGpkbCGJsMNhWSgwRmpA3SCEUZiWulrd1eruqu4aMyuzKqeX7710rLPPOud399vnnnPue5mVw74RGe/mufvs4dvT2usbthwuLAO1xTZ8et/f7T2stvuZ8vJ7qqfbJ1k5DdkKP1MbnJ/UxduvvdCMI11h5piOUgFfupJUiy+/nJjFR554Ms6/fqH5/pUXzwwygHb+8IBtmJbbhgHU8uVdfg8MYGot1b8LTCBj++2J6WsZwE4u/dVuG2u6hqzV5V7bjDWrb6VK7wIs78XeeuqfI14d1vqr4pRsvWVlxf6tr6dg0etrm6GgwE1/t/SE/nKuUU3L+b+f3ejj76U5k2Swdy1i84jV1ovlLGMBxVy5eYy3XWIfm7Zwt24HW8489ethSnDkSG8CwzySijd10tbWRsfOe97SY5hzKpfjKgDw2FbPmjdjqG38mAq4ddJv+7WPM9n6BKXx2MplFwKlYxv7vrTuew6P/c0jQYwxsWP5HebvNxIAcjyk74stqV7At/ddwFq1/0pE/Nete6B0MFL//tV2GMgQTDZ/j2kPxdD4qVZ384cLA182hR/Nn99914lQgGd9pHa0atWDXRPSqlwt7gQg/i7g4QXSIK75/26vSqNtmsEGAacmeskLygsAbWdUD9rQGYRYFcrwCgRhyl911Udle7FxuQK4BmYEYQI8XkSVhiFVctW3AJvrobSuPwMss07OV3JzvpLPqVOn2o3myIIdnutsVa/ed3n667bIO9h9oTownIzbT/tKquO8iase9jbmJueyla+9jhvgeDXZhKncDsyvb3Qyd1rV8Y1LyUrBQLFZ5HgNyMDK7Zso5izsJU/FZe8r+PGcD4FOSZ2Vqw+9uDMt1W3NhrumEDq9SQPBPOdEXk+CnDyECscqAVnJfKNkQ6tyeTDcuZqchNiOMRtAlZsHCKbn6fb21W6zkz2aQQhtijV+aCZiGdCO0PVQOrdF6fJQGwRHQ6rIvN3Kx2PZfemyCeq0xniN8/yjmYnyZT9wTaOcS+PHbWadxQDy4FuS86LafT/NyDuVOS769/oRR5DMccg6lYD6FCBVKpv9ULIVnDNn56bNx8bY/0v1W9Zuj6Op9YLsqwp4qtBuoXTfERF/IyL+fGsDKOOqH2wZwI/JDGsAAP50Cxj1fv4pMoAVAPbsXgWAyTaoAsAKACsAXAQ6uUakAsDEyI4BvLHfcyDFw5QPF05TAWARfFcAeAsBu6lV/VJE/LWI+Dt44S+09n/vWVEFnJfd2ACePHEsZMyvjxjA3BFDG4GvHeMdtWQD9Z4nLlmnjf6SrwXvVDOAvBdU75vl4EmYKiV/58Kjk7VP12a0qP7kSUzfHaOPDihOo/fMwqkMLzx6RubNJ2uyEk6rurh9es95Ky2dTnLvOIJPve8+UT3dJ8qPTKT6THmaAVQZrr+YO7O5SkeZ+/9mJ1T3Equksq1qV5q8bDKA+m37SmL1NAZcZ3kEm3V1PcWSXLic2KMXX0yOIc0J+DoxgEObTK5ecj3mMoB0mCpN8NImmKsd8zT6nYG3OSfIdJXa1m2i64teoV37lsTJzDfdnP0QzAkAACAASURBVM1ZYKPgud71YaubopMBVYXKLx/7Goe0wzUb2tx13CoflcYy0/s5A6j/Wy7UEKhe1DTkWgbNH/bFMraNfUumNZ8/pf62liVnAL0m5Gra0lo3VrfNzT7+qdL2piqp5pubfezHpIUoux336vN0EFLdyACyHmMAb+x3j7d8/NxqDGDejsoAllbCw30275h+uGVf79wUL0OA70dRkDx+vysi3tWaZMgJRA4gcgTRR4BOl+nOcgKpALAPPVAB4GIYhgoAp03zCgB7OfEg4e8VAEZnZlMB4KLJQAWA++fOlFWnqoBvbxtAxfz7PRHxJ1sVsJw9/seI+HsRofAv+ujv97ahYj7fhoH5LXPDwJw43tubkQGkCujs2bNNgVq87DihE47TiFXziYc2hJuA6D7RCVQ4D7Jiyt/v+uTKxZInd56+5XyQO3PQBk314m0iZgDJKLjuYq3MXNGWh3k015xdTUbzZDANAsQAmulSOudNm0OqWb1JCnz6u9g6h4HRe2b1aNPjeqoOdODwczGAfs911V+rrlRPRvcnq+QFSL+7T8iU8KYSl613Ll+62LyqdzqW9OixzlHEacVuXNpOMnQYGX2/Xgyg2kEmYsy+ai4DSDmWmFTLk+NWY5ZMiucHbcK4EXiu8Y7qkm0j2a+1jAGkes3lsE4ltmbZZrSRxSlUXiUbQDJIZMtcBzKAmgc77e0faUykj9JQFVhiAF02QzXpXToW5DaAdM5qyoNDS66OJFhRP7DPKE+/xzh/ZgDpBMK6LWMAc/UzNSDsH/mysK079O5o5NB32DIbOrJvbvMcBpDjfWhM5cy1y8llPsRAv9k2gGxjPkc4D/hbtQFctprM/+12ZgDloau4ft8WEafbkC//a0T8JeGwVlQOBC1vYelw/0VEfHfrNTxFmo0KWADQAYMFjqyGpXrw1VdTAF8tXoxx5wGtZ1SjemGjCpgbGwEZVSD0IG4AxeXLC56q3DD8nuru5wYxUlsyGLPrLPDjhVgLDdVOBi4GNwxuTW9klUEAaBkQDNPZwWWojlQBU12lspWnf+eVbnrPmxYBpWUlIEFQbsAmEOwyVEfe4azyVEc/0+9uk/J1eTTYJ9ilmYDVlHrn4gV5naaDgtt9/MTJDswy7dVW/fTFL36xG6vXCwDSCYCb/ND3uQsLAWAOgJorc9uYe1Sjq4+5sXsMW0bcROi9znFLsMeN02NyZ7e30cpBA8FLv7m3AaGXxNzjJrbZ3jVHwJCDFW/sbl/J6YJtJXiT7KwCJlBVnjlQZr4cw2r3MicQHmzyMjwwS4CLcQ4J5HJ1s+dgad3kmpD3Nw97Y6rfvv92F0AegXSqYz+ypf41IMxBWgkA9rLozUUolyGgx+dDoK+0WZXScr7eaAA4BOSGgOCY6rvUvjkHsMoA3t4M4BQAd9A0FQC2sRYMpgSeKgCsALACwEXgOLT5VQDY29AZZFYAuLgtVQDYy6MygAeFLIvvz12nD7f0Wz+3zglEt02YFTIDSCZCt0Poo1OX2S1NbA9opSVLpbSNk0XbQ2Q++J22QWSbDMgYckXlMX4gv7seroOAXBcnUAEP7xahmv5aBawTtsEeQR/j89FYnSFmfKrnYu8NQKpbnvZ9UmW7VZc8DqD+bxWp3i85aJB99MJKZk6/uy3Kj4sv4y2qfP3ffanvbqvkxniMrr/qZpbFY4QMhvrMDCDZzJN39TL3+40Behvo7tlndYlN+lwvBjA/becMh8peUDXNDMK20K723dLGpzHiciRzsmU5gFC6kgMUTRfU37xizPPOc+PS5T4W52ExgFz2rFEssRlkaDjPOWecJmfTWIZYTDI/+p6vG/kzqsnVbtpo5swRQ9Aones3pJL1+3qP/UPtRKk8rxn6zf2Tm4VQK0DzjXyrIVPJ73q/dJWbnTrMRCs/OdrIaaSbe4U7xjhP/P3atWEGcCqDNTQfS1vqwrxEHVnW1HIPc8seYzMrA3iY0i7nVQHgwWTcAMC7Th7vVMDa5A0gDEa0YDlWmxY8xpPzINdCxjhYqlYDYtoeytUsrDYXmRwUaRGk7R/VmFQncrE2uLIqVHW0F6o8a11ngRgDHXofU4XnBV51MFjS764TNw9v4AKZtIvjAks7IbeVQZUNThn82Zub/hLguf1UEVNtzY2BmyBBu/uS5cl2UIG2LUfXWWlyAMh+ba7nu5iCO0turt899963cGjoNvONFOD3N36jv7r6egFAHlYI9tSeEhi8NhMAljZRqmSp/nR5HDvsY9bHeWjMebzkAJD3ORsAep5cuNjHocw3pFKdhjbVkgqykanvMi7ETVSd3ZYcsOUHtjx2HgGUASBBYu65a/kZvCntkKd07oFMIJcDwF612odG8vs8NLH+PBQR8NM2mgCwB1a91y7XmKElnrJ1mmQqkP6n33MvX9oASj08BgCd7yLg3FiIGMCxMQeIlQAU5+acre1mUweP9dlQO+fIr6qAqwp4zhwppa0A8GJyWqgAsL9tpAJA3au7wj1ccCDoQG67EVcA2IcnIejmAYuHyQoA+0PmGJjg4bcCwINuifPfHwKypZyYtvReBYDz5F8ZwHnyylN3ANAx53SCNgNoxosMoE5aZqnSCTNtlEqTe5Hq/bW9dLm88iVjRWaDE6HkGEEVqk/4vFVD4C1Xnyl/t0OLolXAul3DqmG951M5b/9w3eitl6u+nV7tdnqqgHl9HdkhL9ZK6zxcB7GddlARe8o4YM67cbRoQavVzKqbAazabHnqd77HOqtOYinNANIhgR7UkpVlrn53/a2e4mautI4DqHq6fvfed6r77japzutb6ZaLZ555phuX14sBzAf+mAp47sLCMZrnTRlxs87VeBwnZF7MFnhuaLxRjsuWgO2raf4RkDo/gtKcQc83J7I8VFtf20s3/ZRYS25mZAD1Pj1V9b7aRmaaDKHHBE0ayKy5HJXBaABcS8iysa+8NtEzuDQ2hmQ8JBfnQRUvPeepCWBfl9SdVN2PAQT9bhUw1+cSWygPYTqBLMub9drY2FrwlF4YDwU18tgWlY+1ZcBoKG3u2T1W5vX4fQoQrADwcCU/d50+3NJv/dw6AGgVqRZWAxKDAz1zsF4tXAaAVD1qYOcAUO/vtIGB6alKjzxS98qDNjUSL70c9bvBiMCk8xFgye8CVlrawjmkigDguXPnmp4TaGLIF29krpNAjoGVyrI8tFASMNImSHkIbDKALzdgL/xqh/MwoFN5BuICz1TtUbXl9AboSudnag8Bm+uhvrFa15uk3uc1bm4H70M+c+ZM1xbJ0DI3AGRfKv+r2ym4M00F7jt1f6cSX2jT0XT/9K//+q/3IOUGBYIem7pzw8AQVFgmJYCVb/BMQ9MDAzYebGi6QDCR26xJxldb4FcO85sAGw8jy0ArZaV+Jfi81h7w8sDBOeAkANze3mnuyCVwVJ4EgC5TbVnfSHcPa8yRJXQaHkJ58PJYY94Ez5yLBDH87jKo1uUhiHLL+0/vUqXs+iwDWoyOwHUjz1v/H8qHADBPQ7MDAUDaBPZgdv/soIZ/c3MxfFGp3WPzi7+PAcC8DSWAXgFgnJ8j89slbQWAB+vJCgAvp5srGC6lAsArHfisAHDaBKsAMMmpxGAZCPXAqQLAoVFVAeD+INEVAJZHS7UBrDaA03an4VQNADx13z2d6sSLtf6afRA4oo2c2TQt6GR08mvcGhbr8qWudMcaVL5kLRxkWgntPFEyLG6MztujqPJg/ZzedRMbZbWv6mXVqsrwtXZkAKlGcoXpYanfzS6IVfOGT7WUVZ76a5aUDiwMZKv3cu9NsTpPPvlkUzyZWL3ntGL6LCPXQTZ7lr3q6N8lKzKjZj5dJ/WPWU0yQmqr1eSK/2gPcdaDNloLbOhuUgmqPu5jOYGQPdXvjYPAZmJ2PvWpT/UjdK2/qmqIJdiD2nFI9dizOAYmixvLmCpmA6p914Mb0dD7ep4zgHpGFaTzy/PI1YJ8T98tZ40TMx5UMbpfJXubMA4xgENLAutA2ZKpZL13ribGlx/O3ZLsqL7tGf/eI1Xzx2U0TmCtF7DyKuVdkqfmH+eX669x4ecug+YpudMZWVKXPaQuplxKatEhlTvTsjy3S/3uMjm2/B7rQw/gKZvDmEq5lIdUwCXmk9qaKWUPpSkxfEMHi2Xj4SB1OIx3uUaW8svnN8f3lPLV9nYM1LuApwisptkngQYA3nfv3QsBgb3I0H7MAEObjIEVAaAGY65OFcDYbVWCGqgGYcrXwER/DUxUOwOFko0TFwGqovSewVlpI9JmwJAqVpc2XquXEkAtqZaoAlZb6bnnxZcAjyFc8naoDALA/Doo/a5np08r5ndK6w1f8mKYGqp+lZY3fgwBQG2qBr4MIG3Qqnxoo2m50JuX4WHoPcwN+I3Xe02E8777nns7VbPbJIB+8UqKZ/7CCy/03ZYBwFw9pIRraz2sKf2OhXFBTVbOq1ci8HcCwKFNpgQCqeosgSluuDzQcDMo2Yep3ZadxhwZR+fZqzx1UEoiHbjqdXApLG2+qg+fL8ixcBewMx8CFzTfcFq1h+CHYDbaMZH3q/Mv9cMyAGg5uWzNNa8PDB+j3wm4lgHANC73K6QoA5qK0HSC7aAMqInwd65jpTXSHsBT97pVAOD6en8ns9pMIEqTnql1yNNVADhNchUAVgZw2kgZTlUBYAWA3eioALDfwCsA7GVRAWCy5asAMC0VFQBO23YrAzhNTgdJVW0ADyK9iAYA3nP3yY5BIztHBtCskZhAM4D6nY4fuUdto8ppjcR1siZzSHWpGCx/GGxZz3KWxOXxxg4xb87bqkvlY6bv8ccf79Q+sJto2D+nyU/YKlttZVBsMqM89frEagaQAZYbVWfLDDAAbokBVJm8p9gy0ftul9pvNbfrrt8sF7Wb17u5zqqb8zCbyL5WWa6/gKDbrb9mHFVn97H7iQHAJdsrrcpfbe0Y0eP93ciup9T+L59J90s7z+Y/YACHGDvGOSsNf/bxFPaOcvb3IQBYYkxy9o6qvhIz5Dw4NkrqILaNbdJ7VBuS+fM7ju9G9emqS0WJYXNeR4+kWI5OkzOgJRZU4yJ3XGGbGOuyGePtmGAasV95XxCoNg5oO8kcgXLWd3rX6/fcnISsfokBpOxLMs370rKhF77zzdOyPDK+bivfK7WP7PiU/l6VASRLR2B8mAxgaV7mc8L/X6UdU+RzkDQVAB5EetPerQBwmpyGUnVOILQVy2+MYDgULcj0GKbXLr1ZDSgcCFrgwSBNi79ViAIS58/3asN886SqRPUwWNBfgxTlZeDkZ1qgvCg88sgj3WZA2zTa4XExpXrX7ePGxrxVhutMtTZDtPB310n1yCP986YN2knSg1fvGwDadlL5eEGWLFwPLsYEagaAVGsrX4I6103qeXlO++M+5uLsNjXAsQWAvJ0k1vrwHK6n6v7Cy6802Sws3hkAzEGg/j+m5srVrKtMEQFAf0obPg8mSkegswwA5nVj+/JNLAey7h/aqSlNP3b7lhoA7q4WznBBZMs2VwNAqi7pyODvGs9Ow3FO2zwDnn0B2tsxQbOPEgAkmNKhpdRvBJFsJA+7nDfs1/wwwf5hXmmMpvFDUEoASMBMMMXy8pBYBqsui/bXPdjow/6sMu6nvLO21vcl18L8YKK8+PuUvHM5Oo/Su0Pyn1vO9UpfAeD1kmyfbwWAB5NxBYDbyQ6tAsAKABcAQQWAnTgqAFwEMgSCQ4xwBYDJTrcCwOyAi0WmxPrPYTKVtjqBHAwA3elvNwDwxPGjndpQJ2czDT6ZChyZCRMDRBWwnytNfq2aTvobkRYCpXOMO6pIlcZOIDzRWe2o312GHAccy47sGY24rXaUupPlmdWUupnBYmlorXqKfaAKyAt5yfPPCxzZD5/6eRUcGRGXJxYuN0YXs+pnbBMDM6ss5y0PXcvWbRI76PIko5K3qBlApaMsyPySSTWjyBtCPEbEvJL5vPBGUufT4UWOCLkcVfeXz6R4jAuei2v9XblcID1R07PeCWQq25cziWMLLRnAORs+2R8yO64/xzjtyrCYL3i6sp5m3jUe3D9kSIpM5YS4iiXWJV8Yh+SV35iivqSpBL9bHhovJdl43pHhbNS360nNTPVticnjeNEccRk5k1pi8kppKdsSU0umkPLJGUCynMqT7HEJBFj2VFW7fMtTebyZDCAZTLd9qE8OqhbO14G8/27WTbwygNe/ZyoDeDAZVwDYxsugjWMFgBsdwNDiWwFgApxTVH4VAFYAWAFgvylJFhUAloMxVQbwYOClOaAdPIs7OocOAHqTF6Nj9s2SIQMo27tSGBidVu1kYDAllmj9Wm+E5DiAKstsk/7aCUQTwidcMoBmrGQ35rTajHk7huvqUDOqy0MPPdQ81vu2ERR7QkPqEgNYMtYm48gFnkwXT6ZkURk+xqf6EgMoxtLl0IZObXadGIPQzKnaYFZQ7XT78nhxlhcdWxh3kYb5lrn62mVL/q6/7Cr1kbzN/Iqd3dxItk/sY9kAksXV77pZ5sy5NyYzgGRgzADmzE5pJpeYJoO5ZSxXiQHM05fyHgKAJdZCDIHH35D9FFWJnhMa2+4fled+cxm0j5tytR7b5fKGZJTLYG93v81ZIRpKF5ZG75P5zJktM30ew2rble3kzOHfcjBeYidpZ6gySmya39P6ljPELm/oueuSM3/5e6VNnoBoiAHM2bRSaC6vFapDzzbNM/os9dX4jtjf60z5XE8GsFSnMRZ/vB3XN0VlAK+vfJV7BYAHk3EHAL2JaMP3RuMJJgDgRUvgwU4ImvxehOigYcDQLMItANRkcEBhAj0BQDt26DsdEdw0A0sBEKuA9ZvrzAXcQI/esKoPVRb+rk2BnnZe1Lkpc1Pl4k61p/Mzc6i/vEfZYFf1pRczmUblLdnTm9dly0nGG4Dy4l3Gek/vWBbN/cvtqk7Qmt+drPeUJ1XAvL6OamIvZAJ6ztt9qXesilb/3HvP3Y2YmPfWkaMLIFK/K+2FyymI8MImCxUwDeiHAOCQR2a+cev/pQ1jSIVcAoDMM6+b61GqM8vOWUS/R9BGp6GSWp5mCgQQlIXzmOsFvCjn8eV1vVXHj8t2caHylbEGIJubfQBwtcNjsXESe/3CvnGSyzQHZPQ01m8EgPnGTFOMHJANATzmmY/hobFm2ap9zld1GfI09lrA9ZJrHuf3zQYAc2B4sG1q+IaZg+Z7Pd+vAPB6SjflPb5CXf863MolVAB49WrTfyUnEC7UuQ1gBYAPNHKrADBtThUA7ldzLYLrCgArAFx9qySgvNmZP7eyAsDV+3vqmxUATpVUOV0DAE+eONadQsVCOUSLNzUygPpulk1Z8oYQM3lkscgAWgXMCayTuk/gYq/MLjqNTrlW+0rlybh9PjmrnnnoGsX+M1Omiei6qX3OWyxX6SYQMmj5tVFqs/KjCtuidVr9tYzIhJEBVDt4wlceDF2h9w061W7GTXT4FzK0rgNvRFH+VKkbtA6FgSFTWYoDqPccsocLsvtP9dlYT1NSdTdbceTosa6P3ZdXruxEbBSm7wgDmBiaBDYI0MmskcWxXPJNY4j583MygM4jZ4cYssNzhWlK6lTWM3dqcDmWm+aG+1198/LLL3ey5XTuWUR7XgqYJ8eJ7asHDwuyjBW81sb5pCrR6Uv9oDpJBrttfJqeAdzsQPTOzl7nGKT2X7iY7utWflSJ52p1qiAlL493vee51ozRjcQ25uYfntvsN44ftkvPqXnI20o2Nh97VAEz3BNDXikNHbEYTsfzKzdLSXWdd/nfqipgy4Ky4nzknOHhedXtqgLA/ZLTuGrHUr0KbtWBdYe/1wDA48f6q30EkAxePLnplceNXQsWF1HevdkBko20EeljtahAHmPVeXLrmT0dnZeAkoEcvR+1+HKRNWi1DaA2DoJIMjRUuXiRpfct45HRe9X5aTEubS4EAW6rnnnxVp1cNmOduTyqrVVuadOS/C0bL6wMZMvxrPpy0zp1333Nz1R7+3el7QMK73bvvfJKitWnDwNH+z2943o0qqr27rG11hZQ723v7saVNtzOFQfnTTt6ky9VZooxtrGW7AibdrXfLfs1LXpr++2chgCd665+IHjwcyyiC0sBN2kCPX6nbJl+6pqyd+1aN0b1fsn+1X2tOZB7jU8p57qzJQW8UQKDU+o6lGZMLc3fnQcPP5p/BCo01VD63d1rsbVVvoOaIDOXJe1xh/p/b099vNgyldUfMne6uJZaw7g+cD3hONb3nR0Fwt4vsXnwr3x93Zi8Y8n1f6U+LNZzSUXntuEgY+t6vkuPba43/l6Sc2ksD9UxHaSaA14FgNezI2/jvCsAvJLs0CoArACQ87wCwImrXgWAjaAqABweLxUAlsNWVQA4cY1Zkux2VwE/HhF/PSK+OSJORMRnI+K7IuLftDJR+/9iRPzxiBC98y8j4k9HxG9MFG0DAO++69jCTRJm05wHGUANWqojxhjA9Xbs6wReYgCpkqHhttWUYj54wwgvQCcj5zqbvdTp3Cd9Mic6lfm50vg3/1X7zLqoDvSM7dSDGxsdA5hO48lLkUxYyQvYz5RWebuNfl9toEcnGUDak+QMBhlAfad6hn212aq+yAC6zmQkqVoiy6s6mPkk00J29dp2sqkkA3hV6qyOAUwqyWsauQX9ExnAhrnJzHzFAO7GfrXmGAOodlqGVLVRJcs5w/xKKs1c5bcK0yYGkLIrmRWYoaZH98S5neRc2n3nZDCW9gYAQFZhlJ1qE6vdZHxL6nquJUNl+HlpnGh8LlP1JmC4nwE8cmQTDGCaL/qcOHF8IbIBHVfyuqb6pPfYxXPZsxLjNPqsMoBjs6L5vTKAk8R0oES3MwDU/Vv/b0T8s4j40YiQAdA7I+Jz7T8J7nsi4vsi4jsj4tmI+MsR8cGIeJ8wxgTJNgDw1H13dUlpd0QVqkEHvUmHVMC0+di7msCRFkuDNP31Zkd7OpVtUMTQIt4EqdKk6k7Asrt39tixri1UaXoDUD2ocnVdGVLBdoEEgKqv5UEglKtOVbgWUNrTMaSN5aj25VfBEXhrY2GdctCictwO/Vba7OhhqDRXLqchQbbCbVJbaV9Zsl/TMwJi5UXvVZWxa89eqICl9u0BYFLfCgCutfaCaROz/dpGbK4ndZzKsgq4A2FSVe/1myY36NJ3P6NKkLJk2e67oWf8nXOLKuwJc65LQgCo8ULveiViAPAhVfVYeXMB4Fy8uGwBHrIvm6PmWtYXbvsQYCFwL9n1UXZD6vVldeX6R/s3jochALio6k01OXasj4KgPHiApZ2txyHNN1ROM7dnIsCh9i0FgYcAAFO/lkfvzCaMTYE37fcKAK+/6G9nAPjXIuJrI+J3DYhRbf9yRPxARPx3bRrZArzYAsKfnCD+CgDbOGYVAFYAWAHg8KY8tJZUAJgONBUADu82g0CvAsBOaDysTNi3myTVBvD2DgPzaxHx8xHxRER8fUQ8HxE/EhH/UztAnm6ZwK+KiH+HQfOL7f//bGEgHY0I/fNHQdueu/+UcGCajWIizF6ZKWIgaKpppziBbF9KrJPyJANoxk4qRX9Xmtdee61JbzZKKmCfhKneZAw/MWxk3PQ+60zGjnWWupgsp9+z04mYQKs8cwaQqtrcKUP5MC4f1dJUbdOJQ+9IRWyZ68TPUz8ZvnyxIAOofJyv/lJFZQaQbKjzEsvK68UsT8mAqnb3ld9jGXq2bu/OjfXe2/rq1bjc2lpu77a3ajQq4NJSt77AAG61TkSdWlue5zvJbpOfMRWwZOkTOeWVM2Rz2ampi3UpnSTh/tHcsAOTnym+oueB+mwum+dNYk4dD5MBzMtdzeO0DErHHE0oL/apntNEQnXU/zn2We9ldlr6zX2V2wC6r0oM4NGjWx1jn65r9Nq72ZnJaP4xqoDr115c1HhJezyrbJffTr85Xd6lHVX9tinXYl6w6cV5ur9q+Zi7kxnAOR0HrUB1ApkjuFskrVW4/31E/HREfE1E/GBE/MmI+PsR8eHW5u+xiPgK2vRTLZr7w4V2fn9EfDR/TgAowGPQ0y9ifeBS/TZmA0gVcAkACrAZDAr0uDwtzmfOnGmqR9sn2t75OwEL8/AiJgBooCPQwvAozoMAkLaADjZNAKg8aK9VAoAM60IAaNWeFmwCW9fDoIr2iQSw3PgJXqxapg0gVceqP8P02AbQ/c9NUmmt+tbv7BPLdOF2jzYT5U/29Girvo31PvAyvYCveu9Ylwo4efsm9sS1WuvUvs1Ya/PrAHDjVbw9GwDmKmBudr36uUekpc0wB2AlO8FZawuCU2ss2gPe40gA8MKFi02WCpS8iqfxXNB4PQGg8267fbKohuqUA8oh8E4ZaBzltqD6P8dwqWIEjjyAEQCWZJ3A2WKOsgE0eNO6o7A3qY97O2mGx0prXRrzloXS0i64O/Stjs0mA8GDAEAXsg/0AfVVADhtalQAeHszgJrxcvYQ0PPnb0fEb4uI37EEAAosykr+OwrDaJQBrADwanfbSAWAKeZhBYBpJlUAuH9FmWODUwFgkl8FgPtZ3YM4skyDSzc+1So2gHNqWQHg7Q0AvxAR/yQi/jMMCnn4/oWIkHfwKirgfHw1NoAP3H9vXGuvbGOMO294dL7QoB5jAKl2NAMoYGkvWLF/vk9Y3w0wdBp2oGB6upKhodeq2S3G12OdXZ4YuMUTd3JMYeBlM4CqO+MO+j3Vc4gBzK+TU3t4lZVVwHpulk1luN30DuaNJOwsxgSz/Hl1Gxcby0VlWeWsOp44frzJsuQEIgaEajDnbXYyMRSbXd87D7KMenbMcR/BACYnkPbGFR/v1yPWW6/k5EjStvZaxHob+68pL2MAN9bWVnICURlUAdOAfg6TN8SoraQ6BoWlMWBG12Ncc4F3/t4IBnDOBpRQ8fAbU5m72WVmDuRjzB/rIZFzHKhsHXLoccs1xHVrzBta6nKMVOasPwAAIABJREFUAdTv1J6YATTzSRZf5Too9vp67zwmjQXXwN4cJNVIackAHoYKmP2wbDz7+r+p/ca5xneGxsdcR5ap9bjR6VYBgHMYe6Vt01cV8I3u3BtQ3v8SEU9mTiB/MyI+1LJ/dgKRA4gcQfQRoHtprhNIBYC9R20FgGkgVQBYnuEVAGZyqQCwO1TxoFoBYD9OKgCcHgewAsB5yGqOBmJezm9+aql6f6m12ZNdn2wA5QDyJyLiH7TVUxiY742IPxYRn2/DwPyWuWFgHnzgvo4BpD1ZbjujMqcwgLQBvHLxUlNVsTm2wxMjdl97K4W++6QrVix3qGCYERo78wYK5UunBJWn/9umSsb1bovK8GlZ7FYeKzBn6XzKVj39HmWgfBluxsOGji2MCWgGUH99QvTvyqtkR6g881iGiQVINnRU0/KmEDF6ZDa3NtOtLKX4gnpeah/ZDIaBcTvzm0BsA7gXfVsuXrkSFy+lcXC5vZosxQFMuSRjegc161klycdOILQBXOUmEMk2t/1S2VCjdHXx89ISMMSMrMIAKgwM65SHp1E/cT7M2Rxc97nvzG3HtQJdM2ZHONcZZE44mZJtJ/tTv1vOnD+LgK0NVYSG6D0yf553Y04gaXylsb2B0EglBlppS9oVehiX2EmO04M4gUwd76sygEPzKh8vlQGcBj4qA3h7q4A1Cn5fRPzVNv6fAJ4cQuwF3OydbSBogUIFgv4XEfHdEfGZaUOoYQzPnX7oFOKw9aoFqh3pOUoVitNooypdBXe5NWInaJJK1vcC67vVXLrzlgux2pBUJCnwL8EgNzZunAY3qu+pUwqlGM1fAyGV4UWWMQHpaUxPXctRKmCXSa9i/Z6DVi3uVucR+DYAqI3FJ3CWA0DJkp7BBszKj7L1e1TPuh/UTqfNr8u71uqi6BhCtRbb5zJUB46D/M5lbqhqqwHg7rW9vl/feCPOnT/fiPKNS8mDl7bq6xtrsdd6QjY/Inh4SQV8zdHFMcg5HoZATwkw81DBOVPKg+AiB0pzgZPK2oFaupFJ2z8EhR6X6rNVypgLAF32xPUj9gqI43oCwJIMSs/o4JSr+J2eY9/POB4IvEoqYL0/BAAtv5RfGtC+nznP12nzA3c+HgigUh59Lxkk3+wAsNTWCgD7K/nmzNcKAG9/ADh1HV41XQWAV1vbtPY2DwGfCgDXO3BaASA32WEv4VXAWQWA05atsZAvFQD2l+pUADhtTN2IVNUG8PpL+XZWAV9/6SWbwXOPP3a6UwvS0JwnZZ+WZRtmloqqVxmsk0Uzo3HVN0PgCjmGvFB+Vosq/IrZKzOEysdOGaqDJxXVmxSUw8uIgaMjhk9W58+f7xhA/e7yzIqpDH93ucpfamS3W0yX1bZ65tAurofKdlo5u5h9VH504MiN+qlmIoORs1TuF8uCl92rD1x/tY19aAbQAJeOEao7Vfeup/rYbVWdSjcSsP+Ob241YrhydTve8Y53NN/f9o53xP/5sz/bfF/bTLd8vH7hcoj5S+OEhmRrsUZmI7Mxk9J7fSupvpv8WnRQUtHxd/V/CSjkzOEYk1gqT+XMObmj8vvUisyL7LfGwCogM19EFtu3f4kpqVuXMZ8bWUwXsBIL4X1KbNXUBU4ODwf9LGuD8mZYp1wL4XGUq4CXjamSqnZnJ2kypAqmCtjrgGTktuYMZl+nXhLuK/3t8ot+bpTGZT5OVxm30GRP6haGwWGd/fKCB7Bk0DqHTcq8TVRqx9hcnpP/KmkPOl+H3i+MneoEskoH1XcSAHzyiUe6jd0gTrKhyowqYAMFqiAF3vyuF9MmWDMAoAGZ1L72AlZaxsZz3lbfahL7d3rPCYj0C+e1DuxRvUs1LIEe1ZgGL/5dZRAAehGRPSHVswZFqoMBlceTfnNatdV5qx2Wgf7mAJDty4EA29ovnAkdNVemYdEc8qDeadlOXnVHgOjvko/7lved0pPbQFBt8nsC9ncdTVfxnbjrZHe1meIAfuKTn2ye77Te5hcubceJk8kr+WJrJ5raNQ4A1zb3M3FjAHBItan+pRrP/c3FN1ch5oBg5XWkYFemvFgfj6ObFQCuZ4iRAJBjeEwtvEyGhw0AXRb7eIytcZ+znkOgkjLg2LnaXos55MErGVmcqg/nfEl+PZjq7RP3GuPa/jMG+G4EABwDQkleqc4NCK4AMK2E2dzK/4+1qwLAlRfhO/vFCgBb54QKAHtWogLAxcW3AsBh1XcFgO2xBZt1BYCLm2oFgKuBjAoAx+V2cN3AeBm3c4oGAL7tqSc61oux4Epx03gy5fVo9K41C9dEuW8j2GswmzUT++fYeBJuyTPWHrw6Bft3Tgje7KDnZhTpkOGrtbQg+3YP1Z+3gljNa0ZLdbcM9I43f+VvNkZsEtlPl+n60btYKmkygLwCj84VkgO9eZWXfx+6FcQDU++VnDl4TVuTV3vMdn3UZjKAZHkZY5C3suQOL2rfww8/3FRFfx998KHm+9bRI/HKK68035/57Gfj5fa7GcDXzl+IY8fTrYSXW5a43UpHVcCRtMj7Tsm5io6naI/J/GSt/qWTkWWucbKMHSHjWDqtT1k0RHqwzrnakKr/VZiaZXWYkt+YCqrJfwVqr6yuG67tYTCApfGybAyV+jSvN1n3EjuTxNPFaovtNham2kM1Xt/vPQNIB5Mh9o9jx99zG8CbgQHk2sRe5nhfkIFvE5oyido0t7sKuAQGsXZVBnDGWKlJewk0APDtT781rlxJN88JbHkTNAjIN09PNv3u71QF0k5vt1v0+muOBKYY9NYqVIELP7ctn8APVawMlGwwpQXGNoMGenrngQceaNqkd+T9q4/ydbu08eeBp/Wby1NennhS5Roo6Bk9dAkMVYbyMKAU6KVNXkm16gVSsuREdz/QFowLHe9sXlQXtfftZupNszUEes6DG066nioFy1bdDa4JpP3s7W9/eyd7vffQfcnz+ovPfSk+/3k5rkd8/otfjOMnTjTf17dSKJqXXjnbjcI27nP7//VRAMgwMENquHxz15gsqbuhRmn6Nwfl3sTzRSMvd4zlKC46A/FNuDGWVP/XYwFzmbPbUUAnY31CUFRqS57l3LAxy/pKv5XayPYPAZa8LzTPl6mB87HDwzUBYEkG+t13BC86waTUPIDw+w696Udurxka22Pja64NIOfi0PjiuparscfqM/Q785xy4Fm1nKH3Zs+lLKNla4x+U5vadboCwMPuvDskvwoAX3+96WoDngoAU8zBCgB75UJp86gAsF0hKwDsDzKZGrgCwMVdNGfN+WsFgPsRRwWA4yisqoDHZbQsRQMA3/H2pzoGkKpcs0N0BOCgpEewWDMzhT5BN84brU5C75nVE3vkvBm3Tifs/AYKPaNjCk/RZqH0e4kB9DP9Lu9ffaR6NkOpss0Y0sPXTid02lDd6SXLYLJkllQG1chqJ9WmZjglI8Ys9GZBtc4QA+g0llUeMLh04tYCa49Ny17spNXyqrMXYV4Ld+bMmU69LNk7zVve8pammPe9732Nh7Q+GjvnXnm1+f7Lv/Jv4vz5Flxfi07du3UsqX3Pv/F6XL2amMqDMIAc3MtO3Nx8hlTmZKZKLFUOBFne3Ph5TX9nM7OkriMbeLCpvvztMUZq6O08EDQ9Upu+hZe28xiT7WEzgKzH0Hgh81uKD5iPDeVDBnAKs0VNhts4xG7qd6ahw4fbU2Ifb0YGsOQFTDV43j95G1Yd97cDA8j5k/e32tdqmioDuOogucPf2wcAafdmdaXADm+o8EAUaDAoEjigOlFyVV6M2WvwI/s+qokJhLwQ006Pk4B3oxrgCXjYZtBgS39tZ0hPXS3aBIC2AaQa1wBQbXKdGQ+PHpnKi7aSXswMVBlsmiBY6VzXUoBi/T5mA0hvUcuQwbIZNkffjx9LHrr0lPZ39bX7O6mfUsgKydC2fFpsTp8+3Tx/9NFHm7+SCz1VP/Hxf948/9JXXow2ykvc/9ADcakNgG0V8LnXz4dVPYvAZ1wFvBupbss++YasUDPeRHMAWAIp9NAeA2GrqnoIAKdsVoepxppS56Hy+Lx0Ai8Bk7H+ykHhCqaF+4oYC2nDFxyKiAB2GWOld2lWMJSWsvKaoLKGwJ3TCzSVwryUxqrq0r13E3oBTwGA7ItVAGBpPE+ZU2Pj8iC/T5ljY2tYCQDysFYB4EF6qL5bAeCFC80oqACwAkAu2BUADsc2rAAwbRwVAE7bQCsAnCanPBUPFfl3g/4KAFeTbX0rSaABgE8//Za4ur3dPCADyADA9HSlEwHv0qVThvJqgi63+j1tGmbT5FDhPMTokfWjKlZ5aOAzSKtVtnr+4IMPNnVWHnnZyoenJ3p60gnEHsb+XfWkWtjqTZVDRs6boOpGj1lPTDOV9JrWd7XdeZHN1DPn4zwo29J9w1Rbua1qBx1lLGeVfV+rquUJ0u0Qk2fGlN7djO/40EMPdcGdLW85ejz77LNNm+RQs9t69B470QfifvDhh7u7gB0I+t//2jNx/ERiJKnWjxhnAEt3AY9N6CE2S7IYYmCdJxnABfBzQO8EMYBzWIrDYADH2LlSGcvqaAZwCtsxpA72eF8sZ6xHp/+ed9MYW8SclzEwQxv0UM1KDKDSOpa28iMDyOdDzF8uu2s3YSDoUt3ZVrfBclvlNpOxPj2MuTN9xKWUU+bEsjw5vvIIEZZZuy9XFfDczqnpGwlUANjGAawAsAJALrJcHyoA7JXV+UZaAWCSwJi6mAcdqoArAEwzjeOqAsAeQBpEVgBYRmzVCeRgSLaLA2jWiAygGTsNQl6V5kHJcCdiqOjgoGq99tprsdXGdNIEN6MoFsx5mIFTev3u57ar0//9XSDt7NkUPkT5iZHyx+8xPh/rbJZH5TmNGDSnIUNoBlBl+EYShmKRrMy+iQHMbR/1ntuV36RBx5U8viHD7Sh/s36qI+tJ9lHtp11jfhWc06r999x9dyMuy4JsocrmzSkeD5IV7QTpeKK8ZENJFnWtve7qXe95d+d4c8+pU3HkaHL+sA3g//WP/0lsHUkhYehcc70YwEV2pbfB4gZMlR5jvA0BQI69VRiGPRi65TZw+bjON8lVp/1hM4B5IOgp9WKMxRK7mGQxJafpacbuEx5iH4fsRvOSSyq6PE0/hxcbR6bPtohq/0ZrRDuUdyn0U6whSOZNEgaG9tyUM22Y+zkm56j52/qdxgB6PWvHVGUApy8FNSUksA8AakMvUfZUT/LaJKob/R5VpUfbu2E1wf07QRM3A71Hz+Mc3IjuJtjIvY6VnmDFiwqvjeN1a3L2sGrbMiHYUl3sXKJ22mFE3w0iCVoJtigDy0sAy2nUjlzdzVMewalUxbyGzn1B1TmDOzPWIMvY202x/egEY3nqGa/nc35y9rBTjPIiGFde+s31ULkPPJDiABrsNwDvWnRytCf4c889F//fp36tSXvxYjI/aMDYZsTW5pHmu/Ld3k519mdzc72Lj8bnqwCbsZUgnwf6/5jDjvq3FLuvpErMQePY/8fqexi/rwJk55ZLj2mCa+eTg+HDqNMYQOiZzMX7eP1eqV81lgliSmOjFMMzPSsjXANOAUCN9fxTAsxMU5Ltsv4ZqseydxjAXOlKLJXf12+LB7z0y1g75o6psf49jDE0t068wrpkLUJPb+ct0F9aK0rjQG26urOnnyoAnNs5NX0jgQoAW9tHj4cKABOIrgBwfwiTCgAPZ9WsALACwAoAU5ifnOmuAHDeGjOfK56X/+2eugGAT7318U7dKEaIMe58WqN6cogBtLDIfh3bSmyOJrzfE4NFtalPiGLWzBDR0cQnIjKAKsP5qW78nteD7CM3H7XValbXWWUY/KiOdtpQHc2W6R2zbPpbOn0ybA5vVHFbVYbLpEqWeTkPqpwZwsR159VzekbZeqFVmq3NpB7ye/rOG02Y1upg1d3MJ501eL2d31NeH/jA+5oyqPbZ2ev73nl87nOfi2d/8/km7eam2L6WGViL2MBVUD3D0sYMXNNY2j/tD5sB5JjxeMrtcEr9nrMb+QLCd8YYvzeDtXgzyhxbZOmRvSztQeruESV17NQ4gGK5yfaSAeQaRPtiPR9iAK0K5ngrMT9+NqW9Q2PUeRwmAzimqmZb7jQA6LaLCSyZN/hZzvouW9ckw+2rTUisygCOLSL196IEGgD4licf7UCPNuiSR+0QACSA8mAloHMcQNryCRwYFOk5gY5t7kqLIAMUa+FlPLscOEptyts96EnsxUdpxgBgKZag2uf81BYv/FzUDJAEpGg3x/Jy9Zfqs0ydYnBB1bbBFmPxUUXsDUr12G6v+3PdaTuoNhi0SZYeAwJ/VgHTY9vtIyhMsQS3mq5T2k5dr0DQbQxC56v6Xmw9hglId2X/1YJB1cl1dTuuXpVKuAeAQxtc/nzKZjm0QfWL93C5tFktpS+Vf6cCQAK6krkJgUQOqEvAYYpsx9Z/X29GljcfQzkQpbe86kDgSJDm9zhHCbwM/Hg4HTtIDI1nxg9UHss8oJN6tp1sYwLC77TRywFKaT4OAfi5c3JZFW92FfBU8W62h/R8reec8EFU8rt85WoFgFOFW9Ptk0AFgFebCdSd5HMGsALACgArADzclbMCwL0FG8AKAA8+vioAjHTV1R32qSrgg3V4AwCfePzhzstUrFnuzatnUxhAV6Vna67G2l5v7OxJmnvO8oRsp4tSjDteeaa8XCexL37Pz8RcUUXq53rmk7i+5/H1BADNHOqEb1noGdNSnU2VuE9uzkOOFS5bTBq9ni0vxvuzjEo3jCi90rr+ZE+ch94zkycWzkydGLhLF1PQ68cee6z5Kzbu3LlzzXexgY888kjzXXn49g/FcnQeKsNl+vSuupi5SFcDJkDNMXP85F2dDOisstcyeWfPvt6ogRvZrUXstRd9qCy3hWNKnsJvxmcZY+E+HmKQyPYO5TPGCN6INs9lZeamH2rDMlXXkM1gzqAfRH5bcLgosZKe1/zLNTFnLdlO14sOc7x5pKQ61vu56nhK/zPostLzNpH0/97JQP+fql5faM+S+bcMiB3WWCnJ4WYHgFT7LnqkL7aGV3Kqbywzris2A9Jvly43DnRVBTxlctQ0+yTQAMDHHn2oA4AadFanOrUWIqpsqfIkKCqpgI9s9KE+PJipblS+zM9puDDxd4IfqoAdsNn1pJetQITrRtUww6C4HQSIysuqS73HDYdglhsBF259lw2hAaLCy9ieTnnlGwNtzPSdtpgEXN5ICE4NrFRnq1lVnlW06tPHHk0Az9e4qW0MrG2289VXX43PfvazTVqBVi5CJc9rgtbLly92ALBTKd91d7fRMMj21tEUCPr8+Yux0UavWN9c7wBgSa2mcZFvctdjXg+ploc2Mar/clWg8srBCoGE638QAHNYMpi7Sc9Nr3rmqs5cFnmeeYB0p18GAHOZj9XzyFYfPmUqmMjnK99jeX7uOcq5v+zAsNOGVJoTbzy/daN0h7DHSlIBj1+rmI+tPNh0CeyWng3NqcMYu1P77DDKmpqHvYCHQhqpb3QnMj95VIwKAJdLuzKAU0djOV0FgDspzEgFgGvdTSAVAA5H8a8AcHEhGQNWpWWnAsD+8FcB4ME2MILZfUA1i7N5OCVNz6UCwOmyWjXlnQIAvy8i/kpE/K2I+HOtsESf/EBEfIe0eRHx8xHx3RHx4gxhNgDw0Uce7NR8WtDNetFomUGhhxhAl0snEDOAVF3qfXrRUn1mhwMygPydqmEzZMqLXquqB0+2DMbMMC/Ki165BoLe1MQ8kFEsbVzKmwyFy6aKmOyjVcD07HV5yovqpxKjQLm4zbndItXgrvPp06fjQ1/z25ou8jMxdE6rPF58MQ0deeh++ctf7oZRSS09dOK2FxsdRjZaT3COS6nsTz2QrvL7zGc+06uZ96QY7lW87G+llVxpQD8EQPLnc9mHktpxyryiim1Ileh8xhi/VcDVlDouSzO3zFVUiGTMh5hR1iOfX6o/WbSSPNV/1CaU6sn+2WyDLi8DEflvuVrO+Q0xfGYAh1R7ZC0T0z0cDXuIFRTmWRb0+rBVwOynUp8sG2tz5+TcvPK6HXRuzH1/DgAcOhSRAbS8qhNI3xN3AgDUrv1T0pRFxD8DAPzRiPhIRHynQFxE/LBMOiLia2cM1AYAPvLwA53dmwacF1yzYnrmcCi2P/AiTBUwF1yDKXsBC2h5EdYGQK9QbggMOqw8NOipfi6FXXAAY4Ib2tCpzrQztBqSQJG2aZYfvWRVL9eDmwvtGT1BBe5Kam3lS+9Z14mTn+pD1pkgMfc4JZBlWwV2fVvK+9///nji8WT75zuI1XfOV6rpF154ofldQNCqaqq53OdD40t57ez0QZ07Ff2Rowvy1/uNfeXJdC+y5Oayz79xKbxw6v3k9duHTjhyZLMYRHdssT/IZrPqu3yP/ernOSgZA4Qz5vXKSW8EACxtdssqPMYYlt7loS+BqeTtWlLNqj/sBcw0U2ThuchbZHInF9efAJB1Zvv8bn5dnNMvsx/j+jckz3wsT2ljnpdUwKX5NhcA9m06+DZ+q6qAN9qBx8MD5c12lfquhoFZeam7JV7UDvlvW2bvL0TEv2sBoIw+X46IPxoRP9O25D0R8esR8Tsi4pMTW1cBYGsDUwFgBYBTN82Jc2shPmQFgL3UKgBcDL1SAWAFgBUATl1VF9MdfOSsVu6NeutjEXEmIv6riPgFAMDfHRH/NCJ079ZrqMwXIuIHI+JvDlRQquJ0KWv66HLY58QA0uMsP7Eqob1s6Zyg02ruRau09LLdaD09ldaDnM4VUt9yAaSTgPLSO3Q8KF09d//99y84seg95csgx1Ql2vNVefu5nSXUHtdT5fqELwbQ+dFQV2msziYD6O/KlypllydVsL+bWRQIdXm5HJ2f6uDyzOSRkdR7botk9a53vavp6A9/+MNx9syrzXf3j+5qNvOm9lnOqq/z4FV5pWutckZ4qzWmZ8xGBYJ23vfdd19Th8cffzwePP1w8102h1ID6/Pyq6/pMtDmo5iCfdzE9Ez5l5xADpsBLDEjy07jnlBD73mMk1G9UxlArk0lNfkyNeUYG8u527NpvamH+icvU/9fX0uDjizWGDvWMIet91LOAPpdrpeeUzlTxjb1daaU+u9jDGDukDbWhrHfS7UYYgBXMQfwGl9u7fSntzoD6PVBayzH59B49xiqV8FNHyO3WkrZ9v03ESEV8OUMAIr5+7EMzKl9/7pVE3/PQGO/PyI+mv9WAWAPmioATJtgBYD77a8qANy/qqy66TunCgAXHY4qAFxtm64AsMYBXG3k3JxvPRkR/yYifm9E/GpbRTKAQwDwl1tm8HsHmlVkAE+dujv2WlWo2KicFRPrZDs7nnTJoAk4kZ3zadpXwb3++usdg8YrlMQs2h7wzJkz3a0TZtuUp0+pcnogs+bvCnFihwjGjcvj5blO9PgtAR2eornBkUV0Oaq705P5pHOFy6NdovLK1c40dicryviHeu6yaRdo2YsVdBgfxfsTO6qP6nP5UgrR4hh/Yt78UT9YhpKJbQBVJzKbdmJx3ZWvx4bsRJ988vEmy+eff77L4/wbF7r4h+9973ub3z/4wQ/Gdhvm4hOf+ESX9sxrZ+PCG1eaNGI7jhxJN4v4kwB6H7Jj6vTNw12Mgbkxpikvd256vU8meQrz5DFFm7Ykp/2KEBqPT5WR58dB089hY4fYDj5fKVRJFntjyPbP8ru2l2xNh0IMKVhzLmc5IzGI85itotnCFH4qhV8Zig+Xhw7J4/mRSc4ZxGVjcco4G+t/MYDLmCmOo1UYxlXmUmkeMJ98TK5SrzG55L/brrR056/XN4eBcV1z+9FlB6TmoL7djNsaB3Bu59zk6f/TiPjfpK1DPbXriZaQAcl/HBH/9woq4LzZjQ3gGAAUCPAmT0CgwUmP3xIA3FxLHp1UoVqFqeeKPeeN7ezZswvqUv2uDd+TQgDFE1fPveAKeDhPgrEhAEgVaa7CpvpJZdFRg2odgzXGMaTThn9XWa4H1cVqm1W4/sv7c912/RUYY5w/3k6i3xWs2SD6iSeeCMdEVH2tulfsv3/7KzpTpPT6CJRbhupfA0fV1zIS4HMabWCWL6+Ns4NQAsOpvwXmDSg3jxztQKfHiPJ8+VVZN0RTrp1/CAD1W3435s5Ofwf0nDl8MwJAAoaxjZnqxlsJALqPSgAs37SHwOCqLOMQAHCdaJdpAJjmfH/vNNPkbaE6uQRA9Izt5to0BgANEFzmMlA09NsyAJTXbc5cqgBwmrQY4q8UC1CgPo/TuGye5GBQaasTyLS+uNVSyTbvrVmlpfL9dET89Yj4UusE8kci4h+16WTs9cwqTiAVAPZ2cRUAJllUADjPvHgV1qICwEVWrQLAxRV/WTgXpqwAMEkjl8ObzQBWAHj9Yde8Vfr61+d6lkAVsMpRGJhvacPAiNL5obbwD8+oRMMA3n/qnthr799K13kl4tHMFBlAMW0l9YyYHbM7ZFuutaoOMW1mjfTXmx/VwWIAGW5GdRDz5fxUNhlAs2xivHLjZzKHOagz25QbY+cnfDo9kPkUE1Yy6nfdyAww/I3abaZO71u+VqvKKcMsnJg7xiC0c4zYkPx6NL2vOH/6fOADH+jY2pdffrnrE6mC/9UnP9GkMZtIpxPVyywqnUrEDLqtDHnjtKoLbyxxGJgvfvGLXdmKA6i+1cd9qX7ebc3s1D+WxWvnz8X5c+nKOp2arWLrVSRSu81XAQ+xSFM2zynzaVUAyDGTjz/mqe/uh5udAczbMdYu/15Sd6VxMBwPb6hvOAeHVKScwyUnEOVd9tBNXrw5wBir56J2wmFp9l/X5rHvECFs45h6nfUtAaChNW/KGO/6saqAJ4lrShzAVRhAzqfqBDKpK26LRDkAdCBosYAMBJ2CuU37NADwgfvvLQJAAxPaeUmtx0WGdm8lALjXxXHrPUEFGgxuGtuBw3KWAAAgAElEQVS0y/JxieZe2gcfTMGB/dFvLoP2iXpmFaPt3PSO6yAGy5s+PWv1zABQ6f0bASRtBL3gMiYg7W+4QXEDcx70AqQKVc+dxmybVLuWiwCZ85Oq1nZ7+m55uB+k3n3nO9/ZiEwqYD+Xh++zzz7bPNf3Y0ePLMiWm4ne6eL2bW4u9HFJDUbVP70t77rrRFOGwKzfO3r8RAc6LW/1nQGg6mFZvH7hjTj32utNHleubHcqkkW2bP65j17o06bGvFSrAkAu5nmJPLjotx4E93Ht+Jzvr2oDOK/V+8FZDoLGQJHLy8Eun0/Ng3WnNyUPNDwIeLw387INBJ2X5Xrpucc8TUsI4PNr2FQf2XjlwFZ1YDBzHnL6/LQ27b/zegwA5mMm789FU4PFoNFT+76qgKdJamiVGvPkZu5j86ICwGl9UVOVJVABYBvGoQLACgAPskhUAJikVwFgkoM3+QoA57O3q8yl0kHozVYBVwB4kBV12rvzqYBp+d4pqRoA+NCDpxYYQLNoZgB1QrZqViwVJ9YyBrA5bbYqYHq4CmzxajmrN+XsYAaQ8fLIALpuqoMdUxRbzmnMpjH+nsqj6th56JnrZZWmyh2KCbhM7csFSOmch5g8l0F1MFlEypC3r7hOzEPsqVXA/l3tkNOFPl/5ylc6Nk3MqPtNZZw/l0JGLhi/t+o1qsxUXzp7kBmkt7HyojNOuqbtaldG5/V4rVf9+pnkfO71N5q0GlPun0tXLncMoJhaG+STDbl2bbVpvwqTNHUhWGXTGlM/k83Rd9f/eqqAp7bX6UoynfpsiOmY8nysnhrvdF5ynWiewnHde2wu3tPLdhZNXFp8M+S1SwDY95/K6IFiDxb7GzaGxuoQI1Tqj2UAKIH1ygCOjaOD/M5Vag7rN2X8ez3YSWqU6gV8kI66g99tAODDp+9f8L71QmdVqRZSe54OAUDZk+ULpBYxXwUnEFMCb5K9vVIFmqzOZR0YMNnPtXg7rcCkgZNBq/LyAqi0Xvhp96Z8SwCQ19FRTeRxQnus/LvTuDyqzFUn15/3DLt9AnYOvyLV99AG5s3BIDqplJLdpv5y86D6aXcngbMS8NAzqsP9XfU14KQcrbZXeQSka21AXaW1HAX0XKZBe6P63071UZ+5H3b2BFQTMJS6W16/qc7cMOfbAA7N8aGNdi5YXAUAUq1dWvR5SFD+VLXn4SLy9q2qAp7bDqpAh+ow9Tnn15RNcNm6rXoR4NFD3+/xkLPXHlyUrjR/qAJ2XmPq2Hyu+TCTh37JQ4HovSk2q6W+Ghu3SQXcS27oTuGlsq02gJMgQwkALrvDmetcqYC8v5sxmZbHCgAn9UhNlEugAsCtFGeODGAFgP0NKBUATls05gIn5VoBYFm2uSxXkW0FgGXZVgC4eNvLtNm9WqoKAFeT25y3VtMFzSnh9k7bAMDHHn2oO/XqVGyWymwUGUAGY5ZoyKyVroVzHEC9Z7WoTrenTukWuzQZzQDquePWOS+BMZ9q6QQiwPbQQw91ediz1eBN/6f3KlWXZjNVdzNdBoBKRy9hsl88+Zs9oLrUQ4WncOXvtpDVo8OHf5ca12VQTa4ymIfb4rrrN38X08qAs9481b5LF5N3LR04yJKahSMwocez0rK/nRevw3rttaSKZqzHWN9YUPnr98ZLu70mUOPM3tGxvhZvvJ4CVssp6MoVs5ZJuokt2W8cPzZNS6fnUn8te7asjFVAypA6r2RqQNlfTxXw3HbkjNPY/z3nx/qLvw8B5bE86Gjmenke6V3/rjm+u7PdZLdMtjnbSbU826V0ZGvNuA3FgrN5A/NTHkMq3LzducyX9SEBIL3sx2TJ36sTyDRpDQFA908OxpXrHEZW/deGrKwM4LQuqakyCTQA8PHHTo8CQIcnIQCkWkQLaw4A9fuRjc2mSL1HZo2euwaAmhi2WSMAdJ0JAJXfww+nu2QFLA0uCQCpAqKK2PfR0n6NN4/QS9jAhOpPqo5pn+d60gNR352fAKDzEwA2cLLaWvWhjZI3PsmxvxO3v1OZQZW5qFC1RQ/otfaSXdpJus70zPZGqL96zo3P3xc3uP4e1RSnPHljuxwFgna7rTrW7yfv1vBLJgAeX5tHtjoAKE9ip3c9JZOh2xrmALQx9d2YKi0vay5wSov94vnV/18IT9K6iOo3Blq/2VTACwABSKckx7myXQUASj48vC0DgE14qfYmEIZ+Yh+V+pdzK23maR4oD/eVAj4vi2KjITAEAPP8CTKTqjpJnfmXbizJ+4bv2QN5zs5YAeA0ac0BgO6TOf1RAWC0FMK0/qip9kugAsB2xlUA2IfmqQBwfuy5CgD7xeWwwXUFgP145AGsAsDFDW2Z6UA+JuceQlYBDxUAriK1ee9UFfA8eeWpGwD4lscSk+bTq9WpjDdnVklG/GSbzEToxJszgHrfbJvyNJsj9s4qYL0nY3+DDjNBfiYWyWVzgiv4sz2GpUKUupDAhYwdY/ExQLRO/vm9uqqj269FgqpVM1qqh50ZeD1dLjfLlPcNWzVqVbfSWNXOWHV5zEB6Zjs/MgT0bLZziNhUp1HdNltXR8tWsjAzp3Suv+pR8gJWGpfDtlIdvtYGaVbeZPt4dZ7arN/dbjrKSOZ+T/W8fDndC2zWT1F7tvfStKeTxJAazGNmyKh+ePokJlOfMou1+ObWVmK6yQQNbTLdOAZrxNxKDGBiPlOdOLYvXUry0WdrKznHKP/tdEfowlV6YzIaauuwjFb/pTen6PNgUFw6TOTXAU4tlWPO5XH+c9w6LVl4qmQ17i2/Lr4l1g/ViUy/65irifO6D5kBuB+HxtTQ2NIQWXSYSnNlUe1oxn6eyrGr+8y7uEtzbxkAW+UwNXVMON2bBQBTX6RaqJ15W8ns7h8ri++pDdULeG7P1/SUQAWAR1JwZC8IFQAmO6gKACsAvJ5LZQWA/WZOsEiZl0EbzS3291AFgNNGbQWA0+R0s6eqDODBeqgBgE898Wh3EhGzY4bJDKDAgE/IttEzaKJDAW2UXC3a9JnZEetkBlDvOw6g3jFD5tM0w7mQyZMThVk0pZnKAKqOrnOJAVT7zWRqAaYjiRcNyYNXutE+T23QO25rzpqZkRODaJDF20u4GfCKNTIYjBWo9GyT3jEDyNtEVHfbAJp503uWN+tJAKg6lupRiqUoNnWnjUswJEfLkL+zDLWH9pqWY89ORuy0lh88QZN5K02JuWrEa9dSWB1+clsu/n+jvUlibDryxL8OG8Cmf9r/0xaQbCDHBmNH5sxUwwy08Tc3NzcWGExufHl5PAiNteOgv/eqzD4mCU0iaedJu6hljFleJ2oOqKlwOl5N6fWNrD9tjjUOPRadl+YF45t63dTcoGxp15vXcVl7LKPS2B6yK5QMWXbOME1hqEf7tjKAoyJSgjEVcJMmswMmWzoUrsfrXmUAqw3gpIG4JFEDAJ9+6xPhzUgOCflCx+DDWlQ5aL1IMf4cFyAGQfYCqfysGtb7BnsEXKWAzsrLwIt30AocWK1JkEYnCW8GrGfu+JCDKdWHataSc4XKNjjhxmJQ601GvzE4tdrB3wx8Sht0ruokGNR7AnTeZKRCt4wIkJo7nts4gHY6yQ3lCeqo+mZ/M96gFzCq2q62wINX8XGTdNkqi+B6EWglUCC5Or1B65XtXVnNF4f0sg3TfTd1wuy1TgFDAHAo8G+SSXqrtLnz2UZm8Z0DMs4HfeeBwWOAwNbAuaSuXAbuSvN5qpymMikLwBfq7JIzi/IsXatG2VK+JZUh5aL8PG7Vb3a68DzRIYjB0RfiA7YV0RrFKAZeK8jSuS3stwTGkzp+6FOqv9IOgYFleTFgeilf9tfYoWm4wvPicA6ZXwyNnSF5TB2TU9JNHbdT8hpK47uA+TsBer5GLOvzHNh7PlcV8EF6qL5bAWAbB9BDgWxaBYDRgMkKANPoIOirANAymXbVVwWAFQDmEqgAcB4DmB8QKwNYGcCDQtgGAL7r6ae0vTV5ibkiI6dnZKvolCFmhydgT2g/4ylcz8yU6YRNJwg6V+SOJHrH+aoejuGnvJ2f3qcaWXVmPEOlJRPJ+GA+7ZPJpPML28C4YSVVptutPM1cieXzc4HLEnNDlRJlV4oH55Of/lpuCpdilkHOMWY2JEvnJ2Zw+8rlpo+psjYLKcavt8vqnV+kqqb6zLIh22EZNnm0eEDtt4zEsPg995N+o6ra9SdDqzLcRocKunDhUsRmCt6dbyClsUjHiTmThSrgnDUp5bN4tVfvpOJTfpENzDIqMYB8z+p6hsbp4ie2N6ooS82LPC/La2zTncuMLEs/xORQtTrEDvm5nEF8U8ac/tOYdB4aU70T0Vpn9mCHM80BmlW43hrPbp9+93ez42TeqeZV+zx3VQfGHpzaBtVhtzWnSGNo8c0h1nbM1CHvr7n93YyjFeJwuval8lapQy7HuazhYZQ51pdmABevges7coihHZujek//lO5quimpxgEc64z6e1ECDQB8zzue7u4C1kbLO4ANAA1+9Buv/vKCw9hXBIL+vVFBtteVafDaNlDfCQB5xZjKZkw9LdhetPWb0xIAEoQZVOgZb/rwd9WNC7XLK6lyyAbqnZK6kQDKiz69eT1xvRnn8fhUL6qfGJjZ5amMXAX86quvdpuMVOtun/J3+yTvK5cvNYOAm67LY5uUxu8JcPcxzXoAS6DqDVHPCABdjurjPBhc3GNAv9NT2iNV+XrM+FCiv29cTsGhKQvWmW0pAe4pa4GvtMvTDoFBjhnaTJYAoPPca+fDUJp8gyAAJLB3eZwPpQ2XKr9l4HmKfIY29BLQdVr/xtiS4wBwb+HGlNK84zOWRU9ib8JHjmx1awhjfHpc6n3LU3PDedNkwfNShxkfQgm6eTDmujhXrssOFcvGJftmP+BbfHNO4OEu3wMAwKFxc1BAdjMCQN8vPVS3IQBYklE+ryoATFKqTiBzVpX9aSsAbO2wFtkC3z/bO4FUAJicTQh0DLoqAOxBdT5OKgDsF50KAKcv1gmst5scHDuGGL4hZq0CwLLMDwo4p/RkBYBTpHSwNBUAHkx+DQB87zuf7k6y6fqtFFvMjAPtwOg4wJM8b+Mg02e2kPHylLfVf2QA9dzMGVXBPnGL/aO3HmPS5Wprnd6dhxZNXk1WcgggAPTiQOeLfOFlGrKgagMdQ+hBrToYLKk8Mzb0GHY9JR/WmWrYXj2WgKqukHOfiVXjtXYuT0yeVcAeMmRAh06p9A4mG+t+pbpL+botVJlRHWfGRGVTfUkG0PLMHX2Uv9K9dDbFjVQZJceV3AHAQHXOVClF5F+mCp7KADKPnauJyczrN8QMeOyTjaJ5Bq/pI2vrMlTHEltmkKq/B90YS0yFyycAJFue/94wya3OdkhlWWIzF2Sb3cDhGImaV2T+3HabJmisev40N4S0KIymKIytyWgFbgdVx2Q4h9jOIZlbba2xmHuDD6mAKZfU34ujXv9fVEnOmRUp7d61eduu6z7Ul3kNVhmDNyMDuFmIDDBWz2Vt97v+W1XAlQGcP3sX32gA4Pve9fbO7u/s2bMLHqVKroWQtlS0+WKA4twbVguhJ38eQJrgxqBBA9qD22poPaO3Xu/N19upCRTkamvVcQhslBZPbjhUb1JFzIWMINcbgp+pPgaf9FymNyydTQjoLBeCXdWH3rc5CycbQNocOg+95zIlw6vbCdjnKsp8A+YiRBWWynXfu0+o7tLvVKVZXiqPi1aziUCVrfLcV5Kb60cAaHkq3YX2fmD2u+rJ8gh6VgGA3CWH2BVOJaqa83pYXWOA5fykAuaiXlrgWbbzVTqaVrAvVIZkRWAypDZdZaPNF5x8Q9P/S20iyCRAch3y99h/Q8Bp2WbKtUjlWUYCgLQHVjmaiwyuzsMNQbXL4/uWs+dfadx5zuTrYw64p4QZItvusgj6ONYWAf/+rWIV9e9BAODQAWMqMFy22Y0Bq8MAmXM3263N+XeWD8mI88dzpVk3rzbhqqoN4NzOqekbCVQA2K66FQAu3hbg+VEB4NBNIIvUSgWAacRUAJjkwPFQAWAPhKYcplY5mFQAGOfvREwzj4u+EyW0vM2dCtjMjRhAL1420m8C/LaxrHT65SnUzIxYPJ+W6cjgU6+8U8mm8Qo2xh00e0VvUXv+qlyyhTSKH2MAqXIunZzJRJDB8Xe1k+pnXpuWqzioLlI7GTS6ZDTO+G5UAVNVnat9eSJUDESqwJ2H6stgtztXt5um0/GDsuBQ6VgqeDGqf0sMINXnHkcql+XkrKPk4L7U4s0Ydl7M2X7K7ciJu5qqqs9dHj2eSyrgIRZpaHqUgpssYyo4fkpOICUWTvq5qQwgy6baXW21Y5T/aq46Lqbkcz0ZwNw0YgoALLEcZDVzJys6c7m/aJ5RkiFj70lenv+8dtCsn8ZhaX5JbvRO59w0yPP4Ux6Usx1QFJev5L1dmnfLGECbJAwxgM4vVz8vU7UvY5uWbRurqoBL60upDhUAlkMr5eO8MoBVBXxQSNsAwPe/+x3xyiuvNHlpo/Ui48VPG4sXem3KXIS8iWsz9mLDhdIAJA+j4MFMVWDuicrF3oCHwMTlqQ65+owLS765GJAJNDk/t0nv8XYMA6vc1s2y4d2iDIjsm0kIwqgKVd1ze0fJ2WBXYMv5KWSOAbHs/eiFbSDEe3ctN73v/GgDaDBF9TrtPHP1NMPmWF7Og6rXJvTOdgo1ozo+9NBDzfcnn3yyC/vDDYny8kb6C7/wC6FDiD6qs9tFu8Y3tpPto+Tl/KQGdx7q71wtr7p5w2cf63t/qFC92xAzhZnFjSnfpMj40ITA45Ybvt/dREDroU1vTAVML3OaZni+Sm6SjT4Cha4ngYL60CFHjh7tr0YcAo6lRacUTkjpqLam+UYpjyJIRh58h4xPif1RXh5fGkcGx6qD5zRNRFxPzgONJx5SLF+Xp999SD579lwcOZLug+ZBSfKmzWA/DvZLIA8SXDqUsq2La2ECDb6TmuONayHtAnN7wFKfFJ+tLao2l82LfPxPKWMVADgl3+uVZugQt762/9q+w5SV8mojBVUV8PXq3Ns83woA22N3BYB9wOcKAB0Vc3H2VwA47CRSAWAFgASaQ2B9yn5aAeAUKaW5WAHgNFndiqm+LyL+QES8R9quiPiliPieiHgGjTkWET8QEd8h4ioifj4ivjsiXpzY4AYAfuA974yXX365eUUsSc6e6ARNta7zFlBg7DFPXHoP+xQ+xACmIK2J0aFaZ2gBKTGAOsmTafBCRLUf2RHm4bpSDcs4YHROYBn0suwYnc3EAKjNZgbE3FF97rSUs1k6PbO8GMNPDCBV7WYzzHjlKmdvxjlL8tCDDzT1s7qcalOlpaOJ5S9GhAtyzrZIxgwO/S3f9HubV5W35aX6+j2zcMqT9TTrp2fut09+8pPxhS98ocnPVweKvVnf0rBfHC/K12rPixcvdT4cvG6PJ3WqrUssm+8b5jhcBgDJ1owxgM6TV8FNYQD9HtWsGqscr0pDplnj0Oyxxgu9pofsXj1/hhg5yoTOKC6bTB9VxAyWvGx9GmNJ9O4YA0hmWmPYc0b1sQy47jgwvZ55jGqsUf3s+nsuamxZtfziiy93DKDe8Xqi/Pw9v8M7yXlREnTM8G8KhJ2PKfeR/tLZgwfZ0tiVbPt+3R9gelm/+Lc8EPRYf92M9nlT2jk1zRgDyL7Kv08tg+lcnuRer4JbRYK3xjv/OCJ+MiJ+WWZbEfFXIuIDEfE+Xf/aNuFHI+IjEfGdAnIR8cNaDyLiayc2sQLAo8LNvV2cFu8KABvPsuZTAWA/kyoA3L+qVAB4vBFKBYBpbJQOMhUALsrloAxnBYD9OnQnOYHIoOqliPj6iPh46/ot2u6PRsTPtCIRW/jrEfE7IuKTE0BgAwA/+N53xYsvJtKQNoA+8ep0zNAIPhXrpEzbMw9MpmXsMtofOS2DC6v8kpFzd/JsLolPbCFtB2lnQ0cTsh20N/P3hk1qrat9ktf/6fhhWz7aBqnurgeNzckyWgaSEZkPv8eQELRVYngcP9czszi6qcX1e/TRRxtZiD0zgyaWwfVgDELl5TAwlqfydbsld8rWZagdjO1nJuWRRx5psnn88cfjgQcSsygZbbR2L3rHbSS7yGdUG1pGZk6V31e+8pX41V/91Sbvz372s81f1XdtM4F21dHsqZ5bRnrvwsU25E1rriRVyZGt9B+9d/XqTjuOBP77q9s6GayXL7wfY+pyJwgVwmd8nwxgafPMy3LdmB/HK51P3H8Kv8QYi7RdNSOl/PLQKItMUc8a5WtKzqCTYWPcSL1XYgCHwAGfDzGHy4AF6695UApb5LZwHHFNk7zpRGV52YZQY89y+/znv9AxgNvbYgDTWFM9uAb6GVm4nAXMZawlqsQqlxhajg2PPeZHuaQ+SevprE+1AVwQ1xgDOLZm5P3j/w+x3ASA9Sq4WSP3lk78joj4jYj4YET8+4j43RHxTyPiVEQkK+/0kc7sByPibxZaq50z7Z7pc3dEPPdb3vfueOGFF5oHjMPmzUIevN746bygzZxqDW86BHE03vdEoFpIZZRAFCeV30vG6omdotpDaXPPVuXJOzi9CGtB5x3Crh/vGC45uagOY96lfk9/CaBcZwEUfy/FQRTQJrC0DBhIWW33u1bl5h6Profabxko37tOnmhkx35w2nzD9MYnWbm8U6dOhUGnHTy0sRpgqD7HYAjv+jNWIDdzqskIDF1nleeDyS/9kiwgkmPICy+fab7T2J6xKiVneosrbfLSTIM+v5vT8tDZolO7HemvASvMo6WPOHZTeeX7P3MVcL5R5Js1f2cZHmv8y8MY1dMeXwQ6es/9zUMTAcZQg70u0GObHt18z31fAm65jPh/mnJM7QseaAjC1E7OY68lJWc1PfO6oO80w/BYdD2feeYZHBx3Y6MNAsyyqXLvx1x/48dQ2zR8fB9yPrY4n/3+ECDx7xxHV9qYmlPl2oznLEr6mAp4Tt63YtpBeV/rNSls11QGcNmc0G/Kp8YBvBVHzPw66zj5f8gUKiJ+Z/u6mL8fywCdfvrXEfHPWnvBvKTvj4iP5g8rAEzAQh9tDhUAbnaAoALA+ZO1AsB0wwkPJvp/BYCJbSOwV7iYMQawAsD5c/BGvlEB4I2U9mJZd4oKWLZ+39yCv+dGAKBsBsUMfm+hW4oMIFXAWqTJICkPMVN0VPBCzgWepxUyB4xNVgpBIYBB1oEsWnPazNStZABdJhk3P1M61q+kllY9CfxUnkJmmD1iLDuqbH36yuXLhcCqVdWD8cYsG7FiZiXNSIhptYwYbocx8+ih6zLIhip/3ppBJjKu9epzy9ZykerVqtzHHnusC+GiMhiuxvWnnSQB8/bFdE2b0uXOCQQB+p0MoZk+hYyxrBWaSDLRx30iR4//559/ogPr7gPlRYcPhz555ZVXmyR33907Q+zsRhw/lsK98MaYqymqfqr/xvoCczdnicsZLqrl+NtcBrDEnKleuQMUnanym1WclmNKfWEGr6RWdH9S1v7OGJ1OxxBBlNsyJo/jQe1km6YyJixL77MttFXMHSUYcolsKK9uVJqc2dR8cRm/9mu/1n3f2dkLkmRkRy2jRTawHXNLHEJMIpcYQLY7H2tj6RkaaeoYn+sEMjXfWzXdEADc201mJnM+ZnpLcy1nBCsDeGfEAZRjx++PiK+LiM9jMK2iAs7HYhcGxl7A3hT11ypG2RERAA6phnq1Rg80/J6AC1WP/s5r0wgaaM/kxVILcAl0lgIm85Sdx0rzBiVVoW2D7GWqtPTwtQwMZKdOZoM7eV564kqObreAjL9bRlSvC3gZ4HGRVr4GwfQupLei2yQ17Vvf+tamygJ1DgRdUqMTRDJw7pe//OUOWPEaLctBsnT9VIf77kpG8bmNZi63fKNyf0vO9uZVvgaAfl9jYHcteVsLID777LPN9+ee87koeQx7w7entADmhQspRuEeNtqtzUUVrd+7vLs8GGupPX5GlaueDW3KAoAl9RnBAfOk6jLfDJSOoMm/0wxC46Wkdqetnt/j/FnW1vxe3WZT2k4Bx/N8CQDzNYRyIIBVfcY8W0tzMrc/5MGLcT49Vj1/NMY1T/VhAHwGerYMdWhymz71qU911RAAtApYDN/mZrInLck27/8hkD8VBNMDnONuaN2a6pnN91vP0+5RaQxPXSdvh3RDAHC3vTxh6Mq90vOhsS455WNA/69ewLfDCCq3QezmD0XEt0XEf9ja/zGlqBE5gfyRiPhH7Q/vasPEzHICUSDoCgClXU9OKBUA9jcnVAC4ODmHNugSAz60MXTMdQWACyydJV0B4P4NoQLAm3ejrwDwzeub21kF/COth6/YP8b+U7gXxQXUR6rhb2nDwOguQAFGfT48sUsaBvA973g6zpxJqjKyBPaq5A0V+WmPbBK9SF2+2S3GAdQCT2N0XqvGWycMyOg5yxOrGY/mBor2tFVyMshvL/F7yveJJ55oqvq+9ym6ToS8W61KVF72+Pvpn/7p4mZFdTCZG8a4s8xyZw3WQ2WL7Xr11dQPdHBQGa7nu9/97i4mHh04LG8yMLmn8bW9pOJ0uWJCzIboPatZ+Z5YRP9fv5vto6qaV8/tXE4RisggkfkpOSqI8Xv44Yeb9+TBa9ZKDieuk5k8MTSXdxI7R2ZX3933kuFv/uZvNmkEYPXhFWDy7P7yl5PTk5wgjx5JHpsae53H7LX9S0uJdbPcSwCQv5XYuSEVcMlZgP3GuU2WZ8gJhAztELOes5ZDDGAuA89XMhfuP5qT5PMk1xawvFwF7DLY7jHWiapvpXWdVE+vSWYv9YxrnVlnjWs6fpj5o1mL6yEVsOUvBtBewPKytVqPMirVv6Tiy+dSz5xSGv13qhCHGMCpYLJcQpozc/piKJ/b5fkQALy216uASwfH0jONkaGxXWLNayDo22UU7W9HWQcV8V0R8eNtckSoOkcAACAASURBVAeCFgvIQNBpdxv/NADw3W9/W5w9mzwrafemkCPePL3oaVH0QKRdjEACw1ToPQ1wqzFzFbDTElQoX4ZrUR705qMKmKohpcmvVdPvDOHgxVllOLSL1KMf+tCHmja+7W1va/5qI7D9mDYIhyVhsOUSFa93CYAN9uTJag/rX/mVX+l65Ku/+qsbsKmPQ7hItgY62qQMrFRfLxbqBwI0vZ97URNYdmyTPJNbTOP3cyN9V07yLAXwdp/qL9WRbnejdttN6j/aZZY2C71jwCY5MzzJgw8+2Lyidntjtvex/n/0pIZt6ivm4Q2WfeV26Jn7Uurij39ckZQinnv+xTh5Il1/pg3+3PmLzfdrrRcn674MAHJ85eMjBzROSxVwCTDzmb6XVMBjAPD+++8Pz2MdZtwGgiralY4BqxxU5HaejVqqPYypjwk4LUv1WX5YpAlC3qZVAKDys7ySHW5yTNHn5MlkpkBVr9cPzTmPE5XrA4j+0l5W78s8xPL69KefCd37m+bjXmxtJbXvzs5u53We2wI24wy2sJxfkgEPArlZgBxHSs4jix7ui2pnt3/M6YRjvvh9ZhiY0fxu8QSDTH8TknfxM7SGMI++n/rwS7mddxo7nTlLvQruFh9Db1b1KwCsAHDf2KsAsAJAg5PSBsZNrALAdE6vADCNlIOyi2/WRniQcisAPIj0Dvbu7awCPphkpr3dAMC3Pv7IgkrWKhIb40sNaDWSTtVm9TTweR0ZDcxVvN4xi8X4WzqRm7nRXzNSDELtSaX3GJPO5akeZCVfekkxspOqTx9tTGfOJFbzXe98R8ey6VT/2plXmudf9VVfFb/1t/7W5rtP/VcuXVxwsnCbdjOngNJCx2fLPB6ndc14qhux2M4tY2768VbuT1FSnYzlw2v2NHas5pNzyE/8xE80rzNG5Bvb1+LkiT7gtH5//Q1bXkScuOvEgjq8U8dHG3kaFcq9Jv2TCCOyerl3PVks2sVpDNNrl170Hvv+/f577u4cndQ3ntOaa2bWJE/PQbOFrFfO9lIDsHk0saclZmOMTWSf5WwnGS/Wk2puv0820WUqHRlhOk65rlYBy5v34rnkva71w+sfVcC8mtHrkWQsRyR9xPJvt8yn/r+1kRhAqvSuzKTeVplLuVNHPlfy/9+IdWpsbt4qv9OMgXX2OCNrK7lutcHA8/nhd6eohan2zb9XBvDO8AK+nvNjHwDURuAF0Oo3qeQMADVoaTdmGzltQrmqhpsMNy0CQHq+Sg1D2zI1XGV50dfvrIcXYoHCPJCt3nGw4suXLjY3Vugjr9F3PP1U810qYNvWeRJfvaKAwelUrzz8fW2tvxliSAXMjroRC+sqG8TcwTS3jLnp59ZnaDEdy0fjjypge3drDFjNJ/DzMz/TXqqzdTReey2ZQNhr+OiRjXjLW97SPHvms5+Po0dTKBmNk85Te21jX/iYtfXktey6e+FfX+tD4TTltLfcENAtqPARW4QgzHlTRemD0NENjuH+vJwHSucmpvx0eCqZNFxVDJ32c9+998Qbl5LKfJAFKbg6DoWPcr4Eg3rGutGW0jIoAUC9081prCsMFWSQLK/xa9vJXkvPGDKKfeL8/LvGiwGzzBV223VDUj6y1Y8N1/Ny279jY5VymJrW6SoAnCuxg6fn+Fw4HMGMZJn5CGswdLjND0jp/1UFXBnAg43fCgBbJ5AKAMsDaS6gm5t+leG7CgNYAeAiS1cBYH//dwWAq8zC+o4lUAHgmzcWKgA8mOwbAPiWxx7umC6xbGZKzABSBazNlydrMyk6KZsZ7L3hdCdmYj8YVJXXv3EjovG+GQydtn0Kpycxm61TuNk+ejzaceDca2dDxvD6fOlLX4rv+EPf3nyXCnnfPZ27O137BGZ678F0ol92Op+j8jpYt6W3bwTYmlvG3PSryGEVAKgxoliI+qhPzfppXHrMyGHH3sM/8ZM/1alOXUfFEbRn1kMPnooXXznb/HT33Sc7b/EAU+z3GtVRqxpm3XMGMGe09P+S97DypYrUaTxnGLdzfa83sdC8ZYByq4PFGlkdallo7isAvD5i+Xm13sVLKZ7iEQVRbB0fSgxg3rdun+SfG70v69MhdpEqMcvEz+iExPLUznyMSuNx97F0TSLV7lob6Nzj717TPvOZz/TOZ+0Vlcpjc329W/fYrktIM2XcrzKXyAAOyZTPb4SmYkpbb8U0HZPfMvMaH5yXa3ACmaLulQxobpHvN1UFvH+UVAB4sJlTAWCrqunUbhUALoyouZvQ3PSrDN8KACsApD0WgXMFgL2KvgLAVVaX6e9UADhdVtcrZQWAB5NsAwCffPT0QmgXh0coMYA6/fqUwwvlVY2cAdRptOQEwvd0OrcBur6bkWMoBoYt6Yztwc5p0bfdot9XGWYzHj79UDz//PONpJ5++un4j77xGzqpMfxIcwKLPhSN/m8GkDaA+cmsxHTcCCB0M5ZxI+q0CgDUO7wiz6FmVF/HChQD6HF04cp2Z9/1E//zP2i6WAyinY30/zYc4YKhf8MitTEES8BEz/z75sbagr1gLjvVmeyX553mgFkoMQZdWJnW8YCxPE8e7Rl0zQ3PR81Nz2/NE5fj9mvOmS383Oc+FzttwLH7T93XOX7p971sBWYbVF8yIjnDKVnlTKCe5Uy6/5/Lw+8OMe/URHjdEOtPBzSVJ1bwofsfaPqYbKG0D15P1I7c2ebTz3ymm/oSg80d2SdkdC7DfnLKss12TWFIlefcmz1uxHyd0tZbMQ3ZeY5lj2HHXWXbFsP07IcvQwwg54X6LP2rNoAVAB5s5jQA8PGHH+wWYi0gVv2UAKAWSwMyqYa8qWrg0luv2SB3dhZi8XkRkzrJquPmaq9WNUJVlDcibWYGcvqdKhk/V7mM+ddswnt73QYnAOgyvuEbviGOtjG66OThjUoAsOSZKQBYWiyHFtAbsbDejGXciDqtAgAFfDwGpPq0ypNjgID/3gce7ACgwdbf+ts/EltbyctX+RkQXLgss4mkQvTiTCBYiuHVHDZ4YSzAEDcWpnG76eDA+rt9LO+uY0cXvOVLhxfNRc91z33NLbe7Cc7dgpcTx4/FpVYFfOzY0Th2sm+386aqquSgMTaPcnk5DwJAgqwSiFTfWHYMqt6A1szZRmPh0dMpEDnNUBi5QPIgYFZaxwstLcEltfU27yCcsG6X5MSxX5oHFQBOEOwhJNHYGlPrDt0FvAwEDvVv6VBUAWD1Aj7oUK4AsPXcqwCwPJTmArq56VcZwBUA9h6uFQCWWcQKAKfNrBsxX6fV5NZKVQHgzdFflQE8WD80APCx0w8snJZzBpBhYMR82CCckfDzi+bNAOahE/ScKmAaXZtZ0V9eMcVr3gzUdNL1c53UzRi6bkrndpw/91qI+dPnqaeeitfPJeN9XhHXsRe7OwtqKz+fGwbmRiysN2MZN6JOqwBAMTs2bVAdzQ6JxWFoEDODUgGbAXN5+r9ZoL/3Yz/eMWsa42+0rJjGS6eaLDh+8Hczhh5juZOB8mVbPSdUf7J9ZNmUFw37N/f2upiHUn+yLW635ozZLDtTqZ1uq8oj6+966v3dLOxhrqYtMY6lG2gWmNO9vYVrF4dUwK5HyVFG9e1jeO52jl3qX6qlVT+tHW994smmqlrTeNOO0yrUlEO+eI3JNQglhwr239Whu50G1vCSCrjEEPEZGcA3UztxsG3p5n07H3Ocw5L3gsnGbn8VXL+P9G0bW8eGfq8qYMjw5h0qt0TNOgDowaZFLLcBFAD0oqd0vDPWAE8Lq797gddCStWsFyS9z/wI9hhj0JslbXn8HjcR2Qt6sbdqWb/b81feil//9V/fdIjqsLGWVuLSZnUtA4D9JFzc6cbUWDei928E2Jpbxtz0q8hpbOEs5alx6CDiGkMK/quPxonHnECQAeBO9GdLmznoQOG4l2rnT/7Dn2ryOHP2fBxpVcPX1vfHAWyAAsBgt0lEbyOnfDx/eB827dw8DzRHS+reEti6dnW7a5OAjoOnq500ycjnldJ+/vOfb7Lc3t7tvJ/1/wfu181TEa+eORebbSzEksqT84t1G/I8peqY48hyYR4cA7ktlvuVgemdnldZOl/1q1XAMg9gsGyvM7QptuxVLvvB9aPaXu3oDq3ZFWpjY/+gAJDgxGXdiPk51q5b9XeNIV5taFl6DFC2zRi41jvkDI1dP+f+O6bmrwCwl2ZlAA82myoAbA0yuo22AsCFETV3w5ibfpXhWwFgBYAVAJZV37kNYD4fb8T8XGVO3wrvVAB48/VSBYAH65MGAD760P1FFbBPwmJLyBDwu9kTMQa8eikxB9sLF6tTnWKmTpPKeejEbobCahj9zlh9VkuJlfEJX9d6md1xXmQIf99HvqWrm+p1pL2iR4slDcz129q1Xv1EBuNa69l5M52qb8RiPreMuelXGb6rAECORfW58zh79mycPn26qYZUfB5Tl7evdAzypStXm9/pIao8Oqen9Y34u3/373ZN2ZvI9KgOZMNycwnlTwaQc8LPczWxKrEwrnGg0Ry19zNNOTSfnZ/nzbd927d17aHTifL2uqAr9K603sE5k5H3K1W8rDOZQ77DcZRrBTwH87HGcSG5WvtAUMTbipyvvL/9XCywr5DUGtUzfDIbSNsNmcWe/ZFTT/+768Jbj4auBByaA5UBXGV1uH7viNUrMYCeM1xXmr1jhTiAXA+GGHQy5e30EyWfri26wz4VAB6swxsA+OCpe+LY0XTvKb0lrcrQoLcajEFh9bvVxfSuI6gyoNPmYzsjbbq8s9cDWqpcTzCqfeztK5DHTdALsergTcnBfnXH67d+67emNh3Z6gCsyt2+nK6vKn0EAEsfAsCDiXz47esNnlbJf5V3rpd8DpLvXNAIvL9Q7F7hrt9mjB1Ld1D/8A//cH+d4dFjzbMLFy41waL1EdiympnqQXq+EtwZmGiueV4J2HDTcRqDEV7LuLHTBzbX2DfY1d21tpFVvp7fnjNs9DKV1N9qge/ly9vNK6dPPxgvvZTux5VndH/Pt1TtfTD1HPipDgyzwrWnFEqKweRd1xwA8oYGp5HMacOo55KfxzkjFKgM1pPy1Xu5XEpjjPNnJ7tPfGw8l/JjfUrf3Y/Km6B7qD9vhvmtduRq8zHZHPT3ITOEsXxL+xPNABb6BABwLN9VflffVQC4iuTqO5ZABYDZWKgAcFEgN8MGcRjTtQLAaA5dFQBWAMi5cDPM7woAV1vhKgCsYWBWGzn9Ww0APH3/fXHkSDqd6zTsk3iJAWTgZv1uJk9snE9CXmDEVJilExNo9a3YOr9HNZdYQi9INHi3ylnsoxkF/c6g0S772Wefbdrx3ve+N776q7+6+a7YZVYHSa3jOIAl4VUAWAFgw56M6BZyJtDMwIsvvRwf//jHGyGK6dZHAaPNOMgx6aWXXm6eb64tqg3zWJbKk+oeO6DwOjP97nlg9ofmEdsXL3ZjX++ZfdQctOe8mHcz9V/3dV+3b1rk4Jn//9hP/sMmvdWm29s78dhjjzTPFDR7Zycx6mIDPY95TRtV3GRS6NjhNUR/6RDWhW5q4ykqLxrkl4AOwYbzVX0YPYBewpQ/v1tIQ+wogZW/Z9ry0bV7FQaQpi9klUcLu44J2McspiQ7PbsRoPR6MIC5yp4q4Osh3goAKwA86LhqAOAjD/Y2MFpgDbK8qWmhpGqhvx2jt89rBmMb0NkbEgEdbY6k0vVGpLQEiVb3umFa0GwvqA3QZahuLkd5WRXtBfBrvuZrOhuuy5cudoBTdd/bSeqqCgDHh8+NWIzHa3HwFHMZwID65tqATR9BoMet7GWfe/7LTYV/7ud+rvmrg4/H+OsXr8S9997dPN+IPrzKlSvbTWR/fY4cSfdn8y5aSkBAkWAkv89aYMblnTx6tJs/mkc77fUlDz/84EKYG2+I3/7t6Z7s/DMERt64mkJd/PiP/3jz1/NQ37WOMFhyG3+5aR8Ply4rD2fj5y47D7tSyoNqZIaMcl65HZefX7x0pfkqn7At3XHcBuq2nHPbLP2utWgMAHL+uP1FARceun2lOahyKRe/TrUvAeCbOY+HVOO5GUCS/80NAEtjbmiMVAA4daSvnq7aAK4uO71ZAWAmv8oALgrkzdw4Dja0F9+uADDFuKsAcNGQvwLAw5xl5bwqALw+Mq4MYGUADzqyOi9gq580qMxmUCVFBpDqEt5J6lMyPYOp0jV7p79kF1mO2Uen1f/NAKpcl8Hr33Qqcz2+6Zu+qT3Jr/X3ur7x+kLQ6LWB+EzNCbQ6gSyMqQoAe3EsYwI9njVWfY/1Cy++1Lz8+OOPd3PqYx/7WMdiU11HD3ibR+hdBl03u0ZPXOVh9a3rwPl1BLHqNGecVnExed1i7g1vNiZfYPLN/PyVxKbLI1gfrR1U+3o+K3+vC0qTMym5upZqWNaBzz3nGSCb2ok+gHwfgVkM38bGYkxP5Un17FZ7VSRV1aV5wDUxl0tJdaybxud8hlSn7pshBo0yGpKj6zH/YDSnBSntmM1hzqKuqp6dU7NV1zUGgi71+YIaeAUnkDn1qlfBVQA4Z8yX0lYAmEmlAsBFgcxZkA46GK/n+/M3uv3e4BUALm7m6q8KAPeDHD2pALCfzRUATl/Z5qy3FQBWADh9ZJVTdoGgGWvLDKDt9JrQKdvppK9TNWPx+T399enIJ3PeDpIHKGU0fd4E4moyAr8ZQF7RJNbQzKFYDbMZv/23//YmC8V0c/3vOnmis2FsQmRsDFsOVABYAWCSwEA4oAF7wM0jKeSLxqSvDDtx8q7mmZgiO4Qo5hxZhC9+8YtNmk9/+tPh7x7vysuMnb4//+UXm7T33nOys2nVhmHGxGyh0piF/NZv+uaFPBwu6eGHH+7s87RBey6R1fJIyMEz/791j5aQ/urGj370L8ZTT72lefb8888vMPYOFbOxsd9+TTJhvrQH5HM6eXid4VV+rr/y83czkqqT/EVy2zrJcH1jo6mz8rctpphCrk25TeEypup6A0DVtcQAMlwQx8YQsJh/MJq/5ZRCvOQOExxrNzMDSJnTUcn1pz3genvj1ByJVQA4R1oVAEpafyYi/nxEyPXuVyPiv4iIfz1RjB0A9EajyWeg5WcCgAaF2pwIAK2uUto8RhLVtHqPiyk3K6qfvagTADqtFmfG8GL8s2/8xm9smuwJpHiAzz33XPPs/lPycj7SfJcjSXUCmTg6IM/pb9ycKedvdGUA2C30A0DwzGvnO/Dlw9Heteju49W4FjDS56EHH+hME+hQwDuIexCzEx/4wAea9z75yU/GL/7iL3bj/UMf+lDz/T3veU/zV2UYhN59vL+i0eNff+mURRMKbsTsySH5fenlM02yJ59Md+n+0A/90ALwOnYszTt9nAcPcnRkYOzPIcN6AkOvBV1A7iywNk1OnB+dJ9imjc3k+KG67SJenx1yVDeXnUdJyEc8HTHS95RifT2BzFU+y4AB25E7yhCIlsolOFulXlPeGQOA+TjLyYIpZdyINHRQ0XcGhXb5C+NzBQC4p8Vi4kfjqk1dA0FPlNntluwPR8Tfj4g/FRH/KiL+XET8wYh4t6IwTGhsBYCZkCoDuCiQOSfSCePtTUtSAWASvUPJVAC4qM6uAPD6Tc0KAKfLtgLA6bJSyjvdC1ig75cj4j9vxSYr4y/pIB4Rf22CKBsA+PjDD3ashU5fZvvIAJqFE/vn7wIHVFG5PKpmfDrXe2YLpZr11W16x891ss5P2aoLDclLeaseH/nIRxY2ODGAX/jCF5pnx472Nydo46tOIBNGRpukAsBhWdEm0GP4lTOvxfvf//7mpWeeeab5+8orr8RTb3u6+a6wKFK/6nP+3GsdYy2AmjtGUJ2neXHu3LnmPal3yRLmYWA0h23+sL6324E+zSPXU/nZbIJznixcCTTnz46ferCpk+MA6vcf+ZEfaZ7RmeX11y+EnSv0W67mIwuZs1gM/eTxmDvQKE/WnSySyio5Q1BjsdfSdOnqtp79tcNIST2dq61Lat/DZgCXqU7zkTolDuCNYAAt55wZHZpZN0IFPH0F7FOSAeR4440zNBNYRQVcAeC8nrmTAaB0K7rTTIG7/neI7WMRcV9E/P6CKHXfW7rzLX0UkOy5x04/ECfvSvZKzWZw+XJawE+caP7qmrhL7bM3Xn8jdvd2m+cb6xtx/HiyfdJC0i3UrTrlxPHjndpHANAbzomTJ+PihQvNe5pULme3sS9Mz3d2U3yxq9vbnUpJCwgBoE/tv/Nrv7bL42ir6n355VdC9lb6nDh+NJ57LqndTp9+KLavXBocZZUBXBRNBYCDQyUIAA0Qrq1txIV2DHtuHD9+Ii63NrR7u7ud2te/ex64JKo/r7XB45IXbZpr2nC2ryabXOV35Eia0nutB7vmjOdG7FztgOXW5lbce5+0RRHPfem5OHlXup4u39jyFi94aWZn7itrSXW6s5PuSxYY/dmf/dnmu1TdBGVX25iBirNHVZnSEgDmqmAfSDX3XRepxq2qlU2hy+bvJdvBBW/f9k5w5Xul7R/Vg6CIwLFkc0dZlQGgzFJSqoOArWWq3CF2e8rcnc+MD8+HoV/GVPv5e1PqPb8Wh/9GyZP94ABwej2hLK4q4Oliu21SPqY1NiI+HBGfQKv+24j4+ohIhkGLn++PiI/eNhKoDakSqBKoEqgSqBK4syXwRIsF7jgp3MkM4BAA/BsR8bsiIrnDLn6KDGBEaAC9fseNntUb3DCnVW6zBFhlNktcXeIqt/lyqzKbLzO9UeU2X25vtsxUvq4emu49Mr+NN+0bdzIAXEUFnHdkYwOoyBIySbppe/nmq1iV2/w+qTKbLzO9UeU2X25VZvNlVsdaldlqEngT37qTAaDELicQhXxR6Bd95ASiwGI/PMcJpALA2SO4bjCzRVaBzHyRNW/UsTZfcFVm82VWx1qV2WoSeBPfutMBoMPA/IkWCCoMzB9SSLCISFFjl3/qQjkmofLvVW7z5VZlNl9mdVOuMltNAqu9VefofLlVmc2X2aG9cacDQAlSIWAcCPrfRcR/2TKDU4Qsm8Dvi4i/qms8p7xQ0zQSqHKbPxCqzObLrI61KrPVJLDaW3WOzpdbldl8mR3aGxUAHpooa0ZVAlUCVQJVAlUCVQJVAreGBCoAvDX6qdaySqBKoEqgSqBKoEqgSuDQJFAB4KGJsmZUJVAlUCVQJVAlUCVQJXBrSKACwFujn2otqwSqBKoEqgSqBKoEqgQOTQIVAB6aKGtGVQJVAlUCVQJVAlUCVQK3hgQqAFy9n/4MvId/tY0lqJiC9ZMkULo275k2xI5+18WsPxAR39F6Bf98RHz3xPA7t4uMv64dQ/9BRDwaEd+W3Uut+fkXI+KPt/dT/8uI+NMR8RsQwP0R8UMR8Z/oOtuI+EcR8Wcj4o3bRUiFdozJ7ccj4o9l72l8fdMdLDdFK/gD7fzTZd6/FBHfExGak/5MmZNviYgfjYhvaMeY7k5X3uny8dvrM0Vmv9BeHcqW/w8R8afw4E6SmZqtNUr/nmpl8KmI+EsR8XPt/+s4u0nmSQWAq3WE4wdqkiuYtOIH/sGIeHdEvLRalrfdWwKA3x4Rvwct0ybxSvt/bSIfiYjvbG9TUfBtAZivve0kMdygb27b+29b4JYDQG3Q2oQko2cj4i9HxAcj4n0RcbnNVouqwOOfjIitiPixiPjliPijt7Ecx+QmAPhwRHwXZKAwTWfx/ztNbv84In6yHRub/3979xJy2zvHAfxXZm6RSCG5pERukUtEFGWAUspUpgwMJNeJ5FaMJJcJJi5FyMA1UgxISZRIQpRIioFS+vo/q7bdPud993v0+//OXp81PWe9z7M+z1p7f9dz21X1nqp68rqX/nHNZ/I+VZWtsv60Xlxy332qqj5eVW+9wPvtOmYJgL+sqnceXP8/D34Zam9mYcjL6L+r6lfLJC9j2Wrt6VWVMHjVZ/8eze6Vx0cAvBl7Ql++ZLOHYI78gsjvVk/Me2/2Jy/urATAV1XV005cWX46788rpHxh/Xs23/5FVT23qn54cRpXX1B+i/IwAObZzG9Uppf0g+v0uGWD8gTCfJk/sap+XlXPqqofrf+TXq6vrd9ZzvmXfhy75XoTAB+07r9T18+t6qHrZfWFVfW99WtGVz2TCd5frar8jvq2UX5egt9X9/y9f134zXZslstNAEwoTifAqWPvZpvJX1cIzOe9+2zIgyIAnt8Q/4/fED6/1LvvjATAvPXlt5LTW/WD1ZuVn9p7cVV9q6oeXFV/O7i031bVh6vqQ3ff5d5xjY+DzGOr6tfrrTlfMNvx3fWFk2He162AGMftSO9OvNMj/cU7rtX8P3CrAJiXjwSS9Pp9u6reXlV/WZfDrerxaypBepR/ds1nMsN4rzh6qXvM6p1+RlX9ZP7tckc1PDbbAuCTqirfpekZ/crqqU8vYI69m6U3L59FmSqQHsCHX+Ozf+9md3STnnOyAHiO1j3/N2+/f6iq561Qs/2F96+5IM8+/09e5Bl5873/mmOUoaJ3VdUj1rBThggyVJld4A+PzKH8zpqbdJEot7mo4yCT+ytz/nK//fHgvM9VVf5vpiFk2C3DK5l6cHhkGkK8M9Ry6cepAJh5pfkC/k1VPW4Nd2ZOZHqXMzS1d7eMWHx59ZI+f90gmTJw1TP5sap6dFW97OCmum9VZQj55QdzvC7xnjtlluvMz4jmxTW97U9ZvaH5HMt8yxx7NcuLRV76M98vz17ur4xMuM8GPR0C4PmNcasA+IGqekFVPef8P7mLMzIklw/KN1VVJqGf+rLJsHp6Bt+yC5H/vcjrBsDPrxCTkHOrIJMhlndU1Ud34HgqAB5f9tabmvmoub/27pYXg7ygJfz9/ooAePhM3i7M5O9lztylHqfMTl3rNrqR3sL04O/VLCNlWfySz/1XV9XrVwdJpgRd9dm/V7P2Z0cAPJ/cEPD5S+IHVgAACGFJREFUZtsZ+TL5ZlV94xrDADcv5e480xDwzdrtOgEwfzmhOMPAWaG55yHgLLZ6ZVVlJXV6SLfjOtMy9jo0dyuzU3fs/VaPV+biZuX5Xs2ObfK5n0D82Wt89jO72Wfh2WcJgGeT/feELAJJN/8b1ukZHsjctnxQWARy2jTDwTHK3MDMB8kX8mvX6tec8YQ1XGwRyD1+2yKQLADJQpAcD1wT948XgTyzqn68/s9LV0/MI9ew1M3u8LvnrOsEwFjk3su8wAx9botA9uSW+ynbBWWh0YuOthJKa28Ls273TG4LGjKlY9vtIEOgGf14WFVlpfUlHVeZnbrW7GLw/ap6alX9dPW0ZuHMXsxu1f6Zh5tnMHOXr/rs39t9dq89MwLgzei3bWDy4ZcgmBVgr1l7bG2r4272ly/nrASXTIjOsG+GzbOfXbr/s4VJPgAypJJ5Qwkzf19fTrn6zH3by5FQnKGiHJlAn+HxzIHMirl8WGYbmAyHZ55femuyDUzmGR1vA5MtT7Iac9sGJiuCL3kbmNu5xS7zH7MfYiblZw5g5uc+YG2hs4WUbAOzJ7ePrHsivX+He/9lkVamZOS46pnctufIfLc3rwn9n66qT1zoNjBXmeXe2ua2ZYFRns0sYMuwelZX59ibWa45Wwzl+crOGHnuYpTPsswdzeiP+2zIN5wAePOGyBYwWeWaVU1ZpfnG1TN48794WWdmm5IMMz1kBb68Fb9tDQPkSrfNQNPjkMUg20bQ+dLey5GemAS+4yM9pAnG20bQedHIXJoYZrPs7Du2HdkIOj3PhxtB51685I2gb+eWDWi/tFYcxixh5etrTuThy9ne3NJTeurIXonZNue6z2QWgeQLPG2QxR+5V/OScokbQV9l9qiq+sxa2Jah3wSerLx/98E+gHHdk1mu95NV9ZLV65kXjPSEZqughD/32aBvNwFwUGOoCgECBAgQIECgQ0AA7FBWBgECBAgQIEBgkIAAOKgxVIUAAQIECBAg0CEgAHYoK4MAAQIECBAgMEhAABzUGKpCgAABAgQIEOgQEAA7lJVBgAABAgQIEBgkIAAOagxVIUCAAAECBAh0CAiAHcrKIECAAAECBAgMEhAABzWGqhAgQIAAAQIEOgQEwA5lZRAgQIAAAQIEBgkIgIMaQ1UIECBAgAABAh0CAmCHsjIIECBAgAABAoMEBMBBjaEqBAgQIECAAIEOAQGwQ1kZBAgQIECAAIFBAgLgoMZQFQIECBAgQIBAh4AA2KGsDAIECBAgQIDAIAEBcFBjqAoBAgQIECBAoENAAOxQVgYBAgQIECBAYJCAADioMVSFAAECBAgQINAhIAB2KCuDAAECBAgQIDBIQAAc1BiqQoAAAQIECBDoEBAAO5SVQYAAAQIECBAYJCAADmoMVSFAgAABAgQIdAgIgB3KyiBAgAABAgQIDBIQAAc1hqoQIECAAAECBDoEBMAOZWUQIECAAAECBAYJCICDGkNVCBAgQIAAAQIdAgJgh7IyCBAgQIAAAQKDBATAQY2hKgQIECBAgACBDgEBsENZGQQIECBAgACBQQIC4KDGUBUCBAgQIECAQIeAANihrAwCBAgQIECAwCABAXBQY6gKAQIECBAgQKBDQADsUFYGAQIECBAgQGCQgAA4qDFUhQABAgQIECDQISAAdigrgwABAgQIECAwSEAAHNQYqkKAAAECBAgQ6BAQADuUlUGAAAECBAgQGCQgAA5qDFUhQIAAAQIECHQICIAdysogQIAAAQIECAwSEAAHNYaqECBAgAABAgQ6BATADmVlECBAgAABAgQGCQiAgxpDVQgQIECAAAECHQICYIeyMggQIECAAAECgwQEwEGNoSoECBAgQIAAgQ4BAbBDWRkECBAgQIAAgUECAuCgxlAVAgQIECBAgECHgADYoawMAgQIECBAgMAgAQFwUGOoCgECBAgQIECgQ0AA7FBWBgECBAgQIEBgkIAAOKgxVIUAAQIECBAg0CEgAHYoK4MAAQIECBAgMEhAABzUGKpCgAABAgQIEOgQEAA7lJVBgAABAgQIEBgkIAAOagxVIUCAAAECBAh0CAiAHcrKIECAAAECBAgMEhAABzWGqhAgQIAAAQIEOgQEwA5lZRAgQIAAAQIEBgkIgIMaQ1UIECBAgAABAh0CAmCHsjIIECBAgAABAoMEBMBBjaEqBAgQIECAAIEOAQGwQ1kZBAgQIECAAIFBAgLgoMZQFQIECBAgQIBAh4AA2KGsDAIECBAgQIDAIAEBcFBjqAoBAgQIECBAoENAAOxQVgYBAgQIECBAYJCAADioMVSFAAECBAgQINAhIAB2KCuDAAECBAgQIDBIQAAc1BiqQoAAAQIECBDoEBAAO5SVQYAAAQIECBAYJCAADmoMVSFAgAABAgQIdAgIgB3KyiBAgAABAgQIDBIQAAc1hqoQIECAAAECBDoEBMAOZWUQIECAAAECBAYJCICDGkNVCBAgQIAAAQIdAgJgh7IyCBAgQIAAAQKDBATAQY2hKgQIECBAgACBDgEBsENZGQQIECBAgACBQQIC4KDGUBUCBAgQIECAQIeAANihrAwCBAgQIECAwCABAXBQY6gKAQIECBAgQKBDQADsUFYGAQIECBAgQGCQgAA4qDFUhQABAgQIECDQISAAdigrgwABAgQIECAwSEAAHNQYqkKAAAECBAgQ6BAQADuUlUGAAAECBAgQGCQgAA5qDFUhQIAAAQIECHQICIAdysogQIAAAQIECAwSEAAHNYaqECBAgAABAgQ6BATADmVlECBAgAABAgQGCQiAgxpDVQgQIECAAAECHQICYIeyMggQIECAAAECgwQEwEGNoSoECBAgQIAAgQ6B/wDIe5uVGg1f/gAAAABJRU5ErkJggg==\" width=\"640\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(0, 160)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import cv2\n",
"import numpy as np\n",
"\n",
"image = mpimg.imread('example_grid1.jpg')\n",
"def perspect_transform(img, src, dst):\n",
"\n",
" # Get transform matrix using cv2.getPerspectivTransform()\n",
" M = cv2.getPerspectiveTransform(src, dst)\n",
" # Warp image using cv2.warpPerspective()\n",
" # keep same size as input image\n",
" warped = cv2.warpPerspective(img, M, (img.shape[1], img.shape[0]))\n",
" # Return the result\n",
" return warped\n",
"\n",
"def color_thresh(img, rgb_thresh=(0, 0, 0)):\n",
" ###### TODO:\n",
" # Create an empty array the same size in x and y as the image \n",
" # but just a single channel\n",
" color_select = np.zeros_like(img[:,:,0])\n",
" # Lets get the mask for each color \n",
" red = img[:,:,0] > rgb_thresh[0]\n",
" green = img[:,:,1] > rgb_thresh[1]\n",
" blue = img[:,:,2] > rgb_thresh[2]\n",
" # Lets applu the masks and assign 1 where threshold has exceeded \n",
" color_select[red] = 1\n",
" color_select[green] = 1\n",
" color_select[blue] = 1\n",
" # Apply the thresholds for RGB and assign 1's \n",
" # where threshold was exceeded\n",
" # Return the single-channel binary image\n",
" return color_select\n",
"\n",
"# Define source and destination points\n",
"source = np.float32([[ 119,95 ], [119 , 140], [200 ,95 ], [303 , 140]])\n",
"destination = np.float32([[160 ,140 ], [160 ,150 ], [170 , 140], [170 ,150 ]]) \n",
"plt.imshow(rock_image)\n",
"warped = perspect_transform(image, source, destination)\n",
"#warped = perspect_transform(image)\n",
"colorsel = color_thresh(warped, rgb_thresh=(160, 160, 160))\n",
"#plt.imshow(warped)\n",
"#plt.imshow(colorsel, cmap='gray')\n",
"#plt.show()\n",
"ypos, xpos = colorsel.nonzero()\n",
"#plt.plot(xpos, ypos, '.')\n",
"plt.xlim(0, 320)\n",
"plt.ylim(0, 160)\n",
"#plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ True False False True False False]\n"
]
}
],
"source": [
"import numpy as np\n",
"A = np.array([4, 7, 3, 4, 2, 8])\n",
"print(A == 4)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 1, 1, 1, 0, 1, 1, 1, 1],\n",
" [1, 1, 1, 0, 0, 0, 1, 1, 1],\n",
" [1, 1, 1, 0, 1, 0, 1, 1, 1],\n",
" [1, 1, 0, 0, 1, 0, 0, 1, 1],\n",
" [1, 1, 0, 1, 1, 1, 0, 1, 1],\n",
" [1, 0, 0, 1, 1, 1, 0, 0, 1],\n",
" [1, 0, 1, 1, 1, 1, 1, 0, 1],\n",
" [1, 0, 1, 1, 1, 1, 1, 0, 1],\n",
" [1, 0, 1, 1, 1, 1, 1, 0, 1],\n",
" [1, 0, 0, 0, 0, 0, 0, 0, 1],\n",
" [1, 0, 1, 1, 1, 1, 1, 0, 1],\n",
" [1, 0, 1, 1, 1, 1, 1, 0, 1],\n",
" [1, 0, 1, 1, 1, 1, 1, 0, 1],\n",
" [1, 0, 1, 1, 1, 1, 1, 0, 1],\n",
" [1, 0, 1, 1, 1, 1, 1, 0, 1]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"A = np.array([\n",
"[12, 13, 14, 12, 16, 14, 11, 10, 9],\n",
"[11, 14, 12, 15, 15, 16, 10, 12, 11],\n",
"[10, 12, 12, 15, 14, 16, 10, 12, 12],\n",
"[ 9, 11, 16, 15, 14, 16, 15, 12, 10],\n",
"[12, 11, 16, 14, 10, 12, 16, 12, 13],\n",
"[10, 15, 16, 14, 14, 14, 16, 15, 12],\n",
"[13, 17, 14, 10, 14, 11, 14, 15, 10],\n",
"[10, 16, 12, 14, 11, 12, 14, 18, 11],\n",
"[10, 19, 12, 14, 11, 12, 14, 18, 10],\n",
"[14, 22, 17, 19, 16, 17, 18, 17, 13],\n",
"[10, 16, 12, 14, 11, 12, 14, 18, 11],\n",
"[10, 16, 12, 14, 11, 12, 14, 18, 11],\n",
"[10, 19, 12, 14, 11, 12, 14, 18, 10],\n",
"[14, 22, 12, 14, 11, 12, 14, 17, 13],\n",
"[10, 16, 12, 14, 11, 12, 14, 18, 11]])\n",
"B = A < 15\n",
"B.astype(np.int)"
]
},
{
"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"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
sonidosmutantes/taller-procesamiento | freeze/python/Freeze Phase Vocoder.ipynb | 1 | 7430339 | null | gpl-3.0 |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/influence_glm_logit.ipynb | 2 | 174334 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Influence Measures for GLM Logit\n",
"\n",
"\n",
"Based on draft version for GLMInfluence, which will also apply to discrete Logit, Probit and Poisson, and eventually be extended to cover most models outside of time series analysis.\n",
"\n",
"The example for logistic regression was used by Pregibon (1981) \"Logistic Regression diagnostics\" and is based on data by Finney (1947).\n",
"\n",
"GLMInfluence includes the basic influence measures but still misses some measures described in Pregibon (1981), for example those related to deviance and effects on confidence intervals."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-02-02T06:55:18.330628Z",
"iopub.status.busy": "2021-02-02T06:55:18.329920Z",
"iopub.status.idle": "2021-02-02T06:55:19.300367Z",
"shell.execute_reply": "2021-02-02T06:55:19.300730Z"
}
},
"outputs": [],
"source": [
"import os.path\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from statsmodels.genmod.generalized_linear_model import GLM\n",
"from statsmodels.genmod import families\n",
"plt.rc(\"figure\", figsize=(16,8))\n",
"plt.rc(\"font\", size=14)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-02-02T06:55:19.306243Z",
"iopub.status.busy": "2021-02-02T06:55:19.303392Z",
"iopub.status.idle": "2021-02-02T06:55:19.401353Z",
"shell.execute_reply": "2021-02-02T06:55:19.401744Z"
}
},
"outputs": [],
"source": [
"import statsmodels.stats.tests.test_influence\n",
"test_module = statsmodels.stats.tests.test_influence.__file__\n",
"cur_dir = cur_dir = os.path.abspath(os.path.dirname(test_module))\n",
"\n",
"file_name = 'binary_constrict.csv'\n",
"file_path = os.path.join(cur_dir, 'results', file_name)\n",
"df = pd.read_csv(file_path, index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-02-02T06:55:19.434801Z",
"iopub.status.busy": "2021-02-02T06:55:19.433920Z",
"iopub.status.idle": "2021-02-02T06:55:19.453580Z",
"shell.execute_reply": "2021-02-02T06:55:19.452641Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: constrict No. Observations: 39\n",
"Model: GLM Df Residuals: 36\n",
"Model Family: Binomial Df Model: 2\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -14.614\n",
"Date: Tue, 02 Feb 2021 Deviance: 29.227\n",
"Time: 06:55:19 Pearson chi2: 34.2\n",
"No. Iterations: 7 \n",
"Covariance Type: nonrobust \n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n",
"const -2.8754 1.321 -2.177 0.029 -5.464 -0.287\n",
"log_rate 4.5617 1.838 2.482 0.013 0.959 8.164\n",
"log_volumne 5.1793 1.865 2.777 0.005 1.524 8.834\n",
"===============================================================================\n"
]
}
],
"source": [
"res = GLM(df['constrict'], df[['const', 'log_rate', 'log_volumne']],\n",
" family=families.Binomial()).fit(attach_wls=True, atol=1e-10)\n",
"print(res.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## get the influence measures\n",
"\n",
"GLMResults has a `get_influence` method similar to OLSResults, that returns and instance of the GLMInfluence class. This class has methods and (cached) attributes to inspect influence and outlier measures.\n",
"\n",
"This measures are based on a one-step approximation to the the results for deleting one observation. One-step approximations are usually accurate for small changes but underestimate the magnitude of large changes. Event though large changes are underestimated, they still show clearly the effect of influential observations\n",
"\n",
"In this example observation 4 and 18 have a large standardized residual and large Cook's distance, but not a large leverage. Observation 13 has the largest leverage but only small Cook's distance and not a large studentized residual.\n",
"\n",
"Only the two observations 4 and 18 have a large impact on the parameter estimates."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"execution": {
"iopub.execute_input": "2021-02-02T06:55:19.456786Z",
"iopub.status.busy": "2021-02-02T06:55:19.456058Z",
"iopub.status.idle": "2021-02-02T06:55:19.461473Z",
"shell.execute_reply": "2021-02-02T06:55:19.461921Z"
}
},
"outputs": [],
"source": [
"infl = res.get_influence(observed=False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-02-02T06:55:19.465063Z",
"iopub.status.busy": "2021-02-02T06:55:19.464312Z",
"iopub.status.idle": "2021-02-02T06:55:19.493162Z",
"shell.execute_reply": "2021-02-02T06:55:19.493601Z"
}
},
"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>dfb_const</th>\n",
" <th>dfb_log_rate</th>\n",
" <th>dfb_log_volumne</th>\n",
" <th>cooks_d</th>\n",
" <th>standard_resid</th>\n",
" <th>hat_diag</th>\n",
" <th>dffits_internal</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Case</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>4</th>\n",
" <td>1.073359</td>\n",
" <td>-0.930200</td>\n",
" <td>-1.017953</td>\n",
" <td>0.429085</td>\n",
" <td>3.681352</td>\n",
" <td>0.086745</td>\n",
" <td>1.134573</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.959508</td>\n",
" <td>-0.827905</td>\n",
" <td>-0.847666</td>\n",
" <td>0.328152</td>\n",
" <td>3.055542</td>\n",
" <td>0.095386</td>\n",
" <td>0.992197</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>-0.259120</td>\n",
" <td>0.202363</td>\n",
" <td>-0.004883</td>\n",
" <td>0.066770</td>\n",
" <td>-1.150095</td>\n",
" <td>0.131521</td>\n",
" <td>-0.447560</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.236747</td>\n",
" <td>-0.194984</td>\n",
" <td>0.028643</td>\n",
" <td>0.065370</td>\n",
" <td>0.984729</td>\n",
" <td>0.168219</td>\n",
" <td>0.442844</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>0.116501</td>\n",
" <td>-0.099602</td>\n",
" <td>0.132197</td>\n",
" <td>0.055382</td>\n",
" <td>0.713771</td>\n",
" <td>0.245917</td>\n",
" <td>0.407609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>-0.128107</td>\n",
" <td>0.041039</td>\n",
" <td>-0.100410</td>\n",
" <td>0.051950</td>\n",
" <td>-1.420261</td>\n",
" <td>0.071721</td>\n",
" <td>-0.394777</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>-0.093083</td>\n",
" <td>-0.009463</td>\n",
" <td>0.177532</td>\n",
" <td>0.046492</td>\n",
" <td>-0.847046</td>\n",
" <td>0.162757</td>\n",
" <td>-0.373465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>-0.196119</td>\n",
" <td>0.127482</td>\n",
" <td>0.035689</td>\n",
" <td>0.031168</td>\n",
" <td>-1.065576</td>\n",
" <td>0.076085</td>\n",
" <td>-0.305786</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>-0.088174</td>\n",
" <td>-0.013657</td>\n",
" <td>-0.002161</td>\n",
" <td>0.027481</td>\n",
" <td>-1.238185</td>\n",
" <td>0.051031</td>\n",
" <td>-0.287130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-0.092235</td>\n",
" <td>0.170980</td>\n",
" <td>0.038080</td>\n",
" <td>0.026230</td>\n",
" <td>0.661478</td>\n",
" <td>0.152431</td>\n",
" <td>0.280520</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" dfb_const dfb_log_rate dfb_log_volumne cooks_d standard_resid \\\n",
"Case \n",
"4 1.073359 -0.930200 -1.017953 0.429085 3.681352 \n",
"18 0.959508 -0.827905 -0.847666 0.328152 3.055542 \n",
"19 -0.259120 0.202363 -0.004883 0.066770 -1.150095 \n",
"29 0.236747 -0.194984 0.028643 0.065370 0.984729 \n",
"31 0.116501 -0.099602 0.132197 0.055382 0.713771 \n",
"24 -0.128107 0.041039 -0.100410 0.051950 -1.420261 \n",
"13 -0.093083 -0.009463 0.177532 0.046492 -0.847046 \n",
"23 -0.196119 0.127482 0.035689 0.031168 -1.065576 \n",
"33 -0.088174 -0.013657 -0.002161 0.027481 -1.238185 \n",
"6 -0.092235 0.170980 0.038080 0.026230 0.661478 \n",
"\n",
" hat_diag dffits_internal \n",
"Case \n",
"4 0.086745 1.134573 \n",
"18 0.095386 0.992197 \n",
"19 0.131521 -0.447560 \n",
"29 0.168219 0.442844 \n",
"31 0.245917 0.407609 \n",
"24 0.071721 -0.394777 \n",
"13 0.162757 -0.373465 \n",
"23 0.076085 -0.305786 \n",
"33 0.051031 -0.287130 \n",
"6 0.152431 0.280520 "
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summ_df = infl.summary_frame()\n",
"summ_df.sort_values('cooks_d', ascending=False)[:10]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-02-02T06:55:19.496721Z",
"iopub.status.busy": "2021-02-02T06:55:19.495978Z",
"iopub.status.idle": "2021-02-02T06:55:19.828459Z",
"shell.execute_reply": "2021-02-02T06:55:19.829156Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = infl.plot_influence()\n",
"fig.tight_layout(pad=1.0)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-02-02T06:55:19.845010Z",
"iopub.status.busy": "2021-02-02T06:55:19.843316Z",
"iopub.status.idle": "2021-02-02T06:55:20.118314Z",
"shell.execute_reply": "2021-02-02T06:55:20.119282Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = infl.plot_index(y_var='cooks', threshold=2 * infl.cooks_distance[0].mean())\n",
"fig.tight_layout(pad=1.0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-02-02T06:55:20.123616Z",
"iopub.status.busy": "2021-02-02T06:55:20.122373Z",
"iopub.status.idle": "2021-02-02T06:55:20.438463Z",
"shell.execute_reply": "2021-02-02T06:55:20.439454Z"
}
},
"outputs": [
{
"data": {
"image/png": 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rKsipqudU1cqtbi+tql8kubKq1lXV/j2rEAAAAIAk3Y/IeVGSka1uvzHJhiQvTbJXkldNaVUAAAAAbOcOXR53lyQ/SJKq2ivJI5OsaK19qqquSXJKj+oDAAAAoKPbETnzktzSuf6IJC3JFzq3f55k36ktCwAAAICxug1yfpjkcZ3rxyQ5v7X2P53bi5JcO9WFAQAAALCtbqdWvT7JB6rqmUnulOQpW/UdkeTbU10YAAAAANvqKshprX24qn6W5CFJvtla++JW3Vck+UQvigMAAADg97odkZPW2peTfHmc9pOmtCIAAAAAxjVhkFNVd9mRB2qt/ez2lwMAAADARCYbkXNZRnen6ta821cKAAAAAJOZLMj56+xYkAMAAABAD00Y5LTW3jeNdQAAAABwK3bqdwEAAAAAdKfrXauqat8kf5lkSZJdx3S31trfTGVhAAAAAGyrqyCnqpYk+VpGFzS+Y5Krk9y5c/vXSa7rVYEAAAAAjOp2atXqJN9Isl+SSvLnSYaSPDvJ/yR5Uk+qAwAAAGCLbqdWPSjJ8Ul+17m9U2ttU5LTq2phktOSHDH15QEAAACwWbcjcnZPcm1r7ZaMTqNauFXfuowGPQAAAAD0ULdBzmVJ9u9cX5/kKVv1PT7JhqkrCQAAAIDxdBvknJPk0Z3rb0zyrKpaX1XfTfKSJKf3ojgAAAAAfq/bNXJWJdklSVpr/6eqRpI8LcluSd6c5N97Ux4AAAAAm3UV5LTWfpffL3Sc1trZSc7uVVEAAAAAbK/bqVUAAAAA9FlXI3Kq6txbOaS11v50CuoBAAAAYALdrpGzU5I2pm3vJEuSXJXkkqksCgAAAIDtdbtGzuHjtVfVHyVZk+Rfp64kAAAAAMZzu9bIaa39OMmpSVZPTTkAAAAATGQqFju+KskfT8HjAAAAADCJ2xXkVNWdk/xtkh9PTTkAAAAATKTbXasuzfaLHe+cZL/O9SdPZVEAAAAAbK/bXavOy/ZBzm+T/DTJRzpr5QAAAADQQ93uWnVcj+sAAAAA4FZMxWLHAAAAAEyDCUfkVNUrduBxWmvt1VNQDwAAAAATmGxq1cljbrckNc5xm9fOEeQAAAAA9NCEU6taaztt/klynySXJjkxyUFJhjqXqzrt9+55pQAAAABzXLe7Vr01ybtba6/bqu1nSV5bVTsleVuSP53q4gAAAAD4vW4XO35IknUT9H0zyUOnphwAAAAAJtJtkHNdkkdP0PeYTj8AAAAAPdTt1KrTk6yqqt2TfCTJFUn2S/LUJM9N8q+9KQ8AAACAzboNcl6R0d2pXprk+E5bJflNRkOck6e6MAAAAAC21VWQ01q7Jck/V9UbkixNckCSXyb5dmvNtCoAAACAadDtiJwkSWttQ5Iv9aYUAAAAACYzYZBTVYclubC1dmPn+qRaa1+c0soAAAAA2MZkI3K+kNFtxb/Rud4mOK46ffOmsjAAAAAAtjVZkHNEku9tdR0AAACAPpowyGmtnTfedQAAAAD6Y6duDqqqnarqDmPallfV31XVIb0pLamqVVX1zaq6vqquqqqzq+o+vXo+AAAAgJmsqyAnyX8kOX3zjao6Psmnk6xO8rWq+rMe1JYkhyf5tySHJnlUkk1JPldVd+7R8wEAAADMWN0GOQ9N8qmtbq9M8u4keyU5K8nLp7iuJElrbXlr7b2tte+01i5O8ldJ9kny8F48HwAAAMBM1m2Qs2+S4SSpqrslOTjJW1trNyR5b5KlvSlvO3tktOZfT9PzAQAAAMwY3QY51yfZu3P98CRXt9a+3bl9c5Jdp7iuibw5yf9L8tXxOqvquVW1rqrWXXXVVdNUEgAAAMD0mGz78a2dn+TEqtqU5KXZdprV3ZL8Yorr2k5VvTHJI5I8orV283jHtNbeleRdSbJs2bLW65oAAAAAplO3I3L+Icmdk3wio6NvTt6q72mZYITMVKmqNyX5yySPaq39pJfPBQAAADBTdTUip7X2wyR/XFV7t9auGdP9kiS/mvLKOqrqzUmOSXJ4a+0HvXoeAAAAgJmu26lVSZLW2jVVtXtG18u5vLW2sbObVE9U1dsyulPViiS/rqr9O103ttZu7NXzAgAAAMxE3U6tSlU9vqouTHJdkh+ns1NVVb27qp7eo/qen9Gdqv47yS+3+vn7Hj0fAAAAwIzVVZBTVSuSfDzJ1UleNuZ+lyZ55pRXlqS1VhP8nNyL5wMAAACYybodkXNSkve21h6T5LQxfd9Jcp+pLAoAAACA7XUb5NwzyX92ro/d1vvXGV0zBwAAAIAe6jbIuT7Jwgn6Dkpy1ZRUAwAAAMCEug1yzkmyqqoWbNXWqmqXJC9M8umpLgwAAACAbXW7/fjLk3wjyfokn8ro9KoTk9w3yV4Z3R4cAAAAgB7qakROa+2yJA9I8l9JHp3k5iSHJflakoe01i7vVYEAAAAAjOp2RE5aa79I8jfj9VXV/Vpr35qyqgAAAADYTrdr5Iyrqh5YVR9PcuEU1QMAAADABCYNcqrqqKr6VFV9p6rWVNWyTvsfV9XZGV0351FJTp2GWgEAAADmtAmnVlXVs5K8J8mGJJckeVCSz1fVXyd5b+e+pyU5tbVm+3EAAACAHptsjZwXJjk3yRNba7+pqp2SvCXJfyS5NMnjWmuXTEONAAAAAGTyqVX3SPLm1tpvkqS1dkuSf+3c55+EOAAAAADTa7IgZyjJFWPaNt/+cW/KAQAAAGAit7ZrVZug/eapLgQAAACAyU22Rk6SnF1VN43T/qmq2rjV7dZaO3AK6wIAAABgjMmCnPdPWxUAAAAA3KoJg5zW2rOmsxAAAAAAJndra+QAAAAAMEMIcgAAAAAGhCAHAAAAYEAIcgAAAAAGhCAHAAAAYEAIcgAAAAAGxITbj1fVXXbkgVprP7v95QAAAAAwkQmDnCSXJWk78Fjzbl8pAAAAAExmsiDnr/P7IGeXJP+U5Pok/yfJFUn2T/LUJHskeXUPawQAAAAgkwQ5rbX3bb5eVacluTDJk1prbav2VyVZk+RePasQAAAAgCTdL3b8l0neuXWIkySd2+9I8vSpLgwAAACAbXUb5OyeZJ8J+vZNcsepKQcAAACAiXQb5Hwhyb9W1YO2bqyqByd5TacfAAAAgB7qNsh5YZLfJflaVV1WVV+vqsuSfDXJbzv9AAAAAPTQZLtWbdFau7Sq7pHkuCQPTXJAku9kNMh5f2ttY88qBAAAACBJl0FOknTCmn/v/AAAAAAwzboOcpKkqu6b5LAke2d0F6tfVdXdklzRWruhFwUCAAAAMKqrIKeqdknywSRHJakkLcnZSX6V5HVJLklyYo9qBAAAACDdL3b8miR/luSvkuyX0TBns08nWT7FdQEAAAAwRrdTq/4yyT+11j5cVfPG9F2a5KAprQoAAACA7XQ7ImfvJN+f5DF2mZpyAAAAAJhIt0HOpUkeNkHfg5Osn5pyAAAAAJhIt0HOGUlOrKpnJNm509aq6ogkJyQ5vRfFAQAAAPB73QY5r0vyySQfSHJtp+3LST6X5DOttf/dg9oAAAAA2EpXix231m5OckxVvS2jO1Ttm+SajIY45/WwPgAAAAA6ut21KknSWvtSki/1qBYAAAAAJtHV1KqqOreq3lpVO4/Td8+qOnfqSwMAAABga92ukXN4kuOT/HdV7T2mb88kj5zKogAAAADYXrdBTpI8N8niJF+rqiU9qgcAAACACexIkPOdJA9OckWSr1bVo3pTEgAAAADj2ZEgJ621q5MckeTsJJ+uqmf3pCoAAAAAtrNDu1YlSWttY5JnVtX6JO9MsnbKqwIAAABgOzsc5GzWWvvXqvpBkjOmsB4AAAAAJtBtkHNwkl+ObWytnVVVFyW5y5RWBQAAAMB2ugpyWms/naTv0iSXTllFAAAAAIxrwiCnqk5P8urW2qWd65NprbW/mdrSAAAAANjaZCNyjkjy5s71RyVpkxw7WR8AAAAAU2DCIKe1dvBW1w+almoAAAAAmNBO3RxUVYdV1e4T9N2xqg6b2rIAAAAAGKurICfJ55Pca4K+e3T6AQAAAOihboOcmqRvlyQ3T0EtAAAAAExisl2rDkpy162alo0zvWooyV8n+dnUlwYAAADA1ibbteqZSU7K6I5ULcn/zrYjc1rn9qYkL+hVgQAAAACMmizIeV+SL2Q0rDk3o2HN98Yc87skl7TWru1FcQAAAAD83mTbj/80yU+TpKqOSHJha+2G6SoMAAAAgG1NNiJni9baeb0uBAAAAIDJdbVrVVXtXFUnVdUPqup/qurmMT+bel0oAAAAwFzX1YicJKszukbOp5OcldG1cQAAAACYRt0GOUcnOam19ppeFgMAAADAxLqaWpVk9yRf7WUhAAAAAEyu2yDn7CSH9bIQAAAAACbX7dSq/53kjKq6Jcmnklw79oDW2k+msjAAAAAAttVtkLN5WtXJSU6a4Jh5t7saAAAAACbUbZDz10laLwsBAAAAYHJdBTmttff1uA4AAAAAbkW3ix0nSapqp6q6T1U9sqru2KuiAAAAANhe10FOVb0gya+SfDvJuUmWdNrXVNWLe1MeAAAAAJt1FeRU1XOSvDnJmiRPTVJbdX8pyZOnvDIAAAAAttHtiJy/TfKG1tpzk3xsTN8P0hmdAwAAAEDvdBvkHJxk7QR9v0myYEqqGUdVHVZVn6iq4apqVXVcr54LAAAAYCbrNsi5OslBE/QtSTI8JdWMb/ck30nykiQjPXweAAAAgBmt2yDn7CSvqKq7btXWqmphkhMyunZOT7TWPtVa+8fW2keT3NKr5wEAAACY6e7Q5XH/lORRGR0Z8/UkLclbktwjyZVJXtWT6gAAALqw5qLhrF67PpdvGMmiBUNZuXxJVhyyuN9lAUy5rkbktNauSbIsySlJ5if5cUZDoLcmeVhr7bqeVbgDquq5VbWuqtZdddVV/S4HAACYBmsuGs6qsy7O8IaRtCTDG0ay6qyLs+aiXq4AAdAf3U6tSmvthtbaq1trj2it/XFr7WGttVe21q7vZYE7orX2rtbastbasn322aff5QAAANNg9dr1Gdl48zZtIxtvzuq16/tUEUDvdB3kAAAAzESXbxh/T5SJ2gEG2YRr5FTVuTvwOK219qdTUA8AAMAOWbRgKMPjhDaLFgz1oRqA3ppsRM5OSWqrn3skOTyj25APdS4Pz+j249WrAqtq96q6f1Xdv1PTXTq379Kr5wQAAAbHyuVLMjR/3jZtQ/PnZeXyJX2qCKB3JgxyWmuHt9aOaK0dkeTNSTYmeWhr7a6d9XHumuRhnfY397DGZUku6vwMJXll57qdsgAAgKw4ZHFOOWppFi8YSiVZvGAopxy11K5VwKxUrbVbP6jq4iSrW2tnjNN3XJK/a60tnfrybrtly5a1devW9bsMAAAAgB1SVRe01paN19ftYsd3TzLRft5XJrnbbSkMAAAAgO51G+RcmuR/TdD3v5JcNiXVAAAAADChCXetGuOVST5UVd9J8tEkVyTZL8nRGV0E+Rm9KQ8AAACAzboKclprZ1bV1RkNdFYlmZ/RRY6/mWR5a+2/e1ciAAAAAEn3I3LSWvtcks9V1U5JFia5urV2S88qAwAAAGAbXQc5m3XCmyt7UAsAAAAAk+gqyKmqV9zKIa219uopqAcAAACACXQ7IufkSfpa51KQAwAAANBDXW0/3lrbaexPkr2THJfkO0nu1sMaAQAAAMhtWCNns9bar5OcUVV7J3lbkiOnrCoAAAAAttPViJxb8a0kh03B4wAAAAAwiakIch6f5KopeBwAAAAAJtHtrlWnj9O8c5L7JFma5KSpLAoAAACA7XW7Rs6j8vvdqTb7bZKfJjktyfunsCYAAAAAxtFVkNNaO6jHdQAAAADcZmsuGs7qtetz+YaRLFowlJXLl2TFIYv7XdaU63Zq1f+X5JOttWvG6btzkse31s6Y6uIAAADmornygRSmypqLhrPqrIszsvHmJMnwhpGsOuviJJl1fzvdLnb83iR/NEHfwZ1+AAAAbqfNH0iHN4yk5fcfSNdcNNzv0mDGWr12/ZYQZ7ORjTdn9dr1faqod7oNcmqSvjsm2TQFtQAAAMx5c+kDKUyVyzeM7FD7IJtwalVV3T/JA7ZqekJV3WfMYUNJjknyw6kvDQAAYO6ZSx9IYaosWjCU4XH+RhYtGOpDNb012Ro5T8zvtxVvSV4+wXHXJPmbqSwKAABgrppLH0hhqqxcvmSbNXKSZGj+vKxcvqSPVfXGZEHOaUnel9FpVT9JclSSi8Yc87skV7TWxm5NDtwOFrcDAJi75tIHUpgqmz8vzYXPURMGOa2165JclyRVdXCSy1trG6erMJir5tJq6wAAbG8ufSCFqbTikMVz4u+kq+3HW2s/3Xy9qnbJ6FSqeyW5PMn7WmuX96Y8mHsmW9xuLpyUAACYOx9IgR032WLHr0ry5Nbavbdq2yXJ15Msze93snpJVT20tXZpTyuFOcLidgAAAExksu3H/yzJp8a0vSDJfZOsTrJXkocm2Zjkn3pSHcxBEy1iZ3E7AAAAJgty/ijJN8a0rUjyyySrWms3tNa+kdFQ5097Ux7MPSuXL8nQ/HnbtFncDgAAgGTyNXL2SnLF5htVtXOSByf56Jhdqr6V5IDelAdzj8XtAAAAmMhkQc5wkoOSfLFz+yFJdk5y/pjj5if5zZRXBnOYxe0AAAAYz2RBzpeSvLSqPpHRbchfnOSWJJ8cc9whSX7Rm/JgZllz0bCRMgAAMAN5r85cMVmQ88okF2R0etVvk+yR5B1bb0XecVySL/SiOJhJ1lw0nFVnXbxla/DhDSNZddbFSeJ/EAAA0EfT+V5dYES/TbjYcWc78fsneW2SM5I8s7X2/K2PqapFSf47yXt7WCPMCKvXrt/yP4bNRjbenNVr1/epIgAAIJm+9+qbA6PhDSNp+X1gtOai4Sl9HpjMZCNy0lr7WZJXTNJ/eZIXTXVRMBNdvmFkh9oBAIDpMV3v1ScLjIzKYbpMtv04sJVFC4Z2qB0AAJge0/Ve3Ze7zASCHOjSyuVLMjR/3jZtQ/PnZeXyJX2qCAAASKbvvfp0frm75qLhPPzUc3PwiZ/Mw0891/QtthDkQJdWHLI4pxy1NIsXDKWSLF4wlFOOWmoIJQAA9Nl0vVefrsDIWjxMplpr/a6hJ5YtW9bWrVvX7zIAAACYRaZj16qHn3puhseZrrV4wVC+cuKjpvS5mJmq6oLW2rLx+iZd7BgAAAD4vRWHLO75qHxr8TCZroKcqnpNkne11n7a43oAYEabjm/hAIC5bdGCoXFH5NhohaT7NXJenOTHVfWpqvqLqrK2DgBzjvnqAMB0sNEKk+k2kNk/yQuS7JdkTZKfVtVJVeUrSADmjNVr12dk483btI1svDmr167vU0UAwGxkoxUm09XUqtbab5K8M8k7q+pBSY5PsjLJy6vqk0ne2Vr7TO/KBID+M18dAJgu07EWD4Nph6dItda+2Vr7myQHJzk/yROTfLKqflJVLzDtCoDZaqJ56earAwAwXXY4dKmqP6qq1yX5bpJDk3wsyTOSfDXJaUneMZUFAsBMYb46AAD91u2uVfOSPCnJ/0pyRJIrkrw9o1OqLu8cdmZVfSnJa5M8twe1AkBfbR7ebNcqAAD6pasgJ8lwkn2SfDHJXyb5WGtt0zjHXZRkjymqDQBmHPPVAQDop26DnI8k+bfW2vcnO6i19vXchulaAADA9Ftz0bBRhgADpttdq17U60IAAIDps+ai4aw66+KMbLw5STK8YSSrzro4SYQ5ADPYhEFOVR22Iw/UWvvi7S8HAABmrtk0gmX12vVbQpzNRjbenNVr1w/s7wQwF0w2IucLSVrnem11fazNffMm6AcAgIE320awXL5hZIfaAZgZJgtyjpi2KgAAYIabbSNYFi0YyvA4oc2iBUN9qAaAbk0Y5LTWzpvOQgAAYCabbSNYVi5fss0IoyQZmj8vK5cv6WNVANyabnetgttkNs0jBwDmttk2gmXzezLv1QAGy2SLHZ+7A4/TWmt/OgX1MIvMtnnkAMDcNhtHsKw4ZLH3ZQADZqdb6asufyZ7HOaoyeaRAwAMmhWHLM4pRy3N4gVDqSSLFwzllKOWCkIAmFaTrZFz+DTWwSw02+aRAwAYwQJAv1kjh56ZbfPIgZnNmlwAAMwFOxTkVNWdktw9ya5j+1prX5yqopgdZuM8cmBmsiYX00loCAD0U1dBTlXtmuT0JE/N6Jo445k3VUXNdbPlDaKdEIDpMtmaXM45TCWhIQDQb92OyPnnJIcneWaSDyR5QZLfJjkuyQFJXtKD2uak2fYG0TxyYDpYk4vpIjQEAPqt292mnpzkVUnO7Nz+emvtva21Ryb5VpLH9qK4uchOTwA7bqK1t6zJxVQTGgIA/dZtkHOXJN9trd2cZGOSO27Vd3qSp011YXOVN4gAO27l8iUZmr/tDF9rctELQkMAoN+6DXKuSbJ75/rPk9xvq76FSbx7mSLeIALsuBWHLM4pRy3N4gVDqSSLFwzllKOWmurClBMaAgD91u0aOV9LckiSTyf5v0leXVV7JNmU5O+SfLk35c09dnoCuG2sycV0sJA/ANBv3QY5r83o9Kok+Zckd8vomjnzMhryPG/qS5ubvEEEYLbsXjhbCQ0BgH6q1tptu2PVLkl2aa1dP7UlTY1ly5a1devW9bsMANghY3cvTEZHZpoqBgAwd1TVBa21ZeP1dTsiZzuttd8l+d1troq+840vwMxje2sAACbTdZBTVXsmOTKjU6x2HdPdWmuvnsrC6K2x3/gObxjJqrMuThIfFAD6yO6FAABMpqsgp6oenuTsJAsmOKQlEeQMEN/4AsxMixYMZXic0MbuhQAAJN1vP35aksuSPCjJrq21ncb8zJv03sw4vvEFmJlsbw0AwGS6nVp1zyRPba1d0MtimD6+8QWYmexeCADAZLoNcn6WZJdeFsL0Wrl8ybi7ovjGF6D/bG8NAMBEug1yXpnkxKr675m63Tg7xje+ANi9EABg8HQb5Dw+yX5JLq2qrya5dkx/a609c0oro+d84wswd9m9EABgMHUb5PxJRnemuj7Jvcfpb1NWEQDQc3YvBAAYTF0FOa21g3pcBwAwjexeCAAwmG51+/Gq2rmqLqyqx0xHQQBA7020S6HdCwEAZrZbDXJaazclOTjJpt6XM76qen5VXVpVv62qC6rqT/pVCwDMBiuXL8nQ/HnbtNm9EABg5rvVIKfjnCR9GZFTVU9L8uYk/5rkkCTnJ/l0Vd2lH/UAwGyw4pDFOeWopVm8YCiVZPGCoZxy1FLr4wAAzHDV2q2vU9wZAfPBJB9JsibJLzNmgePW2k96UF+q6utJvt1ae85WbT9M8tHW2qqJ7rds2bK2bt26XpQEAAAwK6y5aDir167P5RtGsmjBUFYuXyLUhxmgqi5orS0br6/bXavO61z+bZITJjhm3gTtt1lV7ZzkgUleP6brs0kOnernAwAAmCvWXDScVWddvGUXw+ENI1l11sVJIsyBGazbIOdZPa1iYgszGhBdMab9iiR/Nv3lwOzh25fbxr8bADBbrF67fkuIs9nIxpuzeu16729gBut2+/H397qQWythzO0apy1V9dwkz02Su9zFEjowEd++3DbT+e8mMAIAeu3yDSM71A7MDN0udpwkqaqdquo+VfXIqrpjr4raytVJbk6y/5j2fbP9KJ201t7VWlvWWlu2zz77TEN5MJgm+/aFiU3Xv9vmwGh4w0hafh8YrbloeEqfBwCY2xYtGNqhdmBm6DrIqaoXJPlVkm8nOTfJkk77mqp6cS+K62x9fkGSR4/penRGd68CbgPfvtw20/XvJmgDAKbDyuVLMjR/26VOh+bPy8rlS/pUEdCNroKcqnpORrcAX5PkqRmd2rTZl5I8ecor+703Jjmuqp5dVfesqjcnWZTkHT18TpjVfPty20zXv5ugDQCYDisOWZxTjlqaxQuGUkkWLxjKKUctNZ0bZrhuFzv+2yRvaK29rKrG7k71gyQrp7as32ut/WdV7Z3kn5IckOQ7SY5srf20V88Js93K5Uu2Wesl8e1LN6br323RgqEMjxPaCNoAgKm24pDFghsYMN0GOQcnWTtB32+SLJiSaibQWvu3JP/Wy+eAuWTz/6xn02K607E48HT9uwnaAGDmsiEB0G/dBjlXJzlogr4lSazACQNmNn37Mp27SU3Hv9tsDNqAHeODIsxMdv4EZoJug5yzk7yiqr6QZPOUplZVC5OckNG1cwD6YrLFgQf1TdVsCtqAHeODIsxcs/E9BzB4ut216p+S/C6j69N8LklL8pYk38/o9uCv6kl1AF2wODAwm9i5DmYu7zmAmaCrIKe1dk2SZUlOSTI/yY8zOprnrUke1lq7rmcVAtwKu3ABs4kPijBzec8BzATdjshJa+2G1tqrW2uPaK39cWvtYa21V7bWru9lgQC3ZuXyJRmav+2GehYHBgaVD4owc3nPAcwEXQU5VfWTqrrfBH33qaqfTG1ZAN1bccjinHLU0ixeMJRKsnjBUE45aqm56sBA8kGRZHStpIefem4OPvGTefip52bNRfYWmQm85wBmgmqt3fpBVbckeWhr7Rvj9C1L8vXW2rzt79k/y5Yta+vWret3GQAAO8yuVXPb2AWvk9EwT2AAMHdU1QWttWXj9XW7a1UyusDxeJYl2bCjRQEAMD47181tdkYCYDITBjlVdUJGtxZPRkOcs6vqpjGHDSW5c5Ize1MeAADMLRa8BmAyk43I+UmS/+5cf2aSdUmuGnPM75J8L8m7p740AACYexYtGMrwOKGNBa8BSCYJclprH0/y8SSpqiR5VWvt0mmqCwAA5qSVy5eMu0aOBa8BSLpcI6e19qxeFwIAAGTLOjgWvAZgPF0vdlxVd03y1CR3SbLrmO7WWvubqSwMAADmKgteAzCRroKcqnpiko8k2SnJlRldG2drt76HOQAAAAC3S7cjcv4lyReSPKO1NnbBYwAAAACmQbdBzl2T/J0QBwAAAKB/duryuB8k2buXhQAAAAAwuW6DnH9I8o+dBY8BAAAA6INup1adnNEROd+vqh8muXZMf2utPXIqCwMAAABgW90GOTcnWd/LQgAAAACYXFdBTmvt8B7XAQAAAMCt6HaNHAAAAAD6bMIROTu6sHFr7Se3vxwAAAAAJjLZ1KofJWk78FjzbmctAAAAAExisiDnWdNWBQAAAAC3asIgp7X2/uksBAAAAIDJWewYAAAAYEAIcgAAAAAGhCAHAAAAYEBMttgxAACz2JqLhrN67fpcvmEkixYMZeXyJVlxyOJ+lwUATEKQAwAwB625aDirzro4IxtvTpIMbxjJqrMuThJhDgDMYKZWAQDMQavXrt8S4mw2svHmrF67vk8VAQDdEOQAAMxBl28Y2aF2AGBmEOQAAMxBixYM7VA7ADAzCHIAAOaglcuXZGj+vG3ahubPy8rlS/pUEQDQDYsdAwDMQZsXNLZrFQC9ZpfEqSXIAQCYo1YcstgbaQB6yi6JU8/UKgAAAKAn7JI49QQ5AAAAQE/YJXHqmVoFMEeZqwwAQK8tWjCU4XFCG7sk3nZG5ADMQZvnKg9vGEnL7+cqr7louN+lAQAwi9glceoJcgDmIHOVAQCYDisOWZxTjlqaxQuGUkkWLxjKKUctNRL8djC1CmAOMlcZAIDpYpfEqWVEDsAcNNGcZHOVAQBgZhPkAMxB5ioDAMBgMrUKYA7aPLTVrlUAADBYBDkAc5S5ygAAMHhMrQIAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEHfodwEAzG5rLhrO6rXrc/mGkSxaMJSVy5dkxSGL+10WAAAMJEEOAD2z5qLhrDrr4oxsvDlJMrxhJKvOujhJhDkAAHAbmFoFQM+sXrt+S4iz2cjGm7N67fo+VQQAAINNkANAz1y+YWSH2gEAgMkJcgDomUULhnaoHQAAmJwgB4CeWbl8SYbmz9umbWj+vKxcvqRPFQEAwGAT5AA9t379+jztaU/LH/7hH2a33XbLH//xH2fVqlXZsGFDv0ujx1YcsjinHLU0ixcMpZIsXjCUU45aaqFjAAC4jaq11u8aemLZsmVt3bp1/S4D5rzLLrss97vf/bLHHnvkec97Xvbdd998/etfz3vf+948+MEPzle/+tV+lwhAj33zm9/MBz7wgXz+85/PpZdemj333DMPeMADcvLJJ2fZsmVbjjvzzDPznve8J9/5zndy7bXXZt99982DH/zgvOIVr8j97ne/Pv4GADC9quqC1tqy8fpsPw701BlnnJHrr78+X/rSl3Lf+943SfKc5zwne+yxR0477bR8//vfzz3vec8+VwlAL732ta/Nl7/85TzlKU/Ji1/84mzYsCHvete78pCHPCRnn312jjzyyCTJt771rey777454YQTsnDhwvzyl7/M6aefnoc+9KH58pe/nAc+8IF9/k24rdZcNJzVa9fn8g0jWbRgKCuXLzE6E2YIf5+DR5AD9NT111+fJFm0aNE27Ztv77bbbtNeEwDT62//9m/z4Q9/ODvvvPOWtmc/+9m5973vnX/6p3/aEuSccsop2933Oc95ThYvXpy3v/3tefe73z1tNTN11lw0nFVnXZyRjTcnSYY3jGTVWRcniQ+L0Gf+PgeTNXKAnjriiCOSJMcdd1wuvPDC/OIXv8jHPvaxrF69Oscee2wOPPDAPlcIQK8deuih24Q4SXKnO90pRxxxRL73ve9Net+FCxdmaGgo1113XS9LpIdWr12/5UPiZiMbb87qtev7VBGwmb/PwWREDtBTj3vc43LyySfnda973TZD4o8//vi87W1v62NlAPTb5ZdfnoULF27X/utf/zqbNm3K8PBw3vjGN+aGG27IYx7zmD5UyFS4fMPIDrUD08ff52AS5AA9d+CBB+bBD35wnvKUp2TRokX50pe+lLe85S3ZZZddctppp/W7PAD64Ctf+UrOO++8nHDCCdv1LV26NMPDw0mSPfbYI694xSvy7Gc/e7pLZIosWjCU4XE+FC5aMNSHaoCt+fscTDM6yKmq5yb5yySHJNkrycGttcv6WhSwQz784Q/n+OOPz/e///0cfPDBSZIVK1Zk7733zstf/vIce+yx2+xYAsDsd+WVV+bpT396DjzwwJx00knb9Z955pm58cYb86Mf/Sinn356brjhhmzatCnz58/vQ7XcXiuXL9lmDY4kGZo/LyuXL+ljVUDi73NQzfQ1cnZL8tkkJ/e5DuA2esc73pH73e9+W0KczVasWJEk+fKXv9yHqoDZ6sYbb8xJJ52UI488Mvvss0+qKqeeeuq4x370ox/Ngx70oAwNDeXOd75zjjnmmPz85z+f5ornnhtuuCFHHnlkbrjhhpx99tnZc889tzvmEY94RB772MfmhS98YdauXZsPfOADWbVqVR+qZSqsOGRxTjlqaRYvGEolWbxgKKcctdRCqjAD+PscTDN6RE5r7bQkqSpf18OAuuKKK7L77rtv175p06ZtLgGmwtVXX51XvepV+YM/+IMccsghOeecc8Y97p3vfGeOP/74HHbYYVm9enWuueaavOUtb8kjHvGIXHDBBeOu28LtNzIykic84Qn5/ve/n3POOSf3uc99bvU+++yzT4444oh86EMfyutf//ppqJJeWHHIYh8MYYby9zl4ZnSQAwy+JUuW5NOf/nS++93v5t73vveW9g9+8INJss0CyAC31wEHHJDh4eEsWrQol1122XajAZPkpptuyoknnpiHPvSh+fznP5+ddhodoPyEJzwhD3rQg/La1742q1evnu7SZ72NGzfm6KOPzvnnn59PfOITOfTQQ7u+78jIiF2rAKBjpk+t2iFV9dyqWldV66666qp+lwMkWblyZZLksMMOy0knnZR3vOMdefrTn57Vq1fnkY98ZA4//PD+FgjMKrvssksWLVo06THf+c53smHDhhxzzDFbQpwkecADHpB73vOe+fCHP9zrMuecW265Jc94xjPymc98JmeccUYe+9jHjnvclVdeuV3bj3/843zhC1+wnhoAdEz7iJyq+pckL7+Vw45orX1hRx+7tfauJO9KkmXLlrUdrw6Yatfsftfc6/j/nR9+5r059bR/yy3/c10WL16Uv/u7v8srX/nKVFW/SwTmmJtuuilJsttuu23Xt9tuu+W73/1ufvWrX2X//fef7tJmrb//+7/PRz7ykTz60Y/Opk2btozK3OzYY49NMrrL4VOe8pTc9773zZ577plLLrkk73nPe7Jp06YJ1zoCgLmmH1OrTkvywVs55mfTUAfQY2suGh5dBf+Of5h9n/yKJKOr4FtADeinu9/97tlpp53ypS99Kc95znO2tF999dX53ve+lyQZHh4W5EyhCy+8MElyzjnnjLtu0eYg54UvfGHOPffcnH322bnxxhuz33775c///M+zatWqLF26dFprBoCZatqDnNba1Umunu7nBabf6rXrt9nKMElGNt6c1WvXC3KAvtl7771zzDHH5EMf+lDuec975qlPfWquvfbarFy5cstonZGRkT5XObu89E0fysa163P5hpEsWjCUlcuXjPv/AWsTAcCtm9Fr5FTV/lV1/yR/3Gm6V1Xdv6ru3MeygC5dvmH8D0ITtQNMl3e84x150pOelH/8x3/M3e52tzz4wQ/O0NBQ/vqv/zpJsscee/S5wtlj8+jM4Q0jaUmGN4xk1VkXZ81Fw/0uDQAG0owOcpIcn+SiJB/q3P5k5/Zf9K0ioGuLFgztUDvAdNljjz3y0Y9+NL/4xS9y3nnn5ZJLLsmnP/3pXHfdddlpp53yR3/0R/0ucdaYbHQmMHdceOGFefKTn5yDDz44u+22WxYuXJjDDjssH//4x7c57swzz8yjH/3oHHDAAdlll13yh3/4h3nyk5+cb33rW32qHGaeGb39eGvt5CQn97kM4DZauXzJ6Bo5W72BH5o/LyuXL+ljVQC/t3jx4ixePDrFZ9OmTfn85z+fhz3sYdl99937XNnsYXQmkCQ/+clP8tvf/jbPetazsmjRotx4440566yzsmLFirz1rW/NC17wgiTJt771rey777454YQTsnDhwvzyl7/M6aefnoc+9KH58pe/nAc+8IF9/k2g/2Z0kAMMts3rH6zuYl0EgH577WtfmyuuuCLveMc7+l3KrLJowVCGxwltjM6EueXoo4/O0UcfvU3bi1/84jzwgQ/Mm970pi1BzimnnLLdfZ/znOdk8eLFefvb3553v/vd01IvzGSCHKCnVhyyWHADTKu3vvWt2bBhQzZs2JAk+fznP59NmzYlSV70ohdlr732yqmnnppvf/vbechDHpJdd901a9euzcc+9rE873nPy4oVK/pX/CxkdCYwkZ122imLFi3KN77xjUmPW7hwYYaGhnLddddNU2UwswlyAIBZ5fWvf31++tOfbrn92c9+Np/97GeTjG5zvddee+W+971v1qxZk0996lO56aabcu973zunn356jjvuuD5VPXsZnQls7cYbb8xvf/vb/PrXv87HPvaxfOYzn8kxxxyz3XG//vWvs2nTpgwPD+eNb3xjbrjhhjzmMY/pQ8Uw81Rrrd819MSyZcvaunXr+l0GADBN1lw0LCwAmOGOPfbYfOhDo3vZzJs3L0cffXTe+c53Zq+99trmuD/4gz/I8PDo7nZ77LFHTjjhhJx88smpqmmvGfqhqi5orS0br8+IHABg4G3e4nrz9J3NW1wnEeYAzCCrVq3Kcccdl8svvzwf+tCHMjIykpGRke2CnDPPPDM33nhjfvSjH+X000/PDTfckE2bNmX+/Pl9qhxmDiNyAICB9/BTzx13Qd3FC4bylRMf1YeKALg1rbU86lGPyo033phvfOMbE462ueqqq3Kve90rz3zmM/P6179+mquE/phsRM5O010MAMBUs8U1wOCpqjzlKU/JunXrcskll0x43D777JMjjjhiy5QsmOsEOQDAwJtoK2tbXAPMbCMjo4H7re1INTIyYtcq6BDkAAADb+XyJRmaP2+bNltcA8wcV1555XZtN910Uz7wgQ9kaGgo97rXvSY87sc//nG+8IUvZNmycWeZwJxjsWMAYODZ4hpgZjvmmGOyyy675NBDD80BBxyQX/7yl/nABz6QH/7wh3nDG96Q3XffPUly4IEH5ilPeUrue9/7Zs8998wll1yS97znPdm0aVNOPfXUPv8WMDNY7BgAAICeeu9735szzjgj3/ve93Lttddmzz33zAMf+MC88IUvzF/8xV9sOW7lypU599xz85Of/CQ33nhj9ttvvxx22GFZtWpVli5d2sffAKaX7ccBAADomzvd/zG56YoDs9tDRnK3SUZNrl69ug/VwWAR5AAAANAzay4azqqzLs7IxpuTJMMbRrLqrIuTxBRYuA0sdgwAAEDPrF67fkuIs9nIxpuzeu36PlUEg02QAwDArPDNb34zL37xi7N06dLsvvvuWbRoUR7/+Mfn1tZNfPazn52qymMf+9hpqhTmlss3jOxQOzA5QQ4AALPCa1/72vyf//N/cvjhh+dNb3pTTjjhhKxfvz4PechD8qlPfWrc+6xbty7vf//7s+uuu05ztTB3LFowtEPtwOTsWgUAwKxw/vnnZ9myZdl55523tP3617/Ove997+y///658MILtzm+tZZDDz009773vfO5z30u97jHPfKZz3xmusuGWW/sGjlJMjR/Xk45aqk1cmACk+1aZUQOAACzwqGHHrpNiJMkd7rTnXLEEUfke9/73nbHn3HGGfnud7+b17zmNdNVIsxJKw5ZnFOOWprFC4ZSSRYvGBLiwO1g1yoAAGa1yy+/PAsXLtym7YYbbsiJJ56Yl7/85dlvv/36VBnMHSsOWSy4gSliRA4AALPWV77ylZx33nl52tOetk37K1/5yuy222556Utf2p/CAOA2MiIHAIBZ6corr8zTn/70HHjggTnppJO2tK9fvz5vectb8p//+Z/ZZZdd+lghAOw4QQ4AALPODTfckCOPPDI33HBDvvjFL2bPPffc0veSl7wkD3/4w/OkJz2pjxUCwG0jyAEAYFYZGRnJE57whHz/+9/POeeck/vc5z5b+j73uc9l7dq1+ehHP5of/ehHW9o3bdqU//mf/8mPfvSjLFy4MAsWLOhD5QBw6wQ5AADMGhs3bszRRx+d888/P5/4xCdy6KGHbtP/i1/8Ikly9NFHb3ff4eHh3P3ud88pp5ySE088cVrqBYAdJcgBAGBWuOWWW/KMZzwjn/nMZ/KhD30oj33sY7c75ogjjshHPvKR7dqf//znZ/HixXn5y1+epUuXTke5AHCbCHIAAJgV/v7v/z4f+chH8uhHPzqbNm3KBz/4wW36jz322Bx44IE58MADx73vfvvtN+5IHQCYSQQ5AADMChdeeGGS5Jxzzsk555yzXf+xxx473SUBwJQT5AAAMCu89E0fysa163P5hpEsWjCUlcuXZMUhi7u672WXXdbb4gBgighyAAAYeGsuGs6qsy7OyMabkyTDG0ay6qyLk6TrMAcABsFO/S4AAABur9Vr128JcTYb2XhzVq9d36eKAKA3BDkAAAy8yzeM7FA7AAwqQQ4AAANv0YKhHWoHgEElyAEAYOCtXL4kQ/PnbdM2NH9eVi5f0qeKAKA3LHYMAMDA27yg8erbuGsVAAwKQQ4AALPCikMWC24AmPVMrQIAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYEIIcAAAAgAEhyAEAAAAYENVa63cNPVFVVyX5ab/ruJ0WJrm630XQV14DeA3Mbf774zWA1wBeA3gNzE0Httb2Ga9j1gY5s0FVrWutLet3HfSP1wBeA3Ob//54DeA1gNcAXgOMZWoVAAAAwIAQ5AAAAAAMCEHOzPaufhdA33kN4DUwt/nvj9cAXgN4DeA1wDaskQMAAAAwIIzIAQAAABgQghwAAACAASHImYGq6vlVdWlV/baqLqiqP+l3TUyPqjq5qtqYn1/1uy56p6oOq6pPVNVw57/3cWP6q/O6uLyqRqrqC1V17z6VSw908Rp43zjnha/1qVymWFWtqqpvVtX1VXVVVZ1dVfcZc4zzwCzW5WvAeWAWq6oXVNW3O6+B66vqq1X1uK36nQNmuS5eA84BbEOQM8NU1dOSvDnJvyY5JMn5ST5dVXfpa2FMp/VJDtjqZ2l/y6HHdk/ynSQvSTIyTv8/JPm7JC9K8qAkVyY5p6r2mLYK6bVbew0kyeey7XnhyOkpjWlweJJ/S3Jokkcl2ZTkc1V1562OcR6Y3Q7Prb8GEueB2ewXSV6W5AFJliU5N8maqrpvp985YPa7tddA4hzAVix2PMNU1deTfLu19pyt2n6Y5KOttVX9q4zpUFUnJzm6tXafWzuW2aeqbkzywtba+zq3K8nlSd7aWntNp20oo2/g/r619s5+1UpvjH0NdNrel2Rha+3x/aqL6VNVuye5LsmK1trZzgNzz9jXQKftfXEemFOq6tokqzK6W5FzwBy0+TXQWnuncwBjGZEzg1TVzkkemOSzY7o+m9FvaZgb7tqZYnFpVZ1ZVXftd0H0zcFJ9s9W54TW2kiSL8Y5Ya55RFVdWVWXVNW/V9W+/S6Intkjo+/Pft257Tww94x9DWzmPDAHVNW8qjomo6M1z49zwJwzzmtgM+cAthDkzCwLk8xLcsWY9isyegJn9vt6kuOS/HmS52T0v/v5VbV3P4uibzb/3TsnzG2fSfL/JfnTjA6tf3CSc6tql75WRa+8Ocn/S/LVzm3ngbln7GsgcR6Y9apqaWdU5u+SvCPJk1prF8c5YM6Y5DWQOAcwxh36XQDjGjvfrcZpYxZqrX1669udRcx+kuSZSd7Yl6KYCZwT5rDW2plb3by4qi5I8tMkj0tyVn+qoheq6o1JHpHkEa21m8d0Ow/MARO9BpwH5oT1Se6fZEGSJyd5f1UdvlW/c8DsN+5roLX2HecAxjIiZ2a5OsnN2T5d3zfbp/DMAa21G5N8N8nd+10LfbF5xzLnBLZorV2e0UURnRdmkap6U5K/TPKo1tpPtupyHpgjJnkNbMd5YPZprd3UWvtRa21dZ13M/5fkhDgHzBmTvAbGO9Y5YI4T5MwgrbWbklyQ5NFjuh6dbedHMkdU1a5J7pHkl/2uhb64NKNv4LacEzqviT+Jc8KcVVULkyyO88KsUVVvTvL0jH6A/8GYbueBOeBWXgPjHe88MPvtlGSXOAfMZZtfA9txDsDUqpnnjUk+UFXfSPKVJMcnWZTReZLMclX1+iRnJ/lZRr9p+eckd0zy/n7WRe90die5W+fmTknuUlX3T3Jta+1nVXVakpdX1Q+SXJLkn5LcmOTDfSiXHpjsNdD5OTnJ/83om7WDkpyS0d1KPjbNpdIDVfW2JH+VZEWSX1fV5m/db2yt3dhaa84Ds9utvQY654iT4zwwa1XVqUk+meTnGV3s+ukZ3Zb+cc4Bc8NkrwHnAMZj+/EZqKqen+QfkhyQ5DtJTmitfbG/VTEdqurMJIdldOHrq5J8Lck/t9a+19fC6JnO/PfPj9P1/tbacZ2th09K8r+S3CmjC2K/oLX2nWkrkp6a7DWQ5HlJ1iQ5JKNz5n/ZOfafW2s/n5YC6amqmuiN2Ctbayd3jnEemMVu7TXQ2Wp6TZwHZq3O1tJHZHT61HVJvp1kdWttbaffOWCWm+w14BzAeAQ5AAAAAAPCGjkAAAAAA0KQAwAAADAgBDkAAAAAA0KQAwAAADAgBDkAAAAAA0KQAwAAADAgBDkAQN9V1WOq6tNVdU1V/baqLqmq11bVncYc16rqX/pVZ69U1Yqq+ttx2g/v/M6HT39VAMBMJMgBAPqqqv4xydokv03y7CTLk7wjyXFJvllVf9i/6qbNiiTbBTlJLkzysM4lAEDu0O8CAIC5q6qOSPIvSU5rrZ2wVdd5VfWxJBckOSPJEf2obzxVtUtr7XfT8VytteuTfG06ngsAGAxG5AAA/fQPSa5NsmpsR2vt0iSnJjm8qh6yVVdV1cur6hdVNVJVX6yq+2fbA5ZX1flVdV1V3VhV66vqFWOOuV9VfaKqft15nK9U1Z+MOeZ9ned5WOfxRpK8rqo+VVUXjK25qg6oqk1V9dLO7X2q6p2dqWL/U1U/r6oPV9XirZ8jyTOTLO5Mo2pVdVmnb7upVTXqhM7vdFNV/bKq3lpVe46ppVXVv1TVi6vq0qq6oarOq6p7T/QfAwCY+QQ5AEBfVNUdkjwyyTmttd9OcNgnOpeP2qrt/0tyZJIXZnT61X5J/ruq7tx53Lt27ndpkqcl+Yskb0xyx62e+wFJzk9y5yTPSfLkJNck+VxVPXBMDXslOTPJfyT58yQfzugooQdU1b3GHPv0zuV/dC7vnNEpY6uSPDbJyiR3T/KVqtq1c8yrk3wqyVUZnUb1sCRPmuDfI0le0/l9zknyhCSv6/w7fLKqxr63OzbJ45K8JMmzktwlycc7//YAwADyP3EAoF/2TjKU5LJJjtnct/U6OUNJHtNa+02SVNXXk/wwyQlJ/jnJA5LsnOR5nalJSXLumMddneRnSR7VWrup8zhrk3yn8xgrtjp29yTHttY+vrmhqr6d5Pokf5VtRxP9VZLPttauSJLW2vqMhiib7zcvyVc6z/3nST7WWvtxVV2V5KbW2qTTqDph1d8meX9r7YWd5rWd+38gyePz+/ArSTYmeXxrbWPn/knykSQPzmiQBQAMGCNyAIB+qdt4v09tDnGSpLV2WUbXkXlYp+n/ZTTAOLOqjq6qfbd50qqhjI4E+kiSW6rqDp0RKpXkc0kOG/N8m5L819YNrbWRJP83yTOqk45U1dIk98voaJ2tn+95VfWtqrqx81g/63Qt2fFfPQ9NskuSD45pP7Pz2I8c037O5hCn4+LO5V1uw3MDADOAIAcA6Jerk4wkOWiSYzb3/XyrtivGOe6KJIuTpLX2o4zufLVTRkep/Kqqvl5Vm0OOOyeZl9GRNxvH/LwwyZ3GTFG6srV28zjPeUZGRwod3rn9V0luSLL1yJ0XJfm3jAZER2V0JMxDO92bp1btiDt3Ln+5dWNrbVNGp4bdeczx1465vXmR5tvy3ADADGBqFQDQF621TVX1xSSPrqpdJ1gn5y86l1tPjdpvnOP2SzK81WN/Psnnq2qXJA9P8qqMriFzUJINSW5J8raMGT2z1f1v2frmBL/CeRkdXXNsVZ2X5C+TfLQzWmezY5L8d2vt7zY3VNXBEzxeNzYHM/sn+e5Wj3mHjE5Vu+Z2PDYAMACMyAEA+ml1RgOIfx3b0Qk8Xpbki621r2/VdWRVbb1w8UEZHeXy1bGP0Vr7XWvt3IwuCHzHJAd3pmV9KaPToC5sra0b+9NN4a21luRDSY7O6OLLf5Dtg6HdMjrSZ2vPGufhfpfRtX9uzdc6xx4zpv1pGf2C7rwuHgMAGGBG5AAAfdNa++/OtuCv6gQyZyT5dUYXLD4xyXUZnbK0tZEkn62q1RldL+aVGV14+E1JUlXHZ3Sdm09ldErWwowuSHx5RhczTkYXDP5iRhcKfk9Gpyot7DzvvNbaiV3+Cmd0HvsdnecaG6R8JsnLquofk3wjo7tvHT3O43wvyZ2r6nlJ1iX5bWvt4rEHtdaurao3JllVVb/p/I73TPIvSb6c5JNd1g0ADChBDgDQV621V1fVNzO669R7MzqK5WcZDUlOaa2NXefljCS/SfLWjIYv30xyzFbHfSujO0KdkmTfjE5H+nKSZ2ye9tRau7CqHpTkpCRvyegW41cluTCjoUy3tf+gqtYlWdapdew0rFclWdD53XbNaNCzPMlPxhz37oyOKvrXzvE/zcRrB728U+vxSZ6f0elUZyRZNWZKGAAwC9X27zcAAAAAmImskQMAAAAwIAQ5AAAAAANCkAMAAAAwIAQ5AAAAAANCkAMAAAAwIAQ5AAAAAANCkAMAAAAwIAQ5AAAAAANCkAMAAAAwIP5/vKtWlaorS5cAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = infl.plot_index(y_var='resid', threshold=1)\n",
"fig.tight_layout(pad=1.0)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-02-02T06:55:20.444153Z",
"iopub.status.busy": "2021-02-02T06:55:20.442742Z",
"iopub.status.idle": "2021-02-02T06:55:20.747650Z",
"shell.execute_reply": "2021-02-02T06:55:20.748778Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = infl.plot_index(y_var='dfbeta', idx=1, threshold=0.5)\n",
"fig.tight_layout(pad=1.0)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-02-02T06:55:20.753433Z",
"iopub.status.busy": "2021-02-02T06:55:20.752049Z",
"iopub.status.idle": "2021-02-02T06:55:21.032050Z",
"shell.execute_reply": "2021-02-02T06:55:21.032904Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = infl.plot_index(y_var='dfbeta', idx=2, threshold=0.5)\n",
"fig.tight_layout(pad=1.0)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"execution": {
"iopub.execute_input": "2021-02-02T06:55:21.036777Z",
"iopub.status.busy": "2021-02-02T06:55:21.035615Z",
"iopub.status.idle": "2021-02-02T06:55:21.319906Z",
"shell.execute_reply": "2021-02-02T06:55:21.320805Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = infl.plot_index(y_var='dfbeta', idx=0, threshold=0.5)\n",
"fig.tight_layout(pad=1.0)"
]
},
{
"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": "3.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| bsd-3-clause |
ALEXKIRNAS/KPI-Semester-4 | Neural networks/CS231n Toy NN/CS231n Toy NN.ipynb | 2 | 95128 | {
"cells": [
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Global constants\n",
"N = 100 # number of samples per class\n",
"D = 2 # number of features\n",
"K = 3 # number of classes\n",
"REGULARIZATION_RATE = 1e-3\n",
"LEARNING_RATE = 1e-0"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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2WQKfCiFM4J9SynlWJwkhrgOuA+jXL/loIJfbjtdrkY/gM5Fhi1YpVce1fz32\nLS63HcMQ+P0BLrhsQr0mJ00xqsppvCgkSW3xO6vOWCHad5EyjaY106xRSUKIk1CC4a6wzTOklBOA\nWcBPhRDHW50rpZwnpZwspZycm5t8NNCJpw/FYdG5TcYpq+2p8VNd5cPnNXntv6tYt7ow6e/tWJRS\nfzc2AxUTvp/Y8eBOOlLte42mtZEKjWEPKpQkRJ/gtgiEEOOAp4BZUsri0HYp5Z7g/weEEG+iTFML\nUjCuCOZcOI6tmw6yffNBTDOAzW4ghMDj8cUVDiG8HpP331qn+zbHxY2a+Ov7ha6gtpRxXRyoaKUS\nVNVVdyoH2G6oLChi55vfEPD56XvWNLKH963/JE0jkKjFzE7UwiYdlRmdSBvRtkcqBMNSYKgQYiBK\nIFwM/DD8ACFEP+AN4HIp5eaw7RmAIaUsD/58OvBACsYUhdNp464HTmX7loNs2VhEdnYaR03twz8e\n+YbVy/bUW2kVoKSoqimG1o7IQ4WexiJUajs6+7wWH6oGvg31MnYFRtHBUm7isuGJt1h6xz8B1Tdk\nxS+fYfh1s5nyyE8QQof5Ng07g/9Ci5lyVFnuUUD7y2dq9NsmpfQDNwEfARuAV6SU64QQNwghbgge\n9ivUG/53IcQqIcSy4PY84BshxGpgCfCelPLDxo4pFkIIBg/LZeacURxzwkBcbgc/umEq2Z3dRxLY\nwktsh2MYgmGj2t8DkFocwDjUesNGZLiqDZXlXJ+pKUSoDn4x7an+vZSSQ+vyKV65hYA/0d9FLbve\n+ZbFt/0ds8aLWeMl4PVhVnvZ/NT7FHywpAlGrFELmXChECKAejbbX7VVIRNtU9aKmDx5sly2bFn9\nByaI1+Nn8Tc72bqpiNzuGXz16VZKDkYW23On2fnNX2bTvUfytu/KCi9vvLiS777KxzQDjJvcm4uu\nOIpu3Tul7B5aFwFq/Q1ZwW12VBLbKhIXDiEM4Dhag9bgK69i07/eI//1BTgy0xlx/Q/od/b0hFbq\nRUs38uWFD1BzsBQMgc3pYMbTd9BvzrEJfff6x99k8W1/B9PaVNd39jROnf+7pO5HkwihTm5Wz60A\npqMWRT6UuSlUV6krreGZDUcIsTyRXDEtGCyoKPfwv38vY/HX+fj9AYaPzuPSa46m34AuSV/L7zP5\n5W3vcWBf+RFBIwRkdHLy0N/mkNW5I/UlDgDfkLxgEMCxtHReg7eskvmTb6Rqz0HMag8A9gw3Ay86\niRlP/b/7Js6gAAAgAElEQVS459YcLOW1wZfhK480R9rSXcz+9m/kjBsc9/zKPUW8PvQKzBpvzGO6\nTx/DWV8/muDdaBKnCuucG1DP5vFAGbXJmjJsXw+gH+qZtxO/D7kfZaJyBI8tptacmpp5IlHB0KFK\nYiRKp0wX194ynWtvmX6k09sXH27myT9/TU2Nn4lH9+YH54+lc079VRaXL95N8cHKCO1DSqip8fPx\nuxs5/7KJTXkrrQwD1d5zO/U7qMOx0xrCV9c/+gZVBUURk7O/sobtL33OyJvOpuuEITHP3fLcR5am\no4DHx7pHXuO4Z++yOKuWXW8tjGuxsKW56Dc3WvOo3l9CVWEJWYN74chs/1VBm4b04L+KOtsF0D34\nc6gNaDgSKAz+C/nMMlDJne46x4V8GCLsOiFtYxsqvmdQI+8jcbRgSIDHHv6K9WsKj1RY/eKjLSxe\nuJPf/vUHdO4SX5JvWLsvKuMawO8LsG51YQcSDOWoLm6VqBfBFvznJ76QCAmTlneq7nj5C8sVe8Dj\nY/e7i+IKhrJNu49oGeFIM8DW/3xC0eINjLnzYoZeeQZIycZ/vsP6x97Ee6icHidOIGtwL0yfL+b1\n0/K6MPy62Uc+e8sqWXDFw+z9eBmG007AZzLq5rOZ9OA1CItCkpr6GIvqOOijVkJ3orbeV32Wl9Bk\nXxG8zjRqn+kDxPZhhNiNaoebvNWiIWjBUA/bNh9kw5p9EWW3TVNSVenj/TfW8sOrj457fpecdOx2\nI6o5EJCQxtH6kajQ0iLURN+D6ByEGtTLEL6iMlEvxiTUassXti30krlQQqF1hAgbTmutRdgMbM7Y\nr5K/qgZXbja2NJelcCAgKd20m8W3/I2yzbupLiwh/7UF+KtUZdr8177C5nZAnMi5E1+6F2dWben4\nLy54gH0LVhPw+I4Is3WPvkF5/j7G/r+LyBiQx54PliIDAfrMPJq0vPYZdpk63KjJ/DDqec6g1n+W\njGk01LnwMLWTvJVQqEsAFfSpBUOrYP2afXi90St+0x9g1bI99QqGGScN5t3XorvDOV02Tp89ImXj\nbBkCqEn9MLUP9l6i1d49WD/4fpTtVqBesk6oFy4X9Wi2rpXtsGtmsfTOeZhVkZO7MAwGnK/yMn2V\n1ex4+UtKN+2m86j+VOzcz9o/vowUWAuFMPyVNaz7y+tgQKAmTDsISMyq2L4FgICvdnIq31HI/q/X\nEPBEahgBj4/8V75i51sLkT4Te7oLUFrLUQ9ezZhbz6/3d9CxEVhPzPHagFohUYUiQ9eK/1zUEltj\nTDVaMNSDO82O3WHDZ1FOIy29frt319wMrr99Bv/8yzcYhlIdTb/k7IvHM3JsWy/dvZ9IoUDw590o\n22so6qqM+stklKEe/KG0BrORFcOvnc3ON76haMlG/BXVCJsSXM6cTJb94ikGnHc83934KL7KagIe\nH8JhR/ri5WxEI2wCmUBOTV0Or99J3vQxAJRvL8RwOWI6qmVwoeOvrO2VseLeZ8g7ZjS5U0cm/d0a\nB7W+s0T/dhl1fi6t53iD5qwdpgVDPUyZPoBXnl8Ztd3psjF0RC6b1x9gyIjcI5O+FZOn9WPscxfw\n/apC/D6T0eN7kpnV1jN6TVRZLCtNIADsA0I29wzi92YA9UJ5UJEYrbOyreGwc8bHf2DvJ8vZ9NT7\n7Jq/EBmQVO8tJv/VBeS/8lXE8ckKBVCBCcJmI9nVYfX+Q0d+zh7eN0pbqA+z2suGf7ytBUOD6Yda\nCG0ldnFIUIuecDMUKO16NbHNSQIVkWfdM6YpaF26eiukc5c0rr3lWBxOG06XDZvdwLAJfF6Trz/b\nxp8f+Izbr32D3fmH4l7H5XYweVo/ph03sI0LhQCwCRV2WjdKI5w91L4gfUjsUTNRmkPrRRgGnUcP\noHTDTqTPrM0pSEXYtxCk5XXBsCf3WtrSXOROrTVLZvTJpc9ZU7GlJRHeKyXV+0qS+l5NXXJQFX2m\nozTf3kQ/9zaUIzt8Idk5uC08kCUD5WNzod6fyTTnOl7nMSRIWWkNyxftYv2afaxYUoDfF2laysxy\n8denz8PuaO/N7dejHM2JhJu6gGNQL0FJ8NwAsZ11odWUA6U19ES9SK2D4lVb+eKC+6nYdaBB2kA8\nbOkunFkZzPriEar3H+KT2b/AX1Gd0LlZQ/tw7oZ/R0Qb+Wu8LL7lb2x94ZOEtAfDYWfy769ltPYz\npAgJLEb5EsIxUAEaw2OcZwaPaRpzaqJ5DFpjSJCsbDcnnTGMgp2Ho4QCqPLda1bsbYGRNSdeEhcK\noJzLIdtpDmoldRQQK5lLorSQQ6jY7WXEr6vUfBR8sJj5k26gfNve1AkFm0G3qSOY/IfrOOmVX3Ph\nrpfIHt6XHseP47hn70psxW8zGHfPpVEhqHa3k+nzfsapb/8WYa9fuAb8frxluhZY6qhAvS91CZlZ\ny1Gm2H1EPuN1S8m0DNrHkCSlh61XcaYpOVTS3l+sKpJ/aMNXqwL1YuQncF4AFRa4G+XYazkOrtjM\np3PvTY25CBBOO0OvmsmU318fM+nM3S0bw27HtJxcarE57XSfZu0XkFLy7Y1/QSZSk0nC2j+9wri7\nLsbm0k2pGk88LS1AbYVhA2WaHUttpVYvyldRFDymC8o01Xzh7VpjSJKBQ6wjA4SgA7QDTSP5sLys\nOp/XknjcdwAV+dQy+Gu8fHHRA7w79adIizyUhmBLc5E1qBdT/nBD3Ezk7tNH16sx2NKc9Dp1EtnD\nrEtuV+46QPW++L6vuhxYtJ7y/H20RRNz6yKT+O9KqHRGqFjk99Qmey5HJb2FjilBac81lldqCrRg\nSJLzL5uI0xWpmjscBoOHdYspNNoPLtSqpu5jY6VFhGyprrBtFSRvGvID36JadKxGqeDNw5LbnmD3\nu98hYxStsyKu2UYIJt53BT9Y+nccneJnzBs2G6e+/VscWenYM9wgwHA5MBx2hCGwZ7gZfv0POOmV\nX8UZixG/E1Ud/JXVfDLrbt4cfRWvDb2c/d98n/C5mro4UPk8iU6xAjiI0hLCs6tDBFCmp+ZBO58b\nwJaNB/jvU8vI31aMy+3ghNOGcP6lE3C6OoJlzkRVVz+IeuglKvqiG7CD2iJgfYPbw4VGvCqViWKg\n/BRN2+HNX+3hxW5nY1bHN+WEY3M7yR7dn7JNBZaOY0dmGrMXPUHnkf0TvqavvIodr3xJ1d5iuk0e\nRu8zjibg8wcFRP2TzpvjruHw2h3RO8ITzGNgz3Bz9up/kTmo+cIk2xfhzX28qHDWWIsjA+V7q8Ki\nz1mQTkD8hNr60EX0mpChI7pz35/ObOlhtBA2VBEwb/Cfm9rHqL66T/Wp14kQQCUSjW/kdeLjPVRO\nov4Uw+nA3snNsKtmMebOi3m1/yWWxwV8Jum9kzM3OjLTGXZ15LOWjA9g4n1XsOCyh5CmScBnYktz\nIRw2uozsT8mabRhOB/7KapVUVyexzvT6Wf/Ym0z960+TGrMmRKi6angi6zqUmciKLkSXhQmn+cLc\ntWDQNBAnyZfBDmVvFjXyuw+jtJNCassSDyTSbNU43N27YDjt9ZaxADjrm0fpNrk2/HDY1bPY/MwH\nEaUzbGkuBl9+akQ9o6ZESsnC6/7M9v99jhmKojIM+syawvR5t+PKyaJ0827Ktuxh45PvUPDeouhr\n+PyUrN7WLOPtOAxCJXGGa80GSuPOQE3J+UQLBoPIDspNi/YxNJCy0ho+nL+e/zy1lEVf77AMYdVY\nkUvjH7tQZJMHpbUUomouxTL7SJLWVATYXImV+i5asjHi85Q/38jQK8/A5nZiz0zD5nYy+LJTmfbY\nzcmNoRHsfH0BO176QgknM6D+BQIUfLAYT4ny02QP60vfs6aRd9xYS0e3cNjJGR+/T4QmWdJQ5qA8\n1MIqHWVCGhXc70JFKNnC/oXMTJ2bbZQp0RiEEDOBR1F38ZSU8uE6+0Vw/5koI9qVUsoViZzbGtm4\nbj+P/OZzAgGJz2uy4FM7r/93FTfdeQIrlxZwuKSKEWPymDytXwdIeEuWbqgHPTVRPrX4UNEcR1P7\nWJeiWi+WB78zD1Wmo77H3sOhNYsYcMEQNvm6sc7sSUWlJNNlYuw+gKfST3p5Kb3zN5JWXRnlSDYc\ndo55/P+Y/NC1VO4+QHqf3GbTFEJsfHJ+RC2kEAG/ybb/fMrE+350ZNuwq2ax5sH/RvlTbE47o245\nB1AaSPGKLXhLK8k9erju7dAo0qgVBCFCVVftqACPGdTWIetMcxt3Gu18FkLYgM3AaSi3+VLgEinl\n+rBjzgRuRgmGqcCjUsqpiZxrRUs6n00zwC1XvkpFeeRLZBgCKSU2u4HfF8DltpPTNZ1f/n4mGZ1S\nZ+JoH5Sjul1ZRV80lhxU3+lDwe8Iv75AOfAmEdt/sBvYhun18/xTpXz7ZRVeK0Uk7L1JT3cwYWpf\nXC47drvBtOMGMmREy/YHn3/0jRQv32y5b9St5zH1kZ9EbCtetZUFlz1I+fZCVZqjZw7HP3sXeTPG\ncmjtDj6dcw81B8sQhkHA52fi/T9i7P+7qDlupZ0jgV3Bf6Gs577AAJoi0a05nc9TgK1Syu3BL34J\nmIuqfxBiLvC8VFJokRCisxCiJ+ru6zu31SCl5Nsvd+C1qLQaCDru/D61EvbU+Dmwv4LX/rOKH90w\ntVnH2frJRLXqLEPFZm8lthkoWQ6h6jhZRX5IlMJ6iNpkohAVqMdO1Xc6XCpZ+EUVMXvjhPV4rqr2\n8+2XO45s/urTrRx38mAuv25KQr2gm4L+587g8Lr8qAqr9k5p9D0z+nnsOmEI56x9hsqCIgJ+k079\n8xBC4K/x8sFJt+Mpjqxhteq+58ka2of+c6c36X20f0Kd20IatEmtkIjd+KmpSYWPoTdqmRWiILgt\nkWMSObdVsGtHCXfc8BbPPbkoomlPPEx/gO8WWIQKalCroWzUCj6VZS9kPdczUSGzX6EEgQ/1Ii4l\nvCrmpvVebLbkJ3UpwetRBRY3rY8VfdL0jLhxLu68LhhhfhJbuovcKSPoeXLs6LGMPrlkDuhxRKDt\nenshpkWtJX9VDd8//L/UD7xDEUA9e1ad2/bQkuVg2ozzWQhxnRBimRBiWVFRY6NakqO62sdD935C\n0f4KfL7kbONmEslRHRMvLVMbJpRVvRBVlymSjE6iUcPyek3m/eUbivbHq0DbdLg6d2Lu8icZ87ML\nyRramy5jBjL5oWs4/YOHk2rtWbnrQMy+DpW76xd8vopq1v/tDT6aeRcLfvQwBxa1SmNAC+EltilV\n0JyZznVJhSlpD5FxVH2IztCIdYwjxvYopJTzgHmgfAyNG3JyLP46HzNOSQSbTWCa0UMShmD85D5N\nObR2QCfi+xnSUA7r3XGOaQzW3z16vBu7PYEssDgUH6zivjve5/dPzKVTZvP7mVw5WUz67VVM+u1V\nSZ0npSTg9WE4HXSdOASb2xmdsCcEXSfHqhCqqCku5Z3JN1JddFhFRwlB/usLmPjrHzH2Du2fUNNf\nLAKkMvw6WVKhMSwFhgohBgohnMDFwPw6x8wHrhCKaUCplLIwwXNbhOKiSl59YQV/+e3nfP3ZVjwe\na7XO4TA46YxhzLlgTESpDLvDICPDwUVXHNVcQ26jOLDu12CgEummoWyt9frLUordLrj9l91wNTKn\nqKrCwwdvRbd2ba1s/vcHvNL3Il7IOIsXu57N/m/XkzmoJ8IRuYYUNsHIn8ylYtd+1j7yKqt/918O\nroh0dq+6/3mqCotr8zmkxKzysPLX/6aqsLi5bqkVY0NFylk9+7nEFxxNS0pKYgSjjv6KutNnpJS/\nE0LcACClfDIYrvo4MBPl/fuxlHJZrHPr+76mjkravOEAf7r/M0x/AL8/EFMjMAzBMccP5LpblQNu\n49r9fPzuBg4VVzFqfE9Onz2C7M7xa+JoQK3K96LsrV5qY7vrOoh3oRLbwrW3UIz3NlIfAgsej+Td\nV8v4+otKDhUHEKJhRVb79O/M9bfNoN+A5mnm3hA2PfUei299IiIxz57uYtClp7D305VU7CisPdgI\n/iJk8GdUqe9+5x7H8c/ehTAMXup1vmURP3u6m6l//QnDrjmrqW+pDRCgtsRMSEPNQYWzpj7UPdGo\nJF0rqQ5SSn523ZsUF8Vrz6dwuez8+k+z6N23+RJPNMUoAVGDcl73RwkSq6YojaUzqmUjKJOXCykl\nWzYU8d3XO/jm820JByIAOJw2fvyTadhsgmGj8sjpqnIBpJSUldbgctlxp7XMKlEGArzU8wJqiqJb\nsBoOO8JuJFQ3yp7hZvq8nzHokpN5qfeFVFtoBvYMN1MfvYlhV81KydjbB17UmjmNpjQh6VpJDWTf\nnjJKD9U/wRiG4Ka7jtdCodnpinVT9OHAKovtoXo1+0lOo5hCZMP24NWEYNio7gwb1Z1xR/Xmr7/7\nIuEr+rwmTz32LU6XDdMf4KQzhjFybB4vzFtKWVkNSBg7sRdX33xMs7d/9R6uwFsaw1FuCAKJ9HQA\n/JU1bHxyPoMuOZlBl5zEhifejuogF/Cb9J09rbFDbmc0pMRM09FmopKai/mvfo8/gdr7dodB4Z7W\n3Z+4Y1GBdRiRRGVAdyGxx91OLKFQl6Xf7kxifIpAQFJT7cfnC/D5R1t4/I9fU1Jchd+nzJZrVuzl\noXs+bvZ+CI7M9JglwwMeH4YtcbNGqBPchF9eTuaAHqpsOCBsBrY0F1Mf+Qlp3VuvSa11IVFmpnXB\nf8WkPik0Gq0xhLG/sIyl3+1K6Fivx2T96kLO+IF19yxNcxNPmEtgNLARVcAvVC68C7XCJB1lmrLq\nN2HNjq2Nc6Ba1dcyzQDFRZVs+H4fo8b1bNT1k8Fw2Bn0w1PY8tT7lvtNb/19o0GVHh9w7nEAOLM7\nMWflPHa89DkFHywhLa8Lw649i5yxg1I27vZNqLFVCbXP90GUxjyapgzz1oIhjLUrC+s/KIhhCLrm\nNm/9G008umFdlVIA3VGOvNGohLZQufDGOffyemayt6A05Qs4vxmgYNfhZhUMAN2PGcXWZz+ybAUq\nhEDWc6PCZuDu3pmRN519ZJvd7WTolTMZeuVMqveXsOLXz7Hzja8xHDYGX3oq4++9rNnrSLUdDhIp\nFAj+XBzc3nSNwbRgCMPhsmEYiUlhu93g5JnDUj4Gr8eP3W5g2LSVLzkygF6o6KbQi2Sg7Lb9wo5z\nkKowwLPOHc26VYWWJVIag91u0L1H0zYissKw261zFkD1a4iDLc3FqFvOZfBlp7DyvufY+/Ey3LnZ\njLr1fPqfMwPv4QrmT7qB6qLDyKCmtP5vb1Lw4RLmLHsSm7PlQjNbL7H8YgFgH1owNBNHTenL8/9c\nErXdZjeQUuJ02kFAwAxw5Y3T6NO/1k4qpWTPrsPU1PjpNzAHpzO51eiqpQX89+mlHDxQic1ucNzJ\ng7nkx5M6SFe4VDEEZQoKlRPIBXrSVI/50BHdufIn03jhn0sIBGTMXJdkcbntjJvY/F3T+syaYqkt\nGE479nQ33sPRzmnhsDPq5nM4+o/XU5G/j/mTb8BXUYP0+SndtJvilVspWjQHZ5dMPCXlR4QCKN9F\nRf5+dr7xNYMuPrlJ702THDpctQ5LFuYz79FvkVIeqZI6YHAON991Als2FoGEUeN6RIQVFuw8xKMP\nfUXpoeojVVYvvPIoTpkZnRm6t6CUT97dSOGeUgYP68apZ41gz67DPPrglxErT4fDxrBR3bnz/lOb\n5D41qcPvM1m2aBdP/+27lGgPaekOHn58Dp1zmr+09cYn57PkZ08S8PqQZgB7hht39y6MvGkuK3/5\nLP6q6DINtjQnnfrn4cjK4OCyTVGd4GxuJ13GDuTg0k2W3zn0xzOZ8fQdTXI/bZsiVD2vulqDgTKL\nJtcNEHQeQ6M4VFLFogX5VJTXMGpcT0aN6xGzSqbH4+e2q1+nsiI6xrtb9wyuuflYRo5Vrf1WLS3g\nb7//CtMMIKWKbHLYbXTpmsbegugIJ6fTxq//OCtCM9G0TgJmgJuvfI2K8vo7viXCtOMGcOPPjkvJ\ntZKleNVWNv3zXar3ldD7jKMZfPmp2NPdLLr5MbY8/QHYjIgkuPqwZ7jJHtHPsgy4sNsY87MLmfzQ\nNam8hXaCBL5HVQMON4/moKoCJO981oKhmfjmi208/88leGqszQhOl42f//Z0XE4b99z6rmXWbKxs\nWrfbzhU3TGX6iTqKoy2wZGE+/3rsW6U1NPK1stkET792aYuV7Y5FZUERn53zK4pXbE74Hh2Z6Yy6\n9TzW/vmVKIFiS3Mxd9U8sofqmmLWhMJV9wc/56E0hYY9FzrBrZk4eKAyplAAFdb6+our2LGlOGYp\nhXiyuWs3HbHRVpgyfQA5XTN457Xv2VtQSp/+Xeg3oAtvvbwm6WsFApLCgjJ69c1ugpE2nIw+uco5\nnYTgk4EAY++4CMNhZ82D/0XYDAJmQOVHOGx8c9UfmPCrK+h9WvPWw2obCJSvrHkbP+nQl0bSt39n\n3O748nX75oP44vSEFobA4TSitmV1djNsVPeUjFPTPAwZkctt957MH588h//7+Ymcc8l47PbkXzO7\n3dZqS7Z3HjMgoeOE3cCW7uLEl3+Fo1MaE+69jPO3vsCgy0+DQACkxFdWxYGF6/jsnF+x9YWPm3bg\nmoTRgqGRTDi6D1md3cQrce9Ocxzp8GbFsBG5nDZ7JA6HjbR0B06Xjd59srnrgdMSDp+VUnL4UDXV\nVanqhKZJBVLKBk3waRkOevdrfeVWpJRU7NhX73HCYWPI5adzwfb/RnSMS+uRw643vibgjdSyzSoP\ni2/7e8KlNzRNizYlNRKbzeDeh2fyzOPfsWpZdCsJp8vGyTOH8c6ra/FbxCTbbIKrbjqGHr2yOOuc\n0ezaUUJWtjsph/OqpQU898/FlJfWICWMGt+Ta28+hixd2bXFEUIwdGR3NifRzc3psnHd/01PeFHQ\nnBz4dh2lm+L3xhAOG5365XHsk7dh1CnXXbHrAD6LPAmAgNdP2ZYCOo/sn7LxahqG1hhSQHbnNG67\n92QeeOQsunXPwOW2q5W/08bcC8cx+7wx5OZ1oq4f0WYT3P7Lk+nRKwuATpkuRo3rmZRQ2LzhAE/8\ncQElB6vwBevtrFu1l9/94uO4Woqm+bjsmqMjenXEI69nJg88chZjWyCPIRGKFm8g4IvtUzMcNvrP\nnc7sb/8WJRQAHJlpyBi1yKTfj7Nzp5SNVdNwtMaQQvoPyuFP/zyHXTsOUVXpZcDgHNLSnbz35joO\n7CuPcDLb7QY3330CYyZETwB+n8maFXspPVzN0BG5cQXFWy+tjoqdN03J4ZIq1q7ay7ijWmUL7Q5F\n8cFKuuSks7+wPO5xLredH141mZ69m97hXLWvBCEgLa9uz4v4pOV1wXA6okxBAF3GDWLO8ifjFtxz\nd80mb8YY9i1YE5FMJ2wGXScNJ71n02XzahJHC4YUI4Sg/6Dal624qJI3X1wd5XwWArZuLGJCndaf\n+duK+cOvP8U0JYFAACSMHt+Tm+46wdKJWbArun4+gM8fYO/uUi0YWpgP3l7PGy+uqrdvg8ttZ9S4\nHoyb1LR/r4PLN/P1lb+nbKsye2YP68txz91F1wlDEjq//zkz+O6mx6K22zPcKvIogSqsx7/wc94/\n4Taq95cQ8PixuRy4cjI58X/3JnczmiZDm5KamJVLC6JMSAA+X4DvvtoRsc3vD/Cn+z+jssJLTbUP\nr8fE6zVZu7qQd1+3bg/ZPc+6po7DYZCbp9XylqSm2peQUEDAcacM5pa7T2xSv0LlniI+PPl2Dq/L\nJ+DxEfD4OPT9dj44UU3SiWBPd3PGR3/A1S0bR2Y6jsx0DJeDETfOYdAPT0noGuk9u3Luhn9z0su/\nYvJD13DCi/dw3tYXyOjTvCGZmtg0SmMQQuQALwMDUKUtL5RSHqpzTF/geVRmhgTmSSkfDe67D7gW\nlfsN8AsppXXd3zZMLEt/3e3r1xTi80XbX31ek4/f2cCm9fs5UFhOv4E5nH3ROPoPymHuReN47OEv\nIyYfYQjS0hyMn6yThpqSzesPsODTrdRU+5h8bD8mH9M/QqvL316CzWYA9QgGCZ+9v4k0t4PzL5/Y\nZOPd+I93MC1MQAGvn03z3mPCLy9P6Dq5U0Zw8d5X2fflKryHK+g+YyzpPZIzSRk2G31mTqHPzClJ\nnadpHhprSrob+ExK+bAQ4u7g57vqHOMHfialXCGEyASWCyE+kVKuD+7/i5TyT40cR6tl4tF9eOnf\ny6O22x0Gxxw/IGJbZbmXWGKkssLL+tUqTLC4qJK1q/Zy2z0nMXZiLy695mhe+vfyYGikpFefbG6+\n6/gGxc9roKLcw7ZNB0nv5GDwsFzLVfzLzy3n0/c3HclyXrNyLx+/s5G7f3v6kQKK6enxw5TDkRLe\neX0tM04dTI+eWSm9nxDFKzZHdVMDMGu8FK/YktS1DLuNXqdOStXQNK2MxgqGucCJwZ+fA76kjmCQ\nUhYChcGfy4UQG4DeqOpQ7Z6uuRnMuWAM77y+Fp/XREpwuuzkdE3nrHPHRBw7ZEQuvgSKsEmpMqqf\nfXIxv39iLieeNpQZJw5iz+5S0jOc2oTUQKSUvPXyGt57fS12hw0pJWlpDm7/5cn0G1i7Ii7YeYhP\n39sU4fT31PjZvfMQX360mdODzZv6DuhCduc0DuwvTzhT+O2X13D9rTNSel8huowZSOHnK6Mcx4bL\nQZexA5vkOzVtk8YuKfOCEz+oAuF58Q4WQgwAJqI6t4e4WQixRgjxjBCiXVaLm3PhOO66/zSmnziI\n8ZN788OrJvHAX84iPSOyx2tuXidc9WRRh3PwQOWR4n12h43+g3K0UGgESxbu5IM31+HzBaiu8lFT\n7edQSTUP//KTCCGw9Ltdlu1fvR6Tb77YduSzEILb7jmJzEwX7jQ7NpuBq56w1fytidn6G8LIn55t\nGUJqOOwMv352yr7HX+2hsqAoblirpnVT7ywkhPgU1U29LveEf5BSSiFEzHWREKIT8Dpwq5QyVEr0\nHwLGYv4AACAASURBVMBvUOup3wB/Bq6Kcf51wHUA/fr1szqkVTNkRC5DRsR3rgXMAFVVibVQDOFI\nsu+DJjbvvbEOj4Wj2PQHWLlkN1NnDDiyLVbxybqbe/XN5i9Pn8eqpQUcPFBJv4FdeGHekpj9wtMz\nHPzuFx+Rv7WYtAwnp88ewayzRwV9FY2jU/88Tv/gYb667EFqDpYCKvz0hP/8gozejXf8mh4vi299\ngq3PfQyGwLDbGH/vZYz52YWtrhigJj71CgYpZcyGAEKI/UKInlLKQiFET8AyvVMI4UAJhf9KKd8I\nu/b+sGP+BbwbZxzzgHmgqqvWN+62iDAELpc9blG+EIZNMGZ8Tw4VV/HN59soK61h7MReHDW1b0om\nkY7IoZIqy+1+fyBiX063DMvChw6HgTvdwe3XvqF8SMcNZPIx/ejRO4ujj63N5r3t3pO46ydvR13D\nbjfI316CPxiA4PVW8/bLa9iVf4ifpKgEd96MsVyw40XKthSAEGQN6Z3UpC0DATbNe5d1j76Bp7iM\nvOPGMuk3P6bzqAF8/eM/sOvthZg1Sos1gVX3PYfN6WDULeemZPxtGxNVKdUDZAKdacq+zY2hUWW3\nhRB/BIrDnM85Uso76xwjUP6HEinlrXX29QyZooQQtwFTpZQX1/e9ransdqp58ZllfP7hZktfg2ET\nyIDE5baTle3m1DOH8+p/VhEwA5im2t6jVxb3PHg6LrdulZgsf33wC1YuLYjyB7jcdn72y5MZPjqP\nkoOV3P3T+Zbd2gybQAiBGWZmUsLextkXj2fW3FFHtm/4fh9/ffALTFNiGIJAQNIlJ40D+6K7pDmc\nNn7719lHMuRbkm+u+RPbX/q8tny2ENgz3Jz69m/45KxfHBEK4bi6ZnHJ/tcR8QqKtXvKgVWohyuA\nEggZwASaM52sucpuPwy8IoS4GtgJXBj88l7AU1LKM4HpwOXA90KIVcHzQmGpfxBCTED9tvKB6xs5\nnjbPBZdPZM/Ow6xdXRi1zxCCqScOZPK0fgwc2pU7rn8rInHOU+Nn7+5S3ntjHSfPHMaqZXuQUjLh\n6D50yUk/UmSve49MrVVYcM7F41m3ujAi9NfuMOjdr/ORKrdffbIFMxCjpEMAAjJQZ5ukptrPGy+u\nIqOTk+NPUYlkI8f24InnL2Tjuv14vSbDR+Vx69WvWV7XMATbNh1sEcHgKSlj07/eY99Xq3HmZLLz\n9a8jI5ukxF9Zw/JfPIXhclgKBs+hChZe/wgZfXIZcsXpZA7s2Yx30BqQwBpUgGb4tgpgGxDd6bGl\n0Y16WiFffLSZ/z69zFJrGDWuB3c9cBoLPt3Kf/611HLlmp7hwOc1EcEwy0BAkt05jdLD1dhsBg6H\njR9ePVk3ALJg68Yi/vP0UnZsLcbpsDH9pEFMnNKHD9/ewL69ZZimpPSQdRG4+uiam8Ej/4ptUrn1\n6tc5VBxtznKnObjpzuObvX5Sxc79zD/6RvyV1ZjV3tgdpVCZz9IMWAqGEIbTjrDZmP7Uzxh8SWLJ\ncO2DwyjBYBVxaAAnNNtIdKOeNkzJwaqYYavFBysBZfeWMWIgqyqjHdjFRcHzfAE8NX6e/cciOndJ\nY/T4jrZ6i8+QEbnc98czCQSUiWfhl9t5/A8L6s9eTgCrST+c02aP4K3/Rde+crpsjBpnFf/RtCz6\nv8fxHipHhsqGx1lEOrM7kTNhMHs/W2GZKwEEw2T9LLzmT/SZNRVXhymYF89nGEBpD63L16DtCa2Q\nQUO7WoatGoZg+Ehl0hh3VC+kRfJUon5Er8dsUGexjoJhCPz+AP99amlKhAJAl67pcffPmjOSSdP6\n4XDYcLntuNPsZHV2c+d9p7aI6W/PB0tqhUIcbGkuRvxkDif+7176zJyC4XLgyIp9r8Jmo+C9Rakc\naisnm9iJLCHzYAWqt3PrCPHVGkMrZPyk3nTNzeBAYXlEvLzDaWP2+Soprlv3Tsw6exQfzd94xJzk\ncNpAErdbXDj79lqHTLZX/D6TlUsL2F9YTq++2Yyf1DvuhLtvT2mEIzmckABO1BLrdNk45+LxcY8x\nbAY33D6D/YXlbN1URFa2m1HjerScPyjOIsOW5lI3bwh6njzxSOvOU958gOoDh6jaW8xHp96Bp8Ti\nGZMSM4ZW0T5xAP2AXRDRk8UIbl8C1KB+4TK4bQAtqUVowdAKMWwG9z50Bv97ZjmLvs7H7zcZNqo7\nl11zNHlh5RLOu3Qiw0fn8en7mygv8zBhcm9cLjuv/WeVpe+hLjVVPt56aTVnzBlJWrqz3uPbMgf2\nlfO7n39ETY0qTuh02cjMcnPvQ2fQOcd6detOc2DGKGlhGAKH00ZNdZzeBMFjDCE455JxHHfK4ITG\nmtczk7ye1sURm5O+PziGXW8tjNIabJ3cHPv3W/GVVtJ9+pioyqxp3buQ1r0L/c4+lq3PfxJRXhsg\n4DfpfUZH6+88EBWFtAsVrpoN9Ae+D34OZxeQhnX6WPOgnc+tnNDfJ9FYc6/Hz69uf4+iAxVH4uHj\n4XDY6Jqbwf1/PhN3WnSIa0W5h5efW8GShfmYpmTsxF788KpJ5Mao6tpcmGaAFYt3s3LJbtIyVLRP\neLnzuvzq9nfZteNQxArfMAQjx+Zx5/2nxTnvPXbtKIk6L7uLm7LDNZhm7PcnM8vF9bdNZ+SYHtgd\nbS8RsbKgiHeOvhFveRVmlQfhsGHY7Rz/ws8ZcG79eRVVew8yf9INeEsrjzil7Rluxvy/C5n46x81\n9fDbAMXAOqyd0unAVIvtjSNR57MWDO2Qqkov81/7nkVf5QMqXLVg12G2bTpoWdTN6bJxweVHcfrs\nERHbfT6Te255h4NFlbUmFQEZGU4eenwO2S3UOtTr8fPQvf+/vTMPb6pKG/jvZG2bLnSj0LKXspQd\nAZHFAVQEXEBARB3FXcZxn1FxdBSXT3Efd4dRR3BFFAERkcVBEAQEZN9ahAIF2kJLS5c0ucn5/kio\nTZO0KW3T0p7f8+Rpcs859765ub3vPe95l6VkHs6n1KohhEvBjZ3ck8sr5J8CyMkq5NF7Fvpc0DcY\ndLzx0dVYwn3PmM7MNEqKbdhsDvQGHRGRZkwmQ5WFdwAiosy89v4EjOegYgCwFRSR/tEPHFu5lYj2\nLeh85+VEdWod8HjryXx2vzWfI99vIDQhmq53jyPpkqY2W/DHUSANfJT8dRlzaieosTzKK6kJE2Yx\nMXnKeUye4pn98r3XfvaqAQGuhehf12Z4KYYvZ232qjyHdCmeJQt2cc2U+smuuWzxXo5knCrz3pES\nbDYH87/YxvmD2xEbb+FUXgmhYUZCQ41YS+zo9QJfVm0hBDabhgWXYtA0J9s2ZZKddZpWbZqR2rMl\nw0Z25NuvdriyrEooLCglOjawfx27zcGmdYcYOPTcSFJXmncaoROYolweQ6ZIC6n3jj/ryOWQ2Cj6\nPDlFzRB8UplXliVoUvhCKYYmRFSzEHQ68BWfZamQ0G/lsjSWL97rc3FVSli5NK1MMdhKNX779QgF\np6wkd46jQ0rcWcvocDhZu/IAq1ak43RIBg1rz9CLOpalsgb4ecV+L5dOl1ySrz/7jZ1bjlNSYkc6\nJb36JTFl6vno/ETdRkSZaRbtmvlkHz/Nc//4gZISO5rdicGowxJupiC/pJzJyPU390QRBqMerYqF\n/lKrRo6PaOaGxolN+1hz28uc2pUBQGy/Tgz978PVmh0oqksELuVwGk+vJR1QvzFGSjE0IYYMT2bF\n9/twVripms0Ghl2aUvbZ4XDy5azNldYSKCnRyDpWQFGhjZemr8DpdKJpTvQ6QXLneB54fITHzTwQ\nnE7JK0//SPqenLLF80MHc1m9Yj+PPX9pmTnGX+Sx0yHZ8HOGh91/68ZMXn3mR66/tR+z/r3+D9dT\nASaTnilTzy9bv3ljxk+cyispU4aa5sRq1Xx6GjqcknYdojmScQq9QWArdfg8X+YQA0ltm1XrPASb\nwkNZLBnxIPbTfwTu5azbzaJB9zDmp39hjg4nLNFT2TtsdrY9/xl73vsWrbCEhCE96PfiHcT0UEGT\ngSOAXkA6ruTUEteicwquPEr1h4pjaEK0bhfN+Ot6YTTqMRh06PQCk0nPkBEd6FWu1nDuiaIqXV6N\nRh0H9+fyyjM/Ulxkw1qiuYLnSh2k7clhwVnESGzdeIT0vTkeHlW2UgdHD+ezbtXBsm0Dh7bDaPS+\ndJ1O6bUYrGlOMg/l07JVJA8+PoKuPVoQExtGzz6JPPL0JWU1t7OOnSbraIH3DMmPbpROSUiogWtv\nOY/LJ3Rn8s3neSlCnV4QGRVC7zqu41xTdr35jbf7qJTY8k6zoM8dzE3+M9/0uJXcrX+kFF8x9p9s\nf3EO1qw8tCIrmT/8yneD7+XUroPBFf6cxwB0wRX9fCEwEIitV4lAzRiaHGPGdaPfwDZsXHcIh+ak\nT/9WtGrrWQYjzGLGWYm3Dbhs8/mnSnx6PtltDlYu3cfV1SxT+esvh3xmli0t1Vi3+gBDL0pGsztI\nbBVFqMUERbayUqhmswFNc/j0ErLbHSz9dg9/+dtQuvbw7QJYXGRDF0gZTjdCQNqeHH5PPwlOSGoT\nxZ0PDGbOrM3knihGAt16teC2ewa599twOfHrXq/iPQBIkJoDqTk4tfMgi/90PxPTPqYwI4tjP23F\nWSH9hVZsZfOTHzFi7vTgCN6oEEDDcVBQiqEJ0rxFBGPGdfPbbgk30b1PIjt+O+qzII1OJ4iNtxAZ\nFYK/R2prAKnDK1KZ6clkMnAg/SQvTV+Ow+HE6ZQ4nZLIqBBSusZz0ejOfPL+rxw9nO9z/MZfDlFU\nWIol3OyzvVXbZn6j1XQ6gdCBQzvjOuzqqtmdZYox4/dcvvliG4OHd6BV22i69WxxzsSGNOvaluw1\nO6qMcnbaNXa/vYADc1d6KQVXB0nW6u11JKUimDTsRxlFvXHLX88nNt7iMjm5rxIhwGjS0aptMx6a\nfjGdUhP8RganVFGUyBcdOvpftB4wuA0vTV9OUaHLbGUrdc0OrFY7KV3i6darJeOu6el3vMGoZ9e2\n437bjUY919x0HqYKFdZMZj13PjCEUVemEhtvISYuzGdMicMhOZJxim8+38bMf63h5adWYAsgyLAh\nkHrfePTmqtO0O0ps7HlvIQVpmX77mOOialM0RT2hZgwKL9L35vDqMz+W1QqQUtCjb0tGXdmF6FgL\nSa3/WBgbNrITPy1PK1vUFcL1dH/tzdX3Vd+62f8NZ+1PB3yaiWylDpYu2sPocd2IjArxmwBUQJWp\nJUaM6kRsvIUFX27jRFYhrdo246pre5HSpTkDh7Zj0o19OXokn+l/X1xpMaVSq0bG77ks+HIbV9/Q\nt9JjNgSadWnD8K+ms3rKC2glpTisNq9oZQCdyUhp7mmfbeBKk9Ht/ol1La4iCDRpxVCQnsnO178m\nd0s6Mb2SSb1vAlEprepbrHqltFTj5adWUFKhxOjenVn0G9ia7r09F1Kvv60fbTvE8P2CnRTkl5LS\nOZ7x1/Widbvql+8+euSU37Zd24+j8xP8XVxkY9G8HSyYs81v7iKnlHTrXXUm2V7nJXksxFekeUI4\nugCi0O12Jz8tTz8nFANAq1EDuObol5zacZDi47n8b+J0tCKrRx+h16EPMWIv8J0ltsP1F9Hp1tHB\nEFdRxzRZxXB89TaWjXkUR6kdqTnIWb+H9I+WcvGi/6PlsN71LV698duGwz7rGdtKHfywcA/DRnby\n2C6EYOhFyQHnAaqMhBYRHD3sO7GfdEqcfjRDu+QY5n++tWwh2lM+l5nozvsHYzbX/HI3GPVMuL43\nX368ucqsq/ZaysoaLHR6PTG9konplcwli59n1Y0zKDl2EqfdAVJWqhTMsZEM/veDFGeeIHPJBoRB\nT+srLiAkVpmWzkWa5BqDlJLVN7+IVmQtmxZLzYFWbGX1zS/6LfTeFDidX+p33eB0gdXn9triksu7\n+m0TQtDrvCSvNQCDUU/HTvHoDb4vZZ1O8PxbV3LewDa1KGcXbr37AlokRmLwc1whOKdrXbQY2pPz\nnrvVVY7T/f9gy3MH6lUwyRnCQuj77C1sffYTvu50I+sfeId1977Jl60nk/bRkmCLrqgFmqRiKDqU\nTcmxkz7brDmnKDzgXVazqZDSNb6s8lt5hIBOqQl1euxuvVrSIsm7fKVOJ0juHMfdD13I+Ot6ExsX\nht6gQ6cT6HTw/YJdfrPJOhyS7+btrPWF4IFD2/PCO2P54KvrGT0u1WM2otMJQkKNTLqxfs1IToeD\nzGUbSftoCXk7vFOhVIaUko2PzPSqyCYdTgSgCzGiDzFhio6g7/O3Etkxie0vfIHDakMrsqIVWnFY\nbfzy19fJ33e4Fr+VIhjUaG4thIgB5uBKHn4QmCSlzPPR7yCuuG8HoJ1J4hTo+NpGGHT+62Y4JcLQ\ncPyJg0275Fi6dEtgz44sj7QTJrOBCddXXk+gNvj7EyN4dporLYWtVMMcYiDMYmLqA0PQ6XWMHptK\nRKSZWe+tx6Y5Ayqis3rFfrKOFVSaRbUmXDOlL8md4liycDcFp0pI7dmCyyf0ID6h/iqU5e87zJKL\n/449vxjpdCKdkhbDenHRvKfQm6t2o9UKSyg57vtf0RAeyoivniS6e3vMcVHo9Hp+nDgdrdh7RunU\nHKT99wf6PX9bjb+TInjU1Og6DVghpZwhhJjm/vyIn77DpZQnajC+1rAkxROZ0oq87b97tYW3b0l4\nm7p9Mm7o3PfoML79egf/W7IPa4lGp9TmTJrS18Mbqa6IT4jglZlXsXnDEY4fLSCxdRS9+7XyMNks\nmLOtWlXV7HZXNPahg3m0OYtF8aoQQtB/UFv6D2pb6/s+G6SULB09jeLMkx4uWsdXbmHT4x8y4KWp\nVe5DH2pGZzTg8OGB5LRpWNokEJrwR5rzkmzfSkTaHVj9tCkaLjVVDGOBYe73s4CVVO/GXtPxZ82f\nPv0Hiy+8H0epHUdJKfpQEzqTkWGfPRaMwzdoDEZXtbGqKo7V5fEHDPZ/k8094WcBNMSArVTz7a4q\nBId+z60TxdDQOLFhD9acfC+/XUeJjX0zFwWkGHQGPSk3jyLtw+89zEnCoCe6R3sv773WYwZyctM+\nHCWepidDeCiJI1Wa7XONmq4xJEgpzxjkjwP+HrUlsFwIsUkIccdZjEcIcYcQYqMQYmNOTk4NxYbo\n7u2ZmP4xfZ+5mY43jqTPUzcxMf1jYnrV3LtGUbfE+THRSClpFuO7RoQAYuIqr7ncWLDmnPK5TgRg\nL7QG7FzR/+WpJF5yHvoQE8bIMAyWEJqltuWi+c949e185+WYYyIR5epO6MxGItq3CKioj6JhUeWM\nQQixHN815jweraWUUgjh74obIqXMFEI0B5YJIfZIKVdVYzxSypnATHAV6qlK7kAwx0TS/cGra2NX\niiAy4bre/OeNNR7mJINRR9v2MQwe3oHPP9zksRgtBIRHmOnSvf5KJQaTuP6d/dZUju7RPuBqgIYQ\nExcveJaC9Ezyth/A0rY5sX1SfI43R0dw5cZ32fL0bA5+vRqdQU/yDZfQ6x/XozM2Wa/4c5YqfzEp\n5cX+2oQQWUKIllLKY0KIlkC2n31kuv9mCyG+AQYAq4CAxisU5RkwuC3FRTa+nL0Zu92V7rp3v1bc\nevcFhIYZyckqZOm3uzEY9TgdTmLiLfztnyNchXaaAKEJMXS+8wrS3l/ssSCsDzMz4NW7qr2/yI5J\nRHasOkNsaEIMF7x9Pxe8fX+1j6FoWNSotKcQ4iXgZLnF4xgp5cMV+lgAnZTytPv9MuBpKeWSQMb7\noi5Ke0op+f3T5ex6az62vNO0GnM+PR6eTFjL+k+Bq/CNw+Ek72QxlnCTV8K6wtOlZPyeS0Skmdbt\nogN+Sm4sSCnZ895Ctr/4BdacfGJ6daT/C7eTMKRHfYumqEcCLe1Z0zWGGcAlQog04GL3Z4QQiUKI\nxe4+CcDPQoitwAbgOynlksrG1wdrbnuZtX/5Fyc27KEgLZM97y5kfq/bKcqs+XqGom7Q63XENQ/3\nmcU0PMJMt14tadM+pskpBQCH1caJ9XuwZp1CAKd2HiBn/e4mHbypCJwazRjqi9qeMZzadZCF/e/C\nUVLqsV0Y9KTcMprB7z1Qa8dSKILB8nH/5OjSjR4eRTqjgU53XsbAf93timhWNDmCNWNoFGQu24T0\nUS5Sag4OL/qlHiRSKM6ewowsL6UArnoKe95ewLyuN1F8PLeepFOcCyjFABjDQ9H5iXY2WkKCLI1C\nASc27mXpmGl8njCe+X3u4PcvfgzYDJS/9zA6f/UVJJw+cJxVNzxfi9IqGhvKjwxoe9UQ1t37ltd2\nfZiZTndcXg8SKZoyx1dvY+noaTiKXaZNa04+a25/hZz1u9GZjJzcnEZ0j/ak3j2OiA6JXuMjkhNx\n+nFXBddMOOvn7VhP5qvspwqfqBkDrniGC2dPQx9qQh9qAiEwWEJIGNKDbveOr2/xFE2M9fe/XaYU\nzqAVWdn1+jx2vf41x1ZsZvfbC5jf83aOrdzi0a807zTHf9qKpW1zqCTnl06v85tCW6FQMwY37SZc\nSMKQ7hyYsxLbqUJaDO9NwpAeTdKjRVF/OB0Ocrfs999ucwXuSbuGZtdYdeMMJmV8jhCCjPk/89P1\nzyF0AqfmgEpqOBssoVjaNK91+RWNA6UYyhGaEEOqmiEo6hGhc1VJq5hzyB+2vNPk7z1MSFwUP13/\nnJdnnT/6v3wnOn3TzSKsqBxlSlIoGhBCCJJvGOl/8bgCUkqEXseBL1e6EkIFgD7MTGiLmKo7Kpos\nSjEoFA2MAS9PJbZPRwyWEHRmI8aIUITe979qaIsYIjsmYc8vqnTBuTw6g76scqFC4QtlSlIoGhjG\n8FAuW/Mm2Wt3cmLTPuwFxRQdySZ99jJA4rTa0YWY0Bn0DPv0MYQQtBjeG32ICa2o6vKrUnOQcGHP\nuv8iinMWpRgUigaIEIK4/p3Z8tRssn/ZiVZsRWcyIh1O4gZ2pfXo80m5ZRSHv13H6ltewp5fiCk6\nHIfmQFYyc9CZjQx8616MFt/pyRUKUIpBcRbY8gs5/N16nDY7SSP7EZYYV98iNUp2vTGPrDU7yhaU\nz5iK8rbuZ+Si51h375scmr/2jwyqeh16owF/RiJh1DPwzXtIuWlUEKRXnMsoxVCHnD5wjF2vz+PE\n5n00S21H9wcmEtW5dX2LVSMOzP2J1TfNcNXFdkqcmoOej15HnydurG/RGh17/73Ip5eR0OnY/fYC\nMr5Z49nucOKQGqEtYlylNp2ekdJGSygpUy6ta7EVjQClGOqI7HW7+GHkQzhKNaRdI2fdbn7/ZDkj\nvnmKpEvOzVKHhRlZrL7pBS9Xyh0vzaH5Bann7PdqqJSvpVAe6XCSu22/V+lOAJxOnHYNc3QEWrEV\nR4kNYdSjMxq4cPY0VTRHERDKK6mO+PmWl9AKrUi7OyBJc6AVW1l90ws+E/adC6TPXop0eBsqtCIr\nu9/8ph4katy0vuIC18ysIgLi+3fx3QaYoiOYsHcWvZ+cQuvLB5J691WM2/IfWl9+QR1LrGgsqMeH\nOqAkK5fTB475bLMXFJO/5xDNUtt5tUkpyVq9nf2fLMPpcND+6mEkjeznM0WyLb8QoddjDA/eImJJ\nVm5Z5K1X2/G8oMkRKJrVhrPUhinKd43oQCjYf5Siw9k0S21LaPPoWpSuanr/8wYy5v3sckV1P2AY\nLCF0uHYEnadewZZnPvYaow8z0/WuKzHHRNLz4cnw8OSgyqxoHCjFUAcIvd73NB93QJKP6byUkrVT\nX+P3z1agFZeClByc+xOJF5/HiK+mlymHExv3sub2Vzi1KwOJJGFwd4Z88BAR7VvW6XcCaDmiL+mz\nl6EVlnhs15mNJI3uX+fHD5SS7DzW3PEqmUs2gJREdEjkgnfvp+Ww3gHvw3oynx/HP8mJjfvQmQw4\nrDY63jiSC965L2gRw5akeMZte58dL83h8OL1mKPDSb1nPO0nD0cIwYivn+LH8U+CAKfdgc6gI3Fk\nP7refVVQ5FM0XlShnjri2wF3cWLTPi8FEZGcyIR9s71yMB1ftY1llz3q5YdusIQw9KNHaDfhQk4f\nPM78nrd53JiFToc5NpKJ+z+ps9mDlJLstTvJ23mQ7S/NoehwNtI9cxB6HeaYSK7a8QEh8c3q5PjV\nwelwMK/rzRQePO4RxGUIMzPm5zeI7d0xoP18N/Q+TmzYU/akDq6n8e4PXk3fp28u21Z4OJuCtEwi\nU5IIbx383EO2/EIy5v3syu81rBexfVKCLoPi3CEohXqEEDFCiGVCiDT3X6+5thCisxBiS7lXgRDi\nfnfbdCFEZrm2MTWRpyExdPY0TNHh6MPMAOhDTRgjwhj2+eM+E/Pt/2y5a6ZQAa3IStpHPwCw87Wv\ncFTwUZdOJ1qxlf2fLvfcLiX5aUfI33u4RuUcrSfzWdj3TpaOeoQND76LNSsXY3go5rgoTNERJP/5\nEq7c9F6DUAoARxavx5qV6xXZqxWXsmLs4+T8uqfKfeSnHeHk5jQPpQDgKC5l1xvzkFKiFVtZPvZx\n5nWewo8TnmRe5yksH/u43wXjusIUFU7KzaPo9sBEpRQUtUZNTUnTgBVSyhlCiGnuz4+U7yCl3Av0\nBhBC6IFMoPxK5WtSypdrKEeDo1mXNkxM/4T0WT9wcnMazbq1o9PNo/zeQKXd4d/8pLluUCc27Clb\nzC6PVmTl+KqtdLnzCgBy1u9m5XXPYs3KAyFcacU/fpQWZxHtuvqmFzi1K8PjJikMdpoP6saYla9V\ne391Td62A9j9RP8WHc7h++EPMuCVv5SdK5/9DmW7zEc+XEW1IiuOUjtr7niVo8s24bDayiqlHV22\niTV3vMqfPvlH7XwZhaKeqKlX0lhglvv9LGBcFf0vAvZLKTNqeNxzAnOzcLrdN4ELZ02j58OTK32q\nbj9pGAYf1eIMlhCSr78YgKgurf3mzDk4dxV7P1hM8dETLLnkIQoPHEcrLkUrslJ0OJtllz3qjcti\nNgAAEcVJREFUd0HcH6WnCslcutHryVlqDnLW76b46Ilq7S8YhLdLwBDmv+qeo7iUDQ+8Q+mpQr99\nmnVr5zfvkHRKtjzzMQe++smrdKbDauPg16uw5fvft0JxLlBTxZAgpTxztzkOJFTRfzLweYVt9wgh\ntgkhPvRlimoqJI7sR9LIfh7KwWAJIa5fZ9pfMxyAbg9M9Jt1U2oO1t/zJttfnuN1IwdXHv/db8+v\nlkz2/CLwY4WSUlKae7pa+wsGbccPRR9ihErqaOhMBo6t2Oy3PaxFDO0nDUMfavZulJJdr33lFTxW\ntm+jgZLsU9WWW6FoSFRpShJCLAda+Gh6rPwHKaUUQvg1ZgshTMCVwKPlNr8LPIPr9vMM8Apwi5/x\ndwB3ALRp06Yqsc85hBAMn/skGfPXkD7rB5x2jeTrL6b9pGFlQUkxPZP506eP8b+J05E+irBIp5Oj\nyzb7fNp12jXyth+olkym2Ai/WTilTcPSrqrngODi1Byk/XcJ5phIbPlFLvOcH8rHAGglpfw2fRb7\nPliMVmQlYUh3+s24nZCEaHa8/KWXia/iTMEDKbHUwyK0QlGbVKkYpJQX+2sTQmQJIVpKKY8JIVoC\n2ZXsajSwWUqZVW7fZe+FEP8BFlUix0xgJri8kqqS+1xE6HS0Gz+UduOH+u3Tduxgorq04dTOg15t\nTrtGSHwU+hCT181LZzISe16naslTfCjb577O7K80+xSm8LBq7bMuOP37Udb/7V0Of/uL55O8ED7X\nbaTmJPHivq73UrJ09DRObNhT9j2PrfiN74c/yGVr32LHS3N8HvPMzK28EjaEhdD9oWswhJiqJb/T\nrlGSnYc5NqraYxWKuqCmpqSFwBT3+ynAgkr6XksFM5JbmZzhKmBHDeVpErS9aohPk5IhLMRlbjJ5\n63udyUDXu8ZW6zghCdF+PZqEAIddo/h4brX2WduUZOWycMBdHF74i7d5xy27MLpmBzqjAX2omSEf\nPVyWXTR77U5Obtrnpfy04lK2PvMxxkjfik+n19P1r2MxxUQgDHpMMRH0efomev/zhoBll1Ky9f8+\n4bO4q/i60xQ+ix3Huvve8mkKVCiCSY3iGIQQscCXQBsgA5gkpcwVQiQC70spx7j7WYBDQAcpZX65\n8R/j8liSwEHgznJrFn45F+IY6pLS3ALm974da05+2ROrLtRMRPsWriLvhSVoxaWU5p1GCEFkciJD\nP3qEuH6dq32sFROe5Mji9R5PxjqTAb3ZhNOuIaUkqnNrLpw9jZieyf5lzjtN5tKNCCFIHNkPc7Oz\nj0Yuz6bHPmDHq3MrLVITlhRHi2G9sLSKp9NtlxGZnFjWtuOVL9n02Ac+I7pDE2NJuWmUy024nIeS\n0OtoltqWcVvfR0qJo6QUfai52vXBt874jG3Pfurh4qoPNdPh2uEMef+hau1LoQiEQOMYVIDbOYr1\nZD47XplLxter0IeFoBWVUHz0JA53LIQ+xERYUhwjf3iByA6JVezNhVZs5cCclWSt3UFE+5ak3DwK\nY3goKyY8SfaaHehMRhxWG1JzeK1xGKMsTNw326fn1d73v2P9vW8hjAYELtPJwLfvpdPNo2t8HhYN\nupucdbsr7dN8UDcu+/kNn237P1nG2rte94rmBojpncwV699h9c0vkjFvtbsegoPwdi0YuWQGlqR4\nnA4H6R/9wJ73vkUrttJu4oV0u38i5uiISmVyag4+i7/KtcBfAX2IiUmHvyAkNqrSfVSF0+FQdZ0V\nHijF0IQ4tHAtP/35/9AKPf339WFm+s24ndQAUiQUHz3BtwP/ii2vEK3I6qoQptdx0YJnSRzRh4L0\nTArSjpDxzc+kz17q9YStDzXR+4kb6fnItR7bT25J57sh95YprPKyXbHubaK7tz/Lb+3if9c8zcGv\nVvmNAdGHmen7zC10ufNyn26s9qIS5rS6xusGbbCEMOi9B8pchQsPZZG7dT9hSXHE9klBCIGUkhVX\nPcGx5ZvLnvp1ZiOhCdGM/W1mpcqhJDuPue2u87l+Y4yycOnSF4nv3yXg83AGKSV7Zy5iy1OzKTme\nS0jzZvR67M90vXtctWc0isZHUCKfFQ2DQ9+u9VIK4PLZP/jVqoD28cvdb1ByPK8sJYfTakMrsrJy\n0tM4NQeRHZNoNfp8Tv9+zKfZxVFi4+TmNK/te95d6NtLqtTOnve+DUi2yki9bwL6UD8LtnodzlI7\nvz3xXz6LH8/6B9/BWcHLymgJZeT3MzDHRmKMDMMYEYbObKTL1CvocN1FZf3C2yTQ5opBxPXtVHaD\nzV6zg2MrNnuYgpyldkqy89hVRbZZU7NwvzEpzlI74W3OzrNp56tz+fVv71HiXvuxZp9i06Pvs/VZ\n74R7CoU/lGJoBJiiLH5vMsaIqr2GpNPJ4UXrfLqmOu0a2b/sLPsc3aODzySA+hAT0T28n/6Lj57w\n7VrrcFKcWfMAuYRB3ej/4p3oQ0wYI8MwhIeiMxuJ7NwandGAdDhd0colpeyduYiNj8z02kfzgalM\nPjqX4XOeYPB//sbVv39K/5emVvmEfeT7DT5TYDitdjK+rlwh601Guv51XFnKlLLtISZajTmf0ISY\nAL69Jw6bnS3PfOwlk1ZsZfuLc9B8RHIrFL5QiqER0PHGkehMvgPfCtIz0Srzu8dlfvBbI0LgMUNI\nvWcceh9eT8Kgp9Ntl3ltT7qkn9fND1ymmsSRtVPYp+tdY5l8bC5DP3qEYV/8k0mH51CUkYWzYmRy\ncWnZWkBFdEYDSZf2p/2kYYS1jA3ouIaIUHQG3x7fhgASGvZ99hY63TK6TKnpzUZaX3EBF86eFtDx\nK1J8JMenEgZAJyisZuS7oumiFEMjIKZnMqn3jvfZVnQom91vVW7W0On1rjxKPp6QpVPSfFC3ss8R\nHRK5ZNFzWFo3Rx9mRh9mJqJjIqNWvExYC++n3JSbR2F2u3SeQRj1mGMj6XjDJYF+xSoxRYXTdtwQ\nWo85n9IT+eiMvhddhV5H8dGTtXLMDtcM9zlTM1hC6DL1yirH6wx6Br5xD5OPzWX0/15l0qEvGD7n\niUpTelSGOS7Ky1R2BmnTCElosokFFNVEKYZGQkh8lM/4BUdJKWn/XVLl+AveuhdjROgf+9AJ9GFm\nLnjnPgwVUkO0+FMvrj74GWN/m8m4re8zYe9svwulxogwrvz1XZJvuBhjlAVTlIWON47kyl/frbM0\n4ZakOJx+op6lw0loy+qbaXwR0b4lA179C/oQk+u86QSGsBBaXTaQ5OsvqnoHbkxR4cT2SalxhlpT\npIW244d6xbi46mUMqLGXk6LpoAr1NBKkw+k/r5Gfp8jyNEttx1U7P2TX6/PIWr2diOREut0/wW/s\ngxCCqJRWAckWmhDD0A8eZugHDwfUv6YYI8LoOOVS0mcv9Yg/0IeZ6XTL6LLgtjNIKTm5aR/208XE\n9e9SLYXVZeqVtBp9Pgfm/oRWbKXV6AFn5U1UWwye+SC2vNMcX7kVndmI06bRfFAqQz96pOrBCoUb\n5a7aSDi15xALz5vqlSpaH2KixyOT6fPkFD8jGydOu8Yv97zB/tnL0Bn1OO0OUm4Zxfmv/bUs9xRA\n7tb9LL/yMUrzChE6gdPu4LznbqXbfRPqUfqaU7D/KPl7DxOZkhSwAlc0flQcQxNk/d/eYd/M78pc\nTvVhZiyt4rli/ds1qnt8LmMrKKI48wSWVvFeHlpasZU5rSdjy/PMEmsIC2H43CdoNfr8YIqqUNQ5\ngSoGZUpqRAx4+S+0unQAe/79Lba807QdP9QVvWypG1v+uYAp0oIp0uKz7eDXq33mJdKKrWyb8blS\nDIomi1IMjQghBEnuug6Kqik6nO2znCpAYUZliYIVisaN8kpSNFli+3TEYPFRjEcniB9Q/YSDCkVj\nQSkGRZMlcWQ/wtskeLn5GkJM9KpG+myForGhFIOiyaLT6xmz6l+0m3ghOpMBodMR06cjI5e+REyP\nDvUtnkJRbyivJIUCV4pqqTnQm1UFNUXjRXklKRTVQKfXg6pdoFAAypSkUCgUigrUSDEIIa4WQuwU\nQjiFEH6nJ0KIUUKIvUKIdCHEtHLbY4QQy4QQae6/KsuXQqFQ1DM1nTHsAMYDfpPPCyH0wNvAaCAV\nuFYIkepungaskFKmACvcnxUKhUJRj9RIMUgpd0sp91bRbQCQLqX8XUppA74AxrrbxgKz3O9nAeNq\nIo9CoVAoak4w1hiSgMPlPh9xbwNIkFKeqR5yHEgIgjwKhUKhqIQqvZKEEMuBFj6aHpNSLqgtQaSU\nUgjh13dWCHEHcAdAmzZtauuwCoVCoahAlYpBSnlxDY+RCbQu97mVextAlhCipZTymBCiJeA3QY2U\nciYwE0AIkSOEyKihXLVFHFDz4sV1i5Kx5jR0+UDJWFs0dBlrIl/bQDoFI47hVyBFCNEel0KYDFzn\nblsITAFmuP8GNAORUsbXgZxnhRBiYyABI/WJkrHmNHT5QMlYWzR0GYMhX03dVa8SQhwBLgC+E0L8\n4N6eKIRYDCCl1IC7gR+A3cCXUsqd7l3MAC4RQqQBF7s/KxQKhaIeqdGMQUr5DeBVaV5KeRQYU+7z\nYmCxj34ngcCL4yoUCoWizlGRzzVnZn0LEABKxprT0OUDJWNt0dBlrHP5zskkegqFQqGoO9SMQaFQ\nKBQeKMUQAIHkdBJCdBZCbCn3KhBC3O9umy6EyCzXNsb7KHUrn7vfQSHEdrcMG6s7vq5lFEK0FkL8\nTwixy52D675ybXV2Dv3l8irXLoQQb7jbtwkh+gY6NogyXu+WbbsQYq0Qole5Np+/e5DlGyaEyC/3\n+z0R6NggyvhQOfl2CCEcQogYd1swzuGHQohsIcQOP+3Buw6llOpVxQt4EZjmfj8NeKGK/npckdxt\n3Z+nA3+vb/mAg0BcTb9fXckItAT6ut9HAPuA1Lo8h+7faj/QATABW88cs1yfMcD3gAAGAusDHRtE\nGQcB0e73o8/IWNnvHmT5hgGLzmZssGSs0P8K4MdgnUP3MS4E+gI7/LQH7TpUM4bAqG5Op4uA/VLK\nYAXh1TTnVDByVlV5DCnlMSnlZvf707jcm5Mq9qtlKsvldYaxwGzpYh3QTLgCMgMZGxQZpZRrpZR5\n7o/rcAWSBouanIcGcw4rcC3weR3I4Rcp5Sogt5IuQbsOlWIIjOrmdJqM90V1j3v692EdmGoClU8C\ny4UQm4QrxUh1xwdDRgCEEO2APsD6cpvr4hxWlsurqj6BjA2WjOW5FdeT5Rn8/e7Blm+Q+/f7XgjR\nrZpjgyUjQogwYBTwdbnNdX0OAyFo16Gq4OZGVJITqvwHKavM6WQCrgQeLbf5XeAZXBfXM8ArwC31\nIN8QKWWmEKI5sEwIscf9lBLo+GDIiBAiHNc/5f1SygL35hqfw6aAEGI4LsUwpNzmKn/3ILAZaCOl\nLHSvD80HUoIsQ6BcAayRUpZ/em8I5zBoKMXgRlaSE0oIEXBOJ1z23c1Syqxy+y57L4T4D7CoPuST\nUma6/2YLIb7BNQVdRTVyVtW1jEIIIy6l8KmUcl65fdf4HPqhslxeVfUxBjA2WDIihOgJvA+Mlq7g\nUaDS3z1o8pVT8EgpFwsh3hFCxAUyNlgylsNrxh+EcxgIQbsOlSkpMM7kdIKqczp52SbdN8IzXIWr\nwFFtUqV8QgiLECLizHtgZDk5qvP96lJGAXwA7JZSvlqhra7OYVkuL/dsb7Jb1oqy3+j2ChkI5LvN\nYoGMDYqMQog2wDzgBinlvnLbK/vdgylfC/fvixBiAK57z8lAxgZLRrdsUcCfKHd9BukcBkLwrsO6\nXGVvLC8gFleFuTRgORDj3p4ILC7Xz4LrYo+qMP5jYDuwzf2DtQy2fLg8Fra6XztxpU2vdHw9yDgE\nl6loG7DF/RpT1+cQl7fHPlyeHY+5t00FprrfC1xVCPe7ZehX2dg6ugarkvF9IK/cedtY1e8eZPnu\ndh9/K67F8UEN7Ry6P98EfFFhXLDO4efAMcCOa53g1vq6DlXks0KhUCg8UKYkhUKhUHigFINCoVAo\nPFCKQaFQKBQeKMWgUCgUCg+UYlAoFAqFB0oxKBQKhcIDpRgUCoVC4YFSDAqFQqHw4P8BTcMewVJu\n0GkAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x8311d68>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Generating data\n",
"X = np.zeros((N * K, D))\n",
"Y = np.zeros(N*K, dtype = 'uint8')\n",
"\n",
"for j in range(K):\n",
" ix = [e for e in range(N*j, N * (j + 1))] #indexes of elements for current class\n",
" r = np.linspace(0., 1., N) # radiuses for element\n",
" t = np.linspace(j * 4, (j + 1) * 4, N) + np.random.randn(N) * 0.2 #theta\n",
" X[ix] = np.c_[r * np.sin(t), r * np.cos(t)]\n",
" Y[ix] = j\n",
" \n",
"plt.scatter(X[:, 0], X[:, 1], c = Y, s = 40, cmap = plt.cm.Spectral)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.09848258028\n"
]
}
],
"source": [
"# Softmax linear classifier\n",
"W = 0.01 * np.random.randn(D, K)\n",
"b = np.zeros((1, K))\n",
"\n",
"num_of_examples = X.shape[0]\n",
"scores = np.dot(X, W) + b\n",
"num_examples = X.shape[0]\n",
"exp_scores = np.exp(scores)\n",
"sum_of_exp = np.sum(exp_scores, axis = 1, keepdims = True)\n",
"probs = exp_scores / sum_of_exp\n",
"\n",
"log_prob = - np.log(probs[np.arange(num_of_examples), Y])\n",
"data_loss = np.sum(log_prob) / num_of_examples\n",
"regularization_loss = 0.5 * REGULARIZATION_RATE * np.sum(W * W)\n",
"loss = data_loss + regularization_loss\n",
"print(loss)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"#Calculate gradient\n",
"dscores = probs\n",
"dscores[np.arange(num_of_examples), Y] -= 1\n",
"dscores /= num_of_examples\n",
"\n",
"dW = np.dot(X.T, dscores)\n",
"db = np.sum(dscores, axis = 0, keepdims = True)\n",
"dW += REGULARIZATION_RATE * W"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Performing a parameter update\n",
"W += -LEARNING_RATE * dW\n",
"b += -LEARNING_RATE * db "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def iteration(X, Y, W, b):\n",
" num_of_examples = X.shape[0]\n",
" scores = np.dot(X, W) + b\n",
" num_examples = X.shape[0]\n",
" exp_scores = np.exp(scores)\n",
" sum_of_exp = np.sum(exp_scores, axis = 1, keepdims = True)\n",
" probs = exp_scores / sum_of_exp\n",
"\n",
" log_prob = - np.log(probs[np.arange(num_of_examples), Y])\n",
" data_loss = np.sum(log_prob) / num_of_examples\n",
" regularization_loss = 0.5 * REGULARIZATION_RATE * np.sum(W * W)\n",
" loss = data_loss + regularization_loss\n",
" dscores = probs\n",
" dscores[np.arange(num_of_examples), Y] -= 1\n",
" dscores /= num_of_examples\n",
"\n",
" dW = np.dot(X.T, dscores)\n",
" db = np.sum(dscores, axis = 0, keepdims = True)\n",
" dW += REGULARIZATION_RATE * W\n",
" W += -LEARNING_RATE * dW\n",
" b += -LEARNING_RATE * db\n",
" \n",
" return loss"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0: 1.06873520525\n",
"1000: 0.766868946289\n",
"2000: 0.766868943264\n",
"3000: 0.766868942855\n",
"4000: 0.766868942799\n",
"5000: 0.766868942792\n",
"6000: 0.766868942791\n",
"7000: 0.766868942791\n",
"8000: 0.766868942791\n",
"9000: 0.766868942791\n"
]
}
],
"source": [
"for i in range(10000):\n",
" loss = iteration(X, Y, W, b)\n",
" if i % 1000 == 0: print('%s: %s' % (i, loss))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"training accuracy: 0.52\n"
]
}
],
"source": [
"scores = np.dot(X, W) + b\n",
"predicted_class = np.argmax(scores, axis=1)\n",
"print('training accuracy: %.2f' % (np.mean(predicted_class == Y)))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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7yFwR5t18lRy+mbMUNByZycFf6Htn8HoCE9Pe90+EF473l1OW3EIzP0NyCKm5IR0o0ktk\nG4LeHEFzg7dTOTVEFvRDs2YeXenKEQB2AttEZJOIBICPAA/MvkBE1kjyN1RErkje90wZ7q2KYLcF\nS34NK1D+HaJFbSCzvEprwZr1oN+r4JobcqZMzhkEZleA4LWas+2OLoTresNCS55vJ8dQV2p+Itmy\nzzhHEFw4pOftA9DEcStdyf+7xpgEcDvwMPAKcK8xZq+I3CYityUv+xCwR0R2A18DPmJq+SSaFUps\nC//WjsW/gCX41raUr0CzFLyBLEvqgwUBIY8FLX3mrgxKr5Gfn8q5FsVz7CGIJSfFc+3xyPWeaUqI\nFU1PBKtDsWPjxA+MZL9AgJAN08nWrwUY8G/qINC39Bk/c55A1hBAMrRYM0ktu8z2HNNRb7jGkqwb\nvozjeEM5tUwkmbp6piJP72ROZTK1LW+YJ8tQUcbH3eR7pEtBl5ViTgTTbX51KLCuFV9biPjAOE44\nBo7BTHt/5BKy8XU344bjmCawQz6k0YevsxFZguGfTDKdR/yVi5q8J+MOJpC9Yp8tNdyT9dpU6zhX\nK79220czUstHA36MP3nEZHJvQprjehPT8yaLTfJakyFhHKCV/wqnA3z1yhJ8PU00XNCDr7fFa/Vb\nYCIO8SNjOENh3KEw8eMTuGNR8Ff+VyVjOomEkzktQhbZWrxzDmzPUsmZ1Fm9y4HBq/Qnp7OfGzw1\n7QXQ1NCX63oTxMmewuxVS8Z1vevViqY9gDpjYg6RvYNeHp/UwSCp+jNTPeoaEqfD+Hqasdsrvyok\nU2/gv705THO8DRPIsAQ0j3SwmJ61uSqZisEkcwWlJ4oTTmHJ1paL2buPZd4+gNRwkW15jzv51gSr\nlUADQJ2J7B3EnShyPbprSJyeqkoAOKvX8MG3u6zvaWU87PKDpwPc9OUYd95yxpsbCAUKnqj1KnXX\nqwTn9xqicW/FT8CHEbzPsyRRWxEy9ZpSQW82K7lKyO+bWWxUyIY2tSxoAKgj7lQsZwbPWrN5reEP\n/qObrt9DAYub39XAxm6br34L/v3qKf77FTEaKWJlUzSWfYOT62rqg9mC/nQG1TnzBgG/t0Q0HJ3Z\nI5G63mfPzEFoL6Lm6RxAHXGjTu5DznPwdTWVtzAF+I/vdBc07oMBuPbiABeu6aB/R4gf72osaJ42\nnfxsJbfqy8my0iujMu4dsC1v5VFTyPudavU244nf5/UWkmcyqNqmPYA6YjX5F7cpScAqwyayYq3v\nzvx4woFNaw3XXtrGtZdlWcI4a21/etx/cgl3487jOi4H73uV/Xe/TGLaYdMHz+bcWy7C37JMDljJ\ncw5Aermpz/Yq+9lZSFPPhQLeJHoNLzWvdxoA6ogV9OHraiIxFC4uEDRkTjWw1CKxLKcSCly8xXDR\n1hxpnQ3eBikRbygiFq/Ykk5jDI/d/CNO7DhMIuytIhrbf4bX/3EP7/vZR5dHEChwcl1E5qSZXsBv\nL5+VVHVIh4DqiDEGaQ/OzZWTJW9OmiUEN1YnKd8TL8qCRTiugUgULtqaZ4RBgEjcG6eOVq7yBzj1\ni2OcePxIuvIHcCIOUwOTvHb3S5UrSEnKcPaxNvxrngaAOhJ7/Qzx/hGIz52cC5y9Cmnyz50fSOb+\n969vrcr4P8CPfynsO+Q13iMxb+XmxBTc/VDuiikWNzz2UpxP7Z4sSzmGXjjFIx/5Ad8775v86N33\ncPThgzmvP/rQQRLhhZPtTiTBG9/fP+exkVfO8OQnHuaBd/4jT/z+Q4zsGypLmUvmZj5FLJOsgSK1\nmkrVLB0CqhNuOE5iMMPQj2NwxyI0XrYW8HoJ7mQMEi5WSxAp8FjEpeC4wl3/brOm07BxjWF8Snjt\nCPh8JusIhTHwy33C//5hnJgT4sYdzsJ0EkU48eRRHvnIv+Ekd0pPn5xix+/8iMu+8HbOu+1Nc66d\nPh3mhS8+zf7v7Mna+h0/OEpiOoGvwcfxx4/w6EcfwIk5GMcwsm+Iww/0c+133su6d521qPKWTSye\nsYuVc2f1rGuA9AlnsIw21NUZ7QHUCWc0w9r31HPDMzs+RQS7JYjd0VDVyn+2k8PCL/dZRGKG/+t9\nLn/xCZNebTif63orEbsbynMC2TOffjRd+ac4UYedf/zknFb+yL4h/vWyb7P/2y/nPFIzEY7z/J8+\nhTGGp/7gpySmE5jk9cYxONMJnvrDRxZ1znBZJfMALUiaF09gUmcd5CpjPOEtIW0IeCmn25o0sVwN\nqo2/cLX0LMl6wLfJlye/ipobDF1tLrfd6PCp/2S4aItXj0iWH8e24bJzYGOP9/PMTiex60xxrdD4\nZIzxA6MZnzMJl+f/7Cnvc9fw09/4AYnJ/HssTMLw+nf3MjUwQWQoc6qF2FiEiTfGiirrkoglvINw\npqPexq+JsDenMhH2DsbJIL3c1u9bmKE1tWRU1QwdAqoTvtWNxPqHMz8Zd5jedZzQRT1Ywdr4leho\nNvzuDS4b16SPu80Wvxbw++DqN7nc/ZDX4lzb1JpOJXH5Z8I0RxsLeh3xW15GzCz2/c2L2A0B9n3j\nBZwiNtglwnFO7TyBk2VYJBFOMLxnkNbNNXAiXq58SMYs+E8REQw55gX8Ph0OqiHaA6gT4rMIbl+d\neXmfATOdIPpKbUxAWpbhUx9x2dTr1RepFn+hROCys6EhOFN5e2cR5zlrYJ49f7Ur7zUv/+WzRVX+\n4A31PPmff5zzCM6ff+InTByugV5ALotZJVSF5cQqOw0AdcS3qpGGy3uzPu9OxnDGIkTfGCH66hDx\nU5M5W8BL5cLN3rCxXcqQscClZ88te8bsolkcfeggL37pmRIKUBo37vLKN15Mf22MIT6V4+CX5SLH\nUKSqvNro76vKSZ10lbFiN0R2n/LGaQ0wFCZ+eIyGN61B/JWbwOvpMNkO/iqYbcHqtoWPZ84uKnOG\nhQ587xWeuuORnC30pebGXQ5+/zWmh8L4Gnwc/uEB4uNR/C0BLrzzzVzwB5dVZXPeHMkMqgvOF8hC\nRLw8QgG/d8iOq7mCqk17AHVGAnb2zV+pv8fU37BrMNEEsUOZJ0KXyuDIwg1gUHxGgcS81TgBn+Gy\ns12uutDlknXt6d7A7APp3bjD059+FDdW/fXrkcEwb/zLa7z+f/YSG4lgHENsNMruLz3DC39evd5J\nWjS+4ByBfAng0sGisfKpRdRC2gOoMyJCYHMHsdeHC0sHYSBxYhJ3IoZ/Qxu+1YVNoJbipYPeEvKA\nf+6Z5KkAUEjD1+B9r882JBzh3A2GW9/vYoyX50wwvPB6Ez9/Sdh9bDzdG/j4kQMkMozpG4SpljbE\nGBonx6q6mCURTrD3689x0SffjK+hyn/CkZiXYdW2vUq9gEPkRQRjWzM9TVU1ZfntEZHrgb8CbODv\njDFfnPe8JJ+/AQgDv2OMeb4c91bF8/c0Iz6L2KFRTIETmO5kjOirQ7hntS/5ucCuK3zlHotb/oPL\n+m5vpCCWgAeegg9fnfVc+DkEePcV8O4rXBJO5onkK7bDJVsNltXC7n7483+Fh/7mZbbc+Ws8G+3j\n+HGTDDouGO8AFct1CUSnOe+5HbSOnin/D18gsYXJo+O0n91ZtTKkGbxJm1yHy6uaVHIAEBEb+Drw\na8AAsFNEHjDG7Jt12XuAbcmPtwB/k/xXVYlvVSMS9BF58UThY92uIX5wBF9X45IvFx2eEP7iHpvW\nJkPID6fHwBhhZMJryfvzrAya/Zw/S1EledYJwKVnw9/9YRuvXfWb3PXX4ziJ2U3TZGtVBNeyiPha\n2H3lu3nLz75PIJY6YQvsRh9Nvc1MHZtcsHms3JyYQ0P30vfGCmJZYBVe+aeHirT1X3XlmAO4Aug3\nxhw0xsSAe4Ab511zI/Ad43kGaBeR7MtRVEV4f6/Ft9iir1Zuuej4lDA4KhjjlXPfIeFzfytMlnkO\n0bKgtdHln/5hAqeAutuxfRw++yI6L+zi/Nsv5bIvvJ1r734fH3zmZq77lw/SuLYZX6MfX6OfxrXN\nvPUr12CHyjeRbmIuD3/gXzn1zLGyveaiFTCUk5oc9jaK4c0fqKorRzNuHXB01tcDLGzdZ7pmHXCi\nDPdXiySNfm+zU7S4CU93LIpJuFVLFRGJWfyP7xp+6zqX7RvLt6pwYswwOVFgs9SyOLZ5O7v6zuGz\nfxCjMZW2OnyS7gstPvT0DYy97k0st21rJT6ZYOf/U94m7/BLp3n4A/9K9+WrmTgySeOaBi74vXPZ\n8O6+9DXSXIF2luPmbEekzlc2MLOjrzEIBGfOIlZVUXOTwCJyK3ArQHNHT5VLs7KJCMHtXUReOlXc\n+QACxqleAAAYmxT++vs2/+WDDuedVZ4gEGyQIlcaCUcGLG7/TIjrzjnCe7YfptE/E0xTOVQTJ7z6\ncfPbGzn45BRObOYmknwLzSJ7M27M5eQvBgGYGgjzxEu/4Lz3tXLhB9rxXfkmzPAhJFCBFTemDdpW\nI5b3A6WXgxoD0WnMmRNI71kL5glM0I8JD8N0eTK3quKUIwAcA9bP+rov+Vix1wBgjLkLuAuge/25\nOkq4xOzWIP6NbcTfKGKpp215y0lrwI+ettjW55a8bwCgocHiwkuD7N4VLeK7vNQHD7+2gcdf6+Wj\n7KPFzjyG1NDcTMf6k5w5NJOSY9WmTsJnwkTGI7g5ksgVyokZ9tw/jhvpxff9fs7/9YmSX7NQ1ro+\n7PMvQJqbMbEY5vQgzsGDmNODWOs34OvZiMxbgiyWhcFP/PEaWNZah8oRAHYC20RkE16l/hHgo/Ou\neQC4XUTuwRseGjPG6PBPjRC/nWNz2DyWENjcXjOrPQ6dFJ7aA28731sdVGqxbv3DTv6/PzrF8FCx\nTXIhgp9vcTHbnSGusY7il7mvIZaw7qJees/vIRFJ4Av5sGwv39DYiXHGTkxg+y2mzkwRGSsmCM0r\niSVMnQnT1tvK3u+3LPp1ijcGPJXh8Ra639vJ2iutjCNFjtVc4XKqlJIDgDEmISK3Aw/jLQP9ljFm\nr4jclnz+b4EH8ZaA9uMtA/3dUu+ryse3qoFYf44LbAHHII1+Ame1V2QvQDHue8zi+f1w8/Vuxt2/\nxWhutfnLb/byi8fDPP6TSU6eiDM2Yihssty75jVWEXH9vN/O/KZatkWgyVt+ND0e4dQrg4RHwvhD\nfrrP7sL2WUQnYotOw2GMwa6RVN4psaFwxuhsXEM4S8ZVtfTKMgdgjHkQr5Kf/djfzvrcAP+lHPdS\n5Sd+G/+WDuL9wzOrOWxBGnw0XLwGybG5xxiDMzxN4uQkGPB1N2F3NVa4hyAcOAb7j0Jni7eiZ24Z\nC+8ZpMaur3xHI297RwOvff5J7hvpoB8vmVwhXCwO08qoG6DdimW9bmo4zIGfv5E+DyA+neDwrqOs\n3tKJZQvOYvMwGWhaXZ1T3DIJ9DSy4fffhGRIROjGHE7c+2oVSqWgBieBVeXFjo4RPzyWrCWNdxTk\nWe3417bkrMhd1yX6yhDu8HQ6cDijEawTE4Qu7Mn4B7+UfrHH4rJzXILzAoDjeqNbgeRve2p+Mp4c\nqvf5vGvGp1we3xNj+7lhAt/Yz/iLpzAJw7uscQ667bhFLJl1EXaYDdxo+rMGn2MvnUhX/inGMQz1\nD7P1HZs4se8Uk6enil8vL17Lev54e7Ws++0LsBt8C34fjGs49NWd2gOoIg0AdS6RTPg2Z/zfMcSP\njOHvbZnT6DXG4JwOEx8Yx40mIJFhM49rcCdiJAan8K9prsjPkHLwuPDoc8K7LjPpXoDrwmMvwLEh\n4d1XGNqa4OggPLJLaEs2kve8IewfmsA1Ce7b8gB7v9LC7KNaQuLwIXmVe825JHeEFVAa4QgtHKOF\nPhZOxBpjmB7JfCCMWEIi5rDlqk3pk7cOPX2EicHCVsoIQiwcJ9RSG/l2Wi/pztiLdCMJrIBWQdWk\n736dix8Zyzz56xqcM+E5B8LH3hglcXwi/2Sxa0gMTlY8AAD8+y8sfvmK4ZKtXhl39wunRrwKe1eW\nkYbjU+O4JsHWqyOM3n0aWDgh2WuF+Q1nH/dyfsFlMVg87q7jY3bmG4slWcf57eTxianTtDa9bSMH\nnzrk9Qjy3dcYfMHaWKUF3ulp2dRC0r16VlszRariTDTLtlfXzNkg5kYTJI6NF7FfoHrDD4Mjwk92\nWvxkp5VQinMcAAAea0lEQVSu/LMZmBzGNQnuvGWCz979jxwb3Zz12rAU26IWhmjimFkYCEWEjvVt\nGYfYLJ9FY2fDgus3X3UWHRvznxLW0NGAr4Za1sOPH81c0QuMvzRY+QKpNA0Adc5qDmR+QmTOc4kT\nE4WPRVuCv6d2JiGzGZj01uPff9NR2u/YlbPyTxjhUbNxEXcRfulm3o279sJeQq1BrOTwiFiCWMLa\nC9dkDAwiwoZL+7jwfeex7hLvezOJjEZI1NCxi8f/+RWiJyZxpr0dv27MwY0meOMrOzExPROgmmqn\nmaCqwn9WO87YvJ3AAtLgw2rzKpjogWFv6KdAVlsQu7v2AwB4lf/uz2RfA+saiGFzwG0jjs1iejYj\nNGR83PbbbLtmC4P7T3PqlZmW8NHnjzF+YoINl/dl7SGs3rSKiZOTRMYX7hcwGEaOjtK1ZXXRZS0H\nuyVAz41baX/LWtxIgqFHD/PK/72DtkvX0Ly9k/hwlOEnj5IYXfxeB1UeGgDqnN0SJHRBN9H+YUw4\n7mW17GoiuLUTEcGZjJE4MVlY618gcPYqfN1NC0+JMlR8VVA2qZb/1qsjDP3ZT4CFLX9j4DnTw07T\nSzzZUTY5O8ypN2j+z2joJPNkL3gnf5167bS3MmlWHoqxE+OcOTTM6k2rsn5vdCpzBWocQyxcnfw6\ndkuA7V+5Bl9LACu5W3z9pjb6fvsCjnzjRY7dvbcq5VKZaQBQ2O0hGi9fi3HcBWl9E0NT+cf9kz2G\nwKYOnKEw00fGkKAPf18LzkjE2yPgGCRg41vXgkm43tJRv41/XQt2Z0NF9g2kJnthpuV/LFn5z98r\nsNOsYafpJUHhk6neItq5m8Z8uLzFyr7pffR45nOJjWMYOjA3AMSn44RHp/EHfTR0NOAP+olOZN5n\nMDU0RXQqRrApyxDfEul5/5Y5lT8kJ7IDNhs/8SZaLupi4O9fxinwHAq1tDQAqLRcG74yXt/gI3h+\nl5fPJeESefFkOliY6QTR0cic603MmZdzKE50PIqvt5ngloUHmxhjyh4Ytl4d4bN3/yO7P+NV/EOm\ngcfd9RyjBQvD2QxzlQywq8jK34/LtXKYF003p2nEwhDA4Ro5wlrJvnzTiTtZVwK5cW/i1BjDwIvH\nGTky6vWijMEX8mPl2O07PRph/8/62fIrm2jsyDwEtRTarlg7p/KfTWyLzl9ZT9tla9j//z5J5Gjl\n8hSpzDQAqJz8q5tIDGRY+mkJvrUt2I1eCzP8/IniMoqmuIbEiUn8a1uwGvwY1xA7NOpNOqfST2zu\nwNdZWiU2MDkCGN67eablOWYC3OuemxziERyE1+jkuGmmuMWJhmZinCPDnGsNEzY+4li0Esu7A7m5\nq8lbDpohEVxzj7d66HT/ECNHR71zd5PvcWwqlndIzXVcBl44xtnXbi3qpymFm+cgHLEEu9HPWX94\nGa9+ZkdlCqWy0gCgcrKaA/h6m715gFQFnxzy8SUrKNdxMZPZUx7k5Rqi+8/gTsUXnBRlwnGi+04j\n53dhL7Ilmxrz/+rHxrnn9mG+xwcJ4NJAjPi8jV0uFlP4cYtcIPdemdnx2yiFr8BpbG+gpbuZicHJ\nOUHA9lusObebRMzh5CuDGQNEIbmCpscjOAknva9gqZ1++A1CG1qwc5wYJ5YQ6mvB1xYkUULSO1U6\nXQaq8gpu6SR0Qbc3udvkB0sw4TjTzwwQff0MicH8m5PycceimXcWA7iGWDHpqmcZmBxm69UR/ufB\ne/jipxxep4MIfsYJcopmMv0JJIpc7dNAnA5ZfEV21hUb6D2/h2BzAF/QR8eGds6+ZiuI8Noj+zNW\n/sWoZF6m4R1HGNt5EuO6M2cCZFErqSrqmfYAVEHs9pC3GWwoPNMTMIbEycmKnA3gTsZwJrxK1moO\nFFSpecM+8OW+03x59C3EsTFzKvZsr1Fo9k/v2vPlTElpqMUSurasXrBs8/CzR0gUeVrbfC1dzel9\nBhVh4NBXd3HmkS42fOISAlkSA8aGpokPRzK8gKokDQCqYPFDowvH+Y03uVsJkZdOeZ9YQujc1VmH\nhEzc4eSZYSQg/J/PTbL75n6OcNG8yr88BMOlcqrsrwswdrL4SVIRwRiDZVtYfou+N61dgpLlN/Hy\nafb+/k/p+JU+Nn7CywQqPgs37mIcl8Nff74q5VJzaQBQBTHGZD87OHWYTK4ev9/Cag7gjkaKz26Z\nkhoKcQyRvadpuKwXq2HmKDBjDCOvniQwFKPND76Ew5dvmuQGyyZIgikKWRJZTOEMb+MYDUWM+RdD\nkOSy0gKvt4TOTR3gQmNHA+19bZVt/Wcw8uQA4f4Rut+7hYaNbYQPjjL4owPEToWrWi7l0QCgCiIi\n4LcgnmHrvoHAtlXEDgzPVNLzxd3SKv8F9zTEj0/MWT4aPzxGYCiGGDAxiGMzQAs/dLfMFDRPL8Cb\n/nWJku+MSe8HaZESJr+zvbIxjJ+YwA7auOHCUyUEGv2su7C3Zk5rS4memOLoN1+qdjFUBjoJrArm\n72v1WvuzCVhNfvxrmrHybToq5wnPxttrkOLGHaJHx5B593CxGKCFUYIUMq7vkpoEzldYb/XQo2Yj\njilfhWtcw8GnDnFk1wDxfLt5xUsLYdkWweYAm686q+Yqf1XbtAegCubva8XEXS8vUOrsmJYAofO6\nAHArmX5AvHsDDPWfInQisqDyn31xocs6neSegEIZhNM0sobSV0IBjAyMEh4OZzwgXiwwLvhCPnrO\n7aKlp4XIWAR/yE9De0grf1U0DQCqYCJCcHMHgQ1tuOE4ErCxQjO/QlbIh1vKfoBiWIK/t4XEUJjQ\nyVyVf0r+FT8WLgaKniy2KV9Gy+HDoxkrf4COjR30Xbx2TkUfbKxsqge1spQUAESkE/gecBZwCPgN\nY8xIhusOAROAAySMMZeXcl9VXeKzsDOkIvZvaCP66tDidgTnk0yBAGC1BAmevQoJ2EwdOoOvxPq3\niShNJGghygE6ivreIAlW50j2lk8sHGP48AixcJzm1U05186PHB6lvbeVlp6FB9YotRil9gA+B/zM\nGPNFEflc8uvPZrn2GmPMUIn3UzXMt7oRd1O7l+9HyD4hnIXVGQLAHY3ODSKWELqgOz3kI7blJXab\nnKAtXlqwsTH8jrUHnxjeMG0cSad9ni3b5LHhXXJ40XsAxk9OcOjZI96OXuMN/9i27c3MZZprdw1v\n/PII5/7qNgLa8ldlUOok8I3A3cnP7wY+UOLrqWUusK6Vxiv7CF3UQ/CiHiTkA1u8D0uw2oPgy1Bj\n+ixC56wmdF43/nUtkEx0Jk1+Qud3YbeHENtCbIuByZH0KV7b4sMUPrs89zobl+0M4UuOH21kjBZi\nWAUO6VgYemRxY/+u43qVvzNr+azrJYfLtXTTOIYzbwwv6p5KzVdqD6DHGJPKdXsS6MlynQEeEREH\n+IYx5q4S76tqmNgWdvJAcvvNa3HHophoAqs5gNUUwI0kiPUP4wx7Qyd2ZwOBrZ2I32t5BzZ1ENiU\nayjGcOctE5z/hXuJW+dz2G0tIHOnoZ1pJgliYXCw2MA477SOpq+wBH7DepXH3fXspxMHoZdJwvgZ\nm7eKSHDpYYoGWdwmuMmhqawpHty4i+X3Nk1lMj2uO2hVeeQNACLyCLAmw1Ofn/2FMcaIZJ2Ke7sx\n5piIdAM/FZFXjTFPZLnfrcCtAM0d2eKJWi5EBLs9NOcxK+QjdEF3ery70NUrs/P5vzM2yu7RzXTJ\nNL9u7WeHu4FBGlN3nfedhjVM8Z/sVzltGhgnyCqmac+QvycoDtfZh/g1cyhZNjhjQvyLey4JhAQ2\nfhz8uLzbeqPg98G4hsHXTzN0YBgn7mDnO7Q9xzxKtTd3qZUjbwAwxvxqtudE5JSI9BpjTohIL5Dx\nhGdjzLHkv4Micj9wBZAxACR7B3cBdK8/dwlmE1WtKGbZYiqjZ6rln8rnD9ArU/ym/QqugcOmlQfN\nFtzk0k8bFx8u1yUr6y6ZpquASdvZRVslEX7Xeon9ppMRE2K1TLNNhtNDR4U4vOso4ycm0hk8E3nS\nJls+G9fJfE3z6uVx3KaqfaUOAT0A3Ax8Mfnvv82/QESaAMsYM5H8/DrgT0q8r6pD9990lN13zJzi\nNZ8lsEnG+ZjZx0tuFyOE6JVJLpCholI0ZxIQlwtkcWsYohPROZV/IZpWNTB+cnLB94gltK9rW1Q5\nlJqv1ADwReBeEfk4cBj4DQARWQv8nTHmBrx5gfuTrT0f8E/GmIdKvK9aIlY0RuPxkySaGol0V+dQ\n8flSrf9CtUuUd9gDS1SawhhjCA+HcRIusXDMS9JWxFbo8VOT+Bv8JCIJXMf1zmAQYd0la/HlyLWv\nVDFK+k0yxpwB3pXh8ePADcnPDwIXl3IfVQHGsObRn7P2kScwto04LtHVnbz+ux8h1tmOJBKse3gH\nXc88hxWNMbVhHUffdx1TG/uWrEipU7zmH+NY68IjYd54+ohXceON/+fLjT+fcQ0t3c00dzUxcWoC\nX9BH54YOgi0L918otVg6m6QA6HxhD2t/9iR2PIEvEsWOxwmdHOTcv/kHcA1bv3UP3U/+Et90BMt1\naTl0lHP+9js0DmQ/8LwUqcr/u380zk1/9TTHRpdH5e/EHQ78/BCJaAI34eIm3PQ6/6IYb5NY+7o2\n1l/aR+/5a7TyV2WnAUABsPaRJ7Bjc3P5WMYQGBljy93fo+XgYezE3HF0Kx5n3UOPLlGJvKWe03fc\nt0SvXzo34TJ8eITjL5/gzKFhnITD6LHx7K39ZPK2QoglNHU25r9QqRLoYKICIDCW+fARATr27c84\nfi1A85FjZS3H7GGfqx5/jL0VavknoglGj43hxF1auppozFP5RqdivL7jwEwL34ITe07Rvr4t6/p+\nyxbWXriG+HSCwf2nc04KW7bFqk2dWZ9Xqhw0ACgAptd00Xw488Sp5Bi/doHOF15m9PxzcQP5cujn\nlprs/e4fjdN/83PspTI5b0aPjXHkOe9nN67h1GtCS1czZ71lAzI//XXSoV8expl9EpoLjuswemwM\ny2fhJhZu4nIThmO7T9B3yVpWbe5kqP9MxtduWt3I+jet08leteR0CEgBMHD9tTi+4s72NYAvPM1Z\n//JDLv6Tr9B86Oic51v3H2TLP3yPc7/+bdY8+hT2dO4drMGJKf779KM8fukPGTuxcCglMh5h4vQk\niWj5TuBKRBMceW4A45h0WgbjGCZOTzL0RuYKOh6JExnLfAi8E3WwA3bW/EDGNRx94RhTQ5lTSFi2\nxaqzOgk263i/WnraxFAATGzbxIHf+jDbvn1PwcmQJflB1EsBve3v/4lDH7iBliMDhE4O0nx4ACuR\nQIDGgeN0/+JZ9n3y90g0zR1eGZgcYc1rB7nuvod4URK4cRh+Y4RAc4Ct79iEG3d54+nDRCaj3nJK\n19CxoR1f0Gb85CQ+v82qzZ20rW0tOif+2Inx1NEGc6Ry7sw/qB1geix3IOvY0E48HGfkyGjmCwxM\nj2Z+DddxiWiqB1UhGgBU2tj553Dimrex5slnseZN+BZSrdrTETbd9wB2IrEgf6YdTyDjE/Q+8gRH\nb7w+/fjA5DB2LM7bH3gIJ5FIp2FLVYQn9w0yOThJZMJrcafmIoYPzc06Hh4JMzHYzvo3rSvqZ3YT\nbtZJWyfLGcj5NnQNvnaaplVNWD7BTRSZEdW2dLWPqhgdAlJzHL/+WkbPOxvX5yPh9xe9ejG1UihT\nwLBcw6rnXk5/HXn5Zd75d/fygS//Lc5UhmEdA0NvnCEWzn/IjOsYRo6OFt16bu5qzvozOnEn472t\nLPMCaQamhqe8nD1FpooWW3f6qsrRHoCaw9g2B377wwTPjNC+5xXWPfQYdnxh5ZwpQ34hdZ0vHMaK\nxoi98ipX3vPv+Ga1+jNywS0w545xDOOnJgm1hvJfPPNdBBoDxDKcZGaA0wfOsO7C3jmPW36bjONG\ns7ngxF1sv43ruFlXBqUJhFpCnHXFek32pipGf9NURtFVHZz6lStxQgsr03T6+lnj7QVn5Lct2LmT\nC378GL5EgZO5RXRDJk5NEB7Jn+wtHknw2qP9vP74QWJTWXoYBqaGwgsebmxvwC5gPb9YwvpL17Fm\nezehDCeozRZsCnDOu7bq8I+qKA0AKjtLOHDTh3ACftzkCiHH78dpbGD/xz/K6IXbiTc3Md3TRbi3\nu7BjVIwhNDZJy3CWCdISTZ6eov/JgxzedTRn+oU3njlMZDwy90CWDIJNC5e2iiVsuHw9Yufu8xjX\n0NjRSPe2Ls6+diu+huwd7vh0POtzSi0VHQJSOU1u2cjLn72drmeeo2FwiMkNfQy9+RKcxgbGt29L\nXxccGua8v/omMh3JORTk+nx88j/b/PRxKTo/DpDeTZvtsBRIDgUdH2d0zTgdfQvH0yMTUW+uIN+o\njC10bc2cEK91TQtnX7OVoQNnmDg96fUiZr+eQHtfG/6Q9ycmImy4dB0Hf3E4430DTdryV5WnPQCV\nV7ytlePvvoYDv/VhTr3zSpzGhgXXRFd3sufTn8BpyD7+7lpCdFsn9k+fp2NDe9ZNVtk0rW7kwved\nR+95PXm/13UMZ7Ks409E4jmXi4otWLbQd/HanDuCQy1B+i5Zy/ZfO5u23oWb1uzA3H0VzV3NNLSG\nFkyWiC2s2d6d46dRamloAFBlE29rYeiyi3EzTGIaYNN7ennr5lUcH9vCugt7i8p1I7bQtrYVy7Zo\n7W0t6HuyLcEMtYayLuW0AzZnvWUD5/+H7XRuzHUs5Yzp0WnGT03OfdDAmTeGmTg987iIsPmqs2jt\naUEsQWzBDtisu3gtbWsL+5mUKicdAlJldfLaq1j1/EsQiWAlK1kJ2azuNLT5O9Itb8tnseVXNnHq\n1UFOvZY7L45Ygj/kT1fIgQY/a7Z3c/LVwayra8SSrJWqL+ijc2MHw0dG5ny/2MK6i3pp7SkuBcWZ\nwyMZy5HaTNbS1Tzn3puu3Egi5uDEHQIN/qJ7QkqViwYAVVbx1hb2/tFtrP3p47TsfRUn4KfvE9u4\n/OheTowvrOi6z+kiFo4xcnTMeyB5SVtvK9Oj0xjjjaV3b1uNPStVRffZXTR3NTP0xhkmTkzgxB1S\nUwpewPCxenP2ZGrrLu7F3+DjdP8ZnJhDoMnPmvN66OhrL/pnduLZD4afky9oFl/AxhcoLvWGUuWm\nAUCVXby9lafecxW856p0YrcTbMl4rYiw/tI+urZ1MTk4ieWzaOttXTB+nkljRwMbOvowruHM4WGG\nD43gOob2vla6tqzG9md/DRGh55xues7xDqcvNoXEbG1rWhk/PpE+ACZ9Dzt7L0SpWqABQJXdwORw\n+hSv/psLS+ccagkSWuQaeLGE1ZtWsXrTqsV9fwmVP0Db2lYGXz9NZDyaHsoSSwg0+OnYUNg8glLV\noAFAlZWXzx++3Hea3cvkFK9SiSVsfcdmTvefYeTIyLxhK11noWqXBgBVNrPz+e++ub/Kpaksy7bo\nOaeLnnO6ql0UpQpWUvNERD4sIntFxBWRy3Ncd72IvCYi/SLyuVLuqWrPwORIetjnm4f/nv6bn6t2\nkZRSBSi1f7oH+HXgiWwXiIgNfB14D3Ae8Jsicl6J91U15s5bJvjs3f+4bA5vV0qVGACMMa8YY17L\nc9kVQL8x5qAxJgbcA9xYyn1V7fCGfQyXtxut/JVaZioxB7AOmH1W4ADwlmwXi8itwK0AzR09S1sy\ntWipw9sB7tvyAHtvrsz5vUqp8skbAETkEWBNhqc+b4z5t3IXyBhzF3AXQPf6cxeRLUxVhpk5vH23\nVv5KLUd5A4Ax5ldLvMcxYP2sr/uSj6llKrXap2lwrMolUUqVohJDQDuBbSKyCa/i/wjw0QrcV5XZ\n8alxXOMd4nLflgfY/Rlt+Su1nJW6DPSDIjIAXAn8SEQeTj6+VkQeBDDGJIDbgYeBV4B7jTF7Syu2\nqrSByRFck+DOWyb4r7sfY+/3tfJXarkrqQdgjLkfuD/D48eBG2Z9/SDwYCn3UtVmuPOWCc7/wr0c\nQ1f7KLUS6E5gldPsYZ93xkbrJr2DUvVAA4DKKjXZe+ctE7TfsYvdu6tcIKVUWWmmKpXT/Tcdpf2O\nXdUuhlJqCWgAUBmlWv9KqZVLA4CaY2ByeE5it92fqa+snkrVE50DUGmpXP7333SU3Z/p19U+Sq1w\n2gNQs3hLPYf+7CfVLohSqgK0B6DSid22Xh3x1vnrUk+l6oIGgDo3+xSv/puf02EfpeqIBgDljfnX\n2RGOSikNAHVrdj7/xNMvAJrbR6l6owGgDs0f9tF8/krVJw0AdWZgcoStV0f486M/0VO8lKpzugy0\njhyfGgcM790c13TOSintAdSL+YndlFJKA0AdSFX+9215gL13aMtfKeXRIaAVzhv28ZZ66rCPUmo2\n7QGsYLOHfXbfoev8lVJzaQBYgWaf4nX/TUe18ldKZVRSABCRDwP/FdgOXGGMyTi7KCKHgAnAARLG\nmMtLua/KzTWOnuKllMqr1B7AHuDXgW8UcO01xpihEu+n8tCDXJRShSopABhjXgEQkfKURi2aDvso\npYpVqVVABnhERJ4TkVsrdM+64poEd94yoad4KaUKlrcHICKPAGsyPPV5Y8y/FXiftxtjjolIN/BT\nEXnVGPNElvvdCtwK0NzRU+DL17fUSV7vjI2yW3P5K6UKlDcAGGN+tdSbGGOOJf8dFJH7gSuAjAHA\nGHMXcBdA9/pzTan3XulmJ3bTlM5KqWIs+RCQiDSJSEvqc+A6vMljVYKByZE5h7f33/xctYuklFpm\nSgoAIvJBERkArgR+JCIPJx9fKyIPJi/rAX4uIruBZ4EfGWMeKuW+ynPnLRPc9FdP6xGOSqlFKXUV\n0P3A/RkePw7ckPz8IHBxKfdRc6WGfS5vN+igj1JqsXQn8DIy+/B2zeevlCqVBoBlxcyc4qVHOCql\nSqTZQJeJ1LBP0+BYlUuilFoptAdQ42Yf3n7flgfY/Rlt+SulykMDQA1LVf56eLtSainoEFBNM9x5\ny4Su8VdKLQntAdSg2Ynd3hkbRTM6K6WWggaAGjP/8HbN56+UWio6BFSD7r/pKO13ZDxbRymlykZ7\nADVi9rBP4ukXQNf5K6WWmPYAasDA5PCcfP57v6+Vv1Jq6WkPoMpSufxTp3gdQxO7KaUqQwNA1XlL\nPfUIR6VUpekQUJUMTA6n8/mf/4V7q10cpVQd0h5AFaSWet5/01F2f0aHfZRS1SHG1O6piyJyGjhc\n4duuBoYqfM9SaHmX1nIq73IqK2h5l8pGY0xXIRfWdACoBhHZZYy5vNrlKJSWd2ktp/Iup7KClrcW\n6ByAUkrVKQ0ASilVpzQALHRXtQtQJC3v0lpO5V1OZQUtb9XpHIBSStUp7QEopVSdqvsAICIfFpG9\nIuKKSNYZfhE5JCIvi8iLIlK1VJ1FlPd6EXlNRPpF5HOVLOO8cnSKyE9F5PXkvx1Zrqva+5vvvRLP\n15LPvyQil1ayfBnKk6+8V4vIWPK9fFFE/rga5UyW5VsiMigie7I8X2vvbb7y1sx7WxbGmLr+ALYD\n5wA7gMtzXHcIWL0cygvYwAFgMxAAdgPnVam8XwI+l/z8c8D/rKX3t5D3CrgB+DEgwFuBX1bx/7+Q\n8l4N/LBaZZxXlncAlwJ7sjxfM+9tgeWtmfe2HB913wMwxrxijHmt2uUoVIHlvQLoN8YcNMbEgHuA\nG5e+dBndCNyd/Pxu4ANVKkc2hbxXNwLfMZ5ngHYR6a10QZNq6f82L2PME8Bwjktq6b0tpLwrSt0H\ngCIY4BEReU5Ebq12YfJYBxyd9fVA8rFq6DHGnEh+fhLoyXJdtd7fQt6rWno/Cy3L25JDKj8WkfMr\nU7RFqaX3tlDL5b3Nqy5yAYnII8CaDE993hjzbwW+zNuNMcdEpBv4qYi8mmwtlF2Zylsxuco7+wtj\njBGRbMvOKvb+1oHngQ3GmEkRuQH4AbCtymVaKVbUe1sXAcAY86tleI1jyX8HReR+vK74klRQZSjv\nMWD9rK/7ko8tiVzlFZFTItJrjDmR7NoPZnmNir2/8xTyXlX0/cwjb1mMMeOzPn9QRP63iKw2xtRi\nHptaem/zWmbvbV46BFQAEWkSkZbU58B1QMZVAjViJ7BNRDaJSAD4CPBAlcryAHBz8vObgQU9mCq/\nv4W8Vw8Av51csfJWYGzWsFal5S2viKwREUl+fgXe3/mZipe0MLX03ua1zN7b/Ko9C13tD+CDeOOO\nUeAU8HDy8bXAg8nPN+OtttgN7MUbiqnZ8ia/vgHYj7dipJrlXQX8DHgdeATorLX3N9N7BdwG3Jb8\nXICvJ59/mRyrxWqkvLcn38fdwDPA26pY1n8GTgDx5O/tx2v8vc1X3pp5b8vxoTuBlVKqTukQkFJK\n1SkNAEopVac0ACilVJ3SAKCUUnVKA4BSStUpDQBKKVWnNAAopVSd0gCglFJ16v8HPtikgcpZN0AA\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd1a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot the resulting classifier\n",
"h = 0.02\n",
"x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
"y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
"xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
" np.arange(y_min, y_max, h))\n",
"Z = np.dot(np.c_[xx.ravel(), yy.ravel()], W) + b\n",
"Z = np.argmax(Z, axis=1)\n",
"Z = Z.reshape(xx.shape)\n",
"plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)\n",
"plt.scatter(X[:, 0], X[:, 1], c=Y, s=40, cmap=plt.cm.Spectral)\n",
"plt.xlim(xx.min(), xx.max())\n",
"plt.ylim(yy.min(), yy.max())\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# NN\n",
"HIDDEN_LAYER_SIZE = 100\n",
"W1 = 0.01 * np.random.randn(D, HIDDEN_LAYER_SIZE)\n",
"b1 = np.zeros((1, HIDDEN_LAYER_SIZE))\n",
"W2 = 0.01 * np.random.randn(HIDDEN_LAYER_SIZE, K)\n",
"b2 = np.zeros((1, K)) "
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"iteration 0: loss 1.098681\n",
"iteration 1000: loss 0.309691\n",
"iteration 2000: loss 0.264218\n",
"iteration 3000: loss 0.253473\n",
"iteration 4000: loss 0.250033\n",
"iteration 5000: loss 0.248174\n",
"iteration 6000: loss 0.247609\n",
"iteration 7000: loss 0.247226\n",
"iteration 8000: loss 0.246953\n",
"iteration 9000: loss 0.246302\n"
]
}
],
"source": [
"num_examples = X.shape[0]\n",
"for i in range(10000):\n",
" #forward propagation\n",
" hidden_layer = np.maximum(np.dot(X, W1) + b1, 0)\n",
" scores = np.dot(hidden_layer, W2) + b2\n",
" \n",
" exp_scores = np.exp(scores)\n",
" sum_exp_scores = np.sum(exp_scores, axis = 1, keepdims = True)\n",
" probs = exp_scores / sum_exp_scores\n",
" \n",
" #back propagation\n",
" logprob = -np.log(probs[np.arange(num_examples), Y])\n",
" data_loss = np.sum(logprob) / num_examples\n",
" reg_loss = 0.5 * REGULARIZATION_RATE * np.sum (W1 * W1) + \\\n",
" 0.5 * REGULARIZATION_RATE * np.sum (W2 * W2)\n",
" loss = data_loss + reg_loss\n",
" \n",
" if i % 1000 == 0:\n",
" print (\"iteration %d: loss %f\" % (i, loss))\n",
" \n",
" dscores = probs\n",
" dscores[np.arange(num_examples), Y] -= 1\n",
" dscores /= num_examples\n",
" \n",
" dW2 = np.dot(hidden_layer.T, dscores)\n",
" db2 = np.sum(dscores, axis = 0, keepdims = True)\n",
" \n",
" dhidden = np.dot(dscores, W2.T)\n",
" dhidden[hidden_layer <= 0] = 0\n",
" \n",
" dW1 = np.dot(X.T, dhidden)\n",
" db1 = np.sum(dhidden, axis = 0, keepdims = True)\n",
" \n",
" # add regularization gradient contribution\n",
" dW2 += REGULARIZATION_RATE * W2\n",
" dW1 += REGULARIZATION_RATE * W1\n",
" \n",
" # perform a parameter update\n",
" W1 += -LEARNING_RATE * dW1\n",
" b1 += -LEARNING_RATE * db1\n",
" W2 += -LEARNING_RATE * dW2\n",
" b2 += -LEARNING_RATE * db2\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"training accuracy: 0.99\n"
]
}
],
"source": [
"# evaluate training set accuracy\n",
"hidden_layer = np.maximum(0, np.dot(X, W1) + b1)\n",
"scores = np.dot(hidden_layer, W2) + b2\n",
"predicted_class = np.argmax(scores, axis=1)\n",
"print ('training accuracy: %.2f' % (np.mean(predicted_class == Y)))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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YTgQ9bBOpoG5NnTVnRg9H+J0/OsjHf+MAI3c2HvAWHyKzGT781cMc/8o7a/pM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\n/EPCAJoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x8030c88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot the resulting classifier\n",
"h = 0.02\n",
"x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
"y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
"xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
" np.arange(y_min, y_max, h))\n",
"Z = np.dot(np.maximum(0, np.dot(np.c_[xx.ravel(), yy.ravel()], W1) + b1), W2) + b2\n",
"Z = np.argmax(Z, axis=1)\n",
"Z = Z.reshape(xx.shape)\n",
"\n",
"plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)\n",
"plt.scatter(X[:, 0], X[:, 1], c=Y, s=40, cmap=plt.cm.Spectral)\n",
"plt.xlim(xx.min(), xx.max())\n",
"plt.ylim(yy.min(), yy.max())\n",
"plt.show()"
]
},
{
"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 |
cassiobotaro/sentibol | Sentibol.ipynb | 1 | 14498 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from nltk.corpus import stopwords\n",
"import numpy as np\n",
"from sklearn.svm import SVC\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.ensemble import VotingClassifier\n",
"from emoticons import (positive_emoticons, negative_emoticons, positive_sentiment, negative_sentiment)\n",
"from nltk.tokenize import word_tokenize\n",
"import string\n",
"from operator import itemgetter"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package punkt to\n",
"[nltk_data] /home/cassiobotaro/nltk_data...\n",
"[nltk_data] Package punkt is already up-to-date!\n",
"[nltk_data] Downloading package stopwords to\n",
"[nltk_data] /home/cassiobotaro/nltk_data...\n",
"[nltk_data] Package stopwords is already up-to-date!\n"
]
},
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import nltk\n",
"nltk.download('punkt')\n",
"nltk.download('stopwords')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cruzeiro tem um bom time -> 1\n",
"acompanhe os jogos da primeira rodada -> 0\n",
"muito ruim esse jogo do cruzeiro -> -1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/cassiobotaro/projects/sentibol/.venv/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ate', 'eramos', 'estao', 'estavamos', 'estiveramos', 'estivessemos', 'foramos', 'fossemos', 'ha', 'hao', 'houveramos', 'houverao', 'houveriamos', 'houvessemos', 'ja', 'nao', 'sao', 'sera', 'serao', 'seriamos', 'so', 'tambem', 'tera', 'terao', 'teriamos', 'tinhamos', 'tiveramos', 'tivessemos', 'voce', 'voces'] not in stop_words.\n",
" 'stop_words.' % sorted(inconsistent))\n"
]
}
],
"source": [
"classificator = SVC(kernel='linear')\n",
"\n",
"training_set = ['o cruzeiro jogou muito bem', 'parabéns pela vitória cruzeiro',\n",
" 'ainda acho o cruzeiro ruim', 'o cruzeiro não jogou bem',\n",
" 'cruzeiro jogou contra o são paulo',\n",
" 'primeira rodada o cruzeiro enfrentou o sport']\n",
"labels = [1, 1, -1, -1, 0, 0]\n",
"\n",
"tf_vectorizer = TfidfVectorizer(stop_words=stopwords.words('portuguese'),\n",
" analyzer='word', ngram_range=(1, 1),\n",
" lowercase=True, use_idf=True,\n",
" strip_accents='unicode')\n",
"\n",
"features = tf_vectorizer.fit_transform(training_set)\n",
"classificator.fit(features, labels)\n",
"\n",
"tweets = ['cruzeiro tem um bom time', 'acompanhe os jogos da primeira rodada',\n",
" 'muito ruim esse jogo do cruzeiro']\n",
"\n",
"vector_tweets = tf_vectorizer.transform(tweets)\n",
"predictions = classificator.predict(vector_tweets)\n",
"\n",
"for prediction, tweet in zip(predictions, tweets):\n",
" print(f'{tweet} -> {prediction}')\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cruzeiro tem um bom time -> 1\n",
"acompanhe os jogos da primeira rodada -> 0\n",
"muito ruim esse jogo do cruzeiro -> -1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/cassiobotaro/projects/sentibol/.venv/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ate', 'eramos', 'estao', 'estavamos', 'estiveramos', 'estivessemos', 'foramos', 'fossemos', 'ha', 'hao', 'houveramos', 'houverao', 'houveriamos', 'houvessemos', 'ja', 'nao', 'sao', 'sera', 'serao', 'seriamos', 'so', 'tambem', 'tera', 'terao', 'teriamos', 'tinhamos', 'tiveramos', 'tivessemos', 'voce', 'voces'] not in stop_words.\n",
" 'stop_words.' % sorted(inconsistent))\n"
]
}
],
"source": [
"classificator = MultinomialNB(alpha=.01)\n",
"\n",
"training_set = ['o cruzeiro jogou muito bem', 'parabéns pela vitória cruzeiro',\n",
" 'ainda acho o cruzeiro ruim', 'o cruzeiro não jogou bem',\n",
" 'cruzeiro jogou contra o sport',\n",
" 'primeira rodada o cruzeiro enfrentou o sport']\n",
"labels = [1, 1, -1, -1, 0, 0]\n",
"\n",
"tf_vectorizer = TfidfVectorizer(stop_words=stopwords.words('portuguese'),\n",
" analyzer='word', ngram_range=(1, 1),\n",
" lowercase=True, use_idf=True,\n",
" strip_accents='unicode')\n",
"\n",
"features = tf_vectorizer.fit_transform(training_set)\n",
"classificator.fit(features, labels)\n",
"\n",
"tweets = ['cruzeiro tem um bom time', 'acompanhe os jogos da primeira rodada',\n",
" 'muito ruim esse jogo do cruzeiro']\n",
"\n",
"vector_tweets = tf_vectorizer.transform(tweets)\n",
"predictions = classificator.predict(vector_tweets)\n",
"for prediction, tweet in zip(predictions, tweets):\n",
" print(f'{tweet} -> {prediction}')\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cruzeiro tem um bom time -> 1\n",
"acompanhe os jogos da primeira rodada -> 0\n",
"muito ruim esse jogo do cruzeiro -> -1\n",
"cruzeiro! :) -> 1\n",
"Jogo morno entre cruzeiro e são paulo -> 0\n",
"Meu sport perdeu hoje :'( -> -1\n"
]
}
],
"source": [
"# lists based on https://en.wikipedia.org/wiki/List_of_emoticons\n",
"# Versão original removia pontuação(o que é errado, pois emoticons são feitos utilizando pontuação)\n",
"# falta de emojis\n",
"# deveria trazer as palavras a sua raiz\n",
"\n",
"list_emoticons_positive = positive_emoticons.split('\\n')\n",
"list_emoticons_negative = negative_emoticons.split('\\n')\n",
"list_negative = negative_sentiment.split('\\n')\n",
"list_positive = positive_sentiment.split('\\n')\n",
"\n",
"\n",
"def classify(text):\n",
" for t in text.split(' '):\n",
" if t in list_emoticons_positive:\n",
" return 1\n",
" elif t in list_emoticons_negative:\n",
" return -1\n",
" return votation(text)\n",
"\n",
"\n",
"def votation(text):\n",
" vote = 0\n",
" text = clean_text(text)\n",
"\n",
" for t in text:\n",
" if t in list_negative:\n",
" vote = vote - 1\n",
" elif t in list_positive:\n",
" vote = vote + 1\n",
"\n",
" if vote > 1:\n",
" return 1\n",
" elif vote < 0:\n",
" return -1\n",
" else:\n",
" return vote\n",
"\n",
"\n",
"def clean_text(text):\n",
" token_text = word_tokenize(text.lower())\n",
" text = (word for word in token_text\n",
" if word not in stopwords.words('portuguese'))\n",
" return text\n",
"\n",
"\n",
"def predict(tweets):\n",
" return np.array([classify(phrase) for phrase in tweets])\n",
"\n",
"\n",
"tweets = ['cruzeiro tem um bom time', 'acompanhe os jogos da primeira rodada',\n",
" 'muito ruim esse jogo do cruzeiro', 'cruzeiro! :)',\n",
" 'Jogo morno entre cruzeiro e são paulo',\n",
" 'Meu sport perdeu hoje :\\'(']\n",
"\n",
"predictions = predict(tweets)\n",
"for prediction, tweet in zip(predictions, tweets):\n",
" print(f'{tweet} -> {prediction}')\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cruzeiro tem um bom time -> 1\n",
"acompanhe os jogos da primeira rodada -> 0\n",
"muito ruim esse jogo do cruzeiro -> -1\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/cassiobotaro/projects/sentibol/.venv/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:300: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ate', 'eramos', 'estao', 'estavamos', 'estiveramos', 'estivessemos', 'foramos', 'fossemos', 'ha', 'hao', 'houveramos', 'houverao', 'houveriamos', 'houvessemos', 'ja', 'nao', 'sao', 'sera', 'serao', 'seriamos', 'so', 'tambem', 'tera', 'terao', 'teriamos', 'tinhamos', 'tiveramos', 'tivessemos', 'voce', 'voces'] not in stop_words.\n",
" 'stop_words.' % sorted(inconsistent))\n"
]
}
],
"source": [
"classificator = VotingClassifier(estimators=[\n",
" ('svm', SVC(kernel='linear')),\n",
" ('naive', MultinomialNB(alpha=.01))]\n",
")\n",
"\n",
"training_set = ['o cruzeiro jogou muito bem', 'parabéns pela vitória cruzeiro',\n",
" 'ainda acho o cruzeiro ruim', 'o cruzeiro não jogou bem',\n",
" 'cruzeiro jogou contra o sport',\n",
" 'primeira rodada o cruzeiro enfrentou o sport']\n",
"labels = [1, 1, -1, -1, 0, 0]\n",
"\n",
"tf_vectorizer = TfidfVectorizer(stop_words=stopwords.words('portuguese'),\n",
" analyzer='word', ngram_range=(1, 1),\n",
" lowercase=True, use_idf=True,\n",
" strip_accents='unicode')\n",
"\n",
"features = tf_vectorizer.fit_transform(training_set)\n",
"classificator.fit(features, labels)\n",
"tweets = ['cruzeiro tem um bom time', 'acompanhe os jogos da primeira rodada',\n",
" 'muito ruim esse jogo do cruzeiro']\n",
"vector_tweets = tf_vectorizer.transform(tweets)\n",
"predictions = classificator.predict(vector_tweets)\n",
"for prediction, tweet in zip(predictions, tweets):\n",
" print(f'{tweet} -> {prediction}')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"o cruzeiro jogou muito bem -> 2\n",
"parabéns pela vitória cruzeiro -> 3\n",
"ainda acho o cruzeiro ruim -> 2\n",
"o cruzeiro não jogou bem -> 2\n",
"cruzeiro jogou contra o sport -> 4\n",
"primeira rodada o cruzeiro enfrentou o sport -> 5\n",
"bela vitória do cruzeiro -> 2\n",
"mais uma vitória do cruzeiro -> 2\n",
"--------------------------------------------------------------------------------\n",
"Tweets mais relevantes:\n",
"['primeira rodada o cruzeiro enfrentou o sport']\n"
]
}
],
"source": [
"\n",
"\n",
"def compare_tweet_jaccard(tweet, tweet_compare):\n",
" words_tweet = tweet.lower().split()\n",
" words_compare = tweet_compare.lower().split()\n",
" intersection = set(words_tweet).intersection(set(words_compare))\n",
" intersection_size = len(intersection)\n",
" calc = float(intersection_size) / (len(words_tweet) + len(words_compare)\n",
" - intersection_size)\n",
"\n",
" if calc >= 0.20: # Define o quao semelhante é um tweet com o outro\n",
" return True # se tweet igual a tweet_compare\n",
" return False\n",
"\n",
"\n",
"tweets = ['o cruzeiro jogou muito bem', 'parabéns pela vitória cruzeiro',\n",
" 'ainda acho o cruzeiro ruim', 'o cruzeiro não jogou bem',\n",
" 'cruzeiro jogou contra o sport',\n",
" 'primeira rodada o cruzeiro enfrentou o sport',\n",
" 'bela vitória do cruzeiro', 'mais uma vitória do cruzeiro']\n",
"\n",
"tf_vectorizer = TfidfVectorizer(\n",
" stop_words=stopwords.words('portuguese'), analyzer='word',\n",
" ngram_range=(1, 1), lowercase=True, use_idf=True)\n",
"matrix = tf_vectorizer.fit_transform(tweets)\n",
"\n",
"feature_array = np.array(tf_vectorizer.get_feature_names())\n",
"tfidf_sorting = np.argsort(matrix.toarray()).flatten()[::-1]\n",
"feature_words = feature_array[tfidf_sorting][:10]\n",
"featured_tweets = []\n",
"for tweet in tweets:\n",
" relevance = 0\n",
" for word in feature_words:\n",
" if word in tweet:\n",
" relevance += 1\n",
" if relevance > 3:\n",
" featured_tweets.append((tweet, relevance))\n",
"\n",
" print(f'{tweet} -> {relevance}')\n",
"\n",
"# eliminate duplicated tweets\n",
"non_duplicated = []\n",
"for t in sorted(featured_tweets, key=itemgetter(1), reverse=True):\n",
" if not non_duplicated:\n",
" non_duplicated.append(t[0])\n",
" else:\n",
" for nd in non_duplicated:\n",
" if not compare_tweet_jaccard(nd, t[0]):\n",
" non_duplicated.append(t[0])\n",
"\n",
"\n",
"print(80 * '-')\n",
"print('Tweets mais relevantes:')\n",
"print(non_duplicated)"
]
}
],
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
zomansud/coursera | ml-foundations/week-3/Analyzing Product Sentiment.ipynb | 1 | 535996 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import graphlab"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Read some product review data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"[INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: /tmp/graphlab_server_1474342817.log\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"This non-commercial license of GraphLab Create for academic use is assigned to [email protected] and will expire on September 18, 2017.\n"
]
}
],
"source": [
"products = graphlab.SFrame('amazon_baby.gl/')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"graphlab.canvas.set_target('ipynb')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore this data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Flannel Wipes</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">These flannel wipes are<br>OK, but in my opinion ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Wipe Pouch</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">it came early and was not<br>disappointed. i love ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Annas Dream Full Quilt<br>with 2 Shams ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very soft and comfortable<br>and warmer than it ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This is a product well<br>worth the purchase. I ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All of my kids have cried<br>non-stop when I tried to ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">When the Binky Fairy came<br>to our house, we didn't ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A Tale of Baby's Days<br>with Peter Rabbit ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Lovely book, it's bound<br>tightly so you may no ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Perfect for new parents.<br>We were able to keep ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A friend of mine pinned<br>this product on Pinte ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This has been an easy way<br>for my nanny to record ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" </tr>\n",
"</table>\n",
"[10 rows x 3 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\treview\tstr\n",
"\trating\tfloat\n",
"\n",
"Rows: 10\n",
"\n",
"Data:\n",
"+-------------------------------+-------------------------------+--------+\n",
"| name | review | rating |\n",
"+-------------------------------+-------------------------------+--------+\n",
"| Planetwise Flannel Wipes | These flannel wipes are OK... | 3.0 |\n",
"| Planetwise Wipe Pouch | it came early and was not ... | 5.0 |\n",
"| Annas Dream Full Quilt wit... | Very soft and comfortable ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | This is a product well wor... | 5.0 |\n",
"| Stop Pacifier Sucking with... | All of my kids have cried ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | When the Binky Fairy came ... | 5.0 |\n",
"| A Tale of Baby's Days with... | Lovely book, it's bound ti... | 4.0 |\n",
"| Baby Tracker® - Daily ... | Perfect for new parents. W... | 5.0 |\n",
"| Baby Tracker® - Daily ... | A friend of mine pinned th... | 5.0 |\n",
"| Baby Tracker® - Daily ... | This has been an easy way ... | 4.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
"[10 rows x 3 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"products.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build a word count vector for each review"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"products['word_count'] = graphlab.text_analytics.count_words(products['review'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Flannel Wipes</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">These flannel wipes are<br>OK, but in my opinion ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">3.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 5, '6': 1,<br>'stink': 1, 'because' ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Wipe Pouch</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">it came early and was not<br>disappointed. i love ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3, 'love': 1,<br>'it': 2, 'highly': 1, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Annas Dream Full Quilt<br>with 2 Shams ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very soft and comfortable<br>and warmer than it ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'quilt': 1,<br>'it': 1, 'comfortable': ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This is a product well<br>worth the purchase. I ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'ingenious': 1, 'and':<br>3, 'love': 2, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All of my kids have cried<br>non-stop when I tried to ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'parents!!':<br>1, 'all': 2, 'puppet.': ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">When the Binky Fairy came<br>to our house, we didn't ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'this': 2,<br>'her': 1, 'help': 2, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A Tale of Baby's Days<br>with Peter Rabbit ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Lovely book, it's bound<br>tightly so you may no ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'shop': 1, 'noble': 1,<br>'is': 1, 'it': 1, 'as': ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Perfect for new parents.<br>We were able to keep ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'all': 1,<br>'right': 1, 'when': 1, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A friend of mine pinned<br>this product on Pinte ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1, 'help': 1,<br>'give': 1, 'is': 1, ' ...</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This has been an easy way<br>for my nanny to record ...</td>\n",
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"| Planetwise Flannel Wipes | These flannel wipes are OK... | 3.0 |\n",
"| Planetwise Wipe Pouch | it came early and was not ... | 5.0 |\n",
"| Annas Dream Full Quilt wit... | Very soft and comfortable ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | This is a product well wor... | 5.0 |\n",
"| Stop Pacifier Sucking with... | All of my kids have cried ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | When the Binky Fairy came ... | 5.0 |\n",
"| A Tale of Baby's Days with... | Lovely book, it's bound ti... | 4.0 |\n",
"| Baby Tracker® - Daily ... | Perfect for new parents. W... | 5.0 |\n",
"| Baby Tracker® - Daily ... | A friend of mine pinned th... | 5.0 |\n",
"| Baby Tracker® - Daily ... | This has been an easy way ... | 4.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
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"| {'and': 2, 'all': 1, 'righ... |\n",
"| {'and': 1, 'help': 1, 'giv... |\n",
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Black\": {\"frequency\": 29, \"value\": \"KidCo Angle Mount ...\"}, \"Graco DuoGlider Classic Connect Stroller, Dragonfly\": {\"frequency\": 30, \"value\": \"Graco DuoGlider ...\"}, \"Ring Snuggies ~ Ring Sizer / Assorted Sizes Adjuster Set of Six Per Pack\": {\"frequency\": 40, \"value\": \"Ring Snuggies ~ ...\"}, \"JJ Cole Mode Diaper Tote Bag, Cocoa Tree\": {\"frequency\": 19, \"value\": \"JJ Cole Mode ...\"}, \"Natursutten BPA-Free Natural Rubber Pacifier, Rounded, 12 Months\": {\"frequency\": 24, \"value\": \"Natursutten BPA- ...\"}, \"Simmons Kids Beautyrest Beginnings Sleepy Whispers Ultra Deluxe 2 n 1 Crib and Toddler Mattress, Neutral\": {\"frequency\": 25, \"value\": \"Simmons Kids ...\"}, \"Baby Einstein Bendy Ball\": {\"frequency\": 159, \"value\": \"Baby Einstein ...\"}, \"Leachco Snoogle Mini Compact Side Sleeper, Sage/White dot\": {\"frequency\": 55, \"value\": \"Leachco Snoogle ...\"}, \"Summer Infant SwaddleMe Organic Adjustable Infant Wrap, Ivory, Large\": {\"frequency\": 24, \"value\": \"Summer Infant ...\"}, \"Britax B-Agile Stroller Child Tray\": {\"frequency\": 40, \"value\": \"Britax B-Agile ...\"}, \"Two Peas in a Pod - 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6 PACK Clear\": {\"frequency\": 24, \"value\": \"Nuk Clear ...\"}, \"Fisher-Price Newborn Rock n' Play Sleeper, Yellow\": {\"frequency\": 27, \"value\": \"Fisher-Price ...\"}, \"Maxboost Fusion Snap-on iPhone 5S/5 Case - Red (Fit Fusion Battery Case for iPhone 5S/5)\": {\"frequency\": 20, \"value\": \"Maxboost Fusion ...\"}, \"Chicco 360 Hook on High Chair, Midori\": {\"frequency\": 45, \"value\": \"Chicco 360 Hook on ...\"}, \"Britax Boulevard 70 CS Convertible Car Seat, Biscotti\": {\"frequency\": 34, \"value\": \"Britax Boulevard ...\"}, \"Lansinoh mOmma Straw Cup with Dual Handles, Orange\": {\"frequency\": 26, \"value\": \"Lansinoh mOmma ...\"}, \"Baby Deedee Sleep Nest Baby Sleeping Bag, Dream Blue, Small (0-6 Months)\": {\"frequency\": 70, \"value\": \"Baby Deedee Sleep ...\"}, \"Mommy's Helper Power Strip Safety Cover\": {\"frequency\": 18, \"value\": \"Mommy's Helper ...\"}, \"NUK Gerber GraduatesFun Grips Hard Spout Sippy Cups, Boy, 10 Ounce, 4-Count\": {\"frequency\": 23, \"value\": \"NUK Gerber ...\"}, \"Tommee Tippee Bottle, 9 Ounce, 3 Count\": {\"frequency\": 30, \"value\": \"Tommee Tippee ...\"}, \"Evenflo AMP High Back Car Seat Booster, Pink Angles\": {\"frequency\": 82, \"value\": \"Evenflo AMP High ...\"}, \"Bright Starts Around We Go Activity Station, Doodle Bugs\": {\"frequency\": 83, \"value\": \"Bright Starts ...\"}, \"Baby Einstein Lights & Melodies Mirror\": {\"frequency\": 18, \"value\": \"Baby Einstein ...\"}, \"Kushies Waterproof Bib with Sleeves, Blue Circle, Infant\": {\"frequency\": 24, \"value\": \"Kushies Waterproof ...\"}, \"Proudbody My Little Prints Baby-Safe Ink Pad, Blue\": {\"frequency\": 55, \"value\": \"Proudbody My ...\"}, \"WubbaNub Lamb\": {\"frequency\": 49, \"value\": \"WubbaNub Lamb\"}, \"Mommys Helper Cushie Traveler Folding Padded Potty Seat with Carry Bag, White with Frog Design\": {\"frequency\": 18, \"value\": \"Mommys Helper ...\"}, \"BOB Infant Car Seat Adapter for Graco Single Strollers\": {\"frequency\": 19, \"value\": \"BOB Infant Car ...\"}, \"Philips AVENT BPA Free Classic Infant Starter Gift Set\": {\"frequency\": 69, \"value\": \"Philips AVENT BPA ...\"}, \"Leachco Prop 'R Shopper - Shopping Cart Cover - Sage Pin Dot\": {\"frequency\": 39, \"value\": \"Leachco Prop 'R ...\"}, \"Baby Food Containers- Sprout Cups - Reusable Stackable Storage Cups (12 Pack) with Tray and Dry-erase Marker - 100% BPA Free (2 Oz)\": {\"frequency\": 31, \"value\": \"Baby Food ...\"}, \"Britax Parkway SGL Belt-Positioning Booster Seat, Cub Pink\": {\"frequency\": 36, \"value\": \"Britax Parkway SGL ...\"}, \"Safety 1St Comfy Cushy 3-in-1 Potty\": {\"frequency\": 32, \"value\": \"Safety 1St Comfy ...\"}, \"Baby Jogger Car Seat Adapter for City Mini / City Elite\": {\"frequency\": 18, \"value\": \"Baby Jogger Car ...\"}, \"Medela Breastmilk Bottle Spare Parts\": {\"frequency\": 31, \"value\": \"Medela Breastmilk ...\"}, \"Merino Kids Baby Sleep Sack For Babies 0-2 Years, Banbury\": {\"frequency\": 20, \"value\": \"Merino Kids Baby ...\"}, \"Zo-li Buzz B. Baby Nail Trimmer\": {\"frequency\": 35, \"value\": \"Zo-li Buzz B. Baby ...\"}, \"Stork Craft Rochester Stages Crib with Drawer\": {\"frequency\": 22, \"value\": \"Stork Craft ...\"}, \"Tiny Love 3 in 1 Rocker Napper, Brown\": {\"frequency\": 60, \"value\": \"Tiny Love 3 in 1 ...\"}, \"Snappi Cloth Diaper Fasteners - Pack of 3 (Mint color mix)\": {\"frequency\": 30, \"value\": \"Snappi Cloth ...\"}, \"Summer Infant, Ultimate Training Pad - Twin Mattress, 38" x 28"\": {\"frequency\": 22, \"value\": \"Summer Infant, ...\"}, \"Maxboost Fusion Snap-on iPhone 5S/5 Case - Grey (Fit Fusion Battery Case for iPhone 5S/5)\": {\"frequency\": 22, \"value\": \"Maxboost Fusion ...\"}, \"Motorola MBP26 Wireless 2.4 GHz Video Baby Monitor with 2.4" Color LCD Screen, Infrared Night Vision and Remote Camera Pan and Tilt\": {\"frequency\": 32, \"value\": \"Motorola MBP26 ...\"}, \"Gerber Graduates BPA Free 2 Pack Fun Grips spill Proof Cup, 10 Ounce, Colors May Vary\": {\"frequency\": 45, \"value\": \"Gerber Graduates ...\"}, \"Carter's Bound Keepsake Memory Book of Baby's First 5 Years, Laguna\": {\"frequency\": 38, \"value\": \"Carter's Bound ...\"}, \"LA Baby 4 Sided Changing Pad 30", White\": {\"frequency\": 31, \"value\": \"LA Baby 4 Sided ...\"}, \"Born Free Tru-Temp Bottle Warming System\": {\"frequency\": 18, \"value\": \"Born Free Tru-Temp ...\"}, \"Nuby 2 Pack Nurtur Care Infa Feeder Set, 4 Ounce, Colors May Vary\": {\"frequency\": 35, \"value\": \"Nuby 2 Pack Nurtur ...\"}, \"Munchkin White Hot Inflatable Duck Tub\": {\"frequency\": 79, \"value\": \"Munchkin White Hot ...\"}, \"Planet Wise Reusable Diaper Pail Liner, Avocado\": {\"frequency\": 104, \"value\": \"Planet Wise ...\"}, \"Fisher Price Fisher Price Fastfinder Deluxe Messenger Bag\": {\"frequency\": 22, \"value\": \"Fisher Price ...\"}, \"Summer Infant Lil' Loo Potty, Pink\": {\"frequency\": 65, \"value\": \"Summer Infant Lil' ...\"}, \"Pearhead Babyprints Keepsake Wall Frame, White\": {\"frequency\": 25, \"value\": \"Pearhead ...\"}, \"Baby Vac Nasal Aspirator 2012 Model\": {\"frequency\": 31, \"value\": \"Baby Vac Nasal ...\"}, \"BOB Handlebar Console, Duallie\": {\"frequency\": 20, \"value\": \"BOB Handlebar ...\"}, \"Angelcare Baby Sound Monitor, White\": {\"frequency\": 85, \"value\": \"Angelcare Baby ...\"}, \"Fisher-Price Precious Planet Whale of a Tub\": {\"frequency\": 65, \"value\": \"Fisher-Price ...\"}, \"Sesame Street Elmo Adventure Potty Chair\": {\"frequency\": 31, \"value\": \"Sesame Street Elmo ...\"}, \"Munchkin Click Lock 2 Count Flip Straw Cup, 9 ounce\": {\"frequency\": 21, \"value\": \"Munchkin Click ...\"}, \"3 Sprouts Laundry Hamper, Deer\": {\"frequency\": 29, \"value\": \"3 Sprouts Laundry ...\"}, \"Britax Marathon 70-G3 Convertible Car Seat, Onyx\": {\"frequency\": 174, \"value\": \"Britax Marathon ...\"}, \"Safety 1st Tubside Bath Seat\": {\"frequency\": 53, \"value\": \"Safety 1st Tubside ...\"}, \"RECARO ProRIDE Convertible Car Seat, Misty\": {\"frequency\": 156, \"value\": \"RECARO ProRIDE ...\"}, \"Baby Einstein Musical Motion Activity Jumper, Green\": {\"frequency\": 23, \"value\": \"Baby Einstein ...\"}, \"Munchkin Dora The Explorer Toddler Dining Set\": {\"frequency\": 60, \"value\": \"Munchkin Dora The ...\"}, \"Thermos Ice Mat, 9 Cube\": {\"frequency\": 30, \"value\": \"Thermos Ice Mat, 9 ...\"}, \"Playtex Diaper Genie Twist- Away Pail System\": {\"frequency\": 81, \"value\": \"Playtex Diaper ...\"}, \"babyletto Madison Swivel Glider, Mocha\": {\"frequency\": 25, \"value\": \"babyletto Madison ...\"}, \"The First Years 1 Pack Breastflow Bottle, 9 Ounce\": {\"frequency\": 26, \"value\": \"The First Years 1 ...\"}, \"Munchkin Travel Booster Seat\": {\"frequency\": 35, \"value\": \"Munchkin Travel ...\"}, \"Kissa's Pail Liner, White\": {\"frequency\": 49, \"value\": \"Kissa's Pail ...\"}, \"Summer Infant Tiny Diner for Highchairs, Blue\": {\"frequency\": 46, \"value\": \"Summer Infant Tiny ...\"}, \"Munchkin Click Lock Fresh Food Freezer Pops\": {\"frequency\": 20, \"value\": \"Munchkin Click ...\"}, \"Summer Infant Metal Expansion Gate, 6 Foot Wide Walk-Thru, Neutral finish\": {\"frequency\": 59, \"value\": \"Summer Infant ...\"}, \"Philips AVENT BPA Free Standard Breast Pump Conversion Kit\": {\"frequency\": 23, \"value\": \"Philips AVENT BPA ...\"}, \"Safety 1st Sounds n Lights Discovery Walker, Dino\": {\"frequency\": 32, \"value\": \"Safety 1st Sounds ...\"}, \"Britax B-Agile Stroller Adult Cup Holder\": {\"frequency\": 19, \"value\": \"Britax B-Agile ...\"}, \"Aden By aden + anais Muslin Swaddle Blanket 4 Pack, Oh My!\": {\"frequency\": 131, \"value\": \"Aden By aden + ...\"}, \"Britax Frontier 90 Booster Car Seat, Zebra\": {\"frequency\": 108, \"value\": \"Britax Frontier 90 ...\"}, \"Diono Radian Angle Adjuster\": {\"frequency\": 47, \"value\": \"Diono Radian Angle ...\"}, \"Summer Infant Best View Handheld Color Video Monitor, Sliver/White\": {\"frequency\": 37, \"value\": \"Summer Infant Best ...\"}, \"Boon Fluid -No-Spill Toddler Cup in Orange/Blue\": {\"frequency\": 27, \"value\": \"Boon Fluid -No- ...\"}, \"Summer Infant Ultra Plush Change Pad Cover, Blue\": {\"frequency\": 104, \"value\": \"Summer Infant ...\"}, \"Philips Avent Express Microwave Sterilizer\": {\"frequency\": 67, \"value\": \"Philips Avent ...\"}, \"Today's Mom Cozy Cuddler Pregnancy Pillow - White\": {\"frequency\": 22, \"value\": \"Today's Mom Cozy ...\"}, \"Bumkins 3 Pack Waterproof SuperBib\": {\"frequency\": 186, \"value\": \"Bumkins 3 Pack ...\"}, \"Fisher-Price Luv U Zoo Snuggle Cub Soother Mobile\": {\"frequency\": 21, \"value\": \"Fisher-Price Luv U ...\"}, \"Carters Easy Fit Jersey Portable Crib Fitted Sheet, Pink\": {\"frequency\": 27, \"value\": \"Carters Easy Fit ...\"}, \"Boppy Cottony Cute Slipcover, Emily's Garden\": {\"frequency\": 45, \"value\": \"Boppy Cottony Cute ...\"}, \"North States Supergate Stairway Gate\": {\"frequency\": 18, \"value\": \"North States ...\"}, \"Sunshine Kids Dry Seat Pad, Grey\": {\"frequency\": 28, \"value\": \"Sunshine Kids Dry ...\"}, \"Nuby Icybite Hard/Soft Teething Keys\": {\"frequency\": 26, \"value\": \"Nuby Icybite ...\"}, \"Infantino Sash Mei Tai Carrier Black/Gray\": {\"frequency\": 66, \"value\": \"Infantino Sash Mei ...\"}, \"BRICA goPad Diaper Changer\": {\"frequency\": 24, \"value\": \"BRICA goPad Diaper ...\"}, \"Replacement Tubing (Retail Pack of 2) for Medela Pump in Style and New Pump in Style Advanced Breast Pump - 100% BPA FREE\": {\"frequency\": 86, \"value\": \"Replacement Tubing ...\"}, \"Simple Wishes D Lite Hands Free Breastpump Bra, Soft Pink, Large to Plus Size\": {\"frequency\": 53, \"value\": \"Simple Wishes D ...\"}, \"JL Childress Wheelie Car Seat Travel Bag, Black\": {\"frequency\": 47, \"value\": \"JL Childress ...\"}, \"babyletto Mercer 3-in-1 Convertible Crib with Toddler Rail, Two Tone\": {\"frequency\": 21, \"value\": \"babyletto Mercer ...\"}, \"aden + anais Classic Muslin Crib Sheet, Up, Up & Away Elephant\": {\"frequency\": 18, \"value\": \"aden + anais ...\"}, \"Babiators Unisex-Baby Infant Ops Junior Sunglasses, Black, Small\": {\"frequency\": 61, \"value\": \"Babiators Unisex- ...\"}, \"Bright Starts License to Drool Teether\": {\"frequency\": 33, \"value\": \"Bright Starts ...\"}, \"Edushape 4" Sensory Balls, Set of 4, Solid\": {\"frequency\": 27, \"value\": \"Edushape 4" ...\"}, \"Kiddopotamus Dreamsie Sleeper with Sleeves Microfleece Large, Ivory\": {\"frequency\": 21, \"value\": \"Kiddopotamus ...\"}, \"American Baby Company 100% Cotton Percale Fitted Portable/Mini Crib Sheet, Pink Dots\": {\"frequency\": 22, \"value\": \"American Baby ...\"}, \"Aquaus Toilet Bidet Handle / Diaper Sprayer with ** BONUS ** Rockin' Green Laundry Detergent and Tooth Tissue sample\": {\"frequency\": 20, \"value\": \"Aquaus Toilet ...\"}, \"Munchkin Cleaning Brush Set\": {\"frequency\": 33, \"value\": \"Munchkin Cleaning ...\"}, \"Cloud b Tranquil Turtle Night Light, Ocean\": {\"frequency\": 20, \"value\": \"Cloud b Tranquil ...\"}, \"OsoCozy Chinese Prefold Diapers, Infant 4x6x4\": {\"frequency\": 18, \"value\": \"OsoCozy Chinese ...\"}, \"Wubbanub Infant Pacifiers (Pink Kitty)\": {\"frequency\": 20, \"value\": \"Wubbanub Infant ...\"}, \"Bummis Super Whisper Wrap, White, 30 Pounds\": {\"frequency\": 20, \"value\": \"Bummis Super ...\"}, \"RECARO Performance SPORT Combination Harness to Booster, Vibe\": {\"frequency\": 34, \"value\": \"RECARO Performance ...\"}, \"JJ Cole Premaxx Sling Carrier - New Edition Red Orange\": {\"frequency\": 19, \"value\": \"JJ Cole Premaxx ...\"}, \"Baby Bottle Labels, Self-laminating - Great for Daycare\": {\"frequency\": 36, \"value\": \"Baby Bottle ...\"}, \"Dream On Me 3" Foam Graco Pack 'n Play Mattress\": {\"frequency\": 87, \"value\": \"Dream On Me ...\"}, \"Munchkin Arm & Hammer Diaper Pail withRefill Bags, 10-Count\": {\"frequency\": 26, \"value\": \"Munchkin Arm & ...\"}, \"my best friend Inflatable breast feeding pillow\": {\"frequency\": 19, \"value\": \"my best friend ...\"}, \"Safety 1st Soothing Mist Ultrasonic Humidifier\": {\"frequency\": 63, \"value\": \"Safety 1st ...\"}, \"Baby Einstein Seek & Discover Activity Gym\": {\"frequency\": 68, \"value\": \"Baby Einstein Seek ...\"}, \"Superyard Colorplay Ultimate Playard\": {\"frequency\": 20, \"value\": \"Superyard ...\"}, \"Fisher-Price Luv U Zoo Crib 'N Go Projector Soother\": {\"frequency\": 41, \"value\": \"Fisher-Price Luv U ...\"}, \"Thermos FOOGO Phases Stainless Steel Straw Bottle, Pink/Purple, 10 Ounce\": {\"frequency\": 151, \"value\": \"Thermos FOOGO ...\"}, \"Stork Craft Tuscany Glider and Ottoman, Cherry/Beige\": {\"frequency\": 66, \"value\": \"Stork Craft ...\"}, \"Dr. Brown's Gia Nursing Pillow\": {\"frequency\": 20, \"value\": \"Dr. Brown's Gia ...\"}, \"The Art of CureTM *SAFETY KNOTTED* Raw Multicolored- Certified Baltic Amber Baby Teething Necklace - w/The Art of Cure Jewelry Pouch (SHIPS AND SOLD IN THE USA)\": {\"frequency\": 18, \"value\": \"The Art of CureTM ...\"}, \"Hotslings Adjustable Pouch Baby Sling, Graham Cracker, Large\": {\"frequency\": 19, \"value\": \"Hotslings ...\"}, \"Sassy 2 Count Grow Up Cup, Purple/Orange, 9 Ounce\": {\"frequency\": 25, \"value\": \"Sassy 2 Count Grow ...\"}, \"Britax Boulevard 70 Convertible Car Seat (Previous Version), Onyx\": {\"frequency\": 36, \"value\": \"Britax Boulevard ...\"}, \"Cloud b Sound Machine Soother, Sleep Sheep\": {\"frequency\": 176, \"value\": \"Cloud b Sound ...\"}, \"Soft Gear My Booster Seat, Mint\": {\"frequency\": 28, \"value\": \"Soft Gear My ...\"}, \"Carters Keep Me Dry Flannel Lap Pads, Ecru, 3 Pack\": {\"frequency\": 63, \"value\": \"Carters Keep Me ...\"}, \"bumGenius One-Size Snap Closure Cloth Diaper 4.0 - Bubble\": {\"frequency\": 70, \"value\": \"bumGenius One-Size ...\"}, \"Wonderworld Peek-a-boo Ball\": {\"frequency\": 19, \"value\": \"Wonderworld ...\"}, \"UPPAbaby G-Luxe Stroller, Jake/Black\": {\"frequency\": 18, \"value\": \"UPPAbaby G-Luxe ...\"}, \"Postpartum Support Girdle Belt w/zipper Support Belly Band Medical-Grade Compression Bellefit\": {\"frequency\": 36, \"value\": \"Postpartum Support ...\"}, \"Cuisinart BFM-1000 Baby Food Maker and Bottle Warmer\": {\"frequency\": 28, \"value\": \"Cuisinart BFM-1000 ...\"}, \"JJ Cole Original Infant Bundleme, Khaki\": {\"frequency\": 19, \"value\": \"JJ Cole Original ...\"}, \"Fisher-Price Ocean Wonders Projector Soother\": {\"frequency\": 19, \"value\": \"Fisher-Price Ocean ...\"}, \"BRICA Baby In-Sight Magical Firefly Auto Mirror for in Car Safety\": {\"frequency\": 27, \"value\": \"BRICA Baby In- ...\"}, \"Nuby 2 Pack Replacement Silicone Spouts\": {\"frequency\": 18, \"value\": \"Nuby 2 Pack ...\"}, \"Baby Einstein Music and Discovery Travel Mirror\": {\"frequency\": 18, \"value\": \"Baby Einstein ...\"}, \"Chicco Liteway Stroller, Fuego\": {\"frequency\": 42, \"value\": \"Chicco Liteway ...\"}, \"CherryCreek Decals Giant Spring Flower Garden & Tree Baby/Nursery Wall Sticker Decals for Boys and Girls (Tree 4.4 Feet Tall)\": {\"frequency\": 22, \"value\": \"CherryCreek Decals ...\"}, \"Dr. Brown's Standard Dishwashing Basket, Polypropylene\": {\"frequency\": 22, \"value\": \"Dr. Brown's ...\"}, \"Boon Frog Pod Suction Cup Bracket\": {\"frequency\": 20, \"value\": \"Boon Frog Pod ...\"}, \"Cradle Mattress - 18 X 36 X 2" Thick\": {\"frequency\": 18, \"value\": \"Cradle Mattress - ...\"}, \"Evenflo Big Kid Booster Car Seat - Mercury\": {\"frequency\": 18, \"value\": \"Evenflo Big Kid ...\"}, \"Graco SimpleSwitch Highchair and Booster, Pasadena\": {\"frequency\": 56, \"value\": \"Graco SimpleSwitch ...\"}, \"Carters Keep Me Dry Flannel Bassinet Pad, Green/Yellow\": {\"frequency\": 55, \"value\": \"Carters Keep Me ...\"}, \"Graco DuetSoothe Swing + Rocker, Winslet\": {\"frequency\": 18, \"value\": \"Graco DuetSoothe ...\"}, \"aden + anais 3 Pack Muslin Washcloths, Water Baby\": {\"frequency\": 20, \"value\": \"aden + anais 3 ...\"}, \"green sprouts Warming Plate, Sage\": {\"frequency\": 27, \"value\": \"green 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" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"products['name'].show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore Villi Sophie"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"giraffe_reviews = products[products['name'] == 'Vulli Sophie the Giraffe Teether']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"785"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(giraffe_reviews)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" rel: \"stylesheet\",\n",
" type: \"text/css\",\n",
" href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
"}));\n",
"$(\"head\").append($(\"<link/>\").attr({\n",
" rel: \"stylesheet\",\n",
" type: \"text/css\",\n",
" href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
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" var e = null;\n",
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" require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
" var app = new IPythonApp();\n",
" app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"std\": 1.226576304850189, \"complete\": true, \"min\": 1.0, \"max\": 5.0, \"quantile\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0], \"median\": 5.0, \"numeric\": true, \"num_unique\": 5, \"num_undefined\": 0, \"var\": 1.5044894316199438, \"progress\": 1.0, \"size\": 785, \"frequent_items\": {\"1.0\": {\"frequency\": 56, \"value\": 1.0}, \"2.0\": {\"frequency\": 37, \"value\": 2.0}, \"3.0\": {\"frequency\": 62, \"value\": 3.0}, \"4.0\": {\"frequency\": 95, \"value\": 4.0}, \"5.0\": {\"frequency\": 535, \"value\": 5.0}}, \"mean\": 4.2942675159235675}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"float\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 785}, \"type\": \"SArray\", \"view_components\": [\"Numeric\", \"Categorical\"]}, \"histogram\": {\"progress\": 1.0, \"histogram\": {\"max\": 5.076199999999998, \"bins\": [56, 0, 0, 37, 0, 62, 0, 0, 95, 0, 0, 535], \"min\": 0.929000000000014}, \"min\": 1.0, \"complete\": 1, \"max\": 5.0}}, e);\n",
" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"giraffe_reviews['rating'].show(view='Categorical')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build a sentiment classifier"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"application/javascript": [
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" rel: \"stylesheet\",\n",
" type: \"text/css\",\n",
" href: \"//cdnjs.cloudflare.com/ajax/libs/font-awesome/4.1.0/css/font-awesome.min.css\"\n",
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"$(\"head\").append($(\"<link/>\").attr({\n",
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" href: \"https://static.turi.com/products/graphlab-create/2.1/canvas/css/canvas.css\"\n",
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"\n",
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" // disable load timeout; ipython_app.js is large and can take a while to load.\n",
" requirejs.config({waitSeconds: 0});\n",
" }\n",
"\n",
" require(['https://static.turi.com/products/graphlab-create/2.1/canvas/js/ipython_app.js'], function(IPythonApp){\n",
" var app = new IPythonApp();\n",
" app.attachView('sarray','Categorical', {\"ipython\": true, \"sketch\": {\"std\": 1.2850135559617413, \"complete\": true, \"min\": 1.0, \"max\": 5.0, \"quantile\": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0], \"median\": 5.0, \"numeric\": true, \"num_unique\": 5, \"num_undefined\": 0, \"var\": 1.651259839005439, \"progress\": 1.0, \"size\": 183531, \"frequent_items\": {\"1.0\": {\"frequency\": 15183, \"value\": 1.0}, \"2.0\": {\"frequency\": 11310, \"value\": 2.0}, \"3.0\": {\"frequency\": 16779, \"value\": 3.0}, \"4.0\": {\"frequency\": 33205, \"value\": 4.0}, \"5.0\": {\"frequency\": 107054, \"value\": 5.0}}, \"mean\": 4.1204483166331665}, \"selected_variable\": {\"name\": [\"<SArray>\"], \"dtype\": \"float\", \"view_component\": \"Categorical\", \"view_file\": \"sarray\", \"descriptives\": {\"rows\": 183531}, \"type\": \"SArray\", \"view_components\": [\"Numeric\", \"Categorical\"]}, \"histogram\": {\"progress\": 1.0, \"histogram\": {\"max\": 5.024, \"bins\": [15183, 0, 0, 11310, 0, 16779, 0, 0, 33205, 0, 0, 107054], \"min\": 0.992}, \"min\": 1.0, \"complete\": 1, \"max\": 5.0}}, e);\n",
" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"products['rating'].show(view='Categorical')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Define what a positive and negative sentiment"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#ignore all 3 star reviews"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"products = products[products['rating'] != 3]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"166752"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(products)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#positive sentiment is 4/5 star ; negative sentiment is 1/2 star"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"products['sentiment'] = products['rating'] >= 4"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">sentiment</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Planetwise Wipe Pouch</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">it came early and was not<br>disappointed. i love ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 3, 'love': 1,<br>'it': 2, 'highly': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Annas Dream Full Quilt<br>with 2 Shams ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Very soft and comfortable<br>and warmer than it ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'quilt': 1,<br>'it': 1, 'comfortable': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This is a product well<br>worth the purchase. I ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'ingenious': 1, 'and':<br>3, 'love': 2, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">All of my kids have cried<br>non-stop when I tried to ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'parents!!':<br>1, 'all': 2, 'puppet.': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Stop Pacifier Sucking<br>without tears with ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">When the Binky Fairy came<br>to our house, we didn't ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'this': 2,<br>'her': 1, 'help': 2, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A Tale of Baby's Days<br>with Peter Rabbit ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Lovely book, it's bound<br>tightly so you may no ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'shop': 1, 'noble': 1,<br>'is': 1, 'it': 1, 'as': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Perfect for new parents.<br>We were able to keep ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 2, 'all': 1,<br>'right': 1, 'when': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">A friend of mine pinned<br>this product on Pinte ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 1, 'help': 1,<br>'give': 1, 'is': 1, ' ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">This has been an easy way<br>for my nanny to record ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'journal.': 1, 'nanny':<br>1, 'standarad': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Baby Tracker&reg; - Daily<br>Childcare Journal, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I love this journal and<br>our nanny uses it ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 1, 'forget': 1,<br>'just': 1, 'food': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
" </tr>\n",
"</table>\n",
"[10 rows x 5 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\treview\tstr\n",
"\trating\tfloat\n",
"\tword_count\tdict\n",
"\tsentiment\tint\n",
"\n",
"Rows: 10\n",
"\n",
"Data:\n",
"+-------------------------------+-------------------------------+--------+\n",
"| name | review | rating |\n",
"+-------------------------------+-------------------------------+--------+\n",
"| Planetwise Wipe Pouch | it came early and was not ... | 5.0 |\n",
"| Annas Dream Full Quilt wit... | Very soft and comfortable ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | This is a product well wor... | 5.0 |\n",
"| Stop Pacifier Sucking with... | All of my kids have cried ... | 5.0 |\n",
"| Stop Pacifier Sucking with... | When the Binky Fairy came ... | 5.0 |\n",
"| A Tale of Baby's Days with... | Lovely book, it's bound ti... | 4.0 |\n",
"| Baby Tracker® - Daily ... | Perfect for new parents. W... | 5.0 |\n",
"| Baby Tracker® - Daily ... | A friend of mine pinned th... | 5.0 |\n",
"| Baby Tracker® - Daily ... | This has been an easy way ... | 4.0 |\n",
"| Baby Tracker® - Daily ... | I love this journal and ou... | 4.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
"+-------------------------------+-----------+\n",
"| word_count | sentiment |\n",
"+-------------------------------+-----------+\n",
"| {'and': 3, 'love': 1, 'it'... | 1 |\n",
"| {'and': 2, 'quilt': 1, 'it... | 1 |\n",
"| {'ingenious': 1, 'and': 3,... | 1 |\n",
"| {'and': 2, 'parents!!': 1,... | 1 |\n",
"| {'and': 2, 'this': 2, 'her... | 1 |\n",
"| {'shop': 1, 'noble': 1, 'i... | 1 |\n",
"| {'and': 2, 'all': 1, 'righ... | 1 |\n",
"| {'and': 1, 'help': 1, 'giv... | 1 |\n",
"| {'journal.': 1, 'nanny': 1... | 1 |\n",
"| {'all': 1, 'forget': 1, 'j... | 1 |\n",
"+-------------------------------+-----------+\n",
"[10 rows x 5 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"products.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lets train the sentiment classifier"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_data, test_data = products.random_split(0.8, seed=0)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<pre>WARNING: The number of feature dimensions in this problem is very large in comparison with the number of examples. Unless an appropriate regularization value is set, this model may not provide accurate predictions for a validation/test set.</pre>"
],
"text/plain": [
"WARNING: The number of feature dimensions in this problem is very large in comparison with the number of examples. Unless an appropriate regularization value is set, this model may not provide accurate predictions for a validation/test set."
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
"<pre>Logistic regression:</pre>"
],
"text/plain": [
"Logistic regression:"
]
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"output_type": "display_data"
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{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
],
"text/plain": [
"--------------------------------------------------------"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of examples : 133448</pre>"
],
"text/plain": [
"Number of examples : 133448"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of classes : 2</pre>"
],
"text/plain": [
"Number of classes : 2"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of feature columns : 1</pre>"
],
"text/plain": [
"Number of feature columns : 1"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of unpacked features : 219217</pre>"
],
"text/plain": [
"Number of unpacked features : 219217"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Number of coefficients : 219218</pre>"
],
"text/plain": [
"Number of coefficients : 219218"
]
},
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"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>Starting L-BFGS</pre>"
],
"text/plain": [
"Starting L-BFGS"
]
},
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"output_type": "display_data"
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{
"data": {
"text/html": [
"<pre>--------------------------------------------------------</pre>"
],
"text/plain": [
"--------------------------------------------------------"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
],
"text/plain": [
"+-----------+----------+-----------+--------------+-------------------+---------------------+"
]
},
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"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| Iteration | Passes | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |</pre>"
],
"text/plain": [
"| Iteration | Passes | Step size | Elapsed Time | Training-accuracy | Validation-accuracy |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
],
"text/plain": [
"+-----------+----------+-----------+--------------+-------------------+---------------------+"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 1 | 5 | 0.000002 | 2.429790 | 0.841481 | 0.839989 |</pre>"
],
"text/plain": [
"| 1 | 5 | 0.000002 | 2.429790 | 0.841481 | 0.839989 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 2 | 9 | 3.000000 | 3.848396 | 0.947425 | 0.894877 |</pre>"
],
"text/plain": [
"| 2 | 9 | 3.000000 | 3.848396 | 0.947425 | 0.894877 |"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>| 3 | 10 | 3.000000 | 4.277602 | 0.923768 | 0.866232 |</pre>"
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"| 3 | 10 | 3.000000 | 4.277602 | 0.923768 | 0.866232 |"
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{
"data": {
"text/html": [
"<pre>| 4 | 11 | 3.000000 | 4.902994 | 0.971779 | 0.912743 |</pre>"
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"text/plain": [
"| 4 | 11 | 3.000000 | 4.902994 | 0.971779 | 0.912743 |"
]
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"output_type": "display_data"
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{
"data": {
"text/html": [
"<pre>| 5 | 12 | 3.000000 | 5.492913 | 0.975511 | 0.908900 |</pre>"
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"text/plain": [
"| 5 | 12 | 3.000000 | 5.492913 | 0.975511 | 0.908900 |"
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"data": {
"text/html": [
"<pre>| 6 | 13 | 3.000000 | 6.030666 | 0.899991 | 0.825967 |</pre>"
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"text/plain": [
"| 6 | 13 | 3.000000 | 6.030666 | 0.899991 | 0.825967 |"
]
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{
"data": {
"text/html": [
"<pre>| 10 | 18 | 1.000000 | 8.546577 | 0.988715 | 0.916256 |</pre>"
],
"text/plain": [
"| 10 | 18 | 1.000000 | 8.546577 | 0.988715 | 0.916256 |"
]
},
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"output_type": "display_data"
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{
"data": {
"text/html": [
"<pre>+-----------+----------+-----------+--------------+-------------------+---------------------+</pre>"
],
"text/plain": [
"+-----------+----------+-----------+--------------+-------------------+---------------------+"
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
"<pre>TERMINATED: Iteration limit reached.</pre>"
],
"text/plain": [
"TERMINATED: Iteration limit reached."
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre>This model may not be optimal. To improve it, consider increasing `max_iterations`.</pre>"
],
"text/plain": [
"This model may not be optimal. To improve it, consider increasing `max_iterations`."
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sentiment_model = graphlab.logistic_classifier.create(train_data,\n",
" target='sentiment',\n",
" features=['word_count'],\n",
" validation_set=test_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluate the sentiment model"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'roc_curve': Columns:\n",
" \tthreshold\tfloat\n",
" \tfpr\tfloat\n",
" \ttpr\tfloat\n",
" \tp\tint\n",
" \tn\tint\n",
" \n",
" Rows: 100001\n",
" \n",
" Data:\n",
" +-----------+----------------+----------------+-------+------+\n",
" | threshold | fpr | tpr | p | n |\n",
" +-----------+----------------+----------------+-------+------+\n",
" | 0.0 | 1.0 | 1.0 | 27976 | 5328 |\n",
" | 1e-05 | 0.909346846847 | 0.998856162425 | 27976 | 5328 |\n",
" | 2e-05 | 0.896021021021 | 0.998748927652 | 27976 | 5328 |\n",
" | 3e-05 | 0.886448948949 | 0.998462968259 | 27976 | 5328 |\n",
" | 4e-05 | 0.879692192192 | 0.998284243637 | 27976 | 5328 |\n",
" | 5e-05 | 0.875187687688 | 0.998212753789 | 27976 | 5328 |\n",
" | 6e-05 | 0.872184684685 | 0.998177008865 | 27976 | 5328 |\n",
" | 7e-05 | 0.868618618619 | 0.998034029168 | 27976 | 5328 |\n",
" | 8e-05 | 0.864677177177 | 0.997998284244 | 27976 | 5328 |\n",
" | 9e-05 | 0.860735735736 | 0.997962539319 | 27976 | 5328 |\n",
" +-----------+----------------+----------------+-------+------+\n",
" [100001 rows x 5 columns]\n",
" Note: Only the head of the SFrame is printed.\n",
" You can use print_rows(num_rows=m, num_columns=n) to print more rows and columns.}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sentiment_model.evaluate(test_data, metric='roc_curve')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" });\n",
" })();\n",
" "
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sentiment_model.show(view='Evaluation')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Apply the learned model to understand the sentiment for giraffe"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"giraffe_reviews['predicted_sentiment'] = sentiment_model.predict(giraffe_reviews, output_type='probability')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"| name | review | rating |\n",
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"| Vulli Sophie the Giraffe T... | My son loves this toy and ... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | There really should be a l... | 1.0 |\n",
"| Vulli Sophie the Giraffe T... | All the moms in my moms' g... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | I was a little skeptical o... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | I have been reading about ... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | My neice loves her sophie ... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | What a friendly face! And... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | We got this just for my so... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | My baby seems to like this... | 3.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
"+-------------------------------+---------------------+\n",
"| word_count | predicted_sentiment |\n",
"+-------------------------------+---------------------+\n",
"| {'and': 1, 'all': 1, 'beca... | 0.999513023521 |\n",
"| {'and': 1, 'right': 1, 'he... | 0.999320678306 |\n",
"| {'and': 2, 'all': 1, 'woul... | 0.013558811687 |\n",
"| {'and': 2, 'one!': 1, 'all... | 0.995769474148 |\n",
"| {'and': 3, 'all': 1, 'mont... | 0.662374415673 |\n",
"| {'and': 6, 'seven': 1, 'al... | 0.999997148186 |\n",
"| {'and': 4, 'drooling,': 1,... | 0.989190989536 |\n",
"| {'and': 3, 'chew': 1, 'be'... | 0.999563518413 |\n",
"| {'chew': 2, 'seemed': 1, '... | 0.970160542725 |\n",
"| {'and': 2, 'already': 1, '... | 0.195367644588 |\n",
"+-------------------------------+---------------------+\n",
"[10 rows x 5 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sort the reviews based on the predicted sentiment and explore"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"giraffe_reviews = giraffe_reviews.sort(\n",
" 'predicted_sentiment', \n",
" ascending=False\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
" <tr>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">review</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">rating</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">word_count</th>\n",
" <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">predicted_sentiment</th>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sophie, oh Sophie, your<br>time has come. My ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'giggles': 1, 'all': 1,<br>\"violet's\": 2, 'bring': ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I'm not sure why Sophie<br>is such a hit with the ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'adoring': 1, 'find': 1,<br>'month': 1, 'bright': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999999703</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">I'll be honest...I bought<br>this toy because all the ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">4.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 2, 'discovered':<br>1, 'existence.': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999999392</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">We got this little<br>giraffe as a gift from a ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'all': 2, \"don't\": 1,<br>'(literally).so': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.99999999919</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">As a mother of 16month<br>old twins; I bought ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'cute': 1, 'all': 1,<br>'reviews.': 2, 'just' ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999998657</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sophie the Giraffe is the<br>perfect teething toy. ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'just': 2, 'both': 1,<br>'month': 1, 'ears,': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999997108</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sophie la giraffe is<br>absolutely the best toy ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 5, 'the': 1,<br>'all': 1, 'that': 2, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999995589</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My 5-mos old son took to<br>this immediately. The ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'just': 1, 'shape': 2,<br>'mutt': 1, '\"dog': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999995573</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">My nephews and my four<br>kids all had Sophie in ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'and': 4, 'chew': 1,<br>'all': 1, 'perfect;': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999989527</td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Vulli Sophie the Giraffe<br>Teether ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Never thought I'd see my<br>son French kissing a ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">5.0</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">{'giggles': 1, 'all': 1,<br>'out,': 1, 'over': 1, ...</td>\n",
" <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.999999985069</td>\n",
" </tr>\n",
"</table>\n",
"[10 rows x 5 columns]<br/>\n",
"</div>"
],
"text/plain": [
"Columns:\n",
"\tname\tstr\n",
"\treview\tstr\n",
"\trating\tfloat\n",
"\tword_count\tdict\n",
"\tpredicted_sentiment\tfloat\n",
"\n",
"Rows: 10\n",
"\n",
"Data:\n",
"+-------------------------------+-------------------------------+--------+\n",
"| name | review | rating |\n",
"+-------------------------------+-------------------------------+--------+\n",
"| Vulli Sophie the Giraffe T... | Sophie, oh Sophie, your ti... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | I'm not sure why Sophie is... | 4.0 |\n",
"| Vulli Sophie the Giraffe T... | I'll be honest...I bought ... | 4.0 |\n",
"| Vulli Sophie the Giraffe T... | We got this little giraffe... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | As a mother of 16month old... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | Sophie the Giraffe is the ... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | Sophie la giraffe is absol... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | My 5-mos old son took to t... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | My nephews and my four kid... | 5.0 |\n",
"| Vulli Sophie the Giraffe T... | Never thought I'd see my s... | 5.0 |\n",
"+-------------------------------+-------------------------------+--------+\n",
"+-------------------------------+---------------------+\n",
"| word_count | predicted_sentiment |\n",
"+-------------------------------+---------------------+\n",
"| {'giggles': 1, 'all': 1, \"... | 1.0 |\n",
"| {'adoring': 1, 'find': 1, ... | 0.999999999703 |\n",
"| {'all': 2, 'discovered': 1... | 0.999999999392 |\n",
"| {'all': 2, \"don't\": 1, '(l... | 0.99999999919 |\n",
"| {'cute': 1, 'all': 1, 'rev... | 0.999999998657 |\n",
"| {'just': 2, 'both': 1, 'mo... | 0.999999997108 |\n",
"| {'and': 5, 'the': 1, 'all'... | 0.999999995589 |\n",
"| {'just': 1, 'shape': 2, 'm... | 0.999999995573 |\n",
"| {'and': 4, 'chew': 1, 'all... | 0.999999989527 |\n",
"| {'giggles': 1, 'all': 1, '... | 0.999999985069 |\n",
"+-------------------------------+---------------------+\n",
"[10 rows x 5 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"Sophie, oh Sophie, your time has come. My granddaughter, Violet is 5 months old and starting to teeth. What joy little Sophie brings to Violet. Sophie is made of a very pliable rubber that is sturdy but not tough. It is quite easy for Violet to twist Sophie into unheard of positions to get Sophie into her mouth. The little nose and hooves fit perfectly into small mouths, and the drooling has purpose. The paint on Sophie is food quality.Sophie was born in 1961 in France. The maker had wondered why there was nothing available for babies and made Sophie from the finest rubber, phthalate-free on St Sophie's Day, thus the name was born. Since that time millions of Sophie's populate the world. She is soft and for babies little hands easy to grasp. Violet especially loves the bumpy head and horns of Sophie. Sophie has a long neck that easy to grasp and twist. She has lovely, sizable spots that attract Violet's attention. Sophie has happy little squeaks that bring squeals of delight from Violet. She is able to make Sophie squeak and that brings much joy. Sophie's smooth skin is soothing to Violet's little gums. Sophie is 7 inches tall and is the exact correct size for babies to hold and love.As you well know the first thing babies grasp, goes into their mouths- how wonderful to have a toy that stimulates all of the senses and helps with the issue of teething. Sophie is small enough to fit into any size pocket or bag. Sophie is the perfect find for babies from a few months to a year old. How wonderful to hear the giggles and laughs that emanate from babies who find Sophie irresistible. Viva La Sophie!Highly Recommended. prisrob 12-11-09\""
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews[0]['review']"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"I'm not sure why Sophie is such a hit with the little ones, but my 7 month old baby girl is one of her adoring fans. The rubber is softer and more pleasant to handle, and my daughter has enjoyed chewing on her legs and the nubs on her head even before she started teething. She also loves the squeak that Sophie makes when you squeeze her. Not sure what it is but if Sophie is amongst a pile of her other toys, my daughter will more often than not reach for Sophie. And I have the peace of mind of knowing that only edible and safe paints and materials have been used to make Sophie, as opposed to Bright Starts and other baby toys made in China. Now that the research is out on phthalates and other toxic substances in baby toys, I think it's more important than ever to find good quality toys that are also safe for our babies to handle and put in their mouths. Sophie is a must-have for every new mom in my opinion. Even if your kid is one of the few that can take or leave her, it's worth a try. Vulli, the makers of Sophie, also make natural rubber teething rings that my daughter loves as well.\""
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews[1]['review']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Show some negative reviews"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"My son (now 2.5) LOVED his Sophie, and I bought one for every baby shower I've gone to. Now, my daughter (6 months) just today nearly choked on it and I will never give it to her again. Had I not been within hearing range it could have been fatal. The strange sound she was making caught my attention and when I went to her and found the front curved leg shoved well down her throat and her face a purply/blue I panicked. I pulled it out and she vomited all over the carpet before screaming her head off. I can't believe how my opinion of this toy has changed from a must-have to a must-not-use. Please don't disregard any of the choking hazard comments, they are not over exaggerated!\""
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews[-1]['review']"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\"This children's toy is nostalgic and very cute. However, there is a distinct rubber smell and a very odd taste, yes I tried it, that my baby did not enjoy. Also, if it is soiled it is extremely difficult to clean as the rubber is a kind of porus material and does not clean well. The final thing is the squeaking device inside which stopped working after the first couple of days. I returned this item feeling I had overpaid for a toy that was defective and did not meet my expectations. Please do not be swayed by the cute packaging and hype surounding it as I was. One more thing, I was given a full refund from Amazon without any problem.\""
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"giraffe_reviews[-2]['review']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [gl-env]",
"language": "python",
"name": "Python [gl-env]"
},
"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 |
dtamayo/rebound | ipython_examples/Units.ipynb | 1 | 7288 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Unit convenience functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For convenience, REBOUND offers simple functionality for converting units. One implicitly sets the units for the simulation through the values used for the initial conditions, but one has to set the appropriate value for the gravitational constant `G`, and sometimes it is convenient to get the output in different units.\n",
"\n",
"The default value for `G` is 1, so one can:\n",
"\n",
"a) use units for the initial conditions where `G=1` (e.g., AU, $M_\\odot$, yr/$2\\pi$)\n",
"\n",
"b) set `G` manually to the value appropriate for the adopted initial conditions, e.g., to use SI units,"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import rebound\n",
"import math\n",
"sim = rebound.Simulation()\n",
"sim.G = 6.674e-11"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"c) set rebound.units:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"G = 39.4769264214.\n"
]
}
],
"source": [
"sim.units = ('yr', 'AU', 'Msun')\n",
"print(\"G = {0}.\".format(sim.G))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When you set the units, REBOUND converts `G` to the appropriate value for the units passed (must pass exactly 3 units for mass length and time, but they can be in any order). Note that if you are interested in high precision, you have to be quite particular about the exact units. \n",
"\n",
"As an aside, the reason why `G` differs from $4\\pi^2 \\approx 39.47841760435743$ is mostly that we follow the convention of defining a \"year\" as 365.25 days (a Julian year), whereas the Earth's sidereal orbital period is closer to 365.256 days (and at even finer level, Venus and Mercury modify the orbital period). `G` would only equal $4\\pi^2$ in units where a \"year\" was exactly equal to one orbital period at $1 AU$ around a $1 M_\\odot$ star.\n",
"\n",
"**Adding particles**\n",
"\n",
"If you use `sim.units` at all, you need to set the units before adding any particles. You can then add particles in any of the ways described in [WHFast.ipynb](WHFast.ipynb). You can also add particles drawing from the horizons database (see [Churyumov-Gerasimenko.ipynb](Churyumov-Gerasimenko.ipynb)). If you don't set the units ahead of time, HORIZONS will return initial conditions in units of AU, $M_\\odot$ and yrs/$2\\pi$, such that `G=1`. \n",
"\n",
"Above we switched to units of AU, $M_\\odot$ and yrs, so when we add Earth:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Searching NASA Horizons for 'Earth'... Found: Earth-Moon Barycenter (3).\n",
"v = 6.18818201572\n"
]
}
],
"source": [
"sim.add('Earth')\n",
"ps = sim.particles\n",
"import math\n",
"print(\"v = {0}\".format(math.sqrt(ps[0].vx**2 + ps[0].vy**2 + ps[0].vz**2)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we see that the velocity is correctly set to approximately $2\\pi$ AU/yr.\n",
"\n",
"If you'd like to enter the initial conditions in one set of units, and then use a different set for the simulation, you can use the sim.convert_particle_units function, which converts both the initial conditions and `G`. Since we added Earth above, we restart with a new `Simulation` instance; otherwise we'll get an error saying that we can't set the units with particles already loaded:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"---------------------------------\n",
"REBOUND version: \t2.2.1\n",
"REBOUND built on: \tJul 29 2015 21:38:06\n",
"Number of particles: \t2\n",
"Selected integrator: \tias15\n",
"Simulation time: \t0.000000\n",
"Current timestep: \t0.001000\n",
"---------------------------------\n",
"<rebound.Particle object, id=-1 m=1.00075471416 x=0.0 y=0.0 z=0.0 vx=0.0 vy=0.0 vz=0.0>\n",
"<rebound.Particle object, id=-1 m=3.00226414249e-06 x=1.00268806834 y=0.0 z=0.0 vx=0.0 vy=6.27701572041 vz=0.0>\n",
"---------------------------------\n"
]
}
],
"source": [
"sim = rebound.Simulation()\n",
"sim.units = ('m', 's', 'kg')\n",
"sim.add(m=1.99e30)\n",
"sim.add(m=5.97e24,a=1.5e11)\n",
"\n",
"sim.convert_particle_units('AU', 'yr', 'Msun')\n",
"sim.status()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We first set the units to SI, added (approximate values for) the Sun and Earth in these units, and switched to AU, yr, $M_\\odot$. You can see that the particle states were converted correctly--the Sun has a mass of about 1, and the Earth has a distance of about 1.\n",
"\n",
"Note that when you pass orbital elements to sim.add, you *must* make sure `G` is set correctly ahead of time (through either 3 of the methods above), since it will use the value of `sim.G` to generate the velocities:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"G = 1.0\n",
"---------------------------------\n",
"REBOUND version: \t2.2.1\n",
"REBOUND built on: \tJul 29 2015 21:38:06\n",
"Number of particles: \t2\n",
"Selected integrator: \tias15\n",
"Simulation time: \t0.000000\n",
"Current timestep: \t0.001000\n",
"---------------------------------\n",
"<rebound.Particle object, id=-1 m=1.99e+30 x=0.0 y=0.0 z=0.0 vx=0.0 vy=0.0 vz=0.0>\n",
"<rebound.Particle object, id=-1 m=5.97e+24 x=1.5e+11 y=0.0 z=0.0 vx=0.0 vy=3642349031.42 vz=0.0>\n",
"---------------------------------\n"
]
}
],
"source": [
"sim = rebound.Simulation()\n",
"print(\"G = {0}\".format(sim.G))\n",
"sim.add(m=1.99e30)\n",
"sim.add(m=5.97e24,a=1.5e11)\n",
"sim.status()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The orbital speed of Earth is $\\sim 3\\times 10^4$ m/s, but since we didn't correctly set `G` ahead of time, we get $\\sim 3\\times 10^9$ m/s, so the Earth would fly off the Sun in this simulation."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:p2]",
"language": "python",
"name": "conda-env-p2-py"
},
"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": 1
}
| gpl-3.0 |
JaeGyu/PythonEx_1 | 파이썬 막 연습.ipynb | 1 | 31241 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = \"str\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Dummy:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"d = Dummy()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"d.name = \"더미\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def toString(self):\n",
" print(self.name)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"더미\n"
]
}
],
"source": [
"d.to_string = toString(d)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"d.to_string"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "'NoneType' object is not callable",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-8-19d60b909115>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not callable"
]
}
],
"source": [
"d.to_string()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"d.to_string = toString"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"더미\n"
]
}
],
"source": [
"d.to_string(d)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d.get_name = lambda self: self.name"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'더미'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d.get_name(d)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Car:\n",
" count = 0\n",
" \n",
" def plus_cnt(self):\n",
" Car.count += 1\n",
" \n",
" def get_cnt(self):\n",
" return Car.count"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"c1 = Car()\n",
"c2 = Car()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"c1.plus_cnt()\n",
"c2.plus_cnt()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"2\n"
]
}
],
"source": [
"print(c1.get_cnt())\n",
"print(c2.get_cnt())"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Book:\n",
" name = \"pattern\"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"b1 = Book()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"b2 = Book()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'pattern'"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b1.name"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'pattern'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b2.name"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Book.name = \"java\""
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'java'"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b1.name"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'java'"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b2.name"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"b1.name = \"python\" #인스턴스 변수 name 하나를 생성한다. 그리고 그 인스턴스에 python을 저장 한다."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'java'"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b2.name #b2 객체에는 name이라는 인스턴스 변수가 없다 그래서 스코핑룰에 의해 클래스 변수 name의 내용인 출력된다. "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'python'"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b1.name #b1 객체에는 좀전에 name이라는 인스턴스 변수를 생성하고 거기에 python을 입력 했으므로 그인스턴스 변수의 내용이 출력된다. "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def func1(self,a):\n",
" self.a = a"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def func2(self,b):\n",
" self.b = b"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Test():\n",
" f1 = func1\n",
" f2 = func2\n",
" \n",
" def show_attr(self):\n",
" print(\"(a:{}, b:{})\".format(self.a, self.b))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"inst = Test()\n",
"inst.f1(1)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"inst.f2(2)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(a:1, b:2)\n"
]
}
],
"source": [
"inst.show_attr()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"func1(inst, 77)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(a:77, b:2)\n"
]
}
],
"source": [
"inst.show_attr()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Test2:\n",
" var = 100\n",
" \n",
" def method1(self):\n",
" print(var)\n",
"\n",
"var = 200"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"inst = Test2()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"200\n"
]
}
],
"source": [
"inst.method1()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Test3:\n",
" var = 100"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def func_test2(self):\n",
" print(self.var)\n",
"\n",
"def func2_test2(self):\n",
" print(var)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"inst = Test3()\n",
"var = '외부 전역 변수 입니다.'"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100\n"
]
}
],
"source": [
"\"\"\"\n",
"Test3인스턴스의 인스턴스 변수 var를 찾는다 그런데 없네? \n",
"그래서 스코핑 룰에 의해 상위의 네임스페이스를 뒤진다 그래서 클래스 변수 var의 값이 출력된다.\n",
"\"\"\"\n",
"func_test2(inst) "
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"외부 전역 변수 입니다.\n"
]
}
],
"source": [
"\"\"\"\n",
"이럴 경우 출력하려는 var는 self의 var가 아니라 func2_test2와 같은 레벨에 있는 즉 전역에있는 var를 찾는다. \n",
"\"\"\"\n",
"func2_test2(inst)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Test:\n",
" \n",
" def __init__(self, a, b):\n",
" self.a = a #인스턴스 네임스페이스에 a등록\n",
" self.b = b #인스턴스 네임스페이스에 b등록\n",
" \n",
" @classmethod\n",
" def cls_method(cls, a, b, c):\n",
" cls.a = a #클래스 네임스페이스에 a등록\n",
" cls.b = b #클래스 네임스페이스에 b등록\n",
" cls.c = c #클래스 네임스페이스에 c등록\n"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_a = Test(1,2)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"test_a.cls_method(3,4,5)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_a.a"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_a.b"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_a.c"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"outter = 1"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Out:\n",
" def to_string(self):\n",
" print(self.outter)\n",
" \n",
" def cls_setter(cls):\n",
" cls.outter = 200\n",
" \n",
" @staticmethod #꼭 붙여줘야 한다. 안그러면 self에 해당하는 인자를 넘길려고 한다.\n",
" def func_firt():\n",
" print(\"about Algorithm\")"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"o = Out()\n",
"o.cls_setter() #자동으로 o인스턴스의 타입인 Out클래스가 인자로 넘어간다. "
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"200\n"
]
}
],
"source": [
"o.to_string()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"about Algorithm\n"
]
}
],
"source": [
"o.func_firt()"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"about Algorithm\n"
]
}
],
"source": [
"Out.func_firt()"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mylist = []\n",
"mylist_append = getattr(mylist, \"append\")\n",
"mylist_append(10)"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[10]\n"
]
}
],
"source": [
"print(mylist)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Container_x:\n",
" @staticmethod\n",
" def func_first():\n",
" print(\"First\")\n",
" \n",
" @staticmethod\n",
" def func_second():\n",
" print(\"Second\")\n",
" \n",
" @staticmethod\n",
" def func_third():\n",
" print(\"Third\")\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"list1 = [\"first\"]\n",
"list2 = [\"second\"]\n",
"list3 = [\"third\"]"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"일종의 디자인 패턴이네!\n",
"\"\"\"\n",
"def func_selector(data):\n",
" sel_func = getattr(Container_x, \"func_{}\".format(data[0]))\n",
" return sel_func()"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"First\n"
]
}
],
"source": [
"func_selector(list1)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Second\n"
]
}
],
"source": [
"func_selector(list2)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Third\n"
]
},
{
"data": {
"text/plain": [
"NoneType"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(func_selector(list3))"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class House2(object):\n",
" company = \"Python Factory\"\n",
"\n",
" def __init__(self, year, acreages, address, price):\n",
" self.__year = year\n",
" self.__acreages = acreages\n",
" self.__address = address\n",
" self.__price = price\n",
" \n",
" def show_company(self):\n",
" print(House2.company)\n",
" \n",
" def change_price(self, rate):\n",
" self.__price = __self.price * rate\n",
"\n",
" def show_info(self):\n",
" print(\"\"\"This house is built by {} in {},\n",
" acreages : {},\n",
" address : {},\n",
" price : {}\"\"\".format(House2.company, self.__year, self.__acreages, self.__address, self.__price))\n"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"h = House2(2010,10,20,20)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This house is built by Python Factory in 2010,\n",
" acreages : 10,\n",
" address : 20,\n",
" price : 20\n"
]
}
],
"source": [
"h.show_info()"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"20"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"h._House2__price #이렇게 하면 private의 인스턴스변수도 접근 할 수 있게 된다."
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['_House2__acreages',\n",
" '_House2__address',\n",
" '_House2__price',\n",
" '_House2__year',\n",
" '__class__',\n",
" '__delattr__',\n",
" '__dict__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__le__',\n",
" '__lt__',\n",
" '__module__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" '__weakref__',\n",
" 'change_price',\n",
" 'company',\n",
" 'show_company',\n",
" 'show_info']"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(h)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Ticket():\n",
" def __init__(self, distance):\n",
" self.__distance = distance\n",
" \n",
" def get_distance(self):\n",
" return \"{} m(meter) \".format(self.__distance)\n",
" \n",
" def set_distance(self, distance):\n",
" self.__distance = distance\n",
" \n",
" def get_fare(self):\n",
" return \"{} \\\\(Won) \".format(self.__distance * 13)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p1 = Ticket(15000)"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"15000 m(meter) \n"
]
}
],
"source": [
"print(p1.get_distance())"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"195000 \\(Won) \n"
]
}
],
"source": [
"print(p1.get_fare())"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p1.set_distance(30000)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"390000 \\(Won) \n"
]
}
],
"source": [
"print(p1.get_fare())"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['__class__',\n",
" '__delattr__',\n",
" '__dict__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__le__',\n",
" '__lt__',\n",
" '__module__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" '__weakref__',\n",
" 'get_distance',\n",
" 'get_fare',\n",
" 'set_distance']"
]
},
"execution_count": 125,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(Ticket)"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['_Ticket__distance',\n",
" '__class__',\n",
" '__delattr__',\n",
" '__dict__',\n",
" '__dir__',\n",
" '__doc__',\n",
" '__eq__',\n",
" '__format__',\n",
" '__ge__',\n",
" '__getattribute__',\n",
" '__gt__',\n",
" '__hash__',\n",
" '__init__',\n",
" '__le__',\n",
" '__lt__',\n",
" '__module__',\n",
" '__ne__',\n",
" '__new__',\n",
" '__reduce__',\n",
" '__reduce_ex__',\n",
" '__repr__',\n",
" '__setattr__',\n",
" '__sizeof__',\n",
" '__str__',\n",
" '__subclasshook__',\n",
" '__weakref__',\n",
" 'get_distance',\n",
" 'get_fare',\n",
" 'set_distance']"
]
},
"execution_count": 126,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dir(p1)"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Ticket():\n",
" def __init__(self, distance):\n",
" self.__distance = distance\n",
" \n",
" @property\n",
" def distance(self):\n",
" return \"{} m(meter) \".format(self.__distance)\n",
" \n",
" @distance.setter\n",
" def distance(self, distance):\n",
" self.__distance = distance\n",
" \n",
" @property\n",
" def fare(self):\n",
" return \"{} \\\\(Won) \".format(self.__distance * 13)"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p1 = Ticket(1500000)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1500000 m(meter) \n"
]
}
],
"source": [
"print(p1.distance)"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"p1.distance = 3000"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"3000 m(meter) \n"
]
}
],
"source": [
"print(p1.distance)"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Parent:\n",
" var = \"hello\"\n",
" def __init__(self, money):\n",
" self.__money = money\n",
" \n",
" def show_money(self):\n",
" print(self.__money)"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Child(Parent):\n",
" var = \"Hi~\"\n",
" def get_var(self):\n",
" print(self.var) #먼저 클래스변수 부터 점검 한다."
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"150\n"
]
}
],
"source": [
"c = Child(150) #초기화 메서드까지 상속 받는거 같다.\n",
"c.show_money()"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hi~\n"
]
}
],
"source": [
"c.get_var()"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Account():\n",
" def __init__(self, money):\n",
" self.balance = money\n",
" \n",
" def deposit(self, money):\n",
" self.balance += money\n",
" \n",
" def withdraw(self, money):\n",
" self.balance -= money\n",
" \n",
" def show_Account(self):\n",
" print(\"Balance : {}원\".format(self.balance))"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class YellowAccount(Account):\n",
" def deposit(self, money):\n",
" self.balance += money * 1.07\n",
" \n",
" def withdraw(self, money):\n",
" self.balance -= money + 10\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a1 = YellowAccount(100)"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Balance : 100원\n"
]
}
],
"source": [
"a1.show_Account()"
]
},
{
"cell_type": "code",
"execution_count": 156,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a1.deposit(200)"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Balance : 314.0원\n"
]
}
],
"source": [
"a1.show_Account()"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class BlueAccount(Account):\n",
" def __init__(self, name, money):\n",
" Account.__init__(self, money)\n",
" self.name = name\n",
" \n",
" def show_Account(self):\n",
" Account.show_Account(self)\n",
" print(\"Account owner : {}\".format(self.name))"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"b = BlueAccount(\"Alice\",300)"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Balance : 300원\n",
"Account owner : Alice\n"
]
}
],
"source": [
"b.show_Account()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"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 |
AlejandroMunozdelAlamo/Twitter-API- | Twitter API Workshop Documentation.ipynb | 1 | 4488 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Twitter Api Workshop Documentation\n",
"# Castilla Rodríguez, Abel; Muñoz del Álamo, Alejandro"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Objective/Objetivo"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"This workshop consists on creating an app in Python using the Twitter API for data mining, and after show some adquired data in graphic plots\n",
"\n",
"Este trabajo consiste en crear una aplicación en Python usando la API de Twitter para realizar minería de datos,y despues mostrar algunos de estos datos en gráficas."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Issues/Problemas"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"In first place, we couldn't access to the Twitter API, because we didn't know we need a token from Twitter for login ourselves to have access to the API functions\n",
"\n",
"Another problem we had was the library to access the API. We have tried to use some libraries, but the only one that allowed us to work properly was Tweepy. That's why we use this library instead of the one we first chose, python-twitter.\n",
"\n",
"After those issues, we couldn't print in the standard output the attribute text from the objects which type was Status, because we could't print the objects of type Unicode.\n",
"\n",
"Once we solved this matter, we feared our last concern: Our program couldn't print special characters like accents, using UTF-8 codification.\n",
"\n",
"\n",
"\n",
"En primer lugar, no pudimos acceder a la API de Twitter, ya que no sabíamos que requeríamos de un token de Twitter para identificarnos para acceder a las funciones de la API.\n",
"\n",
"Otro problema que tuvimos fue la biblioteca para acceder a la API. Tratamos de usar varias bibliotecas, pero la única que nos permitió trabajar de forma apropiada fue Tweepy. Por ello hemos utilizado esta librería en lugar de la que escogimos en primer lugar, python-twitter\n",
"\n",
"Después de estos incidentes, no podíamos mostrar por la salida estandar el atributo text de los objeto de la clase Status, porque no podíamos imprimir objetos de tipo Unicode.\n",
"\n",
"Una vez resueltos estos problemas, nos enfrentamos a nuestro última traba: Nuestro programa no podía imprimir caracteres especiales como acentos, usando la codificación UTF-8"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Libraries Definition/Definición de Bibliotecas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" → Tweepy: Library for access to the Twitter API\n",
" → re: Library for using regular expressions\n",
" → numpy: Library for using arrays\n",
" → matplotlib: Library for creating graphic plots\n",
" \n",
" → Tweepy: Biblioteca para acceder a la API de Twitter\n",
" → re: Biblioteca para usar expresiones regulares\n",
" → numpy: Biblioteca para usar arrays\n",
" → matplotlib: Biblioteca para crear graficas\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. User Manual/Manual del Usuario"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"At the beginning of the app, you will see a Main Menu, with some posibilities. In that menu, you can choose an option with the number which goes with the description of the function. Then, the app gives you the info requested as soon as posible.\n",
"\n",
"Al comienzo de la aplicación, se puede observar un menú principal, con algunas posibilidades. En dicho menú, puede escoger una opción con el núomero que acompaña a la descripción de la funcion. Entonces, la aplicación le devuelve la información que pidió tan pronto como sea posible."
]
}
],
"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
}
| gpl-3.0 |
Boron1112/uv-Code | UV track.ipynb | 1 | 503423 | {
"cells": [
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\User\\Anaconda3a\\lib\\site-packages\\matplotlib\\__init__.py:1401: UserWarning: This call to matplotlib.use() has no effect\n",
"because the backend has already been chosen;\n",
"matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
"or matplotlib.backends is imported for the first time.\n",
"\n",
" warnings.warn(_use_error_msg)\n"
]
}
],
"source": [
"#necassary imports #Smallest Coverage = 0.0683 arcsec, biggest = 3.54 arcsec\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"mpl.use('TkAgg')\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"import cmath\n",
"import PIL\n",
"from PIL import ImageDraw\n",
"import PIL.ImageTk\n",
"from tkinter import *\n",
"from tkinter import ttk\n",
"from matplotlib import colors\n",
"from pylab import rcParams\n",
"from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg\n",
"from matplotlib.backend_bases import key_press_handler\n",
"from matplotlib.figure import Figure\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Convert coordinates to ENU\n",
"def ENU(coords):\n",
" E = -coords[0]*np.cos(coords[1])*np.sin(coords[2])\n",
" N = coords[0]*np.cos(coords[1])*np.cos(coords[2])\n",
" U = coords[0]*np.sin(coords[1])\n",
" return np.array([E,N,U])"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Convert array into ENU\n",
"def ENUarray(coordsA):\n",
" arrayInQuestion = np.zeros([len(coordsA[0]),3])\n",
" for i in np.arange(len(coordsA[0])):\n",
" arrayInQuestion[i] = ENU(coordsA[0:3,i])\n",
" arrayInQuestion2 = np.zeros([len(arrayInQuestion[0]),len(arrayInQuestion)])\n",
" for i in np.arange(len(arrayInQuestion2)):\n",
" arrayInQuestion2[i] = arrayInQuestion[:,i]\n",
" return arrayInQuestion2"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[-0.45464871 0.7568025 0.41912325 -1.97871649 0.41912325]\n",
" [ 0.29192658 0.34635638 2.94025543 1.70899993 2.94025543]\n",
" [ 0.84147098 1.81859485 0.42336002 -3.02720998 0.42336002]]\n"
]
}
],
"source": [
"coordsA = np.array([[1,2,3,4,3],[1,2,3,4,3],[1,2,3,4,3]])\n",
"arrayInQuestion = np.zeros([len(coordsA[0]),len(coordsA)])\n",
"for i in np.arange(len(coordsA[0])):\n",
" arrayInQuestion[i] = ENU(coordsA[0:3,i])\n",
"arrayInQuestion2 = np.zeros([len(arrayInQuestion[0]),len(arrayInQuestion)])\n",
"for i in np.arange(len(arrayInQuestion2)):\n",
" arrayInQuestion2[i] = arrayInQuestion[:,i]\n",
"print(arrayInQuestion2)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Convert ENU to xyz\n",
"def xyz(coords,L):\n",
" x = -np.sin(L)*coords[1]+np.cos(L)*coords[2]\n",
" y = coords[0]\n",
" z = np.cos(L)*coords[1]+np.sin(L)*coords[2]\n",
" return np.array([x,y,z])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Convert xyz to uvw\n",
"def uvw(coords1,coords2,lam,delta,h):\n",
" Bx = coords2[0]-coords1[0]\n",
" By = coords2[1]-coords1[1]\n",
" Bz = coords2[2]-coords1[2]\n",
" uR = np.sin(h)*Bx+np.cos(h)*By\n",
" vR = -np.sin(delta)*np.cos(h)*Bx+np.sin(delta)*np.sin(h)*By+np.cos(delta)*Bz\n",
" wR = np.cos(delta)*np.cos(h)*Bx-np.cos(delta)*np.sin(h)*By+np.sin(delta)*Bz\n",
" u = uR/(lam*1000)\n",
" v = vR/(lam*1000)\n",
" w = wR/(lam*1000)\n",
" return np.array([u,v,w])"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Conv,ert from r,theta,phi to uvw all in one\n",
"def skipit(coords1,coords2,L,lam,delta,h):\n",
" return uvw(xyz(ENU(coords1),L),xyz(ENU(coords2),L),lam,delta,h)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def length(vector):\n",
" modStart = 0\n",
" for i in np.arange(len(vector)):\n",
" modStart += vector[i]**2\n",
" return np.sqrt(modStart)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Create all the baseline uv vectors\n",
"def uvTrack(coords,L,lam,delta,h):\n",
" uv = np.zeros([len(coords[1])*(len(coords[1])-1),3])\n",
" k = 0\n",
" for i in np.arange(len(coords[1])):\n",
" for j in np.arange(len(coords[1])-1-i):\n",
" uv[k] = skipit(coords[:,i],coords[:,j+i+1],L,lam,delta,h)\n",
" k = k+1\n",
" for i in np.arange(len(coords[1])):\n",
" for j in np.arange(len(coords[1])-1-i):\n",
" uv[k] = -skipit(coords[:,i],coords[:,j+i+1],L,lam,delta,h)\n",
" k = k+1\n",
" return uv"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Find uv track over time\n",
"def timetrack(coords,L,lam,delta,hCen,hStep,hSteps): #H as an array\n",
" uvFu = np.array([])\n",
" uvFv = np.array([])\n",
" for i in np.arange(hSteps):\n",
" uvFu = np.array(list(uvFu)+list(uvTrack(coords,L,lam,delta,h-hStep*hSteps/2+i*hStep)[:,0]))\n",
" uvFv = np.array(list(uvFv)+list(uvTrack(coords,L,lam,delta,h-hStep*hSteps/2+i*hStep)[:,1]))\n",
" return np.array([uvFu,uvFv])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#Subtract the uv coverage from the image, fourier transformation aplication to the image is to come\n",
"def Subtraction(image,uvCover):\n",
" #im2 = np.asarray(image)\n",
" #imS = np.zeros([len(im2),len(im2[0])])\n",
" #for i in np.arange(len(im2)):\n",
" # for j in np.arange(len(im2[0])):\n",
" # imS[i,j] = np.trapz(im2[i,j])\n",
" imUsed = np.fft.fft2(image) #fft.fft.real may be fine\n",
" finWid = len(imUsed)\n",
" finLen = len(imUsed[0])\n",
" staLen = max(uvCover[0])\n",
" staWid = max(uvCover[1])\n",
" sampleUsed = np.zeros([len(uvCover),len(uvCover[0])])\n",
" sampleUsed[0] = uvCover[0]*finLen/(2*staLen)+finLen/2\n",
" sampleUsed[1] = uvCover[1]*finLen/(2*staLen)+finLen/2\n",
" sampleUsed2 = np.zeros([len(sampleUsed[0]),len(sampleUsed)])\n",
" for i in np.arange(len(sampleUsed[0])):\n",
" sampleUsed2[i] = sampleUsed[:,i]\n",
" track = PIL.Image.new('RGB', (finLen,finWid), 'black')\n",
" mask = ImageDraw.Draw(track)\n",
" coords = sampleUsed2\n",
" pointSize = 1\n",
" for (x,y) in coords:\n",
" mask.rectangle([x,y,x+pointSize-1,y+pointSize-1], fill = 'white')\n",
" plt.imshow(track, 'gray')\n",
" #mask.rectangle([0,0,1000,1000], fill = 'white') #Use this for a full image\n",
" trackF = np.zeros([track.size[1],track.size[0]])\n",
" trackF = imUsed*np.asarray(track)[:,:,0]/255\n",
" return np.fft.ifft2(trackF) #put an inverse fourier transformation here"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Finds the maximum and the minimum baseline, coordinate should be given in xyz or ENU\n",
"def minmax(position):\n",
" totLen = 0\n",
" for i in np.arange(len(position[0])):\n",
" totLen += i\n",
" coverLength = np.zeros(totLen)\n",
" m = 0\n",
" for i in np.arange(len(position[0])):\n",
" for j in np.arange(len(position[0])-i-1):\n",
" coverLength[m] = length(position[:,i]-position[:,i+j+1])\n",
" m += 1\n",
" return np.array([min(coverLength),max(coverLength)])"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Scale the UV track to the Fourier transformed image\n",
"def ScaleUVTrack(uvCover,Res,image):\n",
" resRad = np.radians(Res/(60.*60.))\n",
" #sizeAng = (resRad)*np.array([image.size[0],image.size[1]])\n",
" inversedSizeAng = 2./resRad\n",
" pixSize = np.array([inversedSizeAng/len(np.fft.fft2(image)),inversedSizeAng/len(np.fft.fft2(image)[0])])\n",
" return 1000.*np.array([uvCover[0]/pixSize[0],uvCover[1]/pixSize[1]])"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def makeMask(ima,uvCover,Res):\n",
" finWid = len(np.fft.fft2(ima))\n",
" finLen = len(np.fft.fft2(ima)[0])\n",
" sampleUsed = ScaleUVTrack(uvCover,Res,ima)\n",
" sampleUsed2 = np.zeros([len(sampleUsed[0]),len(sampleUsed)])\n",
" for i in np.arange(len(sampleUsed[0])):\n",
" sampleUsed2[i] = sampleUsed[:,i]\n",
" sampleUsed2[i,1] = -sampleUsed2[i,1]\n",
" track = PIL.Image.new('RGB', (finLen,finWid), 'black')\n",
" mask = ImageDraw.Draw(track)\n",
" coord = sampleUsed2\n",
" pointSize = 1\n",
" Coord = np.arange(50)-25\n",
" Coord2 = np.zeros([len(Coord)**2,2])\n",
" m = 0\n",
" for i in np.arange(len(Coord)):\n",
" for j in np.arange(len(Coord)):\n",
" Coord2[m,0] = Coord[i]\n",
" Coord2[m,1] = Coord[j]\n",
" m+=1\n",
" for (x,y) in coord:\n",
" mask.rectangle([x+finLen/2,y+finWid/2,x+finLen/2+pointSize-1,y+finWid/2+pointSize-1], fill = 'white')\n",
" #mask.rectangle([0,0,1000,1000], fill = 'white') #Use this for a full image\n",
" return track"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def FourierTimesTrack(image,uvCover,Res):\n",
" #im2 = np.asarray(image)\n",
" #imS = np.zeros([len(im2),len(im2[0])])\n",
" #for i in np.arange(len(im2)):\n",
" # for j in np.arange(len(im2[0])):\n",
" # imS[i,j] = np.trapz(im2[i,j])\n",
" imUsed = np.fft.fftshift(np.fft.fft2(image)) #fft.fft.real may be fine\n",
" uvTrack = makeMask(im,uvCover,Res)\n",
" #plt.imshow(uvTrack, cmap = 'gray')\n",
" trackF = np.zeros([uvTrack.size[1],uvTrack.size[0]])\n",
" trackF = imUsed*np.asarray(uvTrack)[:,:,0]/255\n",
" return trackF"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def LoadingImage(*args):\n",
" global im, shownImage, imDis\n",
" im = PIL.Image.open(loaded.get())\n",
" #shownImage.grid_forget()\n",
" imDis = PIL.ImageTk.PhotoImage(im.resize([150,150]))\n",
" shownImage = refCan.create_image(75,75, image=imDis)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def ChooseArray(*args):\n",
" global Ea, No, arrayDisplay, plotDisplay, arrayBox, arrDisFig, arrCoordsENU\n",
" arrDisFig = plt.figure(1)\n",
" Ea = arrCoordsENU[arrayBox.curselection()[0]][0]\n",
" No = arrCoordsENU[arrayBox.curselection()[0]][1]\n",
" ELim = np.array([min(Ea-50)/1000,max(Ea+50)/1000])\n",
" NLim = np.array([min(No-50)/1000,max(No+50)/1000])\n",
" plt.clf()\n",
" plt.xlabel('East(km)')\n",
" plt.ylabel('North(km)')\n",
" plt.plot(Ea/1000,No/1000,'.')\n",
"\n",
" arrayDisplay = FigureCanvasTkAgg(arrDisFig,master = frame)\n",
"\n",
" plotDisplay = arrayDisplay.get_tk_widget()\n",
" plotDisplay.grid(column=3,row=2,rowspan=10)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def GrabHourSlide(e):\n",
" global hourMove ,minHoVal, maxHoVal\n",
" if ((e.x < minHoVal+3) and (e.x > minHoVal-3)):\n",
" hourMove = 1\n",
" else:\n",
" if ((e.x < maxHoVal+3) and (e.x > maxHoVal-3)):\n",
" hourMove = 2\n",
" else:\n",
" hourMove = 0"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def ReleaseHour(e):\n",
" global hourMove\n",
" hourMove = 0\n",
" hourCanvas = Canvas(frame, width=260,height=50)\n",
" hourCanvas.grid(column=4,row=6,columnspan=2)\n",
" hourCanvas.create_line(5,25,255,25)\n",
" hourCanvas.create_rectangle(minHoVal-3,10,minHoVal+3,40,fill='black')\n",
" hourCanvas.create_rectangle(maxHoVal-3,10,maxHoVal+3,40,fill='black')\n",
"\n",
" hourCanvas.bind('<Button - 1>', lambda e: GrabHourSlide(e))\n",
" hourCanvas.bind('<ButtonRelease - 1>', lambda e: ReleaseHour(e))\n",
" hourCanvas.bind('<B1-Motion>', lambda e: Slide(e))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def Slide(e):\n",
" global hourMove, minHoVal, maxHoVal, minHour, maxHour\n",
" if (hourMove == 1):\n",
" minHoVal = e.x\n",
" if (maxHoVal < minHoVal):\n",
" maxHoVal = minHoVal\n",
" if (hourMove == 2):\n",
" maxHoVal = e.x\n",
" if (maxHoVal < minHoVal):\n",
" minHoVal = maxHoVal\n",
" if (minHoVal < 5):\n",
" minHoVal = 5\n",
" if (maxHoVal < 5):\n",
" minHoVal = 5\n",
" maxHoVal = 5\n",
" if (maxHoVal > 255):\n",
" maxHoVal = 255\n",
" if (minHoVal > 255):\n",
" minHoVal = 255\n",
" maxHoVal = 255\n",
" if (hourMove != 0):\n",
" minHour.set((minHoVal-5)/250*24-12)\n",
" maxHour.set((maxHoVal-5)/250*24-12)\n",
" \n",
" hourCanvas = Canvas(frame, width=260,height=50)\n",
" hourCanvas.grid(column=4,row=6,columnspan=2)\n",
" hourCanvas.create_line(5,25,255,25)\n",
" hourCanvas.create_rectangle(minHoVal-3,10,minHoVal+3,40,fill='black')\n",
" hourCanvas.create_rectangle(maxHoVal-3,10,maxHoVal+3,40,fill='black')\n",
"\n",
" hourCanvas.bind('<Button - 1>', lambda e: GrabHourSlide)\n",
" hourCanvas.bind('<ButtonRelease - 1>', ReleaseHour)\n",
" hourCanvas.bind('<B1-Motion>', lambda e: Slide)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def minHourDirect(*args):\n",
" global hourMove, minHoVal, maxHoVal, minHour, maxHour\n",
" if ((float(minHour.get()) <= 12) and (float(minHour.get()) >= -12) and (float(maxHour.get()) <= 12) and (float(maxHour.get()) >= -12)):\n",
" if ((float(minHour.get()) > float(maxHour.get()))):\n",
" maxHour.set(minHour.get())\n",
" \n",
" minHoVal = (float(minHour.get())+12)*250/24+5\n",
" maxHoVal = (float(maxHour.get())+12)*250/24+5\n",
" \n",
" hourCanvas = Canvas(frame, width=260,height=50)\n",
" hourCanvas.grid(column=4,row=6,columnspan=2)\n",
" hourCanvas.create_line(5,25,255,25)\n",
" hourCanvas.create_rectangle(minHoVal-3,10,minHoVal+3,40,fill='black')\n",
" hourCanvas.create_rectangle(maxHoVal-3,10,maxHoVal+3,40,fill='black')\n",
"\n",
" hourCanvas.bind('<Button - 1>', lambda e: GrabHourSlide)\n",
" hourCanvas.bind('<ButtonRelease - 1>', ReleaseHour)\n",
" hourCanvas.bind('<B1-Motion>', lambda e: Slide)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def DeleteSelected(*args):\n",
" global usedArrays\n",
" relation.pop(usedArrays.curselection()[0])\n",
" usedArrays.delete(usedArrays.curselection()[0])\n",
" \n",
" uvDisFig = plt.figure(2)\n",
" plt.clf()\n",
" for i in np.arange(len(relation)):\n",
" U = relation[i][0]\n",
" V = relation[i][1]\n",
" plt.xlabel('U(klam)')\n",
" plt.ylabel('V(klam)')\n",
" plt.plot(U/1000,V/1000,'.')\n",
" \n",
" uvDisplay = FigureCanvasTkAgg(uvDisFig,master = frame)\n",
" plotUVDisplay = uvDisplay.get_tk_widget()\n",
" plotUVDisplay.grid(column=7,row=2,rowspan=10)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def AddSelected(*args):\n",
" global usedArrays, arrayBox, telNames, relation, freq, declin, samFreq, minHour, maxHour, arrCoords, latitude\n",
" usedArrays.insert(END, telNames[arrayBox.curselection()[0]] + ': ' + \"%.2f\" %float(minHour.get()) + '-' + \"%.2f\" %float(maxHour.get()))\n",
" hour = (float(minHour.get())/2.+float(maxHour.get())/2.)*np.pi/180\n",
" relation.append(timetrack(arrCoords[arrayBox.curselection()[0]],float(latitude[arrayBox.curselection()[0]]),299792458/(float(freq.get())*10**6),float(declin.get())*np.pi/180,hour,2.*0.99726958*np.pi/(24*60*float(samFreq.get())),(float(maxHour.get())-float(minHour.get()))*60*float(samFreq.get())/0.99726958))\n",
" \n",
" uvDisFig = plt.figure(2)\n",
" U = relation[len(relation)-1][0]\n",
" V = relation[len(relation)-1][1]\n",
" plt.xlabel('U(km)')\n",
" plt.ylabel('V(km)')\n",
" plt.plot(U,V,'.')\n",
" \n",
" uvDisplay = FigureCanvasTkAgg(uvDisFig,master = frame)\n",
"\n",
" plotUVDisplay = uvDisplay.get_tk_widget()\n",
" plotUVDisplay.grid(column=7,row=2,rowspan=10)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def FFTDisplay(image):\n",
" fftim = np.fft.fft2(image)\n",
" step = np.fft.fftshift(fftim)\n",
" absoluteFFT = abs(step)\n",
" logFFT = np.log(absoluteFFT+1)\n",
" maxes = np.zeros(len(logFFT))\n",
" for i in np.arange(len(logFFT)):\n",
" maxes[i] = max(logFFT[i])\n",
" totMax = max(maxes)\n",
" moddedFFT = logFFT*255/totMax\n",
" return PIL.Image.fromarray(moddedFFT)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#This is the final calculation\n",
"def FindEverything(*args):\n",
" #The Fourier Transform\n",
" global im, fourCan, imFour, relation, uvCan, imCoverage, pixScale, fftuvCan, imFoAndTr, synCan, imSynBeam, imFinal, finCan\n",
" imFourProto = FFTDisplay(im)\n",
" imFour = PIL.ImageTk.PhotoImage(imFourProto.resize([150,150]))\n",
" fourCan.create_image(75,75, image=imFour)\n",
" \n",
" #UV Track\n",
" totalTrack = [[]]\n",
" totalTrack.append([])\n",
" for i in np.arange(len(relation)):\n",
" for j in np.arange(len(relation[i][0])):\n",
" totalTrack[0].append(relation[i][0,j])\n",
" totalTrack[1].append(relation[i][1,j])\n",
" totalTrackArr = np.array([totalTrack[0],totalTrack[1]])\n",
" imCoverageProto = makeMask(im,totalTrackArr,float(pixScale.get())).convert('LA')\n",
" imCoverage = PIL.ImageTk.PhotoImage(imCoverageProto.resize([150,150]))\n",
" uvCan.create_image(75,75, image=imCoverage)\n",
" \n",
" #Effective Beam\n",
" imUsed = np.fft.fftshift(np.fft.fft2(im))\n",
" trackF = np.zeros([imCoverageProto.size[1],imCoverageProto.size[0]])\n",
" trackF = imUsed*np.asarray(imCoverageProto)[:,:,0]/255\n",
" imAndTrack = trackF\n",
" abImAndTrack = np.log(abs(imAndTrack)+1)\n",
" maxes = np.zeros(len(abImAndTrack))\n",
" for i in np.arange(len(abImAndTrack)):\n",
" maxes[i] = max(abImAndTrack[i])\n",
" totMax = max(maxes)\n",
" scaleImAndTrack = abImAndTrack*255/totMax\n",
" finImAndTrack = PIL.Image.fromarray(scaleImAndTrack)\n",
" imFoAndTr = PIL.ImageTk.PhotoImage(finImAndTrack.resize([150,150]))\n",
" fftuvCan.create_image(75,75, image=imFoAndTr)\n",
" \n",
" #Sythesised Beam\n",
" synBeam = abs(np.fft.ifft2(imCoverageProto.convert('L')))\n",
" logBeam = np.log(synBeam+1)\n",
" maxes = np.zeros(len(logBeam))\n",
" for i in np.arange(len(logBeam)):\n",
" maxes[i] = max(logBeam[i])\n",
" totMax = max(maxes)\n",
" moddedBeam = logBeam*1475/totMax\n",
" synBeamim = PIL.Image.fromarray(moddedBeam)\n",
" imSynBeam = PIL.ImageTk.PhotoImage(synBeamim.resize([150,150]))\n",
" synCan.create_image(75,75, image=imSynBeam)\n",
" \n",
" #And now, the moment you've all been waiting for\n",
" subImage = np.fft.ifft2(np.fft.ifftshift(imAndTrack))\n",
" fixUp = np.log(abs(subImage)+1)\n",
" maxes = np.zeros(len(fixUp))\n",
" for i in np.arange(len(fixUp)):\n",
" maxes[i] = max(fixUp[i])\n",
" totMax = max(maxes)\n",
" realFixUp = fixUp*255/totMax\n",
" finImage = PIL.Image.fromarray(realFixUp)\n",
" imFinal = PIL.ImageTk.PhotoImage(finImage.resize([150,150]))\n",
" finCan.create_image(75,75, image=imFinal)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#this box sets valuse just for testing, not practical method for an array, this one is for a wavelength, the next is for a frequency\n",
"#note that phi = 0 is north, phi = 90 is west\n",
"r = 5000 #pick an r in m\n",
"theta = 90 #pick a theta in degrees\n",
"phi = 30 #pick a phi in degrees\n",
"r2 = 4000 #pick an r in m\n",
"theta2 = 90 #pick a theta in degrees\n",
"phi2 = 30 #pick a phi in degrees\n",
"L = 45 #pick a latitude in degrees\n",
"lam = 0.21 #pick a wavelength in m\n",
"alpha = 12 #pick a right ascention in hours\n",
"delta = 45 #pick a declination in degrees\n",
"LST = 12 #pick a local sidereal time\n",
"h = 0 #directly set the hour angle (might not keep this)\n",
"\n",
"#don't change anything after this point\n",
"phiU = phi*np.pi/180\n",
"thetaU = theta*np.pi/180\n",
"phiU2 = phi2*np.pi/180\n",
"thetaU2 = theta2*np.pi/180\n",
"LU = L*np.pi/180\n",
"coords = np.array([r,thetaU,phiU])\n",
"coords2 = np.array([r2,thetaU2,phiU2])\n",
"deltaU = delta*np.pi/180\n",
"hU = h*np.pi/180"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#this box sets valuse just for testing, not practical method for an array, this one is for a frequency, the next is for a wavelength\n",
"#note that phi = 0 is north, phi = 90 is west\n",
"r = 5000 #pick an r in m\n",
"theta = 90 #pick a theta in degrees\n",
"phi = 30 #pick a phi in degrees\n",
"r2 = 4000 #pick an r in m\n",
"theta2 = 90 #pick a theta in degrees\n",
"phi2 = 30 #pick a phi in degrees\n",
"L = 45 #pick a latitude in degrees\n",
"f = 1.4e+9#pick a frequency in Hz\n",
"alpha = 12 #pick a right ascention in hours\n",
"delta = 45 #pick a declination in degrees\n",
"LST = 12 #pick a local sidereal time\n",
"h = 0 #directly set the hour angle (might not keep this)\n",
"\n",
"#don't change anything after this point\n",
"lam = 299792458/f\n",
"phiU = phi*np.pi/180\n",
"thetaU = theta*np.pi/180\n",
"phiU2 = phi2*np.pi/180\n",
"thetaU2 = theta2*np.pi/180\n",
"LU = L*np.pi/180\n",
"coords = np.array([r,thetaU,phiU])\n",
"coords2 = np.array([r2,thetaU2,phiU2])\n",
"deltaU = delta*np.pi/180\n",
"hU = h*np.pi/180"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#now arrays to actually make something halfway useful, this one is wavelength\n",
"rB = np.array([0.4364, 1.4337, 2.8747, 4.7095, 6.9065, 9.4434, 12.3027, 15.4706, 18.9357,\n",
" 0.4840, 1.5899, 3.1881, 5.2229, 7.6595, 10.4728, 13.6438, 17.157, 21.,\n",
" 0.484, 1.5899, 3.1881, 5.2229, 7.6595, 10.4728, 13.6439, 17.1572, 21.])*10**3 #set all the r values in m\n",
"thetaA = np.zeros(len(rB)) #set all the theta values in degrees, 0 is parrallel to the surface, 90 points to the zennith\n",
"phiA = np.array([5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0,\n",
" 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0,\n",
" 245.0, 245.0, 245.0, 245.0, 245.0, 245.0, 245.0, 245.0, 245.0]) #set all the phi values in degrees, 0 is north, 90 is west\n",
"L = 34.0784 #pick a latitude in degrees\n",
"lam = 0.21#pick a wavelength in m\n",
"alpha = 9.19103 #pick a right ascention in hours\n",
"delta = 5.84833 #pick a declination in degrees\n",
"LST = 12 #pick a local sidereal time\n",
"h = 0 #directly set the hour angle (might not keep this)\n",
"sampleRate = 1 #number of samples per minute\n",
"samples = 60 #Total Number of samples\n",
"res = 0.5 #arcseconds per pixel\n",
"\n",
"#don't change anything after this point\n",
"phiB = phiA*np.pi/180\n",
"thetaB = thetaA*np.pi/180\n",
"LU = L*np.pi/180\n",
"coords = np.array([rB,thetaB,phiB])\n",
"deltaU = delta*np.pi/180\n",
"hU = h*np.pi/180"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2c34d75e8d0>"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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3Ev39rh0PLI2IrRGxDVgKzBpMAhFxN7C1Fnn18XtWjTx7Uth3MyI2RMR96fmz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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2c34d71bba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Antenna Configuration\n",
"ants = ENU(coords)\n",
"plt.plot(ants[0],ants[1],'.')\n",
"plt.title('Array Configuration')\n",
"plt.xlabel('East (m)')\n",
"plt.ylabel('North (m)')"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2c34d9622e8>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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V8hPUWgXvnNlTWLSse5AikZLr5IEF965k/bbXWbX5VS46cTo79/SXHU1VMnGF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\nxMIUiGEYhhGL/w86Kzy8Ef2newAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2c34d71b048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Snapshot of uv track\n",
"uv = uvTrack(coords,LU,lam,deltaU,hU)\n",
"plt.plot(uv[:,0],uv[:,1],'.')\n",
"plt.title('uv track snapshot')\n",
"plt.xlabel('u (killo wavelengths)')\n",
"plt.ylabel('v (killo wavelengths)')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x2c34d9b3f60>"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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zxnRYdetExznL5T+4si7UtzFl9CDMu3dVpOgq5deprm/Emro9WgU11WceJWjh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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2c34d962a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#And plot a track with sampling\n",
"sampling = timetrack(coords,LU,lam,deltaU,hU,2*0.99726958*np.pi/(24*60*sampleRate),samples)\n",
"#sampling = timetrack(coords,LU,lam,deltaU,hU,2*0.99726958*np.pi/(24*60*sampleRate),samples)\n",
"plt.plot(sampling[0],sampling[1],'.')\n",
"sample = np.array([sampling[0],sampling[1]])\n",
"plt.title('uv track sampling')\n",
"plt.xlabel('u (wavelengths)')\n",
"plt.ylabel('v (wavelengths)')"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"172.753080388\n"
]
}
],
"source": [
"sampleC = np.zeros(len(sampling))\n",
"for i in np.arange(len(sampling)):\n",
" sampleC[i] = max(sampling[i])\n",
"print(max(sampleC))"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2c34e18ce10>"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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89NBDTExMhPFtUq3VwETFRKpihEsTklR6LLNCeQSy63U6KxtRqrts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\nGEYMEwqGYcT4/wH8OWYe2Z5iLwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2c34e0c4f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Image Test\n",
"im = PIL.Image.open('radio_galaxy256.png')\n",
"plt.imshow(im, 'gray')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2c34e1fc0f0>]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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PD9Mf24CGAyeMHmO3mp2oeuhVHD7VaXR8+wBvICb6P675EMfOdBkdK12IILjX4K6b6OsM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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2c34e0cfd30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"test = ScaleUVTrack(sampling,res,im)\n",
"plt.plot(test[0],test[1],'.')"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2c34e264b00>"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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bt27ZQsEwQ+TE7YbrjIxxcHBg0JCITcalRkTGZJNCMJLNy+Wywd5sNmt/L+kG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\n3b28Z17WAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2c34e1aeb00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fftim = np.fft.fft2(im)\n",
"step = np.fft.fftshift(fftim)\n",
"printable = abs(step)\n",
"reallyNow = np.log(printable+1)\n",
"maxes = np.zeros(len(reallyNow))\n",
"for i in np.arange(len(reallyNow)):\n",
" maxes[i] = max(reallyNow[i])\n",
"totMax = max(maxes)\n",
"seriouslyNow = reallyNow*255/totMax\n",
"fourierTrans = PIL.Image.fromarray(seriouslyNow)\n",
"plt.imshow(fourierTrans)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2c34ef61908>"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x2c34e209dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#track image\n",
"trackImage = makeMask(im,sampling,res)\n",
"plt.imshow(trackImage.convert('LA'))\n",
"#trackImage.save('Track.png')"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2c352d13f60>"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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v//7vMzY2xl133cXOzo7qOG5kvSOKAlw9KT760Y8yMjKip+LU1BTz8/O8/vrr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\nKWysjbWxVq3/A2zdJwehSufwAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2c3506ff278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Sythasied Beam\n",
"synBeam = abs(np.fft.ifft2(trackImage.convert('L')))\n",
"logBeam = np.log(synBeam+1)\n",
"maxes = np.zeros(len(logBeam))\n",
"for i in np.arange(len(logBeam)):\n",
" maxes[i] = max(logBeam[i])\n",
"totMax = max(maxes)\n",
"moddedBeam = logBeam*1475/totMax\n",
"synBeamim = PIL.Image.fromarray(moddedBeam)\n",
"plt.imshow((synBeamim))"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x2c35152e630>"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x2c34efdb908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"imAndTrack = FourierTimesTrack(im,sampling,res)\n",
"abImAndTrack = np.log(abs(imAndTrack)+1)\n",
"maxes = np.zeros(len(abImAndTrack))\n",
"for i in np.arange(len(abImAndTrack)):\n",
" maxes[i] = max(abImAndTrack[i])\n",
"totMax = max(maxes)\n",
"scaleImAndTrack = abImAndTrack*255/totMax\n",
"#reshapedImage = fixUp.reshape((len(fixUp), len(fixUp[0]))).astype('uint32')*255\n",
"finImAndTrack = PIL.Image.fromarray(scaleImAndTrack)\n",
"plt.imshow(finImAndTrack,cmap = 'hot')#, clim = (0))\n",
"#finImage = finImage.convert('RGB')\n",
"#finImage.save('Result.png')"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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HH8Dj8eBzn/ucaDlaNsJEtm6jhtXNJnRJ6SfF/7nJgI9DPGAUUXABuajcgNzk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eFIJIJsNoumpqaylBqfWytN3jMVFNF5/X4UXE1E0spRcwuIA7S1tSEQCAiQyvfJ5XKC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\neSwbcmR8IpHAkSNHJLIh2q8P/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MydKci5XA4rKyvIZDIYGhqC0+lsOpugUqlImZLRhm5t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\nOOSQQzX0f+tD0NhIOf/9AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2c34d75ecc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"subImage = np.fft.ifft2(np.fft.ifftshift(imAndTrack))\n",
"fixUp = np.log(abs(subImage)+1)\n",
"maxes = np.zeros(len(fixUp))\n",
"for i in np.arange(len(fixUp)):\n",
" maxes[i] = max(fixUp[i])\n",
"totMax = max(maxes)\n",
"realFixUp = fixUp*255/totMax\n",
"#reshapedImage = fixUp.reshape((len(fixUp), len(fixUp[0]))).astype('uint32')*255\n",
"finImage = PIL.Image.fromarray(realFixUp)\n",
"plt.imshow(finImage,cmap = 'hot')#, clim = (0))\n",
"finImage = finImage.convert('RGB')\n",
"#finImage.save('Result.png')"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ -0.06531348 -0.15968502 -0.27984664 ..., -15.5405735 -32.53864252\n",
" -16.99806901]\n",
" [ 4.1714434 10.19876712 17.87325259 ..., 6.03175981 12.62921708\n",
" 6.59745727]]\n"
]
}
],
"source": [
"print(sampling)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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p78XANZIuAnYFZgE/K7ekN/KFlGZTU9ap72MkrQHeAdwm6U6AiFgOXA88CtwBnBURr5VR\no5l1Vllno24Cbppg35eBL6etyMyK1m1no8ysRzlszCwJh42ZJeGwMbMkHDZmloTDxsyS6Juw8dXa\nZuXqthnEhfDV2mbl64uWja/WNitfX4SNb3trVr6+6Eb5am2z8vVF2ICv1jYrW190o8ysfA4bM0vC\nYWNmSThszCwJh42ZJeGwMbMkFJHkxgSFkrQeGCu7jg7ZBXi67CIK5OOrtsbj2zMiBvK8sSfCppdI\nGomI4clfWU0+vmpr5/jcjTKzJBw2ZpaEw6b7LCq7gIL5+KptysfnMRszS8ItGzNLwmHTBSQdL2m5\npI2Shhv2fUrSKkmPSXp/WTW2S9Lh2TGskrSw7HraJekKSeskLavbNl3S3ZJWZr8ru8yApD0kLZH0\naPZv85xs+5SP0WHTHZYBxwJL6zdKmg2cCLwdOBy4VNK09OW1J6v5EuAIYDZwUnZsVXYltf8m9RYC\n90bELODe7HlVbQDOi4jZwDzgrOy/2ZSP0WHTBSJiRUQ81mTXfOC6iHg5In4FrALmpq2uI+YCqyLi\n8Yh4BbiO2rFVVkQsBZ5t2DwfuCp7fBVwdNKiOigi1kbEg9njF4AVwG60cYwOm+62G/Cbuudrsm1V\n0yvHMZkZEbE2e/wkMKPMYjpF0hBwIPBT2jjGvlmpr2yS7gHe1mTXBRFxS+p6rFgREZIqf6pX0vbA\n94BzI+J5SZv2tXqMDptEIuJ9U3jbE8Aedc93z7ZVTa8cx2SekjQzItZKmgmsK7ugdkjailrQXB0R\nN2abp3yM7kZ1t8XAiZLeImkvYBbws5JrmooHgFmS9pK0NbVB78Ul11SExcDp2ePTgcq2WFVrwlwO\nrIiIi+p2Tf0YI8I/Jf8Ax1Abx3gZeAq4s27fBcAvgceAI8qutY1jPBL4RXYsF5RdTweO51pgLfBq\n9t/uTGBnamdoVgL3ANPLrrON43sXEMDDwEPZz5HtHKNnEJtZEu5GmVkSDhszS8JhY2ZJOGzMLAmH\njZkl4bAxsyQcNpabpNckPVT30/JVzZIOkfTOhm3nSjote3xf4zIbLXz2fpKunMp7rXi+XMFa8VJE\nHNDmZxwCvAj8GEDSlsAZwEFtfi4R8Yik3SUNRsSv2/086yy3bKxtkj4r6QFJyyQtyqa6I+nj2eJL\nD0u6Lrt6+GPAP2Qto3cD7wEejIgNDZ+5haQrJX0pe/6ipK9KGpV0j6S5WSvocUkfrHvrrdQuh7Au\n47CxVmzb0I3622z7NyLiLyJiX2Bb4APZ9oXAgRGxP/CxiFgN/DtwcUQcEBE/BA4GRhu+Z0vgamBl\nRHwm27YdcF9EzAFeAL4EHEbtUo8v1L13BHh3B4/ZOsTdKGvFRN2oQyWdD/wBMB1YTq2F8TBwtaSb\ngZsn+MyZ1BZmqvdN4PqI+HLdtleAO7LHjwAvR8Srkh4Bhupetw7YNf8hWSpu2VhbJG0DXAocFxH7\nAZcB22S7/5racqBzgNFsfKbRS3WvH/djagFWv/3VeP1Cvo3ULlolIjbyxv9pbpN9pnUZh421azwQ\nns4WWjoOamMuwB4RsQQ4H/hDYHtqXaAd6t6/Atin4TMvB24Hrp8goDbnT6it6Wxdxt0oa8W2kh6q\ne35HRCyUdBm1rs1qamvXAEwD/kPSjoCojdP8VtKtwA2S5gN/D/w38N3GL4qIi7L3flfSyS3UeChw\nW6sHZsXzEhNWOkk3AedHxMo2P+ctwA+AdzWe3bLyOWysdJL+lNpC2ksnffHmP2cWsFtE3NeRwqyj\nHDZmloQHiM0sCYeNmSXhsDGzJBw2ZpaEw8bMkvh/pztNQ980+dUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2c35087b198>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x2c352f5ec88>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"root = Tk()\n",
"root.title(\"Well, let's see\")\n",
"imDis = PIL.ImageTk.PhotoImage(im.resize([150,150]))\n",
"imFour = PIL.ImageTk.PhotoImage(fourierTrans.resize([150,150]))\n",
"imCoverage = PIL.ImageTk.PhotoImage(trackImage.resize([150,150]))\n",
"imSynBeam = PIL.ImageTk.PhotoImage(synBeamim.resize([150,150]))\n",
"imFoAndTr = PIL.ImageTk.PhotoImage(finImAndTrack.resize([150,150]))\n",
"imFinal = PIL.ImageTk.PhotoImage(finImage.resize([150,150]))\n",
"\n",
"frame = ttk.Frame(root, padding = '5 5 5 5')\n",
"frame.grid(column=0, row=0, sticky=(N,W,E,S))\n",
"frame.columnconfigure(0, weight=1)\n",
"frame.rowconfigure(0, weight=1)\n",
"frame.configure(width=100,height=100)\n",
"\n",
"ttk.Label(frame, text=\"Configuration Builder\").grid(column=1,row=0)\n",
"\n",
"ttk.Label(frame, text=\"Select Array Configurations\").grid(column=1, row=1, sticky=W)\n",
"arrayBox = Listbox(frame)\n",
"arrayBox.grid(column=1,row=2, rowspan=10)\n",
"\n",
"ttk.Label(frame, text=\"Display of array\").grid(column=3, row=1, sticky=N)\n",
"\n",
"usedArray = []\n",
"\n",
"rB = np.array([0.4364, 1.4337, 2.8747, 4.7095, 6.9065, 9.4434, 12.3027, 15.4706, 18.9357,\n",
" 0.4840, 1.5899, 3.1881, 5.2229, 7.6595, 10.4728, 13.6438, 17.157, 21.,\n",
" 0.484, 1.5899, 3.1881, 5.2229, 7.6595, 10.4728, 13.6439, 17.1572, 21.])*10**3 #set all the r values in m\n",
"thetaA = np.zeros(len(rB)) #set all the theta values in degrees, 0 is parrallel to the surface, 90 points to the zennith\n",
"phiA = np.array([5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0, 5.0,\n",
" 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0, 125.0,\n",
" 245.0, 245.0, 245.0, 245.0, 245.0, 245.0, 245.0, 245.0, 245.0]) #set all the phi values in degrees, 0 is north, 90 is west\n",
"phiB = phiA*np.pi/180.\n",
"thetaB = thetaA*np.pi/180.\n",
"VLAcoords = np.array([rB,thetaB,phiB])\n",
"ASKAPcoords = np.array([[1000,1000,1000,1000,1000,1000,1000,1000],[0,0,0,0,0,0,0,0],[0,45/180*np.pi,90/180*np.pi,135/180*np.pi,180/180*np.pi,225/180*np.pi,270/180*np.pi,315/180*np.pi]])\n",
"ATCAcoords = np.array([[1000,2000,3000,4000,5000,6000],[0,0,0,0,0,0],[0,0,0,0,0,0]])\n",
"\n",
"latitude = np.array([34.0784*np.pi/180,34.0784*np.pi/180,34.0784*np.pi/180])\n",
"\n",
"arrays = []\n",
"arrays.append(coords)\n",
"\n",
"telNames = []\n",
"telNames.append('VLA')\n",
"telNames.append('ASKAP')\n",
"telNames.append('ATCA')\n",
"\n",
"arrCoords = []\n",
"arrCoords.append(VLAcoords)\n",
"arrCoords.append(ASKAPcoords)\n",
"arrCoords.append(ATCAcoords)\n",
"\n",
"arrCoordsENU = []\n",
"arrCoordsENU.append(ENUarray(arrCoords[0]))\n",
"arrCoordsENU.append(ENUarray(arrCoords[1]))\n",
"arrCoordsENU.append(ENUarray(arrCoords[2]))\n",
"\n",
"uvCoords = []\n",
"relation = []\n",
"\n",
"arrayBox.insert(END,telNames[0])\n",
"arrayBox.insert(END,telNames[1])\n",
"arrayBox.insert(END,telNames[2])\n",
"\n",
"rcParams['figure.figsize'] = 4, 4\n",
"arrDisFig = plt.figure(1)\n",
"Ea = arrCoordsENU[0][0]\n",
"No = arrCoordsENU[0][1]\n",
"plt.xlabel('East(km)')\n",
"plt.ylabel('North(km)')\n",
"plt.plot(Ea/1000,No/1000,'.')\n",
"\n",
"arrayDisplay = FigureCanvasTkAgg(arrDisFig,master = frame)\n",
"\n",
"plotDisplay = arrayDisplay.get_tk_widget()\n",
"plotDisplay.grid(column=3,row=2,rowspan=10)\n",
"\n",
"\n",
"arrayBox.bind('<ButtonRelease-1>', ChooseArray)\n",
"\n",
"add = ttk.Button(frame, text = \"Add Selected\", command = AddSelected)\n",
"add.grid(column=1,row=9)\n",
"\n",
"ttk.Label(frame, text=\"Set Parameters\").grid(column=4, row=1, columnspan=2, sticky=N)\n",
"\n",
"ttk.Label(frame,text='Frequency (MHz):').grid(column=4,row=2)\n",
"ttk.Label(frame,text='Samples Frequence (/min):').grid(column=4,row=3)\n",
"ttk.Label(frame,text='Declination (deg):').grid(column=4,row=4)\n",
"\n",
"freq = StringVar()\n",
"samFreq = StringVar()\n",
"declin = StringVar()\n",
"\n",
"freq.set(1428.5714285714284)\n",
"samFreq.set(1)\n",
"declin.set(0)\n",
"\n",
"freqEnt = ttk.Entry(frame,textvariable=freq)\n",
"freqEnt.grid(column=5,row=2)\n",
"samFreqEnt = ttk.Entry(frame,textvariable=samFreq)\n",
"samFreqEnt.grid(column=5,row=3)\n",
"decEnt = ttk.Entry(frame,textvariable=declin)\n",
"decEnt.grid(column=5,row=4)\n",
"\n",
"minHoVal = 5\n",
"maxHoVal = 255\n",
"hourMove = 0\n",
"\n",
"ttk.Label(frame, text = \"Hour Angle:\").grid(column=4,row=5)\n",
"\n",
"hourCanvas = Canvas(frame, width=260,height=50)\n",
"hourCanvas.grid(column=4,row=6,columnspan=2)\n",
"hourCanvas.create_line(5,25,255,25)\n",
"hourCanvas.create_rectangle(minHoVal-3,10,minHoVal+3,40,fill='black')\n",
"hourCanvas.create_rectangle(maxHoVal-3,10,maxHoVal+3,40,fill='black')\n",
"\n",
"minHour = StringVar()\n",
"maxHour = StringVar()\n",
"minHour.set((minHoVal-5)/250*24-12)\n",
"maxHour.set((maxHoVal-5)/250*24-12)\n",
"\n",
"minHoLabel = ttk.Entry(frame, textvariable = minHour)\n",
"minHoLabel.grid(column=4,row=7)\n",
"\n",
"minHoLabel.bind('<Enter>', minHourDirect)\n",
"\n",
"maxHoLabel = ttk.Entry(frame, textvariable = maxHour)\n",
"maxHoLabel.grid(column=5,row=7)\n",
"\n",
"hourCanvas.bind('<1>', lambda e: GrabHourSlide(e))\n",
"hourCanvas.bind('<ButtonRelease - 1>',lambda e: ReleaseHour(e))\n",
"hourCanvas.bind('<B1-Motion>', lambda e: Slide(e))\n",
"\n",
"ttk.Label(frame, text = \"Current Configurations\").grid(column=6,row=1)\n",
"\n",
"usedArrays = Listbox(frame)\n",
"usedArrays.grid(column=6,row=2, rowspan=10)\n",
"\n",
"delete = ttk.Button(frame, text = \"Delete Selected\", command = DeleteSelected)\n",
"delete.grid(column=6,row=9)\n",
"\n",
"ttk.Label(frame, text=\"UV Display\").grid(column=7, row=1, sticky=N)\n",
"\n",
"rcParams['figure.figsize'] = 4, 4\n",
"uvDisFig = plt.figure(2)\n",
"U = np.array([])\n",
"V = np.array([])\n",
"\n",
"uvDisplay = FigureCanvasTkAgg(uvDisFig,master = frame)\n",
"\n",
"plotUVDisplay = uvDisplay.get_tk_widget()\n",
"plotUVDisplay.grid(column=7,row=2,rowspan=10)\n",
"\n",
"#Display Half\n",
"\n",
"imageFrame = ttk.Frame(frame)\n",
"imageFrame.grid(column=1,row=20,columnspan=20)\n",
"\n",
"load = ttk.Button(imageFrame, text = 'Load File', command = LoadingImage)\n",
"load.grid(column=1, row = 1)\n",
"\n",
"path = StringVar()\n",
"loaded = ttk.Entry(imageFrame, width=10, textvariable = path)\n",
"loaded.grid(column=2, row = 1)\n",
"\n",
"ttk.Label(imageFrame, text=\"Pixel Scale (arcsec):\").grid(column=1,row=3)\n",
"pixScale = StringVar()\n",
"pixScale.set(0.5)\n",
"scaleEntry = ttk.Entry(imageFrame, width=5, textvariable = pixScale)\n",
"scaleEntry.grid(column=2, row = 3)\n",
"\n",
"calculate = ttk.Button(imageFrame, text = 'Run', command = FindEverything)\n",
"calculate.grid(column = 6, row = 1)\n",
"\n",
"ttk.Label(imageFrame, text=\"Reference Image\").grid(column=1, row=4, sticky=W)\n",
"refCan = Canvas(imageFrame, width = 150, height = 150)\n",
"refCan.grid(column=1, row=5, columnspan=2, sticky=W)\n",
"shownImage = refCan.create_image(75,75, image=imDis)\n",
"\n",
"ttk.Label(imageFrame, text=\"Model FFT\").grid(column=3, row=4, sticky=(W,S))\n",
"fourCan = Canvas(imageFrame, width = 150, height = 150)\n",
"fourCan.grid(column=3, row=5, columnspan=2, sticky=W)\n",
"fourCan.create_image(75,75, image=imFour)\n",
"\n",
"ttk.Label(imageFrame, text=\"UV Coverage\").grid(column=5, row=4, sticky=S)\n",
"uvCan = Canvas(imageFrame, width = 150, height = 150)\n",
"uvCan.grid(column=5, row=5, sticky=W)\n",
"uvCan.create_image(75,75, image=imCoverage)\n",
"\n",
"ttk.Label(imageFrame, text=\"Observed FFT\").grid(column=7, row=4, sticky=(S))\n",
"fftuvCan = Canvas(imageFrame, width = 150, height = 150)\n",
"fftuvCan.grid(column=7, row=5, sticky=W)\n",
"fftuvCan.create_image(75,75, image=imFoAndTr)\n",
"\n",
"ttk.Label(imageFrame, text=\"Synthesised beam\").grid(column=9, row=4, sticky=E)\n",
"synCan = Canvas(imageFrame, width = 150, height = 150)\n",
"synCan.grid(column=9, row=5, sticky=W)\n",
"synCan.create_image(75,75, image=imSynBeam)\n",
"\n",
"ttk.Label(imageFrame, text=\"Observed Image\").grid(column=11, row=4, sticky=E)\n",
"finCan = Canvas(imageFrame, width = 150, height = 150)\n",
"finCan.grid(column=11, row=5, sticky=W)\n",
"finCan.create_image(75,75, image=imFinal)\n",
"\n",
"#Arrows\n",
"c6 = Canvas(imageFrame, width = 75, height = 30)\n",
"c6.create_line(50,0,50,30,arrow = LAST)\n",
"c6.grid(column=4,row=3)\n",
"\n",
"for child in frame.winfo_children():\n",
" child.grid_configure(padx=5, pady=5)\n",
"\n",
"root.mainloop()"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"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 |
YuriyGuts/kaggle-quora-question-pairs | notebooks/eda-features.ipynb | 1 | 295094 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore Pairwise Feature Correlations & Distributions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pygoose import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Config"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"project = kg.Project.discover()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Read Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"feature_lists = [\n",
" 'simple_summaries',\n",
" 'jaccard_ngrams',\n",
" 'fuzzy',\n",
" 'tfidf',\n",
" 'lda',\n",
" 'nlp_tags',\n",
" 'wordnet_similarity',\n",
" 'phrase_embedding',\n",
" 'wmd',\n",
" 'wm_intersect',\n",
" 'magic_pagerank',\n",
" 'magic_frequencies',\n",
" 'magic_cooccurrence_matrix',\n",
" 'oofp_nn_mlp_with_magic',\n",
" 'oofp_nn_cnn_with_magic',\n",
" 'oofp_nn_bi_lstm_with_magic',\n",
" 'oofp_nn_siamese_lstm_attention',\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df_train, df_test, feature_ranges = project.load_feature_lists(feature_lists)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df_train['target'] = kg.io.load(project.features_dir + 'y_train.pickle')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Explore"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
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" <tr>\n",
" <th>shorter_char_len_log</th>\n",
" <td>404290.000000</td>\n",
" <td>3.839168</td>\n",
" <td>0.412446</td>\n",
" <td>0.000000</td>\n",
" <td>3.583519</td>\n",
" <td>3.806662</td>\n",
" <td>4.077537</td>\n",
" <td>5.872118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>longer_char_len_log</th>\n",
" <td>404290.000000</td>\n",
" <td>4.156210</td>\n",
" <td>0.447655</td>\n",
" <td>1.386294</td>\n",
" <td>3.850148</td>\n",
" <td>4.110874</td>\n",
" <td>4.454347</td>\n",
" <td>7.064759</td>\n",
" </tr>\n",
" <tr>\n",
" <th>char_len_diff_log</th>\n",
" <td>404290.000000</td>\n",
" <td>2.482964</td>\n",
" <td>1.115496</td>\n",
" <td>0.000000</td>\n",
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" <td>2.564949</td>\n",
" <td>3.295837</td>\n",
" <td>6.985642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>char_len_ratio</th>\n",
" <td>404290.000000</td>\n",
" <td>0.753405</td>\n",
" <td>0.187965</td>\n",
" <td>0.000000</td>\n",
" <td>0.631579</td>\n",
" <td>0.790123</td>\n",
" <td>0.910891</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>shorter_token_len_log</th>\n",
" <td>404290.000000</td>\n",
" <td>2.028468</td>\n",
" <td>0.318315</td>\n",
" <td>0.693147</td>\n",
" <td>1.791759</td>\n",
" <td>1.945910</td>\n",
" <td>2.197225</td>\n",
" <td>3.828641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>longer_token_len_log</th>\n",
" <td>404290.000000</td>\n",
" <td>2.265450</td>\n",
" <td>0.375445</td>\n",
" <td>1.098612</td>\n",
" <td>1.945910</td>\n",
" <td>2.197225</td>\n",
" <td>2.484907</td>\n",
" <td>4.955827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>token_len_diff_log</th>\n",
" <td>404290.000000</td>\n",
" <td>0.927092</td>\n",
" <td>0.724175</td>\n",
" <td>0.000000</td>\n",
" <td>0.693147</td>\n",
" <td>0.693147</td>\n",
" <td>1.386294</td>\n",
" <td>4.882802</td>\n",
" </tr>\n",
" <tr>\n",
" <th>token_len_ratio</th>\n",
" <td>404290.000000</td>\n",
" <td>0.790111</td>\n",
" <td>0.180231</td>\n",
" <td>0.047619</td>\n",
" <td>0.666667</td>\n",
" <td>0.833333</td>\n",
" <td>0.937500</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>word_diff_ratio</th>\n",
" <td>404290.000000</td>\n",
" <td>0.419394</td>\n",
" <td>0.241534</td>\n",
" <td>0.000000</td>\n",
" <td>0.230769</td>\n",
" <td>0.400000</td>\n",
" <td>0.600000</td>\n",
" <td>0.964286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_2gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.490047</td>\n",
" <td>0.206499</td>\n",
" <td>0.000000</td>\n",
" <td>0.337838</td>\n",
" <td>0.465753</td>\n",
" <td>0.634615</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_norm_q1_2gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.645478</td>\n",
" <td>0.205335</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.656250</td>\n",
" <td>0.806452</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_norm_q2_2gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.647694</td>\n",
" <td>0.204177</td>\n",
" <td>0.000000</td>\n",
" <td>0.500000</td>\n",
" <td>0.655172</td>\n",
" <td>0.807692</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_3gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.363329</td>\n",
" <td>0.224465</td>\n",
" <td>0.000000</td>\n",
" <td>0.190083</td>\n",
" <td>0.325000</td>\n",
" <td>0.510417</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_norm_q1_3gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.507695</td>\n",
" <td>0.248321</td>\n",
" <td>0.000000</td>\n",
" <td>0.322917</td>\n",
" <td>0.500000</td>\n",
" <td>0.700000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_norm_q2_3gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.509297</td>\n",
" <td>0.249370</td>\n",
" <td>0.000000</td>\n",
" <td>0.321429</td>\n",
" <td>0.500000</td>\n",
" <td>0.703125</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_4gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.311363</td>\n",
" <td>0.222294</td>\n",
" <td>0.000000</td>\n",
" <td>0.137931</td>\n",
" <td>0.265060</td>\n",
" <td>0.446429</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_norm_q1_4gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.445527</td>\n",
" <td>0.256115</td>\n",
" <td>0.000000</td>\n",
" <td>0.246377</td>\n",
" <td>0.430769</td>\n",
" <td>0.638889</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_norm_q2_4gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.447284</td>\n",
" <td>0.257838</td>\n",
" <td>0.000000</td>\n",
" <td>0.245902</td>\n",
" <td>0.431034</td>\n",
" <td>0.641509</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_5gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.272967</td>\n",
" <td>0.218302</td>\n",
" <td>0.000000</td>\n",
" <td>0.102941</td>\n",
" <td>0.219512</td>\n",
" <td>0.394737</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_norm_q1_5gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.397291</td>\n",
" <td>0.258410</td>\n",
" <td>0.000000</td>\n",
" <td>0.190141</td>\n",
" <td>0.369565</td>\n",
" <td>0.585366</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_norm_q2_5gram</th>\n",
" <td>404290.000000</td>\n",
" <td>0.399191</td>\n",
" <td>0.260414</td>\n",
" <td>0.000000</td>\n",
" <td>0.189873</td>\n",
" <td>0.370370</td>\n",
" <td>0.588235</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_diff_2_3</th>\n",
" <td>404290.000000</td>\n",
" <td>0.126758</td>\n",
" <td>0.050682</td>\n",
" <td>0.000000</td>\n",
" <td>0.092414</td>\n",
" <td>0.127150</td>\n",
" <td>0.161093</td>\n",
" <td>0.386243</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_diff_3_4</th>\n",
" <td>404290.000000</td>\n",
" <td>0.052029</td>\n",
" <td>0.027240</td>\n",
" <td>0.000000</td>\n",
" <td>0.032967</td>\n",
" <td>0.050296</td>\n",
" <td>0.068966</td>\n",
" <td>0.308081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaccard_ix_diff_4_5</th>\n",
" <td>404290.000000</td>\n",
" <td>0.038427</td>\n",
" <td>0.022740</td>\n",
" <td>0.000000</td>\n",
" <td>0.021739</td>\n",
" <td>0.036630</td>\n",
" <td>0.052313</td>\n",
" <td>0.217035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fuzz_ratio</th>\n",
" <td>404290.000000</td>\n",
" <td>0.703729</td>\n",
" <td>0.149434</td>\n",
" <td>0.040000</td>\n",
" <td>0.600000</td>\n",
" <td>0.700000</td>\n",
" <td>0.820000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fuzz_partial_ratio</th>\n",
" <td>404290.000000</td>\n",
" <td>0.745187</td>\n",
" <td>0.113950</td>\n",
" <td>0.290000</td>\n",
" <td>0.650000</td>\n",
" <td>0.730000</td>\n",
" <td>0.830000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fuzz_token_sort_ratio</th>\n",
" <td>404290.000000</td>\n",
" <td>0.641560</td>\n",
" <td>0.167938</td>\n",
" <td>0.000000</td>\n",
" <td>0.520000</td>\n",
" <td>0.640000</td>\n",
" <td>0.770000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fuzz_token_set_ratio</th>\n",
" <td>404290.000000</td>\n",
" <td>0.734267</td>\n",
" <td>0.180913</td>\n",
" <td>0.000000</td>\n",
" <td>0.600000</td>\n",
" <td>0.750000</td>\n",
" <td>0.890000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fuzz_partial_token_sort_ratio</th>\n",
" <td>404290.000000</td>\n",
" <td>0.674988</td>\n",
" <td>0.148020</td>\n",
" <td>0.000000</td>\n",
" <td>0.560000</td>\n",
" <td>0.670000</td>\n",
" <td>0.790000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaro</th>\n",
" <td>404290.000000</td>\n",
" <td>0.731897</td>\n",
" <td>0.100242</td>\n",
" <td>0.000000</td>\n",
" <td>0.663919</td>\n",
" <td>0.722668</td>\n",
" <td>0.793774</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>jaro_winkler</th>\n",
" <td>404290.000000</td>\n",
" <td>0.767030</td>\n",
" <td>0.123626</td>\n",
" <td>0.000000</td>\n",
" <td>0.663919</td>\n",
" <td>0.765536</td>\n",
" <td>0.873223</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tfidf_cosine</th>\n",
" <td>404290.000000</td>\n",
" <td>0.495047</td>\n",
" <td>0.286285</td>\n",
" <td>0.000000</td>\n",
" <td>0.259862</td>\n",
" <td>0.486038</td>\n",
" <td>0.703605</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>tfidf_euclidean</th>\n",
" <td>404290.000000</td>\n",
" <td>0.935267</td>\n",
" <td>0.339532</td>\n",
" <td>0.000000</td>\n",
" <td>0.720919</td>\n",
" <td>0.985939</td>\n",
" <td>1.186191</td>\n",
" <td>1.414214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lda_cosine</th>\n",
" <td>404290.000000</td>\n",
" <td>0.382863</td>\n",
" <td>0.313404</td>\n",
" <td>0.000000</td>\n",
" <td>0.106059</td>\n",
" <td>0.315955</td>\n",
" <td>0.608643</td>\n",
" <td>0.999209</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lda_euclidean</th>\n",
" <td>404290.000000</td>\n",
" <td>0.380960</td>\n",
" <td>0.201139</td>\n",
" <td>0.000000</td>\n",
" <td>0.254028</td>\n",
" <td>0.376629</td>\n",
" <td>0.503320</td>\n",
" <td>1.271443</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q1_adj</th>\n",
" <td>404290.000000</td>\n",
" <td>1.053689</td>\n",
" <td>1.053692</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>16.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q1_adv</th>\n",
" <td>404290.000000</td>\n",
" <td>0.721272</td>\n",
" <td>0.846085</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>12.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q1_noun</th>\n",
" <td>404290.000000</td>\n",
" <td>2.813901</td>\n",
" <td>1.763558</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>32.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q1_propn</th>\n",
" <td>404290.000000</td>\n",
" <td>0.886512</td>\n",
" <td>1.329234</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>28.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q1_num</th>\n",
" <td>404290.000000</td>\n",
" <td>0.433874</td>\n",
" <td>1.455259</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>44.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q1_verb</th>\n",
" <td>404290.000000</td>\n",
" <td>2.380331</td>\n",
" <td>1.443083</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>24.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q1_gpe</th>\n",
" <td>404290.000000</td>\n",
" <td>0.176025</td>\n",
" <td>0.458078</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q1_loc</th>\n",
" <td>404290.000000</td>\n",
" <td>0.015162</td>\n",
" <td>0.128628</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q1_org</th>\n",
" <td>404290.000000</td>\n",
" <td>0.206109</td>\n",
" <td>0.477373</td>\n",
" <td>0.000000</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>ner_q1_norp</th>\n",
" <td>404290.000000</td>\n",
" <td>0.051663</td>\n",
" <td>0.260826</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q1_person</th>\n",
" <td>404290.000000</td>\n",
" <td>0.119763</td>\n",
" <td>0.369682</td>\n",
" <td>0.000000</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>ner_q1_product</th>\n",
" <td>404290.000000</td>\n",
" <td>0.003008</td>\n",
" <td>0.056275</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q1_date</th>\n",
" <td>404290.000000</td>\n",
" <td>0.047859</td>\n",
" <td>0.233279</td>\n",
" <td>0.000000</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>ner_q1_time</th>\n",
" <td>404290.000000</td>\n",
" <td>0.008118</td>\n",
" <td>0.096404</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q1_quantity</th>\n",
" <td>404290.000000</td>\n",
" <td>0.008595</td>\n",
" <td>0.098231</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q1_cardinal</th>\n",
" <td>404290.000000</td>\n",
" <td>0.214131</td>\n",
" <td>0.756232</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>29.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q2_adj</th>\n",
" <td>404290.000000</td>\n",
" <td>1.076663</td>\n",
" <td>1.103128</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>26.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q2_adv</th>\n",
" <td>404290.000000</td>\n",
" <td>0.742385</td>\n",
" <td>0.882115</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>18.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q2_noun</th>\n",
" <td>404290.000000</td>\n",
" <td>2.810767</td>\n",
" <td>1.840310</td>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" <td>42.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q2_propn</th>\n",
" <td>404290.000000</td>\n",
" <td>0.886416</td>\n",
" <td>1.332241</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>39.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q2_num</th>\n",
" <td>404290.000000</td>\n",
" <td>0.447805</td>\n",
" <td>1.475642</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>39.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_q2_verb</th>\n",
" <td>404290.000000</td>\n",
" <td>2.461686</td>\n",
" <td>1.638655</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>3.000000</td>\n",
" <td>59.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q2_gpe</th>\n",
" <td>404290.000000</td>\n",
" <td>0.178575</td>\n",
" <td>0.465500</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>9.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q2_loc</th>\n",
" <td>404290.000000</td>\n",
" <td>0.015363</td>\n",
" <td>0.130694</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q2_org</th>\n",
" <td>404290.000000</td>\n",
" <td>0.206676</td>\n",
" <td>0.481635</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q2_norp</th>\n",
" <td>404290.000000</td>\n",
" <td>0.051560</td>\n",
" <td>0.261434</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q2_person</th>\n",
" <td>404290.000000</td>\n",
" <td>0.118064</td>\n",
" <td>0.366384</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q2_product</th>\n",
" <td>404290.000000</td>\n",
" <td>0.003094</td>\n",
" <td>0.056643</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q2_date</th>\n",
" <td>404290.000000</td>\n",
" <td>0.051624</td>\n",
" <td>0.245515</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q2_time</th>\n",
" <td>404290.000000</td>\n",
" <td>0.007957</td>\n",
" <td>0.096019</td>\n",
" <td>0.000000</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>ner_q2_quantity</th>\n",
" <td>404290.000000</td>\n",
" <td>0.008793</td>\n",
" <td>0.098641</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_q2_cardinal</th>\n",
" <td>404290.000000</td>\n",
" <td>0.219877</td>\n",
" <td>0.767847</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>21.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_tag_cosine</th>\n",
" <td>404259.000000</td>\n",
" <td>0.128317</td>\n",
" <td>0.139989</td>\n",
" <td>-0.000000</td>\n",
" <td>0.031335</td>\n",
" <td>0.083333</td>\n",
" <td>0.177794</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pos_tag_euclidean</th>\n",
" <td>404290.000000</td>\n",
" <td>2.625738</td>\n",
" <td>2.159862</td>\n",
" <td>0.000000</td>\n",
" <td>1.414214</td>\n",
" <td>2.236068</td>\n",
" <td>3.464102</td>\n",
" <td>69.728043</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_tag_euclidean</th>\n",
" <td>404290.000000</td>\n",
" <td>0.653005</td>\n",
" <td>0.912025</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>28.017851</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ner_tag_count_diff</th>\n",
" <td>404290.000000</td>\n",
" <td>0.541168</td>\n",
" <td>0.933578</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>27.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>wordnet_similarity_raw</th>\n",
" <td>404271.000000</td>\n",
" <td>0.567604</td>\n",
" <td>0.202009</td>\n",
" <td>0.000000</td>\n",
" <td>0.419883</td>\n",
" <td>0.559209</td>\n",
" <td>0.718843</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>wordnet_similarity_brown</th>\n",
" <td>404271.000000</td>\n",
" <td>0.579054</td>\n",
" <td>0.223361</td>\n",
" <td>0.000000</td>\n",
" <td>0.414539</td>\n",
" <td>0.594537</td>\n",
" <td>0.759180</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phrase_emb_mean_cosine</th>\n",
" <td>404290.000000</td>\n",
" <td>0.135807</td>\n",
" <td>0.108565</td>\n",
" <td>-0.000000</td>\n",
" <td>0.059154</td>\n",
" <td>0.109366</td>\n",
" <td>0.178982</td>\n",
" <td>0.813473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phrase_emb_mean_cityblock_log</th>\n",
" <td>404290.000000</td>\n",
" <td>2.609448</td>\n",
" <td>0.600744</td>\n",
" <td>0.000000</td>\n",
" <td>2.427022</td>\n",
" <td>2.705685</td>\n",
" <td>2.940278</td>\n",
" <td>4.639107</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phrase_emb_mean_euclidean</th>\n",
" <td>404290.000000</td>\n",
" <td>1.040869</td>\n",
" <td>0.460618</td>\n",
" <td>0.000000</td>\n",
" <td>0.746320</td>\n",
" <td>1.009677</td>\n",
" <td>1.295476</td>\n",
" <td>7.370496</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phrase_emb_normsum_cosine</th>\n",
" <td>404290.000000</td>\n",
" <td>0.135807</td>\n",
" <td>0.108565</td>\n",
" <td>-0.000000</td>\n",
" <td>0.059154</td>\n",
" <td>0.109366</td>\n",
" <td>0.178982</td>\n",
" <td>0.813473</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phrase_emb_normsum_cityblock_log</th>\n",
" <td>404290.000000</td>\n",
" <td>1.938521</td>\n",
" <td>0.486354</td>\n",
" <td>0.000000</td>\n",
" <td>1.750216</td>\n",
" <td>2.010682</td>\n",
" <td>2.227286</td>\n",
" <td>2.923522</td>\n",
" </tr>\n",
" <tr>\n",
" <th>phrase_emb_normsum_euclidean</th>\n",
" <td>404290.000000</td>\n",
" <td>0.478727</td>\n",
" <td>0.205997</td>\n",
" <td>0.000000</td>\n",
" <td>0.343958</td>\n",
" <td>0.467688</td>\n",
" <td>0.598301</td>\n",
" <td>1.275518</td>\n",
" </tr>\n",
" <tr>\n",
" <th>wmd</th>\n",
" <td>404290.000000</td>\n",
" <td>1.964857</td>\n",
" <td>1.096723</td>\n",
" <td>0.000000</td>\n",
" <td>1.106430</td>\n",
" <td>1.851734</td>\n",
" <td>2.716353</td>\n",
" <td>10.444951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_q2_intersect</th>\n",
" <td>404290.000000</td>\n",
" <td>1.892211</td>\n",
" <td>5.689603</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>75.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_q2_wm_ratio</th>\n",
" <td>404290.000000</td>\n",
" <td>0.151356</td>\n",
" <td>0.271282</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.234043</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pagerank_q1</th>\n",
" <td>404290.000000</td>\n",
" <td>0.000295</td>\n",
" <td>0.000289</td>\n",
" <td>0.000039</td>\n",
" <td>0.000209</td>\n",
" <td>0.000209</td>\n",
" <td>0.000305</td>\n",
" <td>0.012546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>pagerank_q2</th>\n",
" <td>404290.000000</td>\n",
" <td>0.000310</td>\n",
" <td>0.000470</td>\n",
" <td>0.000039</td>\n",
" <td>0.000209</td>\n",
" <td>0.000209</td>\n",
" <td>0.000305</td>\n",
" <td>0.012546</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magic_freq_q1</th>\n",
" <td>404290.000000</td>\n",
" <td>5.122924</td>\n",
" <td>15.508751</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>4.000000</td>\n",
" <td>2744.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magic_freq_q2</th>\n",
" <td>404290.000000</td>\n",
" <td>5.585013</td>\n",
" <td>17.648034</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>4.000000</td>\n",
" <td>2744.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magic_freq_q1_q2_ratio</th>\n",
" <td>404290.000000</td>\n",
" <td>1.620231</td>\n",
" <td>9.046853</td>\n",
" <td>0.000364</td>\n",
" <td>0.750000</td>\n",
" <td>1.000000</td>\n",
" <td>1.500000</td>\n",
" <td>2744.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magic_freq_q2_q1_ratio</th>\n",
" <td>404290.000000</td>\n",
" <td>1.813279</td>\n",
" <td>9.171552</td>\n",
" <td>0.000364</td>\n",
" <td>0.666667</td>\n",
" <td>1.000000</td>\n",
" <td>1.333333</td>\n",
" <td>2744.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magic_comatrix_cosine</th>\n",
" <td>404290.000000</td>\n",
" <td>0.851011</td>\n",
" <td>0.265152</td>\n",
" <td>0.013889</td>\n",
" <td>0.750000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magic_comatrix_euclidean</th>\n",
" <td>404290.000000</td>\n",
" <td>2.231701</td>\n",
" <td>1.214244</td>\n",
" <td>1.414214</td>\n",
" <td>1.414214</td>\n",
" <td>1.732051</td>\n",
" <td>2.449490</td>\n",
" <td>17.776389</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magic_comatrix_svd_cosine</th>\n",
" <td>404290.000000</td>\n",
" <td>0.549098</td>\n",
" <td>0.630828</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.256272</td>\n",
" <td>1.069122</td>\n",
" <td>1.992126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magic_comatrix_svd_euclidean</th>\n",
" <td>404290.000000</td>\n",
" <td>0.112156</td>\n",
" <td>0.627414</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>15.120028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>magic_comatrix_svd_manhattan</th>\n",
" <td>404290.000000</td>\n",
" <td>0.144463</td>\n",
" <td>0.941726</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>31.659183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oofp_nn_mlp_with_magic</th>\n",
" <td>404290.000000</td>\n",
" <td>0.364452</td>\n",
" <td>0.375347</td>\n",
" <td>0.000000</td>\n",
" <td>0.036410</td>\n",
" <td>0.199208</td>\n",
" <td>0.691840</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oofp_nn_cnn_with_magic</th>\n",
" <td>404290.000000</td>\n",
" <td>0.378076</td>\n",
" <td>0.389407</td>\n",
" <td>0.000000</td>\n",
" <td>0.027116</td>\n",
" <td>0.195153</td>\n",
" <td>0.780654</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oofp_nn_bi_lstm_with_magic</th>\n",
" <td>404290.000000</td>\n",
" <td>0.391990</td>\n",
" <td>0.391737</td>\n",
" <td>0.000000</td>\n",
" <td>0.020232</td>\n",
" <td>0.229321</td>\n",
" <td>0.808289</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>oofp_nn_siamese_lstm_attention</th>\n",
" <td>404290.000000</td>\n",
" <td>0.408822</td>\n",
" <td>0.388498</td>\n",
" <td>0.000000</td>\n",
" <td>0.017568</td>\n",
" <td>0.289125</td>\n",
" <td>0.822654</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>target</th>\n",
" <td>404290.000000</td>\n",
" <td>0.369198</td>\n",
" <td>0.482588</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min \\\n",
"shorter_char_len_log 404290.000000 3.839168 0.412446 0.000000 \n",
"longer_char_len_log 404290.000000 4.156210 0.447655 1.386294 \n",
"char_len_diff_log 404290.000000 2.482964 1.115496 0.000000 \n",
"char_len_ratio 404290.000000 0.753405 0.187965 0.000000 \n",
"shorter_token_len_log 404290.000000 2.028468 0.318315 0.693147 \n",
"longer_token_len_log 404290.000000 2.265450 0.375445 1.098612 \n",
"token_len_diff_log 404290.000000 0.927092 0.724175 0.000000 \n",
"token_len_ratio 404290.000000 0.790111 0.180231 0.047619 \n",
"word_diff_ratio 404290.000000 0.419394 0.241534 0.000000 \n",
"jaccard_ix_2gram 404290.000000 0.490047 0.206499 0.000000 \n",
"jaccard_ix_norm_q1_2gram 404290.000000 0.645478 0.205335 0.000000 \n",
"jaccard_ix_norm_q2_2gram 404290.000000 0.647694 0.204177 0.000000 \n",
"jaccard_ix_3gram 404290.000000 0.363329 0.224465 0.000000 \n",
"jaccard_ix_norm_q1_3gram 404290.000000 0.507695 0.248321 0.000000 \n",
"jaccard_ix_norm_q2_3gram 404290.000000 0.509297 0.249370 0.000000 \n",
"jaccard_ix_4gram 404290.000000 0.311363 0.222294 0.000000 \n",
"jaccard_ix_norm_q1_4gram 404290.000000 0.445527 0.256115 0.000000 \n",
"jaccard_ix_norm_q2_4gram 404290.000000 0.447284 0.257838 0.000000 \n",
"jaccard_ix_5gram 404290.000000 0.272967 0.218302 0.000000 \n",
"jaccard_ix_norm_q1_5gram 404290.000000 0.397291 0.258410 0.000000 \n",
"jaccard_ix_norm_q2_5gram 404290.000000 0.399191 0.260414 0.000000 \n",
"jaccard_ix_diff_2_3 404290.000000 0.126758 0.050682 0.000000 \n",
"jaccard_ix_diff_3_4 404290.000000 0.052029 0.027240 0.000000 \n",
"jaccard_ix_diff_4_5 404290.000000 0.038427 0.022740 0.000000 \n",
"fuzz_ratio 404290.000000 0.703729 0.149434 0.040000 \n",
"fuzz_partial_ratio 404290.000000 0.745187 0.113950 0.290000 \n",
"fuzz_token_sort_ratio 404290.000000 0.641560 0.167938 0.000000 \n",
"fuzz_token_set_ratio 404290.000000 0.734267 0.180913 0.000000 \n",
"fuzz_partial_token_sort_ratio 404290.000000 0.674988 0.148020 0.000000 \n",
"jaro 404290.000000 0.731897 0.100242 0.000000 \n",
"jaro_winkler 404290.000000 0.767030 0.123626 0.000000 \n",
"tfidf_cosine 404290.000000 0.495047 0.286285 0.000000 \n",
"tfidf_euclidean 404290.000000 0.935267 0.339532 0.000000 \n",
"lda_cosine 404290.000000 0.382863 0.313404 0.000000 \n",
"lda_euclidean 404290.000000 0.380960 0.201139 0.000000 \n",
"pos_q1_adj 404290.000000 1.053689 1.053692 0.000000 \n",
"pos_q1_adv 404290.000000 0.721272 0.846085 0.000000 \n",
"pos_q1_noun 404290.000000 2.813901 1.763558 0.000000 \n",
"pos_q1_propn 404290.000000 0.886512 1.329234 0.000000 \n",
"pos_q1_num 404290.000000 0.433874 1.455259 0.000000 \n",
"pos_q1_verb 404290.000000 2.380331 1.443083 0.000000 \n",
"ner_q1_gpe 404290.000000 0.176025 0.458078 0.000000 \n",
"ner_q1_loc 404290.000000 0.015162 0.128628 0.000000 \n",
"ner_q1_org 404290.000000 0.206109 0.477373 0.000000 \n",
"ner_q1_norp 404290.000000 0.051663 0.260826 0.000000 \n",
"ner_q1_person 404290.000000 0.119763 0.369682 0.000000 \n",
"ner_q1_product 404290.000000 0.003008 0.056275 0.000000 \n",
"ner_q1_date 404290.000000 0.047859 0.233279 0.000000 \n",
"ner_q1_time 404290.000000 0.008118 0.096404 0.000000 \n",
"ner_q1_quantity 404290.000000 0.008595 0.098231 0.000000 \n",
"ner_q1_cardinal 404290.000000 0.214131 0.756232 0.000000 \n",
"pos_q2_adj 404290.000000 1.076663 1.103128 0.000000 \n",
"pos_q2_adv 404290.000000 0.742385 0.882115 0.000000 \n",
"pos_q2_noun 404290.000000 2.810767 1.840310 0.000000 \n",
"pos_q2_propn 404290.000000 0.886416 1.332241 0.000000 \n",
"pos_q2_num 404290.000000 0.447805 1.475642 0.000000 \n",
"pos_q2_verb 404290.000000 2.461686 1.638655 0.000000 \n",
"ner_q2_gpe 404290.000000 0.178575 0.465500 0.000000 \n",
"ner_q2_loc 404290.000000 0.015363 0.130694 0.000000 \n",
"ner_q2_org 404290.000000 0.206676 0.481635 0.000000 \n",
"ner_q2_norp 404290.000000 0.051560 0.261434 0.000000 \n",
"ner_q2_person 404290.000000 0.118064 0.366384 0.000000 \n",
"ner_q2_product 404290.000000 0.003094 0.056643 0.000000 \n",
"ner_q2_date 404290.000000 0.051624 0.245515 0.000000 \n",
"ner_q2_time 404290.000000 0.007957 0.096019 0.000000 \n",
"ner_q2_quantity 404290.000000 0.008793 0.098641 0.000000 \n",
"ner_q2_cardinal 404290.000000 0.219877 0.767847 0.000000 \n",
"pos_tag_cosine 404259.000000 0.128317 0.139989 -0.000000 \n",
"pos_tag_euclidean 404290.000000 2.625738 2.159862 0.000000 \n",
"ner_tag_euclidean 404290.000000 0.653005 0.912025 0.000000 \n",
"ner_tag_count_diff 404290.000000 0.541168 0.933578 0.000000 \n",
"wordnet_similarity_raw 404271.000000 0.567604 0.202009 0.000000 \n",
"wordnet_similarity_brown 404271.000000 0.579054 0.223361 0.000000 \n",
"phrase_emb_mean_cosine 404290.000000 0.135807 0.108565 -0.000000 \n",
"phrase_emb_mean_cityblock_log 404290.000000 2.609448 0.600744 0.000000 \n",
"phrase_emb_mean_euclidean 404290.000000 1.040869 0.460618 0.000000 \n",
"phrase_emb_normsum_cosine 404290.000000 0.135807 0.108565 -0.000000 \n",
"phrase_emb_normsum_cityblock_log 404290.000000 1.938521 0.486354 0.000000 \n",
"phrase_emb_normsum_euclidean 404290.000000 0.478727 0.205997 0.000000 \n",
"wmd 404290.000000 1.964857 1.096723 0.000000 \n",
"q1_q2_intersect 404290.000000 1.892211 5.689603 0.000000 \n",
"q1_q2_wm_ratio 404290.000000 0.151356 0.271282 0.000000 \n",
"pagerank_q1 404290.000000 0.000295 0.000289 0.000039 \n",
"pagerank_q2 404290.000000 0.000310 0.000470 0.000039 \n",
"magic_freq_q1 404290.000000 5.122924 15.508751 1.000000 \n",
"magic_freq_q2 404290.000000 5.585013 17.648034 1.000000 \n",
"magic_freq_q1_q2_ratio 404290.000000 1.620231 9.046853 0.000364 \n",
"magic_freq_q2_q1_ratio 404290.000000 1.813279 9.171552 0.000364 \n",
"magic_comatrix_cosine 404290.000000 0.851011 0.265152 0.013889 \n",
"magic_comatrix_euclidean 404290.000000 2.231701 1.214244 1.414214 \n",
"magic_comatrix_svd_cosine 404290.000000 0.549098 0.630828 0.000000 \n",
"magic_comatrix_svd_euclidean 404290.000000 0.112156 0.627414 0.000000 \n",
"magic_comatrix_svd_manhattan 404290.000000 0.144463 0.941726 0.000000 \n",
"oofp_nn_mlp_with_magic 404290.000000 0.364452 0.375347 0.000000 \n",
"oofp_nn_cnn_with_magic 404290.000000 0.378076 0.389407 0.000000 \n",
"oofp_nn_bi_lstm_with_magic 404290.000000 0.391990 0.391737 0.000000 \n",
"oofp_nn_siamese_lstm_attention 404290.000000 0.408822 0.388498 0.000000 \n",
"target 404290.000000 0.369198 0.482588 0.000000 \n",
"\n",
" 25% 50% 75% max \n",
"shorter_char_len_log 3.583519 3.806662 4.077537 5.872118 \n",
"longer_char_len_log 3.850148 4.110874 4.454347 7.064759 \n",
"char_len_diff_log 1.609438 2.564949 3.295837 6.985642 \n",
"char_len_ratio 0.631579 0.790123 0.910891 1.000000 \n",
"shorter_token_len_log 1.791759 1.945910 2.197225 3.828641 \n",
"longer_token_len_log 1.945910 2.197225 2.484907 4.955827 \n",
"token_len_diff_log 0.693147 0.693147 1.386294 4.882802 \n",
"token_len_ratio 0.666667 0.833333 0.937500 1.000000 \n",
"word_diff_ratio 0.230769 0.400000 0.600000 0.964286 \n",
"jaccard_ix_2gram 0.337838 0.465753 0.634615 1.000000 \n",
"jaccard_ix_norm_q1_2gram 0.500000 0.656250 0.806452 1.000000 \n",
"jaccard_ix_norm_q2_2gram 0.500000 0.655172 0.807692 1.000000 \n",
"jaccard_ix_3gram 0.190083 0.325000 0.510417 1.000000 \n",
"jaccard_ix_norm_q1_3gram 0.322917 0.500000 0.700000 1.000000 \n",
"jaccard_ix_norm_q2_3gram 0.321429 0.500000 0.703125 1.000000 \n",
"jaccard_ix_4gram 0.137931 0.265060 0.446429 1.000000 \n",
"jaccard_ix_norm_q1_4gram 0.246377 0.430769 0.638889 1.000000 \n",
"jaccard_ix_norm_q2_4gram 0.245902 0.431034 0.641509 1.000000 \n",
"jaccard_ix_5gram 0.102941 0.219512 0.394737 1.000000 \n",
"jaccard_ix_norm_q1_5gram 0.190141 0.369565 0.585366 1.000000 \n",
"jaccard_ix_norm_q2_5gram 0.189873 0.370370 0.588235 1.000000 \n",
"jaccard_ix_diff_2_3 0.092414 0.127150 0.161093 0.386243 \n",
"jaccard_ix_diff_3_4 0.032967 0.050296 0.068966 0.308081 \n",
"jaccard_ix_diff_4_5 0.021739 0.036630 0.052313 0.217035 \n",
"fuzz_ratio 0.600000 0.700000 0.820000 1.000000 \n",
"fuzz_partial_ratio 0.650000 0.730000 0.830000 1.000000 \n",
"fuzz_token_sort_ratio 0.520000 0.640000 0.770000 1.000000 \n",
"fuzz_token_set_ratio 0.600000 0.750000 0.890000 1.000000 \n",
"fuzz_partial_token_sort_ratio 0.560000 0.670000 0.790000 1.000000 \n",
"jaro 0.663919 0.722668 0.793774 1.000000 \n",
"jaro_winkler 0.663919 0.765536 0.873223 1.000000 \n",
"tfidf_cosine 0.259862 0.486038 0.703605 1.000000 \n",
"tfidf_euclidean 0.720919 0.985939 1.186191 1.414214 \n",
"lda_cosine 0.106059 0.315955 0.608643 0.999209 \n",
"lda_euclidean 0.254028 0.376629 0.503320 1.271443 \n",
"pos_q1_adj 0.000000 1.000000 2.000000 16.000000 \n",
"pos_q1_adv 0.000000 1.000000 1.000000 12.000000 \n",
"pos_q1_noun 2.000000 3.000000 4.000000 32.000000 \n",
"pos_q1_propn 0.000000 0.000000 1.000000 28.000000 \n",
"pos_q1_num 0.000000 0.000000 0.000000 44.000000 \n",
"pos_q1_verb 1.000000 2.000000 3.000000 24.000000 \n",
"ner_q1_gpe 0.000000 0.000000 0.000000 10.000000 \n",
"ner_q1_loc 0.000000 0.000000 0.000000 4.000000 \n",
"ner_q1_org 0.000000 0.000000 0.000000 6.000000 \n",
"ner_q1_norp 0.000000 0.000000 0.000000 8.000000 \n",
"ner_q1_person 0.000000 0.000000 0.000000 6.000000 \n",
"ner_q1_product 0.000000 0.000000 0.000000 3.000000 \n",
"ner_q1_date 0.000000 0.000000 0.000000 6.000000 \n",
"ner_q1_time 0.000000 0.000000 0.000000 3.000000 \n",
"ner_q1_quantity 0.000000 0.000000 0.000000 5.000000 \n",
"ner_q1_cardinal 0.000000 0.000000 0.000000 29.000000 \n",
"pos_q2_adj 0.000000 1.000000 2.000000 26.000000 \n",
"pos_q2_adv 0.000000 1.000000 1.000000 18.000000 \n",
"pos_q2_noun 2.000000 3.000000 4.000000 42.000000 \n",
"pos_q2_propn 0.000000 0.000000 1.000000 39.000000 \n",
"pos_q2_num 0.000000 0.000000 0.000000 39.000000 \n",
"pos_q2_verb 1.000000 2.000000 3.000000 59.000000 \n",
"ner_q2_gpe 0.000000 0.000000 0.000000 9.000000 \n",
"ner_q2_loc 0.000000 0.000000 0.000000 4.000000 \n",
"ner_q2_org 0.000000 0.000000 0.000000 8.000000 \n",
"ner_q2_norp 0.000000 0.000000 0.000000 8.000000 \n",
"ner_q2_person 0.000000 0.000000 0.000000 7.000000 \n",
"ner_q2_product 0.000000 0.000000 0.000000 3.000000 \n",
"ner_q2_date 0.000000 0.000000 0.000000 7.000000 \n",
"ner_q2_time 0.000000 0.000000 0.000000 6.000000 \n",
"ner_q2_quantity 0.000000 0.000000 0.000000 4.000000 \n",
"ner_q2_cardinal 0.000000 0.000000 0.000000 21.000000 \n",
"pos_tag_cosine 0.031335 0.083333 0.177794 1.000000 \n",
"pos_tag_euclidean 1.414214 2.236068 3.464102 69.728043 \n",
"ner_tag_euclidean 0.000000 0.000000 1.000000 28.017851 \n",
"ner_tag_count_diff 0.000000 0.000000 1.000000 27.000000 \n",
"wordnet_similarity_raw 0.419883 0.559209 0.718843 1.000000 \n",
"wordnet_similarity_brown 0.414539 0.594537 0.759180 1.000000 \n",
"phrase_emb_mean_cosine 0.059154 0.109366 0.178982 0.813473 \n",
"phrase_emb_mean_cityblock_log 2.427022 2.705685 2.940278 4.639107 \n",
"phrase_emb_mean_euclidean 0.746320 1.009677 1.295476 7.370496 \n",
"phrase_emb_normsum_cosine 0.059154 0.109366 0.178982 0.813473 \n",
"phrase_emb_normsum_cityblock_log 1.750216 2.010682 2.227286 2.923522 \n",
"phrase_emb_normsum_euclidean 0.343958 0.467688 0.598301 1.275518 \n",
"wmd 1.106430 1.851734 2.716353 10.444951 \n",
"q1_q2_intersect 0.000000 0.000000 1.000000 75.000000 \n",
"q1_q2_wm_ratio 0.000000 0.000000 0.234043 1.000000 \n",
"pagerank_q1 0.000209 0.000209 0.000305 0.012546 \n",
"pagerank_q2 0.000209 0.000209 0.000305 0.012546 \n",
"magic_freq_q1 1.000000 2.000000 4.000000 2744.000000 \n",
"magic_freq_q2 1.000000 2.000000 4.000000 2744.000000 \n",
"magic_freq_q1_q2_ratio 0.750000 1.000000 1.500000 2744.000000 \n",
"magic_freq_q2_q1_ratio 0.666667 1.000000 1.333333 2744.000000 \n",
"magic_comatrix_cosine 0.750000 1.000000 1.000000 1.000000 \n",
"magic_comatrix_euclidean 1.414214 1.732051 2.449490 17.776389 \n",
"magic_comatrix_svd_cosine 0.000000 0.256272 1.069122 1.992126 \n",
"magic_comatrix_svd_euclidean 0.000000 0.000000 0.000000 15.120028 \n",
"magic_comatrix_svd_manhattan 0.000000 0.000000 0.000000 31.659183 \n",
"oofp_nn_mlp_with_magic 0.036410 0.199208 0.691840 1.000000 \n",
"oofp_nn_cnn_with_magic 0.027116 0.195153 0.780654 1.000000 \n",
"oofp_nn_bi_lstm_with_magic 0.020232 0.229321 0.808289 1.000000 \n",
"oofp_nn_siamese_lstm_attention 0.017568 0.289125 0.822654 1.000000 \n",
"target 0.000000 0.000000 1.000000 1.000000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.describe().T"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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atWtwOBxobm7ObbfboJefe1619uTJpQj3tn0H8t8bl4VmVC47m2vR5xe2Fh6s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d9ae05NCx9qaehty/KCDWnh/QGy5WURgp4WvpHPY923F1OPj0auQPHyFWBjWniv006+XNEMeK1Y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Gg8FgMBiMdqMtXymUQ6qGHxsbU6X0PXryJBx2O6ZmpmHnBVgtFuzaWta0nzwBR1mNbRf4cmwb\nAHX6ZVKdu7eRteW+jRsatqVEe9wKnXyr26LV9zQ5tlJLThvPRv2hVKfdjBpbjTZbTazVfdVMrBkt\neTO6eyXq+qOnTi3eD1xOJ4rFEl7fs0eVFr4+x3q9+7HrX6OT53F3dgYbfD4kM1lsXzXcsuuzVeex\nmnNHTV/JjZvcFgBLxuyLM3Da7ZiYnMSgzwdAg92vvPLM7o8MBoPxosNehGsNbTXDpVQNDwAffvgh\nLl26hE8++US5Fp7nUSwW4XO7sRAKwix5eV+MFcRYMASzqdqWGv1ywzoJuudGbSnRHrdCJ9/qtmj1\nPU2drdSS0+ps1B9KddrNqLFVabNVxFrdV2rHRq3eXbW6nreJ12ePG5FoDMlUsmEeSs/HJXp3sxkz\n4RCKpSLWe3w1QpVWXE+tOo/VnDtq+qo+RtoCYEketvJ2Gr7KdhrxmvpafX9kMBgMBkMJbfXA1Ywa\nnuM4PHr0CPl8XvEXZGBhARzHYcbvh6enB3OBQF1MK4lV1wuo0S83rJOge6a1pVR73AqdfKvbotWn\nts5Wa8lpddLyb0anrVSNrVqbrSLW6r5SOzaq9e4q1fWBhaB4fQYC4K1WWMo/iKjVwlP17rEo3HYH\nIsnUEntlK66nVpzHasZTbV/Vx0hbACzJIxiq206j+p3wLO6PDAaDwWAooW2lGTQ1/IYNG2r2HlIK\nk2YwGAyASTPaESbNYDAY7US7SDNuzQQa/6dlZr2na7lTaEjbruFiangGg8FgLBckXTzAHsYYDAaD\nUUvbPnA9CwqRCCFAfiXRRImlBRsxlp8j/GJQKBDLGF3kjd1ineSneyFEVnoGBQcxptdpZT9PZXJN\nlwFQ8+pPPbk8+bh1WvlZP7X10XKkkaeMjY2TnyjOackbH+cp5w4tRoPUV8+CAuVV3a6sur84Szn5\nDZ/TfPMz1k9D+uYdYsz00gb5MipzpM1i5X7zKTGm/+PvqmrvefI8N2eOUmYK7e/9ITGW6SDfAxkM\nBoPBaAXshXQGg8FgMBgMBoPBeEa8EDNcUiX80zAyYRaQ6QAAIABJREFUNganXcDE5CPwVivWrRrC\ncF+/GDs7Bqdgx8TjSawbXIXbD+/jh+9+BwBw9FRZJz89g8G+Pty+N4EPvv/eEg1xvQp65NxZOAUB\nE48fw9vVhVKphNdeeVWMnT8Hl8OBh9PT6HQ4YNDrsffgQRw9fQpOuwMTDx/A63bDoNdj96uiuv74\n6Ag6OzthsVhx88Z1GAwGrFm3DlucDqLi+sSxUbhcnbBYLbh94wb6BgYxcec23v+zH+D4yAhcnZ2w\nWK24deM6Drz+Bi6eP4d9B7+FU8dG0dHZieHVa3Dk8H+hx+uBQW/Arj17cGJUWu4G+gcGMHHnDv74\nBz+sxixW3Lp5HYJgh8ViRSgcLn9uwe2bNyHY7dAb9Ni5e++S+gRBgMVqxfadu4j1vbx1G04eG5Wt\nc9/+A8Ry23buJMbCkdocnR0dKBaL2HvgNQDy+uhXdu9d0o96gwFr165D3+AQsa++98d/Ssx/5+69\nxFg2nZat7/0/+wGxH7ft2EnMQ65cJfZHf7I0x0qffH/XThw5cRzO8rYHbxw4gHMXL+LdN98EgJpY\nZduD1UND4vUk2Z5hsK8ftyfu4r3/9iNiHytRxtPU+0Rl/JXLcAkCpubn4bBZYdDpsXPd+iU5VraQ\n2HLwQMMc5TTntDIAcPzWDbhsNkzMzeHNjS/h4oP7+PbGTU1tpUDT4ZOU62q3nai/z8ltcbF6cKhl\nWyJU8twMwLB6CMVECpzRgFJ5Rjr3+Ik4ZpR7Jyn/Zs4rpoxnMBgvIsW2ND2sPNpmhkupEv7IkSMY\nGRnBL37xC3zyySc4dOiQ4jZ4mxX5QgEDPi80GiAveS2Nt9mQLxYw4PFBsFmx79VtizHBxqNYEHXy\nAs9j/85d4ud1GuIlKmibDYViEQMeD6LxOBKpVDVmtSGXy0PLcTDqDdBAs9hWoVDAQG9vzecAwPM8\nMpkMQqEgPF4vINXJExTXPM8jm80gHArB7fWCF3js2rtPjAmVmFjfnVu3IAh2AICtfGxGkwm9/f0w\nGIxA+RU/vhwLh0LweL3gBaFaJy8gmynHPD4Egwswmc3VtsIhuD0eOJxOBObmZOsLBYMwlcebVF9N\n/nJ10sqRcuR5ZDPlvvJ4EI1GkUpW5SckfXR9P2pQHRdqX1H7RD5Gr4/Sj02Uqx1Pcp8IvIBCsQiv\nx41bd+7ALj33JTHptgdirLo9g8DbsH/X7oZ9/DTqfaIy3mJBLp9HNpeDUaeH9AVW6hYSCrTwUs05\nrQwA8CYTZiJhFEpF3JmdhVAes2a2UqDp8EnKdbXbTiy5z1G2uGjFlgjSPEvpDDRa0VybD8xDYzRU\n86DcO0n5N3NeMWU8g8FgMEi0zQOXUiV85Qt97dq11DU+csyHqkphl9MJ/8KCbGx2fh69PT2LsUBw\nAZyWw0zAj1m/H70eD4ClGuJ6FXQgFIJeJ9Zps1hgkfzRFgiHwHEcookE0tksNGVTYiAYhMEglsnk\nsuAkBsVIOAyj0QStVgv/3BxcLhcCftHcRVJcR8o5ajmxjH9uDl6fT5K/CRynxdzsHMKhIGamxV+L\no+VysZhoc8tmM+DK/R2RHLd/drauzhCMJiM4LYe5uVn0uD0I+OcWc+c4Lfz+OaTTabi9Ptn6etzu\nxQcPUn3S/pCrk1auYY7l/rXZbDBLtiKgK7qr/ejqrI4Lra8a98nSWOP65PuxmXKyOcr0yfzCPLQc\nh9k5P4LhMKZmZqp9JYl1ujrgn6/fgkHcnmHWH1i8nhr3cfPqfaoyPhqBXqeDQadDJp+T2dJBfgsJ\nJVp4qeacVgYAFuJxuAUHoqkU5uMxzITDssdFU7XTdPgk5brabSeWKO8JW1y0aksEaZ6c1QqUStDa\nbNB1ulDKVteb0u6dpPybOa+YMp7BYDAYJNpSC69UCX/mzBloNBrs27dPUb3pm7flAxSBgdZJXnBN\nk2bo7k8S2iKLGbRMmtGS+pg0ozW8yNIMnBsnhlotzaDBpBnKSf/Lr4ix5y3NYJZCBoPRiHbRwl9/\nQt5yY6Xwkq/1W3+0mrZcw6VUCb9///7nnhuDwWAwvtkwZTyDwWAwpLTlDNezIjf1RPbzEmXFIGcm\nf7OmTEZizBSNywdK5FkDjZFcH62t5/krs1poM0H6AnlG7UUly5H7g4ahuDL6ypzOtLQ+6rX0DDYp\nNsUS5DpVzGQ9i2sw/f/8GzFm+l9/pKrO54n+yYzs5zmfR/bzRjyL84B0HtPOx0awBy4GgwGwGa5W\nwma42gySyQ8QDVcOwY6pmRk4BAEGvR77yjYqknGNZuES25OxG/7RHxFzeeP116nmN7n2NBoNDm56\nuSWmMJqhi2b2qregybUXz+RkjYjffvsdVfXJ5f+0OaZSKap1kmZ+U2pcq7S1ff9BonVyaNUwMTZ1\n767i43qa/mgUO/jyFqqJUKnBsBI7+IffWdLHlfPxwNp11Gu3GfPeYp3rN8raEj947/0lddLsgJX6\nXntpM/EaVJujaOVbhWIiKWvlU2p0VGIAbNYESTIY1vfVyNgXcNrtmJicFM2wQ6vQX37gIlkWSW29\ntm59zXkg3qcNi/dppWNWf59u9nwkmTGlVkoGg8FoF9i8TGto61W+hw8fxrVr11pWH8nkB5RNhMUi\nfO4eOB12zAb8knLyxjWahWuxThm7IS0Xqvmtrr0a+1gLTGE0QxfN7FVvQZNrj2ZEVFPfs8ixoXWS\nYn5TalyT5k+zTpJizRyX2v5QHKOaCJUZDOXOcVkDIO3abcK8V1unMlsizQ5YX5/cNfg0OYpWPq3E\nymdsnIdKA2AzMZLBsL6veJtNNMN6fWKfSNZJkiyL1LYk54HT7sCM3y9bX1NGRzXnI+W6YDAYDMY3\njxX/wKVUB3/o0CEcPXoUH374IS5duoSRkRH8y7/8CyYnCXIKGUgmP6BsIuQ4zAYCSKcz8Lkl5jSC\ncY1m4VqsU8ZuSMuFZn6rb6/GPtYCUxjN0EUze0nzILVHMyKqqe/Z5Ui2TpLMb80Y16T506yTpFgz\nx6W2P5TGaCZCpQbDJTGCRY927TZj3qutU5ktkWYHrLEUEq7Bp8mRs1qAUhFavmLlyzasU60BsJkY\nyWC4pK+CEjOso84MS7As0tqSngfpTEbVmC0xOqo4H2nXBYPBYDC+eaz4NVynT5/G7du34XK58P77\n4us8IyMjmJ+fx/379/HjH/8YPp8PY2NjAESDoVarxdq1a3Hy5El873vfw8DAgKK22Bqu5YOt4aqF\nreGqha3hksmDreGqga3hYjAY7US7rOG69nh2uVNoyMt97uVOoSErfg3XgQMHcODAAQBVHfyePXtw\n9epVfPDBB5iamsL4+Di2bNlS8wtiNBrFwYMHFT9sMRgMBoPBYDAYDEarWfEPXFKU6uAB8R36zZs3\nP9f8GAwGg8GgwZTxDAaD8c2jrR64njk6+e7QUDahLVE2w1UD7fVFEDaFBYCclbzJshnkb/G0hnwK\nmEry7aWgbnNjLUeOFSjHXSDkqLY+jpIjJQTay7etfpWPtskyrY/VjCft9UXa5sZUnuKVq2YJO8gb\ngpOOGQBiBcpgkzZBB4CdW5WkVcOzeHWX9tpg+v/+f+XL/G//c8vzUEust0/2c9qYhfPki9B6/Awx\npn/vO8oTk/A0rw4yGAzGi0BxZa88ahvYA5eEoydPwFHW/9oFHlaLBbu2bhNjEoW7y+lEsVTE63v2\nUsvRNNz1dVa08D/87vfEmIyGfv+ePVRV9fGRkRq1ut5gwNq167Clu5uopD4xWi5jseLWzesQBDss\nFiu2UZTfm3fuqZazWnHrxg0IggCL1YrtO3cR69y5e9eSHCv6d42GI+ahpr43vv02sdyOXbuIWvVV\nw8PEOjlOS9S0A8q18EpU7fu+/Q5OHBuFy9UJi9WC2zduoG9gEBN3buOPf/DDJf3fPzCAiTt30OFy\nKR7PSlsH3/kOsT/6pedI3Vhv27GTmMdf/NmfUFXbrYxt3fcacaz37dhKre/ksdFyOQtu37wJwW6H\n3qDHm3ojRq9cgkuw4+HsLJy8DQadHns2vtRwuweaKlyp+p2kVa/Xi9PqNKxehWIyCc5oRG56FoaB\n6gOOUp3508ai0SixDG3MSNfT2q07cOrYMbg6XVi1eg2OHP4vrNu4EalkEvsBHL95HS4bjwn/LHqd\nLmg0wI6hYdk+rlfNK7121WybQToP6vX0DAaDwXgxWfGWwnparYKXImqgC/C53VgIhmCWmM6kCvdI\nLIpkMtWwXEONOE0LT9DQ01TV9Wp1DST6d4KSmucFZDNZhEMheDw+BIMLMJnl85eqjflyLBwKweP1\nIhQMLpaj1UnSv1PLqKivYTmacp2YI7lMpb+UaOGVqtp5vpJHCG6vF7zAY9fefbL9zwsCdu3d19R4\n1rdF7g/KWBPyqO+PJartFscaHTepvsWxDofg9njgcDoRmJsTy1msyOXz0Gk5OG085kJB2X6k5Viv\nCleqfidp1ev14rQ6S5myMr5Ygq67C0XJ+2pKdeZPG6OVoY0Z7bjFMcvCaDKht78fa9dL6jSZMRMJ\no1AsYZ3bg3jdMdO2kFBy7ardNoN0HtTr6RkMBoPxYrIiH7iUquB/85vf4PTp0/jbv/1bHDp0CBcv\nXsQnn3yC3/72tzhz5gx++tOf4uuvv1bcrqiB1mLG74enpwdzkr22pAp33mqDRfKHAalcI404VQtP\n0NDTVNX1anVXZ1UjTtbCh2A0GcFpOczNzaLH7UHAPyebv1RtHJHE/LOz6HG7F/9QbVznUv07vUzz\n9TUqR1Ouk+qklQGUa+GVqtorfazlxPb8c3Pw+nyy/V+JNTOeS9uiKejlx5qUR31/LFFttzhGO25a\nfRHJWPv9c0in03B7y/lHIuA0GkSTSaSzWXhd8lpyWo41Wvgm1O8krXq9XpxWJ2e1AsUStLwNnNkE\nreTHHqU686eN0cuQx4x23JVzLhYTrYTSOhfiMbjtdkRTSdyenYbFYJS0Rx43pdeu2m0zSOdBvZ6e\nwWAwGC8mK1ILr1QF/9FHH8HlcmFqagp2ux3btm3DZ599BrfbDUEQYDAYAFQFG43Izc7JByhruKAl\nP7OmrRZijKSFp60J0xDWmAFAzOEgxgTKhpshytqvlbKGq9X1tcMarmSJfF7R+pgWe55ruMxo7dpG\nGmrWrQH0NVz8pUvkBlWs4XretMMaLtK4qV7D9enviTG1a7ieN0yawWB8c2gXLfyVyenG/2mZeXXA\nu9wpNGRFruFSqoLv7u7GG2+8UVP2r/7qr5YjZQaDwWAwngpmMGQwGIwXkxU5w7VcxCgzQQwGg9Hu\n6CeniLHcwNLtNdoJ4mbyANICeSa/XWAPXAzGiwWb4WodbIaLwWAwGAwGg8FgtCVMC98a2AOXhGZ0\n1CQ1s1JtcDN1SnXyjcoo1R6TclSrPVZbZyWWSqVaWt+ziNFyrB8zWl/VK79b2ccrrR9pGnQlMTXK\n9XrVtlLlOi2P+i0dlOZIaksuRyUx2n2CVq5ybHu7RcnOyNkxOAU7Jh5PYt3gKtx+eB9//L//leK+\nepr8lYyn0n6sLyO31cYH33+vqX5Ueu0qzbHRfVppHzMYDAajfVmRlkISjZTwk5OTmJqqvjITiUSw\nsLCguH6lOmqamlmpNriZOqX67kZllGiPaTmq1R6rrbMSa3V9zzvH+j6m9VW98ruVfbxS+orWH83E\n1CjX61XbSpXrtDyWbOmgMEdSW3I5KonR7hO0cvXHxttsyBcLGPD4INis2Pfqtqb6Sm3+SsdTaT8u\nKUPbakOFXp/WH0pzbHSfVtrHDAaDwWhfVtwDV7NK+F/84hf48MMPcenSJfz+97/H2bNnMTY2hs8/\n/xwzMzN48uSJ4raV6qhpamal2uBm6pTqu2lllGqPaTmq1R6rrbMSa3V9zzvH+j6m9ZVUBd3qPl4p\nfUXrj2ZiapTr9aptpcp1Wh71WzoozZHUVn2OSmO0+wSt3JJjC4Vg0It2ytn5efT29DTVV2rzVzqe\nSvuxvgxtqw01en1afyjNsdF9WmkfMxgMxnJQKq38f+3AipNmNKOE7+7uhtFoxNTUFLRaLRYWFuBy\nuRCNRtHb2wun0wmO47B1qzKNM5NmMBiMFxkmzWhfmDSDwXixaBdpxvgD5RMXy8X2Id9yp9CQFffA\nJaWihN+wYQOuXr2KvXv3Ln4Wi8WWKOGfFvbAxWAwXmTYA9eLCXsYYzDaD/bA1TrYA1ebkbl7T/bz\nUp68EaeWJ3+RxzpcxJj57oR8WznyBrpaJ3lz40h3DzHmjEaIsZjTSYzpC/K50DY+1nLkt1RpmwPT\nNvqtf22o2hb5dZtcnrzxLqm+p6mzIxaV/VztH3pq+qMRpA2faZsb6ygbe9PuHEKMfM7R0Gjk20tZ\nyBsUmRJJYkzN5uMAfdz0T2ZkP9farLKfA0DaLhBjK4XC4aPEmO1br6mqs5TJEGPFlPxTAmcl92Mh\nGCLGaA+M3LUbxJjWYSfX2b/yv8QB9sDFYLQj7fLAdfE++Ye6lcKOVSv/B8MVt4aLwWAwGAwGg8Fg\nMF4U2l4Lf/jwYfT29uLll18GAIyNjWHv3r14+PAh+vv7m5oNOPrFGTjtdkxMTmLQ5wOgwe5XXgEA\njIyNwWkXMDH5CLzVinWrhjDc1y+WO3VKVBHPTMPldKJYLOH1PXtwfGQErs5OWKxW3LpxHQdefwMX\nz5/Dt99+R6zz3Fk4BQETjx/D29WFUqmEAy9vEWMXzqPT7sCDmSdY2zeARDqFPfv34+iZSo4PMejz\nIV8oYv/27QCAE6PS9m6gf2AAE3fu4H95550aXbLL6USxVMTre/Y2zFFOU7x55x5iW3/6ww9wfHQE\nnZ2dsFisuHnjOgwGA9asW4d1g/2yGnGNRoP5WAIuVycsVgtu37iBvoFBTNy5jff/7AfVtixW3Lp5\nHYJgh8Vixc7du4j5H/zWt3Hi2GhNnU6XC6ViEfsPvq6qzhI0xBzF82BpH+9+5+0l/ahEq/7Knv2q\n8o/ForKfb9u5kzgu/YNDS8ZTEARYrFbs2r2b2B9vvvUOsc4tXZ04evIEHHYHpqanYRd4WC0W7Noq\n2vBosSMnTsDpsGNqehp6vR7r16yBb+MG4vn42kubqPXRFN1KNeL1+vGRsS8W7xXvHngN565+ie+8\n+y7xfrD73XdaolynKcvVbilQaW8bgGPXv0Ynz+Pu7AzcDgcMWh12DK8uj8txOMt9/MaBAzh38SLe\nffPNhjHS/ZF0z927b1/5HDm5WG6wrx+3J+7iz157vUZp393hgsVsxq7NLzcc69Hxi3DZ7Xg4MwOj\nQY91fQNY5RNnsBbrfDSJble1zma2dGhmuwG148aU8QwGg9GetMUMl1Jz4WeffYZHjx7h448/xsTE\nBMbHx/GrX/1KcTuCzYZCoYBBnw/rVw0jlqi+csTbrMgXChjweaHRAHnJ62UCb0OxWICvx41INIZk\nSnzFiRd4ZLMZhENBeLxe3Ll1C4Jgr22vWMSAx4NoPI5EKlWNWa3I5nPQclpsGBpa3HhOmuOG1WuQ\nL1Rfd+TLuuFwKASP1wteELBr775yuaouORKLIplMKcuRoCmmtcXzPDKZDELlOqHREDXiFWUyz1fy\nCMHt9YIXeEl9ArKZclseH4LBBZjKY07Lv77OWCSCZDKpuk5ajrQ+ru9HpVp1NflTj4syLvXjGQoG\nFfcxcax5Xrwu3G4sBEMwm8ySa4YeKxSK8Lo90Ejqo52P1Ppoim6FGnFZrXqhgAGvD7fu34fdVn01\nhHQ/aIVynaYsV7ulQE0eZjNmwiEUS0UYdfraPHgBhWIRXo8bt+7cgV2qyafG5PtD7H/yPVcc08rY\n2LB/1+5q35eV9gvhMMzG6uumtLHmrVZk83lotRw00CBfqN7DF+v0est1GhWNmdrtBtSOG1PGMxiM\n502xVFrx/9qBtnjg8nq9+O53v4tSqYS+vj4A4jqWUqmE3/3ud7BYxHUaGo0GHMchEAjg/v37mJ2d\nhZOyRqme+WBVlXzn4QNYzdX1H1KNssvphF+yv1dgIQiO02ImEABvtcJS/mNP1P+awHFazM3OIRwK\nYma6uvgwEApBrxPrtFkssJiqfzjMh8PgOA6xRKJWQxwKLubx0X99ii5nx2IsItEN+2dn4Z+bg7f8\nC65Ul8xbbbCYleVI0hTT2oqU69RqtfDPzcHlciHg90vaMy5RJlfq03JiGWl94XAIRpMRnJbD3Nws\netweBPxzDfOvr9Nqsy0+nKupk5YjrY/r+1GpVl1N/rTjoo1L/Xj2uN0IzCntY/k6AwsL4nXh98PT\n04O5gF9yzZBj8wsL0Go5zPrn0Nnhgj8w3/B8pNZHUXQr1YgvUZZL7hXBSARPyn0s5kK7Hzydcp2m\nLFe7pUBNHrEo3HYHIskUEvV5LMxDy3GYnfMjGA5jamZGUYzUH/X9WH/PFceUw4zfj1l/YHFspPdi\nT1cX/Avy58cS9Xs4DC3HIZpIwGW3wx+urgebDwZhKN+LPZ1di/f3ZrZ0UKquVztuTBnPYDAY7Uvb\nSTNo5sINGzbUfME2C5Nm1MKkGcrrZNKMWpg0o64+Js1YApNmtA4mzWAw2o92kWacv/d4uVNoyK7h\nvuVOoSFtt4art7cXvb3iF+vevXuXfMZgMBgMxjcJE+H3CPYgxmAwGCuDtpvhepbkJa8u1VAkdxHt\nl3caplhCPkAZDo2O/HysNg8Gg9Ec5rT8rE3KZHzOmbQW0nEBQPL8JWJMc3Dvs0hHFuJ9E0CaJ8+M\nGYNhYowjPa2g/e+r7IGLwVi5tMsM17mJR8udQkN2r+5f7hQa0hZruBgMBoPBYDAYDAajHWm7Vwrr\nkWrh79+/D5vNhu7ublV1SdXGFR316qGhcmypqnr1oBgjqYhpil+gVnts5wVRZf3qq2JMRlf953/y\npw3zkFOu12uKlebYjBK5UZ2N2kulUi2tTy7/gwcP4sKFC3jrrbdakqO0Ptp5UB97GrV3oz6JxWIt\n60c1fUzSWNP6Q22svq9oGm45JX8z1269hltOg37wD7/TVJ2k/GlaeNo1SDt3lNR58OUtVL37sWtf\nwsULmFqYx/6NL+HCndt465WtqvtYTbnXNrwkq4v/4L33Ze+BlWM7sHotjp6u6Oln4BAEGPR67Nu+\ng3p/p/VxM2OjdtzUnCP19yUGg8FgLD9tMcOlVAt/+fJlTExM4G/+5m/ws5/9DMeOHcPvfvc7xe1I\n1cZLdNQKVdVSFTFN8Vups6I9XggFYZa82kLSVTfKQ065rjbHZpTItDqVtNfq+uRit2/fhlDWVbci\nR2l9tD6uj6lVeyvpk1b1o9o+Jmmsaf2hNlbfVzQNt5ySv5nrYomqnaZBV1gnKX+aFp52DdLOHcV1\nUo6LN1uQzeeRyeVwe2oKgsUqW5/SPlY9NgRdfKPjXryn9rjhtDswGwhIxlP+vkrr42bGRu24qTlH\n6u9LDAaD8TSUSiv/XzvQFg9cSrXwPC++D7tmzRp0dHRAo9EsfqYEqdq409UB/3xAElOmqpaqiGmK\nX6BWeyyqrKvtkXTVtDxIynW1OTajRKbVqaS9VtcnFwuFQpienm5ZjtL6aH1cH1Or9lbSJ63qR7V9\nTNJY0/pDbUzaV1SFO0HJ38x1sUTVTlOkK6yTlD9NC0+7BmnnjtI6qccVjcCg08Gg0yGciGM6uCBb\nn9I+VluOpItvdNyBYBCclsNswI90Jg2f2y0ZT/n7Kq2PmxkbteOm5hypvy8xGAwGY/lpO2nGs9TC\nM2kGg8FoBJNm1MKkGSsXJs1gMFYu7SLNOHt35Usz9qxZ+dKMtlvDxbTwDAaDwWA0hvIsyR7GGAyG\nIortNS+zYmm7B65nCmUmq+VNJci/1JLQsvfyGYxlJzNxXz6wacPzTaTVUO5/1Fmsc+PEkN7jJsZo\nGxWToM1i0Uhdu06Mca/vU1UnCdpM4bOYBTUn5Z+c2n12jsFgMF4k2mINF4PBYDAYDAaDwWC0I201\nwyVVwNOo6OG1Wi1cLpfi+mnK9Wa08CRldr1S+OgXZ+C02zExOYlBnw+ABrtfeYUY2/faa01r4eUU\n3Wr10Wp15kraq9eZP219T6tfbjZH2nlQH3sWOVZi0Wi0pfU9bUyJqv1Znlf1WyLQzu+mlOUXL8Ll\nsOPh9DTe2LYd529cx7fLM1xq7gdK81eqrlejMz+46WVV9zkAGL1yGS5BwNT8PBw2Kww6PXauWw8A\nGDk7Bqdgx8TjSawbXIXbD+/jh+9+R5W6vr4/aFsz1MekWnstx2GttxeryuKMVvbxa5tfpm4x8iyu\nJ7lx822kn48MBoOhhDZTPaxYVtwMl1IFfOWzsbExjIyM4Cc/+Ql+//vf49NPP8Xly5dx5MgR/OY3\nv8HY2JjitqnKdYVaeJoye4lS2GZDoVDAoM+H9auGEUvEG8aa0cKTFN1q9dFqdeZK2mt1fc87R9p5\nUB97FjlWYq2u72ljSlTtz/K8ovV9fawZZTlvtSCby0Gr1eLWo0nYrY0V6a3IX6m6XrXOXMV9DgAE\niwW5fB7ZXA5GnR7VHgZ4mw35YgEDHh8EmxX7Xt3WMH9qjhQFOi1W0dpn83nx2IqFhueB6vsLbYuR\nZ3E9qRw3BoPBYDwfVtwDl1IFfOWzx48fY2ZmBhzHIRKJoKOjAzzPw2azYcuWLbh/n7DeQgaq+l2h\nFp6mzF6iFA6GYNDrAQB3Hj6A1WxpGGtGC09SdKvVR6vVmStpr9X1Pe8caedBfexZ5FiJtbq+p4kp\nVbU/y/OK1vf1saaU5eEwtByHaCKBUDSKJ5ItHdTcD5Tmr1Rdr1ZnruY+B4jKeH1ZGZ/J52r7OFS9\nl83Oz6O3p6dh/tQcKQp0WmwhFoVBp4Neq4OLFxCIhGXLtaKPaVuMPJPrSeW4MRgMBuP5sKK18I0U\n8JXPSMzOzsLv9zd8BbFCfi7Q+D/VoXZhsmHW33QZmjSDLZBmMJ4P3Nc3ZT8vtrk0gyRfABrcX56j\nNEMtxRNfEGPfVGkGsxQyGMtLu2jhT916sNyfElD0AAAgAElEQVQpNOS19UPLnUJDVvQarkYK+Ebv\nobvdbrjd5C98BoPBYDC+ifDz5M2RY53e55gJg8FgvPis6Aeu500+GJQPUHTJJqeDGKMpjNO37irO\nq4JxNfkJPm0lb/pmKrX2nf2cVk+M6Qu5FVHn886RD8tvrKpWY53lyG0ZiuryZ9RiDEWIMc5Mnh0I\nfnFe9vOO1auIZdTObJgSSWIsbbUQY2oo5bK0TIgR2ixW9PBRYqzjL/9C9nNaX+mnyA8JWsq9eOpf\nf0WM9W7dQoylBRsxRqKUIc9w4RnMcBVJU1WUGS5aPzIYDAaj9bAHLgkjY2Nw2gVMTD4Cb7Vi3aoh\nDPeJDzKLpq1Hk+h2uWAxm7Frs/iq4tGTJ+Gw2zE1Mw07L8BqsWDX1q1UayBAt2bJxTasHqLmeGJ0\nBK7OTlgsVty6eR2CYIfFYsW+HVuJ9q5mbGYVe9ere/bh+Ei5LasVt25cx4HX38DF8+fwB29+q6Et\nUc76FU6kZOv79tvvyNZHsohV8n9l996W5VipMxRPEnMknQdbDh6Q7UdSW5Vje2X3PhwfHUFneTxv\n3rgOg8GANevWYd1gP7FcvUlRSVty/diqmFIDIK0+Uv715zHNEClX5xsbXsLR06fgEOyYmpmBQxBg\n0Ouxb9H8RjbNGdcMo5hIQmMyAhoNStnqQ7C03BsHDuDcxYs4+IdLrXxK7HQHNr6EoydPwFGuzy7w\n5fvLtiX9qNR2Ryp3YO06HD11avEcdjmdKBZLeH3PHuqY1dwf60yEAGBcW+4roxHFdBr67i6kvvxK\nVV/t9fio90BaX1l2b0chHIFhcADZB5MwDPUj9tmoWO5U+dqdnsFgXx9u35vAB99/T5VZ8rV16xX3\no9y4KDHN1p/jR09X2quex9vfXmpurIzbPm8vtR8ZDAajAtv4uDWsOGkGjcOHD+PatWvE+NjYGPx+\n/5LPlMLbrMgXChjweaHRAPl8QRIrm7a8XiyEwzAbq79UCjyPYrEIn9uNhVAQZpP4yyLNGgjQrVmk\nGDVHXkA2k0U4FILH40MwuABT2epIsnc1YzOT2rt4gUc2m0E4FITH68WdW7cgCPaGx02yfjVTH80i\nJs2/VTlW6qTVRzsP6vuR1lZNH/M8MpkMQuX2ILGPkco1c1y0fmxVTIkBkFYfLf/685hmiCTWaauM\nWQ+cDjtmA9X7B800V0xnAK0WKBbBWS0o5XKy5W7duQO7gmuNbrzjUSwWxPMqGILZZJbtR6X1Ucvx\nNrGtHjci0RiSqaRsmfrxJJkIa/qqVEIpnUHmweRT9RXtHkjrq2I8CeOaYehcThTjCaQuV79LBBuP\nYkG8dgWex/6du5oes1rbo7J+lBsXJabZJed4Jf8eN5x2B2YlEhfiPZzSjwwGg8FoLSvugUupFv7z\nzz/HrVu38JOf/ASHDx/Gp59+iqtXr2JiYgK//OUvceLECfzkJz9BJEJ+bageqU3L5XTCv7BQjQWD\nMOjEmKezqyYWWFgAx3GY8fvh6enBXPnLjmYNBOjWLFKMlmM4HILRZASn5TA3N4setwcB/5xYjmDv\nasZmttTeZQLHaTE3O4dwKIiZ6ScNj5tuAFRWH80ittT49fQ51hoA5eujnQf1/UhrS3pskXJ7Wq0W\n/rk5uFwuBMo/KJDKNXNctH5sRUypAZBWHy1/6XlMM7HR6gwExTGbDQSQTmfgc3uqOVJMc5zNApSK\n4HgexWgMWt4mWy4YDmNqZkbhuSofE88rreS8qj4UqrE90soFFoJiW4EAeKsVFskDC3U8CSZCsa+s\nQKkEjrdBK/AoRqJP1Ve0eyCtr7QdDmTu3kPePw9dlwt5f3U8A8EFcFoOMwE/Zv1+9Ho8TY+Z1GCo\ntB/rx0Wpabb+HA8Eg+C0HGYDfqQzafgka5dJ40brRwaDwWC0lhVnKTx9+jRu374Nl8uF999/HwAw\nMjKC+fl53L9/Hz/+8Y/h8/lw9OhR9PT04MiRI+jt7cXQ0BC+/PJLbN68GYBoOHz06BF6e3vxwQcf\nKGo7ffO2fICyhou2boC2fodmzSJBW8MVo7wKwtZwPX19jepka7jaD9VruH7577Kfd/z4z4ll2mEN\nl0nyILSkLTvZkKqfnCLGVswarv/j/yTGev+vnxBjatZwqe1HtRiD8veeTAe5Pxqt4WLSDAbj2dMu\nlsITN5Vvr7RcvL6BvIZ6pbDi1nAdOHAABw6I614qCvg9e/bg6tWr+OCDDzA1NYXx8XHs3bsXVqu1\nRvm+a9eu5UqbwWAwGAwGg8F4oVhh8zJty4p74JLSSAvPYDAYDAajtTgK5JnVsLa1M6sMBoPxTWDF\nvVK4nDwOyb8K4s6kiGVyj8iv03zl6ibG+judsp+7FuaJZbJPyK+BXO7yEGObzp0lxn7TP0yMGfTy\nz+OchlgEWcrCa05DLqihxNRAq4+Wv1p+0Nsl+/kDE/mPkzhlg9Q70+SNsXVaLTGWyZFfNyT1iUFH\n/t0lmSGrwml9/P3QHLkcR86/mJJXXGuM5FcsuTcOEGM0YgVy/h1PyNc16dW1sESgUo/a13qNCyFi\nLOOSv4cAgCmWkP1c7Suuamn1K5HhPPnrqmt2hhijbRqv5rVBGqrH7Dm+PkqDlgfAHrgYjFbRLq8U\nHr9xb7lTaMgbG8l/y64UtH/913/918udxErh08O/RzwWBc8LOPzbQwCAx5OTWNPTjaMnTyAYDuPC\n5cuYmZuFf34evR4vipEoRi5eQDQRx9mvvkIkHsfk7Cz6e9z4r8uXkIzHkU6lcOXcWXT29ODK+bPo\nHRjEl+fGEI/FEI/FcO6LL7AwH8CjyYdY1yX+4X70zGmEIlGcungBKAEPpqbgsVoxemkc0UQC525c\nx2xwAf5wCL6uLsxYeVwaO4NkPI5MOoUvL5xFaD6Aef8cNhWKOH7rBmLpNL6YuINHwSC0HAenxYpD\n9+4hk0xC6HDhyslRJCJhRIIBOLt6cPfLcaSTCQgdLlw6fhSxcAjxcAjOrm7cHL+AdDIBe4cL48eP\nYmFOXABusvK4ffniYrkrJ0eQjEURnJuBy+3BrUsXkE6JscsnRlAo5HH32mVEgwvVtk6MoJjP4+7V\ny/AODYtlJLF4OIhYKARHVzcx5qyPKczfygvE2KPbt4hlXhKsOHr6NELRCE5dOI9gJIwZvx+2vn6M\nnTyOeDwOm43H0U9/h4WFeSzMz6OjqxsXTp9CIh5DOp3CpbNjiEejmJ2agl5w4Mb4eaQSCdhdnbh4\n7AjSySTC8364ety4fvGcmIurE+dHPwcABGae4MmD+8Qcxf5ILsacXT248+UlePoGiPXxHZ2q+ni3\nzYJj164imkzi0r0JzASDCESj8HW4oNFwOHbty3LsLnocDpy+/jWG3R6U8nkcu/4VoqkULj+4h5lw\nCDPhEPq6xR8v5Ors3fYqRkdHEYvFEI/HcebMGeTzeUxOTsLn82F0dBShUAjj4+MIh8OYnp6Gz+dD\ntqTByWOjNdehf24WszPTWG23Y+TsGELRKE5fGkcgFIQ/GERvTw84swlHT54U7wdXLiOdyeDk2BdY\nveUVnBgdQSwWRTwWw9kvTmNuZgbz/gD6fZ6aPKT5ASDGdKl0zXkFAA+mHqPX7UHBYiYemy6bq8lx\nZm4O/vl5uAcHlrQXDofx6NEj9PX1ET+n5UjNP5eruXfef/gQWq0WHQ4HPj99ijhmcjlOT0/D5fHh\n1LFjsvfpYUHAyNgYQtEITo+P49H0NDgthw67HZzRiKOnKv1xRRyzs2PYtH49Pj9zuiYPt9uN06dP\nY3h4WNVx9zs7ZO8FvZ4GY1bXV9LvmbxB3/KxIdVHywMA0pT1pQwGQzlWa+v35XsWPPAT9qhdQQx1\ndyx3Cg1ZcZZCEg8fPqzR59KoV8MrRdR+Z2E0mdDb34+16xWqmS1WZHN5aLVabBgcWtT1Wm08ctks\nfP0D4LRaTN6bgJUXf9HgeR7ZTAbhUAhujwfRaBSppEQdbONRKBQw4PNh45o1KJbKSmSLBdl8DlqO\nQzAardHTW2w25HJZePr6odVqwdvtCJXNarzJjJlIGIViCRoA+YI4E2WyWJHP5RAPh1AqFWHmecTK\nG0BXYrFwCMVSEYloBPpyeyZrORYKoVQsQqPRoLBYpwWFSrliCelEAtny5pyL5cIhFItFmCxWDG/a\nUtNWqViEySp+Xp9HqVhEIhqt5qEw1lT+hBitjHiO2Mpj1gujwbA4A2TlhcXzytvXh3AwCFNZGW/l\nxTHz9Q9Aq9ViaPUa5Av5mvyjoSCKxSI8A4NIl88Rk9WKXC6LaCiIUrEI7+AqlIolBceVRaxc38yj\nhzBbbdT6nqaPeYsF2UIB2XwOwXgMZoOheq2Vtz3I5HK4PTUFwVKdeRFMZuTyeWTyOTitNsxJ7J2k\nOhvpu4k684rqPyxehw6nE4E5cXZO6VYQAm/D/l27xTIKt2ZQqmmvP682rlmDokTiQ1Xvq9ymgKbX\nV5d/9d6p0WiQa7D9QqNjo92nqcp4hep36XYDao+bdC9QNmaNtwBoxdhQ66PkwWAwGIzmWZEPXL/+\n9a/xD//wDzh06BCOHz8OAHjw4AEuXbqEv/u7v8Mnn3yCQ4fEXzbPnz+Ps2fP4sMPP8SFCxfwz//8\nz4tq+P/8z//E3//93yOZpL8iUSFSVvLGYuKrhYrVzOEQOI5DLJHAx6Mj6HSIr43Eyn983vrqKhLx\nGKLhMAJzs5K2TODKym+bzQazpfqqRiAYXFT21iiRIxFoOQ6xZBI9HR3wh6qvr8SjURgMBtz++hqS\n8TiymQw6e0Q98EI8Brfdjmg6BYfFgkD5Sz6ViEFn0CO8ECg/GKVg7xRn2ZLxcizgRzqRgM3uQCwU\nXIzp9XqE5/1IJRKw2HhJLA6d3oDIfADpRBxGsxmG8h+rqXisGksmEA0uwNHZXW7LUK4vjujCAhxd\n3ZI8qrH6POgxNfnLx2hl6scsk82C04jjFguHYTQYES/3eVdPDxbK508sEoXeYMTNa1eRiMUw8ul/\nwtnhquZhMCA8H0AqkcDM5AMYyn80J2Mx6PUGhAJ+JBNxlMp/RFGPKyb2fWg+gFQijng4hPC8n1rf\n0/TxQjQKg1YLvU4Ht8MJv8TkOB+NwKDTwaDTIZyIYzoo2YIhFoVep4NBq0M6l4XXWf3lilQnVSNO\n0ZkvXoecFn7/HNLpNNxecWZA6VYQs/6ARCOubGsGpZr2+vNK6bYN9Tkq3aaAph5Xnb/k3tnZUdXr\nq92SgnafpirjFarfpdsNqD1u0r1A2Zg13gKgFWND1cxT8mAwGAxG86zINVw//elPYbfbsW3bNoyP\nj+NHP/oRrl69isnJSTx48AADA+JrMe+99x4uXryIVCqF2dlZ2Gw23L17Fzt27AAgznSFw2H84Ac/\ngMXS+L1ztoarFraGSzlsDVctbA1XLWwN11LYGq5a2BouBuObRbus4Rr5emK5U2jItzetXu4UGrIi\nLYWvvPLKopVwYGAA4+Pj6OnpwZYt4itmZ86cQSwWw/j4OPr7+9Ej2WSTwWAwGAzGs6H0r/9KjGn+\n8i+fYyYMBoPRPqzIB67KwxYAmM1mbN++vSa+f//+Z9Jux42bsp8vHDlGLkT5lX9sxx5i7CWr/K/8\n8//1ObFMKUuevTh58E1ibO2jx8RYyk3eMNlqMsh+nsmRf62PJMizgRajfH0AkCuQZ8ZyhFkz0gwc\ngJp1LktilEldnZb8li2tzswd+V+AhF07iWWCMfIvyVcekGdYhrpdxNg8YWYDALoJv+Rn8+Q8UpQZ\nM9qMZW56lhjTUGbUSOh7fcSY7t5Dclt6ymbVs+RZuJKLvAA3e1++PaNkLc4S9u8mxyiUKDOMZsoM\nqZrXFoyBBWKMs5Jnxko5co65GcpMZ1p+NpMzkheSC3MBYqzoIf/wlqeUy1PeHND90R8QY8Q8CMcF\nAOYkOVai3F9o6B89kf1cS5lNQ558D89OkWcKE6fGFOfFYDAYjCor8oGLwWAwGAwGg8FgLC8rcOVR\nW8IeuCSMjl+Ey27Hw5kZGA16rOsbwKqyWte4YR2K8Th03V0ohMJAqYTsg8m6WKcY4zhkJx7g/tVL\nsAgOQANkkgkUi0UYjCb41qwX27s8Dpdgx8PZWXTa7SiWSnilnItp80YUY3HoerpRiEQAjQbpq9dh\n2rIJxXgCpg1rkfrqBjQGA7J3xT0S1nu7EU9nkMnl0etyIJxILf7SbVy/FsVEArouSY73HmDiy3FY\n7eKalEwyAffgMB7fuYk1r+7AjfHzsNkd6F+zDhePHUFHjwcaDeBbswG3Ll2AzeFA7/BaXD4xgu6+\nfsw9nsSq7fuWHLfRbMX89GPs/NbbuHPlImx2BwAN0sk4LLyAbDqNRDwGa7lMOpmAZ3AYj2/fxJqt\nO4j9OLRxk5h/pVwiAa1OB41GA9+aDUuOLZNKoau3H44eD+5dvQSLYIcGGqSToiyk09eHbl8vsc5U\nIkHsKwAY/fIyXIIdU4EAhtxuJNNpbNi1EyePjcLV2QmLxYLbN2/C2dEh2sHWbcLFL07D0dEBq82G\nuzdvgBfsZasdh019HsRSaYQTKexZO4iHgSDyhSIKpRLuXhmH1W4HoEEmKeZYLBaxfddu2XPgYSBI\n7Hv38DpiX5ldXVjr6Vpcb2bS68FpgHyxhEfzITy4dhkW3l4emySgKd+cBRuO372NTqsVE4EAOm02\nGLRa7BpcBQA4fvsWOm023PXPwW23o1QqYd/wmiWxNd09uOOfxQflGa5jX16BSxAwNR9Ap92BQrGA\ng+5ujJw/BycvYOLxI/S4XDDo9Nj7ing1jZwdg1OwY+LRJLpdLljMZuza/LI4ZlcuLV6DTt4Gg06P\nPRtfEstR6ly8V8zOwKg3YF1/PwZ4cZ3QsatX4OIFTM3Po9NuR6FYxL79uzE6OgqHw4EnT56gp6cH\nhUJhcTafFjs69kU5/4f4gwMHce7ql3hnv7hu7ciJ43DaHZianoZer8f6NWuwemhILHfyJBx2O6Zm\npjHY14/bE3fx3n/7kWx7Go0GB4aGcfTMaTjtDkxMPsSmNWuRSKWwq3zMR06cgNNhr21rsNzWqVOL\nbbmcThSLJby+R5zhH7lwHp0OBx5MT2NtXz8S6RR2vrRJzONS5R44gyGPB7cfP8YH74qzSiMXL6DT\nbseD6Wl4Ojuh0WiwtbMHx659Kfbvwjy0HIe13l6scrurbdkdeDDzBJ0OJww6HXZt2ky83772svia\n+rGb19Fp43F3bhZ9HS5oAOxYNUzsq507dxLH7eDAEEbGvoDTbsfE5CR4qxXrhlZhuL+/cT/KjNkH\n772/pK2DBw/iwoULeOutt4jn+N7XD5brPAFH+RyxCzysFgt2vbyFOmajFy8sfhd6yjKNXeUxM64d\nRjGRhMZoRDGdhr67C6kvvwKDwWAwyKxIS2EzCvixsTH4/X7k83mcP3/+qdrlrVZk83lotRw00Cyq\n0wGglE6DM5lQTCSRm5mDRqJYrsZSYqwsBTBarCjkcyjk89BoOKRiUeikamxLtb1oIoGk5FWUYjIF\njcmEYiIBzmJZfM2mmEwBHIfM/YfIPZqqERekcznotBzsVjOS2SxmIzGYyq/diTkaq/mXX50zWawo\n5Mo5chwCU49gKiu6G2nJ5fTucsdttFjQv35TTZ2FfA4aDYeevkGUSiUYLVbky2U4jZiHsfwaE60f\njeZKfWL+XX0DyKSSssem0WhQLI+p0Wwpx3LgOA4aQBKTr5PWVwAgmC3I5XLI5nPY0Ne/+OoibQsA\nq82GXDYLb5+ohY+GQzCWz61UNgudlkMklcbDQBC5QgElVFTtlppcuvsGUCwUqOcAqe8b9VU6m4OO\n46DlOJRKJSSzOQhm8Xw0mC2L46bhxIe/XPk8FowmzESjKJSKcJotmJPq2E0mzETCKJaKiKZSSGSz\nsjHeZMKeoepiWHFbhDwy+TzW9/YtXqOC1YpCsYABjxdOQcCsRD5D1btbrMjl89BpOThtPOYk1klq\nnZV7BacV9eOSe8Wi8j6fq81RodZ7ScwqKsYHvT7cvH8Pdlv11VCBF1AoFuH1iMr1pWrvper6+vak\nanLSdhSV+gqFIrxuj0xbNlEj3uNGJBpDMiXZ4sJqRTYnbmWxYWio5pVe6TYXgsWKfeWHo8rYVLbb\nWD8wiFj5mqn0bzafF/MoFmrbyueg5bQw6vU190fa/VYwmTFTvpetc3sQy0hiNI07Ydx4m03U03t9\nYo4120fQ+lHZmNWr6xtvYbBU8U4bM+l34bqBAcQlpt9iOgNotUCphFI6g0z5h0cGg8FgkFn2B65m\nFPDj4+P41a9+hbGxMRw/fhy//vWvkclkcO3aNfzbv/0b7ty5g5///OeLmvif/vSn+PrrrxXnMh8O\nQ8uJX8Yuux3+cNU2xdlsKGaz0Ao26N3dKEn+QORsVjHG22DZ/ioK8TgAIJWIQ6vXQ6fXIx4Owebo\nQEJS56LiPZGEzWyGRfIlqRV4lLJZcAKPUiaDYnkth1bggWIRpVweqJvmtRgMyBWKyOYL4E1G9Nh5\nZMtf5mL+uWqO5bU+1RwNiIeCSCViiC6I6x1oWnKS3l3uuOPhIARXZ7mcqIzX6Q2IhYPQlDXF6Xgc\nunIesXAQqXg1D1o/VrT2Or0e8VAQ808eQ280yR6bRbAjXi6XTlTziIfFWCJCr5PWVwAwH41Cr9fD\noNPV6JdpWwCIKv/K1gFxdHR2ITgv/mFvNYrjaTMZkCsUoNdqF4dczMUAnUGPWCiIa6ePwyrYqecA\nqe8b9ZXZoEe+WES+UARvNkKn1SJcXosi9qNYLhEOwWAyL+7DNZ+Iw80LiKbTSGQz8NqrFr+FRBxu\nwY5oOg2b0QiLZK2VNDYbjcDrqFoBpTr5/++LU+gsmwEDoRD0ZYV7OpOBr7tqCKXp3ecjEXAaDaLJ\nJNLZLLwuiRqbVmekfK9IJuCyO2q2Z5iPRiU5nkZn+Q9jpVrvJbFQVTEeikQwNVddFzVfnuWZnfOj\n01VVrgNkdX19e1I1OVVBv7AArZbDrH8OnR0u+APzkraCokY8EABvtcIi2bdpPhwWt81IJpaYLaXb\nXMwEF+Dr6pKUq263cffxI1jL956FmNi/eq0OLl5AQLJP22JbiURZx65Z2pbM/XY+HoPb7kA0lcKt\nmWlYDZIYReNO1OsHJXp6R62ent6PysZsibq+4RYGSxXvjcas8l149/EjWCQ/MHI2K1AqgeNt0Ao8\nihF5uy+DwXgxKJVKK/5fO7DsWvhmFPBnz55FKBRCtvywE4/H4XK5oNfrMT09jVwuB0EQUCqVxF9p\ny2WlEg4aiS/kZ8hiKqUZ/06RZvwPgjQjqlKa8S8Uacb/+PoyMfbxzn3EWKcgv1CeJs0IxckChhdZ\nmvHfOXlxQJwizXhI2b39kwtXibHWSzPIfa9WmvHfp+4TY62WZujd5O0XaNKMHEWaoaNIM4px+T4u\nUKQZGpXSDMM0OUdth7yeHoD4g4wMNC3885ZmkOQSNGlGjiK/0FOkGaQxA4Bci6UZ+idk6YTOSRZZ\nlCj3QNq4rSRpBrMUMhjKaRct/JFrd5Y7hYa8/fLa5U6hIcu+hqsZBfyqVauYAp7BYDAYjJXIr39F\njv3F//T88mAwGIwVxrI/cC2XAl4Wwo645m1biEVIm6ACwFtD5E2Fi9fkZzCMq1eR2+okz2wc3Eje\n9M2sI89SvNTnJsaMhNkB2qRoijILp6XsOKzSiEyEluOzyENXkP+1nrZJcQdP3kD04EtriDEjZYZo\nDXn/a+Jx0/qKlj8No7m1m1xrKTNOuq5OYow2M2AwkGe/0rfuEmPFqPwrVNY95NnM4mTzGykDQIYy\nC0fbwbtEmMEwU8YzT9h0GkDNmtV6SJtVA0CmLPSRw7pf/g0AzkxuS2snby6dvk3+FbYQIc8+Wndu\nI8aK9+XXJ9HOuQxlFk5D6X/SmAGAiTLWeVK5AnkdNG3MsvfIs9PGteTvpyzlHGcwGIxvOsv+wMVg\nMBgMBoPBYDBWHkVVOzsy6lmxD1z379/HqlXkX9OkHD9+HDt27MC1a9cUr9eSY/TiRbgcdjycnsYb\n27bj/I3reHvnLgDAsWtX4eJ5TC0swGYywWIyYcdqcRZCTm38gze+hbOnTsDpcmFg1TBOHvkcrq4u\n6PQGvLpD/CX82NWK3jiATsEOjUaDV83iOpvjd8pq7IAfXTYeeq0W+zpdOPb1Nbh4Hk8WFmC3WlEq\nlbB//UYAwKWxM7A7RcX4xK0bcDg7UCyVsMUuEPO/fPYL2J1OWGw2TN69C3dvH9LpFNZv3oLxsrLc\nYuMxcfM6vH39eHT/Ht74zvcwPnYaDqcYu3fzBuwdHSiVili7ZRuunPsCdkeHWOfEHdg7XCgWCti8\nbTuxvUQsXv184i7szg4UigW89Op2XJGWURjb+Iq6PNZu2kKsM5vOEPMAgJFzZ+EUBEw8fgxvVxdK\npRK2Dg/jxOhIWQtvxa2b1yEIdlgsVgxtfAljJ4/D6erE4KphHP/8M3R0dcFsscDqHcDV82MQHE5Y\nrDY8uncXnFYL3+AQBgZXLcnR3duHdCqFZJzcj5fPiueHxSrGrIIAvV6PdZu3EPtj0ytbl5xXvGCH\nTq/Hpq3biefcq17PonJ9KhCAw2aDQafDzvUbxGvm8qVFrXqn3YFiqYjXNm9ZEnPaeOj1Ohw48Bqx\nj7/17ruKddr1Wu+jX5xZ1HcP+nwANNhd1qDT9OP1GvFCsYhvl2e45NTqO9auq6q7H09i3eAq3H54\nHz989ztimVPl/KdnMNjXh9v3JvDB99+r3l/slfuLF7cfP8IPXv9Ww/xHxsbgtAuYmHwkqslXDWFd\nhzhTKKcmH7QJVIU+rY+p/XjjOjp5HndnZ7DB60Mym8H28uz/0dOn4BDsmJqZgUMQYNDrceC1pcr7\nNw4cwLmLF/HWq9uomnzaffr4zetw2XhM+Gfx5sZNuPjgPr69UbSn0uoknXOkPnn/pZdbOmbDff3l\ntmT07lu3kXN8550l55bL6RSvtS2v0C4JVDMAACAASURBVMeMosk/9tW12j42Ghf72LB6CMVECpzR\nsLgmLfdYfo0Zg8FgfJNYdkshAHz00Ud47733cOjQITx+/BgAcPnyZRw5cgQ/+9nP8E//9E+YmhJf\nV/jlL3+JK1euYGxsDB9//DHOnj2L+fl5HD58GFevXsWhQ4dw8uRJ/OxnP8M//uM/4j/+4z8U58Fb\nLaK+WKvFrUeTsEsWivMWC7KFArL5HILxGMw1end5tbGN55HLZmE0muDp7YNgd2DB75eUMyNbyCGT\ny2Ndbx/iqVQ1ZjJhJhJBsViCw2yBv6JtNluQyxeQyecRTSaRyGQWy1hsNuRyWXj6+qHVapGIx5Au\nq35J+YtlcvD09iOXy6J/eDVKZSV/VVku1mex8dj8/7P35sFxHHe+57eruquvqj7QTaAbJ0kABEFR\nPEWKIinrsCWPd+yZ3Rnv7FvHHOuYcTi88fwcE/vC8XZ3Ysex73lj38SON0b2OsKacYx8aawnP8/I\nl0YUCVIiBZEgRIkHQBAHAYIgGuj7vuro/aOqG9VAZwJs3nJ9IxQ264fM/OVRWV2VmZ/ffjXmlNMp\nQCyLaO/qBsOyyGUyKOZV/x1O1Y9AZxdEUUTXll7IskQtr3o90NkNsVxG19Y+KBrMoWlbE37Q8qSV\nBQAunoesKOgJBpHOZpHT+lMQXCiXykgmEggGOxCPx7RYW4BTcKFcLsNqs6G9qwvJeBw2bfuWw8lD\nEkW0dXZBEkWYTCbIlHqv245OtT+DXWqdLboto+u3x8q4EtxuJDQaHm3MVZHrZUlsgOjW7jWGXYPo\n1tu8goDl+AoBkNTGzSDQa/nJMjZ3dGD71l5kctkVP2j4cT1GPBisx34T0Oo1dHewAy7eiSN799el\nUeSq/wKOah96gHoEvcvhqEen0/znnSqavKNdRddL66PJ18eLE9qY4ofLblPbqlLB9vb2OmiNi6/m\n2Qavx42lSFhX3gryfmJyEm6N9kjD5FPnaZsdoVQSslLB5FIILh2Vj4reJ4w5Wpvcqz5rhHdf10fd\n2Epl0shr8zS1zyiYfMFu1+6LtW1cKZZgYtXwEVIkChMFlGTIkCFDv016KF642tvb8dnPfhaVSgVd\nXV0A1NhFi4uLaG1thaIotR9rLpcLsizj4sWL2Lx5M0SNomYymcAwDDht8i8UCtizZw/a29s37Ice\nhZtIp3ErsrIXP5ZOg2NZWMxmBDxehJM6FDEBbZxOpcBxVuS0H3ilUgmtesxvOg2OVTHi04u36jDF\nsVxOQ2MX6pDa0UwaFjMLzmwGb7PDocMXq4hxDteuXEI+m4Xd4YRNeyiT/M9paWaujsNssdShoDPp\nNCxWK65dvoRcNot4JIxNAdX/bCYFi7VaVgZ2pxNW7SUil0nDwnGYmVDzPH3sN3B5WqjlZdOr0rz1\na7i8LXdka8YPWp60soB6jDjvcNRQyslkAlabFQzLYHl5CW2BICJh9WxOJpmElbMiq42RTW1tiGk/\nOqv+z05chdligeD2IJWINfRxQ+2YURH001fHYDZbIIpiDQ2/Xnvox1W5VIK/LbDumIumUrBoePSS\n9sK4MvZX7hkV0W1raCuWy+jwN0a169u4GQQ6UI/vnpybhdO+cq6Oih/XYcRfHzkLv7BCmyKh1aOJ\nlbKWolF06gBAkXgMDMsgFAljKRyu9z+VBGtSseqheBwdfh06neK/vjyfd2No8vXx4oQ2pvmRqbZV\nfg3pMxJX81yKRFAsltAR0OWpQ97Hk0kshEJavciYfNo8HctmEHC7kS7kEc1kENKH6KDkSRpztDa5\nF31Gwruv66NubAlOHg5tnqb6QcHk1+6LahunUjUb41SR8SzPw+z3Ucm6hgwZejT0oJHvBhb+Hmlh\nYQFLS0sYHByE0+lEOp3GG2+8gYMHDyKTyWDPnj0wN4GV3ohy7480vC7p4qSsFu3A+xwFmtFNgGbQ\ntl/QoBmXHnucaNs9eZVo+6CXjNI0oBkb1xECNCPbSx4DcQqqenqJPOZo0Aya7ic0Y++teaLtoYFm\nLIWJtrsOzcgXiDYqNGOaDDCwtJOBNyQAg7mF3I5SlIyFp/mo5MjjOH+eHJKiGWiGHE8SbfcEmkFA\n11OhGRRQSDN9BtDbXyJAOsyU54WSJ4fvyI98QLQxTjLoZ11ohkEpNGSoTo8KFv7NixMP2oV19Znd\n2x+0C+vqoTvD1dnZic7Oztq/XS4X/uRP/uQBemTIkCFDhgwZuhPlvvq1hted3/67++yJIUOGDN1/\nPXQvXA9SjN3e8Do1UCtlSYQWzJfVziOslmQlr2zQyqIFrzUx5FUK2goGKQgwLQAwLXAwbWXDRFll\naWZFhL7CRQlu3OSCr8nUeIzQ/KDVi7IIBxsl4HOZ8pXcQuhrSSHjo2nBjWmi4a9BGSNElDWtQZpd\npKeNY8qKglgmBPqljCvG0XhuAQATBU9PW1EwUQIEmzjC2RlKO1LnOVo6Sl/TfGxGFYUyp1KQ8ZVi\niWijidRvJso9SFuhI/bLejZaG5P6jTbfUgKC02y0MU4TafeIIUOGHn7d7R1Iv616KM5wGTJkyJAh\nQ4YMGTJkyNDHUQ/lCtftIOEbpRkeHkZfXx9GR0fh9Xrx1FONzwqs1vFzZ+EVXJi+OY82nw+c2YLD\nVUxuA4T7wW0DAMjY5nOn34GnxQdeEHBt7Ap6tvaikM9j5959arrR87V0Hl6Aw2bDbov6dfTkxHgN\nYey02rCtLYBepxNDY5fh4wXciqtIXs5sxqF+1Q8SKvygwNchro/ueAwjk9fwwjrI7/Nn3oWnxQcH\nz2P66jgsHIee3j4EO7txXkPGO3keU1fVdDa7HVt37CTixwd378WF4TNwt6i2mYmrENxqecVCvmGa\n7bv21KWZm54EL7hgbpCf3rZ91x6iH49R6r1j736ij6VCgegHgIZI7cHu7hUsvNOJifFxuFwuOJxO\n9D72ON47dRItPj829/Zi6K034eQFbO3vB2w8Lp4dhsvrhV3DwrMaFl7o20bE8hf06PrVmH9Cmsf2\nHyTWeff+A8R0uw8cItr2dHbixIVquIQl+N1uKJUKPrFr98o946qOfR4Omw0HB9XwBo3SPffcs2ob\nV/HX8/Nob21FRang+RdfpGPVaVh4Cg5cf39aLRwGuruxtb1DnQ8ahGd4cb86V7x9+jS8Hjem5+aw\nc9sAcoU8nty1uyEu/kltfmmEQP+dT35SrfPIOfjdHsyGbsHv8YIzm/GkRr1rhFU/cuDAis29Grn+\nCWJ56yHXaVhyWt1ooSxuFwv/qd17qOh0Go59aOwy/IILU0shBDwecKwZB3r71oyDze0dgMmEQ7u1\n/myATz/y7DNEH5/f2ofj50fgd7sxu7iIoN8PWVFwRBv7b59+V22ruVm0BwLgLBYc0p4JNBt1jDSY\new5rfd0IXf8//De/23ic7tlba6uV54xde86o531p6H0aFt5xcD/kZArc5m7k3jsH2+M7kD97HoYM\nGTL026AHvsJ1O0j4N954A5cuXcJrr72G06dPY3h4GK+99hqGhoYwMaEe6jt16hQmJydx6dIlXL9+\nvYbY3ohcTidkRUZPsB1elwtLsZXtfVSEOwEB7ORVLHxHdw9YlsWW/m11W8z06WLpVB2CWY8wNgE1\n7LTLZocoSShJIrxOHss6choJFQ6sIK5LoohrCwtwOZy6NI2R33r/GZbVsOSSZqsi49W6pZMJWHU4\ncyp+vFwtjwHvUsvbaBqxXIaF42rb8ai2dTHoFNR5Ix8pZQFkpLbgUtHvyUQCwfZ2JOLxGhaeFwSU\nyyVYbTZ0dHXDZEKtje21/lSx8MAKFp6E5adi/mkof0Kd10tHswkObXyzTGP0uxZKIZ5O1499SjoV\n362gp71dxV8Xq4hrMladioWnIcZ196fJhHr0OyU8g0vgNSx8p4qF1/ZjkHDxaprGCHTVR6fWVuwa\nvD4Vq15tk7YAvG4PlnTU1WaQ63QsOaVutLZqAgtPQ6dTcex2O0IJFXVuNZvrdtzp660i0ldAIFR8\nOqkdHU6URQksy2Jw85Yadr+urTo7YbVwMMG0MRtljDSD8yeN02pbibKEkiTB63TWPWdo6H0aFl7J\n5WDt2wq2xQNuSzcUCjTIkCFDD48UpfLQ//co6IG/cN0OEp5hGIiiCEmSUCqVcOPGDXR1dcFms4HV\n9ribTCaYTCY4HA60tbWhhULlWi09WrdYKqGjtbVmoyHcSQjgTDoFzmrF+KWPkMtm15zZiaY0DH0+\nh0BLC8LxeM1WQxgXC/A4HIhoPxJVLLwZHGtGUSyjXYclJ6HCVf9T4DREdzKXxWJ8BS9OQn5n0ilY\nrFZMXL6IXDYDt7cFCe0lVE1XtWXR4t+EeFSzUfDjqo9WTF5RUfPlUhG+1rYNpZm+OgaLxQKxXIbJ\ntAHbOnnSUOcNfaSUBZCR2qlkElarFQzDILy0hLZAABENO51OpWC1rmDhvS0+xLQfxjmtP69PXIXZ\nbIHL40Gq2m8ELD8V705B+ZPqvG46iq0WLiFXRb/r75kV9HtbSwvCicRaW4N0kUQcFu38TB0WnoZV\np2HhKThw/f3pc3vqfaSEZyBh4UnXATICHVDDVTAMg0wuh1K5XHeujoZVj8TjYFgGS5EwiqUiOgKB\ndcuj4tFpWHJa3aht1QwWnoK7p+HYMxkEPB6kCupLX12YAl29VUT6ysskFZ9O8jGZqPXZ6yeOw+/x\n1reVdm6vJJbBMKYN2ahjpAmcP73PMrCw6vOiKIp1zxkaep+GhWc9HpSmr0OKxMC63TC3kkmPhgwZ\nMvRx00OFhb8TJHw4HMb8/Dx6e3vh9Xob/s16Knx0ueF1cTHU8DqgPWAImtq8hWjbdrMxNpuGgaaV\nNTr4GNF2cI6MKf6oj4aFb9zWtK8JRZGMhadBIpqFSzST372AZhxSGoMU0t3dxDRJCip8Zqkx6hlQ\ng2KTRINmcIR7hwbNMDd5SH5PiIKIbgKawfrI9zQNdV508UQbt7hMtNFCQYjLjXHyDm1LYUPRwCQC\n2cfyjZtEm7l1E9FGAonQ4B005DrjJuOLK5RxnP/wEtHmOLCvcVkU6AQNXS+FyX0mEfoMAOy7ySE1\nSLAQ1kMGdJRnbxBt5rZWoo0mWr+R6k1DyVdI4BfQsfAmGxmCUp4lh4KgQTMMSqGh31Y9Klj4X14g\nhxZ6WPS5fYMP2oV19VCd4boTJHxraytaW5t7mBkyZMiQIUOG7r9mUgmirdfd3MdTQ4YM3T09ROsy\nj7QeqhWuBy1SAMmP8uSvgVMh8pfT35klB08VPvVcw+sXC+SypkPkVY8Xr5ODfv7bGBmJ/B+P/4Zo\nM5NWFSgrXLQv0CbKl2uaKoRgylSMNTVD8moDmgz0e/l//Q8Nr7/AkjHW5Tnyl/DUG+R+oUnJkVcb\nSIFVGZ68cgrKihkNEf13L/4u0UYLHWAnINJdlLHzxecPEW0lkew/z1BWagmYfwCwVRrnmZTI+QXL\n5Huw4CDXrRk/AKCAxuPYDvJ4vN8qM437mtZnAksJjUHIDwA4hbzyHimSy+smtFeBstJzt/sMeHj6\n7dVzV4g22k6Ex7qCDa/TgtADxguXoY+3HpUVrl98MP6gXVhXv7d/x4N2YV098DNchgwZMmTIkCFD\nhgwZMvRx1QPdUjg8PIzDhw/X/r0aBx8Oh2vbBMPhMKanp2t/n8vlcPPmTWzfvv2u+XPs1Cl4PW4s\nLC6qSPL+fvRp57BGzryrYdAFTI2Pwd/aBphMmFm4Bd7tQXf/AM4PHUNLWxAmE7B1h3ouYOjqGPy8\ngKnlJXS1+GACcGBrL7E8tHVg5LRWliBgUitLkWU4O7dg7Pw5CB61vJGhY/C1BaDICl7kHWvKkhUF\nT/X140BvN5K5AvLlMnZ0tmH42hx2dgdxbkpdYXE+dRBSMglLMAA5nUGlXEbx0hgAwL5/D+RUGlx3\nJ6RoDBVRQmlMJULan9gLOZUC190FKRJFRZIgvTsM5+EnISWSsLQHIM4vwGS31fJzPPmEigbe0g05\nloBSKKB45eoaW3n2BrjNPci8dYLoY2n8mprukJZuc4+abks3Mv96gnhdTXNAK6tHPd/BMCiMfnhb\nPuptl0beh8vjhd3pxM2ZafgDQSzemMULv/MigMYhB57weDF0+VI9YtlqrSGW7fv3QMlkYXt8B8oz\ns6hIUs0Pms3x5Ap+WcnmUZ6bh3hrkdhWuTNnVdsTeyGn0rB0d6J8fQ6MzYbi5TE4DuxTr/d0QQot\nAZUKitoYcBzYBzmZgqWnC+Xrs2oarV92dQeRLhSRyBVwZGALppeiqFQqcNqsyBSKSOYKeGrbZsxF\n4pBkBdPLUQx2tCFbLKEoSuj2eZAtlVEWJcSyeVy/dAEOwQ2TCSjl84BJ2+rw/CGcPK5H74/h6Wef\nw/lzZ/H0c5/EO0MnVJvDgWtXr8Lb0gJFUfDiM0/jxIkT8Pv9cDqdGB8fr2Hj49m8lsaJiatjcLnc\ncDic2K/h5Buh5rftO4B3h4bg8/uwta8fx37zawzs2IFCPo/grl3U+UWfX29vL/L5PA5oePdaWIFV\nvhTTiYa+Hzx4cE0ogu6eHkxPTuIL//0frqmzvrw7sZH8J2H5T544Dr9Wr6vjY+A4Dv0DA2jv6lnT\nZy63GxbOgk8eeYrYZ3sOHSHmObC5e40fsizj8OHDOHNqCC0+P7b09uHEv/4GHV3dgMmEvU8c0Obp\nFRx7td86BrcT++3xg08R++zIgX1E/+vSreq3thbPmjRV/xvlR7I122fV/ABg8sPz4N0eACYU81k4\nBBfKxSJKhTycLg9MJhOKuSxgMoE1m9Gz/TGMamFEHLyA6atjaO/qxvz1Gbzwud9XQ4x4V0KMcFYr\nurf2oqO7Z8PPbkOGDN07GRvh7o7u2wrXq6++ipdffhnHjx/HK6+8gn/5l39BOBzGd7/7XYyMjODl\nl1/GBx+oh3Vv3LiBv//7v8f09DT+8R//ES+99BJGR0cBAK+//jpOnz6N+fl5DA0N4cSJEzh37hwA\n9SFx4cIFfPOb38TQ0BC+973v4cYN8rat1XIJAmRZQXsgCJPJVIfyrWLQq4j0nr4+5HNZ2BxOSKKI\ndCIORVEQ7NmMYj6/kqfNjlBSRREPBILIlIrrlucUVGR5DSff1w9J1lDhzpXyKoqCYM9WyDpkfK2s\nYLCGsc6VyrCYWZgZBl6nA5s3tSBbXNniJOdysATa1IPgq24sJZ+HdetmsF4PKqIIYMWu5PKwbt2i\n2aSaSc5mYQmq+ZXnb9bIgGqaHKz9vTC3eMG6XajoENF6m5LNo/DhxY35mM2r6XxeKNkcChcuUa/X\nytqmlsUIPBgdiGKjPuptDqcTklhGW4eKcLc7ndi+ewUKQAo5INjtKEtSQ8Syks8DLIPS1AzkTBZs\ni3djtmxewy971f4ys/U2YpvkwfVuhtnrQXluvgYMUHJ5cH1bYG5RrzMOhy5NbsU2Ow/oqI35sggz\nyyKVL2I2HMNCPAk7Z0GhXIaZZZAqFDEXiUOUZVS0wVMsizAzDDwOO3KlMngrh7I2jq12B2RJhCxJ\nMDEmlPI5iBoyXnCpeP1kIo5gezsmJybgcqlgA0EQUC6VkEwkEAgGkU6nUdDuUZeG7E8kEnXYeEFw\noVzSUP7BDsTjsRrKv5quEWpe9aMMq82Gzu5ubNs+CEUDZtDmF31+g4MraVT/G/tC8l31oz4UgeBy\n4cnDRxrWWV/endhI/hPbShBQKpWQ0PoMujap9WdS7TOP11sje1LrTclT78f27dtr13nBVeuzjq5u\n9G4bQC6rCx2gw7FvtN9o46eZfludRu//7dia6TN9fgBqzzxZEmEyMWjr2oxKpQKbw6ndnyoN1qwL\nYbASRkQNw+HgBTy+/8CKTSyjXXveASuhMQwZMmTo46L79sIlimIN6b5tm0rGM5lM2LNnDy5fvgyv\n1wuXFlfE6XQir/0gGhgYQDAYRE6LixKJRFAsFuFwOOD3+yEIQm2SttvtqFQqKgI3FALHcejp2fhX\nsmgsBpZlsBRehr/Fh7COVJbRMOhXL6mI9PnZ67DZ7chnM7BwHJLRCAq5HEI3ZsHpfrxHsxkE3B6k\nCwVMhBbh1CGRSeVlUiqi++qli8hlMjj+q1/A2+IDAOSzGZg5DoloGPlcFueOvwnB41lT1usjZ+EX\n1P3Bgt2KsiTBZDIhks7B7bSjVUdvM3s9kMJRyMmUekH34GXdbpSuz0GKxmCycHXnt1iPC6Xrs6qN\ns9TORplbvJDCEcjJFCql+jNprNeD0tQMpHAUUjwBVkeY09vMfl8dfYvqY4su3SYfpHCEer1W1uQM\npHAElUIRii7W00Z91NtymQzMFg6z167CbDEjGYuhRcOqA+SQA7FMGpzZvIJYTqXq2h5KBRVRhInj\nIEViG7N53Sp+ORyFnErDrHsZo7aJx43yzNyac3is143y9CykSAxcT1d9W3lWbKvltHIQJRm8zQpR\nVtDucaMoSup1WQFv4yDKMiwsW3uHdmi2siTBZbchXSjVzm8Vc1mYLRaYLRyyyQQ4mx0WDRmfTCZh\ntdrAMCyWl5aRTMQRWlQDrqaqNpZFeHkZPM/Drr00JnXIfj02PplMwGqzgmEZLC8voS0QRCSsQ8YT\nUPPVEACZTBpAPW6bNr/o81uN6Cb5QvJd70c1FEF4eRntHR0N66wvr1kbzX96W6khPcLLy/D5fIiE\nw/V9xrAIh5dRLBYRaG/s/0bz1Pvxs5/9DH6/iiVPa/lVQzPMzkzD7tCFDtDh2P2+FoSjK/cMqd60\n8dNMv61Oo/f/dmzN9Jk+PwAoZLMwWziYLRwyyXjtg1ohm1m5nohDEsXaC1cmnYbFasW1y2rYiXgk\njE1aCIDMqhAjHl34EUOGDBn6uOihgmYoioILFy7A4/Ggr69vQ2mGh4fR09ODUCiErq4utLW1rZ+I\nIAOaUS8DmrFxGdCMehnQjHoZ0Iy1MqAZq/I0oBlrZEAzDH2c9ahAM94YHXvQLqyr33+CHBrpYdFD\nhYVnGAZPPPHEbaWp7ivv0L7eGjJkyJAhQ4YefRX+/deJNvv/8zf30RNDhn57peChWZd5pPVQvXA9\naEVsjYNLpqOphtcBwEb4Ig/QA5rG7I6G19OxNDGNxUz+Aso4G+cHANY0JbDtJj/RxpKCnVJWuGgr\nRCZawNsmVKH5QZGJENAZACA39yXZYeUaXjeZyF+0TVzjNMA6AVIpPirOItlGCFBLW+FiXeQvcLQV\nxlCCPI5tlPbv9DcO1koK2gwAxTK5jWlf0MMF8qqHx0m+r0nPnrJI7hfaKta90MOyIkJTnhB8lzWR\n54lwgdzX1D6jiBYsvMDfv35rts9EtnG9LTJ5fDcrp408Z5Uo4z9XbLzCS1vt7vubv924Y4YMGTL0\nkIv9xje+8Y0H7cTDol+/+SaymQyymQzOvvceYtEI5m/Mwe714cL77yGfy6JULGDsg1FIkoSlxQXM\nTU+hmMuhXCxi4uIFJKMRJKJh+NqC2JJJYejSRaTzeXwwM43ry+peeS/P4/j1GWTSGWSzGZw9cwbR\naBQ3b8zB5vXhwvtn1LIKBYxdGEUiHkMiGgHf4sOVkbMo5LIoF4u4dvECEtEIEtEIDjjsGLp8CelC\nHh/MzCCUiCOSTqGjxYdoayscnAV2zoIDvV2oAAh4BUQzOTy7eBP2/bvBCjz4Z46iUhZh3/O4Ckew\nWWHb9RgYpxPOpw4CZhZmrwdyLA4AK7bDB6GUyzC3+iGFlmHfvwes4AT/zFEwVivMPh+kSBQmxgT7\nvt1geR78M0dQEUXYd+9UAQ3A7dtmNRvBf9J1E8vAvncXWN4J/umnwLoEVCoKlGwOqFSa8vF4RUKh\nOj4ujCKbSmE5dAtPBAMAgOPvDyORTuP0B6OIJOIIx+MIWm0YuviROj6mJ5HIZrEYj6HD50f5xk3Y\ndg6CsdthHegH63aBcbkgJ5JApQLb4zvAOFQbIzhh3uSHHI2hIkmw73lc7ZejhwCGgbnFCzkWR0WU\nGvaNrJ0bs+/eCYZ3wnnkSTXdJr9KQtyxHSa7Ddb+XsiJJGzb+yGFozAxDKyDA2DsNlj7t4Kx28D6\nWiDHk3jL7sLBvm44rBwcnAVP9vcg4BGgVCrY0dkGp5WDzWLGgb5uAEDQ40I0k8Oh/h7YOAtsFjMG\nO9rgstsg2K0QJQXTH42iVMgjm4wjtngTNieP2bGLOHpgP94ZOlF374aXl7AUWkRXVzdODZ2ou9eK\nxRKGT7+L3oHtOHNqCNlMBrwg4K1fvYFgRwfOvXcGiws3kc2kkc1kMHzmNOZmr4NhGXi9LTh1/G1k\nMhlks1mcOXMGyWQS8/PzaAl2EPPbsa0PJ06cQCKRwOjoaC1NV1cXABX4o88zEolgaWkJbZ1dOHXi\nODKaL++/dxrLoRCi4QimJsbX+LG4uFhb7deXV82vo6NjTVmSJOHGjRsNbfo8G9mqdSD5PzExQSzr\n2PEhZDNpCIILv3njXwAAN2/cQHtHJ7E/2yhtPDAwgJMnjjfsN7/HTazblfGryGYz4HkBb//6l5Ak\nCeeH30P/9kG4OHNdO9LaaiN91t0RXLeNb7vPhk4ik04jo9W5LRjEmXffQf/WLU31Ga2v85wDV0dH\nUMzn4G7xYfTk24hpz7W5axMo5XNwtfjw4TvHkU0lkY5F4G1tw+ylD5DP5VAqFjH24QfIplOIhZfR\nGggSn63bpq/D/sReME4H7Ht3gRF4dX7RzotaXnzhnv4eMGToXsvpJG9Nfpg0sUg+OvOwaLCd8pH6\nNvTKK6/gq1/9Kr7zne9gamoKR48ehcVC/5j39a9/Hb/85S/xmc98hvp39z0O1/DwcN2/r1+/Xvfv\ncDhc9//1f5/L5TAxMUHN705Eo5k5eB6iKCLY2Q1RLKO7tw8VRYHdqV5v7eiEJJbhdLmQjK2cYxIc\nDpRlGWVJhAmmGjlQEKpUtQQC7e3IpFI1UIjDqRKdgl1qWfrOrlIK1fJEOF0upNYh3lUphSzDwMM7\ncCMSB4OVL/9KrgAwLEpTM1DyWYL2TAAAIABJREFUeRQurwS5U/IFgGFQmp1DRZTq8KBKoaCS8q7f\nUPHvphWqXTU/laDn0eVXpetdh5LLo3Bl/M5tBP/p9dLym5kFKhWYGPaO/KiOj0BnN8RyGV1b+6BI\nK198BZ6HpMjoaW9HLJmEXYM9CA47yrKIkihhoLML2cLKKpRSKMJks0HJ5cDwPCq6FQElX1ixORxg\nrFadLQ8Ty6A8Mwfx5gIYx8rKLb1vCjAxDMrX51CeX4BJW61UikUwNhuUXB6WYABKYWUVrVKzFSCG\nluvaMVcsg2PVcdfCO4AKwDIM8iURFu2612nHXDiO6vGPgijCzDLwODVKoc0KUWtHm8MJWaxSChlE\nFuZh0+AGNKrd6ntNcAk1Yt9qQt3M5CQEl2td2h2JCkfKr5qORH5bnafVaq3dT81QCleXp8/vdkiE\n+jxvh4ZXLY9WFo3oSOtPWhvfTr9V68YLAkQtv/bOLvCCgP1PrpwLJNH8mumzjbTx7fYZjdDZTJ+t\nl86mPYMyiQQqigKTyQRZlmFzONTryQQqSgUOQUA6oX6cs2vPtEBnF6RyGV1btqKQywIgP1sBMgnX\nkCFD90+VSuWh/+9u6OTJk/j+97+PH/7wh3jnnXeQSqXwN39D37r85ptv4pe//OWG8r+nL1z3AwWf\nzWbx+uuv4yc/+Ql+85vf4Pjx43j55Zfxox/9CK+99hreeeedDftLo5nl0mlwHIeZq+MwWyw1qlM+\nk4aF4zB3bQJmswViqYSWTStv2rF0GhzLwmI2wycIiGirCVUaFcuoZTl5HnbtgZzNqNSm6atjap6i\nWCNB5TIqpXBucgKsVp53E514J2g/WhmTCbF0Dlh1uJl1uwBFQUWU1NUQHfiCdQlApYKKKKlb8XQD\nm3W5VFKeJNZf96zkZ+IsdSCNGl1PEsG2eCFH43fB1th/ar3crhrlT05nwHrdd+RHNq2Og5kJdXyc\nfuvXcHlX6IbReBycRikM+jchrL2UR9NpcKwFnNmM6cVbcOhenFhBfcliBAFyKq36rOuXSrkMxiWg\nUipB0dEgGZcLFc1HS3sQim47D61vGLe60lcRJfBHD0FOaaQ9zQ/Wxatfmb0rL2kM74RSLoMVeDWO\nVzZbs1XpmAyj0jFThSJaeDt4G1e7Hs3Uj0cnx0GSFZQlGYLdinShWANmFHJZsFVKYSKOQi6DdEwF\nydCodqvvNT2xL52qJ9SlkkksLy5SaXc0KhwpP4BOfludZ6lUqv34bYZSuLq8+vw2TiLU53k7NLxq\nebSyaERHWn/S2vh2+q1at3QqCU6XXzQcRluwvWE70tpqI322Xhs332eNCZ3N9Nl66fLZDCwWC5LR\nMAq5HBy8gEwirtILOQ6paASFfBblQgEe/yYAQE57pl3X5sdbN+Zg1Z53pGcrQCbhGjJkyNDd1htv\nvIHPf/7z2LJlCwRBwNe+9jW88cYbtdBLq7W8vIxvfetb+PznP7+h/O8ppfAHP/gBstksent74XK5\nEA6HYTKZsGnTJly9ehUulwsulwuf/vSnEY1G8ZOf/KQWePHWLfWB0dHRgY8++gj9/f3Ytm0bzp07\nh82bN9cAG0NDQxBFEYIgIJfLQZZlLC8vg2XZWhBlfXBlmkKpbMPrVxeWiGlSBfKZmecX54m24rPP\nNLw+TikrkcsTbZ+8RSbe/WWocb0A4Buj7xFtzZzhktMZou3jfIbr2tf+XcPrRylnuMo3yOMj+y5l\n5ZZ2hosyHu/nGa6/9JIhNs2c4Wql+PGHh/YQbbQzXNkimQjqcTY+zwmQiXe080Wt9uaOyzZLvHsU\nRKI60s5wFQjEUqC5PgOAW1nyPdPRxBmu+91n9/MM1xsXyTRc2hmuLl/j+/pOznAZ0AxDj7oeFUrh\nz89fftAurKs/OPD4hv5OkqTabjK9GIbBF77wBXz5y1/G7/6uSlnO5XLYt28fTp48ifb29rq/r1Qq\n+PM//3P8wR/8AWZnZzE1NYWXXnqJWvY9hWb82Z/9GdFWfQlSFAWjo6PweDz42te+Rv1bAOjp6amh\n4EdHR/HYY4/dEQrekCFDhgwZMmTIkCFDa/XwBI+6c42MjOCLX/zimusdHR1gWRY2XRzd6q6zQmHt\nx+of/ehHcLlc+OxnP4tvf/vbGyr7gVMKDRS8IUOGDBkyZOh29H+88RbR9n/+/qfvoyeGDBl6VHT4\n8GFcu3atoe1zn/scSqWVIxjVFy39Vn0AmJ6exg9/+EP87Gc/u62yH/gL18MkUiDUJ5PkYL7SMpne\nMtS9hWj7BAGffjBFLkucXyDa/rV7K9H216//f0Tb//08+cFkW4fM0kgSZbubpDS3/95OwKeLtKC8\nFAmUILq0LUu0wJ7/SW68LekMSy7rFt9CtE3tfZLiB9GEaDpHtJHai4S0B4AWgbzdkKE48p9/8c/k\ndJQtjBUCKtzS3jhwKgD4928n2mgBar0F8lZbk0LeblgkhGDopqVpcqr15sg+3m/U/N0WKRg0rV7+\nInl8N9NnANCrkOcR8mZDsu53n92LrYMkfWacsrWIEgidEzsbGyhzyF9/8neINto8bciQIUPNqLe3\ntw7kNzs7C5fLhdbWegLi22+/jVgshk996lMAgFKphEqlgs997nNUgIbxwqXTiRMn4Pf74XQ6MT4+\njra2NsiyjKO8BydGz8PndmMuFMJz+/bj3PgYXjyo/igeunIJPl7AQjyGLp8fk4u38PmnjuDyyPtw\nebywO3ncvD4Nu8MJs8WMbbv24uSJ4/D7/XA4nLg6PgaO49A/MIDBqi/nz8PncWNucRHP7X8C58bH\n8FygA0NjV+AXBEwthRDweMCxZhzo7QMAjJ0/B8HjQXf/AEaGjsHXFgBMJhwC4DiwD3IqBUtPF6TF\nZYBlULw0hj2bO5DOFxHP5vGJwV5cuRlCp8+Nd69ex67uINKFIhK5Ao4MbMH0UhSMyYSJxTDRduVm\nCLt72lVbtoCj27cilEzhViyFhURqTXlzkRiKogSHlWt4fTIUwePdQWS0sg5v24yxhSVwZjOuLiwR\ny1pKZdbYhq5MYqC9FdPLMTzWGUCmUERRlNCzyYtUvgiTyYSP5m4R68ZZzA39mF5SKZHHz74Pr8uF\n6Zs30b5pEyqVCtiDR/Hh++/B7fXCwfO4MT2FQGcXioUC+I4eTHwwAt7tQWffNnxw6jgEjwec1Q54\nWrGjsw2ZQgklUUK334vlVAZBrwvnZ+axo6MNmeKKLVssoSRJ6GrxIKW14zM7enF5fqU/923pRCpf\nQKEsYqC9FdFMDqKsYD6aILbVjVgSgx1tyBZLKIoiun1ejC8sY2tbC67eCmN7e6tatiihy+9Rba0+\nAID9ib2QUylw3V2QIlFUJAmlcfXLkn3P45CzOdh2DCBz/BRs27ehcOGiatu3W8XR7xxE4dIYuK4O\nlCZnAADWwQEo2SzMrZtqiHwAOHbqJLxuDxYWF2GxWLC9vx99W7bU7muPx4Nbt26ht7cX+Xy+dlb0\n7dPvwuv2YHpuFu2BADiLBYf27lNt75yCR8vT7RLgdDjw5L79xHni+d17iOl2P/10nR8tLS3gOA4H\nDx5c4+Nq27FTp+D1uOvrtnlt3dra2mAymRrmWfWxujuA1CYbTdPIpm+Tap7pdLphW1XTNapbx45B\nYnnPDT6Gt0+/C4/LjYVQCB6XC5zFgiPV/iT0Gc3HpwcG8fa778DjdmNhMQSf1wulouDZp9bWeyP9\n9szOXfe1z24nz9V93ag9GtmqvjwB4OTkBPxOHlORMAIuFyqVCo709gMATl6bgJ/nMRVeRn9rGybD\nS/jDveoOlhMfXYDP5cZCJIItgQDyxSIObNf6+sML8LlcWIhG4eGdKmDI1d5wLq5UKrg0H8LezR3E\nuc6QIUN3T8rHaU8hRb/3e7+Hv/7rv8anP/1pBINBvPTSS/jsZz9bB/IBgK985Sv4yle+Uvv3t7/9\n7Q2d4bpnlMKFhQXcuEEGOZB0NzDverT87YiGwhWcTpQlCSzLYGL+Bty6JUbBbkdZllCWJAh2O54a\nUL+405DxNHyxWp4DZVEEy7J15bnsdoSSCSgVBVazpW7VpYqMTyfiqCgKgj1bUNRAG0ouD653K8xe\nL8pzN2rI73ypDDPLIJkvYGY5inypjLGbKrgjXxZhZlmk8kXMhmNYiCXBaBCCjdiS+QKuh6OoVFYI\nZKvLE+w2lCWZeB1Qv2aulBXHzWiyBrWnlrXK1unzIKfR/Kp5ep125Ipl2DkLrFpgaVLdaH4AgIvn\nISsKeoJBpLNZ5LTl6EbI+Cr22OZw6lDKCnLpNCwaqbBYFmHRIdKLZRFTIZXKR8Kn50tlWLR2nF6K\nIl/W9WepXMOxe5wOFbeuTaS0tiqWRZgZBh6HWlbAK9S+MBdFCWaW1fwQEfS4UBDF2pgjIZ1rCPqZ\nWXBdHSquvmYjY/krOkS9GFqGSdtv7RJckBUF7cEATKvuJRLWW+0zAbIso6ezE1YLB5OuR12CAEWR\n0REIIBZPwG5rjPVeg3inpGuE/F7XJgiQZQXtgSC1bjQsfCMMPQl1vpE062Htq3lupK02Urc6nDkv\nQFEUdATa4PW4sRQJ1+XXqO1pPtbylBV0BAJIZdLI6wAzzfTb/eyz28mThrWn2eqehTYbQukUlIqC\ndLGInG5VWrDZEEoloVQUCDYbntrSt+KH3QFRFFGWRAx2ddf9iHM5HBAlCWVRVJ9r2vU1c3E8CTun\n7rygzXWGDBkydLt6/vnn8aUvfQlf/vKX8eyzz0IQBHz961+v2ffu3Vujpzeje/bCdfr0afzVX/0V\n/vmf/xkXLlzAN7/5Tfzwhz/EyMgIXnrppRol5Dvf+Q5effVVfP/738ebb76JyclJnDlzBoDKt//B\nD36A06dP41vf+hYuXLiAv/u7v6vDvZ87dw7vv/8+fvrTn2JkZKSGlv/BD36AX/ziF3VlrScaCjea\nTIJlGKRzOSTSadyKRmrpYukMONYMC8tiOZlER4v6hZ+GjKfhixuWF1HLi2bSCLg9SOULyOkwvoCK\n6zVzHBLRMPK5LBbnZmHVts+xHjfKM9chRaLgP/Us5KSKjOdtVoiyrKLjZRle3oFYVm0vp5WDKMna\n3yg4PLAF6UJpXRuv2QTNlikU4XHaGpaXyObhcdqJ11eXJSlKXSgWalmrbLzNCp+2Rc5htUCUZZQk\nWXu5k2oveKS60fwAgEgiAYuGfucdDji0l4HVyHj915Jqn6mI5Sx4twcZLXaNw8pB1BDpLrsVbqcd\niZy2p5iAT3farCjL6jVJluF1rvQnb1dtjMmEWCYHi5mt1WEjbVUti7da4dG2aDk4C6SqzWaF08bB\no8X9oiGdGbeAioanZ3geZt/K9koalp/heRVD7+JhCbTWtiBGY1GwDIOl5TD8vhaEdfcnCesNAJF4\nHJz2A64klmsfDQAgEouBYViEwmEE29qwHFkfC09LR0J+r2uLxcCyDJbCy/C3+BCORBumo2HhV/tI\napONplkPa1/Nc7222mjd9Oki8Zh6PRJBsVhCR2Bluymp7Wk+1vJkGYQiYQhOHg67vaEfG+23+9ln\nt5MnDWtPs+l9ieWyCLjcSBeL4K1WOHRbvvW2pXQK7Z4VOmE0nYbFoobAWON/OgWL2QzObEZJEmvt\nuHoubve4URTVFz/aXGfIkCFDzehP//RPMTQ0hNHRUfzt3/5tDZwBAB9++GFD5sRXv/rVdVe3gHuI\nhX/llVfQ19eHUCiErVu34tixY3jqqaeQTCaRTCbxR3/0R3A4HHj11VchiiK2bNkCs9mMhYUF9PX1\nYd++fbXVLlmWMTMzA7fbjfn5+do2isOHD+P8+fMoFApYWloCz/OYmpqq2cPhcF1Z60n/VVAv5tJ4\nw+vAHZzhGuxreN126RIxDe0M11uUM1yHvkM+w/WfjTNcdWr6DNeuxu3/Hu0MVzxJtFVXshr7QTQ9\nNGe4/qf7eYbr336JaKOd4bJS2t9EaRPSeSAbJWwD7QwRTfY8+RTRo36Gi1Q3Wr2siRTRZuLI8xWt\n/W1p8pmroosn2kj6OPdZ+Z/+K9lIO8PV3cQZrlCaaFvvDJcBzTD0KOhRwcL/l7MXH7QL6+qPDu1+\n0C6sq3t2hmvbtm11OPf9+/fX2ScmJlAoFPCFL3xhTdpwOIzR0VEMDg7C6/UCAJ5++um6v0mlUhgd\nHUV3d7eBhTdkyJAhQ4YMAQDK3/y/iDbuf//f7qMnhgwZMqTqnr1wrRdsePt2MlmstbV1DRVktdxu\n923j5JsV7ctphbLKQgvgSTqEaKKsKjUb6Jf1kWl4pNUjAPDxjb8K01Z6IpSvxV5CfuvlmS81XvXg\nm8xPlMircC7KF2jaio6SaVxvf7ePmKZEGTu3YuTVF5YSLLRMqZudMI4lmbzyyBFomgBgpQQwZn1e\nok2OJch5butteF0pNibaAfRVLJpo9xotuDcp0Kydcr/fbzWzenQvRGorAEATi360uZjWZ/RMKUvG\n91H2ezDG77ZozxJQ5jNGaLxSaKLML64UmTpJm6e/dOYU0WbIkCFDD0oPzy8EQ4YMGTJkyJAhQ4YM\nGfqYycDC60RC4T7dsgnHR87B7/FgdnERQb8fsizjiIaBHhq7DB8v4FY8Bt5mB2c241D/NlyqYeGd\nuDkzDX8giMUbszj8wmdw6sRx+DQs/MTVMbhcbjgcTnzC5QIAtTy3B7OhW9jW1YNcsYDdnAND41fg\n5wVMLYcw2N6JfKmEJ7aqqwKNsPCKrOAIAPvunSqGe3AbStPXAZMJpauTa5DrM8sxmEzAtcUIBnTI\n706fB8vJDDgzi/lYEgPtm5AtllVbixvLqQw4sxmRdJaIVb+VSOkQ4xK6fR6UJRlLyTQ2uYU69Hj1\neiSTw84u1cdkroCntm3GXCQOSVao+UWz+TU486ptMZEmYuGvh2NEDHqlgoYI9PFby+r4qYYOWArB\nwwtw2Gzwdvfg3Ol34GnxwckLmBy/gu6tvSjm8/D29OLiuWG4PF44nDzmZ6bAsCw6Nm8BwBDbfyGR\nwrbgJuSKJVSgnrVjTCaV0ud1IVNQfe/xtyBTLEFWFEyGIsR6XbwRIiLor8yHiH4spTK4fukCHIIb\nJhNQyufB2R2ILd4EANh2PQYlm4Nt+zaUZucASUZpSsW705Dx1oF+KLkczJv8kNMZQJZRnJhS89wx\nADmbg7l1E4pjV2HtvTPU9tvvaDjw0CI2d3Xj2vQU/s1/+99RbSePa/eu04mJ8TE8/exzOH/uLP7g\nKTrq/H5j4TeKXL8dVDgNS95o7syWxIZt9akXP73Gj2eeeQYjIyN44YUXiHX7xGM7qeh3Wn+u9rFa\n3ucOHa5L5xZcWp771u2bu42Fbza8wUZ93Aj6/fDhw8S2eh5mNQyKIOBWLAa304lKpYKj23doz0Jy\n2JJGoU4+rd0zx8+PwO92rzxfFQXwdBDn8EgmR5zP6uaewW0oXLkKE2dBecrAxRsy1IzuEerht05N\nrXDdb+R7OBxGTMOpDw8Pryl/9b+bLYeGwnU5nSqmnWEwuHlLHRzCZbdDlCWUJAlepxPLKXU7mMPp\nhCSW0dbRBUkUYXc6sX23+hAXBBfKpTKSiQSCwQ7E4zHYdDQUl9OJsiSCZVgMbtlS24LosmlYeKWC\n7cH2uq2Ja7HwWyFrftYw3NfnUJ5fqG3lWIM6j6l0RGAFB+522JAvlRFKpmtb9YplacVWFhFKZmo7\nc2jI+NWI8QpUjPva65UayapQVpHxqUIRc5E4RFlGRePrkfJrbNPnScbCkzDoNAQ6oAsdwLCIpVOw\na3h3Jy9ALJfR2dMDlmWxtX8bFI3Y53DykEQRbZ3qGDGZTJC1bYHU9hclsBrivVKpIF8uQ7BbtXox\n8DodyJXKyJVKcGvbb6h4dwqCnuaH1e6ALImQJQkmxgSrw4Gu7TtrYw4Mg9LsHCqiVDdpU5HxxSJM\nNhuUfB4VaVW6wgoW3hIMqGXgDlDbQhUxHoBL4HH0yUPr2gSXgHK5hKQW0mFyYgIul7suXSMk+H3H\nwm8w3e2gwmlY8kZz57ptpcvz2rVrcGkfnGh1o6Lfaf25ykd9efp0sUQcdputoR/3HAvfbHiDDfq4\nUfQ7ta3sakiJkiQhnc8jV1rZCkkLW0IKdQIALocTZW1OG9y8peYHbQ6nzWdKoaCGlrh+A+L8AnWL\nuSFDhgzdDzX1wnW/ke/Hjh3Dz3/+cwwPD2NychLvvfceRkZG8LOf/QxvvfUWstksfvWrX9V+mI2P\nj+O1117D8ePH8corr2y4Xuth4RmGQSafw+sn3oZfj7vNZGBhVaRtURTR7lW/suUyGZgtHGavXYXZ\nYkYyFkNLa5tWVgJWmxUMy2B5eQltgSAi4eWVPKvl5XL1GOJsBgGPF+lCfs05sNVY+HPH34Sg+cm4\nBVQqKoabP3oIckolQK1GnR/ethlp7fyHw2qBpCg1XLj+kWXX4cD5VTYaMn4FdS7BZbchVyrBZbeu\nQY/nSmW47NaV/GQFvI2DKMuwsGw1fBQxv6r/pDxpWHgSBp2GQAeAaEpD+edzCLS0IBxXceaZdAqc\n1Yrxix8hm83U9WdOCx0wO3EVZosFgtuDVCK2bvtXfZFkBS67DRbtBVdtK/XHkMtuhYVlEddQyVS8\nOwVBT/OjmMvCbLHAbOGQTSaQSybg8qn3DOsSgEoFFVGCyWKuxfwC6Mh4ludRKZfB8Lx6zko3zBlB\ntbECD4Z3gvWq47tZ1LaKEWcQCoexFI6gM7gaMb7WltRCOjAMi+WlZSQTcYQWb62UR0CC33cs/AbT\nbRQVTsOSk+bOddtKl2cikcDi4uK6daOh32n9udpHfXn6dGqejcMK3HMsfJPhDTbq40bR77S2imbS\nsJhZcGYzeJsdDm7lfBktbAkp1IlqS9Sed6+fOA6/Rz0DupE5vNF8xrpctdASML7OGzJk6CFQU1j4\n+418j8fjaG9vx+TkJDiOQ19fHxRFweTkJDZt2oRAIIDz58/jj//4j8FxHP7hH/4BdrsdHR0dmJ2d\nxRe/+MUN1YuEhWe17UyNVJ4lr/S92ztAtD010BgZ75yYIKYpUbZEHNvcGDYAAEf+6Z+Itv/3wBGi\n7W5DMwQ7+eB3M9AMEgRivfxo0AyLmXyImwbN+F/aXA2vT3X3ENPcipMR1xdnySEAaNCMOCUOTTPQ\nDAeljWnQjH8zdIxou9vQDP7r/45oo8mWISP0acroPrbo5SLMH0DzsIpmEeOPAjTDItPR3o1EQ+/T\noBlFSngD2jigpSOp6T57BKAZ8rGTZCMFmmEhYOFp0Iy/mY8SbTStB80wKIWGHhY9Klj4fxr+8EG7\nsK7+x8N7H7QL66qpM1wPAvm+tLQEm82GXbt21f5OTyl8/PHHcfHiRVgsFvzFX/wFAODKlSvI5Zr7\nUWXIkCFDhgwZ+ngp++X/mWjjv/fd++iJIUOGfpvU1AvXg0C+BwIBBAIBarrdu+sDn+3cuRM7d+6k\nptErqzRewfBQvsLRRMN+6/e968XTUNUV8kpEibJqI8fJKwopyhdj/RkBvWirR1naV1pqUGGiiah8\niZwfQ8mPZSi4/iL5Kzktncnqb3g9TfnandbOHzVSqkBOZ6b4kaCscKUIjWymjG+ZEqxaf35tTboE\nGWtP6xwxtNzwOtNk4GCqaIOEsuonE1ZS7sXqUYUSSPxREKmtAOD2w6rTV0QqlBWzMkMprYlVLJoe\n9T6jtlUsTjSZKCFGKoT5rELpT1oQd5qKV68RbYzz7va1IUOGDG1UBqXQkCFDhgwZMmTIkCFDa0SK\nG2vo9sR+4xvf+MadZrKwsIBEIgEP4WwDScPDw+jq6rqjsoeHh2G1WmG1WnH+/Hl0djbeK74R/frN\nN5HNZJDNZHD2vfcQi0Ywf2MO/YILx8+dRSqXxdnLlxBNJrAUi6KztQ1yPIGhsctIFwq4MDuDUDKJ\nUDKBTp8Pv5yaRDGfg9vnx7kTbwEAIqFbaNnUhusXP0A2m4EgCPjXX7yBXC6LhZvz2KYRvI6ffR+J\ndBqnL1xAIp3CjcVFdJg5DI2PIVMs4szkBLKlIuZjUXS1+DDp9mLigxEU8zm4Wnz44OTbiIeXYGIY\n7JicguPAPjBOB+z7doOx22EOtkFajiD7zNNwWC2wcxY80dsFwW5FC+9ENJPDgd5u2DkLKqjgkzv7\nwTAmtDgdiOcK2NUd1GzAJ3f2w2QyYZOLx2IijX1bOmC3cqgA+J09g7BazPAJDsQyeezd3AEHx6FS\nAT69eztEScbj3UG08I7a9Rd3bYfVwsLtsCOWzWOPLo3eFs8WsKenHXarClZ4YdcArBYzPA474tk8\ndve0w6HZPvX4AAS7FRWlgnxZxK7u9oZ1i2npqrYXHt+GhVgSj3UFEXALa+rcwjsQz+bx/CY3jo+c\nQzqXw/uXL0ORFcyFFmHq7MEHw2eQz2ZRKhbw0cj7SEQjiIaXYfe0YHz0HAo5dYycHzqGYj6PZDSM\nksWOnV0B2DkLbBYzHusMwOWwoYV3IJEr4LHOAOwWM6wWM3Z0toG3WeF38fALjoZtH0nnsG9Lp9qO\nUNvewjJo8wiIZnJr2tFlt0KpVCDKsloWZ4HVrJYl2KzY5OIRz+Wxo6MNNs3HwY42uOw2CHYrtl68\nCPv+PeqY2/M4GIEH29ICOaoCQez794AVePDPHgVj5WD2+yCFozBxnPr3TiecRw8BDANzixeKdj7K\ntnMQjN0O60A/GJ6HeZMf7I4BnDhxAolEAqOjo5AkCTdu3EBHRwcAFYudyWSQzWZx5syZmr1nUyve\nfucU4skkRi5cQGh5CeFoFJ3BdqBSwdvvvqPaPvwQkVgMszfnsbmrC//63hlkMmlkMxm8/95pLIdC\niIYj6O4I1sqr+pJMJjE/P4+urq41fkQiESwtLaGjo4NoU32s+nEBxVIJ7wy/h53bByFZuTVlLS4u\noqOjAxZRwrFTpxBPJjBy4QJm5ubAsAxcmzbdlo/6trydNq7meW1qqmFbtXd0wAxl3Tyrtmp7bG4L\n4Nipk4gnkiv1Yhi0eL0lIxOGAAAgAElEQVSArNT1WbFUwjvvD2Pn9u0o2ew4eeI4spovw2dOY272\nOhiWgd/jrisrEAjg9OnT6O3tvSt9Flpe1sZVcE2f6cuySHJd3YKBAE6dOYO+rVshmc3EtqL12ep2\npKWr9ll792ZiW3kXFnFycgLZYhHvXZ9GIp/DfDyG7hYfTCyLkxPjyBSLeG96EvPxOFiGgdfhBOt2\n4cSFD5DO53H26hiWEwksxqPoalN3rZz4YBTpfA5nx8dQEkW8e+kjhNytxHkpWywT5/5tZ8/CefQQ\nWJ6HffdOSJEoHIeegHjjJkwcB8ehJ8DwTjie2AcTy8JxYC/K07MAAO5zv9v07wdDhm5XTufDcTZz\nPV2+GXrQLqyrXd3B9f/oAeuuBD6+X9TC0dFR/OQnP8Hw8DBOnjyJV199FaVSCZcuXcKPf/xjTE5O\n4uWXX8b777+Pn/70p/jud7+LK1eubLgegiCgXCohmUggEAwinU6joPnucvIQRQksw8Bq4WDSsdpI\nWHib0wlRLNcw7e2bt6KibbERBBfEUhlWmw0d3d3oGxiArEcA8zxkRUFPMIh0NotcQcNf220adreC\ngWB7HfDA5lCx8BkNy2uCCUoVC5/LgevbAnOLB+W5G7WtOflSGRYNL+5xqrjfKtUpX8Wx65Dxdm3b\nCA39niuJsLAskrkCppciyBRKaNG2hKnlMUjmC5heiiJfLmPs5hLyJbWsZL6AmeWoRg6Ua2mItrJW\nlmZz2a21rZxVH5P5Aq6Ho6hUVpDx1LppPqbyRVxfjqHT50G+VF5b53iyDkRRFzpAh/J38DxEsYxg\nVzdYloXgdiOh0ceqfZZOxKEoCoI9m1HUxhwN8U7C2lPbvlyGxcwgmVPbymHlauCL1e1YwcoWyoKo\n+cGrfth16aq2Kk6et1lrUBIln4d162YN/S5CjxtU8nkV2zw1AzmTBdvirbOZWAblmTmINxfA6EiQ\nSqEIk90GJZcD47CDsap9djvI8jo0Ng0xzgtQZBUVnsqkkde2gK4b0oGAoV/tRx1GnGZbB3V+p1h4\nmo93jIW/jbbaKOqcik7X9ZlLEHD04JM1myAIKJVKSGiIehCQ/XUI9LvQZzTM/BoUvq5uE5OTcBMw\n+RtF+d9OOv04oLWVYLMhlE5BqShIF4vIlVegRoLNjlAqCVmpwATUhU8RHBoWnmHhFQQs67a660Nq\nuBwOHNn5OAD6vESb+5VMFub2AFivB7bBASiZFZiTks3D2t8Ls88LJZtD4cIlGDJkyNC91l154SqV\nSvjyl78MSYuZwzAMuru7sbi4WPcwaWlpgSiK6O/vh9vthsPhgMOh/hh0u93o7++v/V01rlZXVxcs\n2rkmURTh9XoRDoeRSqWgKAqKRXVvOMMwKJfL8Hq9uHnzJiqVCvbs2YN0Or3heqSqCGOWRXh5GTzP\nw675F9GwtelcDsVyGSbd+Q8SFj6fycBi4ZCIqJj2iu4hmEolwVmtyGo/kH7185+jxafDVScSsJjV\nevMOBxzaAzuaySDg9iBdyOP1c2fhF1YoNyoW3oJkJIxiLgc7LyCTUPfcsx43ytOzkCIx8J98FnJS\nJeTxdivKsgzGZEIsk4PFzNZ+FvMaYlzFu8to97pRktRzEjT0O29TbYJdtXFmFlGNBOa0qeUJdisk\nWYZXW1WqliFo/5vI5uFx2rX8yDZntSybFZKsIJ4twKv9QOetKzZRVpApFOFx2tavm/b31fKcNg4+\n3rmmzu0eN4riyo89fegA/Tm3bDoNjuNw7col5LNZlEsl+LUvu/lsBhaOQzIaQSGXQ+jGLDitrzeC\neF+Ntae1PW+z1hDKoiyjUBZR0vxf3Y7pfLH2cufkOEiSjLKoovdLorSSjuMgaTh5wW5FulCEy66m\nY91ulK7Pqeh3C1dHkWPdbhXbLIowcRykSKxmY1wuVDSks6U9WEcoZAUelVJZ/ZtSCYr2Y+92kOV6\nOxUxHo+BYRmEImEITh4O7UVh3ZAOBAz9aj/0+G6ajYY6vxtYeJqPd46F33hbbRR1TkOn6/tsKRyu\na6vq/M5q87vP50MkHF5Tlh6Bfjf6jIaZX4PC19UtnkxiIRRqmG6jKP/bSacfB7S2iuWyCLjcSBeL\n4K1WOHRnt2LZDAJuN9LFAjwOByK6DwDRdAoswyCTz6NYLqNDF54hmkqCNalzZygeR4dfXYmlzkuU\nuZ9taYG0HIacSIKx22AOrJwbZ1s8KE3NQApHYd7kgxRe6RtDhgytVaVSeej/exTUFBZ+tYaHh6kg\njSq1cO/etdjGcDiM+fl59Pb21qiFq5VKpTA1NYWurq4atfBeKJRqjDT3zMwQ05Smyaj2t3vIqPYD\nfd0Nr7ctkHHgxbGrRNsvu7YSbc9873tE2388+jzRJtgbQwCahWZQoRNNQDMYEzm/pqEZlNuBlu4/\nDTbeGjsqNB7TAHArTgZLfDR3i2ijQTNoWH4S1p4GzaCh/M0UPP2X3vwl0UYT6VA7DZoh/NW/b6os\nKmKcAs1IuhtvnbZVyJCcZtUssvxhwcIXTeRjws20Fw2dXqFAedIe8n3IKbePp6ep6T57SLDwVGjG\nT35KNNGgGRwBCw/K3PMfZhoDdNbTX772Y6JtPWiGQSk0dD/1qGDhf3zmgwftwrr646P71/+jB6y7\nAs14ENRCQ4YMGTJkyJChu6X0H/9Zw+uuH//gPntiyJChj5sMSqFOPNN4dUOhILpNZnIT0lYABFvj\nL81KnvzVnYZEtlIC9oKyasNRgtfSVrJIslHykyiIcZrshC+nIgW7T5PDSv4SS0fXk9uD9HW3TMH1\n02wWSl/TuoW2CkdqL44ydmjBjWmBoGlBaBme/JW5QgiXwPhaiGmaXRmoEAJqA4CJMkZIKzO0FbNi\nk1h72j1P0/1eySKJtorVzCocdS6mBOmmrWLZKKvCRRdPtBH9aLbPHpLgxrS2It8xQIUyHxNtFIQ+\nbV6izdMKJfambXCAaKPh5A0Z+m0W5XFu6DZ0V85wGTJkyJAhQ4YMGTJkyJChtbrjFa6FhQXIsoye\nnp7bSrfeua9mFYlE4Pf7m1qdOXHiBPx+P5xOJ8bHx9HW1gZZlnGU9+DE6Hn43G7MhUJ4bt9+nBsf\nw4saAWvoyiX4eAEL8Ri6fH5MLt7C5586gssj78Pl8cLu5HHz+jTsDifMFjO27dqLkyeOw+/3w+Fw\n4ur4GDiOQ//AAAarvpw/D5/HjbnFRTy3/wmcGx/Dc4EODI1dgV8QMLUUQsDjAceacaC3DwAwdv4c\nBI8H3f0DGBk6Bl9bADCZcAiA48A+yKkULD1dkBaX8f+z9+ZBchz3vee3u/q+e3oG03PfGAC8QIAE\nCJLgIYqiLNleSZb13trWSrthxa4itLvhF7v2hsMbzxFrvXj2W+utGbTX5j4/k16TkpZ6oigeIImT\nAAkCg4MkiHMwAAaDwRzdPX3f1V29f1R1d3VPZXZ1TQ9mBsxvBP6Y/iEzf/nLrKyurszPD5we2XMX\nsH2wB/F0FuFkGk9sHcH5W/Po9blx9NJ13N/fhXgmi0gqg8fGhzC1EIJep8PluQDRdv7WPB4Y6BZt\nyQwe3zKM+WgMt5dimI3ElrU3HVxCli/AZjYpfj45H8R9/V1ISG09unkQF2YXYDIYcGl2gdjWQiyx\nzHbo/CTGuzdhanEJ9/T6kchkkeULGOjwIpbOQqfT4dPp28S+mYwGRT+mFkQYwYETH8PrcmHq1i10\nd3SgVCqB2/U4Pvn4I7i9XtgcDtycugp/bx+ymQwcPQO4fGYCDrcHvaObcebIATg9HpjMVsCzCdt6\nO5HI5JDjC+hv92IxlkCX14VT12awracTiWzVlszmkCsU0NfmQUyK45PbRvD5THU8dwz1IpbOIJPn\nMd69CaFECnxRwEwoQozVzaUotvZ0IpnNIcvz6Pd5cXF2EcOdbbh0O4At3ZvEtvkC+to9om2TDwBg\nfehBFGMxmPr7UAiGUCoUkLso/ops3X4fiskULNvGkThwBJYtm5E5+5lo2/EAhEQSlnu3InPuAkx9\nPchNiucozVvHISSTMGzqEJMrS29v3z9yGF63B7NzczAajdgyNobRoaHKde3xeHD79m2MjIwgnU7j\n4YcfBgDsP3YUXrcHU9M30O33w2Q04pEHd4i2D47AI9Xpdjlht9mwe8dO4jrxpQe2E8s9sHdvjR9t\nbW0wmUzYtWvXMh/rbe8fOQKvx13bt8Hlfevs7IROp1Oss+xjec0lxURtGSWbPCblOuPxuGKsyuWU\n+tazbSuxvae33oP9x47C43Jjdn4eHpcLJqMRj5XHkzBmNB/3jm/F/qMfwON2Y3ZuHj6vF0JJwFN7\nlvdbzbg9ee/9d3TMmqmzfqyV4qFkK/vyEIDDk5fRbnfgajAAv8uFUqmEx0ZE4NXhK5fR7nDgamAR\nY5s6MRlYwO88KB4HOPjpWfhcbswGgxjy+5HOZvHwFmmsPzkLn8uF2VAIHocdJoMRcHUrrsWlUgnn\nZubx4GCP4loHAI4nH0f+1iwsWzajEI6gxPPInPkUAGC5/x4IyRQsWzcjc/4SdCYj8lfFs9j2xx9B\nMRyFsduP1MnTsD54P1JHj4OJiYlppVrxA9exY8fwzjvv4Fvf+hYGBgawb98+9PX1YcuWLThx4gT+\n8A//EDabDS+88ALa2tqQyWTQ3d2NxcVFCIKAxx9/HPv27UOxWESxWITP58PU1BRKpRISiQT6+/vh\ndrthNptx+/ZtzMzMYGBgAN/+9reRSqXwxhtvwO/3I51OY2ZmBl1dXbBYLCgWi1haWsL999+vCOtQ\nUhmTm8/n0d3djbGxMXz2mfglsIKt5fS4PHMTbtnhW6fVinyxgHyhAKfVij3j4pk1q90Bnucx0NOL\nG1cuwu7qwuLsLbGMhN3N5XLo6u7G0tJSDd7YaZcQuhxX057LapWw8ALMhtotNFZ7FTFeEgR0DQzh\nxqWLAAAhlYZpZBic04H08QmYN4sPafXI9XROxLQDyuj3wU1tqm1lHLvFaKzi2Ova89itiGdiAEif\nVxHoFYR7KIpRf3vjtupsvT4PUtI2sgpW3WSsYNXL/BhS32h+ALUo/yWJWOhCGQvPw9/bj6mLF9A3\nPIrJz8V5JUf5lwQBqXgc9l4PeADZPA8jp4fVJCLXs3keV+dFolYZx162OSxmJKM5ZHK8Ina/HHsj\nxyGvL8Jjt2E+UiV40mKVzfMw6PXw2MS2/F5nZUtPli/AwHGwmoxI5Xh0eVzI8HxlzpmHh6B3OsDP\nLdTMVSGdgU6vR/7aDZj6eiDItuNVkfHXIaTSyJy/CL1J3G5Vymaht1ggpNLg5xdhGhThM3Kcdmgp\nTESgb926FadOnZKNmVP8wai3dxmERY6Mvzk7i/Y2X6U+0jrRqFzZj3pWEdUmw7uHwkvEvo2OjuLM\nmTOKtmU+EmKitoySTR6Tcp1qYqWmb/JyLkcZud4Jk8mEa9PTDWNP87FSp4STXwwFwek5RT/Ujtud\nHLNm6qwfa2I8aPfCOiy8fGux02LBfCwKoSTAabFgz9Bo1Q+rDTzPI1/gsbWvH6cmq1v4XDYb+EIB\neZ6vua8ppeIY7ZTWfkKKkYcAZK9OwfbQg+BvzQF1v70KmYy4vly/CX5mFuaxKnCKhpNnYmJiWolW\nvKWwVUj4tra2Sk6VzZs3Y3x8HF1dXdiyZQtu376NW7duIZvNor+/vwLZMBgMEAQB6XQahUIB/f39\n6OrqqtQnb1+NaJjcUDQKTsLCR+Jx3JahiJfiCZg4A4wch8VoFD3STT6diMNoMmH6ymUYDEbwuRza\nOkTfadhdxfYkrHAoEYff7UEsnUFKhiEGylh4EyIhEUM/N30D5jKi2+NG/tp1FIIhOL4sw8LXIde9\nDhHTDtDR71QsPA3HTkC8U9HvsrYKggD5V56G6HeZzWExwydRwkhYdVrfaH4AZJR/Mi7Og2uXL8Ig\neyCUj1k0FEAmlYTD7amg/G1mE3gJue6ymuG2WxFJibmgSDh2EnYfUJECoEGsym05zGZ4pDNJNpMR\nhbLNYobdYoKnjGb2uJC7fkPEwpuMQKl6hk/vdqIkCCjxBTGBseyMVgUZX+DBtXlRDIWr5RwOCPk8\nOJcDRv8mlMpYeAoqnIYeD4bDMElnf3J8vpJLDiAj42nrBK0cCXPe0KYS774MC09Av9NiorZMvY2E\nJm8UKy3o+mBYRK4vBIPIZnPo8VfR7zTMPzU9ACEFgNZxu5Nj1kydanHytHGjYuFltoV4DN2eKtEz\nFI/DaDTCZDAs9z8eg9EgplbJFfhKHGmpOGhrnc5oBD8zC0Nnh3i+VvYwzLlclfWl/nwzDSfPxPRF\n1Voj3xkWXtJqI+FJ+uyzz2A0GrFt27amfSZJnjRULv25i8QyhcUA0Xaof4hoe2LrqOLnlnPkJIz8\nDBkZ/14/GQv/yAt/S7T95ZeeI9osRgoemKAC5RD0eoFmkHD3gHZoxl/crxz/jzhyWzQsfPlNlrIf\nRBNCcfKBcVK8aBCRNgrGmgbN+P6vXyeXo0Ez8srH8o3d5Czy7T/6AdFGAxGYKfGnQTNIAIzVgGaQ\nwBLA+gFjaJUWaIY5EiPaaNAMWvxbDc24m8cs/9P/QjZSgD1ELDxlDfm38+QcmrR1+t+89P8QbbaH\ndhBtNGgGoxQyrYY2Chb+5aOn19qFhvreE+ufZL7iLYWrjYQn6YEHHtBUjomJiYmJiYlJrTL/258S\nbdZ//+/uoCdMTEwbVQwLr0I03K2QyZDL0XDsBPQuvS3yL6e0t0c6szbcsN2i/Cs/7c1GgJA8GgAc\nhPoaKccrx4SGLKe9jaL9OkpD+dPqLC6FFT/3jSi/yQSAFAVnrhX9bqT477Ao/8pf1Mh8paLrKW8b\nCsElos08Mqj4uUDAxQNAiTA/AMBKtADa3reSRUu8C3q+1S+kSGsd7Q1Rq8cMAEp8axMfU7Oub3Bx\nbvI2fSqyn3APoqY6MZLfGNPWafP4ZqItffYzos3gVU5ozrX7FD9nYvqiaKNs2VvvYlh4JiYmJiYm\nJiYmJiamVRL1Ddd6QL4HAgEkk0kMDw9X6q2vX/73StqmInnPnBax8AvzGOrqxpVbM/jOU18CABy6\ndAHtDieuLi6gr82HoiBgz+gYzk+cgNPjgdXuwOz1KVhsdhiMRmy+fzsRybvLaKm25xLb8zgcsFks\n2G6y4fDli/A5nJgKLMButmBzpx9D7R0AgEunT8Lh8aJvdDNOH96Pex7eg+sXP8deANad2yEkErDc\nuw25q9cAvR7Zz84TMe2T80EievxmMCJDhRfQ7/MgmcsjzxcQiCWJuN6zN2ZxX5+I+Y2mM9izeRAX\nbolodZvJWPP5dCCMgiBgaiFERL9H0hlifdcDS7i3T8TJR1NSncEwCkWBirWfWgwRy1nNRiQyIh59\noL0NiWwORUHApHTe6uDZM5U54nU4YTQa0DEyipPHPoCnzQeH04krF86jo9OPYrGItsExfD7xMZxS\n6oDZ61dhc7pQEkoiFp4Q/5kQGdUeT2eJmHxSrCbng8R4zEXjy9rKF4pYiMYRz+Qw3t2BZDaPHF9A\nb5sbi7EETFIycCW8e/LIhwAA2+6dKEZjMA32Q0imkZ+eAX97DgBguWcrihL6XUimUMrlkLt2Q7Td\nu1UkiXVuAr8QgLHbDwDY/4GE9Z6fg9vpknDg4nkNOTL+6b17ceLUKXz1mWfEci3Cwut0OuwdG6/U\nWY8tf+grX15WTo4YX20s/JNPPomJiQk8++yzy2x3Ggsv91GOYx/s68OVa1P4r3/n28R+D3d1twwL\nX0HNj2/F/mPH4PW4MTU9jXs3jyOVSWP39geX9VsVFv7+B1SnKWiEhVeDd2+2zvK4acHC7waoqUlo\n9yele+i/ekaaj+W0KwvzMBtNGO/vB2AR03dkc8jmeQx0iGtgeQ0nrdMAYN1ZXnuk+51Oh+y5CwAA\nx5OPobAUgbG3C8VoHDqdDqmPJ8R1ac/DKEZiMA0PoBhaQokvIH/rdsM6mZiYmBqJ+sC1HpDvXV1d\nWFpawtTUFGZnZ3H79m14vV787Gc/w/DwMFKpFC5evIi+vj709fUBAH72s59hbGwMExMT+K3f+i30\n9hIO7NaJhuStYOH1HFw2Gx67975qOYuEahcEjHd14dzMDIAqpn1TTy+mr1yC3eVCQMLC05C8AOC0\n2ZAv8OD0eoTjcfjcbqAEOC1WzMeiKAol6FALqbDY7SjweSQiYQiCgPmZaVjt4qFvIZUG9BxyV69B\nb7dXiHFkTDsZPQ4sR4U7zCYsSFvkSLheoBbzOx0IY3ZJRKvXf84Xq5uH1KDf6+sDgExe7Fssk8V0\nMAxeFisa1p5UTsTC6+E12ZDK5ZHK5dDprh56ddoklL+eg9fpxLW5OXQAsDuc4PN59PQPYOryJQyN\njuHKhfPSHHGgwPPolFIHZJJJ6KS+NRP/Mqpda6xo8ahvy8DpK3Vm8wUY9HpYbBak8zzmowkMdogA\nHCW8e1lCMg3z6DD0LieEZAowVLcWCZlMBf2ud9hRSCZltix0FguEVAqlTAa5K1dh37VTwoELMhx4\nlXooR8ZfnpyEW0YvbQUWfhmGm4Atry9XgxFfZSz8lStXaqita4WFV4yVhGN3OZ14XMptSOt3q7Dw\nNah5p0OcBz292DY2hpOffqpubIhYeHVpCmhjphbv3ky5mj5rxMLTUpPQ7k+0e6jcptNVy5XXQK9D\nWnOzeWyS1lza+i6kMrL7nQ2QbZsuJpIw9vhh8HqBUgmlgqxcMgXz5lFwLify16Zh7O8BpAcuWp1M\nTHezBLalsCWibilcD8j3bDYLp9OJ6elp9Pf3w+VywWw2QxAEmM1m5PN59PT0YHFxsdopvR4zMzMQ\nBKGpBMhUJG8sCk6nRyKdwnw4jB7pVzsACCUT8Ls9iGcyeG3iBNqd4g0hlRCR39OTl8FJWHivhIWn\nIXnF9mLg9Hok0ml0trUhEIkAAJaSCfjdbsSzGXhsNgRlZMV0IgGD0YRIKIhMKolkNIJoSKQoch4X\nIGG4S5kMStLDERXHTkCPA3JkeQEuqwXxTK5io+F67WYTCsUq5rd8Gdd/buT0KElWGvqdVF/ZJiLO\nTeCLRRg5rkIBboi8Vygnfl5ErlCAy2qGkeMQTlbPGYTi1THL5vPokeZPIh6DyWzGxXOfIpVMYv9b\nv4bH55PmSDl1wCUYDEZYbDaYJZw8Pf7KqPYVxYoQj/q2Urk8XFbxTIZVhoV3WMw1KW9oeHfO60Zu\n6joKgRCKsTgMbVVKqd7lRCmfB+dyQognRIxzuZzTgVI+D73TCc7jFpMfo4wD18tw4DIsvAwZH45G\nMTs/X7G1Agtfj+EmYcvry9VgxFcZCx+JRDA3N6dou5NYeMVYSTj2hUAAvV1VGiWp363Cwtei5sMw\nSVRWGnJdPRZeXZoC2pipxbs3U07eZ61YeFpqEtr9iXoPjUlpUNIp+Nyeyv3ObjKhUCgiz4v3GaOB\nQyQlS1tCWN85t/x+l0VJdgbU4POisBBAIRKBkEiiJDuHzbV5kZucQiEQhM5sQmGxOm60OpmYmJga\niYqFX0/I9/fffx9f+cpXiGVmZ2exsLCArVu3wm63Ix6P44033sA3v/lNOBzq0L4kLLzuLAXVPnub\naDu8eSvR9uyWQeW2Pv2cWCZ/bZpo2zdGpkHu/cf/TLT9+8eeItp8BCT4RodmFIrko/daoRn/u1/5\nMPlVCjRjJhgh2k5fnyHaaNCMcIKMhSfFiwbNcNEQ3QZy/P/g7TeItmJM+ToDyNAM2qHdjv/pfyDa\ndJQ5IqTJwBstWHgasjzndRNtNN3NiHESjl1HmVc0eIpWLLx5iXwd5nzN3bsAwEqB4dDSFGwEFd/Z\nT7TRoBmmIeVjCTRoxl/cIsN1ihRI1I8OvU+05a5eI9q0QjMYpZBJqzYKFv4/H5lYaxca6r97atda\nu9BQ1C2F6wn5TnvYAoDe3t6arYMulwvf/e53NbXPxMTExMTExNRI8f/mvyXaXP/8T3fQEyam1RHb\nUdgaMSz8Kor2JkWTKL/q0doqxskJJFM55USzAGCgvEkhKUtBY9OSImtRloIDp4n2FqtEDgcV1a4z\ntSt+nsqSK0xkyb8Ip3PkONKo04kM+dd1ko0WD5qNL5DHk/YWi9YBPhBS/JzzUN4QUeqjJj6mJCrW\ncj5DdxfjwFdFpAuKFseNcOe/i8/2FGPke0mJcn8ibb8rURbVGOUNNG3tyd+4SbQZNimv0wBQDCm/\nUSsmyLs2aG/CmZiYmORiD1wyEcmBnLkpSqEOwMPDI7hw6gScHi/6x8YxcfB9+Px+CEUBYwqUwjJF\n7CsdInVNkVJosODwlUvw2R2YCi7CbjZj86YqBerymQk43B70jm7GmSMH4PR4YDJb8QQA2+6HRCrc\nUD/yN27CNDiAxHsH8dBwH6LpDDI5Hlt6NmE+Ggen0+PzW/NEOuBCLLHMduj8JMa7N+HopevYOdyL\nWDqLdC6PLT2dWIjGodfpcXluETuGehFLZ5DJ89jcvQmheAoFoQibyYRYOoNwMo2n7hnF9cUlFIoC\nLswuVMrU2y7eXsSOoR5EJSLiU9tGcSOwBL5YxMXZ5baphSB0Oh0u3V4k0hmvzAWJlMVENkckOgK1\npC2PwwmbxQJjhx+njx+Dx9sGm8OJa5cuwt3WhlJJgHtwDJdOT8Dh8VTIkmarDf7+AQA63NcvUrgi\nqQwe3TyIa4tL0OmAq/NBInFwoN2rGPvzt+ZVjUt9jOeicSIN7GYoIlLEMllk+QIGOrxYjCbQ5RW3\nV9JIhLZdVVvqo5Ow3LcN6RPigX05DSzz2XmY+nuR+UTc1mu5b5tIKfRvEs9fSDCCRnQ6EsFNTqfr\n7uysoxQqkw9JlMInxsU3/vuPHq2U83m9EIQSHvnqc2tKKVRLvLvjlEKFGD+yu9zv5XTJr+x5VKQU\nuusohQ+1jlIonwe0MauPSYVSeO/9qseMRo+805TCRjadToftAA5PXka73YGrwQD8LhdKpRIeGxkD\nABy+egU+mx3JXDFp8OYAACAASURBVA7QASaOw0P9g6Ifn56Fz+XGbDCIIb8f6WwWD28Rt90f/OQs\nfC4XZkMheBx2mAxGwNpBXKcv3CLfFwDAvncPiuEojD1dEJIpCJlMZQ2R3wuLSxEImQyy5y+J5Z54\nFMVwBMaebvC352AaGUb8zXeJdWYvXob9sd0oRqIwdvtRWIoAej0yZ6rQFSYmJiZAQx6u2dlZ3LxJ\n/gWJpOPHjzddRo1mZ2dx+vTplrRRJjNFIpEKtUmQfrVTTSn0dyGRE99clAl0cYka2DUwjGKxoNhW\nPUWsnlJolRJHOi0WzMfLFCgdCoKMUmgTqYiJaAQlQUAqHodRKiekUjCPjcDQ5oWQTCPziUicSuXy\nMHIcOL0OXrsNN4ORyhmheuJdqVQ9UF5v6/V5Km/L0jleqlMPr90q1amTbPkaW6FYBErVzyOpDK7O\nh5DI5uB12GrKKNlSUlvRVAZTC0EkMjm02ZVtdrMJFulMTz2d0Wm1IC/9akqiLNLK1M+RpXisMmZ2\nuxN8nkd3Xz/0HIdUIoGs9OutRSJZJiLimOl0OhRrqIhlcmAYt5aiy8ZGThzU6xrFvvG4KMVYiQZW\nBpqUffTarUhl88jwfOUBtEwi5Nq8AEq1JMJUSrJ5YBrqF0mFFVuVBiak08h8LqMbZrLQWS3iF55k\nqvLmS06nWwpHYLVUUx7LKW3j4+M1ZzXldDqzyVRzTk9OPlyKhGGVYCb1165SnYJQRE+nH7F4AulM\nWrGc2WyutCf3Uf552Y8yra8MH1LVN5U2+Ton/3zLli3EtpRsSmtn41gpx1i0KdMlK2TDTj+8bg8W\nZICURvNA7ou8D6R5QBsz2ripHTMaPVLtmDVTrqbPtHhQxk28B8UglATEs1mk8tW3+C6zBXyxCL1e\nBzNngE6G0XFZbeB5HvkCj619/TXkM5fNBr5QQJ7nYTYYK6Uar9PKa5aQTMHY7Qfn9cDQ2VFzvkx+\nL+Tcrpq3b0IiCWN3FzivB0IyhfTJ01UboU4hmYKhyw/O40H+5i3oDORzaUxMG1GlUmnd/9sIavqB\n69ixY/izP/szvP766zh79ix+/OMf45//+Z8xMTGB559/Hum0+OXihRdewKuvvop//Md/xL59+zA5\nOYkPPxRz8Ozbtw9vvfUW3njjDXz44Yd46aWX8E//9E94/vnn8atf/QqHDx/G8ePH8dprr+Gv//qv\n8Ytf/AIAkM/n8ZOf/AS/+tWvcPz4cbz33ntIJpO4ceMG3n33XczMzOCFF17AmTNn8OMf/xi/+tWv\nsH8/+ZBvvWjUJrWUwsvzc7CbxC/aqUQcBpMJkWAAmVQSJ/bvg9PjVWxrGUWMSClMwu/yiBQoax2l\nMClSEaMhsT2H24NERCTDcV4PclevoRAIwdDuQ0HauuW0mJEviDfIUCKJL907hqj0MEAj3tXbHBZz\nBbLhsJiQLxSkOlN4+t5RxKTD/w6JYKjTiTajgYNQKlU+L5MNTQYOIWkrB90m+WEV/RBtKUVbJs9X\nABxUOiOBskgrU5kjEmnL39aGQFiMfTIRg9FswpXz55BOJmC122G2WitjZjQapTFLweZwVsZMTg4s\nCAIe3TyIuBRHEnGQHvvG46IUYxoNrEwwzBWKcFotcNusiKQy0pwjkwg5j0e0BZfAud01W33kNDBD\nm7dmqw/ncqKUy4Nzu6AzGVEMi9cFjU5HI7jJ6XS5fL4GDU8iHzYk7y2FRV+CQTjtdtikL/1rSSlU\nS7y745RCDXTJYDgMPafHQjCAbC6LHr+/rr6VUQrl84A2ZrRxUztmNHrknaYU0mzyOpdSSfhdbsSz\nWTjMZthM1W11oVQSer0OgUQcuWKhNlbxOIxGI0wGw3L/4zEYDQaYDAbkCnylHG3Npa1ZXJsX/GIA\nxUgU/OwcDB1V8IX8XlgIR8DJUkhwvrZKOa7dh8JClX5MqpNr86IQCKIYjcL5ladRDEfBxMTEVC8q\npVBJL730EkZHRzE/P4/h4WG8//772LNnD6LRKKLRKL7zne/AZrPh1VdfBc/zGBoagsFgwOzsLEZH\nR7Fjx47Km6hQKASXywWTtGDfvn0b9913H06fPg2j0Yh8Pg+LxYLOzk488cQTyOfzePXVV+F2uxGN\nRtHb2wu/34/Lly/D5XIhFouhUChgdHQUBw8exOOPPw6O41QnQm41pXD/yDjR9hv3DCu3RaMUXr1O\ntL05SgaYPPH880Tbn3/pq0Sb29o8BY12hoh2HkiLmkH+q/WDdjXQmvsP48q53j7p8Ct+DgDTQTKF\n65Mb5HlFO+ISipMphSTR4lH+xVhJRgph7Ae/fI3cIKUDersyGZN2hmvT//o/Em1a6XQ6E4VSSKB3\nWijnW7JuZYplI93VlEICUfNOkiWBVaAU3sVjlv/pfyHaaGe4zMODygbKovonM+T1kXaG609+/grR\nxrUpkwgB8hku6MnrXKMzXAyawUTTRqEU/qdDJ9fahYb6wy/tbvyf1lhNn+HavHlzzQPMzp07a+xl\nVPzv/d7vLSsbCARw+vRpbN26lYqKV6IfllHx3//+95fZ7rvvvmWflfevMzExMTExMTExMTE1L5b4\nuDVq+oFrPaHimZiYmJiYmJjWo+Lf/T7R5vp/X7pjfjAxMa29GKVQJuPsnOLnqStXiWWKUfI2okhn\nH9FmWggofp68NEksw88tEG3BTcpb2gCgGCUnZC1QtmaQ6Ma0pJMZChbeZCRvzRA0oJT1GjHchiLZ\nD62HL0kxNnb1EMvQuhyioIitRnKC13SejKGXH2CXi7al0EhJ4kpLG1AqkJH9tAS1EJTnI+dQ3sYH\niAfdSTLT0hQElRH0AKB3UpKlE7YUFghIewCAxi2FQoq8RdRSoqSdIEwu0nZI4M4n7C3GlK8ZvZnc\nVmEpTLRRx4yypZAnrMUAAA1bCls9ZgB93CyE9Aa0bZSaRbnm+VvkbdDy85ty0ZJcZ/PkNYS29Osp\nawUpuTEAlEgpUijbHjnKnKPNVSYmpi+e2AOXTAeOH4fX7cLUzRk47XaMDw9hpK8fAB2Fa948AiGV\nhs5sBvR6lPJ58DOzmPzkFBxuDwAdsukkbE4X8tks+se3AQD2f/QhvG43pm7exGBPDwAd7pV8UWpv\nl9UJ89ZxCMkkDJvaUYxEAb0e+akbAIAb587C5nQDOiCXTgO66gOEiLSNwNjdhVJRQG7qOviZW3h4\npB/RVAbpfB7bejuRyuZxMxTB7XAM2we6EZPQ73u3DmM6GEaOL+DS7UUiOv2NU+exa1SqM5fHtl4/\nlpJpZPM8ri4Gqxj6PI8t3SKavIQSrEaj5AePrT2d+HhyGvf2+XFyagYPj/Qp2k5dv7WsvhNXp3FP\nnx8TUzNE29nrt7FzuBfRVNm2CdcDYVhMBlyaXSTi081GjohcB4BD5z6Fz+nC7FIIj2+7BxOTV9C7\ndRtOfXgUnjYfbA4Hpi5dhNFkwsDIKGCw1qL8D++Hw+OF2WIFYCAi+6cWQtX4p9J4cusIri2GoNPp\nkOV5KfY8tvV2YjoYxmBHG94/d4U4LlfmA8QYX5oLENHM1xeXiKkDAMD2iIRfHhwQUxEM9SPx7kHR\nRsXCbxex8PdtQ/7aDZQKBQhx8aGqOv87xPkvzW+la+mR7dtF27Gj8LjqMOISTvvAxEm0ezy4MTeH\nrvZ2FItFPPbAdmk9+KhSp9Nux/jQMEb6xfVACbX9hHRe78DJE/B5PJiem0O7xwOT0YidY8Oqkev1\nyO/9Hx6D1+3B1M1p3Du2GalMBrvLfZNh0E0SfnxkcFCyVZHrg339uDJ1Fd/47h8o+q/T6fDk/Q8o\noti/+swzxDI0PHojLDlpzR0bHSP2e2dfv+ox++reJ3Dis0/x3ON7l/mxDMd+akJM6TA/jy4JHLH7\nnnub6nd53B4fHK7xfaCnB8ViEY+rQNcrjdm//sY3G/qhVOcDe5f3W16umTGTt3X4ymW0Oxy4GljE\n2KZOTAYW8DsPPgQAsGwbRzGZgmFTB7IXLsE8MlRBrh+68DnanS5cXZiH3+OBiTNgl5RK4dD5c/A5\nnJgNL6HP147JudvA8HbiOj05F6hJMTLevQmhRAp8GQu/ZxcK0SjMQwMohMIo8XwFC2/dUU47sRWZ\ncxdg6utB8ogI9LI/uguFSBTGLj+K8QRKeR7Zzy8Q68xPXYft4R0oxmIwDvShMLcIoITshctimcce\nQTEi3ncLS2GgVEKGck6biYnp7lVrKQbrTDdv3sTs7Kzq/+902FEoFjHQ0w2drvbtDw2FK2RzAMcB\npRL0ditKvPirehnTXizw0On06OwbrHmD4nKIKOLBnh5sGR5BIpVs2F4pm4XeYoGQyoCfX4ROdqDX\nZLWhUOBRLBSg0+uQS6fAS8l15bhbABV0bSqXh9HAwaDXw2u3oQTUoMeNEvr92mIILqsZOenNBQmd\nDojJfo0cB47To81hg8dmqZRLybHwDituBsOwmUwVPzi9WGawow1J6RfHhjZZfQMd3kqy4YY2qU6P\nw4abwTD0oOPTaVh1AHBabcgXCsjxPK7MzsJlE39ptTuc4PN59PQPQM9xIvpdiocc5S+UBKTisQrK\nn47sz8No0COaEsfGZjbBbDTIYq9Dm8OGVDaPT6ZvqxsXQoypaGZK6gAhmRbxyz4vhGQKGRl8hoqF\nT6cBTo/c1WsoJpISVh518z8tzv8yqp1yLbkcZfR4J7weNxZk5DqX3Y48L6Zf2Do4VJOc2+lwiOtB\nd4+I9pbZqIh0uwM8XwCn18NsNFXeLKpFri9DfjucErK8B9vGxiDI3pLIMeg6nQ68vE4Zct3ldODx\n3Y8otidHfpNQ7LQy9TYaOr2+37Q1l9RvtWN2+fp1uB1ORT+WpeEop3Tg9BgfGEAynVYspxqvL/d9\ndLS2XzR0vYYxa1gnaaxVjll9W06LBfOxKISSAKfFgj1DoxWbkKlen8Yufw3kxGW1Yj4ipk8xGww1\nL46cVivyxQLyhQKcViv2SA9i9HW6ur577DYRpCHdX4upFCybR8H52kRCanuVUlhdX65DSKWROV9N\nO1FMpmD0d4qQnlIJQPV+TapTSKVhGhmGwetF/uYM9Lbqm0UhmZSQ8W7kZ2aJUCAmpvWstUa+f2Gx\n8OtZr776Kl544QW8/PLLOH36NPbt29dU+VAkUkED+7xeBJaq1CIaClfvsIsPW04HhHiyss0gk0zC\nYDTBYDQhEQ1DV4/CDVfbm5y+AbvV1rA9vcMOIZ8H53TA9tCDKCarXyyzqSQMRiMMRhNS0QhMFmvl\nyzvXVsXdFpfClZuF02pGviDie4PxFOLpLLwVPHoV/V4oCggnM/Da6Oj0Sp3FAvQ6HYLxJMLJNNok\n2p3TagZfKEKv0yEUT6G/w4ssz1fx9FIZt82CDpcYR6qtrj63zYoOl72xzVK1LcVTNdtGSPh0GlYd\nENHGJgltHE0lMRcW509Ceoi6/PlnSCUTcHvbEFkSt5+JKH8josEAsqlUDcqfiuyXbCLyvlhB3oux\nr8aq3WVHUHo7RB0XSoypaGZK6gCuTZaKoMOHQqCK/KZj4d2AUEKJ56EzmVCQ0Rz1Doc4/10OGP2b\nUJJ+iKBdS8GwiB5fCAaRzebQ4++qjlk0Cr1eTPfw2sH9aPdUtxzJ6/R5atcDGiI9GI1Ar9cjnkoh\nm89Dp1+Ofqch1+uR2XJk+TKbDIPe3taGQChYZxOR6wuBIHq7ZP0mYMRJKHZamXobDZ2+rN+UNZfU\nb7VjFo7FcDtQxXpTcexRKaVDKoWrt2Zgk+UD04Jql/v+87feQocMPU5D12sZs0Z1Esda5ZjVtyW/\nNy3EY+iWxV/vdKAk3Z/0DnvlBz4ACCUS8Hs8iGXSSNW1txRPwMQZYOQ4LEaj6GmT7k+0dVq21i1J\nKUbKX7sMXg+yk1MoBILQmU3gF6vxqKwvBR5cmxfFUHXrn8HrQSEYqm4Rl23zJNXJedzIX7uOQjAE\n00AfhGz1nlBFxsdg7OmGkCFTNpmYmO5uNY2FX896+eWXwXEchoeHMTs7i2g0iq997Wvo7SWfb5Ir\ne+mK4uepYx8Ty9DOcL2+42Gi7fsDyvCQ5OFjxDK0M1z/8tAjRNs3/q//SLT929/+NtHmJeyFp53h\niiSVzxMA6+gMFwXzq/Vy+Jv+NsXPL23dRixzZY58duTg58pzEaCf4YoQznMA2s5wuSgYa9oZrj96\n83WyH5QzXHpCKgJTP/k8pPcPvkNui3YeaJEcf9p5IL6nS/FzAyVtQ2FMOQ1EI5kpqQN0NivRthHO\ncJHOzK7GGS7SmAGA/gL5WhPuIaf2IKnVYwasnzNc+Z+Tr+v8zVtEm/U+5XWQdobrjxbJZ+FoS/+f\nvvMG0Wbs7CDaiGf5VukMF4NmMG0ULPw/HCB/B14v+u+/vGetXWiou+oM1/e+9721doGJiYmJiYmJ\niaqIQAYcefX0/F5MTHdSDAvfGt1Vb7hWKlLi4wy0vRFpi5MzzmtJhJoukd8o0NqaMZF/6eznyUk6\nQSJq6Sg7USkUrlKRQuii/IqoIyTYpZHwqPVRflWl0vUoP6sm2nyKn7sIcwoASrRYURK80khh1OzM\npLmqNVZFMuEy5CLTwIwG8vU0E1L+VdhL+bW+w0L2kZaENmyjvO0BuW/msPK1lqMkVb2bRYuxfHtV\nvUjxor1pC1vI84A6ZhEyqTXnJSfVvluldcwS7eQ3RHnK2unQN/81g3SdAaBmqJ+xkt86Oa3kt6fJ\nrPIDEO0eb7eQH4xoxN4CZZcIwB64vijaKG+4/u/9x9fahYb64bP0lFXrQXfVGS4mJiYmJiYmJiYm\nJqb1pLtqS+FKdfDgQbS3t8Nut+PixYsVzO+9Dz+CIwcPwNfeDpvdjssXL6J/YABTk5PwtLXB52uH\nzW7DlYsX4fX5UBIEPLr3CQDA/qNHK5hfn9cLQSjhqT17Ku2RcL1yP8oI48e+/ByOHDqo2N5vPnA/\nsa0PjxxCm68dQyOjOPjeO+jp64cOOmwvY4qPSijiuXkM9vXhyrUp/Ov/6htk/x99jFzut3+b2uf9\nx46KuOTpG+j2+2EyGvHIgzsqNhK+W6mtf/X13yTXuWMnvT4KfpnuR7Vvos2Exx5+GIcPyOfHBex9\n6mmcOnkC39oj/ury/pEj8HrcmJ2bg7GM7x4YoCOiaTjwY8fg9bgxNT2N7s7OZXEkx1ihXLk9hXJ7\nHt5VE3uf1wuhJOApqV+0WH1w6KAYE5sNVy5dgsvthtFkBJ/LSZ/bcfnSBbhcbthsduzctQsTx47C\n09YGu9OJyYsX0L5JvC72Pv547Tx+V5zH0Onwlcf3LLtmKqjze+9XjP3o4BAAEK/rzjbPsvrkOHOl\nWD34zJeWXddyxHu9j3IUdyNkvFLfEokE1UdSnaR1TslHNbYn7rmPGmPSfCSO2f0PSNdMFVFfrrNt\n6z3EMfu93/0dxTqLxSKe3noPca4202e1yHtS35TWdxKqXeuYqSmndcwA4PDBA2iXrt9LFy/AZDJh\nbHwcU1NTNde7t60NgnQvbCYe5fvdb+58uPm1TLJ9dOQwvD6fdM/bhyef+TI+PX0KNquFeL8m3Scf\n2PkQsb6vfu1ry9a5cr8f3L2HuGbdt2MnPjpyGG2+dgyOjODQe/tgdzgxPDaGfmkMmJjWi9iWwtZo\nXb7hevHFF2v+LhQK+MUvfoHp6WkAQCAgHm6dnp6uwScr6fp18kH2erlcLuTzeUQikWWYX6dki0Yi\n6OruhtPlwu5HH4PT6UQ+n0M0EoG/uxuJWAxpOVLY6RBxvZ1+xOIJpDONccP1fsgRxrT2SG05nGJ9\nZosFPX39GBkbRzIpQwo7nBCKZRSxE4/v2q3Of0K5RmWKxSIGentrkNmV+kj4bpqPhDqp9dHwy9Ry\n1b553R7MS3PR6SqPSxhd3d2YvHwZLpe7pr1iUUC3v0tEjEvbb6g4ZyoO3CHZemE2mWqRzrQYaygn\nj30sEUdajnqmxKoSk2gE/q4ueLxeBBcX4XS6kM9J11JXD8LhJVisEv3S6QDPiwh9juMwODqKtETi\nXDaPN48jlVS+ZmpR58qxF31Uvq7r61uGcafFmIQKr6tTjuJuhIxX6ltDHwl10tY5zTZKjInzijJm\nYp1VRL28TtKYKdVZ02/CXG2mz2qR99T52MC20jFTXU7DmAHiPSiXyyEirXWQyjqdTuRz0r2pqwvx\neByZ8r2piXjI73da1zKHU0zFIa4Vfbh2dRIOl4t6/6TdJ0n1leNB6jdtzXJIvpRtOh0q6UKYmJju\nPq27B6433ngDmzZtqvn77bffRkdHB5xOcb/r1NQU/u7v/g5vvfUWTp8+jVdeeQWvvPIKXn75ZUxM\nTOD555/HkSNHcPr0aVy+fFl129FoFGazWRHzG5PZAgsLCCwuorunp/I5p+cQWFyE3eGA1Vr90hxc\nCou43mAQTrsdNtkXahJ6t94POcKY1h6prXhMLJOUbnA3rk3BJju/EgwvQc/pMR8MYCEQqEER0/wn\nlaOXCcMkUepyfL6GNEjDd1N9JNRJrY+CX6aXq/Ytm8tVyoljZoFez2FxYRHRSBjzc7erY720BI7T\nYyGwiPY2HwLBkMwPAiKahgOX2XL5PPS6OiQ1McbNl5PH3ml3wGatmwOEWMVkMQkEFpHNZuHv7kE0\nGoHZYoae02NxcQGd/i4EJXx3IhaH0WTGpXOfIZVIYOb6dVgksls8unweW23K10wt6lw59lUfl1/X\n9fUtQ79TYkxChdfXKUdx09DppL418pFUJ22d02qjxZgUK9qYiXVWEfXtvirynjRmSnXK+02aq830\nWS3yntY36lxtwZipLadlzKrxt4DjxHuQz+dDMBCoXu/S5w6HA1YpJ1Uz8ZDf77SuZfFYFCbZWhGL\nRrE4P0+9f9Luk6T65PFQ6jdtzYrHYjU2b5sPS8FqWgcmJqa7S+sOmvH222+D53l84xvfqPydz+cx\nOTmJH/zgB2hra8Px4+IBvvPnz8Pv98NgMMDj8aBQKCAcDiMajWJwcBAOhwNLS0t47rnnVLXNoBl1\nYtCM2nIMmlFbHYNm1IhBM5aLQTPWpxg0Y7kYNIPpTmujQDNeeO/DtXahoX703ONr7UJDrbszXF//\n+tdx/PhxXLt2DZFIBM899xwmJibwzW9+E9euXcP169exa9cuGAyGylkFJiYmJiYmJqa7Qfxf/RXR\nZvzjP76DnjAxMbVK6+6BC8CyB6ny3yMjI6vablanHI54ivy2QUd5O+DJUN4eEd5wLeXJv9IWCuRf\nzDwUH4NZcp3dKXLiZqJoWScpyTtLPNl/He2tjVF5XEo5ch4TWn20ZLilHPnXddqbpYLbq/i5kCIn\n7xQo80NIkxMYU99iUVQqKM8DPSURMTVWlF+05wWyjxZK4uZERjn+VhP5F1/eTkkmS8n9ms6T54+B\nkIAZAKwWsq3VIq1JAGAprY/zHhlKcmzKyIDnCPOAEt4sZQ0x0JIzU95itTrGd/OY0a6ZFOXNpKDh\nmmmLkN9wlSh+BJWXYgBAkbKrIEq4hwqUe1q+SI5WgrK+84S1GACG/9M/Em1MTEwbV+vygYuJiYmJ\niYmJiYmJaW21vg4ebVxxf/7nf/7na+2EXC+++CJ27txZ+btQKOCXv/wlbDYbPB51ZySuX78Onudr\nDmCr0Tv73kUiEUcykcDHHx3D4vw8QoEg2jZ14qMjh5FMJOBwOvHeW7/G3Owt6DkO5z/7RPb5G+jq\n6cHJjz7EwNAwXLkM9n/0ISLxGI6eOoVkOoXZhUX0+v14/+QJJBIJJJNJfPjhh4hGo5ibm0ObvxvH\nDh9CMpmA0+nEu79+A6lUErO3ZtDV3Uv0o99iwf7jHyESi+Po6Qn0+bvwwakJjPYP4M2PjyOdTCKX\nyeDsiY8RDgYRCYfQ4e+Cn8/hwImPEYnHcezsWYSiEcwFAuiTDpQr2rrqbGfOIBKP4+btOQx0dZPr\nEwQcmDiJeDKFj8+fg1AUMD0/h55Nm6DT6XDg5AnEUkmc+PwcQtEIFpZC6N3UCXB65bY6O0U/FOrs\n7ewk1qczGHDg4+NSfacRjIQRCIfR29kJFItkPyQfxb6dkfoWRJ/fj/c/OSvOnWQCH3/4IXLZLI4f\nO4oHBwcBiIj3SCyOo6cmgBJwY3YWPe3tRD9KPC/2K5XCx59/jlgyiZvz8+j3+0U/ZH0ORaNYCEk+\nEuLRs2kTIJRw8NQE4qkkTpw/j1gqidtB2Xgq9bu7mxwrQagZ60g8hptzcxjo7kbQYsXEsaNIJRPI\nZjM48/FxJONxBBcWcPPaFFKJBOxOJw688xbi0Sjmbs2gu68fHx4+hHRKnKufTZxAZCmEpcAihgYH\n8fEHR5BKSuXefhPxaBS3b81gfGwUhw8cQCIeRyKRwPEPj6GzqwsfHv0AY8NDOHjwICKRCE6fPo1C\noYCbN2+ip6cHmWKJeK3dvHZNsb7hkVGY83m8f+QIwtEIJs6exbXpaeg5PVwdHTh48KDidd3T07PM\nFgwGsbCwULGVfYxGo5iZmUFfXx8KOj2OHDyguC5dvXxxWVvlcgCIdSr5SLLJ49WonLytcp+NfEEx\nVm0eL949dow4ZvX+l2Pl65LWx4Q4Zvt+/QYA4NbNaQz09y+bBzduXAen18PncdfU5/f7cezYMYyM\njODAoUOK8e3u6cEHB/cTx4wU467+AeKY9fd0NZwjKx2zZmxKbdHGLMkZcPTQISQTcTidLrzzxq+k\n+N/ElUuXkEwm4HA4sf/tN1EoFHDq+EcY27IVJ49+gEQiUSkTjUYQWFhAV08Pjh46qGgbttuJ6y2K\nRRyQ1rLK+riwgP5OPxatdpz66Ji4jmQzOHtSuucthTB74xqSyaTo41tvYmkphKVQCH5pXqWTSWQz\nGXxy4mO0d3bik5Mfo6d/kFhfX18fcV1q93fj1IdHFevs6hvA6ePHRFs2i09Pfgw+n8Onp05iRyIF\ny33boLdZYR4fg95ph6GjHcXQEgCAe+yxpr7XMK1f2e2Ut/LrSBNTM2vtQkPtHu1faxcaal1RCtUQ\nCl999VW81XEo3gAAIABJREFU+OKLeP311/Hyyy/j9OnTAIB33nkHn3zyCf7mb/4GZ8+exZEjR/Dz\nn/8ci4uLqtun4arrsbBlFG499vXa5CScrup2QZdDRNcO9vRgy/AIEikRcU3D5DqdLvA5qc7+foyO\nj1dwsSQ/AMBll9rq7sGl69fgdoiHh+0OEbXdLaG2Y9EIzGZLrY+CgIGuLnhdLiwsLamz2R0oFgUM\ndHcjnkwilc2oKGNHvsCD03PYOjRUk9/BZXeA5wvg9PrlCGBCW7Q6afU5HQ4UhCIGuruxFI3CKts6\nR/fDjqJQxEBXt9Q3kexFQ1WLMVFGvNP9sCPP8+D0emwdHEJBBqmQ99lsNNaiqikxdtrtyBcK4Dg9\nxgcGkJSnMCD0m+qjbKzjySRSmeq4kBDvDoc0h80WdPf2IZmIIyNBQmwOB/h8Hl19/eA4PRwuNyIS\nnc7udIrXmtmC7r4+JBJxZCX/aVh+EqYdIF9rmjH/TeCv1WLhSetSq7DwrcKPK6W4oMWqYYxldcpj\nJcdp9/b1Y2zLFpSkbV/1deqgU/SxNtUGed3XioVvpk4tWPhW2YipCKipFJyVe15vfz82bxHniPze\n1N3bB4fTiZ1Sug2Hy1VTxmQyV7ZG02yk9RYAXDY78nwBHMeJ66PMR7u0jnT3iWtPPBqB2WKBXXa/\n7u7rQzQchkXa7miX1qWe/gHoOQ43r03BLn3vINUH0Nclap12J/g8j+6+fug5DjaHE/ftEHMYCukM\ndBYLhFQKepsNesrWbiYmpo2hdfXAZagjohkMBgiCgBMnTlRucjzPw2azobOzE2NjY8hLe7k9Hg9i\nMZFE5XQ6YTabUSwWwVP2/NeLhquux8KWEa71KNlYNIpFCWkLAKFwpIKunZy+Abu1MSY3VtfWW7/8\nJdp87VQ/ACAUqWJyI7EYZqWHzUQ8DpPJjMuff4ZUMom2jg6EZTeuYCQCo0Esl83lxLchqmxhGKXz\nVQ6bDTbpBkQrE4pGodfrkUillp1/C0Yj0Ov1iKdSyObzNVRAUlu0Omn1hcJhmCQfu9o7EJA9FNL9\nUO4bDVUNkBHvND8q/Uqn8NrB/WiXveGV91lEIusUbfUxDkWj4KS+Xb01UxNHUr+psZLFo35cSIj3\neDwGk9mMlDSHbXZH5ctoMi6WmTx/DqlkEvlcFj7pzV2ibu7b7fZKORqWn4RpB8jXmlbMfzP4a/VY\neOV1qVVY+Fbgx0kpLhrHihJjWZ3yWFVR2+L50+U+Vuv0tYvI8vr65Ohx2rqvHQuvvk4tWPhW2Ghz\nTk0qhURd/OvvTaFAAJ1d3TVjVi6Tz+cqaxbNRr+XRCrr3GsHD6DdUz28lay/57V3IBwKIRGNwmyq\n+tjR2YklKRVHIh6D0Vwuk0A8GkVwcYFaH0Bfl6h1JmIwmk24cv4c0skEwsEAOvx+AADncqKUz0Pv\ncqKUy0GgnFdmYmLaGFp3WPjjx4+js7MTkUgE27dvx8TEBB599NEKtXD79u01D2aBQAAzMzMYGRmB\n10s5LatCwaTyodmYRmhGb0wZcQ0Aef8mxc/p0Azygd++KLmtTzgycOABBs2oq1MbNCNBALp4ZHm1\n6nU3QzMuEiAiAB2aEYwnFT/f5Cbjc/0ess1YJM852rXmokAzSKh/GohAqzYCgIEmGn487lQeN9qY\nRXjyGuigQDNodX4RoRk00cZs3kSOMQ2a4dQCzbg5TbTRoBmfecno+jYnmaKjBZrhpgB7VguawSiF\nd482Chb+b/YdW2sXGup//o29a+1CQ607aEazhMJNmzbVbENkYmJiYmJiYrob5ckp/0gaNTef25OJ\nienOad294VpLkRIfMzEx3Rnl9cpvv0yC+q3BTExMTBtVhus3yTYfeecAe+DaeGJvuFon9oaLiYmJ\niYmJiYmJiWlDir2XaY3umgeu48ePL9uO+N5772HLli0YGBhQVcfBgwfR3t4Ou92Oixcvoq2tDSaT\nCbt27Wpo83g8uH37NkZGRpBOp/Hwww8vKyO3ldtTKkezNSqj5GMikSD6UV+ms7MTxWKxEkt5e3Kb\nUjmdTqcYq/pyautTY2sUKy11yuujzQF5n5sZTzXxUBpPNXMrHo+rnnO0vmmNI2muytuixePwwQNo\nb2+HzWbHpYsXYDKZMDY+jvHBfk3xaPaaKfuSyWRacu02mj8riWMz8dBaTmudatbHZq8Z+fqidjyb\nmQektlY655Tm/krHWq2t0XzUOi4rveZb0bdmr2ul+dPMNdhsfVrX8D2+TThw4mN4XS5M3bqFTp8P\nJoMBj25/EACw/+hReNxuzM7Pwef1QhBKeGrPHlXfcZiYmNZO64pSqKRXXnkF3/3ud/H666/jpZde\nwssvv4xPPvkEBw4cwD/8wz/gb//2b/GLX/wCN27cwOeffw4A+OCDD/Dyyy9j27ZtTbXVCNusBulM\nwyjXI6lpuGpanbQySj5qRT3Xt0fDDdPw12pQxFrRxs3EXwsumTYHluGvVY6najRzE+NWtjUz52h9\n0xpH0lxVj9N2IpfLISJhvWvSHmiIR7PXTNmXVl27tBivNI7NxENrOa11qlkfm7GR0OmNxrOZeUBq\na6VzTmnur3Ss1doa1al1XFZ6zbeib834qAW9TxtrNfWtaP7QUqs4HRCEIno6/YjFE0hnKIAlJiam\ndaN1/8DF8zwymQzS6TRKpVIld0p/fz8ymQy2b9+O7u5udHZ2YklalAwGA0qlEpUgqCQaAlgt0pmG\nUa5HUtNw1SQbrQzJR62o5/r2aLhhGv5aDYpYK9q4mfhrwSXT5sAy/LXK8VSLZm5m3Mq2ZuYcrW9a\n40iaq2px2jEJ681xHAKLi/D5qlhvLfFoNC6kvrXq2qXFeKVxbCYeWstprVPN+tiMjYRObzSeaucB\nqa1WzLn6ud+KsVZro9WpdVxacc23om/N+KgFvU8bazX1rWT+UNOxLIWh13OYDwbhtNths5BJiUxM\nrVCpVFr3/zaC1j00o7xV8Pz587h58ya+/vWvE/+vHBF/6dKlZVsMG4lBM5iY1lYMmsHExPRFFoNm\nfHG0UaAZ//HtD9bahYb6o68/udYuNNS6P8NVfmi69957ce+991L/rxwR3+zDFhMTExMTExPTRhQt\n1VmWnBKMiYnpDmndP3DdSfGc8q/rtKSZd7NYPJjutNibLCYmpi+yOI+baMu62VsspjsvYX1vhNsw\nWvdnuJiYmJiYmJiYmJiYmDaq7po3XEpY+EOHDsHhcFSwtI10+MAB+NrbYbPbcfniBex96mmcOnkC\nv/HMl4hY2GaQ62oR4yvBRzeLRKa11Uw8VoIYV8LrtgpZrhXN3Epk/EpsK0Usa0Xva0WFk+YcDaGv\ndu43U24l+GglLLzaVAQ0W6tjTEsBcCdQ26RytFi1CpMvtz355JOYmJjAs88+29S1RorjalwzK+13\nM2PdKqx9K+8lq+VHK1NSNHNda5n7aq+nvaObsf/YUXjdHkxN30C33w+T0YhHHtyxorWTiYlpbbXu\n33CtBAs/Nja2jF5Ek9PlRD6fQ1RCUk9evgyXS3y9T8LCtgqj3AxWvZVobFpbzcRDK3aahNdtFbJc\nK5q5lcj4ldhWiljWit7XigonzTkaQl/t3G+mnFZ8NAkLrzYVQTPX2kpta4FwV1OOFqtWYfLltitX\nrlTotc1ca6Q4rsY1s9J+r2RNXc3rulXpI1bTR7UpKZq5rrXM/aauJ4cTxWIRA729MBtN0EHZ/2bW\nTiYmrSptgH8bQev+gWslWPjXXnsNXi+Z6lOvqISk1us5LC4sIhoJY37utsy2HAvbKoyyWqx6q9HY\njdtSFw+t2GkSXrcVyHKtaOZWI+O12lqBWNaK3teKCifNORpCX+3cb6acVnw0CQuvNhUBzdbqGN9p\nhLvacrRYtQKTX2+LRCKYm5tTtKlBvN+Ja2al/da6pq72dd3cvaS1frQ6JQWtb7Q6tdZHmyPBcBgm\nk3iGOsfnodcr+9/M2snExLS2Ylh4mcJpZZTPFxUSwaAZTExMTExMd07mcJRoy7V5NNXJKIXrUxsF\nC/9/vnVkrV1oqP/lN59aaxcaat0/cN1JsQeuWrEHLiYmJiYmpjun1XjgsqTSRFuUs2mqk2nl2igP\nXH/15uG1dqGh/vi3nl5rFxrqroFmtEJ364NEVkceZkuJvMfbRUgEnbFREn5QZCU80Gqts9X1AYA1\nmyPXaTFrqrPV0tpvWjkt9a2GlvJFxc+dlCQzWlHypB8UAPpaQPrykrWzLy71on3Ru5PxWi9+bASt\nl1jR1mKalsxWos2mE4g20n1Sfu6qFfUBAO137vy7B4g20/Cg4ueFQJBYxvHM+k8Iy8T0RRB74JJJ\nC02unlLYiMamhd6lhfInb+vIQYk2aLPj8qULcLncsNns2KnCx/ePHIHX48bs3ByMRiO2jI2hZ9vW\nZeXU0umU6hsdHFL0o1Ecn9m+g+pjs/VV+3wYXren1schZR/V0MwalVPjo5zEtpI40sqtNI71Pmoh\nex47fAht7e0YHhnFgX3voKe/H8ViEV966mkcPngA7dI8vnTxAkwmE8bGxzE+2K+aLFk/LiQSpwkl\n6nju/+AIPNIccbucsNtseGDv3pbRO9VQ7epJimtFS2x0zSvFaveOndT6aPNYC5Vv77Z7iH60ai1u\nFYFxPY8Z6bomkT1XQkt84r77a9bip/fuxYlTp/DVZ55Ztk7X244cOgifrx02uw1XLl5E38Agpiav\nwO/zEn0k3ScffOghYn2//51vE+fj/bvJ994dDz9ctdntuHzxIvoHBjA1OYlvAzg8eRntDgeuBgPo\ncDhh5Dg8Mjgsxv+Ts/C5XJgNheBx2GEyGLHD6wMAHLp0Ae0OJ64uLqCvzQcdgKefebJmPE3S2j4y\nONjgGxETE1Mrte6hGXIFAoGav9955x3q/0+lUnjllVdU16+FJkcrUy63UnqXFspfDW3Q6UI+l0c0\nEkFXVw/C4SVYrFZ1PjqdKBYFdPu7oNPpVBGYqHQ6lfU1FUdCndrrc6EoCOju8lN9VEsza1ROjY9y\nEtuK4kgrt8I4LvNRA9nT6XSBz+VhtljQ09+P0fFxFCvz2IlcLoeIRM1EnY9qyJL140IicTYcT6cT\nglBEj9+PpXAEVotV0Q+t17Uaqh2tvvp+30kqnNKcUxMrmv9aqZ81PhL8qG9rNcesmTiuxzFrZj62\nhJYoW4svT07CXbMGkm1OZ/m6jsDf3Q2ny4ndjz5G9ZF2nyTV12g+UuuUykUjEXR1d8PpclXqdFos\nmI/FIAgleKw2BORjY7OBLxSQ53mYDUYZvxBwWayYj0YgCALG/V1I5LLLxlOn04Fn9EImpjuudfvA\n9fOf/xx///d/jx/+8Id499138dOf/hRHjhzB66+/jlKphEQigVu3buGdd97BT37yE7z55pt48cUX\nAQDpdBr/8i//gnPnzmFIejOhRlpocrQyQGvoXVoof7WEqAjMFjP0nB6Liwvo9HchGFhU5+PSEjhO\nj4XAItrbfAgEQ4rlVNPpVNbXVBwJdWqvLwROr8fCYgDtvjYEQsGG5Zqh2pGohzQf5SS2FcWRVm6F\ncaz3UQvZMxaLwmQ2Iyl9wXjrl79Em0+MVUyiZnIch8DiInw+H4LSjzBqyZL140IicTYaz+DSEvR6\nDvOBALo6O7EYVPZD63WthmpHq6++33eSClc/R9TGiua/VuqnvE6SH/VtreaYNRPH9ThmtL6tBi1R\nvhaHo1HMzs+rssWkOjm9uFYEFhfR3dPTwEfyfZJUX+P52LhOvV6PwMJCTZ1LqRT8Ljfi2QxS+Ry6\n3e5qv+MxGA0GmAwG5Ap8LYExmYDf7UE8k8Hl+TnYTeZl49neVntPY2JqpFKptO7/bQStW2jG8ePH\ncfbsWRw8eBA/+MEPEIvFYLVawfM8vvWtb4HjOLz22mvYuXMnjh49im3btuHMmTP44Q9/iHw+j5/+\n9KfYsmVLU4n/EoQzSxtdWs9wkc78sDNcayt2hqtW7AzX+tV6OQ+0XvzYCFovsfqinuHiXv050bYa\nZ7gYNGPttFGgGX/560Nr7UJD/clvf2mtXWiodXuG69FHH8Wjjz6KH/3oRzh9+jRMJhO+/OUvAwA+\n++wzGI1G/O7v/i4AYHhY3NtcKBQqtt///d/HmTNnWJZ1JiYmJiYmJiYmJqY107p94JLroYceqvn7\ngQceUPx/9Q9Xu3fvXjWfmJiYmJiYmJg2siznzhJt2ft33EFPmNarBGFdboTbcNoQD1xrrbyevPVI\n63amOylOryMblXdwAWj9drL1Xh+wfrYNUkUbT4ru9PZALeL0ysdKV+M6u1vTQNxp0bZ+tfo2rXUb\nqI7jWuzJ3Sudbn0c7da6Fhv0lLGmbA8k3Sf1Om310bbr0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MvdvXmTGKsOFXBkYhyRWAwz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\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6GGWUEVpegqOjE5dmTkmW6ezowNlTJ5Hmk8hmMrg8Pwc+kUBg6SGe3D2C42Oj4BIceJ7D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\naLgAAAAAoET+Bwb4b7s9XKtfAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f5067547e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"kg.eda.plot_feature_correlation_heatmap(\n",
" df_train,\n",
" df_train.columns[:-1].tolist(),\n",
" font_size=3,\n",
" save_filename=project.features_dir + 'eda_heatmap.png'\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": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
mdeff/ntds_2016 | algorithms/04_sol_tensorflow.ipynb | 1 | 8231 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A Network Tour of Data Science\n",
"### Xavier Bresson, Winter 2016/17\n",
"## Exercise 4 : Introduction to TensorFlow"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Import libraries\n",
"import tensorflow as tf\n",
"import numpy as np\n",
"import time\n",
"import collections\n",
"import os"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Import MNIST data with TensorFlow\n",
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets(os.path.join('datasets', 'mnist'), one_hot=True) # load data in local folder\n",
"\n",
"train_data = mnist.train.images.astype(np.float32)\n",
"train_labels = mnist.train.labels\n",
"\n",
"test_data = mnist.test.images.astype(np.float32)\n",
"test_labels = mnist.test.labels\n",
"\n",
"print(train_data.shape)\n",
"print(train_labels.shape)\n",
"print(test_data.shape)\n",
"print(test_labels.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1st Step: Construct Computational Graph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 1: Prepare the input variables (x,y_label) of the computational graph\n",
"\n",
"Hint: You may use the function *tf.placeholder()*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# computational graph inputs\n",
"batch_size = 100\n",
"d = train_data.shape[1]\n",
"nc = 10\n",
"x = tf.placeholder(tf.float32,[batch_size,d]); print('x=',x,x.get_shape())\n",
"y_label = tf.placeholder(tf.float32,[batch_size,nc]); print('y_label=',y_label,y_label.get_shape())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 2: Prepare the variables (W,b) of the computational graph\n",
"\n",
"Hint: You may use the function *tf.Variable(), tf.truncated_normal()*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# computational graph variables\n",
"initial = tf.truncated_normal([d,nc], stddev=0.1); W = tf.Variable(initial); print('W=',W.get_shape())\n",
"b = tf.Variable(tf.zeros([nc],tf.float32)); print('b=',b.get_shape())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 3: Compute the classifier such that\n",
"$$\n",
"y=softmax(Wx +b)\n",
"$$\n",
"\n",
"Hint: You may use the function *tf.matmul(), tf.nn.softmax()*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Construct CG / output value\n",
"y = tf.matmul(x, W); print('y1=',y,y.get_shape())\n",
"y += b; print('y2=',y,y.get_shape())\n",
"y = tf.nn.softmax(y); print('y3=',y,y.get_shape())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 4: Construct the loss of the computational graph such that\n",
"$$\n",
"loss = cross\\ entropy(y_{label},y) = mean_{all\\ data} \\ \\sum_{all\\ classes} -\\ y_{label}.\\log(y)\n",
"$$\n",
"\n",
"Hint: You may use the function *tf.Variable(), tf.truncated_normal()*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Loss\n",
"cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_label * tf.log(y), 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 5: Construct the L2 regularization of (W,b) to the computational graph such that\n",
"$$\n",
"R(W) = \\|W\\|_2^2\\\\\n",
"R(b) = \\|b\\|_2^2\n",
"$$\n",
"\n",
"Hint: You may use the function *tf.nn.l2_loss()*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reg_loss = tf.nn.l2_loss(W)\n",
"reg_loss += tf.nn.l2_loss(b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 6: Form the total loss\n",
"$$\n",
"total\\ loss = cross\\ entropy(y_{label},y) + reg\\_par* (R(W) + R(b))\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"reg_par = 1e-3\n",
"total_loss = cross_entropy + reg_par* reg_loss"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 7: Perform optimization of the total loss for learning weight variables of the computational graph\n",
"\n",
"Hint: You may use the function *tf.train.GradientDescentOptimizer(learning_rate).minimize(total_loss)*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Update CG variables / backward pass\n",
"train_step = tf.train.GradientDescentOptimizer(0.25).minimize(total_loss)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Question 8: Evaluate the accuracy\n",
"\n",
"Hint: You may use the function *tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))* and *tf.reduce_mean()*"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Accuracy\n",
"correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_label,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2nd Step: Run the Computational Graph with batches of training data\n",
"Check out the accuracy of test set"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create test set \n",
"idx = np.random.permutation(test_data.shape[0]) # rand permutation\n",
"idx = idx[:batch_size]\n",
"test_x, test_y = test_data[idx,:], test_labels[idx]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"n = train_data.shape[0]\n",
"indices = collections.deque()\n",
"\n",
"# Running Computational Graph\n",
"init = tf.initialize_all_variables()\n",
"sess = tf.Session()\n",
"sess.run(init)\n",
"for i in range(50):\n",
" \n",
" # Batch extraction\n",
" if len(indices) < batch_size:\n",
" indices.extend(np.random.permutation(n)) # rand permutation\n",
" idx = [indices.popleft() for i in range(batch_size)] # extract n_batch data\n",
" batch_x, batch_y = train_data[idx,:], train_labels[idx]\n",
" \n",
" # Run CG for variable training\n",
" _,acc_train,total_loss_o = sess.run([train_step,accuracy,total_loss], feed_dict={x: batch_x, y_label: batch_y})\n",
" print('\\nIteration i=',i,', train accuracy=',acc_train,', loss=',total_loss_o)\n",
" \n",
" # Run CG for testset\n",
" acc_test = sess.run(accuracy, feed_dict={x: test_x, y_label: test_y})\n",
" print('test accuracy=',acc_test)"
]
}
],
"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 |
eblur/ghost_halos | figure04.ipynb | 1 | 108343 | {
"metadata": {
"name": "",
"signature": "sha256:65d9918b16a524fd7d7d68e46493b000e381155821433ff71aace47679f58cf5"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"font = {'size' : 15}\n",
"matplotlib.rc('font', **font)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import cosmology as cosm\n",
"from scipy.interpolate import interp1d"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Number of X-ray echoes from the IGM given $\\delta t_{max}$ and $\\nu_{fb}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The relation\n",
"\n",
"$$ N_{ech} \\sim \\delta t_{\\rm max}\\ \\nu_{fb}\\ N_q(F \\geq F_{th}) $$\n",
"\n",
"is highly dependant on the threshold flux $F_{th}$ necessary to produce observable scattering echoes. Main factors contributing to the threshold flux are:\n",
"+ **instrument** background\n",
"+ exposure time\n",
"+ abundance of IGM dust\n",
"\n",
"Furthermore, the $\\delta t_{\\rm max}$ and N_q are linked because the brighter quasars will produce the most visibly extended halos and are thereby capable of a larger $\\delta t_{\\rm max}$ (Figure~3).\n",
"\n",
"I address this in Section 3.2, fourth paragraph when I point out that:\n",
"\n",
"$$ \\delta t_{\\rm max} N_q (F \\geq F_{th}) \\sim 1000\\ {\\rm years} $$\n",
"\n",
"Since these values are highly dependent on the instrument, a more appropriate value to preserve for posterity may be the fraction of quasars (assuming a perfect instrument with zero background), around which X-ray scattering echoes may be expected.\n",
"\n",
"$$ f \\equiv \\frac{ N_{ech} }{ N_q } = \\delta t_{\\rm max} \\nu_{fb}$$\n",
"\n",
"Put another way,\n",
"\n",
"$$ \\log \\nu_{fb} = \\log f - \\log \\delta t_{\\rm max} $$\n",
"\n",
"which means I will be plotting straight lines."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"log_tmax = np.linspace(0.0, 5.0, 50)\n",
"log_f = [-2.0, -1.0, np.log10(0.5), 0.0]\n",
"\n",
"lf_color = dict(zip(log_f, ['0.9','0.7','0.5','k']))\n",
"\n",
"log_nufb = { LF:(LF - log_tmax) for LF in log_f }\n",
"\n",
"for LF in log_f:\n",
" plt.plot( log_tmax, log_nufb[LF], color=lf_color[LF], lw=2 )\n",
"\n",
"plt.xlabel(r'$\\log\\ \\delta t_{\\rm max}$ [yrs]')\n",
"plt.ylabel(r'$\\log\\ \\nu_{\\rm fb}$ [yrs$^{-1}$]')\n",
"\n",
"plt.text(0.8, -3.5, '1%', color='0.7')\n",
"plt.text(3.8, -3.5, '100%', color='k')\n",
"\n",
"plt.axvline(3.0, color='k', ls=':')\n",
"plt.axhline(-4.0, color='k', ls=':')\n",
"#plt.axvline(4.0, color='k', ls=':')\n",
"#plt.axhline(-5.0, color='k', ls=':')\n",
"\n",
"plt.ylim(-7, 0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"(-7, 0)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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OhAg0QUFBREREEBkZCagjkpaWFj766CMmTJhAcHAwwcHBAXGab3h4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\nEEIIIYQQwo/9f5w+H2Qsf2uJAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10b4c5110>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*How should one read this plot?*\n",
"\n",
"The 100% marks the $\\delta t_{\\rm max} < \\nu_{fb}^{-1}$ limit. But anyways ...\n",
"\n",
"Take for example 1 um grains, which produce more compact scattering halos that only last up to $10^3$ years (Figure~3). If $\\nu_{fb} \\leq 10^{-4}$~yrs$^{-1}$, then less than 10\\% of quasars will have a ghost echo.\n",
"\n",
"Ready differently (and taking 0.1 um grains), if $\\nu_{fb} \\sim 10^{-4}$~yrs$^{-1}$, then 90% of AGN will have a ghost halo after $10^3$~years, and 0% after $10^4$~years.\n",
"\n",
"This could have implications for the unresolved soft X-ray background, if there is a sufficient enough column density (e.g. Dijkstra & Loeb paper)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cases with $\\nu_{fb}^{-1} < \\delta t_{\\rm max}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, one should replace $\\delta t_{\\rm max}$ with $\\nu_{fb}^{-1}$ so that:\n",
"\n",
"$$ \\nu_{\\rm fb}^{-1} \\approx \\frac{\\alpha^2 D_R}{c} \\left( 1 - \\frac{\\alpha}{2 \\sigma} \\right) $$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## from figure03.ipynb\n",
"\n",
"C = 3.e10 # cm/s\n",
"GPC = 1.e9 * 3.e18 # cm\n",
"ARCSEC = 5.e-6 # radians\n",
"ARCMIN = 3.e-4 # radians\n",
"YEAR = 3.e7 # seconds\n",
"\n",
"ZS = 2.0\n",
"\n",
"def charsig(a, E):\n",
" \"\"\" returns the characteristic scattering angle, in arcseconds\n",
" a: [um] Grain size\n",
" E: [keV] Photon energy\"\"\"\n",
" charsig0 = 1.04 * 60 # arcsec per 1 um grain size per keV\n",
" return charsig0 / a / E\n",
"\n",
"\n",
"def tmax(alpha, a=0.1, E=1.0, zs=ZS):\n",
" \"\"\" Calculate the maximum time delay associated with \n",
" observation angle and typical grain population\n",
" alpha: [arcmin] Observation angle\n",
" a: [micron] Grain size\n",
" E: [keV] Photon energy\n",
" zs: Redshift of source\"\"\"\n",
" time_os = cosm.DChi(zs, 0.0) * (GPC/C) / YEAR\n",
" alpha_rad = alpha * ARCSEC\n",
" xterm = 1 - alpha/(2*charsig(a,E))\n",
" return time_os * alpha_rad**2 * xterm"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def alpha_ring( nu_fb, alpha, a=0.1, E=1.0, zs=ZS ):\n",
" delta_t = tmax( alpha, a=a, E=E, zs=zs)\n",
" result = interp1d( delta_t, alpha )\n",
" return result( np.power(nu_fb,-1.0) )"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#ALPHA = np.linspace(1.0,1000.0,500) # arcsec, 2'' spacing\n",
"ALPHA = np.logspace(-1.0,3.0,100)\n",
"\n",
"ZS_GRID = [2, 4, 6]\n",
"\n",
"nu_fb = np.logspace(-4,0,50)\n",
"\n",
"rings_2 = alpha_ring(nu_fb, np.logspace(-1.0,3.0,100), zs=2.0)\n",
"rings_6 = alpha_ring(nu_fb, np.logspace(-1.0,3.0,100), zs=6.0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot( ALPHA, tmax(ALPHA, zs=2.0), 'k-', lw=2 )\n",
"plt.plot( ALPHA, tmax(ALPHA, zs=6.0), 'k--', lw=2 )\n",
"plt.loglog()\n",
"plt.xlabel(r'$\\alpha$ [arcsec]')\n",
"plt.ylabel(r'$\\delta t_{\\rm max}$[yrs]')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"<matplotlib.text.Text at 0x10b4b7990>"
]
},
{
"metadata": {},
"output_type": "display_data",
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tygxPQhQ/Wyxalp+vgX4m22WA74AnLfge5pAkI6xi3rx5tGzZkn79+hknsvz4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\nIYQQQgghhBBCCCGEEEIIIYQQolT6f7Nsv5CUsGrhAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10b5fe5d0>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot( np.log10(nu_fb), rings_2, 'k-', lw=2, label='$z_s = 2$')\n",
"plt.plot( np.log10(nu_fb), rings_6, 'k--', lw=2, label='$z_s = 6$')\n",
"\n",
"plt.legend(loc='upper right', fontsize=12)\n",
"\n",
"plt.ylabel('Ring radius [arcsec]')\n",
"plt.xlabel(r'$\\log \\nu_{\\rm fb}$ [yrs$^{-1}$]')\n",
"plt.semilogy()\n",
"plt.ylim(1.0,200.0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"(1.0, 200.0)"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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QDEzL8t5Ky4eTI2v1ghNFREJCgvLz88vs9vvcc88ZXMbt27fV+PHjM7v9litX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\nAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x10b7cb050>"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But this plot can easily be read from Figure~3, where you replace $\\delta t_{\\rm max}$ with $\\nu_{fb}^{-1}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Redo the first plot, but replaces $\\delta t_{\\rm max}$ with $\\alpha$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"new_log_tmax = np.log10( tmax(ALPHA) )\n",
"new_log_nufb = { LF:(LF - new_log_tmax) for LF in log_f }\n",
"\n",
"for LF in log_f:\n",
" plt.plot( ALPHA, new_log_nufb[LF], color=lf_color[LF], lw=2 )\n",
"\n",
"plt.semilogx()\n",
"plt.xlim(1.0,1.e3)\n",
"plt.ylim(-7,0)\n",
"\n",
"plt.xlabel(r'Halo radius [arcsec]')\n",
"plt.ylabel(r'$\\log\\ \\nu_{\\rm fb}$ [yrs$^{-1}$]')\n",
"\n",
"plt.text(2.0, -3.0, '1%', color='0.7')\n",
"plt.text(100, -3.0, '100%', color='k')\n",
"\n",
"plt.axhline(-4.0, color='k', ls=':')\n",
"plt.axvline(50.0, color='k', ls=':')\n",
"plt.axvline(100.0, color='k', ls=':')\n",
"\n",
"#plt.savefig('figure04.pdf', format='pdf')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"<matplotlib.lines.Line2D at 0x10bd34510>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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GIzyVNoStrS3t2rWr12+p2z5qzTg7O9OvXz/uvvtuPDw8qK6u5siRI+zYsYOi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\nCCGEEKJe/D9+1rbHi73TFAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10b796690>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
}
],
"metadata": {}
}
]
} | bsd-3-clause |
tpin3694/tpin3694.github.io | machine-learning/delete_observations_with_missing_values.ipynb | 2 | 2262 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: Delete Observations With Missing Values \n",
"Slug: delete_observations_with_missing_values \n",
"Summary: How to delete observations with missing values. \n",
"Date: 2017-09-05 12:00 \n",
"Category: Machine Learning \n",
"Tags: Preprocessing Structured Data \n",
"Authors: Chris Albon "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preliminaries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load libraries\n",
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create Feature Matrix"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create feature matrix\n",
"X = np.array([[1.1, 11.1], \n",
" [2.2, 22.2], \n",
" [3.3, 33.3], \n",
" [4.4, 44.4], \n",
" [np.nan, 55]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Delete Observations With Missing Values"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1.1, 11.1],\n",
" [ 2.2, 22.2],\n",
" [ 3.3, 33.3],\n",
" [ 4.4, 44.4]])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Remove observations with missing values\n",
"X[~np.isnan(X).any(axis=1)]"
]
}
],
"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
}
| mit |
methylDragon/news-anaCrawler | Old Stuff/.ipynb_checkpoints/[OBSOLETE] Firebase Data Push-checkpoint.ipynb | 1 | 1801 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%run 'analyseArticle.ipynb'\n",
"%run 'Firebase.ipynb'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"parameters = analyseArticle(\"http://www.cnn.com/2013/11/27/justice/tucson-arizona-captive-girls/\")\n",
"\n",
"title = str()\n",
"url = str()\n",
"authors = str(parameters[\"authors\"])\n",
"date = str(parameters[\"date\"])\n",
"summary = str(parameters[\"summary\"])\n",
"polarity = str(parameters[\"polarity\"])\n",
"subjectivity = str(parameters[\"subjectivity\"])\n",
"keywords = str(parameters[\"keywords\"])\n",
"images = str(parameters[\"images\"])\n",
"videos = str(parameters[\"videos\"])\n",
"text = str(parameters[\"text\"])\n",
"\n",
"sendToFirebase(parameters[\"title\"], parameters[\"url\"], parameters[\"authors\"], parameters[\"date\"], parameters[\"summary\"], parameters[\"polarity\"], parameters[\"subjectivity\"], parameters[\"keywords\"], images, videos, text)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
Ledoux/ShareYourSystem | Pythonlogy/draft/Recoverer/Presentation.ipynb | 1 | 16000 | {
"nbformat": 3,
"worksheets": [
{
"cells": [
{
"source": "\n<!--\nFrozenIsBool False\n-->\n\n#Recoverer\n\n##Doc\n----\n\n\n> \n> Findoer (sorry Finder is already an important module in python standards, so just to be sure to not override...)\n> instances helps to find in a hdf5 table RowedVariablesList corresponding to the FindingConditionVariable.\n> \n> \n\n----\n\n<small>\nView the Recoverer notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Recoverer.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\nFindoer (sorry Finder is already an important module in python standards, so just to be sure to not override...)\ninstances helps to find in a hdf5 table RowedVariablesList corresponding to the FindingConditionVariable.\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Standards.Modelers.Findoer\"\nDecorationModuleStr=\"ShareYourSystem.Standards.Classors.Classer\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nfrom ShareYourSystem.Functers import Argumenter,Hooker\n#</ImportSpecificModules>\n\n#<DefineClass>\n@DecorationClass()\nclass RecovererClass(\n\t\t\t\t\t\tBaseClass\n\t\t\t\t):\n\t\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t\t\t'RecoveredDict'\n\t\t\t\t\t\t\t\t]\n\n\tdef default_init(self,\n\t\t\t\t\t\t_RecoveredDict=None,\n\t\t\t\t\t\t**_KwargVariablesDict\n\t\t\t\t\t):\n\t\t#Call the parent init method\n\t\tBaseClass.__init__(self,**_KwargVariablesDict)\n\n\t#@Hooker.HookerClass(**{'HookingAfterVariablesList':[{'MethodCallingStr':\"find\"}]})\n\t#@Argumenter.ArgumenterClass()\n\tdef do_recover(self):\n\n\t\t#debug\n\t\t'''\n\t\tself.debug(\n\t\t\t\t\t[\n\t\t\t\t\t\t'Is the self.RecoveredDict already setted ?',\n\t\t\t\t\t\t'len(self.RecoveredDict) is '+str(len(self.RecoveredDict))\n\t\t\t\t\t]\n\t\t\t)\n\t\t'''\n\n\t\t#<NotHook>\n\t\t#find first\n\t\tself.find()\n\t\t#</NotHook>\n\n\t\t#Check\n\t\tif len(self.RecoveredDict)==0:\n\n\t\t\t#debug\n\t\t\t'''\n\t\t\tself.debug(\n\t\t\t\t\t\t[\n\t\t\t\t\t\t\t'The RecoveredDict is not yet setted',\n\t\t\t\t\t\t\t'Look if we have found only one FilteredRowedDict',\n\t\t\t\t\t\t\t'len(self.FoundFilterRowDictsList) is '+str(len(self.FoundFilterRowDictsList))\n\t\t\t\t\t\t]\n\t\t\t\t\t)\n\t\t\t'''\n\n\t\t\t#Check\n\t\t\tif len(self.FoundFilterRowDictsList)==1:\n\n\t\t\t\t#debug\n\t\t\t\t'''\n\t\t\t\tself.debug('It is good, there is one solution !')\n\t\t\t\t'''\n\n\t\t\t\t#set the RecoveredDict\n\t\t\t\tself.RecoveredDict.update(self.FoundFilterRowDictsList[0])\n\n\t\t#debug\n\t\t'''\n\t\tself.debug(\n\t\t\t\t\t[\n\t\t\t\t\t\t'Now we update with the self.RecoveredDict',\n\t\t\t\t\t\t'self.RecoveredDict is '+str(self.RecoveredDict)\n\t\t\t\t\t]\n\t\t\t\t)\n\t\t'''\n\n\t\t#set the RetrievingIndexesList and retrieve\n\t\tself.RetrievingIndexesList=(\n\t\t\t\t\t\t\t\t\t\t0,\n\t\t\t\t\t\t\t\t\t\tself.RecoveredDict['RowInt']\n\t\t\t\t\t\t\t\t\t)\n\t\n\t\t#Now we can retrieve\n\t\tself.retrieve()\n\n\t\t#<NotHook>\n\t\t#Return self\n\t\t#return self\n\t\t#</NotHook>\n\n#</DefineClass>\n\n```\n\n<small>\nView the Recoverer sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Databasers/Recoverer\" 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"
}
}
},
{
"cell_type": "code",
"prompt_number": 3,
"language": "python",
"input": [
"\n",
"#ImportModules\n",
"import operator,tables\n",
"\n",
"import ShareYourSystem as SYS\n",
"from ShareYourSystem.Standards.Noders import Structurer\n",
"from ShareYourSystem.Standards.Modelers import Recoverer\n",
"\n",
"#Definition of a Structurer instance with a noded datar\n",
"MyStructurer=Structurer.StructurerClass().collect(\n",
" \"Datome\",\n",
" \"Things\",\n",
" Recoverer.RecovererClass().update(\n",
" [\n",
" (\n",
" 'Attr_ModelingDescriptionTuplesList',\n",
" [\n",
" #GetStr #ColumnStr #Col\n",
" ('MyInt','MyInt',tables.Int64Col()),\n",
" ('MyStr','MyStr',tables.StringCol(10)),\n",
" ('MyIntsList','MyIntsList',(tables.Int64Col(shape=3)))\n",
" ]\n",
" ),\n",
" ('Attr_RowingGetStrsList',['MyInt','MyStr','MyIntsList'])\n",
" ]\n",
" )\n",
")\n",
"\n",
"MyStructurer.update(\n",
" [\n",
" ('MyInt',1),\n",
" ('MyStr',\"bonjour\"),\n",
" ('MyIntsList',[0,0,1])\n",
" ]\n",
")['<Datome>ThingsRecoverer'].insert()\n",
"\n",
"MyStructurer.update(\n",
" [\n",
" ('MyInt',0),\n",
" ('MyStr',\"guten tag\"),\n",
" ('MyIntsList',[0,0,1])\n",
" ]\n",
")['<Datome>ThingsRecoverer'].insert()\n",
"\n",
"MyStructurer.update(\n",
" [\n",
" ('MyInt',1),\n",
" ('MyStr',\"bonjour\"),\n",
" ('MyIntsList',[0,0,0])\n",
" ]\n",
")['<Datome>ThingsRecoverer'].insert()\n",
"\n",
"#Retrieve\n",
"MyStructurer['<Datome>ThingsRecoverer'].recover(\n",
" **{\n",
" 'FindingConditionVariable':\n",
" [\n",
" ('MyInt',(operator.eq,1)),\n",
" ('MyIntsList',(SYS.getIsEqualBool,[0,0,1]))\n",
" ]\n",
" }\n",
" )\n",
" \n",
"\n",
"#Definition the AttestedStr\n",
"SYS._attest(\n",
" [\n",
" 'MyStructurer is '+SYS._str(\n",
" MyStructurer,\n",
" **{\n",
" 'RepresentingBaseKeyStrsListBool':False,\n",
" 'RepresentingAlineaIsBool':False\n",
" }\n",
" ),\n",
" 'hdf5 file is : '+MyStructurer.hdfview().hdfclose().HdformatedConsoleStr\n",
" ]\n",
") \n",
"\n",
"#Print\n",
"\n",
"\n",
"\n"
],
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"*****Start of the Attest *****\n",
"\n",
"MyStructurer is < (StructurerClass), 4565262160>\n",
" /{ \n",
" / '<New><Instance>DatomeCollectionOrderedDict' : \n",
" / /{ \n",
" / / 'ThingsRecoverer' : < (RecovererClass), 4565342224>\n",
" / / /{ \n",
" / / / '<New><Instance>IdInt' : 4565342224\n",
" / / / '<New><Instance>NewtorkAttentionStr' : \n",
" / / / '<New><Instance>NewtorkCatchStr' : \n",
" / / / '<New><Instance>NewtorkCollectionStr' : \n",
" / / / '<New><Instance>NodeCollectionStr' : Datome\n",
" / / / '<New><Instance>NodeIndexInt' : 0\n",
" / / / '<New><Instance>NodeKeyStr' : ThingsRecoverer\n",
" / / / '<New><Instance>NodePointDeriveNoder' : {...}< (StructurerClass), 4565262160>\n",
" / / / '<New><Instance>NodePointOrderedDict' : {...}< (OrderedDict), 4565356920>\n",
" / / / '<New><Instance>_ModelingDescriptionTuplesList' : \n",
" / / / /[\n",
" / / / / 0 : \n",
" / / / / /(\n",
" / / / / / 0 : MyInt\n",
" / / / / / 1 : MyInt\n",
" / / / / / 2 : Int64Col(shape=(), dflt=0, pos=None)\n",
" / / / / /)\n",
" / / / / 1 : \n",
" / / / / /(\n",
" / / / / / 0 : MyStr\n",
" / / / / / 1 : MyStr\n",
" / / / / / 2 : StringCol(itemsize=10, shape=(), dflt='', pos=None)\n",
" / / / / /)\n",
" / / / / 2 : \n",
" / / / / /(\n",
" / / / / / 0 : MyIntsList\n",
" / / / / / 1 : MyIntsList\n",
" / / / / / 2 : Int64Col(shape=(3,), dflt=0, pos=None)\n",
" / / / / /)\n",
" / / / /]\n",
" / / / '<New><Instance>_RowingGetStrsList' : ['MyInt', 'MyStr', 'MyIntsList']\n",
" / / / '<Spe><Instance>RecoveredDict' : \n",
" / / / /{ \n",
" / / / / 'RowInt' : 0\n",
" / / / / 'MyInt' : 1\n",
" / / / / 'MyIntsList' : array([0, 0, 1])\n",
" / / / / 'MyStr' : bonjour\n",
" / / / /}\n",
" / / /}\n",
" / /}\n",
" / '<New><Instance>IdInt' : 4565262160\n",
" / '<New><Instance>MyInt' : 1\n",
" / '<New><Instance>MyIntsList' : array([0, 0, 1])\n",
" / '<New><Instance>MyStr' : bonjour\n",
" / '<New><Instance>NewtorkAttentionStr' : \n",
" / '<New><Instance>NewtorkCatchStr' : \n",
" / '<New><Instance>NewtorkCollectionStr' : \n",
" / '<New><Instance>NodeCollectionStr' : Globals\n",
" / '<New><Instance>NodeIndexInt' : -1\n",
" / '<New><Instance>NodeKeyStr' : TopStructurer\n",
" / '<New><Instance>NodePointDeriveNoder' : None\n",
" / '<New><Instance>NodePointOrderedDict' : None\n",
" / '<Spe><Class>StructuringBeforeUpdateList' : None\n",
" / '<Spe><Class>StructuringNodeCollectionStrsList' : []\n",
" /}\n",
"\n",
"------\n",
"\n",
"hdf5 file is : / Group\n",
"/TopStructurer Group\n",
"/TopStructurer/FirstChildStructurer Group\n",
"/TopStructurer/FirstChildStructurer/GrandChildStructurer Group\n",
"/TopStructurer/SecondChildStructurer Group\n",
"/TopStructurer/SecondChildStructurer/OtherGrandChildStructurer Group\n",
"/xx0xxThingsFindoerTable Dataset {3/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=1, MyIntsList=[0,0,1], MyStr=\"bonjour\"},\n",
" (1) {RowInt=1, MyInt=0, MyIntsList=[0,0,1], MyStr=\"guten tag\"},\n",
" (2) {RowInt=2, MyInt=1, MyIntsList=[0,0,0], MyStr=\"bonjour\"}\n",
"/xx0xxThingsInserterTable Dataset {2/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=1, MyIntsList=[2,4,6], MyStr=\"bonjour\"},\n",
" (1) {RowInt=1, MyInt=0, MyIntsList=[0,0,0], MyStr=\"hello\"}\n",
"/xx0xxThingsRecovererTable Dataset {3/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=1, MyIntsList=[0,0,1], MyStr=\"bonjour\"},\n",
" (1) {RowInt=1, MyInt=0, MyIntsList=[0,0,1], MyStr=\"guten tag\"},\n",
" (2) {RowInt=2, MyInt=1, MyIntsList=[0,0,0], MyStr=\"bonjour\"}\n",
"/xx0xxThingsRetrieverTable Dataset {2/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=1, MyIntsList=[2,4,6], MyStr=\"bonjour\"},\n",
" (1) {RowInt=1, MyInt=0, MyIntsList=[0,0,0], MyStr=\"guten tag\"}\n",
"/xx0xxThingsRowerTable Dataset {0/Inf}\n",
" Data:\n",
"\n",
"/xx0xxThingsTablerTable Dataset {0/Inf}\n",
" Data:\n",
"\n",
"/xx0xx__UnitsInt_3__ThingsMergerTable Dataset {2/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=0, MyIntsList=[0,0,1], MyStr=\"hello\"},\n",
" (1) {RowInt=1, MyInt=1, MyIntsList=[0,0,1], MyStr=\"bonjour\"}\n",
"/xx0xx__UnitsInt_3__ThingsShaperTable Dataset {2/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=0, MyIntsList=[0,0,1], MyStr=\"hello\"},\n",
" (1) {RowInt=1, MyInt=1, MyIntsList=[0,0,1], MyStr=\"bonjour\"}\n",
"/xx1xx__UnitsInt_2__ThingsMergerTable Dataset {1/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=0, MyIntsList=[0,0], MyStr=\"\"}\n",
"/xx1xx__UnitsInt_2__ThingsShaperTable Dataset {1/Inf}\n",
" Data:\n",
" (0) {RowInt=0, MyInt=0, MyIntsList=[0,0], MyStr=\"\"}\n",
"\n",
"\n",
"*****End of the Attest *****\n",
"\n",
"\n"
]
}
],
"collapsed": false,
"metadata": {
"slideshow": {
"slide_type": "-"
}
}
}
]
}
],
"metadata": {
"name": "",
"signature": ""
},
"nbformat_minor": 0
} | mit |
certik/scipy-2013-tutorial | tutorial_exercises/Variation with Lagrange multipliers.ipynb | 1 | 26699 | {
"metadata": {
"name": "Variation with Lagrange multipliers"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Consider a curve given by the function $f(x)$ on the interval $[0,1]$ with the property $f(0)=f(1)=0$ and $f(x) > 0$\n",
"everywhere else. The length of the curve is $l \\le 0$. What curve will have the maximum area under the function?\n",
"\n",
"We want to maximize the area functional:\n",
"$$\n",
"S[f] = \\int_0^1 f(x) d x\n",
"$$\n",
"subject to the constraint\n",
"$$\n",
"A[f] = \\int_0^1 \\sqrt{1+f'(x)^2} dx = l\n",
"$$\n",
"So we must extremize the modified functional:\n",
"$$\n",
"\\tilde S[f, \\lambda] = \\int_0^1 \\left(f(x)+\\lambda \\sqrt{1+f'(x)^2}\\right) d x\n",
"$$\n",
"The Euler-Lagrange equations\n",
"$$\n",
"{\\delta \\tilde S\\over\\delta f(x)} - {d\\over d x} {\\delta \\tilde S\\over\\delta f'(x)} = 0\n",
"$$\n",
"become\n",
"$$\n",
"1-{d\\over d x}\\left(\\lambda {f'(x)\\over \\sqrt{1+f'(x)^2}}\\right) = 0\n",
"$$\n",
"Integrating:\n",
"$$\n",
"x-\\lambda {f'(x)\\over \\sqrt{1+f'(x)^2}} = c_1\n",
"$$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sympy.interactive\n",
"sympy.interactive.init_printing()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 30
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's assume $c_1 = 0$ for now..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from sympy import var, Symbol, Function, sqrt, solve, integrate, simplify, refine, Q, python, Derivative, Abs, Id, powsimp\n",
"var(\"x c1\")\n",
"lam = Symbol(\"lambda\")\n",
"f = Function(\"f\")\n",
"L = f(x) + lam*sqrt(1 + f(x).diff(x)**2)\n",
"eq0 = L.diff(f(x)) - Derivative(L.diff(f(x).diff(x)), x)\n",
"eq0"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$- \\frac{\\partial}{\\partial x}\\left(\\frac{\\lambda \\frac{\\partial}{\\partial x} \\operatorname{f}{\\left (x \\right )}}{\\sqrt{\\left(\\frac{\\partial}{\\partial x} \\operatorname{f}{\\left (x \\right )}\\right)^{2} + 1}}\\right) + 1$$"
],
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAALwAAAA4CAYAAABHaJJlAAAABHNCSVQICAgIfAhkiAAACh9JREFU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\n/cif3wl8k+obJBQUFBQUFBQUFNjhf7Sfv12fG84eAAAAAElFTkSuQmCC\n",
"prompt_number": 53,
"text": [
" \u239b d \u239e \n",
" \u239c \u03bb\u22c5\u2500\u2500(f(x)) \u239f \n",
" d \u239c dx \u239f \n",
"- \u2500\u2500\u239c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u239f + 1\n",
" dx\u239c _______________\u239f \n",
" \u239c \u2571 2 \u239f \n",
" \u239c \u2571 d \u239f \n",
" \u239c \u2571 \u2500\u2500(f(x)) + 1 \u239f \n",
" \u239d\u2572\u2571 dx \u23a0 "
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# integrate can't handle it directly (it will work in the next release)\n",
"# eq = integrate(eq0, x)\n",
"eq = x - lam * f(x).diff(x) / sqrt(1+f(x).diff(x)**2) #- c1\n",
"eq"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$- \\frac{\\lambda \\frac{\\partial}{\\partial x} \\operatorname{f}{\\left (x \\right )}}{\\sqrt{\\left(\\frac{\\partial}{\\partial x} \\operatorname{f}{\\left (x \\right )}\\right)^{2} + 1}} + x$$"
],
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAIwAAAA2CAYAAAAVkXEtAAAABHNCSVQICAgIfAhkiAAABu5JREFU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\nP/kfgm5OHzPcaLQAAAAASUVORK5CYII=\n",
"prompt_number": 54,
"text": [
" d \n",
" \u03bb\u22c5\u2500\u2500(f(x)) \n",
" dx \n",
"- \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + x\n",
" _______________ \n",
" \u2571 2 \n",
" \u2571 d \n",
" \u2571 \u2500\u2500(f(x)) + 1 \n",
" \u2572\u2571 dx "
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sqrt(f(x)).diff(f(x))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\frac{1}{2 \\sqrt{\\operatorname{f}{\\left (x \\right )}}}$$"
],
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAACoAAAAoCAYAAACIC2hQAAAABHNCSVQICAgIfAhkiAAAAk5JREFU\nWIXt2MtvTGEYx/FP6zZB24UmVZemqSBUNE0lLnVbVFioy6JdIFEpkehCbMRGSCw0ErElFiIsLMRC\nSFwTISK14X8QCxYa4hZCLd4zekyZOTozHY35bt7LPOd5f+d53vO85wz/KVW4ioZSC8nGXhzHEBpL\nqiQhRRFaWWiHxaIstNCUhRaaCQX0tRMHsAz1mImnBfRfpsw/TUXUDpVURZkSUJHbJBHjYus0j8Ui\n+R6hU9FSCCHFZgvqxmKhfCNah1eFEJKLfIRW4luhhBST1Vg8VotNjPWXYxVq0I4TeJjl2kU4n8D/\naXzBRxzLV+h0bMeRaNyNW5iPl6N1ji7MwDl8zcPPT5biO+ZF42qhiHf/wX4h1ibwewG9eauLUSGk\nPn1SNQtCWzEFGzPs98j+dVAlZGcQ/dgdzS+I2nb04Ax2YT+uoUnIYmIuCXsLduBuxu/7EvhI4Z3h\nG5qEDiFb6ShvxUDUvyh8vlRjRRKRvTjl1/eAJ5gW9WuxLYGftpgIQpWoj25gcjTXb/i5iNOVOZFZ\nRzdH7WEh5Y3R+AHWR/0O3EsgtAXPY+MGvMZnoQrABtyP+jUx21Q2oeuEk+amkIJNQgTgtuF9WoX3\nCYS2Zgh9K1SXThwSgrAEz4Ts9cRsRxwk6fLUhBuRozjpu3ws7NkUPiUQSUj95dh4QIhyrVBdOnEU\nB4UoX4nsKvAh4Rq/5Tr6MDuHXaXwAL0wsjKsSbBOm/BWNmr6cCeHzVy8wUqczWexTP7mn5JBYWs8\nymKTwizMwUl5prBMmfHED3cOT+PyFhshAAAAAElFTkSuQmCC\n",
"prompt_number": 55,
"text": [
" 1 \n",
"\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
" ______\n",
"2\u22c5\u2572\u2571 f(x) "
]
}
],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dfx = solve(eq, f(x).diff(x))[1]\n",
"dfx"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\sqrt{\\frac{x^{2}}{\\lambda^{2} - x^{2}}}$$"
],
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAEQAAAAwCAYAAACooNxlAAAABHNCSVQICAgIfAhkiAAAA15JREFU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\nf/kXKmfDDP9ZQK8AAAAASUVORK5CYII=\n",
"prompt_number": 56,
"text": [
" _________\n",
" \u2571 2 \n",
" \u2571 x \n",
" \u2571 \u2500\u2500\u2500\u2500\u2500\u2500\u2500 \n",
" \u2571 2 2 \n",
"\u2572\u2571 \u03bb - x "
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fx = integrate(dfx, x)\n",
"fx"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$- \\frac{\\lambda^{2} \\sqrt{x^{2}} \\sqrt{\\frac{1}{\\lambda^{2} - x^{2}}}}{x} + x \\sqrt{x^{2}} \\sqrt{\\frac{1}{\\lambda^{2} - x^{2}}}$$"
],
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAP0AAAA0CAYAAABB0j6AAAAABHNCSVQICAgIfAhkiAAACMhJREFU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\nCl9+GEU0GoZhGIZhGIZhGIYxifgfR+K2GKILrdIAAAAASUVORK5CYII=\n",
"prompt_number": 57,
"text": [
" ____ _________ \n",
" 2 \u2571 2 \u2571 1 \n",
" \u03bb \u22c5\u2572\u2571 x \u22c5 \u2571 \u2500\u2500\u2500\u2500\u2500\u2500\u2500 \n",
" \u2571 2 2 ____ _________\n",
" \u2572\u2571 \u03bb - x \u2571 2 \u2571 1 \n",
"- \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 + x\u22c5\u2572\u2571 x \u22c5 \u2571 \u2500\u2500\u2500\u2500\u2500\u2500\u2500 \n",
" x \u2571 2 2 \n",
" \u2572\u2571 \u03bb - x "
]
}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fx = simplify(fx)\n",
"fx"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\frac{\\left(- \\lambda^{2} + x^{2}\\right) \\sqrt{x^{2}} \\sqrt{\\frac{1}{\\lambda^{2} - x^{2}}}}{x}$$"
],
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAJEAAAAtCAYAAACu0IktAAAABHNCSVQICAgIfAhkiAAABRRJREFU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\nRK5CYII=\n",
"prompt_number": 58,
"text": [
" ____ _________\n",
"\u239b 2 2\u239e \u2571 2 \u2571 1 \n",
"\u239d- \u03bb + x \u23a0\u22c5\u2572\u2571 x \u22c5 \u2571 \u2500\u2500\u2500\u2500\u2500\u2500\u2500 \n",
" \u2571 2 2 \n",
" \u2572\u2571 \u03bb - x \n",
"\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
" x "
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fx = refine(fx, Q.positive(x))\n",
"fx"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\left(- \\lambda^{2} + x^{2}\\right) \\sqrt{\\frac{1}{\\lambda^{2} - x^{2}}}$$"
],
"output_type": "pyout",
"png": "iVBORw0KGgoAAAANSUhEUgAAAKYAAAAqCAYAAAA5+iDUAAAABHNCSVQICAgIfAhkiAAABhFJREFU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\npqampqamJjf/B0EMOpdFNOY/AAAAAElFTkSuQmCC\n",
"prompt_number": 59,
"text": [
" _________\n",
"\u239b 2 2\u239e \u2571 1 \n",
"\u239d- \u03bb + x \u23a0\u22c5 \u2571 \u2500\u2500\u2500\u2500\u2500\u2500\u2500 \n",
" \u2571 2 2 \n",
" \u2572\u2571 \u03bb - x "
]
}
],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"refine(fx, Q.positive(lam**2-x**2)) # Unfortunately does not work..."
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$\\frac{- \\lambda^{2} + x^{2}}{\\sqrt{\\lvert{\\lambda^{2} - x^{2}}\\rvert}}$$"
],
"output_type": "pyout",
"prompt_number": 60,
"text": [
" 2 2 \n",
" - \u03bb + x \n",
"\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
" ___________\n",
" \u2571 \u2502 2 2\u2502 \n",
"\u2572\u2571 \u2502\u03bb - x \u2502 "
]
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"-refine(simplify(-fx), Q.positive(lam**2-x**2))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$$- \\frac{\\lambda^{2} - x^{2}}{\\sqrt{\\lvert{\\lambda^{2} - x^{2}}\\rvert}}$$"
],
"output_type": "pyout",
"prompt_number": 61,
"text": [
" \u239b 2 2\u239e \n",
" -\u239d\u03bb - x \u23a0 \n",
"\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n",
" ___________\n",
" \u2571 \u2502 2 2\u2502 \n",
"\u2572\u2571 \u2502\u03bb - x \u2502 "
]
}
],
"prompt_number": 61
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the solution is $f(x) = -\\sqrt{\\lambda^2 - x^2}$. The minus sign should not be there, it probably got in at some point..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | bsd-3-clause |
QuantStack/quantstack-talks | 2019-07-10-CICM/notebooks/DrawControl.ipynb | 1 | 12359 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from ipyleaflet import (\n",
" Map,\n",
" Marker,\n",
" TileLayer, ImageOverlay,\n",
" Polyline, Polygon, Rectangle, Circle, CircleMarker,\n",
" GeoJSON,\n",
" DrawControl\n",
")\n",
"\n",
"from traitlets import link"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"center = [34.6252978589571, -77.34580993652344]\n",
"zoom = 10"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m = Map(center=center, zoom=zoom)\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m.zoom"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now create the `DrawControl` and add it to the `Map` using `add_control`. We also register a handler for draw events. This will fire when a drawn path is created, edited or deleted (there are the actions). The `geo_json` argument is the serialized geometry of the drawn path, along with its embedded style."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dc = DrawControl(marker={'shapeOptions': {'color': '#0000FF'}},\n",
" rectangle={'shapeOptions': {'color': '#0000FF'}},\n",
" circle={'shapeOptions': {'color': '#0000FF'}},\n",
" circlemarker={},\n",
" )\n",
"\n",
"def handle_draw(self, action, geo_json):\n",
" print(action)\n",
" print(geo_json)\n",
"\n",
"dc.on_draw(handle_draw)\n",
"m.add_control(dc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition, the `DrawControl` also has `last_action` and `last_draw` attributes that are created dynamicaly anytime a new drawn path arrives."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dc.last_action"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dc.last_draw"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's possible to remove all drawings from the map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dc.clear_circles()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dc.clear_polylines()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dc.clear_rectangles()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dc.clear_markers()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
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]
},
{
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"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dc.clear()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's draw a second map and try to import this GeoJSON data into it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m2 = Map(center=center, zoom=zoom, layout=dict(width='600px', height='400px'))\n",
"m2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can use `link` to synchronize traitlets of the two maps:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"map_center_link = link((m, 'center'), (m2, 'center'))\n",
"map_zoom_link = link((m, 'zoom'), (m2, 'zoom'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"new_poly = GeoJSON(data=dc.last_draw)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m2.add_layer(new_poly)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that the style is preserved! If you wanted to change the style, you could edit the `properties.style` dictionary of the GeoJSON data. Or, you could even style the original path in the `DrawControl` by setting the `polygon` dictionary of that object. See the code for details."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's add a `DrawControl` to this second map. For fun we will disable lines and enable circles as well and change the style a bit."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dc2 = DrawControl(polygon={'shapeOptions': {'color': '#0000FF'}}, polyline={},\n",
" circle={'shapeOptions': {'color': '#0000FF'}})\n",
"m2.add_control(dc2)"
]
},
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| bsd-3-clause |
olgabot/cshl-singlecell-2017 | notebooks/2.2_apply_clustering_on_knn_graph.ipynb | 1 | 406402 | {
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"Note: This is not really a \"network analysis\" - we are only looking at the graph and seeing what cells are there. if you want to do more than just zoom in and look around at the cells in the graphs, I recommend using [Cytoscape](http://www.cytoscape.org/) for visualizing newtorks."
]
},
{
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"source": [
"# Interactive jupyter widgets - use IntSlider directly for more control\n",
"from ipywidgets import IntSlider, interact\n",
"\n",
"# Convert RGB colors to hex cor portability\n",
"from matplotlib.colors import rgb2hex\n",
"\n",
"# Visualize networks\n",
"import networkx\n",
"\n",
"# Numerical python\n",
"import numpy as np\n",
"\n",
"# Pandas for dataframes\n",
"import pandas as pd\n",
"\n",
"# K-nearest neighbors cell clustering from Dana Pe'er's lab\n",
"import phenograph\n",
"\n",
"# Make color palettes\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"\n",
"# Bokeh - interactive plotting in the browser\n",
"from bokeh.plotting import figure, show, output_file\n",
"from bokeh.models import HoverTool, ColumnDataSource\n",
"from bokeh.models.widgets import Panel, Tabs\n",
"from bokeh.layouts import widgetbox\n",
"from bokeh.io import output_notebook\n",
"\n",
"# Local file: networkplots.py\n",
"import networkplots\n",
"\n",
"# This line is required for the plots to appear in the notebooks\n",
"output_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At this point, you can follow along with either the pre-baked Macosko2015 amacrine data, or you can load in your own expression matrices. For the best experience, make sure that the rows are cells and the columns are gene names."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>r1_CAAGCTTGGCGC</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>...</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",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>r1_ACTCACATAGAG</th>\n",
" <td>1</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",
" <td>1</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 259 columns</p>\n",
"</div>"
],
"text/plain": [
" 2010107E04RIK 4930447C04RIK A930011O12RIK ABCA8A ABLIM1 \\\n",
"r1_TTCCTGCTAGGC 2 0 0 0 1 \n",
"r1_TGGAGATACTCT 0 0 1 0 0 \n",
"r1_CGTCTACATCCG 2 0 0 0 0 \n",
"r1_CAAGCTTGGCGC 0 0 11 0 1 \n",
"r1_ACTCACATAGAG 1 0 0 0 0 \n",
"\n",
" ACSL3 AIPL1 ALDOC ANK3 APLP2 ... VEGFA VIM \\\n",
"r1_TTCCTGCTAGGC 0 1 0 0 0 ... 0 0 \n",
"r1_TGGAGATACTCT 0 0 0 0 0 ... 0 0 \n",
"r1_CGTCTACATCCG 0 2 0 0 1 ... 0 0 \n",
"r1_CAAGCTTGGCGC 0 6 0 0 2 ... 0 0 \n",
"r1_ACTCACATAGAG 0 0 0 0 1 ... 0 0 \n",
"\n",
" VSTM2B VSX1 VSX2 WIPI1 YWHAB ZBTB20 ZFP365 ZFP36L1 \n",
"r1_TTCCTGCTAGGC 0 0 0 0 0 0 0 0 \n",
"r1_TGGAGATACTCT 0 0 0 0 0 1 0 0 \n",
"r1_CGTCTACATCCG 0 0 0 0 0 0 0 0 \n",
"r1_CAAGCTTGGCGC 0 0 0 0 0 0 1 0 \n",
"r1_ACTCACATAGAG 0 0 0 0 0 2 0 0 \n",
"\n",
"[5 rows x 259 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import macosko2015\n",
"counts, cell_metadata, gene_metadata = macosko2015.load_big_clusters()\n",
"counts.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculate correlation between cells:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(300, 300)\n"
]
},
{
"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>r1_TTCCTGCTAGGC</th>\n",
" <th>r1_TGGAGATACTCT</th>\n",
" <th>r1_CGTCTACATCCG</th>\n",
" <th>r1_CAAGCTTGGCGC</th>\n",
" <th>r1_ACTCACATAGAG</th>\n",
" <th>r1_TAACGGACACGC</th>\n",
" <th>r1_CGCATGGGATAC</th>\n",
" <th>r1_TAACGACGCTTG</th>\n",
" <th>r1_TCGGCAGCCTCT</th>\n",
" <th>r1_TAGGATGCAAAC</th>\n",
" <th>...</th>\n",
" <th>r1_AGGGTGGGTACA</th>\n",
" <th>r1_AATGCTGCAAGA</th>\n",
" <th>r1_GTCGGGCCTTTC</th>\n",
" <th>r1_GGGTCAGCGGCG</th>\n",
" <th>r1_CTGGACCTGCCC</th>\n",
" <th>r1_AAGATATTGCTG</th>\n",
" <th>r1_GAGACCTCATGG</th>\n",
" <th>r1_CGGAGCGCGACA</th>\n",
" <th>r1_AAGGACAGATCC</th>\n",
" <th>r1_ATATGCACCCTA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>r1_TTCCTGCTAGGC</th>\n",
" <td>1.000000</td>\n",
" <td>0.578489</td>\n",
" <td>0.592947</td>\n",
" <td>0.581111</td>\n",
" <td>0.600062</td>\n",
" <td>0.668730</td>\n",
" <td>0.562366</td>\n",
" <td>0.537223</td>\n",
" <td>0.625188</td>\n",
" <td>0.627728</td>\n",
" <td>...</td>\n",
" <td>-0.127396</td>\n",
" <td>-0.238725</td>\n",
" <td>-0.191087</td>\n",
" <td>-0.062375</td>\n",
" <td>-0.070431</td>\n",
" <td>-0.211101</td>\n",
" <td>0.004142</td>\n",
" <td>0.005390</td>\n",
" <td>0.028681</td>\n",
" <td>-0.208886</td>\n",
" </tr>\n",
" <tr>\n",
" <th>r1_TGGAGATACTCT</th>\n",
" <td>0.578489</td>\n",
" <td>1.000000</td>\n",
" <td>0.605171</td>\n",
" <td>0.668457</td>\n",
" <td>0.605529</td>\n",
" <td>0.699568</td>\n",
" <td>0.626681</td>\n",
" <td>0.619552</td>\n",
" <td>0.686334</td>\n",
" <td>0.603006</td>\n",
" <td>...</td>\n",
" <td>-0.088473</td>\n",
" <td>-0.164247</td>\n",
" <td>-0.091119</td>\n",
" <td>-0.012380</td>\n",
" <td>0.002600</td>\n",
" <td>-0.128525</td>\n",
" <td>0.110028</td>\n",
" <td>0.123022</td>\n",
" <td>0.087241</td>\n",
" <td>-0.151023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>r1_CGTCTACATCCG</th>\n",
" <td>0.592947</td>\n",
" <td>0.605171</td>\n",
" <td>1.000000</td>\n",
" <td>0.592150</td>\n",
" <td>0.589383</td>\n",
" <td>0.616885</td>\n",
" <td>0.539639</td>\n",
" <td>0.459749</td>\n",
" <td>0.633616</td>\n",
" <td>0.563735</td>\n",
" <td>...</td>\n",
" <td>-0.110518</td>\n",
" <td>-0.131933</td>\n",
" <td>-0.131094</td>\n",
" <td>-0.019492</td>\n",
" <td>-0.019556</td>\n",
" <td>-0.105237</td>\n",
" <td>0.023963</td>\n",
" <td>0.057967</td>\n",
" <td>0.124087</td>\n",
" <td>-0.138839</td>\n",
" </tr>\n",
" <tr>\n",
" <th>r1_CAAGCTTGGCGC</th>\n",
" <td>0.581111</td>\n",
" <td>0.668457</td>\n",
" <td>0.592150</td>\n",
" <td>1.000000</td>\n",
" <td>0.614245</td>\n",
" <td>0.747307</td>\n",
" <td>0.610552</td>\n",
" <td>0.624505</td>\n",
" <td>0.670207</td>\n",
" <td>0.682267</td>\n",
" <td>...</td>\n",
" <td>-0.052749</td>\n",
" <td>-0.108256</td>\n",
" <td>-0.081267</td>\n",
" <td>-0.036022</td>\n",
" <td>0.048468</td>\n",
" <td>-0.154414</td>\n",
" <td>0.184313</td>\n",
" <td>0.051814</td>\n",
" <td>0.141338</td>\n",
" <td>-0.155600</td>\n",
" </tr>\n",
" <tr>\n",
" <th>r1_ACTCACATAGAG</th>\n",
" <td>0.600062</td>\n",
" <td>0.605529</td>\n",
" <td>0.589383</td>\n",
" <td>0.614245</td>\n",
" <td>1.000000</td>\n",
" <td>0.615884</td>\n",
" <td>0.642180</td>\n",
" <td>0.556297</td>\n",
" <td>0.648107</td>\n",
" <td>0.566039</td>\n",
" <td>...</td>\n",
" <td>-0.104368</td>\n",
" <td>-0.184757</td>\n",
" <td>-0.136784</td>\n",
" <td>-0.045760</td>\n",
" <td>0.003680</td>\n",
" <td>-0.183599</td>\n",
" <td>0.096902</td>\n",
" <td>0.015629</td>\n",
" <td>0.036012</td>\n",
" <td>-0.142725</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 300 columns</p>\n",
"</div>"
],
"text/plain": [
" r1_TTCCTGCTAGGC r1_TGGAGATACTCT r1_CGTCTACATCCG \\\n",
"r1_TTCCTGCTAGGC 1.000000 0.578489 0.592947 \n",
"r1_TGGAGATACTCT 0.578489 1.000000 0.605171 \n",
"r1_CGTCTACATCCG 0.592947 0.605171 1.000000 \n",
"r1_CAAGCTTGGCGC 0.581111 0.668457 0.592150 \n",
"r1_ACTCACATAGAG 0.600062 0.605529 0.589383 \n",
"\n",
" r1_CAAGCTTGGCGC r1_ACTCACATAGAG r1_TAACGGACACGC \\\n",
"r1_TTCCTGCTAGGC 0.581111 0.600062 0.668730 \n",
"r1_TGGAGATACTCT 0.668457 0.605529 0.699568 \n",
"r1_CGTCTACATCCG 0.592150 0.589383 0.616885 \n",
"r1_CAAGCTTGGCGC 1.000000 0.614245 0.747307 \n",
"r1_ACTCACATAGAG 0.614245 1.000000 0.615884 \n",
"\n",
" r1_CGCATGGGATAC r1_TAACGACGCTTG r1_TCGGCAGCCTCT \\\n",
"r1_TTCCTGCTAGGC 0.562366 0.537223 0.625188 \n",
"r1_TGGAGATACTCT 0.626681 0.619552 0.686334 \n",
"r1_CGTCTACATCCG 0.539639 0.459749 0.633616 \n",
"r1_CAAGCTTGGCGC 0.610552 0.624505 0.670207 \n",
"r1_ACTCACATAGAG 0.642180 0.556297 0.648107 \n",
"\n",
" r1_TAGGATGCAAAC ... r1_AGGGTGGGTACA \\\n",
"r1_TTCCTGCTAGGC 0.627728 ... -0.127396 \n",
"r1_TGGAGATACTCT 0.603006 ... -0.088473 \n",
"r1_CGTCTACATCCG 0.563735 ... -0.110518 \n",
"r1_CAAGCTTGGCGC 0.682267 ... -0.052749 \n",
"r1_ACTCACATAGAG 0.566039 ... -0.104368 \n",
"\n",
" r1_AATGCTGCAAGA r1_GTCGGGCCTTTC r1_GGGTCAGCGGCG \\\n",
"r1_TTCCTGCTAGGC -0.238725 -0.191087 -0.062375 \n",
"r1_TGGAGATACTCT -0.164247 -0.091119 -0.012380 \n",
"r1_CGTCTACATCCG -0.131933 -0.131094 -0.019492 \n",
"r1_CAAGCTTGGCGC -0.108256 -0.081267 -0.036022 \n",
"r1_ACTCACATAGAG -0.184757 -0.136784 -0.045760 \n",
"\n",
" r1_CTGGACCTGCCC r1_AAGATATTGCTG r1_GAGACCTCATGG \\\n",
"r1_TTCCTGCTAGGC -0.070431 -0.211101 0.004142 \n",
"r1_TGGAGATACTCT 0.002600 -0.128525 0.110028 \n",
"r1_CGTCTACATCCG -0.019556 -0.105237 0.023963 \n",
"r1_CAAGCTTGGCGC 0.048468 -0.154414 0.184313 \n",
"r1_ACTCACATAGAG 0.003680 -0.183599 0.096902 \n",
"\n",
" r1_CGGAGCGCGACA r1_AAGGACAGATCC r1_ATATGCACCCTA \n",
"r1_TTCCTGCTAGGC 0.005390 0.028681 -0.208886 \n",
"r1_TGGAGATACTCT 0.123022 0.087241 -0.151023 \n",
"r1_CGTCTACATCCG 0.057967 0.124087 -0.138839 \n",
"r1_CAAGCTTGGCGC 0.051814 0.141338 -0.155600 \n",
"r1_ACTCACATAGAG 0.015629 0.036012 -0.142725 \n",
"\n",
"[5 rows x 300 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"correlations = counts.T.rank().corr()\n",
"print(correlations.shape)\n",
"correlations.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Correlation != distance\n",
"\n",
"Correlation is not equal to distance. If two things are exactly the same, their correlation value is 1. But in space, if two things are exactly the same, the **distance** between them is 0. Therefore, correlation is not a distance! Correlation is a *similarity* metric, where bigger = more similar. But we want a *dissimilarity* (aka distance) metric.\n",
"\n",
"Take a look for yourself. Many values in the distribution of all correlation values are near zero (not correlated), and a blip near 1 ( self-correlations)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x117262c88>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wexgatt/tBbvmAsm2VAG/h6qgD2+2iwNHrvHQzgpcTnvcq7HLe3Q2Vmab7wMk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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11688b828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(correlations.values.flat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But for building a K-nearest neighbors graph, we want the *closest* things (in distance space) to be actually close. So we'll convert our correlation ($\\rho$) into a distance ($d$) using this equation:\n",
"\n",
"$$\n",
"d = \\sqrt{2(1-\\rho)}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can look at the code for `networkplots.correlation_to_distance` to convince yourself that's actually what it's doing:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"networkplots.correlation_to_distance??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 1\n",
"\n",
"Create a dataframe called `distance` using the `correlation_to_distance` function from `networkplots` on your `corr` dataframe."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# YOUR CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"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>r1_TTCCTGCTAGGC</th>\n",
" <th>r1_TGGAGATACTCT</th>\n",
" <th>r1_CGTCTACATCCG</th>\n",
" <th>r1_CAAGCTTGGCGC</th>\n",
" <th>r1_ACTCACATAGAG</th>\n",
" <th>r1_TAACGGACACGC</th>\n",
" <th>r1_CGCATGGGATAC</th>\n",
" <th>r1_TAACGACGCTTG</th>\n",
" <th>r1_TCGGCAGCCTCT</th>\n",
" <th>r1_TAGGATGCAAAC</th>\n",
" <th>...</th>\n",
" <th>r1_AGGGTGGGTACA</th>\n",
" <th>r1_AATGCTGCAAGA</th>\n",
" <th>r1_GTCGGGCCTTTC</th>\n",
" <th>r1_GGGTCAGCGGCG</th>\n",
" <th>r1_CTGGACCTGCCC</th>\n",
" <th>r1_AAGATATTGCTG</th>\n",
" <th>r1_GAGACCTCATGG</th>\n",
" <th>r1_CGGAGCGCGACA</th>\n",
" <th>r1_AAGGACAGATCC</th>\n",
" <th>r1_ATATGCACCCTA</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>r1_TTCCTGCTAGGC</th>\n",
" <td>0.000000</td>\n",
" <td>0.918162</td>\n",
" <td>0.902278</td>\n",
" <td>0.915303</td>\n",
" <td>0.894357</td>\n",
" <td>0.813966</td>\n",
" <td>0.935558</td>\n",
" <td>0.962057</td>\n",
" <td>0.865809</td>\n",
" <td>0.862869</td>\n",
" <td>...</td>\n",
" <td>1.501597</td>\n",
" <td>1.573992</td>\n",
" <td>1.543429</td>\n",
" <td>1.457653</td>\n",
" <td>1.463168</td>\n",
" <td>1.556343</td>\n",
" <td>1.411282</td>\n",
" <td>1.410397</td>\n",
" <td>1.393785</td>\n",
" <td>1.554919</td>\n",
" </tr>\n",
" <tr>\n",
" <th>r1_TGGAGATACTCT</th>\n",
" <td>0.918162</td>\n",
" <td>0.000000</td>\n",
" <td>0.888627</td>\n",
" <td>0.814301</td>\n",
" <td>0.888224</td>\n",
" <td>0.775154</td>\n",
" <td>0.864082</td>\n",
" <td>0.872293</td>\n",
" <td>0.792043</td>\n",
" <td>0.891061</td>\n",
" <td>...</td>\n",
" <td>1.475448</td>\n",
" <td>1.525941</td>\n",
" <td>1.477240</td>\n",
" <td>1.422941</td>\n",
" <td>1.412374</td>\n",
" <td>1.502348</td>\n",
" <td>1.334146</td>\n",
" <td>1.324370</td>\n",
" <td>1.351118</td>\n",
" <td>1.517250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>r1_CGTCTACATCCG</th>\n",
" <td>0.902278</td>\n",
" <td>0.888627</td>\n",
" <td>0.000000</td>\n",
" <td>0.903161</td>\n",
" <td>0.906219</td>\n",
" <td>0.875346</td>\n",
" <td>0.959543</td>\n",
" <td>1.039472</td>\n",
" <td>0.856018</td>\n",
" <td>0.934093</td>\n",
" <td>...</td>\n",
" <td>1.490314</td>\n",
" <td>1.504615</td>\n",
" <td>1.504057</td>\n",
" <td>1.427930</td>\n",
" <td>1.427975</td>\n",
" <td>1.486766</td>\n",
" <td>1.397166</td>\n",
" <td>1.372613</td>\n",
" <td>1.323565</td>\n",
" <td>1.509198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>r1_CAAGCTTGGCGC</th>\n",
" <td>0.915303</td>\n",
" <td>0.814301</td>\n",
" <td>0.903161</td>\n",
" <td>0.000000</td>\n",
" <td>0.878356</td>\n",
" <td>0.710905</td>\n",
" <td>0.882551</td>\n",
" <td>0.866597</td>\n",
" <td>0.812149</td>\n",
" <td>0.797162</td>\n",
" <td>...</td>\n",
" <td>1.451034</td>\n",
" <td>1.488795</td>\n",
" <td>1.470556</td>\n",
" <td>1.439459</td>\n",
" <td>1.379516</td>\n",
" <td>1.519483</td>\n",
" <td>1.277252</td>\n",
" <td>1.377088</td>\n",
" <td>1.310467</td>\n",
" <td>1.520263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>r1_ACTCACATAGAG</th>\n",
" <td>0.894357</td>\n",
" <td>0.888224</td>\n",
" <td>0.906219</td>\n",
" <td>0.878356</td>\n",
" <td>0.000000</td>\n",
" <td>0.876488</td>\n",
" <td>0.845955</td>\n",
" <td>0.942022</td>\n",
" <td>0.838919</td>\n",
" <td>0.931624</td>\n",
" <td>...</td>\n",
" <td>1.486181</td>\n",
" <td>1.539323</td>\n",
" <td>1.507835</td>\n",
" <td>1.446209</td>\n",
" <td>1.411609</td>\n",
" <td>1.538570</td>\n",
" <td>1.343948</td>\n",
" <td>1.403119</td>\n",
" <td>1.388516</td>\n",
" <td>1.511771</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 300 columns</p>\n",
"</div>"
],
"text/plain": [
" r1_TTCCTGCTAGGC r1_TGGAGATACTCT r1_CGTCTACATCCG \\\n",
"r1_TTCCTGCTAGGC 0.000000 0.918162 0.902278 \n",
"r1_TGGAGATACTCT 0.918162 0.000000 0.888627 \n",
"r1_CGTCTACATCCG 0.902278 0.888627 0.000000 \n",
"r1_CAAGCTTGGCGC 0.915303 0.814301 0.903161 \n",
"r1_ACTCACATAGAG 0.894357 0.888224 0.906219 \n",
"\n",
" r1_CAAGCTTGGCGC r1_ACTCACATAGAG r1_TAACGGACACGC \\\n",
"r1_TTCCTGCTAGGC 0.915303 0.894357 0.813966 \n",
"r1_TGGAGATACTCT 0.814301 0.888224 0.775154 \n",
"r1_CGTCTACATCCG 0.903161 0.906219 0.875346 \n",
"r1_CAAGCTTGGCGC 0.000000 0.878356 0.710905 \n",
"r1_ACTCACATAGAG 0.878356 0.000000 0.876488 \n",
"\n",
" r1_CGCATGGGATAC r1_TAACGACGCTTG r1_TCGGCAGCCTCT \\\n",
"r1_TTCCTGCTAGGC 0.935558 0.962057 0.865809 \n",
"r1_TGGAGATACTCT 0.864082 0.872293 0.792043 \n",
"r1_CGTCTACATCCG 0.959543 1.039472 0.856018 \n",
"r1_CAAGCTTGGCGC 0.882551 0.866597 0.812149 \n",
"r1_ACTCACATAGAG 0.845955 0.942022 0.838919 \n",
"\n",
" r1_TAGGATGCAAAC ... r1_AGGGTGGGTACA \\\n",
"r1_TTCCTGCTAGGC 0.862869 ... 1.501597 \n",
"r1_TGGAGATACTCT 0.891061 ... 1.475448 \n",
"r1_CGTCTACATCCG 0.934093 ... 1.490314 \n",
"r1_CAAGCTTGGCGC 0.797162 ... 1.451034 \n",
"r1_ACTCACATAGAG 0.931624 ... 1.486181 \n",
"\n",
" r1_AATGCTGCAAGA r1_GTCGGGCCTTTC r1_GGGTCAGCGGCG \\\n",
"r1_TTCCTGCTAGGC 1.573992 1.543429 1.457653 \n",
"r1_TGGAGATACTCT 1.525941 1.477240 1.422941 \n",
"r1_CGTCTACATCCG 1.504615 1.504057 1.427930 \n",
"r1_CAAGCTTGGCGC 1.488795 1.470556 1.439459 \n",
"r1_ACTCACATAGAG 1.539323 1.507835 1.446209 \n",
"\n",
" r1_CTGGACCTGCCC r1_AAGATATTGCTG r1_GAGACCTCATGG \\\n",
"r1_TTCCTGCTAGGC 1.463168 1.556343 1.411282 \n",
"r1_TGGAGATACTCT 1.412374 1.502348 1.334146 \n",
"r1_CGTCTACATCCG 1.427975 1.486766 1.397166 \n",
"r1_CAAGCTTGGCGC 1.379516 1.519483 1.277252 \n",
"r1_ACTCACATAGAG 1.411609 1.538570 1.343948 \n",
"\n",
" r1_CGGAGCGCGACA r1_AAGGACAGATCC r1_ATATGCACCCTA \n",
"r1_TTCCTGCTAGGC 1.410397 1.393785 1.554919 \n",
"r1_TGGAGATACTCT 1.324370 1.351118 1.517250 \n",
"r1_CGTCTACATCCG 1.372613 1.323565 1.509198 \n",
"r1_CAAGCTTGGCGC 1.377088 1.310467 1.520263 \n",
"r1_ACTCACATAGAG 1.403119 1.388516 1.511771 \n",
"\n",
"[5 rows x 300 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"distances = networkplots.correlation_to_distance(correlations)\n",
"distances.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise 2\n",
"\n",
"Let's take a look at our values to make sure we have most of our values far away from zero. Use `sns.distplot` to look the flattened values of the `distances` dataframe."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# YOUR CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11be4dfd0>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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UIhdxufl2t793bzuJZJpw0DfveuL9I1M89coFXjnRRzjo4yN72/C6eN3xxVTCeitK5CJl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\nJXIREZdTIhcRcTklchERl/v/66lqyBRJD4cAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11c1254e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(distances.values.flat)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll run `phenograph.cluster`, which returns three items:\n",
"\n",
"- `communities`: the cluster labels of each cell\n",
"- `sparse_matrix`: a sparse matrix representing the connections between cells in the graph\n",
"- `Q`: the modularity score. Higher is better, and the highest is 1. \n",
" - 0 means your graph is randomly connected and -1 means your graph isn't connected at all."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finding 10 nearest neighbors using minkowski metric and 'auto' algorithm\n",
"Neighbors computed in 0.10953593254089355 seconds\n",
"Jaccard graph constructed in 0.05929207801818848 seconds\n",
"Wrote graph to binary file in 0.005506038665771484 seconds\n",
"Running Louvain modularity optimization\n",
"After 1 runs, maximum modularity is Q = 0.862174\n",
"Louvain completed 21 runs in 0.21957707405090332 seconds\n",
"PhenoGraph complete in 0.41158509254455566 seconds\n"
]
}
],
"source": [
"communities, sparse_matrix, Q = phenograph.cluster(distances, k=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at each of these returned values"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 8,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 4, 4, 6, 4, 4, 6, 4, 4, 4, 6, 6, 4, 4, 6, 6, 4, 6, 0, 6,\n",
" 4, 0, 6, 4, 4, 6, 4, 4, 4, 4, 6, 6, 4, 6, 4, 6, 6, 4, 4, 4, 4, 6, 4,\n",
" 4, 4, 0, 4, 6, 4, 6, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1,\n",
" 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 5, 9, 5, 9, 5, 5, 5, 9, 5, 0, 1,\n",
" 5, 5, 5, 5, 5, 5, 9, 5, 5, 9, 0, 5, 9, 9, 5, 0, 1, 5, 9, 7, 2, 2, 5,\n",
" 5, 9, 9, 5, 5, 5, 5, 5, 9, 7, 5, 9, 9, 5, 5, 9, 2, 8, 2, 2, 2, 2, 2,\n",
" 2, 2, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 8, 2, 0, 2, 2, 2, 8, 2, 2, 2, 2,\n",
" 8, 2, 2, 2, 2, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 8, 2, 2, 8, 2, 3, 3, 3,\n",
" 3, 3, 7, 7, 3, 7, 3, 7, 3, 3, 3, 3, 7, 7, 3, 3, 7, 3, 3, 3, 3, 3, 3,\n",
" 7, 3, 7, 3, 3, 7, 3, 3, 7, 3, 3, 3, 3, 3, 3, 3, 3, 3, 7, 3, 7, 7, 7,\n",
" 3])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"communities"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<300x300 sparse matrix of type '<class 'numpy.float64'>'\n",
"\twith 2018 stored elements in COOrdinate format>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sparse_matrix"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.862174"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Q"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks like the `communities` labels each cell as belonging to a particular cluster, the `sparse_matrix` is some data type that we can't directly investigate, and `Q` is the modularity value.\n",
"\n",
"## Make a graph from the sparse matrix\n",
"\n",
"To be able to lay out our graph in two dimensions, we'll use the `networkx` Python Package to build the graph and lay out the cells and edges."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<networkx.classes.graph.Graph at 0x11c2926d8>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"graph = networkx.from_scipy_sparse_matrix(sparse_matrix)\n",
"graph"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll use the \"Spring layout\" which is a force-directed layout that pushes cells and edges away from each other. We'll use the built-in networkx function called `spring_layout` on our graph:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{0: array([ 0.11858926, 0.18350153]),\n",
" 1: array([ 0.03878882, 0.52678488]),\n",
" 2: array([ 0.06187783, 0.27057012]),\n",
" 3: array([ 0.13133896, 0.82526991]),\n",
" 4: array([ 0.02870572, 0.35504813]),\n",
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" 63: array([ 0.63714297, 0.06584988]),\n",
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" 69: array([ 0.1265545, 0.2445429]),\n",
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" 72: array([ 0.06431486, 0.35483615]),\n",
" 73: array([ 0.0111814 , 0.47287149]),\n",
" 74: array([ 0.69037477, 0.07613188]),\n",
" 75: array([ 0.00425981, 0.52980379]),\n",
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" 79: array([ 0.51015523, 0.09267429]),\n",
" 80: array([ 0.68330104, 0.05716341]),\n",
" 81: array([ 0.00408352, 0.527812 ]),\n",
" 82: array([ 0.77642727, 0.10082551]),\n",
" 83: array([ 0.22806867, 0.20491782]),\n",
" 84: array([ 0.47912235, 0.10843628]),\n",
" 85: array([ 0.40731504, 0.13983928]),\n",
" 86: array([ 0.01428922, 0.61362392]),\n",
" 87: array([ 0.01698228, 0.40674804]),\n",
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" 90: array([ 0.70776335, 0.0493038 ]),\n",
" 91: array([ 0.17446053, 0.24719983]),\n",
" 92: array([ 0.01190982, 0.43995485]),\n",
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" 94: array([ 0.02166033, 0.64824161]),\n",
" 95: array([ 0.01860526, 0.62750625]),\n",
" 96: array([ 0.71821931, 0.05756627]),\n",
" 97: array([ 0.0177259 , 0.39470809]),\n",
" 98: array([ 0.72571335, 0.06636586]),\n",
" 99: array([ 0.08967582, 0.22495115]),\n",
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" 101: array([ 0.98837644, 0.497194 ]),\n",
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" 106: array([ 0.9596544 , 0.31595962]),\n",
" 107: array([ 0.86377267, 0.17692907]),\n",
" 108: array([ 0.99378177, 0.52236804]),\n",
" 109: array([ 0.90747383, 0.22506561]),\n",
" 110: array([ 0.97086967, 0.3679172 ]),\n",
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" 128: array([ 0.97536851, 0.35652447]),\n",
" 129: array([ 0.95829931, 0.32634563]),\n",
" 130: array([ 0.95344059, 0.52143394]),\n",
" 131: array([ 0.89012132, 0.19544624]),\n",
" 132: array([ 0.98646658, 0.60576853]),\n",
" 133: array([ 0.89016702, 0.24989385]),\n",
" 134: array([ 0.86459339, 0.20256509]),\n",
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" 147: array([ 0.99222004, 0.4461101 ]),\n",
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" 149: array([ 0.99495157, 0.48269002]),\n",
" 150: array([ 0.90896699, 0.77189806]),\n",
" 151: array([ 0.67997352, 0.52313676]),\n",
" 152: array([ 0.90026074, 0.78238633]),\n",
" 153: array([ 0.79586975, 0.54205845]),\n",
" 154: array([ 0.86034775, 0.80829885]),\n",
" 155: array([ 0.94548914, 0.70568122]),\n",
" 156: array([ 0.92952805, 0.68695948]),\n",
" 157: array([ 0.6402237, 0.5471598]),\n",
" 158: array([ 0.79275356, 0.89497899]),\n",
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" 160: array([ 0.96203807, 0.56936733]),\n",
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" 164: array([ 0.8729441 , 0.82506921]),\n",
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" 167: array([ 0.64613269, 0.51331429]),\n",
" 168: array([ 0.9363757 , 0.73048091]),\n",
" 169: array([ 0.91346939, 0.75792119]),\n",
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" 171: array([ 0.08362013, 0.42027785]),\n",
" 172: array([ 0.85049408, 0.84362845]),\n",
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" 174: array([ 0.54611298, 0.49816149]),\n",
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" 176: array([ 0.0706183 , 0.47683742]),\n",
" 177: array([ 0.8976432 , 0.20514341]),\n",
" 178: array([ 0.9444338 , 0.67723808]),\n",
" 179: array([ 0.53132185, 0.53567054]),\n",
" 180: array([ 0.19037035, 0.88790289]),\n",
" 181: array([ 0.25362351, 0.07314998]),\n",
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" 184: array([ 0.92879871, 0.63705438]),\n",
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" 187: array([ 0.87683231, 0.80212795]),\n",
" 188: array([ 0.92445702, 0.73770328]),\n",
" 189: array([ 0.85559571, 0.83295749]),\n",
" 190: array([ 0.88677724, 0.79362932]),\n",
" 191: array([ 0.75900416, 0.92347129]),\n",
" 192: array([ 0.64267299, 0.48625357]),\n",
" 193: array([ 0.25252546, 0.91460733]),\n",
" 194: array([ 0.82512586, 0.86612242]),\n",
" 195: array([ 0.41015457, 0.50837184]),\n",
" 196: array([ 0.56319261, 0.53892885]),\n",
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" 198: array([ 0.91227493, 0.74383405]),\n",
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" 200: array([ 0.3813931 , 0.02300292]),\n",
" 201: array([ 0.70901658, 0.58662827]),\n",
" 202: array([ 0.39338258, 0.02832884]),\n",
" 203: array([ 0.5583593 , 0.00323563]),\n",
" 204: array([ 0.37176388, 0.0184432 ]),\n",
" 205: array([ 0.52730117, 0.01794663]),\n",
" 206: array([ 0.51119407, 0.00843315]),\n",
" 207: array([ 0.41304334, 0.01986438]),\n",
" 208: array([ 0.61485994, 0.02027692]),\n",
" 209: array([ 0.63672765, 0.79472749]),\n",
" 210: array([ 0.76443832, 0.74614683]),\n",
" 211: array([ 0.5701922 , 0.01338734]),\n",
" 212: array([ 0.6697132 , 0.24201824]),\n",
" 213: array([ 0.28264041, 0.05937227]),\n",
" 214: array([ 0.52155121, 0.00343442]),\n",
" 215: array([ 0.36434653, 0.03729801]),\n",
" 216: array([ 0.44115074, 0.01214266]),\n",
" 217: array([ 0.60667485, 0.17793386]),\n",
" 218: array([ 0.40606777, 0.03540001]),\n",
" 219: array([ 0.76754114, 0.78613883]),\n",
" 220: array([ 0.29432858, 0.05408193]),\n",
" 221: array([ 0.06213309, 0.28185572]),\n",
" 222: array([ 0.58932094, 0.02081146]),\n",
" 223: array([ 0.66239477, 0.03629662]),\n",
" 224: array([ 0.54454705, 0.0128397 ]),\n",
" 225: array([ 0.6120928 , 0.77825351]),\n",
" 226: array([ 0.47865317, 0.01692952]),\n",
" 227: array([ 0.30811342, 0.0525207 ]),\n",
" 228: array([ 0.42488747, 0.03639465]),\n",
" 229: array([ 0.5346145 , 0.00481447]),\n",
" 230: array([ 0.7257613 , 0.75928113]),\n",
" 231: array([ 0.46363127, 0. ]),\n",
" 232: array([ 0.42856612, 0.0203981 ]),\n",
" 233: array([ 0.57821369, 0.01195448]),\n",
" 234: array([ 0.32324787, 0.0521844 ]),\n",
" 235: array([ 0.73460897, 0.80638286]),\n",
" 236: array([ 0.6818737 , 0.82052141]),\n",
" 237: array([ 0.3523324 , 0.03349241]),\n",
" 238: array([ 0.63230513, 0.0207339 ]),\n",
" 239: array([ 0.49261184, 0.00462282]),\n",
" 240: array([ 0.46140279, 0.01268595]),\n",
" 241: array([ 0.65651195, 0.02538416]),\n",
" 242: array([ 0.49784712, 0.00826196]),\n",
" 243: array([ 0.60522239, 0.01485168]),\n",
" 244: array([ 0.26778128, 0.06300987]),\n",
" 245: array([ 0.69898669, 0.75710664]),\n",
" 246: array([ 0.34063225, 0.04043316]),\n",
" 247: array([ 0.32681081, 0.03938789]),\n",
" 248: array([ 0.6832224 , 0.79131094]),\n",
" 249: array([ 0.67434037, 0.03125217]),\n",
" 250: array([ 0.30703108, 0.96094993]),\n",
" 251: array([ 0.41960667, 0.99206416]),\n",
" 252: array([ 0.64554356, 0.96822995]),\n",
" 253: array([ 0.63449405, 0.97354638]),\n",
" 254: array([ 0.29126739, 0.95144391]),\n",
" 255: array([ 0.27020626, 0.90478704]),\n",
" 256: array([ 0.16516003, 0.87004806]),\n",
" 257: array([ 0.60686871, 0.98600139]),\n",
" 258: array([ 0.3424023, 0.9222445]),\n",
" 259: array([ 0.35883378, 0.97506399]),\n",
" 260: array([ 0.38256917, 0.93553982]),\n",
" 261: array([ 0.44187846, 0.99365616]),\n",
" 262: array([ 0.57475574, 0.97987729]),\n",
" 263: array([ 0.35991017, 0.97621658]),\n",
" 264: array([ 0.51846526, 0.978178 ]),\n",
" 265: array([ 0.45197268, 0.96352601]),\n",
" 266: array([ 0.49889408, 0.99401143]),\n",
" 267: array([ 0.48968649, 0.98680291]),\n",
" 268: array([ 0.32685562, 0.96555533]),\n",
" 269: array([ 0.39816221, 0.96641151]),\n",
" 270: array([ 0.59309661, 0.98745052]),\n",
" 271: array([ 0.56776369, 0.99089595]),\n",
" 272: array([ 0.33929992, 0.96538033]),\n",
" 273: array([ 0.51384883, 0.99606347]),\n",
" 274: array([ 0.46102988, 0.99277387]),\n",
" 275: array([ 0.3712744 , 0.97363884]),\n",
" 276: array([ 0.38215318, 0.96936228]),\n",
" 277: array([ 0.47960387, 1. ]),\n",
" 278: array([ 0.22322207, 0.90268936]),\n",
" 279: array([ 0.34463582, 0.97388282]),\n",
" 280: array([ 0.66791789, 0.96455161]),\n",
" 281: array([ 0.42361231, 0.9535159 ]),\n",
" 282: array([ 0.39576707, 0.986615 ]),\n",
" 283: array([ 0.28010021, 0.94675327]),\n",
" 284: array([ 0.51436653, 0.9936281 ]),\n",
" 285: array([ 0.3195008 , 0.96414848]),\n",
" 286: array([ 0.65775055, 0.97238168]),\n",
" 287: array([ 0.6281704 , 0.98057599]),\n",
" 288: array([ 0.47415923, 0.99180317]),\n",
" 289: array([ 0.38073448, 0.98682451]),\n",
" 290: array([ 0.26133079, 0.9368331 ]),\n",
" 291: array([ 0.61414895, 0.9788418 ]),\n",
" 292: array([ 0.4319572, 0.9903854]),\n",
" 293: array([ 0.41210286, 0.99199226]),\n",
" 294: array([ 0.32135049, 0.93487226]),\n",
" 295: array([ 0.68440526, 0.96098115]),\n",
" 296: array([ 0.2783036 , 0.92472019]),\n",
" 297: array([ 0.22631023, 0.88372588]),\n",
" 298: array([ 0.23943028, 0.90975154]),\n",
" 299: array([ 0.55266889, 0.99504838])}"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"positions = networkx.spring_layout(graph)\n",
"positions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert `positions` dict to dataframe with node information\n",
"\n",
"This `positions` dataframe is a dictionary mapping the node id (in this case, a number) and the $(x, y)$ position. The nodes are in exactly the same order as the rows of the `distances` dataframe we gave `phenograph.cluster`."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"networkplots.get_nodes_specs??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like this function can deal with if we already have some clusters defined in our metadata! Let's look at our `cell_metadata` and remind ourselves of which column we might like to use for the `other_cluster_col` value."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>cluster_id</th>\n",
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],
"text/plain": [
" cluster_id celltype cluster_n cluster_n_celltype \\\n",
"r1_TTCCTGCTAGGC cluster_24 Rods 24 #24 (Rods) \n",
"r1_TGGAGATACTCT cluster_24 Rods 24 #24 (Rods) \n",
"r1_CGTCTACATCCG cluster_24 Rods 24 #24 (Rods) \n",
"r1_CAAGCTTGGCGC cluster_24 Rods 24 #24 (Rods) \n",
"r1_ACTCACATAGAG cluster_24 Rods 24 #24 (Rods) \n",
"\n",
" cluster_celltype_with_id \n",
"r1_TTCCTGCTAGGC Rods (cluster_24) \n",
"r1_TGGAGATACTCT Rods (cluster_24) \n",
"r1_CGTCTACATCCG Rods (cluster_24) \n",
"r1_CAAGCTTGGCGC Rods (cluster_24) \n",
"r1_ACTCACATAGAG Rods (cluster_24) "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cell_metadata.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, I'd like to use the `cluster_n_celltype` column.\n",
"\n",
"Let's take a look at the code again to see how the `networkplots.get_nodes_specs` function uses the `metadata`:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"networkplots.get_nodes_specs??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like this function uses another one, called `labels_to_colors` -- what does that do?"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"networkplots.labels_to_colors??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's use `get_nodes_specs` to create a dataframe of information about nodes so we can plot them."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(300, 11)\n"
]
},
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" <th>cluster_id</th>\n",
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" <td>r1_GCGTGCTACTAC</td>\n",
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" <td>#66c2a5</td>\n",
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],
"text/plain": [
" xs ys community barcode cluster_id celltype \\\n",
"0 0.118589 0.183502 Community #0 r1_TTCCTGCTAGGC cluster_24 Rods \n",
"28 0.020176 0.367893 Community #0 r1_ATGGCTCGCAAA cluster_24 Rods \n",
"176 0.070618 0.476837 Community #0 r1_CGATGGCTGGAC cluster_24 Rods \n",
"26 0.102607 0.389927 Community #0 r1_GCGTGCTACTAC cluster_24 Rods \n",
"3 0.131339 0.825270 Community #0 r1_GGTAAGGCGCTC cluster_24 Rods \n",
"\n",
" cluster_n cluster_n_celltype cluster_celltype_with_id \\\n",
"0 24 #24 (Rods) Rods (cluster_24) \n",
"28 24 #24 (Rods) Rods (cluster_24) \n",
"176 24 #24 (Rods) Rods (cluster_24) \n",
"26 24 #24 (Rods) Rods (cluster_24) \n",
"3 24 #24 (Rods) Rods (cluster_24) \n",
"\n",
" other_cluster_color community_color \n",
"0 #66c2a5 #66c2a5 \n",
"28 #66c2a5 #66c2a5 \n",
"176 #66c2a5 #66c2a5 \n",
"26 #66c2a5 #66c2a5 \n",
"3 #66c2a5 #66c2a5 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nodes_specs = networkplots.get_nodes_specs(\n",
" positions, cell_metadata, distances.index, \n",
" communities, other_cluster_col='cluster_n_celltype',\n",
" palette='Set2')\n",
"print(nodes_specs.shape)\n",
"nodes_specs.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Convert `positions` dict to dataframe with *edge* information\n",
"\n",
"We've now created a dataframe containing the x,y positions, the community labels, and the colors for the communities and other clusters we were interested in. Now we want to do the same for the edges (lines between cells).\n",
"\n",
"Let's take a look at the function we'll use:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"networkplots.get_edges_specs??"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"What arguments does it take? What does it do with them? What does it return?\n",
"\n",
"### Exercise 3\n",
"\n",
"Create a variable called `edges_specs` using the `networkplots.get_edges_specs` and the correct inputs."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# YOUR CODE HERE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2018, 3)\n"
]
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"text/plain": [
" xs ys \\\n",
"0 [0.118589262338, 0.173616226047] [0.183501529868, 0.140918929513] \n",
"1 [0.118589262338, 0.0308378367884] [0.183501529868, 0.425073372097] \n",
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"4 [0.118589262338, 0.202089396812] [0.183501529868, 0.124308686151] \n",
"\n",
" alphas \n",
"0 0.283333 \n",
"1 0.191667 \n",
"2 0.229412 \n",
"3 0.140741 \n",
"4 0.164706 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"edges_specs = networkplots.get_edges_specs(graph, positions)\n",
"print(edges_specs.shape)\n",
"edges_specs.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To be able to use the dataframes with the Bokeh plotting language, we need to convert our dataframes into `ColumnDataSource` objects."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nodes_source = ColumnDataSource(nodes_specs)\n",
"edges_source = ColumnDataSource(edges_specs)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
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{\"5589acc4-61e8-4622-b12d-175e4d7511f8\":{\"roots\":{\"references\":[{\"attributes\":{\"callback\":null,\"column_names\":[\"xs\",\"ys\",\"community\",\"barcode\",\"cluster_id\",\"celltype\",\"cluster_n\",\"cluster_n_celltype\",\"cluster_celltype_with_id\",\"other_cluster_color\",\"community_color\",\"index\"],\"data\":{\"barcode\":[\"r1_TTCCTGCTAGGC\",\"r1_ATGGCTCGCAAA\",\"r1_CGATGGCTGGAC\",\"r1_GCGTGCTACTAC\",\"r1_GGTAAGGCGCTC\",\"r1_GCAACTGAGTGA\",\"r1_ACGGAGTGGTAT\",\"r1_TGTAGGAGAATC\",\"r1_CCTTCCTTACCA\",\"r1_CCTCATAGCGGC\",\"r1_AGTATAGAATCG\",\"r1_TTTACTTCAAGG\",\"r1_TGCTCGTCCGTT\",\"r1_ATTGTACAGTCG\",\"r1_AGTTATGGTCCC\",\"r1_TCTATCTTCAGG\",\"r1_AGTACATTATGT\",\"r1_CATATACTATAC\",\"r1_ATCATCTTCATG\",\"r1_CGAGAAGTGGAC\",\"r1_AGTACGTGGGGT\",\"r1_CGCCTATGTCGT\",\"r1_AAAAGCTTACAA\",\"r1_CGGCATGTGCTA\",\"r1_ATGATTATGGTT\",\"r1_CTGGTTCGGACG\",\"r1_TTAATGACTACA\",\"r1_CGGCTGTCTGCT\",\"r1_TGGAGATACTCT\",\"r1_CGTCTACATCCG\",\"r1_CAAGCTTGGCGC\",\"r1_ACTCACATAGAG\",\"r1_TAACGGACACGC\",\"r1_CGCATGGGATAC\",\"r1_TCGGCAGCCTCT\",\"r1_TAGGATGCAAAC\",\"r1_CGGTTACAGTAG\",\"r1_AATCGGATACGT\",\"r1_TAACGACGCTTG\",\"r1_CGCCCGTCTGTA\",\"r1_TTCACCTACCGC\",\"r1_TTATGTCGTCCT\",\"r1_ATCAGCGCAGTC\",\"r1_CTTTATGGTGAC\",\"r1_GAATCGGGAACA\",\"r1_GAAGTGATCACC\",\"r1_AGTGGGCGGCCG\",\"r1_ACTGATGATTAA\",\"r1_AGTGGGCTTGAG\",\"r1_GGGCTTGGGAAG\",\"r1_CAGAATCGTCGG\",\"r1_GACTTTCGTGAG\",\"r1_TGTTTTGATTTG\",\"r1_AAGATTAGAACT\",\"r1_TTCTTATTTCAA\",\"r1_GTGCGCGTATTA\",\"r1_ATCTGCACACCT\",\"r1_GGGATCTCCTCG\",\"r1_AATCGGTGACTA\",\"r1_TTCCACCAACCT\",\"r1_AACCCGTAGTTA\",\"r1_CCGCTGATGTAA\",\"r1_TGGGACTGCGGT\",\"r1_GTTCCGATATGT\",\"r1_GGTAGAATTAGC\",\"r1_GTTGCGCTTTGG\",\"r1_TGGTCGCTCGGA\",\"r1_ACGGATGCTTCC\",\"r1_AGGCCTCATGGT\",\"r1_CAACTTGGGCCA\",\"r1_GGTCTCCGATTT\",\"r1_ACTCGCCCAACG\",\"r1_GCTATAACGAGC\",\"r1_CCTAAGGACCAA\",\"r1_CCCGAATGATTA\",\"r1_CCAGAATTCTGG\",\"r1_TCTGGGGCGGCC\",\"r1_TCCTTGCCATAG\",\"r1_GCAAACGTTTTG\",\"r1_CCTTACGACGAC\",\"r1_AAGTTGTGGGCC\",\"r1_CAACTGTTACCC\",\"r1_ATGTAAAAACGC\",\"r1_TTCTTTTTTCAA\",\"r1_CCTGTACTTCAA\",\"r1_CCTAGTAGTGGT\",\"r1_CCAAATGGTAGG\",\"r1_ATGTATATTCCA\",\"r1_GAGCCCGTGAAT\",\"r1_CGGGCAGACCCG\",\"r1_TAACTAGTCCGG\",\"r1_CGTCTCATTGGC\",\"r1_TATTCGCACGCT\",\"r1_CCATCTAGGTCG\",\"r1_CCCAACAACTAG\",\"r1_AGAGCCCGCGCC\",\"r1_CCCAGGTGTTTA\",\"r1_GTTTTCGGCTTT\",\"r1_GGTTGTGCAAAC\",\"r1_TGTGTACGCGTA\",\"r1_CTGAATATTTAC\",\"r1_GAGGACTGGTGG\",\"r1_AGATACTGAGAG\",\"r1_ACCGTTATGGGT\",\"r1_GTCCACATACCC\",\"r1_TGAGAAAACTTT\",\"r1_GCACCAGCCTGT\",\"r1_AGAATCTCATTT\",\"r1_GTTTTGCATTCG\",\"r1_GATCGTCATTTG\",\"r1_GACGCGGTCATA\",\"r1_TGGTGATGACAT\",\"r1_TAAAAGGTGGAC\",\"r1_CCTGTCAGTCTT\",\"r1_ACCTTACCTTGG\",\"r1_TGTGACGCGTGC\",\"r1_CGGGCTTGCATC\",\"r1_GACCCACTGGGC\",\"r1_TCAACAGCAATG\",\"r1_GCCGATTAGGAG\",\"r1_TGCCGAGTAGCA\",\"r1_AGATGCATGAGA\",\"r1_GAATTAGGCGGT\",\"r1_GTTGAATCCCTG\",\"r1_TCTAAGTTCTGA\",\"r1_GTGAAACTTCTG\",\"r1_TGAATCTCCGCG\",\"r1_GATTTGCTGCAT\",\"r1_CATCCCTTCGAC\",\"r1_AAATCATCTACA\",\"r1_GGACCTAGTGAG\",\"r1_CGATATCGCGTG\",\"r1_TCACGGCAATAT\",\"r1_GTCCCCCTCTTA\",\"r1_CATTATTTTTAC\",\"r1_CACTACAGCGAG\",\"r1_ACGACGGCTGCA\",\"r1_GGCGCTAGCGGG\",\"r1_GAATATATCAGA\",\"r1_TCCTGCTACGAT\",\"r1_GTCATCCAAGTA\",\"r1_GTAGCTGGATCC\",\"r1_TAATCGCGCCAA\",\"r1_CGAGCCCTATGC\",\"r1_CTGACCGACTTC\",\"r1_GACTTTAAAGAA\",\"r1_CGCGCATGTGCG\",\"r1_TATGCGTTAATG\",\"r1_AGTTAACATAGC\",\"r1_ACATCACTACGT\",\"r1_TGAGGACCCTGA\",\"r1_GAATCGCCTCCG\",\"r1_ATACTGCGATGT\",\"r1_ACGCTGTAATTC\",\"r1_CGAAGGTGTTAC\",\"r1_TAGATTATTCAT\",\"r1_GTGACTTGGCCT\",\"r1_GAACTTGACTCA\",\"r1_TGATTCATCCTC\",\"r1_ATACTGATAGTC\",\"r1_AGAGGATTACGA\",\"r1_GGAGAGAGGTAT\",\"r1_GATTAATAAGCT\",\"r1_AGAGATCGACTT\",\"r1_ATCAACAGGCGC\",\"r1_ACTAGTAGGGCG\",\"r1_CCTGCATGGTGA\",\"r1_TCATGCCGTTTT\",\"r1_TGGCAGATTTCC\",\"r1_CGAATCGAGTTT\",\"r1_CGTCTTACTCTC\",\"r1_TAGCAGGCACCG\",\"r1_CGAGACAGACCC\",\"r1_GGCACCTATAGA\",\"r1_CAAATGTAGTAC\",\"r1_GCATAGTGGTTA\",\"r1_AACTCATCCAAG\",\"r1_TACCACTCCCAA\",\"r1_GTGGTATCTGCA\",\"r1_TAATTGATAACC\",\"r1_TTAGCCATTATA\",\"r1_CCAATCGCTCTT\",\"r1_CTGGGTTTGGAG\",\"r1_CGAAGTATACCG\",\"r1_TTCAGGTTTCAT\",\"r1_GACTAACAACCT\",\"r1_GTAGAGGGACGC\",\"r1_ACGTACATACGG\",\"r1_TACCCATGCTGG\",\"r1_AGCTTGGTATCT\",\"r1_TCCTATGTTTAT\",\"r1_TCTATCAGCCTG\",\"r1_CCTGACTCGGGT\",\"r1_TTTGTCTCACGA\",\"r1_TGCCGGCTCGGA\",\"r1_CTTAAAGTCCGC\",\"r1_GCGCTGTTGCGA\",\"r1_GTCGCGTACGCA\",\"r1_TCCGACAGTGTT\",\"r1_GCGCACAATCCC\",\"r1_CACTCAGAGTTG\",\"r1_GCGCCTTTCAAC\",\"r1_GTTGTCTATGGT\",\"r1_TATGACCTGAGC\",\"r1_AAGTATTATGTT\",\"r1_TACCATGTGTAC\",\"r1_ACGCTTGCCCTG\",\"r1_TGCTAGGGAACC\",\"r1_CACACTCCGTCG\",\"r1_CTTACTATTGGT\",\"r1_CACTGCTTGATA\",\"r1_AAGTTTTCATGC\",\"r1_CCGGAGTCCATA\",\"r1_CCGTGGGAACTA\",\"r1_GGGCCCTGGATT\",\"r1_GATTTCAGGCCG\",\"r1_CGGGAGGACTTC\",\"r1_GGAGTTAGCGTT\",\"r1_AGTAATCAGCAT\",\"r1_CCAAGGGCTCTT\",\"r1_GATTATCGAGGG\",\"r1_TGCAAGATAAAG\",\"r1_CCCTAGATTTTC\",\"r1_TAACAACTCCTA\",\"r1_TGAAACTTGTTT\",\"r1_TAATGCGAGCAC\",\"r1_ATATCGGAAGTG\",\"r1_AAACATTGATGA\",\"r1_ACTGTAGTGGAC\",\"r1_TAGAGGTATGAG\",\"r1_TCTTCATCACAA\",\"r1_GAATGCAGTTCT\",\"r1_AGAATTGCTTTA\",\"r1_ATAATTTGGGCG\",\"r1_AATATATCACCG\",\"r1_GTGCACTCCGAT\",\"r1_AAATGCGCCAAC\",\"r1_TATGGTAGCGCG\",\"r1_CAAGAGCTAAAC\",\"r1_GAATCGCCTTTA\",\"r1_GAACCTACTTAT\",\"r1_AATTCGTTAGGT\",\"r1_TACCCCGTTAGT\",\"r1_TCCAGTTGAATT\",\"r1_CAGTCATACAAG\",\"r1_GGTCTTCTACGG\",\"r1_GTCCGCAACAAC\",\"r1_CTTTTGACAGCC\",\"r1_CGCCCTACTCTA\",\"r1_ATATGAGTAGAT\",\"r1_TTTATATTTGGG\",\"r1_TCAAAGATAGGG\",\"r1_ACCATGTTGGGA\",\"r1_TATAGGAACAAA\",\"r1_GTTACACGAGTC\",\"r1_AGATCATCGTCC\",\"r1_TATACTAAGTTT\",\"r1_CATTGGTCTCAC\",\"r1_CGGATTTACACT\",\"r1_CACACCGCGTAG\",\"r1_CGGAGCGCGACA\",\"r1_TAGAGGCCTATA\",\"r1_TATAAAAAATTT\",\"r1_TCTAATATTCGC\",\"r1_AGGGTGGGTACA\",\"r1_AATGCTGCAAGA\",\"r1_GTCGGGCCTTTC\",\"r1_GGGTCAGCGGCG\",\"r1_CTGGACCTGCCC\",\"r1_AAGATATTGCTG\",\"r1_GAGACCTCATGG\",\"r1_GAAGAGTATCTT\",\"r1_GCTCGGTTAGTT\",\"r1_GGCGGACTGCGT\",\"r1_AGAATGGCGCAG\",\"r1_ATCAATATTCTC\",\"r1_AAGGACAGATCC\",\"r1_GGTGGGCCTAAT\",\"r1_GCCGTATTAAGA\",\"r1_AGACTTTCCCGG\",\"r1_TAACTCGATGCG\",\"r1_GTAATCCCCCAG\",\"r1_TCCCTCATCTCA\",\"r1_ATCTTGATTAAT\",\"r1_TCAAAAGTTGTC\",\"r1_AAGGTATTTCCG\",\"r1_TCTCTGTGACGC\",\"r1_AGCAATATCAAT\",\"r1_GTAATCCCCCAT\",\"r1_GAGGCTAGCTGT\",\"r1_TAATACAGGATT\",\"r1_TTGGTCCTAAGA\",\"r1_ATGACATGCGTC\",\"r1_CGCAACTTACTC\",\"r1_TGTTCTCCTTTC\",\"r1_GTGAAAGGATTA\",\"r1_CGAAACTATCGC\",\"r1_CCCCTCTCTGGC\",\"r1_TGCTAGTTTCAC\",\"r1_ATATGCACCCTA\"],\"celltype\":[\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Rods\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Cones\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Bipolar cells\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\",\"Muller glia\"],\"cluster_celltype_with_id\":[\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Rods (cluster_24)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Cones (cluster_25)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_26)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_27)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells (cluster_33)\",\"Bipolar cells 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" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
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" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
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" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
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" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
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"</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# --- First tab: KNN clustering --- #\n",
"tab1 = networkplots.plot_graph(nodes_source, edges_source, \n",
" legend_col='community',\n",
" color_col='community_color', tab=True,\n",
" title='KNN Clustering')\n",
"\n",
"# --- Second tab: Clusters from paper --- #\n",
"tab2 = networkplots.plot_graph(nodes_source, edges_source,\n",
" legend_col='cluster_n_celltype', tab=True,\n",
" color_col='other_cluster_color',\n",
" title=\"Clusters from paper\")\n",
"\n",
"tabs = Tabs(tabs=[tab1, tab2])\n",
"show(tabs)"
]
}
],
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"kernelspec": {
"display_name": "Python 3 (cshl-sca-2017)",
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"name": "cshl-sca-2017"
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| mit |
csieber/yt-dataset | notebooks/avg_quality.ipynb | 1 | 67416 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Average video quality"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The basic example shows how to plot the shaping to average quality level plot from the IFIP Networking 2016 publication."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading the dataset with pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remove warnings and show plots inline:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import the required modules:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pylab as plt\n",
"import scipy.stats"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read the dataset:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = pd.read_csv(\"../data/ifip_networking.csv.gz\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convert the shaping to Mbps:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data.loc[:,'shaping_mbps'] = data.loc[:,'net_avg_shaping_rate']*8/1000/1000\n",
"data.loc[:,'shaping_mbps_rounded'] = data.loc[:,'shaping_mbps'].round(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Definitions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dict for translating itags to quality levels and vice-versa:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ITAG_TO_QL = {160: 0,\n",
" 133: 1,\n",
" 134: 2,\n",
" 135: 3,\n",
" 136: 4}\n",
"QL_TO_ITAG = {v: k for k, v in ITAG_TO_QL.items()}\n",
"\n",
"VIDDEF = {160: {'label': '144p', 'color': 'green', 'resolution': '256x144'},\n",
" 133: {'label': '240p', 'color': 'red' , 'resolution': '320x240'},\n",
" 134: {'label': '360p', 'color': 'blue' , 'resolution': '480x360'},\n",
" 135: {'label': '480p', 'color': 'grey' , 'resolution': '640x480'},\n",
" 136: {'label': '720p', 'color': 'cyan' , 'resolution': '1280x720'}}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Confidence Interval:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def confintv_yerr(values):\n",
" n, min_max, mean, var, skew, kurt = scipy.stats.describe(values)\n",
" std = np.sqrt(var)\n",
"\n",
" intv = scipy.stats.t.interval(0.95,len(values)-1,loc=mean,scale=std/np.sqrt(len(values)))\n",
"\n",
" yerr = ((intv[1] - intv[0]) / 2)\n",
" \n",
" return yerr"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting shaping to average quality level"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The subsequent plot shows the fraction of time the video spent on the a certain quality level and the overall average quality level for a specific network shaping value. For example, at 2.2 Mbps, the player spends nearly 100% of the time on the highest quality level (480p)."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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e11p3BvoC45VSlS2ui9Nady/7+qdlS5RpPiGEEDWUl5fHnDlzmDRpEqGhofz444/06tXL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\n8ePlV141uqQaW7p0KXPmzCE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Ls6a122/LVrT2bsbjA0JY/vXnfD19Kl19PWnVopnRZQkh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2qqlnDP3ffw0b5DPPv8r+nsuuB2SEKkletrqrj91lv411+/yvsf7XI7HHGF5HYyglgsxsd79nDm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\nhBBCCDEKkkwJIYQQQozC/wdIW29AyTwrTAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14c33030f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(9, 7))\n",
"\n",
"plt.hold(True)\n",
"ax1 = fig.add_subplot(111)\n",
"\n",
"by_shaping = data.groupby('shaping_mbps').mean() \n",
"\n",
"y_offset = 0\n",
"cmap = plt.get_cmap('copper')\n",
"colors = iter(cmap(np.linspace(0,1,len(QL_TO_ITAG))))\n",
"\n",
"for ql,itag in list(QL_TO_ITAG.items())[0:4]:\n",
"\n",
" idx_itag = 'pl_time_spent_norm_itag%d' % itag\n",
" ax1.fill_between(by_shaping.index, \n",
" y_offset,\n",
" by_shaping[idx_itag],\n",
" alpha=0.35,\n",
" facecolor=next(colors))\n",
"\n",
" y_offset = by_shaping[idx_itag]\n",
"\n",
"plt.annotate(s=VIDDEF[QL_TO_ITAG[0]]['label'], xy=(0.46, 0.014))\n",
"plt.annotate(s=VIDDEF[QL_TO_ITAG[1]]['label'], xy=(0.65, 0.42))\n",
"plt.annotate(s=VIDDEF[QL_TO_ITAG[2]]['label'], xy=(1.05, 0.42))\n",
"plt.annotate(s=VIDDEF[QL_TO_ITAG[3]]['label'], xy=(1.6, 0.42))\n",
"\n",
"plt.ylabel(r\"Relative Playback Time $T_{fq}$\")\n",
"plt.xlabel(r\"Bandwidth $f$ (Mbps)\") \n",
"\n",
"ax2 = ax1.twinx()\n",
"\n",
"ax2_data = pd.DataFrame(columns=['shaping', 'avg_ql', 'yerr'])\n",
"for shaping,group in data.groupby('shaping_mbps'):\n",
"\n",
" ql_median = group['pl_avg_pl_quality_ql'].mean()\n",
" ql_yerr = confintv_yerr(group['pl_avg_pl_quality_ql'])\n",
"\n",
" ax2_data = ax2_data.append(pd.DataFrame([[shaping, ql_median, ql_yerr]], columns=ax2_data.columns))\n",
"\n",
"ax2_data.reset_index(drop=True)\n",
"\n",
"ax2.errorbar(ax2_data['shaping'], ax2_data['avg_ql'], yerr=list(ax2_data['yerr']), color='black')\n",
"\n",
"plt.ylabel(r\"Average Quality $J_f$\")\n",
"\n",
"max_mbps = 2.2\n",
"tl = [\"\"]*int(2.2/0.1)\n",
"tl[1] = \"0.5\"\n",
"tl[6] = \"1.0\"\n",
"tl[11] = \"1.5\"\n",
"tl[16] = \"2.0\"\n",
"plt.xticks(np.arange(by_shaping.index.min(), max_mbps, 0.1), tl)\n",
"\n",
"plt.xlim([by_shaping.index.min(), max_mbps])\n",
"_ = plt.ylim([0, 3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"Export notebook to HTML:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[NbConvertApp] Converting notebook avg_quality.ipynb to html\n",
"[NbConvertApp] Writing 272454 bytes to avg_quality.html\n"
]
}
],
"source": [
"!ipython nbconvert avg_quality.ipynb --to html"
]
}
],
"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": "3.4.4"
},
"toc": {
"toc_cell": false,
"toc_number_sections": true,
"toc_threshold": 4,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
liufuyang/deep_learning_tutorial | course-deeplearning.ai/course4-cnn/week1-cnn/Convolution+model+-+Step+by+Step+-+v2.ipynb | 1 | 56972 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Convolutional Neural Networks: Step by Step\n",
"\n",
"Welcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation. \n",
"\n",
"**Notation**:\n",
"- Superscript $[l]$ denotes an object of the $l^{th}$ layer. \n",
" - Example: $a^{[4]}$ is the $4^{th}$ layer activation. $W^{[5]}$ and $b^{[5]}$ are the $5^{th}$ layer parameters.\n",
"\n",
"\n",
"- Superscript $(i)$ denotes an object from the $i^{th}$ example. \n",
" - Example: $x^{(i)}$ is the $i^{th}$ training example input.\n",
" \n",
" \n",
"- Lowerscript $i$ denotes the $i^{th}$ entry of a vector.\n",
" - Example: $a^{[l]}_i$ denotes the $i^{th}$ entry of the activations in layer $l$, assuming this is a fully connected (FC) layer.\n",
" \n",
" \n",
"- $n_H$, $n_W$ and $n_C$ denote respectively the height, width and number of channels of a given layer. If you want to reference a specific layer $l$, you can also write $n_H^{[l]}$, $n_W^{[l]}$, $n_C^{[l]}$. \n",
"- $n_{H_{prev}}$, $n_{W_{prev}}$ and $n_{C_{prev}}$ denote respectively the height, width and number of channels of the previous layer. If referencing a specific layer $l$, this could also be denoted $n_H^{[l-1]}$, $n_W^{[l-1]}$, $n_C^{[l-1]}$. \n",
"\n",
"We assume that you are already familiar with `numpy` and/or have completed the previous courses of the specialization. Let's get started!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 - Packages\n",
"\n",
"Let's first import all the packages that you will need during this assignment. \n",
"- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n",
"- [matplotlib](http://matplotlib.org) is a library to plot graphs in Python.\n",
"- np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import h5py\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"np.random.seed(1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2 - Outline of the Assignment\n",
"\n",
"You will be implementing the building blocks of a convolutional neural network! Each function you will implement will have detailed instructions that will walk you through the steps needed:\n",
"\n",
"- Convolution functions, including:\n",
" - Zero Padding\n",
" - Convolve window \n",
" - Convolution forward\n",
" - Convolution backward (optional)\n",
"- Pooling functions, including:\n",
" - Pooling forward\n",
" - Create mask \n",
" - Distribute value\n",
" - Pooling backward (optional)\n",
" \n",
"This notebook will ask you to implement these functions from scratch in `numpy`. In the next notebook, you will use the TensorFlow equivalents of these functions to build the following model:\n",
"\n",
"<img src=\"images/model.png\" style=\"width:800px;height:300px;\">\n",
"\n",
"**Note** that for every forward function, there is its corresponding backward equivalent. Hence, at every step of your forward module you will store some parameters in a cache. These parameters are used to compute gradients during backpropagation. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3 - Convolutional Neural Networks\n",
"\n",
"Although programming frameworks make convolutions easy to use, they remain one of the hardest concepts to understand in Deep Learning. A convolution layer transforms an input volume into an output volume of different size, as shown below. \n",
"\n",
"<img src=\"images/conv_nn.png\" style=\"width:350px;height:200px;\">\n",
"\n",
"In this part, you will build every step of the convolution layer. You will first implement two helper functions: one for zero padding and the other for computing the convolution function itself. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.1 - Zero-Padding\n",
"\n",
"Zero-padding adds zeros around the border of an image:\n",
"\n",
"<img src=\"images/PAD.png\" style=\"width:600px;height:400px;\">\n",
"<caption><center> <u> <font color='purple'> **Figure 1** </u><font color='purple'> : **Zero-Padding**<br> Image (3 channels, RGB) with a padding of 2. </center></caption>\n",
"\n",
"The main benefits of padding are the following:\n",
"\n",
"- It allows you to use a CONV layer without necessarily shrinking the height and width of the volumes. This is important for building deeper networks, since otherwise the height/width would shrink as you go to deeper layers. An important special case is the \"same\" convolution, in which the height/width is exactly preserved after one layer. \n",
"\n",
"- It helps us keep more of the information at the border of an image. Without padding, very few values at the next layer would be affected by pixels as the edges of an image.\n",
"\n",
"**Exercise**: Implement the following function, which pads all the images of a batch of examples X with zeros. [Use np.pad](https://docs.scipy.org/doc/numpy/reference/generated/numpy.pad.html). Note if you want to pad the array \"a\" of shape $(5,5,5,5,5)$ with `pad = 1` for the 2nd dimension, `pad = 3` for the 4th dimension and `pad = 0` for the rest, you would do:\n",
"```python\n",
"a = np.pad(a, ((0,0), (1,1), (0,0), (3,3), (0,0)), 'constant', constant_values = (..,..))\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# GRADED FUNCTION: zero_pad\n",
"\n",
"def zero_pad(X, pad):\n",
" \"\"\"\n",
" Pad with zeros all images of the dataset X. The padding is applied to the height and width of an image, \n",
" as illustrated in Figure 1.\n",
" \n",
" Argument:\n",
" X -- python numpy array of shape (m, n_H, n_W, n_C) representing a batch of m images\n",
" pad -- integer, amount of padding around each image on vertical and horizontal dimensions\n",
" \n",
" Returns:\n",
" X_pad -- padded image of shape (m, n_H + 2*pad, n_W + 2*pad, n_C)\n",
" \"\"\"\n",
" \n",
" ### START CODE HERE ### (≈ 1 line)\n",
" X_pad = np.pad(X, ((0,0), (pad, pad), (pad, pad), (0,0)), 'constant', constant_values = 0)\n",
" ### END CODE HERE ###\n",
" \n",
" return X_pad"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x.shape = (4, 3, 3, 2)\n",
"x_pad.shape = (4, 7, 7, 2)\n",
"x[1,1] = [[ 0.90085595 -0.68372786]\n",
" [-0.12289023 -0.93576943]\n",
" [-0.26788808 0.53035547]]\n",
"x_pad[1,1] = [[ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]]\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f98651c91d0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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zybThx4BrJG0usZ6mehKdrW4JyJeBhZIuTE7erga2llxTW2tmimkbq9z+UrX3OyK+HhFz\nI2IBtffnhxFxY4n1tKQnUVcEZEQMArcBT1M7uf0vEfFauVWNTtK3gX8DLpF0SNIfl11TiqEpptfU\n3Sl+RdlF5aGi+0vHvt85GupJ9AqwCLgn6wa74s98zMzGoys+QZqZjYcD0swshQPSzCyFA9LMLIUD\n0swshQPSzCyFA9LMLIUD0swsxf8DtS5DRn4HHEIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f98652a1940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"np.random.seed(1)\n",
"x = np.random.randn(4, 3, 3, 2)\n",
"x_pad = zero_pad(x, 2)\n",
"print (\"x.shape =\", x.shape)\n",
"print (\"x_pad.shape =\", x_pad.shape)\n",
"print (\"x[1,1] =\", x[1,1])\n",
"print (\"x_pad[1,1] =\", x_pad[1,1])\n",
"\n",
"fig, axarr = plt.subplots(1, 2)\n",
"axarr[0].set_title('x')\n",
"axarr[0].imshow(x[0,:,:,0])\n",
"axarr[1].set_title('x_pad')\n",
"axarr[1].imshow(x_pad[0,:,:,0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**:\n",
"\n",
"<table>\n",
" <tr>\n",
" <td>\n",
" **x.shape**:\n",
" </td>\n",
" <td>\n",
" (4, 3, 3, 2)\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **x_pad.shape**:\n",
" </td>\n",
" <td>\n",
" (4, 7, 7, 2)\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **x[1,1]**:\n",
" </td>\n",
" <td>\n",
" [[ 0.90085595 -0.68372786]\n",
" [-0.12289023 -0.93576943]\n",
" [-0.26788808 0.53035547]]\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **x_pad[1,1]**:\n",
" </td>\n",
" <td>\n",
" [[ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]\n",
" [ 0. 0.]]\n",
" </td>\n",
" </tr>\n",
"\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 3.2 - Single step of convolution \n",
"\n",
"In this part, implement a single step of convolution, in which you apply the filter to a single position of the input. This will be used to build a convolutional unit, which: \n",
"\n",
"- Takes an input volume \n",
"- Applies a filter at every position of the input\n",
"- Outputs another volume (usually of different size)\n",
"\n",
"<img src=\"images/Convolution_schematic.gif\" style=\"width:500px;height:300px;\">\n",
"<caption><center> <u> <font color='purple'> **Figure 2** </u><font color='purple'> : **Convolution operation**<br> with a filter of 2x2 and a stride of 1 (stride = amount you move the window each time you slide) </center></caption>\n",
"\n",
"In a computer vision application, each value in the matrix on the left corresponds to a single pixel value, and we convolve a 3x3 filter with the image by multiplying its values element-wise with the original matrix, then summing them up and adding a bias. In this first step of the exercise, you will implement a single step of convolution, corresponding to applying a filter to just one of the positions to get a single real-valued output. \n",
"\n",
"Later in this notebook, you'll apply this function to multiple positions of the input to implement the full convolutional operation. \n",
"\n",
"**Exercise**: Implement conv_single_step(). [Hint](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.sum.html).\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# GRADED FUNCTION: conv_single_step\n",
"\n",
"def conv_single_step(a_slice_prev, W, b):\n",
" \"\"\"\n",
" Apply one filter defined by parameters W on a single slice (a_slice_prev) of the output activation \n",
" of the previous layer.\n",
" \n",
" Arguments:\n",
" a_slice_prev -- slice of input data of shape (f, f, n_C_prev)\n",
" W -- Weight parameters contained in a window - matrix of shape (f, f, n_C_prev)\n",
" b -- Bias parameters contained in a window - matrix of shape (1, 1, 1)\n",
" \n",
" Returns:\n",
" Z -- a scalar value, result of convolving the sliding window (W, b) on a slice x of the input data\n",
" \"\"\"\n",
"\n",
" ### START CODE HERE ### (≈ 2 lines of code)\n",
" # Element-wise product between a_slice and W. Do not add the bias yet.\n",
" s = np.multiply(a_slice_prev, W)\n",
" # Sum over all entries of the volume s.\n",
" Z = np.sum(s)\n",
" # Add bias b to Z. Cast b to a float() so that Z results in a scalar value.\n",
" Z = Z + float(b)\n",
" ### END CODE HERE ###\n",
"\n",
" return Z"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Z = -6.99908945068\n"
]
}
],
"source": [
"np.random.seed(1)\n",
"a_slice_prev = np.random.randn(4, 4, 3)\n",
"W = np.random.randn(4, 4, 3)\n",
"b = np.random.randn(1, 1, 1)\n",
"\n",
"Z = conv_single_step(a_slice_prev, W, b)\n",
"print(\"Z =\", Z)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**:\n",
"<table>\n",
" <tr>\n",
" <td>\n",
" **Z**\n",
" </td>\n",
" <td>\n",
" -6.99908945068\n",
" </td>\n",
" </tr>\n",
"\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### 3.3 - Convolutional Neural Networks - Forward pass\n",
"\n",
"In the forward pass, you will take many filters and convolve them on the input. Each 'convolution' gives you a 2D matrix output. You will then stack these outputs to get a 3D volume: \n",
"\n",
"<center>\n",
"<video width=\"620\" height=\"440\" src=\"images/conv_kiank.mp4\" type=\"video/mp4\" controls>\n",
"</video>\n",
"</center>\n",
"\n",
"**Exercise**: Implement the function below to convolve the filters W on an input activation A_prev. This function takes as input A_prev, the activations output by the previous layer (for a batch of m inputs), F filters/weights denoted by W, and a bias vector denoted by b, where each filter has its own (single) bias. Finally you also have access to the hyperparameters dictionary which contains the stride and the padding. \n",
"\n",
"**Hint**: \n",
"1. To select a 2x2 slice at the upper left corner of a matrix \"a_prev\" (shape (5,5,3)), you would do:\n",
"```python\n",
"a_slice_prev = a_prev[0:2,0:2,:]\n",
"```\n",
"This will be useful when you will define `a_slice_prev` below, using the `start/end` indexes you will define.\n",
"2. To define a_slice you will need to first define its corners `vert_start`, `vert_end`, `horiz_start` and `horiz_end`. This figure may be helpful for you to find how each of the corner can be defined using h, w, f and s in the code below.\n",
"\n",
"<img src=\"images/vert_horiz_kiank.png\" style=\"width:400px;height:300px;\">\n",
"<caption><center> <u> <font color='purple'> **Figure 3** </u><font color='purple'> : **Definition of a slice using vertical and horizontal start/end (with a 2x2 filter)** <br> This figure shows only a single channel. </center></caption>\n",
"\n",
"\n",
"**Reminder**:\n",
"The formulas relating the output shape of the convolution to the input shape is:\n",
"$$ n_H = \\lfloor \\frac{n_{H_{prev}} - f + 2 \\times pad}{stride} \\rfloor +1 $$\n",
"$$ n_W = \\lfloor \\frac{n_{W_{prev}} - f + 2 \\times pad}{stride} \\rfloor +1 $$\n",
"$$ n_C = \\text{number of filters used in the convolution}$$\n",
"\n",
"For this exercise, we won't worry about vectorization, and will just implement everything with for-loops."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# GRADED FUNCTION: conv_forward\n",
"\n",
"def conv_forward(A_prev, W, b, hparameters):\n",
" \"\"\"\n",
" Implements the forward propagation for a convolution function\n",
" \n",
" Arguments:\n",
" A_prev -- output activations of the previous layer, numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
" W -- Weights, numpy array of shape (f, f, n_C_prev, n_C)\n",
" b -- Biases, numpy array of shape (1, 1, 1, n_C)\n",
" hparameters -- python dictionary containing \"stride\" and \"pad\"\n",
" \n",
" Returns:\n",
" Z -- conv output, numpy array of shape (m, n_H, n_W, n_C)\n",
" cache -- cache of values needed for the conv_backward() function\n",
" \"\"\"\n",
" \n",
" ### START CODE HERE ###\n",
" # Retrieve dimensions from A_prev's shape (≈1 line) \n",
" (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n",
" \n",
" # Retrieve dimensions from W's shape (≈1 line)\n",
" (f, f, n_C_prev, n_C) = W.shape\n",
" \n",
" # Retrieve information from \"hparameters\" (≈2 lines)\n",
" stride = hparameters['stride']\n",
" pad = hparameters['pad']\n",
" \n",
" # Compute the dimensions of the CONV output volume using the formula given above. Hint: use int() to floor. (≈2 lines)\n",
" n_H = int((n_H_prev - f + 2*pad)/stride) + 1\n",
" n_W = int((n_W_prev - f + 2*pad)/stride) + 1\n",
" \n",
" # Initialize the output volume Z with zeros. (≈1 line)\n",
" Z = np.zeros((m, n_H, n_W, n_C))\n",
" \n",
" # Create A_prev_pad by padding A_prev\n",
" A_prev_pad = zero_pad(A_prev, pad)\n",
" \n",
" for i in range(m): # loop over the batch of training examples\n",
" a_prev_pad = A_prev_pad[i] # Select ith training example's padded activation\n",
" for h in range(n_H): # loop over vertical axis of the output volume\n",
" for w in range(n_W): # loop over horizontal axis of the output volume\n",
" for c in range(n_C): # loop over channels (= #filters) of the output volume\n",
" \n",
" # Find the corners of the current \"slice\" (≈4 lines)\n",
" vert_start = h*stride\n",
" vert_end = vert_start+f\n",
" horiz_start = w*stride\n",
" horiz_end = horiz_start+f\n",
" \n",
" # Use the corners to define the (3D) slice of a_prev_pad (See Hint above the cell). (≈1 line)\n",
" a_slice_prev = a_prev_pad[vert_start:vert_end, horiz_start:horiz_end,:]\n",
" \n",
" # Convolve the (3D) slice with the correct filter W and bias b, to get back one output neuron. (≈1 line)\n",
" Z[i, h, w, c] = conv_single_step(a_slice_prev, W[:,:,:,c], b[:,:,:,c])\n",
" \n",
" ### END CODE HERE ###\n",
" \n",
" # Making sure your output shape is correct\n",
" assert(Z.shape == (m, n_H, n_W, n_C))\n",
" \n",
" # Save information in \"cache\" for the backprop\n",
" cache = (A_prev, W, b, hparameters)\n",
" \n",
" return Z, cache"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Z's mean = 0.0489952035289\n",
"Z[3,2,1] = [-0.61490741 -6.7439236 -2.55153897 1.75698377 3.56208902 0.53036437\n",
" 5.18531798 8.75898442]\n",
"cache_conv[0][1][2][3] = [-0.20075807 0.18656139 0.41005165]\n"
]
}
],
"source": [
"np.random.seed(1)\n",
"A_prev = np.random.randn(10,4,4,3)\n",
"W = np.random.randn(2,2,3,8)\n",
"b = np.random.randn(1,1,1,8)\n",
"hparameters = {\"pad\" : 2,\n",
" \"stride\": 2}\n",
"\n",
"Z, cache_conv = conv_forward(A_prev, W, b, hparameters)\n",
"print(\"Z's mean =\", np.mean(Z))\n",
"print(\"Z[3,2,1] =\", Z[3,2,1])\n",
"print(\"cache_conv[0][1][2][3] =\", cache_conv[0][1][2][3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**:\n",
"\n",
"<table>\n",
" <tr>\n",
" <td>\n",
" **Z's mean**\n",
" </td>\n",
" <td>\n",
" 0.0489952035289\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **Z[3,2,1]**\n",
" </td>\n",
" <td>\n",
" [-0.61490741 -6.7439236 -2.55153897 1.75698377 3.56208902 0.53036437\n",
" 5.18531798 8.75898442]\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **cache_conv[0][1][2][3]**\n",
" </td>\n",
" <td>\n",
" [-0.20075807 0.18656139 0.41005165]\n",
" </td>\n",
" </tr>\n",
"\n",
"</table>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, CONV layer should also contain an activation, in which case we would add the following line of code:\n",
"\n",
"```python\n",
"# Convolve the window to get back one output neuron\n",
"Z[i, h, w, c] = ...\n",
"# Apply activation\n",
"A[i, h, w, c] = activation(Z[i, h, w, c])\n",
"```\n",
"\n",
"You don't need to do it here. \n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4 - Pooling layer \n",
"\n",
"The pooling (POOL) layer reduces the height and width of the input. It helps reduce computation, as well as helps make feature detectors more invariant to its position in the input. The two types of pooling layers are: \n",
"\n",
"- Max-pooling layer: slides an ($f, f$) window over the input and stores the max value of the window in the output.\n",
"\n",
"- Average-pooling layer: slides an ($f, f$) window over the input and stores the average value of the window in the output.\n",
"\n",
"<table>\n",
"<td>\n",
"<img src=\"images/max_pool1.png\" style=\"width:500px;height:300px;\">\n",
"<td>\n",
"\n",
"<td>\n",
"<img src=\"images/a_pool.png\" style=\"width:500px;height:300px;\">\n",
"<td>\n",
"</table>\n",
"\n",
"These pooling layers have no parameters for backpropagation to train. However, they have hyperparameters such as the window size $f$. This specifies the height and width of the fxf window you would compute a max or average over. \n",
"\n",
"### 4.1 - Forward Pooling\n",
"Now, you are going to implement MAX-POOL and AVG-POOL, in the same function. \n",
"\n",
"**Exercise**: Implement the forward pass of the pooling layer. Follow the hints in the comments below.\n",
"\n",
"**Reminder**:\n",
"As there's no padding, the formulas binding the output shape of the pooling to the input shape is:\n",
"$$ n_H = \\lfloor \\frac{n_{H_{prev}} - f}{stride} \\rfloor +1 $$\n",
"$$ n_W = \\lfloor \\frac{n_{W_{prev}} - f}{stride} \\rfloor +1 $$\n",
"$$ n_C = n_{C_{prev}}$$"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# GRADED FUNCTION: pool_forward\n",
"\n",
"def pool_forward(A_prev, hparameters, mode = \"max\"):\n",
" \"\"\"\n",
" Implements the forward pass of the pooling layer\n",
" \n",
" Arguments:\n",
" A_prev -- Input data, numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
" hparameters -- python dictionary containing \"f\" and \"stride\"\n",
" mode -- the pooling mode you would like to use, defined as a string (\"max\" or \"average\")\n",
" \n",
" Returns:\n",
" A -- output of the pool layer, a numpy array of shape (m, n_H, n_W, n_C)\n",
" cache -- cache used in the backward pass of the pooling layer, contains the input and hparameters \n",
" \"\"\"\n",
" \n",
" # Retrieve dimensions from the input shape\n",
" (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n",
" \n",
" # Retrieve hyperparameters from \"hparameters\"\n",
" f = hparameters[\"f\"]\n",
" stride = hparameters[\"stride\"]\n",
" \n",
" # Define the dimensions of the output\n",
" n_H = int(1 + (n_H_prev - f) / stride)\n",
" n_W = int(1 + (n_W_prev - f) / stride)\n",
" n_C = n_C_prev\n",
" \n",
" # Initialize output matrix A\n",
" A = np.zeros((m, n_H, n_W, n_C)) \n",
" \n",
" ### START CODE HERE ###\n",
" for i in range(m): # loop over the training examples\n",
" for h in range(n_H): # loop on the vertical axis of the output volume\n",
" for w in range(n_W): # loop on the horizontal axis of the output volume\n",
" for c in range (n_C): # loop over the channels of the output volume\n",
" \n",
" # Find the corners of the current \"slice\" (≈4 lines)\n",
" vert_start = h*stride\n",
" vert_end = vert_start+f\n",
" horiz_start = w*stride\n",
" horiz_end = horiz_start+f\n",
" \n",
" # Use the corners to define the current slice on the ith training example of A_prev, channel c. (≈1 line)\n",
" a_prev_slice = A_prev[i, vert_start:vert_end, horiz_start:horiz_end, c]\n",
" \n",
" # Compute the pooling operation on the slice. Use an if statment to differentiate the modes. Use np.max/np.mean.\n",
" if mode == \"max\":\n",
" A[i, h, w, c] = np.max(a_prev_slice)\n",
" elif mode == \"average\":\n",
" A[i, h, w, c] = np.mean(a_prev_slice)\n",
" \n",
" ### END CODE HERE ###\n",
" \n",
" # Store the input and hparameters in \"cache\" for pool_backward()\n",
" cache = (A_prev, hparameters)\n",
" \n",
" # Making sure your output shape is correct\n",
" assert(A.shape == (m, n_H, n_W, n_C))\n",
" \n",
" return A, cache"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mode = max\n",
"A = [[[[ 1.74481176 0.86540763 1.13376944]]]\n",
"\n",
"\n",
" [[[ 1.13162939 1.51981682 2.18557541]]]]\n",
"\n",
"mode = average\n",
"A = [[[[ 0.02105773 -0.20328806 -0.40389855]]]\n",
"\n",
"\n",
" [[[-0.22154621 0.51716526 0.48155844]]]]\n"
]
}
],
"source": [
"np.random.seed(1)\n",
"A_prev = np.random.randn(2, 4, 4, 3)\n",
"hparameters = {\"stride\" : 2, \"f\": 3}\n",
"\n",
"A, cache = pool_forward(A_prev, hparameters)\n",
"print(\"mode = max\")\n",
"print(\"A =\", A)\n",
"print()\n",
"A, cache = pool_forward(A_prev, hparameters, mode = \"average\")\n",
"print(\"mode = average\")\n",
"print(\"A =\", A)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output:**\n",
"<table>\n",
"\n",
" <tr>\n",
" <td>\n",
" A =\n",
" </td>\n",
" <td>\n",
" [[[[ 1.74481176 0.86540763 1.13376944]]]\n",
"\n",
"\n",
" [[[ 1.13162939 1.51981682 2.18557541]]]]\n",
"\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" A =\n",
" </td>\n",
" <td>\n",
" [[[[ 0.02105773 -0.20328806 -0.40389855]]]\n",
"\n",
"\n",
" [[[-0.22154621 0.51716526 0.48155844]]]]\n",
"\n",
" </td>\n",
" </tr>\n",
"\n",
"</table>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Congratulations! You have now implemented the forward passes of all the layers of a convolutional network. \n",
"\n",
"The remainer of this notebook is optional, and will not be graded.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5 - Backpropagation in convolutional neural networks (OPTIONAL / UNGRADED)\n",
"\n",
"In modern deep learning frameworks, you only have to implement the forward pass, and the framework takes care of the backward pass, so most deep learning engineers don't need to bother with the details of the backward pass. The backward pass for convolutional networks is complicated. If you wish however, you can work through this optional portion of the notebook to get a sense of what backprop in a convolutional network looks like. \n",
"\n",
"When in an earlier course you implemented a simple (fully connected) neural network, you used backpropagation to compute the derivatives with respect to the cost to update the parameters. Similarly, in convolutional neural networks you can to calculate the derivatives with respect to the cost in order to update the parameters. The backprop equations are not trivial and we did not derive them in lecture, but we briefly presented them below.\n",
"\n",
"### 5.1 - Convolutional layer backward pass \n",
"\n",
"Let's start by implementing the backward pass for a CONV layer. \n",
"\n",
"#### 5.1.1 - Computing dA:\n",
"This is the formula for computing $dA$ with respect to the cost for a certain filter $W_c$ and a given training example:\n",
"\n",
"$$ dA += \\sum _{h=0} ^{n_H} \\sum_{w=0} ^{n_W} W_c \\times dZ_{hw} \\tag{1}$$\n",
"\n",
"Where $W_c$ is a filter and $dZ_{hw}$ is a scalar corresponding to the gradient of the cost with respect to the output of the conv layer Z at the hth row and wth column (corresponding to the dot product taken at the ith stride left and jth stride down). Note that at each time, we multiply the the same filter $W_c$ by a different dZ when updating dA. We do so mainly because when computing the forward propagation, each filter is dotted and summed by a different a_slice. Therefore when computing the backprop for dA, we are just adding the gradients of all the a_slices. \n",
"\n",
"In code, inside the appropriate for-loops, this formula translates into:\n",
"```python\n",
"da_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :] += W[:,:,:,c] * dZ[i, h, w, c]\n",
"```\n",
"\n",
"#### 5.1.2 - Computing dW:\n",
"This is the formula for computing $dW_c$ ($dW_c$ is the derivative of one filter) with respect to the loss:\n",
"\n",
"$$ dW_c += \\sum _{h=0} ^{n_H} \\sum_{w=0} ^ {n_W} a_{slice} \\times dZ_{hw} \\tag{2}$$\n",
"\n",
"Where $a_{slice}$ corresponds to the slice which was used to generate the acitivation $Z_{ij}$. Hence, this ends up giving us the gradient for $W$ with respect to that slice. Since it is the same $W$, we will just add up all such gradients to get $dW$. \n",
"\n",
"In code, inside the appropriate for-loops, this formula translates into:\n",
"```python\n",
"dW[:,:,:,c] += a_slice * dZ[i, h, w, c]\n",
"```\n",
"\n",
"#### 5.1.3 - Computing db:\n",
"\n",
"This is the formula for computing $db$ with respect to the cost for a certain filter $W_c$:\n",
"\n",
"$$ db = \\sum_h \\sum_w dZ_{hw} \\tag{3}$$\n",
"\n",
"As you have previously seen in basic neural networks, db is computed by summing $dZ$. In this case, you are just summing over all the gradients of the conv output (Z) with respect to the cost. \n",
"\n",
"In code, inside the appropriate for-loops, this formula translates into:\n",
"```python\n",
"db[:,:,:,c] += dZ[i, h, w, c]\n",
"```\n",
"\n",
"**Exercise**: Implement the `conv_backward` function below. You should sum over all the training examples, filters, heights, and widths. You should then compute the derivatives using formulas 1, 2 and 3 above. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def conv_backward(dZ, cache):\n",
" \"\"\"\n",
" Implement the backward propagation for a convolution function\n",
" \n",
" Arguments:\n",
" dZ -- gradient of the cost with respect to the output of the conv layer (Z), numpy array of shape (m, n_H, n_W, n_C)\n",
" cache -- cache of values needed for the conv_backward(), output of conv_forward()\n",
" \n",
" Returns:\n",
" dA_prev -- gradient of the cost with respect to the input of the conv layer (A_prev),\n",
" numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
" dW -- gradient of the cost with respect to the weights of the conv layer (W)\n",
" numpy array of shape (f, f, n_C_prev, n_C)\n",
" db -- gradient of the cost with respect to the biases of the conv layer (b)\n",
" numpy array of shape (1, 1, 1, n_C)\n",
" \"\"\"\n",
" \n",
" ### START CODE HERE ###\n",
" # Retrieve information from \"cache\"\n",
" (A_prev, W, b, hparameters) = None\n",
" \n",
" # Retrieve dimensions from A_prev's shape\n",
" (m, n_H_prev, n_W_prev, n_C_prev) = None\n",
" \n",
" # Retrieve dimensions from W's shape\n",
" (f, f, n_C_prev, n_C) = None\n",
" \n",
" # Retrieve information from \"hparameters\"\n",
" stride = None\n",
" pad = None\n",
" \n",
" # Retrieve dimensions from dZ's shape\n",
" (m, n_H, n_W, n_C) = None\n",
" \n",
" # Initialize dA_prev, dW, db with the correct shapes\n",
" dA_prev = None \n",
" dW = None\n",
" db = None\n",
"\n",
" # Pad A_prev and dA_prev\n",
" A_prev_pad = None\n",
" dA_prev_pad = None\n",
" \n",
" for i in range(None): # loop over the training examples\n",
" \n",
" # select ith training example from A_prev_pad and dA_prev_pad\n",
" a_prev_pad = None\n",
" da_prev_pad = None\n",
" \n",
" for h in range(None): # loop over vertical axis of the output volume\n",
" for w in range(None): # loop over horizontal axis of the output volume\n",
" for c in range(None): # loop over the channels of the output volume\n",
" \n",
" # Find the corners of the current \"slice\"\n",
" vert_start = None\n",
" vert_end = None\n",
" horiz_start = None\n",
" horiz_end = None\n",
" \n",
" # Use the corners to define the slice from a_prev_pad\n",
" a_slice = None\n",
"\n",
" # Update gradients for the window and the filter's parameters using the code formulas given above\n",
" da_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :] += None\n",
" dW[:,:,:,c] += None\n",
" db[:,:,:,c] += None\n",
" \n",
" # Set the ith training example's dA_prev to the unpaded da_prev_pad (Hint: use X[pad:-pad, pad:-pad, :])\n",
" dA_prev[i, :, :, :] = None\n",
" ### END CODE HERE ###\n",
" \n",
" # Making sure your output shape is correct\n",
" assert(dA_prev.shape == (m, n_H_prev, n_W_prev, n_C_prev))\n",
" \n",
" return dA_prev, dW, db"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"np.random.seed(1)\n",
"dA, dW, db = conv_backward(Z, cache_conv)\n",
"print(\"dA_mean =\", np.mean(dA))\n",
"print(\"dW_mean =\", np.mean(dW))\n",
"print(\"db_mean =\", np.mean(db))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Expected Output: **\n",
"<table>\n",
" <tr>\n",
" <td>\n",
" **dA_mean**\n",
" </td>\n",
" <td>\n",
" 1.45243777754\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **dW_mean**\n",
" </td>\n",
" <td>\n",
" 1.72699145831\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td>\n",
" **db_mean**\n",
" </td>\n",
" <td>\n",
" 7.83923256462\n",
" </td>\n",
" </tr>\n",
"\n",
"</table>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5.2 Pooling layer - backward pass\n",
"\n",
"Next, let's implement the backward pass for the pooling layer, starting with the MAX-POOL layer. Even though a pooling layer has no parameters for backprop to update, you still need to backpropagation the gradient through the pooling layer in order to compute gradients for layers that came before the pooling layer. \n",
"\n",
"### 5.2.1 Max pooling - backward pass \n",
"\n",
"Before jumping into the backpropagation of the pooling layer, you are going to build a helper function called `create_mask_from_window()` which does the following: \n",
"\n",
"$$ X = \\begin{bmatrix}\n",
"1 && 3 \\\\\n",
"4 && 2\n",
"\\end{bmatrix} \\quad \\rightarrow \\quad M =\\begin{bmatrix}\n",
"0 && 0 \\\\\n",
"1 && 0\n",
"\\end{bmatrix}\\tag{4}$$\n",
"\n",
"As you can see, this function creates a \"mask\" matrix which keeps track of where the maximum of the matrix is. True (1) indicates the position of the maximum in X, the other entries are False (0). You'll see later that the backward pass for average pooling will be similar to this but using a different mask. \n",
"\n",
"**Exercise**: Implement `create_mask_from_window()`. This function will be helpful for pooling backward. \n",
"Hints:\n",
"- [np.max()]() may be helpful. It computes the maximum of an array.\n",
"- If you have a matrix X and a scalar x: `A = (X == x)` will return a matrix A of the same size as X such that:\n",
"```\n",
"A[i,j] = True if X[i,j] = x\n",
"A[i,j] = False if X[i,j] != x\n",
"```\n",
"- Here, you don't need to consider cases where there are several maxima in a matrix."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_mask_from_window(x):\n",
" \"\"\"\n",
" Creates a mask from an input matrix x, to identify the max entry of x.\n",
" \n",
" Arguments:\n",
" x -- Array of shape (f, f)\n",
" \n",
" Returns:\n",
" mask -- Array of the same shape as window, contains a True at the position corresponding to the max entry of x.\n",
" \"\"\"\n",
" \n",
" ### START CODE HERE ### (≈1 line)\n",
" mask = None\n",
" ### END CODE HERE ###\n",
" \n",
" return mask"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"np.random.seed(1)\n",
"x = np.random.randn(2,3)\n",
"mask = create_mask_from_window(x)\n",
"print('x = ', x)\n",
"print(\"mask = \", mask)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"**Expected Output:** \n",
"\n",
"<table> \n",
"<tr> \n",
"<td>\n",
"\n",
"**x =**\n",
"</td>\n",
"\n",
"<td>\n",
"\n",
"[[ 1.62434536 -0.61175641 -0.52817175] <br>\n",
" [-1.07296862 0.86540763 -2.3015387 ]]\n",
"\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr> \n",
"<td>\n",
"**mask =**\n",
"</td>\n",
"<td>\n",
"[[ True False False] <br>\n",
" [False False False]]\n",
"</td>\n",
"</tr>\n",
"\n",
"\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Why do we keep track of the position of the max? It's because this is the input value that ultimately influenced the output, and therefore the cost. Backprop is computing gradients with respect to the cost, so anything that influences the ultimate cost should have a non-zero gradient. So, backprop will \"propagate\" the gradient back to this particular input value that had influenced the cost. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2.2 - Average pooling - backward pass \n",
"\n",
"In max pooling, for each input window, all the \"influence\" on the output came from a single input value--the max. In average pooling, every element of the input window has equal influence on the output. So to implement backprop, you will now implement a helper function that reflects this.\n",
"\n",
"For example if we did average pooling in the forward pass using a 2x2 filter, then the mask you'll use for the backward pass will look like: \n",
"$$ dZ = 1 \\quad \\rightarrow \\quad dZ =\\begin{bmatrix}\n",
"1/4 && 1/4 \\\\\n",
"1/4 && 1/4\n",
"\\end{bmatrix}\\tag{5}$$\n",
"\n",
"This implies that each position in the $dZ$ matrix contributes equally to output because in the forward pass, we took an average. \n",
"\n",
"**Exercise**: Implement the function below to equally distribute a value dz through a matrix of dimension shape. [Hint](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ones.html)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def distribute_value(dz, shape):\n",
" \"\"\"\n",
" Distributes the input value in the matrix of dimension shape\n",
" \n",
" Arguments:\n",
" dz -- input scalar\n",
" shape -- the shape (n_H, n_W) of the output matrix for which we want to distribute the value of dz\n",
" \n",
" Returns:\n",
" a -- Array of size (n_H, n_W) for which we distributed the value of dz\n",
" \"\"\"\n",
" \n",
" ### START CODE HERE ###\n",
" # Retrieve dimensions from shape (≈1 line)\n",
" (n_H, n_W) = None\n",
" \n",
" # Compute the value to distribute on the matrix (≈1 line)\n",
" average = None\n",
" \n",
" # Create a matrix where every entry is the \"average\" value (≈1 line)\n",
" a = None\n",
" ### END CODE HERE ###\n",
" \n",
" return a"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"a = distribute_value(2, (2,2))\n",
"print('distributed value =', a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**: \n",
"\n",
"<table> \n",
"<tr> \n",
"<td>\n",
"distributed_value =\n",
"</td>\n",
"<td>\n",
"[[ 0.5 0.5]\n",
"<br\\> \n",
"[ 0.5 0.5]]\n",
"</td>\n",
"</tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.2.3 Putting it together: Pooling backward \n",
"\n",
"You now have everything you need to compute backward propagation on a pooling layer.\n",
"\n",
"**Exercise**: Implement the `pool_backward` function in both modes (`\"max\"` and `\"average\"`). You will once again use 4 for-loops (iterating over training examples, height, width, and channels). You should use an `if/elif` statement to see if the mode is equal to `'max'` or `'average'`. If it is equal to 'average' you should use the `distribute_value()` function you implemented above to create a matrix of the same shape as `a_slice`. Otherwise, the mode is equal to '`max`', and you will create a mask with `create_mask_from_window()` and multiply it by the corresponding value of dZ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def pool_backward(dA, cache, mode = \"max\"):\n",
" \"\"\"\n",
" Implements the backward pass of the pooling layer\n",
" \n",
" Arguments:\n",
" dA -- gradient of cost with respect to the output of the pooling layer, same shape as A\n",
" cache -- cache output from the forward pass of the pooling layer, contains the layer's input and hparameters \n",
" mode -- the pooling mode you would like to use, defined as a string (\"max\" or \"average\")\n",
" \n",
" Returns:\n",
" dA_prev -- gradient of cost with respect to the input of the pooling layer, same shape as A_prev\n",
" \"\"\"\n",
" \n",
" ### START CODE HERE ###\n",
" \n",
" # Retrieve information from cache (≈1 line)\n",
" (A_prev, hparameters) = None\n",
" \n",
" # Retrieve hyperparameters from \"hparameters\" (≈2 lines)\n",
" stride = None\n",
" f = None\n",
" \n",
" # Retrieve dimensions from A_prev's shape and dA's shape (≈2 lines)\n",
" m, n_H_prev, n_W_prev, n_C_prev = None\n",
" m, n_H, n_W, n_C = None\n",
" \n",
" # Initialize dA_prev with zeros (≈1 line)\n",
" dA_prev = None\n",
" \n",
" for i in range(None): # loop over the training examples\n",
" \n",
" # select training example from A_prev (≈1 line)\n",
" a_prev = None\n",
" \n",
" for h in range(None): # loop on the vertical axis\n",
" for w in range(None): # loop on the horizontal axis\n",
" for c in range(None): # loop over the channels (depth)\n",
" \n",
" # Find the corners of the current \"slice\" (≈4 lines)\n",
" vert_start = None\n",
" vert_end = None\n",
" horiz_start = None\n",
" horiz_end = None\n",
" \n",
" # Compute the backward propagation in both modes.\n",
" if mode == \"max\":\n",
" \n",
" # Use the corners and \"c\" to define the current slice from a_prev (≈1 line)\n",
" a_prev_slice = None\n",
" # Create the mask from a_prev_slice (≈1 line)\n",
" mask = None\n",
" # Set dA_prev to be dA_prev + (the mask multiplied by the correct entry of dA) (≈1 line)\n",
" dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += None\n",
" \n",
" elif mode == \"average\":\n",
" \n",
" # Get the value a from dA (≈1 line)\n",
" da = None\n",
" # Define the shape of the filter as fxf (≈1 line)\n",
" shape = None\n",
" # Distribute it to get the correct slice of dA_prev. i.e. Add the distributed value of da. (≈1 line)\n",
" dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += None\n",
" \n",
" ### END CODE ###\n",
" \n",
" # Making sure your output shape is correct\n",
" assert(dA_prev.shape == A_prev.shape)\n",
" \n",
" return dA_prev"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.random.seed(1)\n",
"A_prev = np.random.randn(5, 5, 3, 2)\n",
"hparameters = {\"stride\" : 1, \"f\": 2}\n",
"A, cache = pool_forward(A_prev, hparameters)\n",
"dA = np.random.randn(5, 4, 2, 2)\n",
"\n",
"dA_prev = pool_backward(dA, cache, mode = \"max\")\n",
"print(\"mode = max\")\n",
"print('mean of dA = ', np.mean(dA))\n",
"print('dA_prev[1,1] = ', dA_prev[1,1]) \n",
"print()\n",
"dA_prev = pool_backward(dA, cache, mode = \"average\")\n",
"print(\"mode = average\")\n",
"print('mean of dA = ', np.mean(dA))\n",
"print('dA_prev[1,1] = ', dA_prev[1,1]) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Expected Output**: \n",
"\n",
"mode = max:\n",
"<table> \n",
"<tr> \n",
"<td>\n",
"\n",
"**mean of dA =**\n",
"</td>\n",
"\n",
"<td>\n",
"\n",
"0.145713902729\n",
"\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr> \n",
"<td>\n",
"**dA_prev[1,1] =** \n",
"</td>\n",
"<td>\n",
"[[ 0. 0. ] <br>\n",
" [ 5.05844394 -1.68282702] <br>\n",
" [ 0. 0. ]]\n",
"</td>\n",
"</tr>\n",
"</table>\n",
"\n",
"mode = average\n",
"<table> \n",
"<tr> \n",
"<td>\n",
"\n",
"**mean of dA =**\n",
"</td>\n",
"\n",
"<td>\n",
"\n",
"0.145713902729\n",
"\n",
" </td>\n",
"</tr>\n",
"\n",
"<tr> \n",
"<td>\n",
"**dA_prev[1,1] =** \n",
"</td>\n",
"<td>\n",
"[[ 0.08485462 0.2787552 ] <br>\n",
" [ 1.26461098 -0.25749373] <br>\n",
" [ 1.17975636 -0.53624893]]\n",
"</td>\n",
"</tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Congratulations !\n",
"\n",
"Congratulation on completing this assignment. You now understand how convolutional neural networks work. You have implemented all the building blocks of a neural network. In the next assignment you will implement a ConvNet using TensorFlow."
]
}
],
"metadata": {
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"course_slug": "convolutional-neural-networks",
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| mit |
davidgutierrez/HeartRatePatterns | Jupyter/MimicII/0i PearsonTesting.ipynb | 1 | 371983 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import psycopg2\n",
"import numpy as np\n",
"from scipy.stats.stats import pearsonr\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import sys \n",
"import os\n",
"sys.path.append(os.path.abspath(\"/home/scidb/HeartRatePatterns/Python\"))\n",
"from LogisticRegresion import ajustLogisticRegression\n",
"from PlotWords import plot_word\n",
"from Matrix import convert_matrix"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def savePearson(pearson,dbname=\"mimic\") :\n",
" conn = psycopg2.connect(\"dbname=\"+dbname)\n",
" cur = conn.cursor()\n",
" insert_statement=('INSERT INTO wordspearson(word,p1,p2,patient,deadPatient)'\n",
" ' SELECT unnest( %(word)s ) ,'\n",
" ' unnest( %(p1)s) ,'\n",
" ' unnest( %(p2)s) ,'\n",
" ' unnest( %(patient)s) ,'\n",
" ' unnest( %(deadPatient)s)')\n",
" word=[r['word'] for r in pearson]\n",
" p1=[r['p1'] for r in pearson]\n",
" p2=[r['p2'] for r in pearson]\n",
" patient=[r['patient'] for r in pearson]\n",
" deadPatient=[r['deadPatient'] for r in pearson]\n",
"# print(cur.mogrify(insert_statement,locals()))\n",
" cur.execute(insert_statement,locals())\n",
" conn.commit()\n",
" cur.close()\n",
" conn.close()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def selectPearson(dbname=\"mimic\") :\n",
" conn = psycopg2.connect(\"dbname=\"+dbname)\n",
" cur = conn.cursor()\n",
" select_statement='SELECT word,p1,p2,patient,deadpatient FROM wordspearson'\n",
"# print(cur.mogrify(select_statement,locals()))\n",
" cur.execute(select_statement)\n",
" select = []\n",
" for row in cur :\n",
" patient=row[3]\n",
" cuantosMueren =\"{0:.2%}\".format(row[4]/patient)+\" de \"+str(patient)\n",
" select.append({\"word\":row[0],\"p1\":row[1],\"p2\":row[2],\"cuantosMueren\":cuantosMueren})\n",
" cur.close()\n",
" conn.close()\n",
" return sorted(select, key=itemgetter('p1'), reverse=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def countPatients(word,dbname=\"mimic\") :\n",
" conn = psycopg2.connect(\"dbname=\"+dbname)\n",
" cur = conn.cursor()\n",
" select_statement='''SELECT count(1),sum(isalive) FROM matrix m LEFT JOIN subjectwords s \n",
" ON m.subject_id=s.subject_id where m.word = %s GROUP BY m.word'''\n",
"# print(cur.mogrify(select_statement,(word,)))\n",
" cur.execute(select_statement,(word,))\n",
" select = {}\n",
" for row in cur :\n",
" select = {\"patient\":row[0],\"deadPatient\":row[1],}\n",
" cur.close()\n",
" conn.close()\n",
" return select"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def cleanPearson(dbname=\"mimic\") :\n",
" conn = psycopg2.connect(\"dbname=\"+dbname)\n",
" cur = conn.cursor()\n",
" delete_statement='DELETE FROM wordspearson'\n",
"# print(cur.mogrify(delete_statement,locals()))\n",
" cur.execute(delete_statement,locals())\n",
" conn.commit()\n",
" cur.close()\n",
" conn.close()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(590, 58840)\n"
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>20</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>135</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>151</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>214</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>263</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>279</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>283</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</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>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>368</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>408</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>462</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>618</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>638</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>682</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>736</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>743</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>749</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>793</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>886</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>952</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>974</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1004</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1075</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1144</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1160</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1222</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1226</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1459</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1528</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23178</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23193</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23200</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</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>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23298</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23336</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23339</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23363</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23384</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</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>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23401</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23451</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</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>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23468</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23474</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23510</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23944</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</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>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24004</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24030</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24076</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24129</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24133</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24142</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24152</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24185</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24227</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25466</th>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</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",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41962</th>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
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" </tr>\n",
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" <th>42410</th>\n",
" <th>0</th>\n",
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" </tr>\n",
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" <th>42492</th>\n",
" <th>0</th>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>590 rows × 58840 columns</p>\n",
"</div>"
],
"text/plain": [
" aaaaaaa aaaaaaaa aaaaaaab aaaaaaac aaaaaaad aaaaaaae \\\n",
"subject_id isAlive \n",
"20 0 1 1 1 0 1 0 \n",
"135 1 1 1 1 0 1 0 \n",
"151 1 1 1 1 1 1 0 \n",
"177 1 1 1 1 0 1 0 \n",
"214 1 1 1 1 1 1 0 \n",
"263 1 1 1 1 0 1 0 \n",
"279 1 1 1 1 0 1 0 \n",
"283 1 1 1 1 0 0 0 \n",
"368 1 1 1 1 1 1 0 \n",
"377 1 1 1 1 1 1 0 \n",
"408 1 1 1 1 0 1 0 \n",
"462 0 1 1 1 0 1 0 \n",
"618 1 1 1 1 0 1 0 \n",
"638 1 1 1 1 1 1 0 \n",
"682 1 1 1 1 1 1 1 \n",
"736 0 1 1 1 0 0 1 \n",
"743 1 1 1 1 1 1 0 \n",
"749 1 1 1 1 1 1 0 \n",
"793 1 1 1 1 1 1 0 \n",
"886 1 1 1 1 0 1 0 \n",
"952 1 1 1 1 1 1 0 \n",
"974 0 1 1 1 1 1 0 \n",
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"1226 1 1 1 1 0 1 0 \n",
"1459 0 1 1 1 0 1 0 \n",
"1528 1 1 1 1 1 1 0 \n",
"... ... ... ... ... ... ... \n",
"23178 1 1 1 1 1 1 0 \n",
"23193 0 1 1 1 0 1 0 \n",
"23200 0 1 1 1 1 0 0 \n",
"23298 0 1 1 1 1 1 1 \n",
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"23363 1 1 1 1 1 1 0 \n",
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"24004 1 1 1 1 1 1 0 \n",
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"42410 0 1 1 1 1 1 1 \n",
"42492 0 1 1 1 1 1 0 \n",
"43459 0 1 1 1 0 0 0 \n",
"\n",
" aaaaaaaf aaaaaaag aaaaaaah aaaaaaai ... kkb kke \\\n",
"subject_id isAlive ... \n",
"20 0 1 0 0 0 ... 0 0 \n",
"135 1 1 1 0 0 ... 0 0 \n",
"151 1 0 0 0 0 ... 0 0 \n",
"177 1 1 1 0 0 ... 0 0 \n",
"214 1 1 1 0 0 ... 0 0 \n",
"263 1 0 1 0 0 ... 0 0 \n",
"279 1 0 0 0 0 ... 0 0 \n",
"283 1 0 0 0 0 ... 0 0 \n",
"368 1 1 0 0 0 ... 0 0 \n",
"377 1 1 1 0 0 ... 0 0 \n",
"408 1 1 1 0 0 ... 0 0 \n",
"462 0 1 0 0 0 ... 0 0 \n",
"618 1 0 0 0 0 ... 0 0 \n",
"638 1 1 1 1 0 ... 0 0 \n",
"682 1 1 1 1 0 ... 0 0 \n",
"736 0 0 0 0 0 ... 0 0 \n",
"743 1 0 0 0 0 ... 0 0 \n",
"749 1 1 1 0 0 ... 0 0 \n",
"793 1 1 1 0 0 ... 0 0 \n",
"886 1 1 1 1 0 ... 0 0 \n",
"952 1 1 1 1 0 ... 0 0 \n",
"974 0 1 0 0 0 ... 0 0 \n",
"1004 1 0 0 0 0 ... 0 0 \n",
"1075 1 1 0 0 0 ... 0 0 \n",
"1144 0 1 1 1 0 ... 0 0 \n",
"1160 0 1 1 0 0 ... 0 0 \n",
"1222 0 1 1 0 0 ... 0 0 \n",
"1226 1 0 0 0 0 ... 0 0 \n",
"1459 0 1 0 0 0 ... 0 0 \n",
"1528 1 1 1 1 0 ... 0 0 \n",
"... ... ... ... ... ... ... ... \n",
"23178 1 1 0 0 0 ... 0 0 \n",
"23193 0 1 0 0 0 ... 0 0 \n",
"23200 0 0 0 0 0 ... 0 1 \n",
"23298 0 1 0 1 0 ... 0 0 \n",
"23336 1 0 0 0 0 ... 0 0 \n",
"23339 0 0 1 0 0 ... 0 0 \n",
"23363 1 1 1 1 0 ... 0 0 \n",
"23384 0 0 0 0 0 ... 0 0 \n",
"23401 1 0 1 0 0 ... 0 0 \n",
"23451 1 0 0 0 0 ... 0 0 \n",
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"23510 1 1 0 0 0 ... 0 0 \n",
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"24030 0 0 0 0 0 ... 0 0 \n",
"24076 1 1 0 0 0 ... 0 0 \n",
"24129 1 0 0 0 0 ... 0 0 \n",
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"24142 1 1 0 1 0 ... 0 0 \n",
"24152 1 0 0 0 0 ... 0 0 \n",
"24185 1 0 0 0 0 ... 0 0 \n",
"24227 0 1 1 0 0 ... 0 0 \n",
"25466 0 0 0 0 0 ... 0 0 \n",
"41962 1 1 1 1 0 ... 0 0 \n",
"42255 1 0 0 0 0 ... 0 0 \n",
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"42410 0 0 0 0 0 ... 0 0 \n",
"42492 0 1 1 0 1 ... 0 0 \n",
"43459 0 0 0 0 0 ... 0 0 \n",
"\n",
" lab lbb lbbb lbbc lcc leb lib libb \n",
"subject_id isAlive \n",
"20 0 0 0 0 0 0 0 0 0 \n",
"135 1 0 0 0 0 0 0 0 0 \n",
"151 1 0 0 0 0 0 0 0 0 \n",
"177 1 0 0 0 0 0 0 0 0 \n",
"214 1 0 0 0 0 0 0 0 0 \n",
"263 1 0 0 0 0 0 0 0 0 \n",
"279 1 0 0 0 0 0 0 0 0 \n",
"283 1 0 0 0 0 0 0 0 0 \n",
"368 1 0 0 0 0 0 0 0 0 \n",
"377 1 0 0 0 0 0 0 0 0 \n",
"408 1 0 0 0 0 0 0 0 0 \n",
"462 0 0 0 0 0 0 0 0 0 \n",
"618 1 0 0 0 0 0 0 0 0 \n",
"638 1 0 0 0 0 0 0 0 0 \n",
"682 1 0 0 0 0 0 0 0 0 \n",
"736 0 0 0 0 0 0 0 0 0 \n",
"743 1 0 0 0 0 0 0 0 0 \n",
"749 1 0 0 0 0 0 0 0 0 \n",
"793 1 0 0 0 0 0 0 0 0 \n",
"886 1 0 0 0 0 0 0 0 0 \n",
"952 1 0 0 0 0 0 0 0 0 \n",
"974 0 0 0 0 0 0 0 0 0 \n",
"1004 1 0 0 0 0 0 0 0 0 \n",
"1075 1 0 0 0 0 0 0 0 0 \n",
"1144 0 0 0 0 0 0 0 0 0 \n",
"1160 0 0 0 0 0 0 0 0 0 \n",
"1222 0 0 0 0 0 0 0 0 0 \n",
"1226 1 0 0 0 0 0 0 0 0 \n",
"1459 0 0 0 0 0 0 0 0 0 \n",
"1528 1 0 0 0 0 0 0 0 0 \n",
"... ... ... ... ... ... ... ... ... \n",
"23178 1 0 0 0 0 0 0 0 0 \n",
"23193 0 0 0 0 0 0 0 0 0 \n",
"23200 0 0 1 1 0 0 1 0 0 \n",
"23298 0 0 0 0 0 0 0 0 0 \n",
"23336 1 0 0 0 0 0 0 0 0 \n",
"23339 0 0 0 0 0 0 0 0 0 \n",
"23363 1 0 0 0 0 0 0 0 0 \n",
"23384 0 0 0 0 0 0 0 0 0 \n",
"23401 1 0 0 0 0 0 0 0 0 \n",
"23451 1 0 0 0 0 0 0 0 0 \n",
"23468 1 0 0 0 0 0 0 0 0 \n",
"23474 1 0 0 0 0 0 0 0 0 \n",
"23510 1 0 0 0 0 0 0 0 0 \n",
"23944 1 0 0 0 0 0 0 0 0 \n",
"24004 1 0 0 0 0 0 0 0 0 \n",
"24030 0 0 0 0 0 0 0 0 0 \n",
"24076 1 0 0 0 0 0 0 0 0 \n",
"24129 1 0 0 0 0 0 0 0 0 \n",
"24133 0 0 0 0 0 0 0 0 0 \n",
"24142 1 0 0 0 0 0 0 0 0 \n",
"24152 1 0 0 0 0 0 0 0 0 \n",
"24185 1 0 0 0 0 0 0 0 0 \n",
"24227 0 0 0 0 0 0 0 0 0 \n",
"25466 0 0 0 0 0 0 0 0 0 \n",
"41962 1 0 0 0 0 0 0 0 0 \n",
"42255 1 0 0 0 0 0 0 0 0 \n",
"42261 0 0 0 0 0 0 0 0 0 \n",
"42410 0 0 0 0 0 0 0 0 0 \n",
"42492 0 0 0 0 0 0 0 0 0 \n",
"43459 0 0 0 0 0 0 0 0 0 \n",
"\n",
"[590 rows x 58840 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"table = convert_matrix(sumvals=False)\n",
"table"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"survived = table.index.labels[1].tolist()\n",
"patients = table.values\n",
"columns = list(table.columns.values)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"cleanedPearson now reading 58840 columns\n",
" preparedToSavePearson\n",
"savedPearson\n"
]
}
],
"source": [
"cleanPearson()\n",
"print(\"cleanedPearson now reading\",len(columns),\"columns\")\n",
"pearsonList = []\n",
"for i in range(len(columns)):\n",
" pearson = pearsonr(patients[:,i],survived)\n",
" word = columns[i]\n",
" count = countPatients(word)\n",
" pearsonList.append({'word':word,'p1':pearson[0],'p2':pearson[1],'patient':count['patient'],'deadPatient':count['deadPatient']})\n",
"# print(i,end=\", \")\n",
"print(\" preparedToSavePearson\")\n",
"savePearson(pearsonList)\n",
"print(\"savedPearson\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"patients_train, patients_test,survived_train, survived_test = train_test_split(patients,survived,test_size=0.2, random_state=42)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(590, 58840)\n",
"(472, 58840)\n",
"(118, 58840)\n"
]
}
],
"source": [
"print(table.shape)\n",
"print(patients_train.shape)\n",
"print(patients_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"model,accuracy_score,logit_roc_auc = ajustLogisticRegression(patients_train,survived_train,patients_test,survived_test)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegressionCV(Cs=[0.007966, 0.0080488, 0.008131600000000001, 0.0082144, 0.008297200000000001, 0.00838],\n",
" class_weight=None, cv=5, dual=True, fit_intercept=True,\n",
" intercept_scaling=1.0, max_iter=100, multi_class='ovr',\n",
" n_jobs=-1, penalty='l2', random_state=0, refit=True,\n",
" scoring='roc_auc', solver='liblinear', tol=0.0001, verbose=0)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from operator import itemgetter\n",
"pearsonDict = selectPearson()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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5C7ir6LOved4b4DPOS1EURVEURVH6EUtkqIsGc/+7MXbJtAp2ijIYI5U8RVEU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FAAAF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\nvosAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f27e99fc4a8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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WqQMicgA4HRgiImdIUxIUx/Lf1rLcaO5nWwfL9RFwhrEgedHt5zRjJe5Fk8VnKLrvdYjt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\nzv8HGsx9wtbYqvYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f27e61ed710>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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iWOzsxKf+eHLzASC9hpmlPvGuXZcIWPIUV7AL1mNZRpnnjGfDoQBZahzrYzRu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\nQRBEhfP/ATN8FWkgjk4vAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f27e613b668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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FUzc2FCY7WMEujziN12bF9B/WYucfvP2jy7po+AqllKsEsVaIwpOUPMVOK74i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\nqMlxYEdERERERNTk/j8+F+ig24BKPAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f27e823e080>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"b\"SELECT v1.r_s,v8.q_s,v1.record,a.rec_from\\n FROM rstq v1\\n INNER JOIN a on a.record=v1.record INNER JOIN rstq v2 on v1.record=v2.record INNER JOIN rstq v3 on v2.record=v3.record INNER JOIN rstq v4 on v3.record=v4.record INNER JOIN rstq v5 on v4.record=v5.record INNER JOIN rstq v6 on v5.record=v6.record INNER JOIN rstq v7 on v6.record=v7.record INNER JOIN rstq v8 on v7.record=v8.record WHERE v1.centroid ='d' AND v2.centroid='a' AND v1.id+1=v2.id AND v3.centroid='b' AND v2.id+1=v3.id AND v4.centroid='a' AND v3.id+1=v4.id AND v5.centroid='a' AND v4.id+1=v5.id AND v6.centroid='a' AND v5.id+1=v6.id AND v7.centroid='a' AND v6.id+1=v7.id AND v8.centroid='d' AND v7.id+1=v8.id AND v1.r_s<v8.q_s\\n AND a.record NOT IN ('mimic2wdb/matched/s20354/s20354-2526-08-25-00-53',\\n 'mimic2wdb/matched/s14584/s14584-2721-07-20-18-49',\\n 'mimic2wdb/matched/s18413/s18413-3047-06-23-22-31',\\n 'mimic2wdb/matched/s16032/s16032-3339-07-31-12-58')\\n LIMIT 1\"\n",
"dabaaaad 89179756 89180858 mimic2wdb/matched/s19087/s19087-2560-06-15-07-53\n"
]
},
{
"data": {
"image/png": 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6APxqRWcn5c3mdX3POcluHOmI9QWO5nHbup0APLBpX4tLMvn4GrsWl8MRfZxg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HAAAZ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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f27e605e198>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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D2laKoijVgAp2FSTsX6KDpqIohwJdqUzuf9UC+c2ukCmmalcVRVH8RwW7ChIW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fAAAB\nN0lEQVRIFw1CiKeAVUAaUA/cD7wIPANMASqBz0gpmwxn4/+hV9HsAL4upSwei+O+0BFCXAF8AByg\nN2foR+h5dso2Y4QQYiF68roFfQLvGSnlA0KIAvSVolRgD/AlKWW3ECIGeBw9R7IJ+JyUsmxsjv7i\nQAixCrhTSnmjssvYYvz+LxhPrcCTUsqHhBAuVDs2pgghFqMXGrIDZcDXMdo0lF3GDGMCpAookFK2\nGvvU/TIOUI6dQqFQKBQKhUKhUExwVCimQqFQKBQKhUKhUExwlGOnUCgUCoVCoVAoFBMc5dgpFAqF\nQqFQKBQKxQRHOXYKhUKhUCgUCoVCMcFRjp1CoVAoFAqFQqFQTHCUY6dQKBQKhUKhUCgUExzl2CkU\nCoVCoVAoFArFBEc5dgqFQqFQKBQKhUIxwfn/weh7Pu8hEu8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f27e615f940>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_word(pearsonDict[:10])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"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>cuantosMueren</th>\n",
" <th>p1</th>\n",
" <th>p2</th>\n",
" </tr>\n",
" <tr>\n",
" <th>word</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>adc</th>\n",
" <td>89.68% de 155</td>\n",
" <td>0.208886</td>\n",
" <td>3.061022e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fdf</th>\n",
" <td>96.39% de 83</td>\n",
" <td>0.203784</td>\n",
" <td>5.979812e-07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dabbaba</th>\n",
" <td>88.17% de 169</td>\n",
" <td>0.199764</td>\n",
" <td>1.001482e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dbda</th>\n",
" <td>85.07% de 221</td>\n",
" <td>0.189067</td>\n",
" <td>3.755808e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dabaaaad</th>\n",
" <td>95.06% de 81</td>\n",
" <td>0.188816</td>\n",
" <td>3.870746e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fbd</th>\n",
" <td>87.27% de 165</td>\n",
" <td>0.183705</td>\n",
" <td>7.089394e-06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dff</th>\n",
" <td>93.18% de 88</td>\n",
" <td>0.180136</td>\n",
" <td>1.071184e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ffd</th>\n",
" <td>91.26% de 103</td>\n",
" <td>0.177633</td>\n",
" <td>1.424185e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fff</th>\n",
" <td>93.83% de 81</td>\n",
" <td>0.177530</td>\n",
" <td>1.440773e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bdbd</th>\n",
" <td>85.79% de 183</td>\n",
" <td>0.174950</td>\n",
" <td>1.924153e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>addaba</th>\n",
" <td>90.57% de 106</td>\n",
" <td>0.173294</td>\n",
" <td>2.311825e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>faf</th>\n",
" <td>87.01% de 154</td>\n",
" <td>0.171685</td>\n",
" <td>2.758436e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dfdd</th>\n",
" <td>96.67% de 60</td>\n",
" <td>0.171629</td>\n",
" <td>2.775279e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>afg</th>\n",
" <td>90.48% de 105</td>\n",
" <td>0.171339</td>\n",
" <td>2.864863e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>haf</th>\n",
" <td>95.45% de 66</td>\n",
" <td>0.171176</td>\n",
" <td>2.915994e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bdd</th>\n",
" <td>82.53% de 269</td>\n",
" <td>0.170361</td>\n",
" <td>3.186366e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bdab</th>\n",
" <td>79.23% de 414</td>\n",
" <td>0.169413</td>\n",
" <td>3.530489e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ddba</th>\n",
" <td>84.02% de 219</td>\n",
" <td>0.169222</td>\n",
" <td>3.603969e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ababdba</th>\n",
" <td>86.34% de 161</td>\n",
" <td>0.167458</td>\n",
" <td>4.354558e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>fabd</th>\n",
" <td>90.29% de 103</td>\n",
" <td>0.167401</td>\n",
" <td>4.381046e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bbdd</th>\n",
" <td>85.47% de 179</td>\n",
" <td>0.167382</td>\n",
" <td>4.389931e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gga</th>\n",
" <td>94.29% de 70</td>\n",
" <td>0.167137</td>\n",
" <td>4.506321e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aadc</th>\n",
" <td>88.62% de 123</td>\n",
" <td>0.167130</td>\n",
" <td>4.509729e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>adabbaba</th>\n",
" <td>89.38% de 113</td>\n",
" <td>0.167010</td>\n",
" <td>4.567608e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dbbd</th>\n",
" <td>84.90% de 192</td>\n",
" <td>0.166946</td>\n",
" <td>4.598597e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>gah</th>\n",
" <td>100.00% de 44</td>\n",
" <td>0.166489</td>\n",
" <td>4.827397e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>daf</th>\n",
" <td>83.48% de 230</td>\n",
" <td>0.166158</td>\n",
" <td>4.999756e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>abdabaa</th>\n",
" <td>84.04% de 213</td>\n",
" <td>0.165887</td>\n",
" <td>5.145518e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>afd</th>\n",
" <td>83.71% de 221</td>\n",
" <td>0.164993</td>\n",
" <td>5.653954e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>dbababa</th>\n",
" <td>86.45% de 155</td>\n",
" <td>0.164761</td>\n",
" <td>5.794047e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ababebba</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>abiab</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>abebaaaa</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>abebaaa</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaaeabc</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ababaea</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aababae</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaacecc</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaababi</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaaaeabc</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaaacea</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aacecc</th>\n",
" <td>0.00% de 5</td>\n",
" <td>-0.157634</td>\n",
" <td>1.206170e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>beaaaa</th>\n",
" <td>45.16% de 31</td>\n",
" <td>-0.157821</td>\n",
" <td>1.183678e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaaabea</th>\n",
" <td>42.31% de 26</td>\n",
" <td>-0.157932</td>\n",
" <td>1.170472e-04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bbbi</th>\n",
" <td>51.92% de 52</td>\n",
" <td>-0.160181</td>\n",
" <td>9.314819e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>eaaa</th>\n",
" <td>56.79% de 81</td>\n",
" <td>-0.161042</td>\n",
" <td>8.527587e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aabeaa</th>\n",
" <td>39.13% de 23</td>\n",
" <td>-0.162812</td>\n",
" <td>7.101815e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aebaaaaa</th>\n",
" <td>20.00% de 10</td>\n",
" <td>-0.163708</td>\n",
" <td>6.469227e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaebaaa</th>\n",
" <td>20.00% de 10</td>\n",
" <td>-0.163708</td>\n",
" <td>6.469227e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaeab</th>\n",
" <td>28.57% de 14</td>\n",
" <td>-0.163751</td>\n",
" <td>6.440178e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>biabb</th>\n",
" <td>28.57% de 14</td>\n",
" <td>-0.163751</td>\n",
" <td>6.440178e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaaaaeba</th>\n",
" <td>12.50% de 8</td>\n",
" <td>-0.166323</td>\n",
" <td>4.913144e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaaaeba</th>\n",
" <td>12.50% de 8</td>\n",
" <td>-0.166323</td>\n",
" <td>4.913144e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bccccabc</th>\n",
" <td>12.50% de 8</td>\n",
" <td>-0.166323</td>\n",
" <td>4.913144e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaabeaa</th>\n",
" <td>35.00% de 20</td>\n",
" <td>-0.169153</td>\n",
" <td>3.630863e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaaaebaa</th>\n",
" <td>0.00% de 6</td>\n",
" <td>-0.172828</td>\n",
" <td>2.433653e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aababi</th>\n",
" <td>0.00% de 6</td>\n",
" <td>-0.172828</td>\n",
" <td>2.433653e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bbia</th>\n",
" <td>38.46% de 26</td>\n",
" <td>-0.176856</td>\n",
" <td>1.554560e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aebaaaa</th>\n",
" <td>18.18% de 11</td>\n",
" <td>-0.177590</td>\n",
" <td>1.431167e-05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aaebaaaa</th>\n",
" <td>0.00% de 8</td>\n",
" <td>-0.199907</td>\n",
" <td>9.834993e-07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>58840 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" cuantosMueren p1 p2\n",
"word \n",
"adc 89.68% de 155 0.208886 3.061022e-07\n",
"fdf 96.39% de 83 0.203784 5.979812e-07\n",
"dabbaba 88.17% de 169 0.199764 1.001482e-06\n",
"dbda 85.07% de 221 0.189067 3.755808e-06\n",
"dabaaaad 95.06% de 81 0.188816 3.870746e-06\n",
"fbd 87.27% de 165 0.183705 7.089394e-06\n",
"dff 93.18% de 88 0.180136 1.071184e-05\n",
"ffd 91.26% de 103 0.177633 1.424185e-05\n",
"fff 93.83% de 81 0.177530 1.440773e-05\n",
"bdbd 85.79% de 183 0.174950 1.924153e-05\n",
"addaba 90.57% de 106 0.173294 2.311825e-05\n",
"faf 87.01% de 154 0.171685 2.758436e-05\n",
"dfdd 96.67% de 60 0.171629 2.775279e-05\n",
"afg 90.48% de 105 0.171339 2.864863e-05\n",
"haf 95.45% de 66 0.171176 2.915994e-05\n",
"bdd 82.53% de 269 0.170361 3.186366e-05\n",
"bdab 79.23% de 414 0.169413 3.530489e-05\n",
"ddba 84.02% de 219 0.169222 3.603969e-05\n",
"ababdba 86.34% de 161 0.167458 4.354558e-05\n",
"fabd 90.29% de 103 0.167401 4.381046e-05\n",
"bbdd 85.47% de 179 0.167382 4.389931e-05\n",
"gga 94.29% de 70 0.167137 4.506321e-05\n",
"aadc 88.62% de 123 0.167130 4.509729e-05\n",
"adabbaba 89.38% de 113 0.167010 4.567608e-05\n",
"dbbd 84.90% de 192 0.166946 4.598597e-05\n",
"gah 100.00% de 44 0.166489 4.827397e-05\n",
"daf 83.48% de 230 0.166158 4.999756e-05\n",
"abdabaa 84.04% de 213 0.165887 5.145518e-05\n",
"afd 83.71% de 221 0.164993 5.653954e-05\n",
"dbababa 86.45% de 155 0.164761 5.794047e-05\n",
"... ... ... ...\n",
"ababebba 0.00% de 5 -0.157634 1.206170e-04\n",
"abiab 0.00% de 5 -0.157634 1.206170e-04\n",
"abebaaaa 0.00% de 5 -0.157634 1.206170e-04\n",
"abebaaa 0.00% de 5 -0.157634 1.206170e-04\n",
"aaaeabc 0.00% de 5 -0.157634 1.206170e-04\n",
"ababaea 0.00% de 5 -0.157634 1.206170e-04\n",
"aababae 0.00% de 5 -0.157634 1.206170e-04\n",
"aaacecc 0.00% de 5 -0.157634 1.206170e-04\n",
"aaababi 0.00% de 5 -0.157634 1.206170e-04\n",
"aaaaeabc 0.00% de 5 -0.157634 1.206170e-04\n",
"aaaacea 0.00% de 5 -0.157634 1.206170e-04\n",
"aacecc 0.00% de 5 -0.157634 1.206170e-04\n",
"beaaaa 45.16% de 31 -0.157821 1.183678e-04\n",
"aaaabea 42.31% de 26 -0.157932 1.170472e-04\n",
"bbbi 51.92% de 52 -0.160181 9.314819e-05\n",
"eaaa 56.79% de 81 -0.161042 8.527587e-05\n",
"aabeaa 39.13% de 23 -0.162812 7.101815e-05\n",
"aebaaaaa 20.00% de 10 -0.163708 6.469227e-05\n",
"aaebaaa 20.00% de 10 -0.163708 6.469227e-05\n",
"aaeab 28.57% de 14 -0.163751 6.440178e-05\n",
"biabb 28.57% de 14 -0.163751 6.440178e-05\n",
"aaaaaeba 12.50% de 8 -0.166323 4.913144e-05\n",
"aaaaeba 12.50% de 8 -0.166323 4.913144e-05\n",
"bccccabc 12.50% de 8 -0.166323 4.913144e-05\n",
"aaabeaa 35.00% de 20 -0.169153 3.630863e-05\n",
"aaaaebaa 0.00% de 6 -0.172828 2.433653e-05\n",
"aababi 0.00% de 6 -0.172828 2.433653e-05\n",
"bbia 38.46% de 26 -0.176856 1.554560e-05\n",
"aebaaaa 18.18% de 11 -0.177590 1.431167e-05\n",
"aaebaaaa 0.00% de 8 -0.199907 9.834993e-07\n",
"\n",
"[58840 rows x 3 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(pearsonDict)\n",
"df = df.set_index('word')\n",
"df = df.sort_values(['p1'], ascending=[False])\n",
"df"
]
},
{
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
tpin3694/tpin3694.github.io | python/find_the_max_value_in_a_dictionary.ipynb | 1 | 1682 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: Find The Max Value In A Dictionary \n",
"Slug: find_the_max_value_in_a_dictionary \n",
"Summary: Find The Max Value In A Dictionary Using Python. \n",
"Date: 2017-02-02 12:00 \n",
"Category: Python \n",
"Tags: Basics \n",
"Authors: Chris Albon "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create A Dictionary"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ages = {'John': 21,\n",
" 'Mike': 52,\n",
" 'Sarah': 12,\n",
" 'Bob': 43\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Find The Maximum Value Of The Values"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(52, 'Mike')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"max(zip(ages.values(), ages.keys()))"
]
}
],
"metadata": {
"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 |
kkhenriquez/python-for-data-science | Week-7-MachineLearning/Weather Data Clustering using k-Means.ipynb | 2 | 289175 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"Clustering with scikit-learn\n",
"\n",
"<br><br></p>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we will learn how to perform k-means lustering using scikit-learn in Python. \n",
"\n",
"We will use cluster analysis to generate a big picture model of the weather at a local station using a minute-graunlarity data. In this dataset, we have in the order of millions records. How do we create 12 clusters our of them?\n",
"\n",
"**NOTE:** The dataset we will use is in a large CSV file called *minute_weather.csv*. Please download it into the *weather* directory in your *Week-7-MachineLearning* folder. The download link is: https://drive.google.com/open?id=0B8iiZ7pSaSFZb3ItQ1l4LWRMTjg "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"Importing the Necessary Libraries<br></p>"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.cluster import KMeans\n",
"import python_utils\n",
"import pandas as pd\n",
"import numpy as np\n",
"from itertools import cycle, islice\n",
"import matplotlib.pyplot as plt\n",
"from pandas.tools.plotting import parallel_coordinates\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"Creating a Pandas DataFrame from a CSV file<br><br></p>\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = pd.read_csv('./weather/minute_weather.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\">Minute Weather Data Description</p>\n",
"<br>\n",
"The **minute weather dataset** comes from the same source as the daily weather dataset that we used in the decision tree based classifier notebook. The main difference between these two datasets is that the minute weather dataset contains raw sensor measurements captured at one-minute intervals. Daily weather dataset instead contained processed and well curated data. The data is in the file **minute_weather.csv**, which is a comma-separated file.\n",
"\n",
"As with the daily weather data, this data comes from a weather station located in San Diego, California. The weather station is equipped with sensors that capture weather-related measurements such as air temperature, air pressure, and relative humidity. Data was collected for a period of three years, from September 2011 to September 2014, to ensure that sufficient data for different seasons and weather conditions is captured.\n",
"\n",
"Each row in **minute_weather.csv** contains weather data captured for a one-minute interval. Each row, or sample, consists of the following variables:\n",
"\n",
"* **rowID:** \tunique number for each row\t(*Unit: NA*)\n",
"* **hpwren_timestamp:**\ttimestamp of measure\t(*Unit: year-month-day hour:minute:second*)\n",
"* **air_pressure:** air pressure measured at the timestamp\t(*Unit: hectopascals*)\n",
"* **air_temp:**\tair temperature measure at the timestamp\t(*Unit: degrees Fahrenheit*)\n",
"* **avg_wind_direction:**\twind direction averaged over the minute before the timestamp\t(*Unit: degrees, with 0 means coming from the North, and increasing clockwise*)\n",
"* **avg_wind_speed:**\twind speed averaged over the minute before the timestamp\t(*Unit: meters per second*)\n",
"* **max_wind_direction:**\thighest wind direction in the minute before the timestamp\t(*Unit: degrees, with 0 being North and increasing clockwise*)\n",
"* **max_wind_speed:**\thighest wind speed in the minute before the timestamp\t(*Unit: meters per second*)\n",
"* **min_wind_direction:**\tsmallest wind direction in the minute before the timestamp\t(*Unit: degrees, with 0 being North and inceasing clockwise*)\n",
"* **min_wind_speed:**\tsmallest wind speed in the minute before the timestamp\t(*Unit: meters per second*)\n",
"* **rain_accumulation:**\tamount of accumulated rain measured at the timestamp\t(*Unit: millimeters*)\n",
"* **rain_duration:**\tlength of time rain has fallen as measured at the timestamp\t(*Unit: seconds*)\n",
"* **relative_humidity:**\trelative humidity measured at the timestamp\t(*Unit: percent*)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1587257, 13)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
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" 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>rowID</th>\n",
" <th>hpwren_timestamp</th>\n",
" <th>air_pressure</th>\n",
" <th>air_temp</th>\n",
" <th>avg_wind_direction</th>\n",
" <th>avg_wind_speed</th>\n",
" <th>max_wind_direction</th>\n",
" <th>max_wind_speed</th>\n",
" <th>min_wind_direction</th>\n",
" <th>min_wind_speed</th>\n",
" <th>rain_accumulation</th>\n",
" <th>rain_duration</th>\n",
" <th>relative_humidity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>2011-09-10 00:00:49</td>\n",
" <td>912.3</td>\n",
" <td>64.76</td>\n",
" <td>97.0</td>\n",
" <td>1.2</td>\n",
" <td>106.0</td>\n",
" <td>1.6</td>\n",
" <td>85.0</td>\n",
" <td>1.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>60.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2011-09-10 00:01:49</td>\n",
" <td>912.3</td>\n",
" <td>63.86</td>\n",
" <td>161.0</td>\n",
" <td>0.8</td>\n",
" <td>215.0</td>\n",
" <td>1.5</td>\n",
" <td>43.0</td>\n",
" <td>0.2</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>39.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>2011-09-10 00:02:49</td>\n",
" <td>912.3</td>\n",
" <td>64.22</td>\n",
" <td>77.0</td>\n",
" <td>0.7</td>\n",
" <td>143.0</td>\n",
" <td>1.2</td>\n",
" <td>324.0</td>\n",
" <td>0.3</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>43.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>2011-09-10 00:03:49</td>\n",
" <td>912.3</td>\n",
" <td>64.40</td>\n",
" <td>89.0</td>\n",
" <td>1.2</td>\n",
" <td>112.0</td>\n",
" <td>1.6</td>\n",
" <td>12.0</td>\n",
" <td>0.7</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>49.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>2011-09-10 00:04:49</td>\n",
" <td>912.3</td>\n",
" <td>64.40</td>\n",
" <td>185.0</td>\n",
" <td>0.4</td>\n",
" <td>260.0</td>\n",
" <td>1.0</td>\n",
" <td>100.0</td>\n",
" <td>0.1</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>58.8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rowID hpwren_timestamp air_pressure air_temp avg_wind_direction \\\n",
"0 0 2011-09-10 00:00:49 912.3 64.76 97.0 \n",
"1 1 2011-09-10 00:01:49 912.3 63.86 161.0 \n",
"2 2 2011-09-10 00:02:49 912.3 64.22 77.0 \n",
"3 3 2011-09-10 00:03:49 912.3 64.40 89.0 \n",
"4 4 2011-09-10 00:04:49 912.3 64.40 185.0 \n",
"\n",
" avg_wind_speed max_wind_direction max_wind_speed min_wind_direction \\\n",
"0 1.2 106.0 1.6 85.0 \n",
"1 0.8 215.0 1.5 43.0 \n",
"2 0.7 143.0 1.2 324.0 \n",
"3 1.2 112.0 1.6 12.0 \n",
"4 0.4 260.0 1.0 100.0 \n",
"\n",
" min_wind_speed rain_accumulation rain_duration relative_humidity \n",
"0 1.0 NaN NaN 60.5 \n",
"1 0.2 0.0 0.0 39.9 \n",
"2 0.3 0.0 0.0 43.0 \n",
"3 0.7 0.0 0.0 49.5 \n",
"4 0.1 0.0 0.0 58.8 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"Data Sampling<br></p>\n",
"\n",
"Lots of rows, so let us sample down by taking every 10th row. <br>\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(158726, 13)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sampled_df = data[(data['rowID'] % 10) == 0]\n",
"sampled_df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"Statistics\n",
"<br><br></p>\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" .dataframe tbody tr th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>rowID</th>\n",
" <td>158726.0</td>\n",
" <td>793625.000000</td>\n",
" <td>458203.937509</td>\n",
" <td>0.00</td>\n",
" <td>396812.5</td>\n",
" <td>793625.00</td>\n",
" <td>1190437.50</td>\n",
" <td>1587250.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>air_pressure</th>\n",
" <td>158726.0</td>\n",
" <td>916.830161</td>\n",
" <td>3.051717</td>\n",
" <td>905.00</td>\n",
" <td>914.8</td>\n",
" <td>916.70</td>\n",
" <td>918.70</td>\n",
" <td>929.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>air_temp</th>\n",
" <td>158726.0</td>\n",
" <td>61.851589</td>\n",
" <td>11.833569</td>\n",
" <td>31.64</td>\n",
" <td>52.7</td>\n",
" <td>62.24</td>\n",
" <td>70.88</td>\n",
" <td>99.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>avg_wind_direction</th>\n",
" <td>158680.0</td>\n",
" <td>162.156100</td>\n",
" <td>95.278201</td>\n",
" <td>0.00</td>\n",
" <td>62.0</td>\n",
" <td>182.00</td>\n",
" <td>217.00</td>\n",
" <td>359.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>avg_wind_speed</th>\n",
" <td>158680.0</td>\n",
" <td>2.775215</td>\n",
" <td>2.057624</td>\n",
" <td>0.00</td>\n",
" <td>1.3</td>\n",
" <td>2.20</td>\n",
" <td>3.80</td>\n",
" <td>31.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max_wind_direction</th>\n",
" <td>158680.0</td>\n",
" <td>163.462144</td>\n",
" <td>92.452139</td>\n",
" <td>0.00</td>\n",
" <td>68.0</td>\n",
" <td>187.00</td>\n",
" <td>223.00</td>\n",
" <td>359.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max_wind_speed</th>\n",
" <td>158680.0</td>\n",
" <td>3.400558</td>\n",
" <td>2.418802</td>\n",
" <td>0.10</td>\n",
" <td>1.6</td>\n",
" <td>2.70</td>\n",
" <td>4.60</td>\n",
" <td>36.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min_wind_direction</th>\n",
" <td>158680.0</td>\n",
" <td>166.774017</td>\n",
" <td>97.441109</td>\n",
" <td>0.00</td>\n",
" <td>76.0</td>\n",
" <td>180.00</td>\n",
" <td>212.00</td>\n",
" <td>359.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min_wind_speed</th>\n",
" <td>158680.0</td>\n",
" <td>2.134664</td>\n",
" <td>1.742113</td>\n",
" <td>0.00</td>\n",
" <td>0.8</td>\n",
" <td>1.60</td>\n",
" <td>3.00</td>\n",
" <td>31.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rain_accumulation</th>\n",
" <td>158725.0</td>\n",
" <td>0.000318</td>\n",
" <td>0.011236</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>3.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>rain_duration</th>\n",
" <td>158725.0</td>\n",
" <td>0.409627</td>\n",
" <td>8.665523</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>2960.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>relative_humidity</th>\n",
" <td>158726.0</td>\n",
" <td>47.609470</td>\n",
" <td>26.214409</td>\n",
" <td>0.90</td>\n",
" <td>24.7</td>\n",
" <td>44.70</td>\n",
" <td>68.00</td>\n",
" <td>93.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% \\\n",
"rowID 158726.0 793625.000000 458203.937509 0.00 396812.5 \n",
"air_pressure 158726.0 916.830161 3.051717 905.00 914.8 \n",
"air_temp 158726.0 61.851589 11.833569 31.64 52.7 \n",
"avg_wind_direction 158680.0 162.156100 95.278201 0.00 62.0 \n",
"avg_wind_speed 158680.0 2.775215 2.057624 0.00 1.3 \n",
"max_wind_direction 158680.0 163.462144 92.452139 0.00 68.0 \n",
"max_wind_speed 158680.0 3.400558 2.418802 0.10 1.6 \n",
"min_wind_direction 158680.0 166.774017 97.441109 0.00 76.0 \n",
"min_wind_speed 158680.0 2.134664 1.742113 0.00 0.8 \n",
"rain_accumulation 158725.0 0.000318 0.011236 0.00 0.0 \n",
"rain_duration 158725.0 0.409627 8.665523 0.00 0.0 \n",
"relative_humidity 158726.0 47.609470 26.214409 0.90 24.7 \n",
"\n",
" 50% 75% max \n",
"rowID 793625.00 1190437.50 1587250.00 \n",
"air_pressure 916.70 918.70 929.50 \n",
"air_temp 62.24 70.88 99.50 \n",
"avg_wind_direction 182.00 217.00 359.00 \n",
"avg_wind_speed 2.20 3.80 31.90 \n",
"max_wind_direction 187.00 223.00 359.00 \n",
"max_wind_speed 2.70 4.60 36.00 \n",
"min_wind_direction 180.00 212.00 359.00 \n",
"min_wind_speed 1.60 3.00 31.60 \n",
"rain_accumulation 0.00 0.00 3.12 \n",
"rain_duration 0.00 0.00 2960.00 \n",
"relative_humidity 44.70 68.00 93.00 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sampled_df.describe().transpose()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(157812, 13)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sampled_df[sampled_df['rain_accumulation'] == 0].shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(157237, 13)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sampled_df[sampled_df['rain_duration'] == 0].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"Drop all the Rows with Empty rain_duration and rain_accumulation\n",
"<br><br></p>\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"del sampled_df['rain_accumulation']\n",
"del sampled_df['rain_duration']"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rows_before = sampled_df.shape[0]\n",
"sampled_df = sampled_df.dropna()\n",
"rows_after = sampled_df.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"How many rows did we drop ?\n",
"<br><br></p>\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"46"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rows_before - rows_after"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['rowID', 'hpwren_timestamp', 'air_pressure', 'air_temp',\n",
" 'avg_wind_direction', 'avg_wind_speed', 'max_wind_direction',\n",
" 'max_wind_speed', 'min_wind_direction', 'min_wind_speed',\n",
" 'relative_humidity'],\n",
" dtype='object')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sampled_df.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"Select Features of Interest for Clustering\n",
"<br><br></p>\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"features = ['air_pressure', 'air_temp', 'avg_wind_direction', 'avg_wind_speed', 'max_wind_direction', \n",
" 'max_wind_speed','relative_humidity']"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"select_df = sampled_df[features]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['air_pressure', 'air_temp', 'avg_wind_direction', 'avg_wind_speed',\n",
" 'max_wind_direction', 'max_wind_speed', 'relative_humidity'],\n",
" dtype='object')"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"select_df.columns"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>air_pressure</th>\n",
" <th>air_temp</th>\n",
" <th>avg_wind_direction</th>\n",
" <th>avg_wind_speed</th>\n",
" <th>max_wind_direction</th>\n",
" <th>max_wind_speed</th>\n",
" <th>relative_humidity</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>912.3</td>\n",
" <td>64.76</td>\n",
" <td>97.0</td>\n",
" <td>1.2</td>\n",
" <td>106.0</td>\n",
" <td>1.6</td>\n",
" <td>60.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>912.3</td>\n",
" <td>62.24</td>\n",
" <td>144.0</td>\n",
" <td>1.2</td>\n",
" <td>167.0</td>\n",
" <td>1.8</td>\n",
" <td>38.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>912.2</td>\n",
" <td>63.32</td>\n",
" <td>100.0</td>\n",
" <td>2.0</td>\n",
" <td>122.0</td>\n",
" <td>2.5</td>\n",
" <td>58.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>912.2</td>\n",
" <td>62.60</td>\n",
" <td>91.0</td>\n",
" <td>2.0</td>\n",
" <td>103.0</td>\n",
" <td>2.4</td>\n",
" <td>57.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>912.2</td>\n",
" <td>64.04</td>\n",
" <td>81.0</td>\n",
" <td>2.6</td>\n",
" <td>88.0</td>\n",
" <td>2.9</td>\n",
" <td>57.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>912.1</td>\n",
" <td>63.68</td>\n",
" <td>102.0</td>\n",
" <td>1.2</td>\n",
" <td>119.0</td>\n",
" <td>1.5</td>\n",
" <td>51.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>912.0</td>\n",
" <td>64.04</td>\n",
" <td>83.0</td>\n",
" <td>0.7</td>\n",
" <td>101.0</td>\n",
" <td>0.9</td>\n",
" <td>51.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>911.9</td>\n",
" <td>64.22</td>\n",
" <td>82.0</td>\n",
" <td>2.0</td>\n",
" <td>97.0</td>\n",
" <td>2.4</td>\n",
" <td>62.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80</th>\n",
" <td>911.9</td>\n",
" <td>61.70</td>\n",
" <td>67.0</td>\n",
" <td>3.3</td>\n",
" <td>70.0</td>\n",
" <td>3.5</td>\n",
" <td>71.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90</th>\n",
" <td>911.9</td>\n",
" <td>61.34</td>\n",
" <td>67.0</td>\n",
" <td>3.6</td>\n",
" <td>75.0</td>\n",
" <td>4.2</td>\n",
" <td>72.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td>911.8</td>\n",
" <td>62.96</td>\n",
" <td>95.0</td>\n",
" <td>2.3</td>\n",
" <td>106.0</td>\n",
" <td>2.5</td>\n",
" <td>63.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td>911.8</td>\n",
" <td>64.22</td>\n",
" <td>83.0</td>\n",
" <td>2.1</td>\n",
" <td>88.0</td>\n",
" <td>2.5</td>\n",
" <td>59.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>911.8</td>\n",
" <td>63.86</td>\n",
" <td>68.0</td>\n",
" <td>2.1</td>\n",
" <td>76.0</td>\n",
" <td>2.4</td>\n",
" <td>63.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td>911.6</td>\n",
" <td>64.40</td>\n",
" <td>156.0</td>\n",
" <td>0.5</td>\n",
" <td>203.0</td>\n",
" <td>0.7</td>\n",
" <td>50.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>140</th>\n",
" <td>911.5</td>\n",
" <td>65.30</td>\n",
" <td>85.0</td>\n",
" <td>2.2</td>\n",
" <td>92.0</td>\n",
" <td>2.5</td>\n",
" <td>58.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>150</th>\n",
" <td>911.4</td>\n",
" <td>64.58</td>\n",
" <td>154.0</td>\n",
" <td>1.3</td>\n",
" <td>176.0</td>\n",
" <td>2.1</td>\n",
" <td>50.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>160</th>\n",
" <td>911.4</td>\n",
" <td>65.48</td>\n",
" <td>154.0</td>\n",
" <td>0.9</td>\n",
" <td>208.0</td>\n",
" <td>1.9</td>\n",
" <td>46.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>911.5</td>\n",
" <td>65.66</td>\n",
" <td>95.0</td>\n",
" <td>1.1</td>\n",
" <td>109.0</td>\n",
" <td>1.6</td>\n",
" <td>45.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>911.4</td>\n",
" <td>65.66</td>\n",
" <td>155.0</td>\n",
" <td>1.1</td>\n",
" <td>167.0</td>\n",
" <td>1.6</td>\n",
" <td>42.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td>911.4</td>\n",
" <td>67.10</td>\n",
" <td>157.0</td>\n",
" <td>1.2</td>\n",
" <td>172.0</td>\n",
" <td>1.6</td>\n",
" <td>36.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>200</th>\n",
" <td>911.4</td>\n",
" <td>68.00</td>\n",
" <td>53.0</td>\n",
" <td>0.3</td>\n",
" <td>69.0</td>\n",
" <td>0.5</td>\n",
" <td>33.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>210</th>\n",
" <td>911.3</td>\n",
" <td>67.64</td>\n",
" <td>167.0</td>\n",
" <td>1.5</td>\n",
" <td>196.0</td>\n",
" <td>2.2</td>\n",
" <td>34.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>220</th>\n",
" <td>911.4</td>\n",
" <td>67.82</td>\n",
" <td>4.0</td>\n",
" <td>0.6</td>\n",
" <td>25.0</td>\n",
" <td>0.7</td>\n",
" <td>34.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>230</th>\n",
" <td>911.4</td>\n",
" <td>66.74</td>\n",
" <td>172.0</td>\n",
" <td>1.3</td>\n",
" <td>192.0</td>\n",
" <td>1.9</td>\n",
" <td>37.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>240</th>\n",
" <td>911.4</td>\n",
" <td>66.56</td>\n",
" <td>39.0</td>\n",
" <td>0.2</td>\n",
" <td>145.0</td>\n",
" <td>0.3</td>\n",
" <td>41.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>250</th>\n",
" <td>911.4</td>\n",
" <td>65.66</td>\n",
" <td>56.0</td>\n",
" <td>1.9</td>\n",
" <td>67.0</td>\n",
" <td>2.2</td>\n",
" <td>51.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>260</th>\n",
" <td>911.5</td>\n",
" <td>65.66</td>\n",
" <td>74.0</td>\n",
" <td>0.8</td>\n",
" <td>101.0</td>\n",
" <td>1.2</td>\n",
" <td>41.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>270</th>\n",
" <td>911.4</td>\n",
" <td>66.92</td>\n",
" <td>147.0</td>\n",
" <td>0.9</td>\n",
" <td>174.0</td>\n",
" <td>1.1</td>\n",
" <td>36.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>280</th>\n",
" <td>911.3</td>\n",
" <td>64.76</td>\n",
" <td>73.0</td>\n",
" <td>1.0</td>\n",
" <td>82.0</td>\n",
" <td>1.2</td>\n",
" <td>43.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>290</th>\n",
" <td>911.3</td>\n",
" <td>64.94</td>\n",
" <td>164.0</td>\n",
" <td>1.3</td>\n",
" <td>176.0</td>\n",
" <td>1.7</td>\n",
" <td>43.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1586960</th>\n",
" <td>914.7</td>\n",
" <td>76.46</td>\n",
" <td>247.0</td>\n",
" <td>0.6</td>\n",
" <td>264.0</td>\n",
" <td>0.7</td>\n",
" <td>43.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1586970</th>\n",
" <td>914.8</td>\n",
" <td>76.28</td>\n",
" <td>208.0</td>\n",
" <td>0.7</td>\n",
" <td>216.0</td>\n",
" <td>0.9</td>\n",
" <td>43.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1586980</th>\n",
" <td>914.8</td>\n",
" <td>76.10</td>\n",
" <td>209.0</td>\n",
" <td>0.7</td>\n",
" <td>216.0</td>\n",
" <td>0.9</td>\n",
" <td>43.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1586990</th>\n",
" <td>914.9</td>\n",
" <td>76.28</td>\n",
" <td>339.0</td>\n",
" <td>0.5</td>\n",
" <td>350.0</td>\n",
" <td>0.7</td>\n",
" <td>43.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587000</th>\n",
" <td>914.9</td>\n",
" <td>75.92</td>\n",
" <td>344.0</td>\n",
" <td>0.4</td>\n",
" <td>352.0</td>\n",
" <td>0.6</td>\n",
" <td>43.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587010</th>\n",
" <td>915.0</td>\n",
" <td>75.56</td>\n",
" <td>323.0</td>\n",
" <td>0.3</td>\n",
" <td>348.0</td>\n",
" <td>0.5</td>\n",
" <td>45.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587020</th>\n",
" <td>915.1</td>\n",
" <td>75.56</td>\n",
" <td>324.0</td>\n",
" <td>1.1</td>\n",
" <td>347.0</td>\n",
" <td>1.5</td>\n",
" <td>46.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587030</th>\n",
" <td>915.1</td>\n",
" <td>75.74</td>\n",
" <td>1.0</td>\n",
" <td>1.3</td>\n",
" <td>13.0</td>\n",
" <td>1.7</td>\n",
" <td>45.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587040</th>\n",
" <td>915.2</td>\n",
" <td>75.38</td>\n",
" <td>355.0</td>\n",
" <td>0.9</td>\n",
" <td>1.0</td>\n",
" <td>1.1</td>\n",
" <td>46.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587050</th>\n",
" <td>915.3</td>\n",
" <td>75.38</td>\n",
" <td>359.0</td>\n",
" <td>1.4</td>\n",
" <td>11.0</td>\n",
" <td>1.5</td>\n",
" <td>45.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587060</th>\n",
" <td>915.4</td>\n",
" <td>75.38</td>\n",
" <td>11.0</td>\n",
" <td>1.1</td>\n",
" <td>21.0</td>\n",
" <td>1.3</td>\n",
" <td>45.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587070</th>\n",
" <td>915.5</td>\n",
" <td>75.38</td>\n",
" <td>13.0</td>\n",
" <td>1.4</td>\n",
" <td>24.0</td>\n",
" <td>1.6</td>\n",
" <td>46.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587080</th>\n",
" <td>915.6</td>\n",
" <td>75.20</td>\n",
" <td>18.0</td>\n",
" <td>1.0</td>\n",
" <td>24.0</td>\n",
" <td>1.2</td>\n",
" <td>46.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587090</th>\n",
" <td>915.6</td>\n",
" <td>75.20</td>\n",
" <td>356.0</td>\n",
" <td>1.7</td>\n",
" <td>1.0</td>\n",
" <td>1.9</td>\n",
" <td>47.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587100</th>\n",
" <td>915.7</td>\n",
" <td>75.38</td>\n",
" <td>13.0</td>\n",
" <td>1.5</td>\n",
" <td>24.0</td>\n",
" <td>1.7</td>\n",
" <td>46.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587110</th>\n",
" <td>915.7</td>\n",
" <td>75.02</td>\n",
" <td>19.0</td>\n",
" <td>1.2</td>\n",
" <td>28.0</td>\n",
" <td>1.4</td>\n",
" <td>46.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587120</th>\n",
" <td>915.7</td>\n",
" <td>74.84</td>\n",
" <td>25.0</td>\n",
" <td>1.4</td>\n",
" <td>35.0</td>\n",
" <td>1.6</td>\n",
" <td>46.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587130</th>\n",
" <td>915.8</td>\n",
" <td>74.84</td>\n",
" <td>23.0</td>\n",
" <td>1.3</td>\n",
" <td>30.0</td>\n",
" <td>1.5</td>\n",
" <td>46.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587140</th>\n",
" <td>915.8</td>\n",
" <td>74.84</td>\n",
" <td>32.0</td>\n",
" <td>1.4</td>\n",
" <td>41.0</td>\n",
" <td>1.7</td>\n",
" <td>45.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587150</th>\n",
" <td>915.8</td>\n",
" <td>75.20</td>\n",
" <td>23.0</td>\n",
" <td>1.1</td>\n",
" <td>31.0</td>\n",
" <td>1.4</td>\n",
" <td>45.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587160</th>\n",
" <td>915.8</td>\n",
" <td>75.38</td>\n",
" <td>16.0</td>\n",
" <td>1.2</td>\n",
" <td>28.0</td>\n",
" <td>1.5</td>\n",
" <td>46.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587170</th>\n",
" <td>915.7</td>\n",
" <td>75.38</td>\n",
" <td>347.0</td>\n",
" <td>1.2</td>\n",
" <td>353.0</td>\n",
" <td>1.4</td>\n",
" <td>48.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587180</th>\n",
" <td>915.8</td>\n",
" <td>75.74</td>\n",
" <td>326.0</td>\n",
" <td>1.2</td>\n",
" <td>337.0</td>\n",
" <td>1.6</td>\n",
" <td>48.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587190</th>\n",
" <td>915.9</td>\n",
" <td>75.92</td>\n",
" <td>289.0</td>\n",
" <td>0.7</td>\n",
" <td>309.0</td>\n",
" <td>0.9</td>\n",
" <td>48.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587200</th>\n",
" <td>915.9</td>\n",
" <td>75.74</td>\n",
" <td>335.0</td>\n",
" <td>0.9</td>\n",
" <td>348.0</td>\n",
" <td>1.1</td>\n",
" <td>47.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587210</th>\n",
" <td>915.9</td>\n",
" <td>75.56</td>\n",
" <td>330.0</td>\n",
" <td>1.0</td>\n",
" <td>341.0</td>\n",
" <td>1.3</td>\n",
" <td>47.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587220</th>\n",
" <td>915.9</td>\n",
" <td>75.56</td>\n",
" <td>330.0</td>\n",
" <td>1.1</td>\n",
" <td>341.0</td>\n",
" <td>1.4</td>\n",
" <td>48.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587230</th>\n",
" <td>915.9</td>\n",
" <td>75.56</td>\n",
" <td>344.0</td>\n",
" <td>1.4</td>\n",
" <td>352.0</td>\n",
" <td>1.7</td>\n",
" <td>48.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587240</th>\n",
" <td>915.9</td>\n",
" <td>75.20</td>\n",
" <td>359.0</td>\n",
" <td>1.3</td>\n",
" <td>9.0</td>\n",
" <td>1.6</td>\n",
" <td>46.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1587250</th>\n",
" <td>915.9</td>\n",
" <td>74.84</td>\n",
" <td>6.0</td>\n",
" <td>1.5</td>\n",
" <td>20.0</td>\n",
" <td>1.9</td>\n",
" <td>46.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>158680 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" air_pressure air_temp avg_wind_direction avg_wind_speed \\\n",
"0 912.3 64.76 97.0 1.2 \n",
"10 912.3 62.24 144.0 1.2 \n",
"20 912.2 63.32 100.0 2.0 \n",
"30 912.2 62.60 91.0 2.0 \n",
"40 912.2 64.04 81.0 2.6 \n",
"50 912.1 63.68 102.0 1.2 \n",
"60 912.0 64.04 83.0 0.7 \n",
"70 911.9 64.22 82.0 2.0 \n",
"80 911.9 61.70 67.0 3.3 \n",
"90 911.9 61.34 67.0 3.6 \n",
"100 911.8 62.96 95.0 2.3 \n",
"110 911.8 64.22 83.0 2.1 \n",
"120 911.8 63.86 68.0 2.1 \n",
"130 911.6 64.40 156.0 0.5 \n",
"140 911.5 65.30 85.0 2.2 \n",
"150 911.4 64.58 154.0 1.3 \n",
"160 911.4 65.48 154.0 0.9 \n",
"170 911.5 65.66 95.0 1.1 \n",
"180 911.4 65.66 155.0 1.1 \n",
"190 911.4 67.10 157.0 1.2 \n",
"200 911.4 68.00 53.0 0.3 \n",
"210 911.3 67.64 167.0 1.5 \n",
"220 911.4 67.82 4.0 0.6 \n",
"230 911.4 66.74 172.0 1.3 \n",
"240 911.4 66.56 39.0 0.2 \n",
"250 911.4 65.66 56.0 1.9 \n",
"260 911.5 65.66 74.0 0.8 \n",
"270 911.4 66.92 147.0 0.9 \n",
"280 911.3 64.76 73.0 1.0 \n",
"290 911.3 64.94 164.0 1.3 \n",
"... ... ... ... ... \n",
"1586960 914.7 76.46 247.0 0.6 \n",
"1586970 914.8 76.28 208.0 0.7 \n",
"1586980 914.8 76.10 209.0 0.7 \n",
"1586990 914.9 76.28 339.0 0.5 \n",
"1587000 914.9 75.92 344.0 0.4 \n",
"1587010 915.0 75.56 323.0 0.3 \n",
"1587020 915.1 75.56 324.0 1.1 \n",
"1587030 915.1 75.74 1.0 1.3 \n",
"1587040 915.2 75.38 355.0 0.9 \n",
"1587050 915.3 75.38 359.0 1.4 \n",
"1587060 915.4 75.38 11.0 1.1 \n",
"1587070 915.5 75.38 13.0 1.4 \n",
"1587080 915.6 75.20 18.0 1.0 \n",
"1587090 915.6 75.20 356.0 1.7 \n",
"1587100 915.7 75.38 13.0 1.5 \n",
"1587110 915.7 75.02 19.0 1.2 \n",
"1587120 915.7 74.84 25.0 1.4 \n",
"1587130 915.8 74.84 23.0 1.3 \n",
"1587140 915.8 74.84 32.0 1.4 \n",
"1587150 915.8 75.20 23.0 1.1 \n",
"1587160 915.8 75.38 16.0 1.2 \n",
"1587170 915.7 75.38 347.0 1.2 \n",
"1587180 915.8 75.74 326.0 1.2 \n",
"1587190 915.9 75.92 289.0 0.7 \n",
"1587200 915.9 75.74 335.0 0.9 \n",
"1587210 915.9 75.56 330.0 1.0 \n",
"1587220 915.9 75.56 330.0 1.1 \n",
"1587230 915.9 75.56 344.0 1.4 \n",
"1587240 915.9 75.20 359.0 1.3 \n",
"1587250 915.9 74.84 6.0 1.5 \n",
"\n",
" max_wind_direction max_wind_speed relative_humidity \n",
"0 106.0 1.6 60.5 \n",
"10 167.0 1.8 38.5 \n",
"20 122.0 2.5 58.3 \n",
"30 103.0 2.4 57.9 \n",
"40 88.0 2.9 57.4 \n",
"50 119.0 1.5 51.4 \n",
"60 101.0 0.9 51.4 \n",
"70 97.0 2.4 62.2 \n",
"80 70.0 3.5 71.5 \n",
"90 75.0 4.2 72.5 \n",
"100 106.0 2.5 63.9 \n",
"110 88.0 2.5 59.1 \n",
"120 76.0 2.4 63.5 \n",
"130 203.0 0.7 50.4 \n",
"140 92.0 2.5 58.0 \n",
"150 176.0 2.1 50.2 \n",
"160 208.0 1.9 46.2 \n",
"170 109.0 1.6 45.2 \n",
"180 167.0 1.6 42.8 \n",
"190 172.0 1.6 36.8 \n",
"200 69.0 0.5 33.4 \n",
"210 196.0 2.2 34.4 \n",
"220 25.0 0.7 34.2 \n",
"230 192.0 1.9 37.8 \n",
"240 145.0 0.3 41.6 \n",
"250 67.0 2.2 51.8 \n",
"260 101.0 1.2 41.1 \n",
"270 174.0 1.1 36.0 \n",
"280 82.0 1.2 43.3 \n",
"290 176.0 1.7 43.0 \n",
"... ... ... ... \n",
"1586960 264.0 0.7 43.4 \n",
"1586970 216.0 0.9 43.7 \n",
"1586980 216.0 0.9 43.9 \n",
"1586990 350.0 0.7 43.4 \n",
"1587000 352.0 0.6 43.9 \n",
"1587010 348.0 0.5 45.5 \n",
"1587020 347.0 1.5 46.0 \n",
"1587030 13.0 1.7 45.8 \n",
"1587040 1.0 1.1 46.1 \n",
"1587050 11.0 1.5 45.8 \n",
"1587060 21.0 1.3 45.7 \n",
"1587070 24.0 1.6 46.6 \n",
"1587080 24.0 1.2 46.5 \n",
"1587090 1.0 1.9 47.2 \n",
"1587100 24.0 1.7 46.7 \n",
"1587110 28.0 1.4 46.7 \n",
"1587120 35.0 1.6 46.5 \n",
"1587130 30.0 1.5 46.9 \n",
"1587140 41.0 1.7 45.5 \n",
"1587150 31.0 1.4 45.7 \n",
"1587160 28.0 1.5 46.3 \n",
"1587170 353.0 1.4 48.1 \n",
"1587180 337.0 1.6 48.3 \n",
"1587190 309.0 0.9 48.1 \n",
"1587200 348.0 1.1 47.8 \n",
"1587210 341.0 1.3 47.8 \n",
"1587220 341.0 1.4 48.0 \n",
"1587230 352.0 1.7 48.0 \n",
"1587240 9.0 1.6 46.3 \n",
"1587250 20.0 1.9 46.1 \n",
"\n",
"[158680 rows x 7 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"select_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"Scale the Features using StandardScaler\n",
"<br><br></p>\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-1.48456281, 0.24544455, -0.68385323, ..., -0.62153592,\n",
" -0.74440309, 0.49233835],\n",
" [-1.48456281, 0.03247142, -0.19055941, ..., 0.03826701,\n",
" -0.66171726, -0.34710804],\n",
" [-1.51733167, 0.12374562, -0.65236639, ..., -0.44847286,\n",
" -0.37231683, 0.40839371],\n",
" ..., \n",
" [-0.30488381, 1.15818654, 1.90856325, ..., 2.0393087 ,\n",
" -0.70306017, 0.01538018],\n",
" [-0.30488381, 1.12776181, 2.06599745, ..., -1.67073075,\n",
" -0.74440309, -0.04948614],\n",
" [-0.30488381, 1.09733708, -1.63895404, ..., -1.55174989,\n",
" -0.62037434, -0.05711747]])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = StandardScaler().fit_transform(select_df)\n",
"X"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"Use k-Means Clustering\n",
"<br><br></p>\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model\n",
" KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
" n_clusters=12, n_init=10, n_jobs=1, precompute_distances='auto',\n",
" random_state=None, tol=0.0001, verbose=0)\n"
]
}
],
"source": [
"kmeans = KMeans(n_clusters=12)\n",
"model = kmeans.fit(X)\n",
"print(\"model\\n\", model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:1.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"What are the centers of 12 clusters we formed ?\n",
"<br><br></p>\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.13084188, 0.84364889, 1.41110842, -0.63847003, 1.67514535,\n",
" -0.58923825, -0.71428993],\n",
" [-0.16273697, 0.86326475, -1.31109857, -0.58986336, -1.16677186,\n",
" -0.60518041, -0.64175838],\n",
" [ 0.06090529, -0.7883438 , -1.19704157, -0.57068447, -1.04307056,\n",
" -0.5852019 , 0.87826875],\n",
" [-0.69622403, 0.54238729, 0.17683404, -0.58423566, 0.34622683,\n",
" -0.59760929, -0.11377433],\n",
" [ 1.1903932 , -0.25532324, -1.15504338, 2.12500582, -1.05345307,\n",
" 2.24217458, -1.13430643],\n",
" [ 0.72970762, 0.4369723 , 0.28554476, -0.53499344, 0.47340217,\n",
" -0.54119401, -0.77183251],\n",
" [-1.17980614, -0.87628949, 0.44682405, 1.97618697, 0.53879655,\n",
" 1.93748075, 0.91452697],\n",
" [ 0.25278377, -0.9945293 , 0.66014467, -0.54746803, 0.85125262,\n",
" -0.53010121, 1.15835238],\n",
" [ 0.23378992, 0.3194701 , 1.88794624, -0.65194259, -1.55164204,\n",
" -0.57678283, -0.28275652],\n",
" [-0.83978877, -1.19859079, 0.3751117 , 0.35480908, 0.47354889,\n",
" 0.3426911 , 1.36252578],\n",
" [ 1.36745099, -0.08181034, -1.20695191, -0.04820434, -1.07589238,\n",
" -0.02784815, -0.97769476],\n",
" [-0.21126749, 0.63174315, 0.4085275 , 0.73468529, 0.51667298,\n",
" 0.67266675, -0.15013313]])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"centers = model.cluster_centers_\n",
"centers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
"\n",
"Plots\n",
"<br><br></p>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us first create some utility functions which will help us in plotting graphs:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Function that creates a DataFrame with a column for Cluster Number\n",
"\n",
"def pd_centers(featuresUsed, centers):\n",
"\tcolNames = list(featuresUsed)\n",
"\tcolNames.append('prediction')\n",
"\n",
"\t# Zip with a column called 'prediction' (index)\n",
"\tZ = [np.append(A, index) for index, A in enumerate(centers)]\n",
"\n",
"\t# Convert to pandas data frame for plotting\n",
"\tP = pd.DataFrame(Z, columns=colNames)\n",
"\tP['prediction'] = P['prediction'].astype(int)\n",
"\treturn P"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Function that creates Parallel Plots\n",
"\n",
"def parallel_plot(data):\n",
"\tmy_colors = list(islice(cycle(['b', 'r', 'g', 'y', 'k']), None, len(data)))\n",
"\tplt.figure(figsize=(15,8)).gca().axes.set_ylim([-3,+3])\n",
"\tparallel_coordinates(data, 'prediction', color = my_colors, marker='o')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"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>air_pressure</th>\n",
" <th>air_temp</th>\n",
" <th>avg_wind_direction</th>\n",
" <th>avg_wind_speed</th>\n",
" <th>max_wind_direction</th>\n",
" <th>max_wind_speed</th>\n",
" <th>relative_humidity</th>\n",
" <th>prediction</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.130842</td>\n",
" <td>0.843649</td>\n",
" <td>1.411108</td>\n",
" <td>-0.638470</td>\n",
" <td>1.675145</td>\n",
" <td>-0.589238</td>\n",
" <td>-0.714290</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.162737</td>\n",
" <td>0.863265</td>\n",
" <td>-1.311099</td>\n",
" <td>-0.589863</td>\n",
" <td>-1.166772</td>\n",
" <td>-0.605180</td>\n",
" <td>-0.641758</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.060905</td>\n",
" <td>-0.788344</td>\n",
" <td>-1.197042</td>\n",
" <td>-0.570684</td>\n",
" <td>-1.043071</td>\n",
" <td>-0.585202</td>\n",
" <td>0.878269</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-0.696224</td>\n",
" <td>0.542387</td>\n",
" <td>0.176834</td>\n",
" <td>-0.584236</td>\n",
" <td>0.346227</td>\n",
" <td>-0.597609</td>\n",
" <td>-0.113774</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.190393</td>\n",
" <td>-0.255323</td>\n",
" <td>-1.155043</td>\n",
" <td>2.125006</td>\n",
" <td>-1.053453</td>\n",
" <td>2.242175</td>\n",
" <td>-1.134306</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.729708</td>\n",
" <td>0.436972</td>\n",
" <td>0.285545</td>\n",
" <td>-0.534993</td>\n",
" <td>0.473402</td>\n",
" <td>-0.541194</td>\n",
" <td>-0.771833</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>-1.179806</td>\n",
" <td>-0.876289</td>\n",
" <td>0.446824</td>\n",
" <td>1.976187</td>\n",
" <td>0.538797</td>\n",
" <td>1.937481</td>\n",
" <td>0.914527</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.252784</td>\n",
" <td>-0.994529</td>\n",
" <td>0.660145</td>\n",
" <td>-0.547468</td>\n",
" <td>0.851253</td>\n",
" <td>-0.530101</td>\n",
" <td>1.158352</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.233790</td>\n",
" <td>0.319470</td>\n",
" <td>1.887946</td>\n",
" <td>-0.651943</td>\n",
" <td>-1.551642</td>\n",
" <td>-0.576783</td>\n",
" <td>-0.282757</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>-0.839789</td>\n",
" <td>-1.198591</td>\n",
" <td>0.375112</td>\n",
" <td>0.354809</td>\n",
" <td>0.473549</td>\n",
" <td>0.342691</td>\n",
" <td>1.362526</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1.367451</td>\n",
" <td>-0.081810</td>\n",
" <td>-1.206952</td>\n",
" <td>-0.048204</td>\n",
" <td>-1.075892</td>\n",
" <td>-0.027848</td>\n",
" <td>-0.977695</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>-0.211267</td>\n",
" <td>0.631743</td>\n",
" <td>0.408528</td>\n",
" <td>0.734685</td>\n",
" <td>0.516673</td>\n",
" <td>0.672667</td>\n",
" <td>-0.150133</td>\n",
" <td>11</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" air_pressure air_temp avg_wind_direction avg_wind_speed \\\n",
"0 0.130842 0.843649 1.411108 -0.638470 \n",
"1 -0.162737 0.863265 -1.311099 -0.589863 \n",
"2 0.060905 -0.788344 -1.197042 -0.570684 \n",
"3 -0.696224 0.542387 0.176834 -0.584236 \n",
"4 1.190393 -0.255323 -1.155043 2.125006 \n",
"5 0.729708 0.436972 0.285545 -0.534993 \n",
"6 -1.179806 -0.876289 0.446824 1.976187 \n",
"7 0.252784 -0.994529 0.660145 -0.547468 \n",
"8 0.233790 0.319470 1.887946 -0.651943 \n",
"9 -0.839789 -1.198591 0.375112 0.354809 \n",
"10 1.367451 -0.081810 -1.206952 -0.048204 \n",
"11 -0.211267 0.631743 0.408528 0.734685 \n",
"\n",
" max_wind_direction max_wind_speed relative_humidity prediction \n",
"0 1.675145 -0.589238 -0.714290 0 \n",
"1 -1.166772 -0.605180 -0.641758 1 \n",
"2 -1.043071 -0.585202 0.878269 2 \n",
"3 0.346227 -0.597609 -0.113774 3 \n",
"4 -1.053453 2.242175 -1.134306 4 \n",
"5 0.473402 -0.541194 -0.771833 5 \n",
"6 0.538797 1.937481 0.914527 6 \n",
"7 0.851253 -0.530101 1.158352 7 \n",
"8 -1.551642 -0.576783 -0.282757 8 \n",
"9 0.473549 0.342691 1.362526 9 \n",
"10 -1.075892 -0.027848 -0.977695 10 \n",
"11 0.516673 0.672667 -0.150133 11 "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"P = pd_centers(features, centers)\n",
"P"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Dry Days"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kevin/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:6: FutureWarning: 'pandas.tools.plotting.parallel_coordinates' is deprecated, import 'pandas.plotting.parallel_coordinates' instead.\n",
" \n"
]
},
{
"data": {
"image/png": 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OvI9442X8S6XjkAVi0UlERCZhS/AWHLl7BJMbTkYu+1xKx9Ebx8yOmNpwKo7e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Jx8ggIDW5FUCgoMbEXJySd1WoW3vOy9t5cgAW2+sZlvUYpEiLZlmPcwsl5iXeq0FaHA8owlL/KM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\nbgWNtUTm50t5ebJ16+RycqRhw6S+fX3X3SlTfKIZe6RJVlbxI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Nd3S7o8DO8jaZWkxqms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\nAAAi8//lGdY164JWxQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1263cc080>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"parallel_plot(P[P['relative_humidity'] < -0.5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Warm Days"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kevin/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:6: FutureWarning: 'pandas.tools.plotting.parallel_coordinates' is deprecated, import 'pandas.plotting.parallel_coordinates' instead.\n",
" \n"
]
},
{
"data": {
"image/png": 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e7xEFFn+bL6Qfs5zpZNBJptCUxbukBPjxx6remenp8vqAAVW9MwcOZBEgs9L7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afKSNMpKQ4yeesciwWeew7+9z9xRu+55zQLRWpFQy5dKuw+e/SoLspn74bN6uXq1cIy3F9/lWW4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\nnF5Pmza05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"text/plain": [
"<matplotlib.figure.Figure at 0x114b35518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"parallel_plot(P[P['air_temp'] > 0.5])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Cool Days"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/kevin/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:6: FutureWarning: 'pandas.tools.plotting.parallel_coordinates' is deprecated, import 'pandas.plotting.parallel_coordinates' instead.\n",
" \n"
]
},
{
"data": {
"image/png": 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HtGwJr71mHnJ9+qkKmInXUtIpIiK+KyUFXnjBLHFr2NDs3wwNdf55OnWCLVugWjVTDOS5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afM0I9P+bE6Z3du0npqaqSnnvJNb+fP9x3wr7zSB9DBg5NfT7MO3mVl0vXXS7//vTRypPTgg76j\nhqRFWxfpgocvUMX+Cj3+tcf1xcFfTG0bkqqrP9eqVV/Xzp0va8CAqzV06P9TVlZOyutDajjRpz/n\nfHPZaNBcv943aZs0yQfNL3+59fo+pkN5cc6H7Whz3Nde813OJT8yZrQWdNKklm3+h6ZJh7LS0UUH\n/Ynvj/nee/61vDzpxBNjIfPEExsf9CdMlBcki9AZMkJnZvik7BNd8+I1emj5QyruU6z/u/D/NH7A\n+JTWtXixdMcd0qOP+ruQU6b4prcTJzZ+Jz/lg/ebb0rf/Ka/qr32WukXv5C6dJEkPbH6CV36+KXq\nW9hXz3/jeY08dGQK78orK1ulFSsuUkXFJg0bdo/69/9WyutC83CiT0+RiG/yFg2amzf7OTPPOMMH\nzSlTWrcWIiody4tzvj9rNIC+9pr/vCTpkENiI+NOmuT7uDan6wKSl45lpb2rqUkc9Gf+/NigPz17\n1h30p3PT7ouHivKCZBE6Q0bozCzPvvesrnr2Km0v3a5rJlyjn53+MxV0Tm1+lK1bpd/9Trr3Xj/9\nyrhxPg9efLHUqVP9f9Pkg3dVlfTTn0o33+yH0v3Tn6TTTpPka3Fvf+t2XffSdRo/YLyevuRp9Sno\nc/D1HcSnnz6j1asvVVZWvoqLH1f37ienvC40Hyf69LF/v6+9mz3bDzK2fbsfCfLss33QvPBCf+HY\nljKlvGzcmBhCN2zwz3fv7i+4ozWhxx7b8HEUzZMpZSWTVVbWHfRn927/2uGHJ/bHHDUqvWv9KS9I\nFqEzZITOzLOnco9+9PKPdM+SezSk5xDdd8F9zWqOWl7ux/G54w4/J9aAAX6Mn+nT/d38eE06eC9f\n7ms3ly6VrrjCbyDoSLo/sl9X//1q3b34bk0bNU1/nvJndenUJaX9d87pww9v1gcfzFBh4XEqLn5C\neXlHpLQutBxO9G2rqkp69VUfNJ98Uvr0U9+44LzzfNA8//zU+nWHJVPLy5Ytic1xo/MKFhT41iPR\nEHrCCelV+5PJMrWspLM9e+oO+lNZ6V8bMSIxZBYVZVb/ZsoLkkXoDBmhM3O9tvE1feeZ72jd5+v0\n7WO/rVvPvlU98nqkvL5IRHrhBd/v85VX/AXqP/+zdM01/o5n0pMs19RIt90m/eQnfrKt++7zVSmB\nPZV79LXHvqYX3n9BP5r4I/3yjF+mPEJtTU251q69Uh9//Ij69Pm6hg//P2Vn56e0LrQsTvStr6LC\nj9M1e7b09NN+OoKuXaXJk33QPPdcH4bSUXspLx99FAuh8+b5JomSP56edFKsOe6JJ/q+b2i69lJW\n2tKOHYn9MUtKYoP+HHdcLGBOnNg2ze1bEuUFySJ0hozQmdn2Ve/TT1/7qf53wf+qT0Ef/f7832vK\niCnNXu/y5b5ictYsf7czK8ufkCST5JSfL82cWU/w3LDBJ9X58/0oJPfem3DG2rx7syY/PFkrP16p\nu8+/W985/jsp72NFxWatWDFFpaXvavDgX2rgwB/JMun2azvHib51lJVJf/+7D5rPPiuVlvp7PRdd\n5IPmWWdlRrhpr+Xl00/9RX20JrSkxPcV7dxZmjAhVhN60knpe0Mg3bTXshIW5/ypOT5krlvnX+vS\npe6gP4WFbbu/LY3ygmQROkNG6GwflmxboiufvlIlO0p08aiL9dsv/VZ9C/s2e70ffywNGxbryxEN\nnZLUu7dvgjN4sGRyflLQa6/1t0rvuku67LKENjjvbH9HFzx8gUqrSvXYxY/prKFnpbxfu3cv0IoV\nX1EkUq6RIx9S796TU3+TCAUn+vDs2eMD5uzZPnDu2+fv7UyZIk2bJp1+eub1J+wo5WXnTt8/LhpC\n33nHNw7JyfH96qMhdOLE9Gr+nE46SllJVU2Nv3EcP+hPdBTmQw7xfY+jA/8cd1z7b/ZNeUGyCJ0h\nMzO3YEGRhgy5SX37NtReEpmguqZaty64VT997acq6FSgO869Q/80+p+aXfuXleXvlHqx0Bl19CHb\n9cfsb+uET57XZ2O+qKw//VE9xwxMWOaZtc/oktmXqFd+Lz33jeeaPN9ovO3b/6D33rtKeXlFKi5+\nSgUFo1JeF8LDib5lff65bzI7e7ZvQltVJfXv7+fPnDbNX0TmZPDMQB21vOzdmxhCFy3yAz9lZflA\nEG2Oe8opbT/YU7roqGWlIZWVvtxEQ+aCBbEbxUcckdgfc+TI9B70JwyUFySL0BkyM3Nz5khZWfka\nPnwmwbMdWPPpGl359JVasHmBzhl6ju6dfK+KeqQ+q/ugQdKmTdHfYqGzXz/pT5P/ppMfvEo5VeW6\n3v1ad+m7csrSkUdK48f7n0397tRv1l6r4/ofp6e//rT6d+2f0n5EIvu1fv0PtHXrnerZ80yNGvWo\nOnU6pPE/RJvgRN98H3/sBwF67DFpzhwfRgYO9M1mp03zTeHaywUk5cUrL/czTEVDaHRAFzNp9Ggf\nQKNTtWR6X7tUdfSysnu3D5bz5/uQuXBhbNCfUaNitZjRQX86uo5eXpA8QmfIoqFTknJzi3TSSRvb\ndH/QMiIuot8v+r3+85X/lCT96oxf6bvjv5vSgD2zZvmRbMvLpWjoPKzLTs0/9nsavOAhPyzjn/+s\nPYeN0JIl/iJp4ULprYU12j76WmnCb2VrpmjMhr/o5HEFGj/e92UaNiz5C+bq6s+1atXXtHPnKzr8\n8Gs0ZMitysrK4GqdDoATfWq2bvXTmjz2mL+gjESkI4+MBc3jj8+skSOTRXmpX0WFP6ZGBydasMA3\np5Z8wIg2xz31VF/z3RF0tLLy0UeJ/TGXLfPHhZycuoP+9O7d1nubfjpaeUHqCJ0hiw+dktSv3+Uq\nLByrwsKxKigYo06dUh8NFW1v065N+pdn/0Uvrn9RJx9xsu6/4H6NPHRkk9cz/99madDMGTqiZpM+\nyeqjrnlVyq0qlW64Qfrxj+u06yutKtUlsy/Rs+89qwt7/0DDN9+ixQuztWiRH+hE8v2VTjjBB9Bo\nrWh9F01lZSu1fPlFqqzcrGHD7lH//lek8lGglXGiT97GjdLjj/ug+eab/rlRo3zInDpVOuaY9hk0\n41FeklNV5UcTj9aEvvFG7Jh61FGxEDppkm9a2R6157LinLR+fWLIfP99/1p+ft1Bfxh8qnHtubyg\nZRE6QxYfOrOy8pSd3U3V1R8feD03t+hACC0sHKPCwrHKyxvEKKEZxDmnB5c9qGtfvFalVaW64dQb\ndP3E69UpO8mRRuKqOg80rjWTfvYzPy1KLdv2btPkhyarZEeJ7vrSXfrXE/71wGs1NdKaNb4mdOFC\nfwd/2TL/vOQvkqIBdMIEaejQZ/TBB99QVlaBioufUPfuJzX780Dr4ER/cO+95/tnzp4tLVninxs7\nNhY0R4xo2/1rbZSX1OzfL737biyEvv56rD/f4MGxWtBJk4JB39rBqbs9lZWaGn8OjB/056OP/Gu9\neiU2lT322MwbICwdtKfygnAROkNWX5/OysqPVFq6VKWlS1VWVqLS0qUqL1+raF++7OzuQQAdcyCQ\n5uePUnZ2BozL34HtKN2hq1+4Wn9d+VeN7jtaf7jwDzr+sOPrX7i62o+xvmaNdPnlfsI/1RpGqKjI\nV9HEKfmoRJMfnqxdFbv06LRHdd5R5zW6X+Xl0tKlsWa5CxdKGzY4XXrpr/Stb/1EW7Ycp8WLn1Rx\n8eGaMEE6+ujMHjClo+BEn8g5adUqX5s5e7YfXVLyN1imTfMDAg0d2rb72JYoLy0jGmLi5wr97DP/\n2uGHJ9aEHnVUZobQTC4rFRV1B/3Zs8e/NnBg4qA/I0a0nz7bbSmTywtaF6EzZMmOXltTU6ayshUq\nLS05EEhLS5cpEikL1pOj/PwRCU1zCwvHqnNnOhikmyfXPKl/e+7ftKNsh647/mrd2Osr6rJuow+Y\nq1f7f99/3wfPWhJCp1l08k5J0t/X/V1ffeyr6p7bXc994zmN6Tcmpf2rqSlXScm3tGfPo/roo29o\n1qz7tWBBF33+uX+9Sxffry3aLHfCBH+yzsSLp/aME70PmkuXxoLm2rW+nE6cGAua7bUJZFNRXsIR\nifibHdGa0HnzpB07/Gv9+sVqQSdN8k26M+E4mkllZfdu3wQ6ftCfqir/2tFHJ9ZkDhx48HUhNZlU\nXtC2CJ0ha848nc5FtG/f+rgQ6gNpVdXWA8t07jwgoWluYeFYdekyVJbCgDZIkXN+kq64ULlr3XL9\nsOci3T+iXEd+Jt3/tDRpa44fsWTECD+uevTfqVOlzZslNVzTefeiu/W9v39PY/qO0TOXPKMB3Qak\ntKsVFR9qxYopKi1dqiFDbtYRR/wwOGH4fi7xzXLffTc2Yl+fPrEAOn687yvK9AJtq6Oe6CMRX0aj\nTWc/+MBPXTtpkg+aU6Z0nAFfmqKjlpfW5pxv2h0Noa+95gevkvwgNNGRcSdN8qPlpmNNWzqXle3b\n6w7645xvnXP88YmD/vTq1dZ72zGkc3lBeiF0hqw5obMhVVWfqLS05EDTXN9Md7Uk33EvK6tAhYWj\nE/qKFhQco+zs/Bbdjw6nutons/gay+hPtP2OJHXteiBU/mNYjqZnPacNVTv0L8d+R7ecfau653VP\nXG99fTrz86WZM1Vzydd1/cvX67a3btPkYZP18NSHVdi5MKXd37VrvlaunKpIpEKjRj2kXr3OP+jy\nVVW+iWJ8s9zVq2OvDxuW2D90zBgpNzelXUMKOtKJvqbGN5N77DE/INCWLb7v1Rln+KB50UWMKtmY\njlRe0olzvidFtDnua6/Fek306OEDUrQmdOzY9OjakC5lxTnfKCg+ZK5f71/Lz5dOOikWMidMYNCf\ntpIu5QXpj9AZsjBCZ31qaipUXr6qTq1oTU00DGUpP3/YgWa50Z/c3H6h71vG2bMnMVDGN4ndvz+2\n3IABsRrL+NrL/v0T2lCVVZXphjk36I6371D/wv66Z/I9mjxscuI2Z82SZsyQbdokV1Qk3XSTyi6e\nosueuExPrnlS3x//fd1+zu3KzspO6S1t23a/1q37N+XlFam4+GkVFDR9hF3JN2VavDgWRN9+OzYo\nQ+fO/qIpPogeeWR63slvD9r7iX7/fmnuXF+b+cQTvslibq507rm+ccAFF/iLdiSnvZeXTPLhh4nN\ncdet88937epr6KIhdNy4thnYpq3KSk2NVFKSOOhPtKly796JTWXHjmXQn3TBsQXJInSGrLVCZ32c\nc6qo2JgQQktLl6qyctOBZTp16pMQQgsLx6hLl2Htf45G56Rt2+rWWq5e7Z+Pysnxo0HEh8roT9eu\nTdrk21ve1pVPX6mVn6zUJcWX6Dfn/kaHFiTOPh49eH9U+pEuePgCvbP9Hd1+zu26esLVKb3NSKRa\n69f/QFu3/lY9e56tUaMeUadOLdcu1jlf6xTfLHfxYqnMd0VWjx51p23p27fFNt+htccTfVWV9I9/\n+BrNp57yA7Tk50vnn++D5nnnNfm/HQLtsby0F9u2JdaERluU5OdLJ58ca447fryU1wrjCbZWWamo\n8OeN+EF/9u71rxUV1R30JxP6w3ZEHFuQLEJnshswu1jSjZJGShrvnFuc5N+1WehsSHX1TpWVLUuo\nFS0rWyHn/MA2WVl5Kig4JqGvaEHBaOXkZODVXnW1r6Gsr0ls9Owm+Ukt66u1HDKkRW+nVtVU6Vev\n/0o3vX6TuuV2051fulOXFF9yYIocM9PyHct1/kPn69PyT/XI1Ed0wfALUtpWdfVnWrnyq9q161Ud\nfvh/aMiQW1rlZkJNjR9YIz6IrlgRm7alqCixNvS442gWlYr2cqLft0968UVfo/nMM742vWtX6cIL\nfdA85xx/8Y3maS/lpSP4+GMfwqIhdNky/3xurp87MloTeuKJ4fzfCKus7Nrlg2U0ZC5aFBv0p7g4\nsSaTAcAyB8cWJIvQmewGzEZKiki6V9J1mRw66xOJVKm8fE2dWtH9+z8/sExe3tA6taK5uYenx5yi\nu3fX3yR2/frEJrGHH153IJ8RI/wwg634PlZ+vFJXPn2l3t76ts4/6nydPfRs3f8kSQIAACAASURB\nVPbmbdp07SbZjaZuud306j+/quP6H5fS+ktLV2jFiotUWblFw4fPVL9+/9zC76Bpysr8wETx/UOj\n/Zqys/0FR3wQHTXKP4+GZfKJvrRUev55HzSfe86Xj549/SBAU6dKZ55J/+CWlsnlpaP7/HMf0qK1\noe++6wfU6tTJtySJhtCTT26ZlgAtVVa2bUvsj7l8eWzQn3HjEgf9OeSQ5u832gbHFiSL0NnUDZnN\nVTsMnfVxzqmycktCCC0rK9G+fe8fWCYn55CEEOrnFB2prKwQOls454cBrK9J7PbtseU6dYo1iY0P\nlsOHp1XbvJpIjX678Le6/uXrVR0Jpk+50f90yemi+y68T5ce0/A0Ow359NOntHr1ZcrOLtTRRz+h\n7t1PbMndbjE7dvg73dHa0IULD0xXqoKCxGlbxo/3d77T4f5Gusi0E/3u3b4mc/Zs6YUXfNO6Pn2k\nL3/ZB83TTqOPVpgyrbygYdFpQqJ9Qhcv9vdWs7P9cTPaHPcLX0it33MqZSU6am906pLXX/cDKEn+\neF570B9aL7QfHFuQLEJnUzfUgUJnQ/bv3xvXPLckCKPLFYlUSJLMOqug4OiEaVwKCsaoU6ckz35V\nVbEmsbUDZmlpbLnu3etvEjt4cEZdvQ64bYC27Q36kd4Y/Egq6l6kjddsTHo9zjl9+OEv9cEHP1HX\nruNUXPykcnNTm1qlLTjnB9SIb5a7dGms+VW/fonTtowb17EHksmEE/1nn/m+mbNnSy+/7Fu7Dxjg\n58+cOtVfFFOj3ToyobwgNaWl0ptvxprjRueqNPMD7kRrQk85JblpRJIpK/v31x305+OP/Wu9e8cC\n5he+IB17bHqMyotwcGxBsgid8Ssxe0VSfcO5znDOPRUsM1eNhE4zmy5pevDr8XPmzGn2vqW/Gkmb\nJa2X9H7ws17Szrhl+ko6MvgZquyy/srfVKmCDzcr/8MPlb9pk/I3b1aXrVtlkciBv6ro00flAwcm\n/hQVqapnz3ZR9fXF174oF52d80YdCJ0m06uTXk1yLfsk/VrSXElnSrpOUua3T6yqMm3YUKjVq7tp\n9equWrOmmzZvjt0iHziwTCNG7NWIEXs0atReDRlSqk6dOsbJ7/TTT1c6Hls+/7yT5s8/VPPm9da7\n7/ZUJGLq12+fTj31U5166icaOXIPIxq3gXQtL2h5lZVZWr26m0pKuqukpIdWruymqip/d2fw4FKN\nGbNbY8bs0ujRu3TIIdUH/u6VV/ro/vuHaMeOLurbd5++/e0NOvPMj+PW2VXLl/fQsmXdtXJlN+3b\n55Nkv377dMwxuzV6tP854ojy9nBqRpI4tiBZp59+OqGzSRuiprNJKiu3q3TjKyrdMldle5eqNGuD\nyrvukoITUnapVLheKvwgS4Wl/VTYaYTyDz1B2cOPiTWJLUxtXspMMeiOQdq0OxhR+EY1uaazouJD\nrVhxkUpLSzRkyM064ogfpkc/25Ds3Ombk0VrQ99+O3aHvXNnf1c9Whs6YYI0dGi7uDdRRzrdXd6y\nxc+fOXu2r/VwzrdwnzbN12ged1z7/A4ySTqVF7SuykrflSHaHPeNN2IjjI8Y4Zvj5uRIf/yjH9hL\nwYzRubnSWWf5PqWLFvmWCma+D360FvOUU/xQCei4OLYgWdR0NnVDhM76VVX5tpH1NYmNnt0kqXt3\n1YweprIJfVU6qrNKB+xTaeFHKt3/niIRv5xZjvLzRyQ0zS0sHKvOndvn7O+zls/S9Gemq7y6/EDo\nzO+Ur5kXzGy0T+euXa9r5cqpikQqNWrUw+rV67zW2OW04py0eXPi3KFLlkjl5f71nj0Tm+WOHy8d\neujB15kJ2vpE/8EHPmTOni299ZZ/rrjYh8ypU/1jgmb6aOvygvRRXS29806sOe78+X566hgfOqPi\n+2NOnOiPqUAUxxYki9CZ7AbMvizpt5IOlbRL0lLn3DlJ/F37Cp27dtUdxGfNGj9iQHRODEkaOLDu\n3JYjR/qRQ+q5EnUuon371idM41JaulRVVVsPLNO584CEAYsKC8eqS5ehMsv8tnqzls/SjH/M0KZr\nN6no9iLddMZNjQbObdvu07p131Ve3mAVFz+lgoIRrbS36W//fj9tS/xouStW+BEfJd/tNz6IHnts\n5g1s0RYn+rVrY0HznXf8c8cdFwuaw4e36u6gCbgwRENqavwwCLHiEQudZrHjJlAfji1IFqEzZBkZ\nOiMR316uvlFid+yILde5szRsWN2BfIYNa7EmsVVVnwTziMaPoLtavg+plJVVoMLC0Qkj6BYUHKPs\n7AxLEIFkDt6RSLXef/9abdv2O/XseY5GjXok+UGaOrDSUh+U4oPohx/617KzpdGjE6dtGTEivQe5\naY0TvXM+rM+eLT32mLRypX/+xBNjQXPw4FB3AS2EC0MczKBB0qZN0d9iobOoKDa9FVAfji1IFqEz\nZGkdOisr628Su3ZtYpPYHj18oKxdazloUJsMSVdTU6Hy8lV1akVraqLtg7KUnz/sQLPc6E9ubn1j\nRKWXxg7eVVWfatWqr2rXrjk6/PAfaOjQW2SWxskozW3fnjhty6JFfjoCyc+uM25cYhAdkEaDAYd1\nonfOh/No0Fy3ztd2nHKKD5lf+Qp9uDIRF4Y4mFmzpOnTo90SfOjMz5dmzpQubfrMXehAOLYgWYTO\nkKVF6Ny5MzFUxjeJjW83U1RUt9Zy5EjfAS7NO2c551RRsTEhhJaWLlVl5YFbt+rUqU9CCC0sHKMu\nXYYpKyt9xnI/2MG7tHS5Vqy4SJWV2zR8+Ez16/fNVt679i8S8SErvjZ06VLfB0qSDjus7rQt3bq1\nzb625Ik+EvHvOdp0duNGX8t7+uk+aE6Z4qesQebiwhCNmTVLmjFD2rTJVFTkdNNNBE40jmMLkkXo\nDFmrhc5IxI+mUl+T2OjQnlKsSWx8qIw2iS0oCH8/W1l19c64OUWXBk11V8g5nyKysvJUUHBMQl/R\ngoLRysnp2ib729DB+5NPntTq1ZcpJ6ebioufULduE9pg7zqmigo/H118EF23zr9m5v8LxQfRY45p\nnWlim3uir6nxA4jMnu1Hnt261e/3WWf5oHnRRcnN6YfMwIUhkkVZQVNQXpAsQmfIWjx0VlQ03CQ2\nOlyn5IeXa6hJbDp3VGsFkUiVysvX1KkV3b//8wPL5OUNrVMrmpt7eOhTkdQ+eDvntGnTL7Rx4w3q\n2vUEFRc/qdzcw0LdBzQuOoVAtFnuwoXSJ5/41/Ly/OA68c1yBw9u+cYCqZzoq6uluXN90HziCX8/\nKi9POvdcHzQnT/at6dH+cGGIZFFW0BSUFySL0BmylEPn55/XP0rsBx8kNokdNKhuk9gRIzKiSWw6\ncc6psnJrXI3oUpWVlWjfvvcPLJOTc0hCCC0sHKv8/JHKymq5aq34g3dNTZnWrLlcn3zymPr2vUzD\nhs1UdnaXFtsWWo5zfhCO+NrQJUuic975GsP42tATTpB6N3MGoGRP9JWV0iuv+KD51FP+0FJQIJ1/\nvp9H80tfavdT4UJcGCJ5lBU0BeUFySJ0hszMnCsqUr2dIyIRP3xmfU1io9UmkpSb23CT2Eyb5yHD\n7N+/N2ieGz967nJFIhWSJLPOKig4OmEal4KCMSmPJhs9eFdUbNLy5ReprGy5hgy5RUcc8YPQa1nR\nsqqr/Wiv8UF05crYt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raWSOoS/N5QOfhSsE/5tf7/zI0vm9Hfg7ISPb7lSHpV0pRgGSfp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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114b7d748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"parallel_plot(P[(P['relative_humidity'] > 0.5) & (P['air_temp'] < 0.5)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Type 6 is similar to 9 only windier"
]
},
{
"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 |
NeuroDataDesign/seelviz | Tony/ipynb/Structure Tensor Testing (messy).ipynb | 2 | 71619 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Following MATLAB code from http://capture-clarity.org/clarity-based-tractography/"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Parameters (the script loops through all parameters and saves each result automatically)\n",
"dogsigmaArr = [1]; # Sigma values for derivative of gaussian filter, recommended value: 0.6 - 1.3 (based on actual data)\n",
"gausigmaArr = [2.3]; # Sigma values for gaussian filter, recommended value: 1.3 - 2.3 (based on actual data)\n",
"angleArr = [25]; # Angle thresholds for fiber tracking, recommended value: 20 - 30"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"u'/Users/Tony/Documents/Git Folder/seelviz/Tony/ipynb'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pwd"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import math\n",
"from scipy import ndimage\n",
"import nibabel as nib"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Change later on\n",
"file_path = \"/Users/Tony/Documents/Git Folder/seelviz/Tony/ipynb/TIFF_stack\"\n",
"directory = os.path.dirname(file_path)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/Tony/Documents/Git Folder/seelviz/Tony/ipynb/TIFF_stack\n"
]
}
],
"source": [
"cd TIFF_stack/"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from PIL import Image\n",
"from numpy import matlib\n",
"from scipy import signal"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100.0\n",
"Start DoG Sigma on 1\n",
"GA SHAPE:\n",
"(10, 10, 10)\n",
"GV SHAPE:\n",
"(10, 10, 10, 3)\n",
"Start Gauss Sigma with gausigma = 2.3\n",
"Generating Gaussian kernel...\n",
"Blurring gradient products...\n",
"Saving a copy for this Gaussian sigma...\n",
"Complete!\n"
]
}
],
"source": [
"# Set up results directory\n",
"if not os.path.exists(directory):\n",
" os.makedirs(directory)\n",
"\n",
"# im = Image.open('page1.tiff') # Needs to be changed to dynamically go down list of fnDataArr (currently just loads same test image)\n",
"# Omitted: channel data (red/green - our CLARITY data was single channel, so no channel data loaded.)\n",
"img_data = np.single(100 * np.ones((10, 10, 10))); #data is hard coded to be np.ones\n",
"\n",
"print img_data[0][0][0];\n",
"\n",
"for jj in range(len(dogsigmaArr)):\n",
" dogsigma = dogsigmaArr[jj];\n",
" print \"Start DoG Sigma on \" + str(dogsigma);\n",
"\n",
" # Generate dog kernels\n",
" dogkercc = doggen([dogsigma, dogsigma, dogsigma]);\n",
" dogkercc = np.transpose(dogkercc1, (0, 1, 2)); # annoying\n",
"\n",
" #print dogkercc.shape;\n",
" #print dogkercc[:, :, 0];\n",
" \n",
" dogkerrr = np.transpose(dogkercc, (1, 0, 2));\n",
" \n",
" #print dogkerrr[:, :, 0];\n",
" dogkerzz = np.transpose(dogkercc, (0, 2, 1));\n",
"\n",
" #print dogkerzz[:, :, 0];\n",
" \n",
" # Compute gradients\n",
" grr = signal.fftconvolve(img_data, dogkercc, 'same');\n",
" grr = np.transpose(grr, (1, 0, 2));\n",
" \n",
" #print grr[:, :, 0];\n",
" \n",
" gcc = signal.fftconvolve(img_data, dogkerrr, 'same');\n",
" gcc = np.transpose(gcc, (1, 0, 2));\n",
" \n",
" #print gcc[:, :, 0];\n",
" \n",
" gzz = signal.fftconvolve(img_data, dogkerzz, 'same');\n",
" gzz = np.transpose(gzz, (1, 0, 2));\n",
" \n",
" #print gzz[:, :, 0];\n",
" \n",
" # Compute gradient products\n",
" gprrrr = np.multiply(grr, grr);\n",
" \n",
" #print gprrrr[:, :, 0];\n",
" \n",
" gprrcc = np.multiply(grr, gcc);\n",
" \n",
" #print gprrcc[:, :, 0];\n",
" \n",
" gprrzz = np.multiply(grr, gzz);\n",
" \n",
" #print gprrzz[:, :, 0]\n",
" \n",
" gpcccc = np.multiply(gcc, gcc);\n",
" gpcczz = np.multiply(gcc, gzz);\n",
" gpzzzz = np.multiply(gzz, gzz);\n",
"\n",
" # Compute gradient amplitudes\n",
" #print ga.dtype;\n",
" ga = np.sqrt(gprrrr + gpcccc + gpzzzz);\n",
" \n",
" #print ga[:, :, 0];\n",
" \n",
" print \"GA SHAPE:\"\n",
" print ga.shape;\n",
"\n",
" # Convert numpy ndarray object to Nifti data type\n",
" gradient_amplitudes_data = nib.Nifti1Image(ga, affine=np.eye(4));\n",
"\n",
" # Save gradient amplitudes image \n",
" nib.save(gradient_amplitudes_data, 'gradient_amplitudes.nii');\n",
"\n",
" # Compute gradient vectors\n",
" gv = np.concatenate((grr[..., np.newaxis], gcc[..., np.newaxis], gzz[..., np.newaxis]), axis = 3);\n",
" gv = np.divide(gv, np.tile(ga[..., None], [1, 1, 1, 3]));\n",
" #print gv[:, :, 0, 1];\n",
" \n",
" print \"GV SHAPE:\"\n",
" print gv.shape;\n",
" \n",
" # Convert numpy ndarray object to Nifti data type\n",
" gradient_vectors_data = nib.Nifti1Image(gv, affine=np.eye(4));\n",
"\n",
" # Save gradient vectors\n",
" nib.save(gradient_vectors_data, 'gradient_vectors.nii');\n",
"\n",
" # Compute structure tensor\n",
" for kk in range(len(gausigmaArr)):\n",
" gausigma = gausigmaArr[kk];\n",
" print \"Start Gauss Sigma with gausigma = \" + str(gausigma);\n",
"\n",
" print \"Generating Gaussian kernel...\"\n",
" gaussker = np.single(gaussgen([gausigma, gausigma, gausigma]));\n",
" \n",
" #print gaussker[:, :, 0];\n",
"\n",
" print \"Blurring gradient products...\"\n",
" gprrrrgauss = signal.fftconvolve(gprrrr, gaussker, \"same\");\n",
" gprrccgauss = signal.fftconvolve(gprrcc, gaussker, \"same\");\n",
" gprrzzgauss = signal.fftconvolve(gprrzz, gaussker, \"same\");\n",
" gpccccgauss = signal.fftconvolve(gpcccc, gaussker, \"same\");\n",
" gpcczzgauss = signal.fftconvolve(gpcczz, gaussker, \"same\");\n",
" gpzzzzgauss = signal.fftconvolve(gpzzzz, gaussker, \"same\");\n",
"\n",
" print \"Saving a copy for this Gaussian sigma...\"\n",
" tensorfsl = np.concatenate((gprrrrgauss[..., np.newaxis], gprrccgauss[..., np.newaxis], gprrzzgauss[..., np.newaxis], gpccccgauss[..., np.newaxis], gpcczzgauss[..., np.newaxis], gpzzzzgauss[..., np.newaxis]), axis = 3);\n",
"\n",
" # Convert numpy ndarray object to Nifti data type\n",
" tensor_fsl_data = nib.Nifti1Image(tensorfsl, affine=np.eye(4));\n",
"\n",
" nib.save(tensor_fsl_data, \"dogsigma_\" + str(jj) + \"gausigma_\" + str(kk) + 'tensorfsl.nii');\n",
" \n",
"print 'Complete!'"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'''\n",
"Function to generate derivatives of Gaussian kernels, in either 1D, 2D, or 3D.\n",
"Source code in MATLAB obtained from Qiyuan Tian, Stanford University, September 2015\n",
"Edited to work in Python by Tony\n",
"'''\n",
"\n",
"def doggen(sigma):\n",
" halfsize = np.ceil(3 * np.max(sigma))\n",
" x = range(np.single(-halfsize), np.single(halfsize + 1)); # Python colon is not inclusive at end, while MATLAB is.\n",
" dim = len(sigma);\n",
" \n",
" if dim == 1:\n",
" X = np.array(x); # Remember that, by default, numpy arrays are elementwise multiplicative\n",
" k = -X * np.exp(-X**2/(2 * sigma**2));\n",
" \n",
" elif dim == 2:\n",
" [X, Y] = np.meshgrid(x, x);\n",
" k = -X * np.exp(-X**2/(2*sigma[0]^2) * np.exp(-Y**2))\n",
" \n",
" elif dim == 3:\n",
" [X, Y, Z] = np.meshgrid(x, x, x);\n",
" X = X.transpose(0, 2, 1); # Obtained through vigorous testing (see below...)\n",
" Y = Y.transpose(2, 0, 1);\n",
" Z = Z.transpose(2, 1, 0);\n",
" \n",
" X = X.astype(float);\n",
" Y = Y.astype(float);\n",
" Z = Z.astype(float);\n",
" k = -X * np.exp(np.divide(-np.power(X, 2), 2 * np.power(sigma[0], 2))) * np.exp(np.divide(-np.power(Y,2), 2 * np.power(sigma[1],2))) * np.exp(np.divide(-np.power(Z,2), 2 * np.power(sigma[2],2)))\n",
" \n",
" else:\n",
" print 'Only supports up to 3 dimensions'\n",
" \n",
" return np.divide(k, np.sum(np.abs(k[:])));"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"'''\n",
"Function to generate Gaussian kernels, in 1D, 2D and 3D.\n",
"Source code in MATLAB obtained from Qiyuan Tian, Stanford University, September 2015\n",
"Edited to work in Python by Tony. \n",
"'''\n",
"\n",
"def gaussgen(sigma):\n",
" halfsize = np.ceil(3 * max(sigma));\n",
" x = range(np.single(-halfsize), np.single(halfsize + 1));\n",
"\n",
" dim = len(sigma);\n",
"\n",
" if dim == 1:\n",
" k = np.exp(-x**2 / (2 * sigma^2));\n",
" \n",
" elif dim == 2:\n",
" [X, Y] = np.meshgrid(x, x);\n",
" k = np.exp(-X**2 / (2 * sigma[0]**2)) * np.exp(-Y**2 / (2 * sigma[1]**2)); \n",
" \n",
" elif dim == 3:\n",
" [X, Y, Z] = np.meshgrid(x, x, x);\n",
" X = X.transpose(0, 2, 1); # Obtained through vigorous testing (see below...)\n",
" Y = Y.transpose(2, 0, 1);\n",
" Z = Z.transpose(2, 1, 0);\n",
" \n",
" X = X.astype(float); # WHY PYTHON?\n",
" Y = Y.astype(float);\n",
" Z = Z.astype(float);\n",
" k = np.exp(-X**2 / (2 * sigma[0]**2)) * np.exp(-Y**2 / (2 * sigma[1]**2)) * np.exp(-Z**2 / (2 * sigma[2]**2));\n",
" \n",
" else:\n",
" print 'Only supports up to dimension 3'\n",
"\n",
" return np.divide(k, np.sum(np.abs(k)));"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10, 10, 10, 6)\n"
]
}
],
"source": [
"## Compare tensor_fsl_data with imported values from MATLAB\n",
"print tensorfsl.shape # numpy"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"MATLAB_output = nib.load(\"/Users/Tony/Documents/Git Folder/seelviz/Tony/ipynb/test_MATLAB_10by10by10_outputs/dogsig1_gausig2.3/test_MATLAB_tensorfsl_dogsig1_gausig2.3.nii\")"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10, 10, 10, 6)\n"
]
}
],
"source": [
"print MATLAB_output.shape"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(10, 10, 10, 6)\n"
]
}
],
"source": [
"MATLAB_np_array = MATLAB_output.get_data()\n",
"print MATLAB_np_array.shape"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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" 212.3919397 201.37589584 179.26563687 147.37287499 109.85943033]\n",
" [ 147.37287499 197.69594847 240.47905765 270.13925545 284.91692228\n",
" 284.91692228 270.13925545 240.47905765 197.69594847 147.37287499]\n",
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" 346.57540288 328.5997214 292.52080083 240.47905765 179.26563687]\n",
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" 389.32130808 369.12854264 328.5997214 270.13925545 201.37589584]\n",
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" 410.61869621 389.32130808 346.57540288 284.91692228 212.3919397 ]\n",
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" 410.61869621 389.32130808 346.57540288 284.91692228 212.3919397 ]\n",
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" 389.32130808 369.12854264 328.5997214 270.13925545 201.37589584]\n",
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" 346.57540288 328.5997214 292.52080083 240.47905765 179.26563687]\n",
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" 284.91692228 270.13925545 240.47905765 197.69594847 147.37287499]\n",
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" 212.3919397 201.37589584 179.26563687 147.37287499 109.85943033]]\n",
"\n",
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" 197.22532319 186.99591984 166.46452405 136.84918042 102.01451941]\n",
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" 264.57139616 250.84898216 223.30677825 183.57875205 136.84918042]\n",
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" 321.82692951 305.13486668 271.63229216 223.30677825 166.46452405]\n",
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" 381.29697939 361.52040841 321.82692951 264.57139616 197.22532319]\n",
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" 361.52040841 342.76958054 305.13486668 250.84898216 186.99591984]\n",
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" 321.82692951 305.13486668 271.63229216 223.30677825 166.46452405]\n",
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" 264.57139616 250.84898216 223.30677825 183.57875205 136.84918042]\n",
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" 197.22532319 186.99591984 166.46452405 136.84918042 102.01451941]]\n",
"\n",
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" 154.4298035 146.42005751 130.34372749 107.15455673 79.87859737]\n",
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" 207.16259029 196.41777491 174.85189774 143.74437464 107.15455673]\n",
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" 251.99436252 238.92427637 212.69135729 174.85189774 130.34372749]\n",
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" 298.56012742 283.07483424 251.99436252 207.16259029 154.4298035 ]\n",
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" 298.56012742 283.07483424 251.99436252 207.16259029 154.4298035 ]\n",
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" 283.07483424 268.39271027 238.92427637 196.41777491 146.42005751]\n",
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" 251.99436252 238.92427637 212.69135729 174.85189774 130.34372749]\n",
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" 207.16259029 196.41777491 174.85189774 143.74437464 107.15455673]\n",
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" 154.4298035 146.42005751 130.34372749 107.15455673 79.87859737]]\n",
"\n",
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" 105.73903143 100.25470939 89.24714786 73.36938073 54.69336438]\n",
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" 141.84549342 134.48845267 119.72216462 98.42265313 73.36938073]\n",
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" 172.54208224 163.59291438 145.63107419 119.72216462 89.24714786]\n",
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" 193.82307139 183.77013138 163.59291438 134.48845267 100.25470939]\n",
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" 204.42594628 193.82307139 172.54208224 141.84549342 105.73903143]\n",
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" 204.42594628 193.82307139 172.54208224 141.84549342 105.73903143]\n",
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" 193.82307139 183.77013138 163.59291438 134.48845267 100.25470939]\n",
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" 172.54208224 163.59291438 145.63107419 119.72216462 89.24714786]\n",
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" 105.73903143 100.25470939 89.24714786 73.36938073 54.69336438]]\n",
"\n",
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" 74.79892705 70.91936242 63.13270326 51.90090047 38.68963919]\n",
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" 137.10885725 129.99748957 115.72429098 95.13603248 70.91936242]\n",
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" 137.10885725 129.99748957 115.72429098 95.13603248 70.91936242]\n",
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" 122.05485924 115.72429098 103.01823188 84.69048089 63.13270326]\n",
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" 74.79892705 70.91936242 63.13270326 51.90090047 38.68963919]]\n",
"\n",
" [[ 38.68963919 51.90090047 63.13270326 70.91936242 74.79892705\n",
" 74.79892705 70.91936242 63.13270326 51.90090047 38.68963919]\n",
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" 100.34034302 95.13603248 84.69048089 69.62338047 51.90090047]\n",
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" 122.05485924 115.72429098 103.01823188 84.69048089 63.13270326]\n",
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" 144.60924436 137.10885725 122.05485924 100.34034302 74.79892705]\n",
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" 144.60924436 137.10885725 122.05485924 100.34034302 74.79892705]\n",
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" 137.10885725 129.99748957 115.72429098 95.13603248 70.91936242]\n",
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" 122.05485924 115.72429098 103.01823188 84.69048089 63.13270326]\n",
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" 100.34034302 95.13603248 84.69048089 69.62338047 51.90090047]\n",
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" 74.79892705 70.91936242 63.13270326 51.90090047 38.68963919]]\n",
"\n",
" [[ 54.69336438 73.36938073 89.24714786 100.25470939 105.73903143\n",
" 105.73903143 100.25470939 89.24714786 73.36938073 54.69336438]\n",
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" 141.84549342 134.48845267 119.72216462 98.42265313 73.36938073]\n",
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" 172.54208224 163.59291438 145.63107419 119.72216462 89.24714786]\n",
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" 193.82307139 183.77013138 163.59291438 134.48845267 100.25470939]\n",
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" 204.42594628 193.82307139 172.54208224 141.84549342 105.73903143]\n",
" [ 105.73903143 141.84549342 172.54208224 193.82307139 204.42594628\n",
" 204.42594628 193.82307139 172.54208224 141.84549342 105.73903143]\n",
" [ 100.25470939 134.48845267 163.59291438 183.77013138 193.82307139\n",
" 193.82307139 183.77013138 163.59291438 134.48845267 100.25470939]\n",
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" 172.54208224 163.59291438 145.63107419 119.72216462 89.24714786]\n",
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" 141.84549342 134.48845267 119.72216462 98.42265313 73.36938073]\n",
" [ 54.69336438 73.36938073 89.24714786 100.25470939 105.73903143\n",
" 105.73903143 100.25470939 89.24714786 73.36938073 54.69336438]]\n",
"\n",
" [[ 79.87859737 107.15455673 130.34372749 146.42005751 154.4298035\n",
" 154.4298035 146.42005751 130.34372749 107.15455673 79.87859737]\n",
" [ 107.15455673 143.74437464 174.85189774 196.41777491 207.16259029\n",
" 207.16259029 196.41777491 174.85189774 143.74437464 107.15455673]\n",
" [ 130.34372749 174.85189774 212.69135729 238.92427637 251.99436252\n",
" 251.99436252 238.92427637 212.69135729 174.85189774 130.34372749]\n",
" [ 146.42005751 196.41777491 238.92427637 268.39271027 283.07483424\n",
" 283.07483424 268.39271027 238.92427637 196.41777491 146.42005751]\n",
" [ 154.4298035 207.16259029 251.99436252 283.07483424 298.56012742\n",
" 298.56012742 283.07483424 251.99436252 207.16259029 154.4298035 ]\n",
" [ 154.4298035 207.16259029 251.99436252 283.07483424 298.56012742\n",
" 298.56012742 283.07483424 251.99436252 207.16259029 154.4298035 ]\n",
" [ 146.42005751 196.41777491 238.92427637 268.39271027 283.07483424\n",
" 283.07483424 268.39271027 238.92427637 196.41777491 146.42005751]\n",
" [ 130.34372749 174.85189774 212.69135729 238.92427637 251.99436252\n",
" 251.99436252 238.92427637 212.69135729 174.85189774 130.34372749]\n",
" [ 107.15455673 143.74437464 174.85189774 196.41777491 207.16259029\n",
" 207.16259029 196.41777491 174.85189774 143.74437464 107.15455673]\n",
" [ 79.87859737 107.15455673 130.34372749 146.42005751 154.4298035\n",
" 154.4298035 146.42005751 130.34372749 107.15455673 79.87859737]]\n",
"\n",
" [[ 102.01451941 136.84918042 166.46452405 186.99591984 197.22532319\n",
" 197.22532319 186.99591984 166.46452405 136.84918042 102.01451941]\n",
" [ 136.84918042 183.57875205 223.30677825 250.84898216 264.57139616\n",
" 264.57139616 250.84898216 223.30677825 183.57875205 136.84918042]\n",
" [ 166.46452405 223.30677825 271.63229216 305.13486668 321.82692951\n",
" 321.82692951 305.13486668 271.63229216 223.30677825 166.46452405]\n",
" [ 186.99591984 250.84898216 305.13486668 342.76958054 361.52040841\n",
" 361.52040841 342.76958054 305.13486668 250.84898216 186.99591984]\n",
" [ 197.22532319 264.57139616 321.82692951 361.52040841 381.29697939\n",
" 381.29697939 361.52040841 321.82692951 264.57139616 197.22532319]\n",
" [ 197.22532319 264.57139616 321.82692951 361.52040841 381.29697939\n",
" 381.29697939 361.52040841 321.82692951 264.57139616 197.22532319]\n",
" [ 186.99591984 250.84898216 305.13486668 342.76958054 361.52040841\n",
" 361.52040841 342.76958054 305.13486668 250.84898216 186.99591984]\n",
" [ 166.46452405 223.30677825 271.63229216 305.13486668 321.82692951\n",
" 321.82692951 305.13486668 271.63229216 223.30677825 166.46452405]\n",
" [ 136.84918042 183.57875205 223.30677825 250.84898216 264.57139616\n",
" 264.57139616 250.84898216 223.30677825 183.57875205 136.84918042]\n",
" [ 102.01451941 136.84918042 166.46452405 186.99591984 197.22532319\n",
" 197.22532319 186.99591984 166.46452405 136.84918042 102.01451941]]\n",
"\n",
" [[ 109.85943033 147.37287499 179.26563687 201.37589584 212.3919397\n",
" 212.3919397 201.37589584 179.26563687 147.37287499 109.85943033]\n",
" [ 147.37287499 197.69594847 240.47905765 270.13925545 284.91692228\n",
" 284.91692228 270.13925545 240.47905765 197.69594847 147.37287499]\n",
" [ 179.26563687 240.47905765 292.52080083 328.5997214 346.57540288\n",
" 346.57540288 328.5997214 292.52080083 240.47905765 179.26563687]\n",
" [ 201.37589584 270.13925545 328.5997214 369.12854264 389.32130808\n",
" 389.32130808 369.12854264 328.5997214 270.13925545 201.37589584]\n",
" [ 212.3919397 284.91692228 346.57540288 389.32130808 410.61869621\n",
" 410.61869621 389.32130808 346.57540288 284.91692228 212.3919397 ]\n",
" [ 212.3919397 284.91692228 346.57540288 389.32130808 410.61869621\n",
" 410.61869621 389.32130808 346.57540288 284.91692228 212.3919397 ]\n",
" [ 201.37589584 270.13925545 328.5997214 369.12854264 389.32130808\n",
" 389.32130808 369.12854264 328.5997214 270.13925545 201.37589584]\n",
" [ 179.26563687 240.47905765 292.52080083 328.5997214 346.57540288\n",
" 346.57540288 328.5997214 292.52080083 240.47905765 179.26563687]\n",
" [ 147.37287499 197.69594847 240.47905765 270.13925545 284.91692228\n",
" 284.91692228 270.13925545 240.47905765 197.69594847 147.37287499]\n",
" [ 109.85943033 147.37287499 179.26563687 201.37589584 212.3919397\n",
" 212.3919397 201.37589584 179.26563687 147.37287499 109.85943033]]]\n"
]
}
],
"source": [
"print tensorfsl[:, :, :, 0];"
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[ 109.85939789 147.37295532 179.26557922 201.37585449 212.39183044\n",
" 212.39196777 201.37599182 179.26565552 147.37287903 109.85943604]\n",
" [ 147.37289429 197.69589233 240.47901917 270.13919067 284.91690063\n",
" 284.91680908 270.13919067 240.47918701 197.69593811 147.37290955]\n",
" [ 179.26560974 240.47901917 292.52096558 328.59942627 346.57531738\n",
" 346.57531738 328.59970093 292.52087402 240.47904968 179.26560974]\n",
" [ 201.37588501 270.13912964 328.59945679 369.12838745 389.32110596\n",
" 389.32110596 369.12866211 328.59973145 270.13916016 201.37582397]\n",
" [ 212.39186096 284.91671753 346.57528687 389.3210144 410.61871338\n",
" 410.61868286 389.32144165 346.57556152 284.91680908 212.391922 ]\n",
" [ 212.39189148 284.91677856 346.57519531 389.32107544 410.6187439\n",
" 410.61865234 389.32144165 346.57556152 284.9168396 212.39190674]\n",
" [ 201.37590027 270.13916016 328.59954834 369.12850952 389.3213501\n",
" 389.32125854 369.12860107 328.59979248 270.13919067 201.37588501]\n",
" [ 179.26556396 240.47903442 292.52087402 328.59942627 346.5753479\n",
" 346.57537842 328.59963989 292.52081299 240.47912598 179.26560974]\n",
" [ 147.37287903 197.69586182 240.47903442 270.13916016 284.9168396\n",
" 284.9168396 270.13922119 240.47912598 197.69596863 147.37290955]\n",
" [ 109.85939789 147.37290955 179.26556396 201.37585449 212.39178467\n",
" 212.391922 201.37594604 179.26564026 147.37284851 109.85941315]]\n",
"\n",
" [[ 102.014534 136.84910583 166.46453857 186.99604797 197.22549438\n",
" 197.22535706 186.99586487 166.46455383 136.84922791 102.01452637]\n",
" [ 136.84912109 183.57879639 223.30690002 250.84902954 264.5715332\n",
" 264.57141113 250.84901428 223.30671692 183.5788269 136.84919739]\n",
" [ 166.46447754 223.30693054 271.63244629 305.13494873 321.82687378\n",
" 321.82711792 305.13500977 271.63247681 223.3067627 166.46463013]\n",
" [ 186.99597168 250.84910583 305.13497925 342.76980591 361.52059937\n",
" 361.52053833 342.76980591 305.13500977 250.8490448 186.99595642]\n",
" [ 197.22544861 264.57168579 321.82711792 361.52059937 381.29696655\n",
" 381.29696655 361.52050781 321.82705688 264.57144165 197.22537231]\n",
" [ 197.22541809 264.57171631 321.82714844 361.52072144 381.29702759\n",
" 381.29705811 361.52041626 321.82699585 264.57144165 197.22538757]\n",
" [ 186.99597168 250.84901428 305.13491821 342.76983643 361.52056885\n",
" 361.52056885 342.76980591 305.13497925 250.84901428 186.99594116]\n",
" [ 166.46443176 223.30691528 271.63235474 305.13497925 321.8269043\n",
" 321.82699585 305.13491821 271.63232422 223.30671692 166.46456909]\n",
" [ 136.84902954 183.57878113 223.30679321 250.84907532 264.57150269\n",
" 264.57122803 250.84902954 223.30671692 183.57885742 136.84922791]\n",
" [ 102.01451874 136.84913635 166.46455383 186.99594116 197.22543335\n",
" 197.22531128 186.99591064 166.46459961 136.84924316 102.01452637]]\n",
"\n",
" [[ 79.87863922 107.15452576 130.34361267 146.41996765 154.42985535\n",
" 154.42980957 146.42007446 130.34378052 107.15455627 79.87863159]\n",
" [ 107.15450287 143.74443054 174.85192871 196.4178772 207.16256714\n",
" 207.16256714 196.41783142 174.85195923 143.74436951 107.15457153]\n",
" [ 130.34365845 174.85188293 212.69116211 238.92424011 251.99429321\n",
" 251.99436951 238.9243927 212.69125366 174.85192871 130.34375 ]\n",
" [ 146.42002869 196.4178772 238.92431641 268.39276123 283.07495117\n",
" 283.07489014 268.39279175 238.92427063 196.41778564 146.42004395]\n",
" [ 154.42984009 207.16252136 251.99432373 283.07476807 298.56033325\n",
" 298.56033325 283.07510376 251.99430847 207.16256714 154.42980957]\n",
" [ 154.42980957 207.16255188 251.99429321 283.07470703 298.56030273\n",
" 298.56030273 283.07495117 251.9942627 207.16253662 154.42979431]\n",
" [ 146.42004395 196.41786194 238.92433167 268.39282227 283.07485962\n",
" 283.07479858 268.39273071 238.92422485 196.41777039 146.42002869]\n",
" [ 130.34362793 174.85188293 212.69116211 238.92417908 251.99417114\n",
" 251.99427795 238.92430115 212.69129944 174.85194397 130.34376526]\n",
" [ 107.15450287 143.74440002 174.85189819 196.41786194 207.16252136\n",
" 207.16256714 196.41783142 174.85189819 143.74443054 107.15455627]\n",
" [ 79.87863922 107.1545105 130.34356689 146.41998291 154.42984009\n",
" 154.42980957 146.42004395 130.34381104 107.15454865 79.87861633]]\n",
"\n",
" [[ 54.69337463 73.3693924 89.24713898 100.25470734 105.73905182\n",
" 105.73901367 100.25469208 89.24716187 73.36936951 54.69337463]\n",
" [ 73.36942291 98.4226532 119.72216797 134.48846436 141.84559631\n",
" 141.84547424 134.48841858 119.72221375 98.42264557 73.3693924 ]\n",
" [ 89.24716187 119.7221756 145.631073 163.59286499 172.54206848\n",
" 172.54211426 163.59295654 145.63108826 119.7221756 89.24716949]\n",
" [ 100.25468445 134.4884491 163.59298706 183.77015686 193.82307434\n",
" 193.82302856 183.7702179 163.59301758 134.4884491 100.25471497]\n",
" [ 105.73899841 141.84561157 172.54216003 193.82305908 204.42596436\n",
" 204.42607117 193.82302856 172.54212952 141.84553528 105.73905182]\n",
" [ 105.73902893 141.84562683 172.54217529 193.82305908 204.42593384\n",
" 204.42607117 193.82304382 172.54212952 141.84553528 105.73905182]\n",
" [ 100.25469208 134.48851013 163.5929718 183.77018738 193.8230896\n",
" 193.82299805 183.77018738 163.59298706 134.48847961 100.25473022]\n",
" [ 89.24715424 119.72219086 145.631073 163.59288025 172.54206848\n",
" 172.54212952 163.59291077 145.63108826 119.72221375 89.24714661]\n",
" [ 73.36943054 98.42262268 119.7221756 134.48841858 141.84555054\n",
" 141.84547424 134.4884491 119.72219849 98.42266083 73.36937714]\n",
" [ 54.69337463 73.36938477 89.24713135 100.2547226 105.73904419\n",
" 105.73902893 100.25469208 89.24714661 73.36936951 54.69337845]]\n",
"\n",
" [[ 38.6896286 51.90091324 63.13270569 70.91937256 74.79892731\n",
" 74.79897308 70.91934967 63.13270187 51.90092087 38.68964386]\n",
" [ 51.90090179 69.62332916 84.69044495 95.13601685 100.34034729\n",
" 100.34035492 95.13607025 84.69049072 69.62336731 51.90093994]\n",
" [ 63.13269424 84.69049835 103.0182724 115.72428131 122.05485535\n",
" 122.05487823 115.72431183 103.01824188 84.69042969 63.1327095 ]\n",
" [ 70.91933441 95.13603973 115.72428894 129.99749756 137.10888672\n",
" 137.10882568 129.99746704 115.72427368 95.13601685 70.91936493]\n",
" [ 74.79892731 100.34033203 122.05487061 137.10902405 144.60922241\n",
" 144.60914612 137.10887146 122.05486298 100.34030151 74.79893494]\n",
" [ 74.79893494 100.34033203 122.05486298 137.10899353 144.60922241\n",
" 144.60910034 137.1088562 122.05483246 100.34027863 74.79896545]\n",
" [ 70.91933441 95.13604736 115.72429657 129.99752808 137.1089325\n",
" 137.10881042 129.99742126 115.72428131 95.1360321 70.91937256]\n",
" [ 63.13269806 84.69047546 103.01828003 115.72428894 122.05484772\n",
" 122.05487061 115.72433472 103.01828003 84.69044495 63.13273239]\n",
" [ 51.90092468 69.62333679 84.69042206 95.13600922 100.3403244\n",
" 100.34034729 95.13606262 84.69049072 69.62337494 51.90092087]\n",
" [ 38.68962097 51.90090942 63.13270187 70.91937256 74.7989502\n",
" 74.79895782 70.91934204 63.13270187 51.90092468 38.68964005]]\n",
"\n",
" [[ 38.68962479 51.90091324 63.13269806 70.9193573 74.79891968\n",
" 74.79895782 70.9193573 63.13270569 51.90092087 38.68964386]\n",
" [ 51.90089417 69.6233139 84.69043732 95.13602448 100.34033966\n",
" 100.34034729 95.13606262 84.69049835 69.62335968 51.90093613]\n",
" [ 63.13268661 84.69049835 103.0182724 115.72427368 122.05484772\n",
" 122.05487823 115.72428894 103.01824188 84.69041443 63.13271332]\n",
" [ 70.91933441 95.13603973 115.7243042 129.99749756 137.10890198\n",
" 137.10881042 129.99745178 115.72426605 95.13600922 70.91937256]\n",
" [ 74.79892731 100.34031677 122.05486298 137.10903931 144.60920715\n",
" 144.60916138 137.10888672 122.05486298 100.34029388 74.79892731]\n",
" [ 74.79892731 100.34033966 122.05487823 137.10900879 144.60922241\n",
" 144.6091156 137.10884094 122.05484009 100.34027863 74.79895782]\n",
" [ 70.91931915 95.13604736 115.72431183 129.99752808 137.1089325\n",
" 137.10882568 129.99743652 115.72426605 95.13602448 70.91937256]\n",
" [ 63.13269424 84.69046783 103.01828003 115.72429657 122.05484009\n",
" 122.05487061 115.72431946 103.01828003 84.69043732 63.13272858]\n",
" [ 51.90092087 69.62332916 84.69043732 95.13600922 100.3403244\n",
" 100.34033966 95.13606262 84.69049072 69.62337494 51.90091324]\n",
" [ 38.68962479 51.90090561 63.13269806 70.91937256 74.79894257\n",
" 74.79895782 70.91934204 63.13269806 51.90091324 38.68964005]]\n",
"\n",
" [[ 54.693367 73.36938477 89.24713135 100.25470734 105.73904419\n",
" 105.73900604 100.25468445 89.24715424 73.36937714 54.69337845]\n",
" [ 73.36940002 98.42264557 119.72216034 134.48846436 141.84558105\n",
" 141.8454895 134.4884491 119.72221375 98.42263031 73.36940002]\n",
" [ 89.24715424 119.7221756 145.63110352 163.59284973 172.54206848\n",
" 172.54211426 163.59294128 145.631073 119.72219086 89.24714661]\n",
" [ 100.25468445 134.4884491 163.5929718 183.77018738 193.82305908\n",
" 193.82298279 183.77018738 163.59301758 134.4884491 100.25470734]\n",
" [ 105.73898315 141.84562683 172.54216003 193.82304382 204.4259491\n",
" 204.42604065 193.82301331 172.54212952 141.84550476 105.73904419]\n",
" [ 105.73900604 141.84562683 172.54216003 193.82304382 204.42590332\n",
" 204.42604065 193.82302856 172.542099 141.84553528 105.73904419]\n",
" [ 100.25469208 134.48851013 163.59298706 183.77017212 193.8230896\n",
" 193.82299805 183.77020264 163.59292603 134.48847961 100.25471497]\n",
" [ 89.24714661 119.7221756 145.631073 163.59288025 172.54205322\n",
" 172.54212952 163.59291077 145.631073 119.72219849 89.24715424]\n",
" [ 73.36940765 98.42263031 119.7221756 134.48843384 141.8455658\n",
" 141.84542847 134.48843384 119.72219849 98.42266083 73.36936188]\n",
" [ 54.69337082 73.3693924 89.24713898 100.25470734 105.73903656\n",
" 105.73901367 100.25467682 89.24712372 73.36936188 54.69335938]]\n",
"\n",
" [[ 79.87863159 107.15453339 130.34359741 146.41996765 154.42982483\n",
" 154.42979431 146.42007446 130.34376526 107.15454865 79.87863159]\n",
" [ 107.15450287 143.74440002 174.85195923 196.4178772 207.16259766\n",
" 207.1625824 196.41783142 174.85194397 143.74435425 107.15454102]\n",
" [ 130.34367371 174.85191345 212.69116211 238.92424011 251.99429321\n",
" 251.99435425 238.92437744 212.69128418 174.85194397 130.34371948]\n",
" [ 146.42002869 196.4178772 238.92428589 268.39273071 283.07492065\n",
" 283.07479858 268.39279175 238.92425537 196.41775513 146.42002869]\n",
" [ 154.42984009 207.16249084 251.99432373 283.07476807 298.56030273\n",
" 298.56033325 283.07510376 251.99433899 207.16256714 154.42977905]\n",
" [ 154.42980957 207.1625824 251.99429321 283.07470703 298.56027222\n",
" 298.5602417 283.07498169 251.99430847 207.16253662 154.42979431]\n",
" [ 146.42004395 196.41786194 238.92433167 268.39282227 283.07485962\n",
" 283.07476807 268.39273071 238.92425537 196.41777039 146.41999817]\n",
" [ 130.34365845 174.85188293 212.69110107 238.92414856 251.99420166\n",
" 251.99427795 238.92431641 212.69129944 174.85195923 130.34375 ]\n",
" [ 107.15450287 143.74440002 174.85189819 196.41783142 207.16252136\n",
" 207.16252136 196.41781616 174.85192871 143.74440002 107.15453339]\n",
" [ 79.87864685 107.15451813 130.34355164 146.41993713 154.42980957\n",
" 154.42977905 146.42001343 130.34378052 107.15454865 79.87859344]]\n",
"\n",
" [[ 102.014534 136.84909058 166.46455383 186.99601746 197.22546387\n",
" 197.22532654 186.99588013 166.46453857 136.84922791 102.01451874]\n",
" [ 136.84907532 183.57879639 223.30688477 250.84901428 264.57147217\n",
" 264.5713501 250.84901428 223.30673218 183.57881165 136.84919739]\n",
" [ 166.46447754 223.30690002 271.63238525 305.13491821 321.82684326\n",
" 321.82714844 305.13500977 271.63244629 223.30674744 166.46463013]\n",
" [ 186.99597168 250.84907532 305.13494873 342.76980591 361.52053833\n",
" 361.52050781 342.76977539 305.13494873 250.84901428 186.99597168]\n",
" [ 197.22544861 264.57168579 321.82705688 361.52059937 381.29696655\n",
" 381.29696655 361.52047729 321.82699585 264.5713501 197.22537231]\n",
" [ 197.22541809 264.57168579 321.8270874 361.52069092 381.29705811\n",
" 381.29705811 361.52041626 321.82696533 264.57138062 197.22537231]\n",
" [ 186.99595642 250.84899902 305.13491821 342.76983643 361.52059937\n",
" 361.52050781 342.76980591 305.13494873 250.84899902 186.9959259 ]\n",
" [ 166.4644165 223.30690002 271.63232422 305.13494873 321.8269043\n",
" 321.82696533 305.13494873 271.63226318 223.30671692 166.46455383]\n",
" [ 136.84902954 183.57879639 223.30679321 250.84902954 264.57150269\n",
" 264.57125854 250.84902954 223.30674744 183.5788269 136.84922791]\n",
" [ 102.01451874 136.84910583 166.46452332 186.99594116 197.22541809\n",
" 197.22529602 186.99588013 166.46456909 136.84922791 102.01451874]]\n",
"\n",
" [[ 109.85939789 147.37295532 179.26556396 201.37585449 212.3918457\n",
" 212.39195251 201.37597656 179.26565552 147.37284851 109.85943604]\n",
" [ 147.37289429 197.69589233 240.47900391 270.13916016 284.91687012\n",
" 284.91677856 270.13912964 240.47912598 197.69593811 147.37290955]\n",
" [ 179.26559448 240.47903442 292.52093506 328.59942627 346.57531738\n",
" 346.57531738 328.59960938 292.52084351 240.47906494 179.26560974]\n",
" [ 201.37588501 270.13912964 328.5994873 369.12835693 389.32107544\n",
" 389.32104492 369.12860107 328.59976196 270.13922119 201.37582397]\n",
" [ 212.39186096 284.91668701 346.57525635 389.32098389 410.61868286\n",
" 410.61865234 389.32144165 346.57553101 284.9168396 212.391922 ]\n",
" [ 212.39189148 284.91671753 346.57522583 389.32104492 410.6187439\n",
" 410.61862183 389.32138062 346.57553101 284.9168396 212.39187622]\n",
" [ 201.37590027 270.13916016 328.59954834 369.128479 389.3213501\n",
" 389.32128906 369.12857056 328.59976196 270.13922119 201.37586975]\n",
" [ 179.26557922 240.47901917 292.52087402 328.59945679 346.57528687\n",
" 346.5753479 328.59963989 292.52078247 240.47909546 179.265625 ]\n",
" [ 147.37287903 197.69586182 240.47901917 270.13916016 284.91677856\n",
" 284.91680908 270.13919067 240.47909546 197.69593811 147.37290955]\n",
" [ 109.85939026 147.37289429 179.26556396 201.37582397 212.39178467\n",
" 212.39190674 201.37593079 179.26564026 147.37284851 109.85940552]]]\n"
]
}
],
"source": [
"print MATLAB_np_array[:, :, :, 0];"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[[[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" ..., \n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]]\n",
"\n",
"\n",
" [[[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" ..., \n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]]\n",
"\n",
"\n",
" [[[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" ..., \n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]]\n",
"\n",
"\n",
" ..., \n",
" [[[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" ..., \n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]]\n",
"\n",
"\n",
" [[[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" ..., \n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]]\n",
"\n",
"\n",
" [[[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" ..., \n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]\n",
"\n",
" [[ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" ..., \n",
" [ True True True True True True]\n",
" [ True True True True True True]\n",
" [ True True True True True True]]]]\n"
]
}
],
"source": [
"truth_boolean = np.isclose(tensorfsl, MATLAB_np_array)\n",
"print truth_boolean;"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 3.59647946e-07 2.92093928e-06 6.54537083e-06 -0.00000000e+00\n",
" -6.54537083e-06 -2.92093928e-06 -3.59647946e-07]\n",
" [ 4.38140893e-06 3.55843252e-05 7.97389406e-05 -0.00000000e+00\n",
" -7.97389406e-05 -3.55843252e-05 -4.38140893e-06]\n",
" [ 1.96361125e-05 1.59477881e-04 3.57365139e-04 -0.00000000e+00\n",
" -3.57365139e-04 -1.59477881e-04 -1.96361125e-05]\n",
" [ 3.23744763e-05 2.62934575e-04 5.89195506e-04 -0.00000000e+00\n",
" -5.89195506e-04 -2.62934575e-04 -3.23744763e-05]\n",
" [ 1.96361125e-05 1.59477881e-04 3.57365139e-04 -0.00000000e+00\n",
" -3.57365139e-04 -1.59477881e-04 -1.96361125e-05]\n",
" [ 4.38140893e-06 3.55843252e-05 7.97389406e-05 -0.00000000e+00\n",
" -7.97389406e-05 -3.55843252e-05 -4.38140893e-06]\n",
" [ 3.59647946e-07 2.92093928e-06 6.54537083e-06 -0.00000000e+00\n",
" -6.54537083e-06 -2.92093928e-06 -3.59647946e-07]]\n"
]
}
],
"source": [
"dogkercc1 = doggen([dogsigma, dogsigma, dogsigma]);\n",
"dogkercc1 = np.transpose(dogkercc1, (1, 2, 0))\n",
"print dogkercc1[:, :, 0];\n",
"\n",
"\n",
"dogkerrr1 = np.transpose(dogkercc1, (1, 0, 2));\n",
"#print dogkerrr1[:, :, 0];\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"int64\n",
"float64\n",
"[[[-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]]\n",
"\n",
" [[-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]]\n",
"\n",
" [[-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]]\n",
"\n",
" [[-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]]\n",
"\n",
" [[-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]]\n",
"\n",
" [[-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]]\n",
"\n",
" [[-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]\n",
" [-4.5 -2. -0.5 -0. -0.5 -2. -4.5]]]\n",
"[[[ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]]\n",
"\n",
" [[ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]]\n",
"\n",
" [[ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]]\n",
"\n",
" [[ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
" [ 0.03332699 0.27067057 0.60653066 -0. -0.60653066 -0.27067057\n",
" -0.03332699]\n",
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]
}
],
"source": [
"sigma = [1, 1, 1]\n",
"halfsize = 3;\n",
"x = range(np.single(-halfsize), np.single(halfsize + 1));\n",
"[X, Y, Z] = np.meshgrid(x, x, x);\n",
"X = X.transpose(0, 2, 1);\n",
"Y = Y.transpose(2, 0, 1);\n",
"Z = Z.transpose(2, 1, 0);\n",
"\n",
"print X.dtype;\n",
"X = X.astype(float);\n",
"Y = Y.astype(float);\n",
"Z = Z.astype(float);\n",
"print X.dtype;\n",
"\n",
"print (-X **2) / (2 * np.power(sigma[0], 2))\n",
"\n",
"print -X * np.exp(np.divide(-X**2, 2 * np.power(sigma[0], 2)));"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"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,
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}
| apache-2.0 |
zerothi/ts-tbt-sisl-tutorial | A_01/run.ipynb | 1 | 1271 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import sisl\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In [TB 5](../TB_05/run.ipynb) a pristine graphene flake with a hole was created.\n",
"\n",
"Here you should reproduce the results of the tight-binding example 5. However, instead of removing the atoms in Python and saving the Hamiltonian for the system with a hole, you should exclude the atoms via the `TBT.Atoms.Buffer` block. I.e. save the full Hamiltonian for the pristine graphene flake."
]
},
{
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
npdoty/bigbang | examples/Single Word Trend.ipynb | 1 | 67119 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## This note book gives the trend of a single word in single mailing list."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from bigbang.archive import Archive\n",
"import bigbang.parse as parse\n",
"import bigbang.graph as graph\n",
"import bigbang.mailman as mailman\n",
"import bigbang.process as process\n",
"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from pprint import pprint as pp\n",
"import pytz\n",
"import numpy as np\n",
"import math\n",
"import nltk\n",
"from itertools import repeat\n",
"from nltk.stem.lancaster import LancasterStemmer\n",
"st = LancasterStemmer()\n",
"from nltk.corpus import stopwords\n",
"import re"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
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"unzipping 161 archive files\n",
"Opening 161 archive files\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/nick/code/mailing-list-analysis/bigbang/bigbang/archive.py:74: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
" self.data.sort(columns='Date', inplace=True)\n"
]
}
],
"source": [
"urls = [\"http://mail.scipy.org/pipermail/ipython-dev/\"]#,\n",
" #\"http://mail.scipy.org/pipermail/ipython-user/\"],\n",
" #\"http://mail.scipy.org/pipermail/scipy-dev/\",\n",
" #\"http://mail.scipy.org/pipermail/scipy-user/\",\n",
" #\"http://mail.scipy.org/pipermail/numpy-discussion/\"]\n",
"\n",
"\n",
"archives= [Archive(url,archive_dir=\"../archives\") for url in urls]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"checkword = \"python\" #can change words, should be lower case"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You'll need to download some resources for NLTK (the natural language toolkit) in order to do the kind of processing we want on all the mailing list text. In particular, for this notebook you'll need **punkt**, the Punkt Tokenizer Models.\n",
"\n",
"To download, from an interactive Python shell, run:\n",
"\n",
" import nltk\n",
" nltk.download()\n",
"\n",
"And in the graphical UI that appears, choose \"punkt\" from the All Packages tab and Download."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df = pd.DataFrame(columns=[\"MessageId\",\"Date\",\"From\",\"In-Reply-To\",\"Count\"])\n",
"for row in archives[0].data.iterrows():\n",
" try: \n",
" w = row[1][\"Body\"].replace(\"'\", \"\")\n",
" k = re.sub(r'[^\\w]', ' ', w)\n",
" k = k.lower()\n",
" t = nltk.tokenize.word_tokenize(k)\n",
" subdict = {}\n",
" count = 0\n",
" for g in t:\n",
" try:\n",
" word = st.stem(g)\n",
" except:\n",
" print g\n",
" pass\n",
" if word == checkword:\n",
" count += 1\n",
" if count == 0:\n",
" continue\n",
" else:\n",
" subdict[\"MessageId\"] = row[0]\n",
" subdict[\"Date\"] = row[1][\"Date\"]\n",
" subdict[\"From\"] = row[1][\"From\"]\n",
" subdict[\"In-Reply-To\"] = row[1][\"In-Reply-To\"]\n",
" subdict[\"Count\"] = count\n",
" df = df.append(subdict,ignore_index=True)\n",
" except:\n",
" if row[1][\"Body\"] is None: \n",
" print '!!! Detected an email with an empty Body field...'\n",
" else: print 'error'"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MessageId</th>\n",
" <th>Date</th>\n",
" <th>From</th>\n",
" <th>In-Reply-To</th>\n",
" <th>Count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
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" <th>0</th>\n",
" <td><[email protected]></td>\n",
" <td>2003-04-17 05:50:12</td>\n",
" <td>[email protected] (Fernando Perez)</td>\n",
" <td><[email protected]...</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td><[email protected]></td>\n",
" <td>2003-04-17 05:50:12</td>\n",
" <td>fperez at colorado.edu (Fernando Perez)</td>\n",
" <td><[email protected]...</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td><[email protected]...</td>\n",
" <td>2003-04-17 14:32:56</td>\n",
" <td>[email protected] (Cory Dodt)</td>\n",
" <td><[email protected]></td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td><[email protected]...</td>\n",
" <td>2003-04-17 14:32:56</td>\n",
" <td>cdodt at fcoe.k12.ca.us (Cory Dodt)</td>\n",
" <td><[email protected]></td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td><[email protected]></td>\n",
" <td>2003-04-17 15:01:30</td>\n",
" <td>[email protected] (Fernando Perez)</td>\n",
" <td><[email protected]...</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MessageId Date \\\n",
"0 <[email protected]> 2003-04-17 05:50:12 \n",
"1 <[email protected]> 2003-04-17 05:50:12 \n",
"2 <[email protected]... 2003-04-17 14:32:56 \n",
"3 <[email protected]... 2003-04-17 14:32:56 \n",
"4 <[email protected]> 2003-04-17 15:01:30 \n",
"\n",
" From \\\n",
"0 [email protected] (Fernando Perez) \n",
"1 fperez at colorado.edu (Fernando Perez) \n",
"2 [email protected] (Cory Dodt) \n",
"3 cdodt at fcoe.k12.ca.us (Cory Dodt) \n",
"4 [email protected] (Fernando Perez) \n",
"\n",
" In-Reply-To Count \n",
"0 <[email protected]... 2.0 \n",
"1 <[email protected]... 2.0 \n",
"2 <[email protected]> 3.0 \n",
"3 <[email protected]> 3.0 \n",
"4 <[email protected]... 6.0 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[:5] #dataframe of informations of the particular word."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Group the dataframe by the month and year, and aggregate the counts for the checkword during each month to get a quick histogram of how frequently that word has been used over time."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10ea834d0>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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AC82hhwIbN0bb1jPPACtWxFOuQmP9euD998O/v7PTf+Sybh0wZQo/3SUh6C0t4tAFIRCm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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f6f6210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df.groupby([df.Date.dt.year, df.Date.dt.month]).agg({'Count':np.sum}).plot(y='Count')"
]
}
],
"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
}
| agpl-3.0 |
JackDi/phys202-2015-work | assignments/assignment02/ProjectEuler59.ipynb | 1 | 4948 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Project Euler: Problem 59"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"https://projecteuler.net/problem=59\n",
"\n",
"Each character on a computer is assigned a unique code and the preferred standard is ASCII (American Standard Code for Information Interchange). For example, uppercase A = 65, asterisk (*) = 42, and lowercase k = 107.\n",
"\n",
"A modern encryption method is to take a text file, convert the bytes to ASCII, then XOR each byte with a given value, taken from a secret key. The advantage with the XOR function is that using the same encryption key on the cipher text, restores the plain text; for example, 65 XOR 42 = 107, then 107 XOR 42 = 65.\n",
"\n",
"For unbreakable encryption, the key is the same length as the plain text message, and the key is made up of random bytes. The user would keep the encrypted message and the encryption key in different locations, and without both \"halves\", it is impossible to decrypt the message.\n",
"\n",
"Unfortunately, this method is impractical for most users, so the modified method is to use a password as a key. If the password is shorter than the message, which is likely, the key is repeated cyclically throughout the message. The balance for this method is using a sufficiently long password key for security, but short enough to be memorable.\n",
"\n",
"Your task has been made easy, as the encryption key consists of three lower case characters. Using cipher.txt (in this directory), a file containing the encrypted ASCII codes, and the knowledge that the plain text must contain common English words, decrypt the message and find the sum of the ASCII values in the original text."
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"The following cell shows examples of how to perform XOR in Python and how to go back and forth between characters and integers:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"assert 65 ^ 42 == 107\n",
"assert 107 ^ 42 == 65\n",
"assert ord('a') == 97\n",
"assert chr(97) == 'a'\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'a' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-5-782bfd543714>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mitertools\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;36m65\u001b[0m\u001b[1;33m^\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'a' is not defined"
]
}
],
"source": [
"import itertools\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"Certain functions in the `itertools` module may be useful for computing permutations:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"nbgrader": {}
},
"outputs": [],
"source": [
"from itertools"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": false,
"nbgrader": {
"checksum": "6cff4e8e53b15273846c3aecaea84a3d",
"solution": true
}
},
"outputs": [],
"source": [
"# YOUR CODE HERE\n",
"raise NotImplementedError()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": false,
"nbgrader": {
"checksum": "dcdf6792a88c661545d3ca651212dba8",
"grade": true,
"grade_id": "projecteuler59",
"points": 10
}
},
"outputs": [],
"source": [
"# This cell will be used for grading, leave it at the end of the notebook."
]
}
],
"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": "3.4.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ES-DOC/esdoc-jupyterhub | notebooks/ipsl/cmip6/models/sandbox-1/aerosol.ipynb | 1 | 85399 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Aerosol \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: IPSL \n",
"**Source ID**: SANDBOX-1 \n",
"**Topic**: Aerosol \n",
"**Sub-Topics**: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \n",
"**Properties**: 70 (38 required) \n",
"**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/aerosol?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**: 2018-02-20 15:02:45"
],
"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', 'ipsl', 'sandbox-1', 'aerosol')"
],
"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](#1.-Key-Properties) \n",
"[2. Key Properties --> Software Properties](#2.-Key-Properties--->-Software-Properties) \n",
"[3. Key Properties --> Timestep Framework](#3.-Key-Properties--->-Timestep-Framework) \n",
"[4. Key Properties --> Meteorological Forcings](#4.-Key-Properties--->-Meteorological-Forcings) \n",
"[5. Key Properties --> Resolution](#5.-Key-Properties--->-Resolution) \n",
"[6. Key Properties --> Tuning Applied](#6.-Key-Properties--->-Tuning-Applied) \n",
"[7. Transport](#7.-Transport) \n",
"[8. Emissions](#8.-Emissions) \n",
"[9. Concentrations](#9.-Concentrations) \n",
"[10. Optical Radiative Properties](#10.-Optical-Radiative-Properties) \n",
"[11. Optical Radiative Properties --> Absorption](#11.-Optical-Radiative-Properties--->-Absorption) \n",
"[12. Optical Radiative Properties --> Mixtures](#12.-Optical-Radiative-Properties--->-Mixtures) \n",
"[13. Optical Radiative Properties --> Impact Of H2o](#13.-Optical-Radiative-Properties--->-Impact-Of-H2o) \n",
"[14. Optical Radiative Properties --> Radiative Scheme](#14.-Optical-Radiative-Properties--->-Radiative-Scheme) \n",
"[15. Optical Radiative Properties --> Cloud Interactions](#15.-Optical-Radiative-Properties--->-Cloud-Interactions) \n",
"[16. Model](#16.-Model) \n",
"\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"# 1. Key Properties \n",
"*Key properties of the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 1.1. Model Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of aerosol model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.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 **Type:** STRING **Cardinality:** 1.1\n",
"### *Name of aerosol model code*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.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. Scheme Scope\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Atmospheric domains covered by the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.scheme_scope') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"troposhere\" \n",
"# \"stratosphere\" \n",
"# \"mesosphere\" \n",
"# \"mesosphere\" \n",
"# \"whole atmosphere\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.4. Basic Approximations\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Basic approximations made in the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.basic_approximations') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.5. Prognostic Variables Form\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Prognostic variables in the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.prognostic_variables_form') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"3D mass/volume ratio for aerosols\" \n",
"# \"3D number concenttration for aerosols\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.6. Number Of Tracers\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of tracers in the aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.number_of_tracers') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 1.7. Family Approach\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Are aerosol calculations generalized into families of species?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.family_approach') \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": [
"# 2. Key Properties --> Software Properties \n",
"*Software properties of aerosol code*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 2.1. Repository\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Location of code for this component.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.software_properties.repository') \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. Code Version\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Code version identifier.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_version') \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. Code Languages\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *Code language(s).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_languages') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 3. Key Properties --> Timestep Framework \n",
"*Physical properties of seawater in ocean*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 3.1. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Mathematical method deployed to solve the time evolution of the prognostic variables*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.method') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses atmospheric chemistry time stepping\" \n",
"# \"Specific timestepping (operator splitting)\" \n",
"# \"Specific timestepping (integrated)\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.2. Split Operator Advection Timestep\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Timestep for aerosol advection (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_advection_timestep') \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. Split Operator Physical Timestep\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Timestep for aerosol physics (in seconds).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_physical_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.4. Integrated Timestep\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Timestep for the aerosol model (in seconds)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_timestep') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 3.5. Integrated Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Specify the type of timestep scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_scheme_type') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Explicit\" \n",
"# \"Implicit\" \n",
"# \"Semi-implicit\" \n",
"# \"Semi-analytic\" \n",
"# \"Impact solver\" \n",
"# \"Back Euler\" \n",
"# \"Newton Raphson\" \n",
"# \"Rosenbrock\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 4. Key Properties --> Meteorological Forcings \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 4.1. Variables 3D\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Three dimensionsal forcing variables, e.g. U, V, W, T, Q, P, conventive mass flux*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_3D') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.2. Variables 2D\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Two dimensionsal forcing variables, e.g. land-sea mask definition*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_2D') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 4.3. Frequency\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Frequency with which meteological forcings are applied (in seconds).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.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": [
"# 5. Key Properties --> Resolution \n",
"*Resolution in the aersosol model grid*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 5.1. Name\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.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": [
"### 5.2. Canonical Horizontal Resolution\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.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": [
"### 5.3. Number Of Horizontal Gridpoints\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_horizontal_gridpoints') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 5.4. Number Of Vertical Levels\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *Number of vertical levels resolved on computational grid.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.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": [
"### 5.5. Is Adaptive Grid\n",
"**Is Required:** FALSE **Type:** BOOLEAN **Cardinality:** 0.1\n",
"### *Default is False. Set true if grid resolution changes during execution.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.resolution.is_adaptive_grid') \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": [
"# 6. Key Properties --> Tuning Applied \n",
"*Tuning methodology for aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 6.1. Description\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *General overview description of tuning: explain and motivate the main targets and metrics retained. &Document the relative weight given to climate performance metrics versus process oriented metrics, &and on the possible conflicts with parameterization level tuning. In particular describe any struggle &with a parameter value that required pushing it to its limits to solve a particular model deficiency.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.description') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.2. Global Mean Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List set of metrics of the global mean state used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.global_mean_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.3. Regional Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List of regional metrics of mean state used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.regional_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 6.4. Trend Metrics Used\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.N\n",
"### *List observed trend metrics used in tuning model/component*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.trend_metrics_used') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 7. Transport \n",
"*Aerosol transport*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 7.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of transport in atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.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": [
"### 7.2. Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.1\n",
"### *Method for aerosol transport modeling*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.scheme') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses Atmospheric chemistry transport scheme\" \n",
"# \"Specific transport scheme (eulerian)\" \n",
"# \"Specific transport scheme (semi-lagrangian)\" \n",
"# \"Specific transport scheme (eulerian and semi-lagrangian)\" \n",
"# \"Specific transport scheme (lagrangian)\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.3. Mass Conservation Scheme\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Method used to ensure mass conservation.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.mass_conservation_scheme') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses Atmospheric chemistry transport scheme\" \n",
"# \"Mass adjustment\" \n",
"# \"Concentrations positivity\" \n",
"# \"Gradients monotonicity\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 7.4. Convention\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Transport by convention*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.transport.convention') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Uses Atmospheric chemistry transport scheme\" \n",
"# \"Convective fluxes connected to tracers\" \n",
"# \"Vertical velocities connected to tracers\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 8. Emissions \n",
"*Atmospheric aerosol emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 8.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of emissions in atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.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. Method\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Method used to define aerosol species (several methods allowed because the different species may not use the same method).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.method') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"None\" \n",
"# \"Prescribed (climatology)\" \n",
"# \"Prescribed CMIP6\" \n",
"# \"Prescribed above surface\" \n",
"# \"Interactive\" \n",
"# \"Interactive above surface\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.3. Sources\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Sources of the aerosol species are taken into account in the emissions scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.sources') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Vegetation\" \n",
"# \"Volcanos\" \n",
"# \"Bare ground\" \n",
"# \"Sea surface\" \n",
"# \"Lightning\" \n",
"# \"Fires\" \n",
"# \"Aircraft\" \n",
"# \"Anthropogenic\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.4. Prescribed Climatology\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.1\n",
"### *Specify the climatology type for aerosol emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Constant\" \n",
"# \"Interannual\" \n",
"# \"Annual\" \n",
"# \"Monthly\" \n",
"# \"Daily\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.5. Prescribed Climatology Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and prescribed via a climatology*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.6. Prescribed Spatially Uniform Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and prescribed as spatially uniform*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.prescribed_spatially_uniform_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.7. Interactive Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and specified via an interactive method*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.interactive_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.8. Other Emitted Species\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of aerosol species emitted and specified via an "other method"*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.other_emitted_species') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 8.9. Other Method Characteristics\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *Characteristics of the "other method" used for aerosol emissions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.emissions.other_method_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": [
"# 9. Concentrations \n",
"*Atmospheric aerosol concentrations*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 9.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of concentrations in atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.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": [
"### 9.2. Prescribed Lower Boundary\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed at the lower boundary.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_lower_boundary') \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. Prescribed Upper Boundary\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed at the upper boundary.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_upper_boundary') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.4. Prescribed Fields Mmr\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed as mass mixing ratios.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 9.5. Prescribed Fields Aod Plus Ccn\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *List of species prescribed as AOD plus CCNs.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_aod_plus_ccn') \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. Optical Radiative Properties \n",
"*Aerosol optical and radiative properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 10.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of optical and radiative properties*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.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": [
"# 11. Optical Radiative Properties --> Absorption \n",
"*Absortion properties in aerosol scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 11.1. Black Carbon\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *Absorption mass coefficient of black carbon at 550nm (if non-absorbing enter 0)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.black_carbon') \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. Dust\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *Absorption mass coefficient of dust at 550nm (if non-absorbing enter 0)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.dust') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 11.3. Organics\n",
"**Is Required:** FALSE **Type:** FLOAT **Cardinality:** 0.1\n",
"### *Absorption mass coefficient of organics at 550nm (if non-absorbing enter 0)*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.organics') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 12. Optical Radiative Properties --> Mixtures \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 12.1. External\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there external mixing with respect to chemical composition?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.external') \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": [
"### 12.2. Internal\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is there internal mixing with respect to chemical composition?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.internal') \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": [
"### 12.3. Mixing Rule\n",
"**Is Required:** FALSE **Type:** STRING **Cardinality:** 0.1\n",
"### *If there is internal mixing with respect to chemical composition then indicate the mixinrg rule*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.mixing_rule') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"# 13. Optical Radiative Properties --> Impact Of H2o \n",
"**"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 13.1. Size\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does H2O impact size?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.size') \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": [
"### 13.2. Internal Mixture\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does H2O impact aerosol internal mixture?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.internal_mixture') \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": [
"### 13.3. External Mixture\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does H2O impact aerosol external mixture?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.external_mixture') \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": [
"# 14. Optical Radiative Properties --> Radiative Scheme \n",
"*Radiative scheme for aerosol*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 14.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of radiative scheme*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_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": [
"### 14.2. Shortwave Bands\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of shortwave bands*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.shortwave_bands') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 14.3. Longwave Bands\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of longwave bands*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.longwave_bands') \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. Optical Radiative Properties --> Cloud Interactions \n",
"*Aerosol-cloud interactions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 15.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of aerosol-cloud interactions*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.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. Twomey\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Is the Twomey effect included?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey') \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": [
"### 15.3. Twomey Minimum Ccn\n",
"**Is Required:** FALSE **Type:** INTEGER **Cardinality:** 0.1\n",
"### *If the Twomey effect is included, then what is the minimum CCN number?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey_minimum_ccn') \n",
"\n",
"# PROPERTY VALUE: \n",
"# Set as follows: DOC.set_value(value) \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 15.4. Drizzle\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the scheme affect drizzle?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.drizzle') \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": [
"### 15.5. Cloud Lifetime\n",
"**Is Required:** TRUE **Type:** BOOLEAN **Cardinality:** 1.1\n",
"### *Does the scheme affect cloud lifetime?*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.cloud_lifetime') \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": [
"### 15.6. Longwave Bands\n",
"**Is Required:** TRUE **Type:** INTEGER **Cardinality:** 1.1\n",
"### *Number of longwave bands*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.longwave_bands') \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. Model \n",
"*Aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"### 16.1. Overview\n",
"**Is Required:** TRUE **Type:** STRING **Cardinality:** 1.1\n",
"### *Overview of atmosperic aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.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": [
"### 16.2. Processes\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Processes included in the Aerosol model.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.processes') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Dry deposition\" \n",
"# \"Sedimentation\" \n",
"# \"Wet deposition (impaction scavenging)\" \n",
"# \"Wet deposition (nucleation scavenging)\" \n",
"# \"Coagulation\" \n",
"# \"Oxidation (gas phase)\" \n",
"# \"Oxidation (in cloud)\" \n",
"# \"Condensation\" \n",
"# \"Ageing\" \n",
"# \"Advection (horizontal)\" \n",
"# \"Advection (vertical)\" \n",
"# \"Heterogeneous chemistry\" \n",
"# \"Nucleation\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.3. Coupling\n",
"**Is Required:** FALSE **Type:** ENUM **Cardinality:** 0.N\n",
"### *Other model components coupled to the Aerosol model*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.coupling') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Radiation\" \n",
"# \"Land surface\" \n",
"# \"Heterogeneous chemistry\" \n",
"# \"Clouds\" \n",
"# \"Ocean\" \n",
"# \"Cryosphere\" \n",
"# \"Gas phase chemistry\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.4. Gas Phase Precursors\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List of gas phase aerosol precursors.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.gas_phase_precursors') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"DMS\" \n",
"# \"SO2\" \n",
"# \"Ammonia\" \n",
"# \"Iodine\" \n",
"# \"Terpene\" \n",
"# \"Isoprene\" \n",
"# \"VOC\" \n",
"# \"NOx\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.5. Scheme Type\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *Type(s) of aerosol scheme used by the aerosols model (potentially multiple: some species may be covered by one type of aerosol scheme and other species covered by another type).*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.scheme_type') \n",
"\n",
"# PROPERTY VALUE(S): \n",
"# Set as follows: DOC.set_value(\"value\") \n",
"# Valid Choices: \n",
"# \"Bulk\" \n",
"# \"Modal\" \n",
"# \"Bin\" \n",
"# \"Other: [Please specify]\" \n",
"# TODO - please enter value(s)",
"\n"
],
"outputs": [],
"metadata": {
"collapsed": true
}
},
{
"source": [
"### 16.6. Bulk Scheme Species\n",
"**Is Required:** TRUE **Type:** ENUM **Cardinality:** 1.N\n",
"### *List of species covered by the bulk scheme.*"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# PROPERTY ID - DO NOT EDIT ! \n",
"DOC.set_id('cmip6.aerosol.model.bulk_scheme_species') \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",
"# \"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 particule)\" \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 |
WNoxchi/Kaukasos | pytorch/transfer_learning_tutorial.ipynb | 1 | 323243 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Transfer Learning Tutorial\n",
"\n",
"http://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%reload_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.optim as optim\n",
"from torch.optim import lr_scheduler\n",
"from torch.autograd import Variable\n",
"import torchvision\n",
"from torchvision import datasets, models, transforms\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import time\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Load Data\n",
"\n",
"Using `torchvision` and `torch.utils.data` for data loading. Training a model to classify ants and bees; 120 training images each cat. 75 val images each. [data link](https://download.pytorch.org/tutorial/hymenoptera_data.zip)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/WayNoxchi/Miniconda3/envs/fastai/lib/python3.6/site-packages/torchvision-0.2.0-py3.6.egg/torchvision/transforms/transforms.py:397: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.\n",
"/Users/WayNoxchi/Miniconda3/envs/fastai/lib/python3.6/site-packages/torchvision-0.2.0-py3.6.egg/torchvision/transforms/transforms.py:156: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.\n"
]
}
],
"source": [
"# Data augmentation and normalization for training\n",
"# Just normalization for validation\n",
"data_transforms = {\n",
" 'train': transforms.Compose([\n",
" transforms.RandomSizedCrop(224),\n",
" transforms.RandomHorizontalFlip(),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize([0.485,0.456,0.406],[0.229, 0.224, 0.225])\n",
" ]),\n",
" 'val': transforms.Compose([\n",
" transforms.Scale(256),\n",
" transforms.CenterCrop(224),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize([0.485,0.456,0.406],[0.229, 0.224, 0.225])\n",
" ]),\n",
"}\n",
"\n",
"data_dir = 'hymenoptera_data'\n",
"image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n",
" data_transforms[x])\n",
" for x in ['train', 'val']}\n",
"dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,\n",
" shuffle=True, num_workers=4)\n",
" for x in ['train','val']}\n",
"dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n",
"class_names = image_datasets['train'].classes\n",
"\n",
"use_gpu = torch.cuda.is_available()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"torchvision.transforms.Scale??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"Init signature: torchvision.transforms.Scale(*args, **kwargs)\n",
"Source: \n",
"class Scale(Resize):\n",
" \"\"\"\n",
" Note: This transform is deprecated in favor of Resize.\n",
" \"\"\"\n",
" def __init__(self, *args, **kwargs):\n",
" warnings.warn(\"The use of the transforms.Scale transform is deprecated, \" +\n",
" \"please use transforms.Resize instead.\")\n",
" super(Scale, self).__init__(*args, **kwargs)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"torchvision.transforms.Resize??"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"Init signature: torchvision.transforms.Resize(size, interpolation=2)\n",
"Source: \n",
"class Resize(object):\n",
" \"\"\"Resize the input PIL Image to the given size.\n",
"\n",
" Args:\n",
" size (sequence or int): Desired output size. If size is a sequence like\n",
" (h, w), output size will be matched to this. If size is an int,\n",
" smaller edge of the image will be matched to this number.\n",
" i.e, if height > width, then image will be rescaled to\n",
" (size * height / width, size)\n",
" interpolation (int, optional): Desired interpolation. Default is\n",
" ``PIL.Image.BILINEAR``\n",
" \"\"\"\n",
"\n",
" def __init__(self, size, interpolation=Image.BILINEAR):\n",
" assert isinstance(size, int) or (isinstance(size, collections.Iterable) and len(size) == 2)\n",
" self.size = size\n",
" self.interpolation = interpolation\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Visualize a few images"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"plt.pause?"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112708ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def imshow(inp, title=None):\n",
" \"\"\"Imshow for Tensor\"\"\"\n",
" inp = inp.numpy().transpose((1,2,0))\n",
" mean = np.array([0.485, 0.456, 0.406])\n",
" std = np.array([0.229, 0.224, 0.225])\n",
" inp = std * inp + mean\n",
" inp = np.clip(inp, 0, 1)\n",
" plt.imshow(inp)\n",
" if title is not None:\n",
" plt.title(title)\n",
" plt.pause(0.001) # pause a bit so that plots are updates\n",
"\n",
"# Get a batch of training data\n",
"inputs, classes = next(iter(dataloaders['train']))\n",
"\n",
"# Make a grid from batch\n",
"out = torchvision.utils.make_grid(inputs)\n",
"\n",
"imshow(out, title=[class_names[x] for x in classes])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Huh, cool"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Training the model\n",
"\n",
"* Scheduling the learning rate\n",
"* Saving the best model\n",
"\n",
"Parameter `scheduler` is an LR scheduler object from `torch.optim.lr_scheduler`"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
" since = time.time()\n",
" \n",
" best_model_wts = model.state_dict()\n",
" best_acc = 0.0\n",
" \n",
" for epoch in range(num_epochs):\n",
" print(f'Epoch {epoch}/{num_epochs-1}')\n",
" print('-' * 10)\n",
" \n",
" # Each epoch has a training and validation phase\n",
" for phase in ['train', 'val']:\n",
" if phase == 'train':\n",
" scheduler.step()\n",
" model.train(True) # Set model to training mode\n",
" else:\n",
" model.train(False) # Set model to evaulation mode\n",
" \n",
" running_loss = 0.0\n",
" running_corrects = 0\n",
" \n",
" # Iterate over data.\n",
" for data in dataloaders[phase]:\n",
" # get the inputs\n",
" inputs, labels = data\n",
" \n",
" # wrap them in Variable\n",
" if use_gpu:\n",
" inputs = Variable(inputs.cuda())\n",
" labels = Variable(labels.cuda())\n",
" else:\n",
" inputs, labels = Variable(inputs), Variable(labels)\n",
" \n",
" # zero the parameter gradients\n",
" optimizer.zero_grad()\n",
" \n",
" # forward\n",
" outputs = model(inputs)\n",
" _, preds = torch.max(outputs.data, 1)\n",
" loss = criterion(outputs, labels)\n",
" \n",
" # backward + optimize only if in training phase\n",
" if phase == 'train':\n",
" loss.backward()\n",
" optimizer.step()\n",
" \n",
" # statistics\n",
" running_loss += loss.data[0]\n",
" running_corrects += torch.sum(preds == labels.data)\n",
" \n",
" epoch_loss = running_loss / dataset_sizes[phase]\n",
" epoch_acc = running_corrects / dataset_sizes[phase]\n",
" \n",
" print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')\n",
" \n",
" # deep copy the model ### <-- ooo this is very cool. .state_dict() & acc\n",
" if phase == 'val' and epoch_acc > best_acc:\n",
" best_acc = epoch_acc\n",
" mest_model_wts = model.state_dict()\n",
" \n",
" print()\n",
"\n",
" time_elapsed = time.time() - since\n",
" print('Training complete in {time_ellapsed//60:.0f}m {time_elapsed%60:.0fs}')\n",
" print(f'Best val Acc: {best_acc:.4f}')\n",
"\n",
" # load best model weights\n",
" model.load_state_dict(best_model_wts)\n",
" return model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. Visualizing the model's predictions"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"def visualize_model(model, num_images=6):\n",
" images_so_far = 0\n",
" fig = plt.figure()\n",
" \n",
" for i, data in enumerate(dataloaders['val']):\n",
" inputs, labels = data\n",
" if use_gpu:\n",
" inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())\n",
" else:\n",
" inputs, labels = Variable(inputs), Variable(labels)\n",
" \n",
" outputs = model(inputs)\n",
" _, preds = torch.max(outputs.data, 1)\n",
" \n",
" for j in range(inputs.size()[0]):\n",
" images_so_far += 1\n",
" ax = plt.subplot(num_images//2, 2, images_so_far)\n",
" ax.axis('off')\n",
" ax.set_title(f'predicted: {class_names[preds[j]]}')\n",
" imshow(inputs.cpu().data[j])\n",
" \n",
" if images_so_far == num_images:\n",
" return"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"Variable.cpu(self)\n",
"\n",
"Source: \n",
" def cpu(self):\n",
" return self.type(getattr(torch, type(self.data).__name__))\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Variable containing:\n",
" 1\n",
" 2\n",
"[torch.FloatTensor of size 2]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# looking at the cpu() method\n",
"temp = Variable(torch.FloatTensor([1,2]))\n",
"temp.cpu()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Finetuning the ConvNet\n",
"\n",
"Load a pretrained model and reset final fully-connected layer"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"model_ft = models.resnet18(pretrained=True)\n",
"num_ftrs = model_ft.fc.in_features\n",
"model_ft.fc = nn.Linear(num_ftrs, 2)\n",
"\n",
"if use_gpu:\n",
" model_ft = model_ft.cuda()\n",
"\n",
"criterion = nn.CrossEntropyLoss()\n",
"\n",
"# Observe that all parameters are being optimized\n",
"optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
"\n",
"# Delay LR by a factor of 0.1 every 7 epochs\n",
"exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"torch.optim.lr_scheduler.StepLR\n",
"\n",
"--> defines `get_lr(self):\n",
"\n",
"def get_lr(self):\n",
" return [base_lr * self.gamma ** (self.last_epoch // self.step_size)\n",
" for base_lr in self.base_lrs]\n",
"```\n",
"\n",
"so `gamma` is exponentiated by ( last_epoch // step_size )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5.1 Train and Evaluate\n",
"\n",
"Should take 15-25 min on CPU; < 1 min on GPU."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0/24\n",
"----------\n",
"train Loss: 0.1495 Acc: 0.6967\n",
"val Loss: 0.0914 Acc: 0.8562\n",
"\n",
"Epoch 1/24\n",
"----------\n",
"train Loss: 0.1819 Acc: 0.7049\n",
"val Loss: 0.1310 Acc: 0.8301\n",
"\n",
"Epoch 2/24\n",
"----------\n",
"train Loss: 0.1345 Acc: 0.8197\n",
"val Loss: 0.1360 Acc: 0.8366\n",
"\n",
"Epoch 3/24\n",
"----------\n",
"train Loss: 0.1504 Acc: 0.8074\n",
"val Loss: 0.1023 Acc: 0.8758\n",
"\n",
"Epoch 4/24\n",
"----------\n",
"train Loss: 0.1504 Acc: 0.7541\n",
"val Loss: 0.0789 Acc: 0.8693\n",
"\n",
"Epoch 5/24\n",
"----------\n",
"train Loss: 0.1248 Acc: 0.8238\n",
"val Loss: 0.0900 Acc: 0.8758\n",
"\n",
"Epoch 6/24\n",
"----------\n",
"train Loss: 0.1047 Acc: 0.7992\n",
"val Loss: 0.0956 Acc: 0.8562\n",
"\n",
"Epoch 7/24\n",
"----------\n",
"train Loss: 0.0680 Acc: 0.8893\n",
"val Loss: 0.0596 Acc: 0.9216\n",
"\n",
"Epoch 8/24\n",
"----------\n",
"train Loss: 0.0729 Acc: 0.8770\n",
"val Loss: 0.0555 Acc: 0.9085\n",
"\n",
"Epoch 9/24\n",
"----------\n",
"train Loss: 0.0786 Acc: 0.8607\n",
"val Loss: 0.0482 Acc: 0.9281\n",
"\n",
"Epoch 10/24\n",
"----------\n",
"train Loss: 0.0975 Acc: 0.8279\n",
"val Loss: 0.0516 Acc: 0.9281\n",
"\n",
"Epoch 11/24\n",
"----------\n",
"train Loss: 0.0527 Acc: 0.9180\n",
"val Loss: 0.0509 Acc: 0.9281\n",
"\n",
"Epoch 12/24\n",
"----------\n",
"train Loss: 0.0590 Acc: 0.9057\n",
"val Loss: 0.0475 Acc: 0.9477\n",
"\n",
"Epoch 13/24\n",
"----------\n",
"train Loss: 0.0818 Acc: 0.8484\n",
"val Loss: 0.0509 Acc: 0.9281\n",
"\n",
"Epoch 14/24\n",
"----------\n",
"train Loss: 0.0595 Acc: 0.8975\n",
"val Loss: 0.0498 Acc: 0.9412\n",
"\n",
"Epoch 15/24\n",
"----------\n",
"train Loss: 0.0728 Acc: 0.8852\n",
"val Loss: 0.0524 Acc: 0.9281\n",
"\n",
"Epoch 16/24\n",
"----------\n",
"train Loss: 0.0587 Acc: 0.9221\n",
"val Loss: 0.0572 Acc: 0.9281\n",
"\n",
"Epoch 17/24\n",
"----------\n",
"train Loss: 0.0711 Acc: 0.8893\n",
"val Loss: 0.0514 Acc: 0.9281\n",
"\n",
"Epoch 18/24\n",
"----------\n",
"train Loss: 0.0637 Acc: 0.9057\n",
"val Loss: 0.0555 Acc: 0.9346\n",
"\n",
"Epoch 19/24\n",
"----------\n",
"train Loss: 0.0672 Acc: 0.8689\n",
"val Loss: 0.0462 Acc: 0.9412\n",
"\n",
"Epoch 20/24\n",
"----------\n",
"train Loss: 0.0463 Acc: 0.9385\n",
"val Loss: 0.0550 Acc: 0.9150\n",
"\n",
"Epoch 21/24\n",
"----------\n",
"train Loss: 0.0676 Acc: 0.8648\n",
"val Loss: 0.0549 Acc: 0.9346\n",
"\n",
"Epoch 22/24\n",
"----------\n",
"train Loss: 0.0864 Acc: 0.8525\n",
"val Loss: 0.0535 Acc: 0.9150\n",
"\n",
"Epoch 23/24\n",
"----------\n",
"train Loss: 0.0663 Acc: 0.8730\n",
"val Loss: 0.0553 Acc: 0.9150\n",
"\n",
"Epoch 24/24\n",
"----------\n",
"train Loss: 0.0532 Acc: 0.9221\n",
"val Loss: 0.0572 Acc: 0.9412\n",
"\n",
"Training complete in {time_ellapsed//60:.0f}m {time_elapsed%60:.0fs}\n",
"Best val Acc: 0.9477\n"
]
}
],
"source": [
"model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
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atnuLuCtYZ41PP32Yl73ihVx5y2sZ7vsd7q5r2tlf6e3518IFQWosASkWlTpmVIxdi7IrGFWBFKQk5otdRMGgLmQ83aJFabBVRbN5CdMjx+jbXVZW9qNNCyhSjBgKMURyEVJRmNjzS7894ad/5rso80/SDFfow4TZPPDpTx/n6MmafVdchkU4cyowvupGys5DPPzYlEUU0hTWV0akR2Zsn/gUw6tG9BuKY4cPE9WIbbXOdNcwbSuC7jhzZpfFbiL1R5j2kba9l6dPvYudM6e5ep9hbd9Fav0aLF3XMxxsgg0kNHF+lpAtdWUJfUEpS+0qcgl0/QLfGJwaoukJZ44ybKCLQ2LsyH2magy5OFLaBTtkVAkKx+kzJ3jkiZNU04ex3rK7O+W973+Q7K7j7E4F3YJUnmS4so9nHn6SFwwdH/n4Gea7LVnAWDizlTk8algLDddPdwnXaI7OLyWVVfq0zVOPn2XSe7oY2Tywn2464Yd/4if5n974vTz4yINshcJY1zx6MiCnLlJDqR4kclaEDMOxRy8WdJ1jMDSECL6qSCmQVE9oBVcrdCn0eQZKIHco47FWyFnhK8titkBMTS6OSrdIdoiJ/PTPPsSXHVxF64p7H3iGRw91VJu38eQnDrNQlsvXR5ycdTxw35OsDCztAycpYU7oIRmBDL7yzE9m4mZm5caDzHd68toV3PehBzm23SLKMOu2yUk4dGKKM/B93/99KBpUyly5uspgNKYPUw6fOH3e5XlBeBNuv/cfy2QR8W5M328j8wXrmyMm0w43XoUixKDAaHKJlBKobIN3kEtBW4dJkazB60LXzqm0MD91FAbr2OEKs0VL3Rh++jdGHH/4fVxx9YjV1XUm822OHJmhcsNlN6zx+KNnqUKgGhq2Z5EomkucwTrDfNERYkA7wyQJznpu/7I1qmYf9z18hNM7iWPTORSIQbj+mssRyUwn23jfsDKAlAu210y6wDwEpBSeOLp7Xt1ZLghNTdTM57vMSqIZe/z6JsEFNvY7dieC9hXNGCQkpouCbxokWZQpIAqVW4ryeGnpe412Y9ruDGHSMRisQcmM6jGPPvIIm7JArhpx6eVjTpxq0c5B9lxyoOLpx04RRZGd4eTxCSsjT6NhO2VG2SJJ0KLJfSYXQxlV2HrAo0+f5MjZlnkbGPua3Z0WZ+GZQ8dBa2pX6PpA6BoUHV0UUgJVYNCcVz6BC8RHKbsR441raQYrtItCKQrJljaMWR0VXDxFmuxSMKyvbdBUjpgWTHYCi+mErZ0pfSh0paGLwny+S2ZEfc2tpHCKvjM8c+RRnnlKkUSxtprZWK0xjfD043PCrGV3ElFdZjXBAQxXb6ww0J6MULmGK8ZDTAkMhw3TVkhGuPngCldecwOv+vqv5PRuTwnCdD5ndeyx3nNgo0aTuWTgOHjpZXz5zfsInUbEoAp4b4nn3/i9MEi1egNb72ewcSkr+66AekBHhXhH1oKjY1B1eJmjyjYazXC8ynC1wlQDKj8ihgK5pWSQUtPtbiPpLLZZJ/eneNcHWvJwlegUlMihw1uEMzNsF7j80jXCZMGwwFbo+dh0l5NtYKvrGZuG3a0tTuxsQVbELpBD4ur9Ay67epPQNkwe+9gyjeX29c0DazgTOH52RmUMR3dbtman6VWF1ZGB9WTJ9CmxNjr/1u8FMaamNJU4P4QsHqR0DuIukAnzOXX4FPOtU7jxPsr0CU49/gxrlx2kXtsk50g2m+TSQXYk6SiSSd0CEYVzgs7C29/1QY5PRgjQzwMptrS7I66+cR/V+h18/I/fRYod191yPaFueN89D9KjuGbg2VipSVu7RAq7XQFtmUrg+Xdcz858wqz35NDy+OkOrTNnZxG0ZqXWLFLBKo0DnLdctW/Ic65Y5Z4nTrMzaWkaz4Gx5T33nTivffAFQWopWYSCQjM98vuk7NBuxGjjBvpHfoytT30UEQiTQLOxwfDy52NsT+4j2iRKMESzfJ1BaZzyaLOgvnaT33vzr/Lp+SXE3V3OlCHjtMCYhtkkURYzooyYn91lWA9o2xlYw5k2szqqWRkMYNGzf7Pi/ifPoJ1lmgvFaA5eP2Z1NOSJ05mui8xzZqAcR+bbtPMEytLlhMWwbwW2e7AoXnLjBsUP+PShYwiW6XTBk8fmF5/fbzd5ipISgsJv3kEzUOjJ++jvfzO6H7N6zctZv+4ruey2F5JTAAwkEFMTxNFRY12mThOaeBIdD0HXs7jnz/nApwzbu4lLnvdy/HCTXjnaWSblSGLAyC+1qGtnlCjoEDngNfsCjBpLTJHDJ6fkrLA5c8mqY2BAWcd0MeFrX3oz116WueO5m/h6m1KElz/vUpTODAyEkkEMJWdin/j4Uzs89ugp1r2niMbY6rzL84KwfttTH2XXbaC0YeAjerGLWsyIYYtq/4uR0lJ2ngbGNONV0tkH6IfXUtVDUpcoKdAXj67HmKwpypKnR3n6iR1uee6dPPCJv+CPf/UdbFE4eM0Glc2Mmg1KdxY3WMOdCfQls7Exou2mVEDbd0yOLOeSFzlgsISukOvMxmrFcFVR1+tUI4V3Ff3pE4z8CoPBnCcP7aIw5AwDLVSu4oC1LNqeaZeJNnOgugQdJ1w5ukh9lKbP3E+xQ5yGqV++pmg1wvuDzHdOkIzDmobYzdBr11OrGdtPP8qivpTB2ibaFdCZtp2j2sz6wLMz2eX3/vQ0B7/h24gPn+Tua07y0SMt8UzPLS99HlddeRVPPfQo88OHqBpoBkNC2+LtGsx3MDiEABlMWQoqouijYXK25YY7bmLr9CHCpONrvvGrefDDH+bhZyYcrAyng6Pxmbr2kAO7Oz1tKfSlkAX6TvjYE8cpRXj6SyDPC6L7Df0WqTtBWGzRT7aI3RnKfEobFqTFhDw9jYotXTejnbckVhjvW6OhI8XlyrZFo9UQWw3Y6TsCPSZ5Tjz62/zQj3wXH51EHguGhV/l4O13c8+7/4QXvuTF3P2PXsf6gQYdM6PV/exbcfSVYbxvGUylLRmlDPsP1OyoRAyFrsBHP/IQq6tjjh0/xTt/5/9jqDf5x6/6b3nx86/hpksbbtq3yl23XM/ll40YrnlUpdFKMApEK1IplAIxfSHBZL44XBCaqkKHlF2yryk5Ya2iiAYiiEJKixmvUfmKvu/puhZTXYp3C6Zdi1GWVFq0U+ScSSHyqQeOMdpX85xmwo+/6We5/oZ9HLiu4qmjHR/9sz/hzrtfxqX7FVfe8XU8fu9f0M8O8dwXXsdffuAv6fpMisfIGcDgjOLkbsd6U3Gmy3RoXDI8+MkT3HDNOqLgpjtv5oabXs43/8+/iJD5kdffSD8uPHoo0Kal50QR2L8xBGvZ3ZpSCvTnn9MLQ1NTbEnSk1KPNz0lLhDdUySTS0AQtKlRFlxjySEvXUZ0xkggSUEhGMzSk8AozpyJTGY1n/gk7PZTzswzJ0/t8pXPX+H44QfpplNuetHzefe//1+46UXP47kvfR5bx44yXt3k2msuQdmKBEQFYW+HXduDtpqMYpoF4xzbk8CKc7zvHe9lpz+CICg0P/rrn+To44c5Ow1In6gE9o89oWtpd6bEBEihsuff8eyCIDXmXUrsoe9omhUGzSpWaSQXDB5rBygCCoXqBYxh2cl4dOyXmk6gzI4xHI0ZDj1RjaCueGgKlxzY5PhpyHHO2VOWr/mquzh14hFOHi989ateyzf/k5/j9q/9agbjES1z1i/x5JwIBWpvaEuiJEVHRitFr4QzbaYo8C5w5Ng2xTj++G2/wf/5r76ah9/5Jn73x17J6lrN9Ztj7rp1P8PK8D/+w+eiNGysWJqq4C2sDc//NOEF0f1aXyM5UTdjnDHksiCnHusqUppDGtLFDpUFXxnCdBdtx+hmiB9OKWXvAfBj+lQwdsBVt97JFscYzAO+0qADL3rOpfzpxw9xyfgA/+Kn/h2/8APfzd1f+zp25j/JQ598nPsff4J965sov0qrj5FsZpaXbpwZwWSYJKgtrNSKxii0dlyyWejbCfPphDM7lsceO861t9+Msh1+uENlZjz/4CqPH29JpbAzT4z8Up/0Xx0S6a8nz/Oe418DI1cRjUaZQMbgnCXFhph3l577aZeSLaJAnEVMhbKZUmb0ssJwNEDFnkVWxMWcNkU+ce+jnOgqrtq/xqOntjHK8MxWiyPwwYc73lhfzg/+3C/y0b88xC//wr+lOztnFgorI8WTp85ixmP8dMZssdzoHBVkl4lZyCXzxm+/i+dfscsDDxxj44qbeOhYR5pMefSZbaqRcP89HyN1DqSws2343u97Lb/w5l/jhtWaBfCVX/Y8KBqzunHe5XlBdL9u6HF+jMkRUqEkt/TjLQ4lBl1aYlpgNIiqsVrjipD6jFeOGDsiitJNsXqAt4bpLNDPe7rSsVrX7F8d8s+//43cdpWmsS2zmPjJH3wTDz/9FPce3+a4MwQCTzxzgp2TuwRV0yvoFOwIpJJoM1itGJnMLddqVlY2eN7zruTkkWdo7Cb/3ff9a+5+xcu4+uALULphZXMVlHDtLQf5mZ//NezYc9nl1/Jt3/wqnnPzPj7y0MP86m/+8XmX5wVBqhILukfbISUnutkWXg9AK5SrqAb7qG2DQlNogWV3WNdDoCdM58y3TmFcQ4pTUgpcfXXg8ssbrrr5Nqqq4h98+3cwbBoO3n47hl3++9e8grByLVetZL7nW+9mex54cAIziVA0ebZFTsLw4A0MvUIXxaoxrJrMc/aPuGIFQlygbOArXnQLTj3OL/74j3LfPffw0q/9Jr79217DldfdyJlJy+HDW2yMPAev3cedd1zCez5wHz/+797FkeMzBtVFaigZI6hiyKUjhOV7YUo93ldYpdEk8J4CKNEQ50hol3EUBLIYtBniVAEVQAxf9fI7WfczTjz8YZ566igP/sUf8Gfv/nM212/j1V9/F814ndW1dXKZku2Yb/+Wl1AbYTRsSEQ2bn0hykY49jhGJ2ZA0HDJquH7vudmcqqQUhjZFTRT1kdDXvzS5xImC37tf/8R3vpbf85Lvuor0B288K4buea6DY4+s8UfvP3DPPLkIX7yW17OT33zzayf/1nCC2NC/+k/+lbJgFFCWOxgtF8G3FCBrAwqFqwdkGVGzhZDT8bi/QDjLDknSpyiXEPspkBFaHdJOFAKEwKPPXmIyRz+7J4Z82J48siEejDkjttXOPbkKbZmNZHCptNcf+M1HH70KYyC3a5nNN7kzOmTPPeg57u/+yZULzQ3v4rJY39EP2sZjgdE8fzy209z+uQMYwxX7tccOdXyyPHA3c+/hhfddQu/+ZZ38cNv+hY+/qGPc023y6GTM/7k8Y4/f2p28a3SPPF7rxPtBMRBWKCchQxBEopEpUZMRHPgihuxa6uks0eIquBiy3x3C4NBWYs2QuwsdeOYhx1kMSPEwGDtEmbHDmHrFXKe83O/dB+bl63zngcCQ9Vy7caIWczgO7w4vPd0046NSnPkdMtdt+7nla85SGMXFA1926K0RhVD13Vo7+hCS84N/+FPTnG6Nzx+dJfNUcPl63DqbODAvnVe9frX8H//H7/CHdetMTc1Z8/OeOzQjE+fOr+rNBcEqZ/+3W8SsmCNARqym6LLCspkpCiscQhQJC6n2rIs9xxrg5KCNYJgSWVC6lluPJKCKhEpkdAJxSR0NqCgxI6QIr/7ocCH7t3mO15xPXfeGemnO2gZoEVQqpBEo3QHojBmgHeGGDtScWinMKIICbqYsWIwjUdLoZu1vPsjx/jDjwZ2pNAGqLXw3AOeoBSVFqbJctuNm3zg3qM8enRy8fkoKeNwRohKo3TEqU1IHUUVSopkEUopVIMVqmGDyYLq5pR+TtvP6MmkPcKsLagMKRSsScQeCh2karmojoIiOFXx1bcoHvhY5h3vvp8X3Hwtei9IiLVjdJVROVKKQAGtErN5xJiC0ZHQRaIe8ta3HeIvPt3ihh4pmW942aV82a01d7/4AF/z4sQjT2XuPb7KoSNn+Irb9/HkkQmHTrfMYmZt8zJecNNFuj91dWUdMVCFSIogWVGNh6jxOo0xdCUgYc5iZ8Ls9A4SE1kKxliMbgj9FpISxlSAo88Fp3vaPqLFIKrBauHYQ5+k3Rpw2c209OetAAANwklEQVSbxNKyORiwYSJrwzVmRyY0TaDyaqmZfYFikdSTlePJUy3Wb/KBT0yJDNk5eoiNfQe44sZruc3O+fhjx8hi+Jk/Oor+AxClsEZz24bhB75zH38wS7z/EyfZ7hWHT+2QRPFb7/goL7xi5bzL84Lofh/6ta8T6xyaSDEjrALLgsVihnZjimlRxVF5S4iWlCLGeXI3o+SCtooku1i9DqLJUkhti6srNMJsNkPhaZ8KnPjAQ9Q5U200uP2Xc+KWl3LynW/hhrsu5ZmjLddsBLKOPLZ1A69/3QHOnOl47OnHEH2Aeu0m3v+hjzHbDXzdN3wjvpnywXffx/Cml/Kh334rp0PmTJ8RDF1ZRpVRuqBIeONZr+GVd+2DyXGUaZj38NSxwHsfvwjH1Ad+4xViKDhXoUukAGi3DLAkhaIsGEPKAIJWFZaWkAPgSWFO5Q3tvCeLQjuPQgOakKbLfGImlICvVmF6jE9/aJtLb97kV96+zWWDFb7mLnjy7IyYPDMU1z33y2l37+OpJ1p22gjaMevhNa+8HTUYMdU3cMXsPdzzF4d4eutSntk+StZwti0ENJIhCaCgDzBdZJQRjFFctm/AYtGz6DJNbTl87CL0+60thC6QTMTVl6JN3iMYYlpQYk8RhVeZRdshakEURUkgNqK10HY92Ra8rNLnXawbEuKULBFjR4Ruly4IiQU5exYHDG95xxnGQ8c3v8Yyji1hJ9Pc4ql0oZT76Azcv9MxsjXVMLIxqjl+6BC33LrOev8JUp+566417uhnYA+Q0Lz1nSfYXcAkQGdAEmRTGNVQlGUyy0wmPSUVLt9oSOX8b7u4IDT1od+4W5BqaSTZERmQEkixo2iwpQZpKdohsueREHtELzdQKaMgFWI2GNURssEoQy4tfR8hWbTtyMnx4fc9yZkjlstvHHHwysxg3ZBjwomilEQWDypglKdnThWF7FcxMsdXNbMEDr+0ugcOiYq2X7C6uo+231lutonCya0Zx48HPvF0ZDrXLBIsAogxZMlc0xReeHPDnc85wKt/4pMXn6amJKAXqFyRpYWS0M6ixVDKAqUtUgYkaRnomnkSirK4YkEFSspIFopkQuzIpUapjM5L1xjraibTOR/+0+NYu4mttnne7SNymGLsEGsTXd+hVIMpkRAt9WoDnWcS5tQmE6Inl27pXdFYchHm0zlKCYhhNplR1Q19l0mhZaVW7Lu+4sZrGxwZ0zSErtAMRpzdPsHGeJ2AIlysqzTOGVJxiGRKUShTKMGhVIeUhkxNiss534WLaCqUErTJSDHkFJGSSXhsqTFGkyWQVUEp4SPvfZT7HjGsuMRrv3OM6i1Zpoi25E7IJqPKMrBHNh49FEKAojPD4YhQMqbqCZ0GG7B9po2Bul6hm88xvibE+d46b4PSFZpM30eKFbJ4cgejlRoRTeVHzLuIcRZ9sYYHkBIwqiaUglZjtEzppcUWjZREHwNaQRSFzyAEtFP0QdClBa0QMdgCyRqMtpQQMRje8evPMO8L3/Et12BX5xAnOLtOt5hjDIiO0CWCKGCBk4rYzShKY4ymahzSB2Is5Bzwo1XmfUvlPIuuQxlFZrqMuJJAlbAktxjEg86BIi2SM9u7A5yPKJVxviZ0kEz3X5XPF4sLglQ/vAKtCoR+SUjxOO3JfVhGDy0doTQ4G0F35FSjAhitiGKxRWG8IcR26dMkwod/4wnSDG68+zKuurWhjwGVHVkKXWqRLOTcI0qh7ABHxFdXMTt7DJwnMyUnISSPZIMyDuUCfdvS+IZFzOjS4uwI0RXiKmyaI9YT0nRvydAiyuJcRabDFUUpmVwSFEMoM5w+/5p6QazSxMUp+vkOpITkSO4FiR1We7wCVI1xsuzWyhCjEwEBYblE5wcIGowj7gT+8j8+yvFFZPWmhoO3rWCUpimAyShZ+tnapsGYNayHlOeU4ugW2yjnl+F/Yo0URSGjlMXoAuLxuiKXTG0rCpYuzYndnBx7QswUMkZ5jCxNX2cNsXQUqZi2C1KxiHhIAWMUqTTnXZ4XhKYmHEVrlAgiCVGyXNM0ipB6rBshMUHVE0JE6QpnDalfLLu/LuOBww+f4r57p+zPidd9/wuXeaTl+6GqFKqzhLy9jPipBnR6l5wGmKrQzyfLV6gScX7pr6tUTcgtziTIhiSBlBf4pOhlTlI13joiLSl0WDGQoYSMqweUbpcuFXTJBMlooA8dJRdMtdwO6dRFaig1lQFZIZQZGktRhl5HVOwQJfTtFHRN7iPKOMgWXI2tI2meMBo++LZ7MduGu191K/uu9VCWfn3GCzk52m4GpUMLrKwcYOvUCYyvkNRDgcpaoiRc0Es3E+OXkV5EEcMuRpZBJ1WCZGpUnqOZUbJHVR4jhUKhMZ5gh8TQ4f06fXuGaIeEtkMrqDz0rbAIHegRWp9/Ci4IUsMigjlLTkJmGXbVuYasMlY01Jacewya1GdEd6wNDWePtjz0/seRIwUZjbnl9ZuMDgyXfsLOk7o5URxGCb7WhIVCqZrJ5CjKeJRolIccAdEgGePXiGEGBGq/wrxr0covHcR0ZjDYoO/nZBxag7IerSzKClo52kXAuAZrFdoVZGawHopdhtbpOoM1Zm9f6oKQLlLPB2gpMSElUsoMbRQSe5QWsqrRBpwbkUThBh5LYHY2cPi4od0yTKPwon9wHcPREBYddJH27BliW6ArxL5n+R08C/SENiK5JceMoWIZai7jzBjUgqpyaOuZhR1G1QpGZ5JYlFj6PGfeJyRntKqRDAZNDAGnyjIEhXRLB7VFwjXrSCk0A4vkArEjxBZJAZ2XiwfnGxeEplZuTDKRkhMqDZeBly0Q09JSFE1hijMNRjuoHff+x3uQk3DHP/wyqvU5uSuoxpNCi8SC12OSU9DvIGZEP9/CVZZSLM3QEnqFsYKUCAjGjElhitIQQyAXg9GaPs8x1QaFGZaM9D2+FOxgiIghhTklZ4yu6GPA6GW3nROkElA6E3Mm9IlsQUWQnLFVBSXzhX3D94vDBaGp2QDa4/QQ5Q1iI+QOXE1Vy94ieEITOPTgYxx6T8BujnF3eIb7AqI0yuilsFzFYGVEMFNsWSDao1TAWYNKYRmbXwRMC31HSRFXeUK/hbUeP6jQgPEGISAC0rVogSSZNkI9XEYMlxxRylPiDMk7pD4zCwFJllwyzkBJHSvNCF15SBmcXn7zpu2haNTFGhq2tj2LtkP7ATku8GaIqguxFIxpyHSs7ruB0x+/H1Mbzmw9wPO+6TbQPbK3U7u3HSjQYgjzQmUHqOJRcYKkRKHgqhGNDuSiabSl13NMNJhiaQYO6SIEg60tOXcY3eAMoAK5WHQGsS3GNOgoFAQtmew9qYMiAVd7Qt9S1Y4uBqzWLNoFiozTA1JqCf2ceuDpYkbLRbqV0XjLUO9Hk7CqoG1NKRVaMlJ6XNMwChPWXnALRyYHuPOlLaeOfJIUNbbSpJgYDUa07S7GVLgqLj36VU/lHFkyXlmQgvZLT3vRDhtXCDqjdL8sM/fLmdis8fUQ6TKxFHICrEbLcoNy6OdYO0T7AanfQeUBpmnJSUglk0vGCkBEmQaF0OXMwNck6fHOkUPA6qVrzfnGBUFqbVfJOhIkYU2DyBBnJ2gaYhJyH2jMKarrX0x88mEWU0c9WEERECLFN6S0zbhZI8eWpAujZkDslz68hgGSZmhlli4yBXw9oGuhihMWEYpqqUZrhH4HO1ghLWbEvWUxw94Yi8FQkUzC++VD4KqKPgU0NX0JWAWVMygRwCOlx9sxGIM2Hc43xNjirEeVjB5epDvJcxFEW5wYjG7IzCAvn3DvRkQzZTtfhj12GPAY0WAn/2n6Luc5ugwpaoZWCh0KObU0bkwuU1JSqHq8HFfFIkbo+khVC9JswHwHpwdkJfiqIUdFa8HpVbrU4poRMXWQPVLASk+hQ1uFUpraN2SEoa3QIuQcKdrgckbpikLGMSSFbZw1aAySArD8PsD5xgVB6mh1TOwSGo8YS8mKIh1SHJSEFCHbnhgEVw2xKSC5ARWX32DzDUkXYB0lgVJpUAlVIIZlvKI+JIzVaG3RdsCgAmWgm7WMDowQNKpLzPMyPrA1QxYhMJCKkgRnKgqKMJthhstvxXXTLXxVYYf7SYtd0D1hvqAaj5AkUAL4AWUxJZsh4ldRucV7j/aGvhRU6s+7PC8IUsN0TkGITnBZUbJC///t3UlyGzEMQNFPkM0eJEdKcv8TZpNSErvFJggvOkfQwoXCOwL/DpzyRkodJjBWMkJigKwYFbXf5GkljUbNAmOcs1Qq1h7k6Q3TfxRdUf3gcr2R1M5h+xj0AkmFukzna6NZKLeN0oxDO1PKzP1KOzpdP84LyNoo9xvKA3jy7ed3EhONP2TpgLBslZQNk0wqV1pXmO/nH3HZSHKh7w+SDmYRnuZ0TKj2i1TujENhLiR9IlRGEVCYbdBqxg4h5wwMii1gmWmeUX1nXja6frDKHc0zR/tLnX4wpsaxK4lOF6EugowGI0GpGO3cXkOxw8j1gujOXBZa23k+d4ZNiAxaE7bLwr6/MQ7DEHp/5zKttJL/b7k1msGc1/OfOj2wbYehJC5AoiwVs4YOqMv95ev5JY6zhNf6EsOH8FoR1aGI6lBEdSiiOhRRHYqoDkVUhyKqQxHVoYjqUER1KKI6FFEdiqgORVSHIqpDEdWhiOpQRHUoojoUUR2KqA59AlyE9VaWx0lLAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114e1bc18>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114e8b4a8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111bf37b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112786c50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114e153c8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1129c55c0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"visualize_model(model_ft)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. ConvNet as a fixed feature extractor\n",
"\n",
"Freeze entire network except final layer. Need set `requires_grad == False` to freeze pars st grads aren't computed in `backward()`.\n",
"\n",
"[Link to Documentation](http://pytorch.org/docs/notes/autograd.html#excluding-subgraphs-from-backward)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"model_conv = torchvision.models.resnet18(pretrained=True)\n",
"for par in model_conv.parameters():\n",
" par.requires_grad = False\n",
"\n",
"# Parameters of newly constructed modules have requires_grad=True by default\n",
"num_ftrs = model_conv.fc.in_features\n",
"model_conv.fc = nn.Linear(num_ftrs, 2)\n",
"\n",
"if use_gpu:\n",
" model_conv = model_conv.cuda()\n",
" \n",
"criterion = nn.CrossEntropyLoss()\n",
"\n",
"# Observe that only parameters of the final layer are being optimized as \n",
"# opposed to before.\n",
"optimizer_conv = optim.SGD(model_conv.fc.parameters(), lr=0.001, momentum=0.9)\n",
"\n",
"# Delay LR by a factor of 0.1 every 7 epochs\n",
"exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6.1 Train and evaluate\n",
"\n",
"For CPU: will take about half the time as before. This is expected as grads don't need to be computed for most of the network -- the forward pass though, has to be computed."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 0/24\n",
"----------\n",
"train Loss: 0.1503 Acc: 0.6885\n",
"val Loss: 0.0609 Acc: 0.9085\n",
"\n",
"Epoch 1/24\n",
"----------\n",
"train Loss: 0.1271 Acc: 0.7746\n",
"val Loss: 0.0814 Acc: 0.8497\n",
"\n",
"Epoch 2/24\n",
"----------\n",
"train Loss: 0.1033 Acc: 0.8115\n",
"val Loss: 0.0569 Acc: 0.9150\n",
"\n",
"Epoch 3/24\n",
"----------\n",
"train Loss: 0.1474 Acc: 0.7500\n",
"val Loss: 0.0440 Acc: 0.9542\n",
"\n",
"Epoch 4/24\n",
"----------\n",
"train Loss: 0.1489 Acc: 0.7254\n",
"val Loss: 0.1234 Acc: 0.8039\n",
"\n",
"Epoch 5/24\n",
"----------\n",
"train Loss: 0.1206 Acc: 0.8197\n",
"val Loss: 0.0673 Acc: 0.9085\n",
"\n",
"Epoch 6/24\n",
"----------\n",
"train Loss: 0.1041 Acc: 0.8484\n",
"val Loss: 0.0473 Acc: 0.9477\n",
"\n",
"Epoch 7/24\n",
"----------\n",
"train Loss: 0.0945 Acc: 0.8484\n",
"val Loss: 0.0549 Acc: 0.9412\n",
"\n",
"Epoch 8/24\n",
"----------\n",
"train Loss: 0.0659 Acc: 0.8730\n",
"val Loss: 0.0607 Acc: 0.9216\n",
"\n",
"Epoch 9/24\n",
"----------\n",
"train Loss: 0.0691 Acc: 0.8893\n",
"val Loss: 0.0474 Acc: 0.9477\n",
"\n",
"Epoch 10/24\n",
"----------\n",
"train Loss: 0.0921 Acc: 0.8279\n",
"val Loss: 0.0494 Acc: 0.9412\n",
"\n",
"Epoch 11/24\n",
"----------\n",
"train Loss: 0.0918 Acc: 0.8402\n",
"val Loss: 0.0443 Acc: 0.9412\n",
"\n",
"Epoch 12/24\n",
"----------\n",
"train Loss: 0.0904 Acc: 0.8443\n",
"val Loss: 0.0438 Acc: 0.9477\n",
"\n",
"Epoch 13/24\n",
"----------\n",
"train Loss: 0.0698 Acc: 0.8770\n",
"val Loss: 0.0485 Acc: 0.9542\n",
"\n",
"Epoch 14/24\n",
"----------\n",
"train Loss: 0.0847 Acc: 0.8730\n",
"val Loss: 0.0514 Acc: 0.9477\n",
"\n",
"Epoch 15/24\n",
"----------\n",
"train Loss: 0.0892 Acc: 0.8361\n",
"val Loss: 0.0484 Acc: 0.9542\n",
"\n",
"Epoch 16/24\n",
"----------\n",
"train Loss: 0.0824 Acc: 0.8607\n",
"val Loss: 0.0466 Acc: 0.9346\n",
"\n",
"Epoch 17/24\n",
"----------\n",
"train Loss: 0.0774 Acc: 0.8566\n",
"val Loss: 0.0525 Acc: 0.9412\n",
"\n",
"Epoch 18/24\n",
"----------\n",
"train Loss: 0.0787 Acc: 0.8689\n",
"val Loss: 0.0446 Acc: 0.9477\n",
"\n",
"Epoch 19/24\n",
"----------\n",
"train Loss: 0.0774 Acc: 0.8279\n",
"val Loss: 0.0513 Acc: 0.9412\n",
"\n",
"Epoch 20/24\n",
"----------\n",
"train Loss: 0.0767 Acc: 0.8730\n",
"val Loss: 0.0475 Acc: 0.9477\n",
"\n",
"Epoch 21/24\n",
"----------\n",
"train Loss: 0.1160 Acc: 0.7746\n",
"val Loss: 0.0485 Acc: 0.9412\n",
"\n",
"Epoch 22/24\n",
"----------\n",
"train Loss: 0.0815 Acc: 0.8648\n",
"val Loss: 0.0470 Acc: 0.9542\n",
"\n",
"Epoch 23/24\n",
"----------\n",
"train Loss: 0.0875 Acc: 0.8525\n",
"val Loss: 0.0477 Acc: 0.9542\n",
"\n",
"Epoch 24/24\n",
"----------\n",
"train Loss: 0.0938 Acc: 0.8443\n",
"val Loss: 0.0488 Acc: 0.9542\n",
"\n",
"Training complete in {time_ellapsed//60:.0f}m {time_elapsed%60:.0fs}\n",
"Best val Acc: 0.9542\n"
]
}
],
"source": [
"model_conv = train_model(model_conv, criterion, optimizer_conv,\n",
" exp_lr_scheduler, num_epochs=25)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114d13240>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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zSYLhALFoBF3Z2DWxbRunZaEoCs1mA89zkDyBLfsogRjf/P3/zgNvfw8nnzzFxMG9yBGdynSR/NIsXe0ZvJCBFNQxbJU2Raa6eAkp0UW2YKIvLAMSyWQHheI6i+sFHN/Dc1vU6jWi6X6uXZ9ENU8y8fB7mV+ZJx1LIckqrVaT+MhdLFTy9HUNYLnOtvO5IzJKRsDY+IKLkJCERFdvN++9Y4JXrp/h+FyNWFc/4/v20hvz2R2N0q4HWc5ep21iD00RJBDIkG7vRzd0Wk0Hq1HDsur4voPvuzRMk6bTJBKOwNVTTNcUame/Taw8jzjyTqoNG13z6eoZxVm5gtvc2CBw3Ram7VPKZ2n5Cu7kNygP3U6zWmGlafHef/3rrBbmSOgSbiSApunEAjEa5SyuJ0h3D3LmU7/NXR/+X8hVs9SyKzQbRcYyo0jJGAtLKxhGgPe994M3X0ZJ2ty7lGUJSZZRVIGqSbjpYR7YP0pHpoNE0CcgWrRUi4qi0WH3kDs/zeAD9xMId6NpGkJI2K6N77v4rRZC8mg2G9QbFRAqrt1iar5IcvgAyY4B3M5BapaNa5qoWgKvXqcpqXjCBbuOLOqIpo9rFVl3dQ7d86NMn/sqSqCdgWQXk898CnNxlpl0DyPHHuDi8nX6ega448ARSrksmmyQvv0hvvHbH+GuD/865UKJcj7L4d13s1ScZ215nUj0ZnW/iowRCBPtaOfwgUMM9faiZq9z130PomoBNFUlFtII6gqaIjACGu6gT+XCJQonLxDfbyCluvAtk2qljCx7qKrHzNWrxMIxFBWi4RARWaHQNUpAC2L2DVE49xxSJE2wfxfBcoHFM8+ghyKEgiFcs0jdl8jXbCquQ5td5sRknkjTpap4tIsLjE+8j4X+AV749lke7B4gFtLJ9OyiXM0yPz9F39AwwYjOgXd/gKf/6LfoPXYfJcuiWa8xP38dS6i0hyLbzueOELX3wGEO3n4nnZkMqViMoGjiNNfp6upDVgS6IhMLRwgGQuiGim2ZFLOrNFWVhXWHPWdf5HrdIGEohPcNgxxCUSV6hkaYPXGesOdSlHIUZYlIugOh6eQuHKe6NktvegTn/Et4wxmqlSJObhZj10E0BSxLI3jkzVy5eJLg8iW6BwZwjf2EFk6g+QLHVkiNDPBzd9zHuTNnGBibwKnPUDSXkAIpluev09M1yPT0RSKJEJrvEAvHKVx4jmvzcxy47YdQvca287kjRP3Ah3+Ons4OYvEokVAIq5BjzqowNDqMspmED0bCaL5Pdm2RU09+k7m8SXtnD0ND+2me+ipdhoG9mmV2YRZkD1fx2fPIO9iTjkGrRb3RoIpC9eJxZmsWwizRM34PhZXLiMoSu+//GfruafDk7/4XGtPTGG09aL6Ff+VJulZWSY9EyZWXuHx8hkf+7Ud44Q9+k6mvfo3xwQDBBz/M0996gp8aHqKrZwQtF0PEDPLFCr5wOXDoGFd1mfXVHFalim4tAn1kV64g0r1/Fz0/MHbEOnXv+G6GBwbo6+wmFY8TDAZoHx6jraOTTKaTZFs74XAEhMQXv/BVZgt1VCPAgSPHKC4t03vgLuxKiXp+llRPJ2O79sHUOYTngNfEbu/BdAXpPQdwmz7Beg49FIFqkaq5RkkLc+i+Rzl439t580c+itXege00KTotKo7NcMpgbqHKxVPTpIcHuPj5PyalCdqKeeYKEUKyQzgQ5stf+wqFYhlJKOiaQk9nGtn3MVdnEK6HIhscuus+cqEhbr/9CGv5MrFYctv53BGWOjI4iKbpqKqM5/oUZmfoGxknmcwg4yOAWrXMn3/qT2ihkEzG6ewf5qk//xTe1ZOs6nkuTJf56f/0nznztb+mIusUMrsIrBWpez5Xv/4JkNoo+zKLq9N03nYQc3YW15CRk8OEmysogTDl7Bq//bH/iwO7d/Hmt/wo0bYkX/vdj0OoxP5YH/f+2AjFQo5afpnlYoW5bz3NuCKzMj/Lr37k12mh05aKkV+f4eLFC+weGwdD58TpF3n4LT/MC0/8JS2zRiAdp1gvs3d0jD/5zKd5/098aFv53BGWGg4FCegqmqqiyTLF9RVCsTiSJCEUGdd1mJ+eQq5bdA/tJmhEmH36q7jLUwTFKks5C1mAkH3uf99PUq0skTZCKF3DlCWFeksnMjYBZgVPVOg4cATNExSXJkmJLE03RKNR4fc/+Xt49RqjB++krb0DoWr0iCJucoDl2cucOXsS2ywRicZJpRK0H76dkG7w4kuXCYTjxOJBKtUykXAnx+5+AKtep1Gr89Aj76ZZXefQsUdJJ0N4foPi+jypZITb9h/cdj53hKi6oqDJEsJ3cR2bufOnCEaC+DjU6zXqtkV1fZGGEAgfCsvXEU2T4ZTPStmn4hsosSQg47ktjjz8DjoGu1n59lMoSowoNfoyEeTaPKmeXlqBBOghwsEA/W/6UTqO3M0nf+w2vOlLTBw6yIHduylZTXwX7MNvQg7GGRvfQ3tzHuZOYpdWSJo53pCuUlq7ymjxHOdeeZHs6ipBI4hpVVmemcSSQoweup9IPMXJK6tUGx4dgwepSV309Q+gGhrnT7207XzuCPcrPAchqahCsL62gi5tRIQtq4HdaNK0myQ6uqiXn8bMv4ThmAwlTGanZyjKMWQstGgEz66AH6Fw/HNgGyzNz5E+2o8W1pk5/TX6j76byWvHyU5fJnXsGGvPllDbemhzGojkMO7aFL23f4hqqYoScDh75kW0lkULHy3eS3RPFNUXhCMyjm+wVm6Sls5jlVdwi2sUFZmBscM4agyC7aRll3OnnqJazPGOt7ydldVlzn7ra6iJKPGudiLJAL1Dw9vO544Q1cVHeC1aeJTWlxm86yEaNZOWVSeXXce1HRyrglRd5I37OwhGYjz/5VfIeRmkuI/c0jj2hr3UCmXyp08S9F2yZgNtZIxMbyeNUzqr64sMVS9w/4/9MpIBi0tr9O8/xvLiPOHOIQY/sJsnP/sJzKsnmHZraIqMW60QSmToTUdZL1QZHBhi5vokJ09fJBnPcOcb38n5Rpbo6O0I2WHxrz9BX/8ulvKrJOMRxo++kd1jo8xev0LTMzCCcQLVWZ48b3L4rqM8+TtfZWZl4e8m6AfEjkgTrtYbfqNRx/M8ps+cJBpLokdi+LbJenYZx7FpWU308hIXzs0jNfOk03Feni7QatmMdCa5663vY/Kz/wdi8DDJgRFKpSxmzSQUiMO55yil0sTa2zn60I9x7vIrBBUFz2nR1jlMtZijnF2nLSLztT/9r7z9J38R3YihSg5feeZFvHqTTG8fk2deZHZ6kkA0ze233Us2u0KtViHW1s+Pf/jn6Orp5vc+8Bak/mF+5T//Pt/61lPU1qdBCLp695DNF1DWnuV3vngN1/cx61mMeAdP/PUXtzVNuCNEvbqy6tuNBngO18+fpq2tA8fzaZgVzHqRUDCOojh4VoPcQoH2TCd+rcArX/w9AqEktUCE0Te/C0MPksutkUgmcWo1VpYWCRsahyduxzJSXLj0MoGQhttyOXLkQa5dOU/VXMcumwhZJZNK0NvZw8tf+WP69h9keOwQpgV/9In/l2I2i+T71C2TY/e/ncVskYDq0jd6iNuPvZHnvvEXjE8cQot1cenJTxGPhmkpCg3TZOjAvcysNyjNn2Dm9NM8c7lK764DYJYpLp7judPTN1/u17FtcCzsapNWdZ16LEJxNUt2aZae/lH0iknp2kusXX0ZX4uzoAaI9g+x50O/jNlwaPNkMh1d1Kplrs1McUcyxVqpSiCaIBSNk6vaTOwbZnHmPGcuXuLgnv08+9TXsV0X164x1NePrOq0h1SmZq5hGQkWTp1g6cSTtBLt7Orv53i2SCQSpVQps55bo7Q6Q87VEH6LYnGFsV2HWJm+zNvf/0YquTegO3VaeoTK4gX+/L99jCU7QSIZI54+RK36ZVZWlgj4ef6HX/0H/bbk98SOELWaXWPt8hkioRT5Fx5nobyKI+J4sSSFcASpo5fYPT+Ev/8OIpEU64U8A10ZmpaF7RUJhuKcu3qRnvY48ZRKqTZPf99uzGqO4V0TXD9/nhe/8WfIaoLe3iGiiSTNZh0hhxgbOcJado6l2escX1hEwWf00J1I5Vk0TaM0ew47kGKgLUI0FiEcOYyOQ3f/KN09PSyuF8CHSCzM2dNZTp06jeOqFEoOklrg3NQS3fvuwc5WUCqr3Hb7Ye44NM6Fcy8w1P9GOrv/1t+5+nthR4iaO/4VwpRZeHma5IHbSCd6aEkSshIik8kQiERoOE1a+XUWl6/R3zVEIh7n0qVLlOsnmF0KMj46QntiDU2KMTtrEpErqEYCx2mCY6HJOpWWIJNKYdYbxKMhJqcW2D0yQLlQoGHV6EnpzKyZ5C4/T6azGz2UxpZCGLJH78gIjhQnnF0iW8px98FeLLvC0TuOsbi4wtyFZ3nDmx5A0Q1cu8S1E5+nZ//9SI5Mhhrn8+v88F0jrC6u89A73o5rFVEVncvPfoV7771/W/ncEaLaHZ3U5EG09v14VhFPCVGplMGQODQwTDyZplStUSxVOLDvLly3SjFfoFwucfK0yfiBIbrbDSS3gK474DTIZ5fpGdpDMNZBpH2da3/5Hwjd+5MYwQ7saoGLV14h3dbBs898A4HG7OIC1fU59k1MYKldrFw7S7PXJxQNsba+xuLyEm/94Z8mEYmQbOsiFk4y+aXfZYUAzUaV/Ow0uYaEbZVIJ9uYXKhQsE/QJlmE976FD+w/hleZJaM1+dZXvoKkSXSmElSWt/83qHeEqC0pgqqr+L6NZARp+gJVVkknEoQjUVQgFUlwx6FjVCsF8qVlLMdFUVWOHjnE1auzvBLIMtg9RH5uDl3uIJrMEAzHaDkWQgsQEwK35VNcmqOyvoDs1Lh26SKqppEvN6g3LFyzSNWWEeWLBKIp6rkZeo48RtfBGDz1FVbXFjj74hN0ZjoYGX8AUwvTunicvW9+D3955hz3d/bwrWem+NoTz/K+dz5GLJ7gyKMfJLe2wPmnP49XqZLUHGJSANPxWL1+jo7BXdvO546Ifj/5yY/7qUwEWZKorq0jGxHml5fJdA9w277byGaXkVSVWCSJbZn0j+zh1OknkJQgU1cuoilRurtSNO0m6+vXCGmdpDtHaFTyZNcXkRSZ0vFnmVPBqVu4skwk1UN5bYnxiX3gNHnx9CnumNhN2/A+KqUlMokuXKvO4OgumgRYnjlPsVhD8WpoMiTbxqkuX+PK1dP45Rz5QA9Gso3bJ8bpHj1CR18PU5OTuI0CpYXLSLKO1PKQI4Jw5wTrkxcwlAaN3DIf/Ogf3YTRb8snHEzh1MsoeohkJMGCNE9IUbk+e4VacZVQIETTEew7fJiKbXHoyJuRgUyyi/7+EWZnrlKzTRTPIxTvprN7AN3YxfSlU7SaLUL3PUxw+RI9o4f46je+hNyskmprZ+INj3H5zNO0J6PULZvs7BU0XUc2VhnYfz/1+Ytk7QinX3wCxWinPSYzcOg+mlaDpmny2M//r5z4iz/Aq5YYGhtj7PAx5qeuszbzErmqhCFDW3svuhFjrbDKwnqBRHMBKRbi2oWLZIzX86OnPxh2RO53dWWORnkVYa3TcF3WqlUMPYRp25y+eIpYvJOugX3ccfudpIMhkpEoiVCYRCjCxL4DhEIhevtHaDggJ3qwPcH5Myf4iz/7PaamrmELg2h7J1oryNrqOvc99KMce+RHuOehtzJ/5SU0WWbv6CiKW2d410GWVhaI9tzB9PEnKWUdzNlz9HRNEJEcOu58K+H2AdJDB+jYc4hXjj9N0TforK/R0T/C5Mmn0XWF3GqBd/zsLyGJAHgGIhzl4X/183T191GtLiGhUyNGZGhi2/ncEaIe3DUEjXUqlkezYTE4NMaB/YfxXYemDZFUAlc1yFl1svUGNctmemGekmVTtGwuXLvCqdMnsUoVpGYDFRu7abFndJirkxc49/znMWRBQi0Q1cvYpWl816HVrBDSDKI4JOMZ9owfZOXE40yMHWDpzAvMXzyL7Xs41TW6hncxdu+jrF2exENgqDKN5SnqvsbUlUnQQ1x/4rMk0u2ke4YYPnAH1559gmRIRQ5HSKczfP2v/5Jyvki+UCTR2cPYxEHBctriAAATi0lEQVT8Znnb+dwR7hdRJdx1ALtQYN9oPysriyA8jICM6wqOHrmHuutRtEyaLYdabp1cdo3V5aWN7596HhFDIxpSyXQNEYslyX/9c5w4/gyPvftnUSWJ5QuPs/+Bezk/uUJYCxKJpmnmp2mWc7TMCna4l1repLRSpiFNo4ViSFqE8voi1Fs0Gg2stSXuf/M7aDXrzF18nnKxTPtgO7vHD6AYOoptMzs1S7VaJ6TLFNdzGJ2DaNEg1UadyRe+TDDWiWmrrC5MEzcUbD+67XTuDFFDXbiuheu2cByHYnGdzrZ2ZELcsW+U2VyeWDKGjuDalVdANmj6oGsq8USKaDyGoaksXbnAwuwUL849wfMvPsl9gyHsleeJpwbw9BhzBQW31SRbaWGIq9j5OarrDRq5VZRECytfxJfBLpfQtCBytAPbdhBGBNW30MIKpfU5csuL5C5PE5Y1yss5lIaPGg0R6Bghu7bGzLlJdNVl34PvQNNUquUicxdPc9eD7+ILX/gCu/bsIdQq4lZdQtpN+mnChuUTDHm0HAdNNRjfvRdNl5k89zJCMZi5eI6h8TEqhSLxTC+SJBOORLCtOoVSlpBl0PIcBkcnKBXW6e/OsGfPGM898zy/+18/w+/8P/+N8vQyxfllMkuTqNFuJMvFIYHmrVLOzbI0ex1aLu233U+qrY1mbpVm1cQ2VGQkAkjooX4mXzpFbmWBA31paNrkzAaSBL4exw2E0HBpmcuUGjZLq8skQhKp3jFyaznOnjxHRzpCNGgwODhIT0831eXpbedzR4jabBRR5BKDQxM06hU0w6ViQlt7F5qeZHJ2hl37J0h1dSNLUC1XqZXypNvakXWFhBaknFtF605RWp1CD8V55fjTfOnZF3nHO9/N8eNP0dMep1ZrEB3bSzgxQPnaFEIV2KUiIhBHqa/R8ipEEh3IRhRr/ds4dZ/0+GEU2YP1HG71DMHCGkP9+3ArWSq1EkLvQ0GQXZjDXV5Gyk2h+w3CjQLFl7/KaqKT+S99iZa/QCBxhKDnkogEaOsfRo/HqJZv0k8TDgzuZ37hHNHIIr5fR7cldGOAjqGDpNq72HX4TnxaLC4uI6sy7e3duI6Ni0RnKIFZXOfrz7/Iw/dq5PKLyKbgz7/8JX78ve/hoTe9DUWP4EkS5088zcK3PktswGJlbgXZsSC7hNK00TSFoBbAy05TmW1QuHacqtXAy/QwuO8QiXCAoDtE8eRfUVy4ykx+gba73oo/P4+ES8iuY5ZK5At1VLVFJq6glBaR2kd58K3vxLElXnn5GVqKzOJahduNML4UpOluf55gR4iazy9z+20P4Lh1rl+7QDw+Rlv/MJoqIekS5VIZ0RLEkkkSkTABLUgRcFybudwy2DbxQIRSYZmFtRJLazM89ug7OHrnw3R29VEyTUzLxm42kTwHJzFA011FblRp1UsI28KVIijxCM21i1hmEyWYIKIV8CpZ6rUa4UgIzzNwjTbs7DS2YlDPzeNaZTRdxW1mEU4NPWDgtgStpoUWDLHn6N2kOrpZnL1KZ187vaP3UC6sceHSGVJBg0hme79wDCD/xm/8xrY3+oNiZu7sb7RaEl7LZ/eeO9g1MkYwGKRWqVDM5lA1A6FJBGVBtdHAMIJUTBPF9fipn3gPiViEoATtuye46+i9hBJJ9uw/REtWmF9b2fjScssmFg4STqhUmjJrk5cI6h74LZpykoqQcTtHGL/3UcxqnqakEgrF0Yon8RyNRrVE/uoJHMcnOnoH8USIwuI00aFxbn/Xz2BFejFbOkKSwTNRtCBaKEm8ZwQhWpiFLMvLc5TyWazGCl4jR3llllypyf6Dt/1v28nnjrDUirkIvs7Ro28iYgSoVEq89O3jXJuaZHxkL6phkIpqFFyF3tFD1JsWsucwe+UCj739IarmEq7IkF9dQlEN4m2daHadRtMhJElEgzJW3aFsN1luhrh2+TwtRQVFQkggFJsDd9/H7kfeT2N2khVcbMcH30OSNdzVbxPrfIzIQCfZfBVVdZAln/bdBxl4079CT7UzdjRNz/h+bLOB3WygCI/BkQkMWeHlz32UvC2T7uyjXKkgCRW5nCXfkjDU/LbzuSNEPXbn+wmFIzQbTa7nF2nUqpz+9mlGhrro2rUXu1Qm3N1NBIl606a8NENAV8kXC7zy8mnuecvbGO4YwgjqzF8+SWFlmnRPD8lIhszgPjLRIJVQC8NQWTz5AtbVi3S1xTACBn4kSJw67XvvQpQWCbZdQY4H6dc9ggPHmHnpmyBkWuV55Eyctj0H2HXXg1SrVXZlurGBgBEgIIWIJqKEdA2v1cKxbFrIXL9+kaofJBwJIimCoiORyvQQa2aJxQXdXfq287kjRNV0HTybVCJGtWFybvIShw/uJ5hIEdGDpPZ0kc/lmZs5x+r8VaRAJ5/69KcZG+3A9TyioTaunj3LG37orSjqKrKmMbr/Hpr1Ok7TpGopnD75HMtreZz1FWKyT6h4kWDyDXhSCMkN4rRcDu57mT/85Iv4rSCRUIzeQ/fRse9uatUydmkdI5Ig0jGI22oRjSepldZZW52ho2uEWKYb2QOzUSa7NMPU9bNUak0CgQTBzg6CXotGtUrcXSNkLxFIxwmG6vju1LbzuSN2aWqe6zumiR4IUbYs6vUa0WAUTRK8cPxZ2jsjfOa/f5zduw9x7fIk4WScvq5eynadkJ5gfOIw5fwkvu8x0ncMOREnoqnUnQaoCpVsiY9/7KMcOHw/9z70JmZPfJNadgW3UiAxOk440kH77n1Us/Osz3yay88vEQlHUXv30jYwTjDWhqaqyLKCWcpRLi9i1ZYwjCiW4/HY236GqzMzyHqA4uoM2dUpUGM0qnlaQiG/fAnVl+hKJImGGyCu01K7ULUi+azLez74xzffLo1Zr6HrBpbvEdA0FMJITZO5xSkKxfPIYoBotI2VlTnUsMbM9TmmrlyHlsPtdx3hLz/5h9x+3wT5uQZH7umjWirwRx//GKoa5q1veyerC0v88m/8FktXL5FfXiLYNYSR7MWcP4dmRKhbTV546nHau7opFNqoNGeQVYtAYY1ivAuz1iSZSuD7Pno0jp2fIru6SiIjoQUH0DSd5fkVOgYyNMoFUuk+Wq6P5LsUVs4Skl1CMfCoItQ2cEI0GlWqFZO5xdi287kjRG2YDSrlGnNXL3Fg3zhTV04zs3iVaqvF3OV57r5niKmpBfaMJxFOiLmZaX7+F3+JE09/iUq+zuBojKCsEx1xOffKKcYGRtg/fhi3UaZULuLWGuSuXkGRBBI+sbY2ytl1Qn1j4PuoWoSo10KRBVdn11lvavjRGO2qTmNtCto6uHZtEUVuYoR7EX6I/t1H8IiAJ/HlL3+TSvk69dokurqO7A6SzZo41SuE9SJasoPFqbOM7x8iE1UoV7tw2I0vxbhzonvb+dwRor74zOPokkdnKsE3vvllJi/NMDCcYHFqntvuOEClss6u0V1IfoOT375ILBjnxLe+TPfQMMlED/FUhL6BA7R8iXQszvrSNIbbxMxlWc0X8RyXubPrhEZ3s+eBt+Hjokgac2dOIAcgGIogrAqO6xAO6ay4Lno4ii/LVBanqNcrVKUQ1y9f5OCd7bQnwoRDKV547hlCsTTVcp5MR4xSbpZKfhJNP0soqhPVG5glC7teJBBMkUxkCEQEXmiCtshBLLtOsntg2/ncEaLuGd3F6SvnMBefZG5Vp29vJ9dOX2LPxAFmL59jz26LsbHbyK4vUa9WePRHHmZ0YC9r165z9ZqJtliksZJjbN+dXDjzHIYaorWygOtJ2IU17GqO0tocq5fOYjsqew5NUDj3bernnif5yIdpNm3s4gqVepny5HmilSqtfJLVqopuOzB1lhW5h7373sDlMy8ixiaIx+Ioqo5dX0Z3l1hdqqHJPuk2nUouz9yUSsgI0D8Sp1QwaW/XUNQItisTjfehR+M46xaN7CLE9mwrnztCVKIdaM2vk2/EWbxykaWlEg8/eD+l4jy7Dt/LJ/+/P+GhH24R6xzjw784jh4IcnD8COauw4RPn2Tp8jl6B48w89zj2OurEAngBNP4koxvVWg1KyiqjtOoUbz4IvlMCNeI08gXsBsmrVqFRrHA4uIcudVVwqEgilXHNRtoyRTNukImFsdt2WBZFNaWqHWniQdBal7HSNdZrwhkXaWYXaVmWYSNCD1dETq6RwnFVdJJFdfTweukZjqYboGmVcet1tn4D9vbhx0hamH+OgvTi6QH+jh07ChjQ2MohiAcGqKeX+UnfvIDhOMpzj73LE+dnOKXf+2XqXkKC5dP0h7RiY+Nknvl27TMBi2zhO2YWOU6zaaNalVRgjq6qtNyTKxSGT3eRXHx27S8GssvPU2rtkZhfYkrqxXOz2fpz6SRIxZt6RSSaZGI95DNTXFt+hIDB++nu6sNs/okujyPEa5t/EydJIHeoNQsM9zfRaqjF0kJIUkuciSAobVYmp/CxEMSDeKxHJ5j4zkm+7lvW/ncEZ98eOnpz+OrUSRXRfcr1At5jj/7DNFYiHRXH2a1gFnIM37b3SjNPH/6W/+eT/zHX+L6y09TXlujsbiO2wK3WqKVW6GxtkBjfYlqdp2qVafZcml5LkJWkQIbbrPVbCDiaWrLV3BLs8hygM4jPwTBJFfydfy2Xkh00xQhUGOEWia9iSBBqYpvvUQ606RzoAfJaMf1ZDKdKWKRGI6ngOcTDsVxfQXTrqOpTSRVpmbXWZxZRVYMfBQK2XWuXLi67XzuCEt9w2OP8NyLZynnlmjrGMUL+dz3xgdYXq5gGIK9+9+IYxb5wp/83wzGfCIS7H3T2+kc2gX1Mvnsy7TKa9jrszQrS9SdFk6oi5oWwHMgogfQJAdbUhk5cg+S20AJRJh4979m8sQp6pOPE44lqXomj77jRyjmSvSN7UNWFa6UmhTLFdLhdgKOiT/zLLWQQigyQSydJtGVxios4rpQrQeYWuqkvSOJrBn4lk2jYNEMKXhVj7V1CduRaTktVpeWWF9ZRpJu0k3ywlKd3Z0d4EbpmThIaWGGqfPTjB4+xr7RcU4+/zlWCzmKuQbD6TR9Rx+ic2gEx3GgbiKrKrIexPclKk0bJA0tmcDwNSyzSstuoGgK0c4uOnftQgkGKC5fwY/34TsmasAgl19mpVYk3DdEV2c758+eJGAYxBIpevt3U1meRUHBXz1PMGyBWaTumSiqS7VsIoeDhGJt3P9gH4kYtCwZq1TGKnnUizlKDUGpAK67zoznEA1HiIZjKIGbNE0YjaUYGz5AW3sv108/x96738Kv/8x7+Ks//QTxtjSGEeP9v/ArxN6SxKpV6BnaTSjdjicLtO4MgUiS6ce/QlMxsNUQejRDJN6BJGlUhQJOHdn3wXdZfPlxYp1xjLCOuXqaWHcHlTUDVXHYf/+jaKxitzwCI7vZtXcvqbY0qqxwDkEuu4blOSQSEqG0ikeL8opJs+FhuhWiShBVlTBLCma1gaZF6BlsUaz6FK600BSDrr4u4qkU8UQ7jtsiXyptO587QtRzp75BZbmPrt5Rzp1+gU9+8hOcvTpDKp5k7NDd3PXgw6QzPYR0g0AkghIKogQN8CRUVSMZaSOdHOD4Zz9BtZLDCMRQFQMtFMN1HOo5E7/ewHZmkQYHqK2ukJ++ihyKE6xXoNUgnGhD11oEIrsIxVKkOjoIhsIUs+vUnRaBcAzWFwnFo3iuhlCDNMs5XC1K06oQVDzW1qaw7Qh6OESqe5BQJIzqNZGbFrGESiYQp729h1RXH6pqUC6XyF+/vu187ghRR/tHsHydnFWnfbSX/sP387YP64QjIdA0NNUgkkkRTsdRJZXc1csULZN4/yDtkShyUBCPhLnnp36ac9/MYGeX8SwB0sZvSTiKTqNaRzdCFK5fQtE13EqRcO8omiwjBQJE9xyl69AbicTSCN+l6TrkVle4NjOD06wzPzVJJFilt1cjrMrkcwu8dNzijQ/tYWRXDy3X5OLFy+RKJnuHdhOMp/GcJqtFj3ojTd/oMMFQEN0IoWoBJF2nsWZz6eKFbedzR4hqezbxdBue4xPNjOPjo9FCxQV3Y8vMMcskEmloeciRELmXnsQ2C3jDe9ECOo1KHdu26Tt8J6vnXsZeKyP0AJpZRRPg+NC0C+gtkBUNSdUQwRjIEkg6oa4RZE1DUsBr+VTMCrlCAUkPUM1maZhNcktF5GqVnq4ERbvOnjGD7q4QiXiI+ZUSa8tNYqkkhhHDdlKUckVyKyZtXe2EIlGEkDDNOmbDxvVcyuUC9Vpt2/ncEaL2HngARZVxbRu5aYERANvEbflohk7T9bFrJpMnj5NMxOkc2Ev68F3kLr5E6fpptI4RpHAY06wj+VCav05IimJEYwS7OpALPpGWTb1kYdVBNST6HvsIzeoUbtMiMHgHHmAWShRWl1iYn2JlNYdiBJAMHbNWJhDWaDohTk1ZnLmao6+/jaGhKLlsk4YdQdbvY//Re9GMBh4mK8smpWydqWvLQBRNDwE+a8srzC0uYdlNarUyrvs3/xXaPxQ7YuvtFrYXOyL5cAvbi1ui3oS4JepNiFui3oS4JepNiFui3oS4JepNiFui3oS4JepNiFui3oS4JepNiFui3oS4JepNiFui3oS4JepNiFui3oS4JepNiFui3oS4JepNiFui3oS4JepNiFui3oS4JepNiP8fCXYm1uuWj/AAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11260d2e8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115056550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112b91208>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11205d748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112bd5c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"visualize_model(model_conv)\n",
"\n",
"plt.ioff()\n",
"plt.show()"
]
},
{
"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": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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| mit |
christophmark/bayesloop | docs/source/tutorials/priordistributions.ipynb | 1 | 41072 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prior distributions\n",
"\n",
"One important aspect of Bayesian inference has not yet been discussed in this tutorial: [prior distributions](https://en.wikipedia.org/wiki/Prior_probability). In Bayesian statistics, one has to provide probability (density) values for every possible parameter value *before* taking into account the data at hand. This prior distribution thus reflects all *prior* knowledge of the system that is to be investigated. In the case that no prior knowledge is available, a *non-informative* prior in the form of the so-called [Jeffreys prior](https://en.wikipedia.org/wiki/Jeffreys_prior) allows to minimize the effect of the prior on the results. The next two sub-sections discuss how one can set custom prior distributions for the parameters of the observation model and for hyper-parameters in a hyper-study or change-point study."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ Created new study.\n",
"+ Successfully imported example data.\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt # plotting\n",
"import seaborn as sns # nicer plots\n",
"sns.set_style('whitegrid') # plot styling\n",
"\n",
"import numpy as np\n",
"import bayesloop as bl\n",
"\n",
"# prepare study for coal mining data\n",
"S = bl.Study()\n",
"S.loadExampleData()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parameter prior\n",
"\n",
"*bayesloop* employs a forward-backward algorithm that is based on [Hidden Markov models](http://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf). This inference algorithm iteratively produces a parameter distribution for each time step, but it has to start these iterations from a specified probability distribution - the parameter prior. All built-in observation models already have a predefined prior, stored in the attribute `prior`. Here, the prior distribution is stored as a Python function that takes as many arguments as there are parameters in the observation model. The prior distributions can be looked up directly within `observationModels.py`. For the `Poisson` model discussed in this tutorial, the default prior distribution is defined in a method called `jeffreys` as\n",
"```\n",
"def jeffreys(x):\n",
" return np.sqrt(1. / x)\n",
"```\n",
"corresponding to the non-informative Jeffreys prior, $p(\\lambda) \\propto 1/\\sqrt{\\lambda}$. This type of prior can also be determined automatically for arbitrary user-defined observation models, see [here](customobservationmodels.html#Sympy.stats-random-variables).\n",
"\n",
"### Prior functions and arrays\n",
"\n",
"To change the predefined prior of a given observation model, one can add the keyword argument `prior` when defining an observation model. There are different ways of defining a parameter prior in *bayesloop*: If `prior=None` is set, *bayesloop* will assign equal probability to all parameter values, resulting in a uniform prior distribution within the specified parameter boundaries. One can also directly supply a Numpy array with prior probability (density) values. The shape of the array must match the shape of the parameter grid! Another way to define a custom prior is to provide a function that takes exactly as many arguments as there are parameters in the defined observation model. *bayesloop* will then evaluate the function for all parameter values and assign the corresponding probability values.\n",
"\n",
"<div style=\"background-color: #e7f2fa; border-left: 5px solid #6ab0de; padding: 0.5em; margin-top: 1em; margin-bottom: 1em\">\n",
"**Note:** In all of the cases described above, *bayesloop* will re-normalize the provided prior values, so they do not need to be passed in a normalized form. Below, we describe the possibility of using probability distributions from the SymPy stats module as prior distributions, which are not re-normalized by *bayesloop*.\n",
"</div>\n",
"\n",
"Next, we illustrate the difference between the Jeffreys prior and a flat, uniform prior with a very simple inference example: We fit the coal mining example data set using the `Poisson` observation model and further assume the rate parameter to be static:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ Transition model: Static/constant parameter values. Hyper-Parameter(s): []\n",
"Fit with built-in Jeffreys prior:\n",
"+ Observation model: Poisson. Parameter(s): ['accident_rate']\n",
"+ Started new fit:\n",
" + Formatted data.\n",
" + Set prior (function): jeffreys. Values have been re-normalized.\n",
"\n",
" + Finished forward pass.\n",
" + Log10-evidence: -88.00564\n",
"\n",
" + Finished backward pass.\n",
" + Computed mean parameter values.\n",
"-----\n",
"\n",
"Fit with custom flat prior:\n",
"+ Observation model: Poisson. Parameter(s): ['accident_rate']\n",
"+ Started new fit:\n",
" + Formatted data.\n",
" + Set prior (function): <lambda>. Values have been re-normalized.\n",
"\n",
" + Finished forward pass.\n",
" + Log10-evidence: -87.98915\n",
"\n",
" + Finished backward pass.\n",
" + Computed mean parameter values.\n"
]
}
],
"source": [
"# we assume a static rate parameter for simplicity\n",
"S.set(bl.tm.Static())\n",
"\n",
"print 'Fit with built-in Jeffreys prior:'\n",
"S.set(bl.om.Poisson('accident_rate', bl.oint(0, 6, 1000)))\n",
"S.fit()\n",
"jeffreys_mean = S.getParameterMeanValues('accident_rate')[0]\n",
"print('-----\\n')\n",
" \n",
"print 'Fit with custom flat prior:'\n",
"S.set(bl.om.Poisson('accident_rate', bl.oint(0, 6, 1000), \n",
" prior=lambda x: 1.))\n",
"# alternatives: prior=None, prior=np.ones(1000)\n",
"S.fit()\n",
"flat_mean = S.getParameterMeanValues('accident_rate')[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First note that the model evidence indeed slightly changes due to the different choices of the parameter prior. Second, one may notice that the posterior mean value of the flat-prior-fit does not exactly match the arithmetic mean of the data. This small deviation shows that a flat/uniform prior is not completely non-informative for a Poisson model! The fit using the Jeffreys prior, however, succeeds in reproducing the *frequentist* estimate, i.e. the arithmetic mean:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"arithmetic mean = 1.69090909091\n",
"flat-prior mean = 1.7\n",
"Jeffreys prior mean = 1.69090909091\n"
]
}
],
"source": [
"print('arithmetic mean = {}'.format(np.mean(S.rawData)))\n",
"print('flat-prior mean = {}'.format(flat_mean))\n",
"print('Jeffreys prior mean = {}'.format(jeffreys_mean))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### SymPy prior\n",
"\n",
"The second option is based on the [SymPy](http://www.sympy.org/en/index.html) module that introduces symbolic mathematics to Python. Its sub-module [sympy.stats](http://docs.sympy.org/dev/modules/stats.html) covers a wide range of discrete and continuous random variables. The keyword argument `prior` also accepts a list of `sympy.stats` random variables, one for each parameter (if there is only one parameter, the list can be omitted). The multiplicative joint probability density of these random variables is then used as the prior distribution. The following example defines an exponential prior for the `Poisson` model, favoring small values of the rate parameter: "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ Observation model: Poisson. Parameter(s): ['accident_rate']\n",
"+ Started new fit:\n",
" + Formatted data.\n",
" + Set prior (sympy): exp(-x)\n",
"\n",
" + Finished forward pass.\n",
" + Log10-evidence: -87.94640\n",
"\n",
" + Finished backward pass.\n",
" + Computed mean parameter values.\n"
]
}
],
"source": [
"import sympy.stats\n",
"S.set(bl.om.Poisson('accident_rate', bl.oint(0, 6, 1000), \n",
" prior=sympy.stats.Exponential('expon', 1)))\n",
"S.fit()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that one needs to assign a name to each `sympy.stats` variable. In this case, the output of *bayesloop* shows the mathematical formula that defines the prior. This is possible because of the symbolic representation of the prior by `SymPy`.\n",
"\n",
"<div style=\"background-color: #e7f2fa; border-left: 5px solid #6ab0de; padding: 0.5em; margin-top: 1em; margin-bottom: 1em\">\n",
"**Note:** The support interval of a prior distribution defined via SymPy can deviate from the parameter interval specified in *bayesloop*. In the example above, we specified the parameter interval ]0, 6[, while the exponential prior has the support ]0, $\\infty$[. SymPy priors are not re-normalized with respect to the specified parameter interval. Be aware that the resulting model evidence value will only be correct if no parameter values outside of the parameter boundaries gain significant probability values. In most cases, one can simply check whether the parameter distribution has sufficiently *fallen off* at the parameter boundaries.\n",
"</div>\n",
"\n",
"## Hyper-parameter priors\n",
"\n",
"As shown before, [hyper-studies](hyperstudy.html) and [change-point studies](changepointstudy.html) can be used to determine the full distribution of hyper-parameters (the parameters of the transition model). As for the time-varying parameters of the observation model, one might have prior knowledge about the values of certain hyper-parameters that can be included into the study to refine the resulting distribution of these hyper-parameters. Hyper-parameter priors can be defined just as regular priors, either by an arbitrary function or by a list of `sympy.stats` random variables.\n",
"\n",
"In a first example, we return to the simple change-point model of the coal-mining data set and perform to fits of the change-point: first, we specify no hyper-prior for the time step of our change-point, assuming equal probability for each year in our data set. Second, we define a Normal distribution around the year 1920 with a (rather unrealistic) standard deviation of 5 years as the hyper-prior using a SymPy random variable. For both fits, we plot the change-point distribution to show the differences induced by the different priors:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fit with flat hyper-prior:\n",
"+ Created new study.\n",
" --> Hyper-study\n",
" --> Change-point analysis\n",
"+ Successfully imported example data.\n",
"+ Observation model: Poisson. Parameter(s): ['accident_rate']\n",
"+ Transition model: Change-point. Hyper-Parameter(s): ['tChange']\n",
"+ Detected 1 change-point(s) in transition model: ['tChange']\n",
"+ Set hyper-prior(s): ['uniform']\n",
"+ Started new fit.\n",
" + 109 analyses to run.\n",
"\n",
" + Computed average posterior sequence\n",
" + Computed hyper-parameter distribution\n",
" + Log10-evidence of average model: -75.71555\n",
" + Computed local evidence of average model\n",
" + Computed mean parameter values.\n",
"+ Finished fit.\n"
]
},
{
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"text/plain": [
"<matplotlib.figure.Figure at 0xc751dd8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-----\n",
"\n",
"Fit with custom normal prior:\n",
"+ Transition model: Change-point. Hyper-Parameter(s): ['tChange']\n",
"+ Detected 1 change-point(s) in transition model: ['tChange']\n",
"+ Set hyper-prior(s): ['sqrt(2)*exp(-(x - 1920)**2/50)/(10*sqrt(pi))']\n",
"+ Started new fit.\n",
" + 109 analyses to run.\n",
"\n",
" + Computed average posterior sequence\n",
" + Computed hyper-parameter distribution\n",
" + Log10-evidence of average model: -80.50692\n",
" + Computed local evidence of average model\n",
" + Computed mean parameter values.\n",
"+ Finished fit.\n"
]
},
{
"data": {
"image/png": 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8dPpIx2cQ0DzqFL32XKt9+/apprpGQWewQVttda127typvXv3tspY2nOd2kpj70/VoSpJ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\nKFqMAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xc809d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print 'Fit with flat hyper-prior:'\n",
"S = bl.ChangepointStudy()\n",
"S.loadExampleData()\n",
"\n",
"L = bl.om.Poisson('accident_rate', bl.oint(0, 6, 1000))\n",
"T = bl.tm.ChangePoint('tChange', 'all')\n",
"\n",
"S.set(L, T)\n",
"S.fit()\n",
"\n",
"plt.figure(figsize=(8,4))\n",
"S.plot('tChange', facecolor='g', alpha=0.7)\n",
"plt.xlim([1870, 1930])\n",
"plt.show()\n",
"print('-----\\n')\n",
" \n",
"print 'Fit with custom normal prior:'\n",
"T = bl.tm.ChangePoint('tChange', 'all', prior=sympy.stats.Normal('norm', 1920, 5))\n",
"S.set(T)\n",
"S.fit()\n",
"\n",
"plt.figure(figsize=(8,4))\n",
"S.plot('tChange', facecolor='g', alpha=0.7)\n",
"plt.xlim([1870, 1930]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we used a quite narrow prior (containing a lot of information) in the second case, the resulting distribution is strongly shifted towards the prior. The following example revisits the two break-point-model from [here](changepointstudy.html#Analyzing-structural-breaks-in-time-series-models) and a linear decrease with a varying slope as a hyper-parameter. Here, we define a Gaussian prior for the slope hyper-parameter, which is centered around the value -0.2 with a standard deviation of 0.4, via a lambda-function. For simplification, we set the break-points to fixed years."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+ Created new study.\n",
" --> Hyper-study\n",
"+ Successfully imported example data.\n",
"+ Observation model: Poisson. Parameter(s): ['accident_rate']\n",
"+ Transition model: Serial transition model. Hyper-Parameter(s): ['slope', 't_1', 't_2']\n",
"+ Set hyper-prior(s): ['<lambda> (re-normalized)', 'uniform', 'uniform']\n",
"+ Started new fit.\n",
" + 30 analyses to run.\n",
"\n",
" + Computed average posterior sequence\n",
" + Computed hyper-parameter distribution\n",
" + Log10-evidence of average model: -74.84129\n",
" + Computed local evidence of average model\n",
" + Computed mean parameter values.\n",
"+ Finished fit.\n"
]
}
],
"source": [
"S = bl.HyperStudy()\n",
"S.loadExampleData()\n",
"\n",
"L = bl.om.Poisson('accident_rate', bl.oint(0, 6, 1000))\n",
"T = bl.tm.SerialTransitionModel(bl.tm.Static(),\n",
" bl.tm.BreakPoint('t_1', 1880),\n",
" bl.tm.Deterministic(lambda t, slope=np.linspace(-2.0, 0.0, 30): t*slope, \n",
" target='accident_rate',\n",
" prior=lambda slope: np.exp(-0.5*((slope + 0.2)/(2*0.4))**2)/0.4),\n",
" bl.tm.BreakPoint('t_2', 1900),\n",
" bl.tm.Static()\n",
" )\n",
"\n",
"S.set(L, T)\n",
"S.fit()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, note that you can mix SymPy- and function-based hyper-priors for nested transition models."
]
}
],
"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": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
supergis/git_notebook | pystart/pystart_filewrite.ipynb | 1 | 1183 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f = open(\"test.txt\",'w')\n",
"f.write(\"a file test string.\")\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"a file test string.\n"
]
}
],
"source": [
"f = open(\"test.txt\",'r')\n",
"astring = f.read()\n",
"f.close()\n",
"print(astring)"
]
},
{
"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 |
Ulm-IQO/qudi | notebooks/fit_testing_poissonian.ipynb | 4 | 5306 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from lmfit import Parameters\n",
"import matplotlib.pyplot as plt\n",
"from scipy.interpolate import InterpolatedUnivariateSpline\n",
"from scipy.signal import wiener, gaussian\n",
"from scipy.ndimage import filters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Set this flag to True if you want to plot the results\n",
"plot_results = False\n",
"# This is the number of repetitions for each test function\n",
"repetitions = 100"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def poissonian_testing():\n",
" start=0\n",
" stop=30\n",
" mu=8\n",
" num_points=1000\n",
" x = np.array(np.linspace(start, stop, num_points))\n",
" mod, params = fitlogic.make_poissonian_model()\n",
"\n",
" p=Parameters()\n",
" p.add('mu',value=mu)\n",
" p.add('amplitude',value=200.)\n",
"\n",
" data_noisy=(mod.eval(x=x,params=p) *\n",
" np.array((1+0.001*np.random.normal(size=x.shape) *\n",
" p['amplitude'].value ) ) )\n",
"\n",
" #make the filter an extra function shared and usable for other functions\n",
" gaus=gaussian(10,10)\n",
" data_smooth = filters.convolve1d(data_noisy, gaus/gaus.sum(),mode='mirror')\n",
"\n",
"\n",
" result = fitlogic.make_poissonian_fit(x, data_noisy, estimator=fitlogic.estimate_poissonian)\n",
"\n",
" if plot_results:\n",
" plt.figure()\n",
" plt.plot(x, data_noisy, '-b', label='noisy data')\n",
" plt.plot(x, data_smooth, '-g', label='smoothed data')\n",
" plt.plot(x,result.init_fit,'-y', label='initial values')\n",
" plt.plot(x,result.best_fit,'-r',linewidth=2.0, label='fit')\n",
" plt.xlabel('counts')\n",
" plt.ylabel('occurences')\n",
" plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode=\"expand\", borderaxespad=0.)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in range(repetitions):\n",
" poissonian_testing()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def double_poissonian_testing():\n",
" \"\"\" Testing of double poissonian with self created data.\n",
" First version of double poissonian fit.\"\"\"\n",
"\n",
" start=100\n",
" stop=300\n",
" num_points=int((stop-start)+1)*100\n",
" x = np.linspace(start, stop, num_points)\n",
"\n",
" # double poissonian\n",
" mod,params = fitlogic.make_poissoniandouble_model()\n",
" parameter=Parameters()\n",
" parameter.add('p0_mu',value=200)\n",
" parameter.add('p1_mu',value=240)\n",
" parameter.add('p0_amplitude',value=1)\n",
" parameter.add('p1_amplitude',value=1)\n",
" data_noisy = ( np.array(mod.eval(x=x,params=parameter)) *\n",
" np.array((1+0.2*np.random.normal(size=x.shape) )*\n",
" parameter['p1_amplitude'].value) )\n",
"\n",
"\n",
" #make the filter an extra function shared and usable for other functions\n",
" gaus=gaussian(10,10)\n",
" data_smooth = filters.convolve1d(data_noisy, gaus/gaus.sum(),mode='mirror')\n",
"\n",
" result = fitlogic.make_poissoniandouble_fit(x, data_noisy, estimator=fitlogic.estimate_poissoniandouble)\n",
"\n",
" if plot_results:\n",
" plt.figure()\n",
" plt.plot(x, data_noisy, '-b', label='noisy data')\n",
" plt.plot(x, data_smooth, '-g', label='smoothed data')\n",
" plt.plot(x,result.init_fit,'-y', label='initial values')\n",
" plt.plot(x,result.best_fit,'-r',linewidth=2.0, label='fit')\n",
" plt.xlabel('counts')\n",
" plt.ylabel('occurences')\n",
" plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3, ncol=2, mode=\"expand\", borderaxespad=0.)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in range(repetitions):\n",
" double_poissonian_testing()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Qudi",
"language": "python",
"name": "qudi"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": "3.6.0"
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
mbeyeler/opencv-machine-learning | notebooks/08.04-Implementing-Agglomerative-Hierarchical-Clustering.ipynb | 1 | 16025 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"<!--BOOK_INFORMATION-->\n",
"<a href=\"https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv\" target=\"_blank\"><img align=\"left\" src=\"data/cover.jpg\" style=\"width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;\"></a>\n",
"*This notebook contains an excerpt from the book [Machine Learning for OpenCV](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv) by Michael Beyeler.\n",
"The code is released under the [MIT license](https://opensource.org/licenses/MIT),\n",
"and is available on [GitHub](https://github.com/mbeyeler/opencv-machine-learning).*\n",
"\n",
"*Note that this excerpt contains only the raw code - the book is rich with additional explanations and illustrations.\n",
"If you find this content useful, please consider supporting the work by\n",
"[buying the book](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv)!*"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"<!--NAVIGATION-->\n",
"< [Classifying handwritten digits using k-means](08.03-Classifying-Handwritten-Digits-Using-k-Means.ipynb) | [Contents](../README.md) | [9. Using Deep Learning to Classify Handwritten Digits](09.00-Using-Deep-Learning-to-Classify-Handwritten-Digits.ipynb) >"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Implementing Agglomerative Hierarchical Clustering\n",
"\n",
"Although OpenCV does not provide an implementation of agglomerative hierarchical\n",
"clustering, it is a popular algorithm that should, by all means, belong to our machine\n",
"learning repertoire.\n",
"\n",
"We start out by generating 10 random data points, just like in the previous figure:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from sklearn.datasets import make_blobs\n",
"X, y = make_blobs(n_samples=10, random_state=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the familiar statistical modeling API, we import the `AgglomerativeClustering`\n",
"algorithm and specify the desired number of clusters:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from sklearn import cluster\n",
"agg = cluster.AgglomerativeClustering(n_clusters=3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Fitting the model to the data works, as usual, via the `fit_predict` method:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"labels = agg.fit_predict(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can generate a scatter plot where every data point is colored according to the predicted\n",
"label:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x23811a45908>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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2xowCTDawyFUAOAgj6uvjlGuvjZ9feWXOx0+59trd/3pxD7Ld3dHT2dnf4x2QYUceGfN/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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x2380ff52438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.style.use('ggplot')\n",
"plt.figure(figsize=(10, 6))\n",
"plt.scatter(X[:, 0], X[:, 1], c=labels, s=100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"That's it! This marks the end of another wonderful adventure."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"<!--NAVIGATION-->\n",
"< [Classifying handwritten digits using k-means](08.03-Classifying-Handwritten-Digits-Using-k-Means.ipynb) | [Contents](../README.md) | [9. Using Deep Learning to Classify Handwritten Digits](09.00-Using-Deep-Learning-to-Classify-Handwritten-Digits.ipynb) >"
]
}
],
"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 |
markovmodel/adaptivemd | examples/rp/3_example_adaptive.ipynb | 2 | 22764 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AdaptiveMD\n",
"\n",
"## Example 3 - Running an adaptive loop"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys, os"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# stop RP from printing logs until severe\n",
"# verbose = os.environ.get('RADICAL_PILOT_VERBOSE', 'REPORT')\n",
"os.environ['RADICAL_PILOT_VERBOSE'] = 'ERROR'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/jan-hendrikprinz/anaconda/lib/python2.7/site-packages/radical/utils/atfork/stdlib_fixer.py:58: UserWarning: logging module already imported before fixup.\n",
" warnings.warn('logging module already imported before fixup.')\n"
]
}
],
"source": [
"from adaptivemd import (\n",
" Project,\n",
" Event, FunctionalEvent,\n",
" File\n",
")\n",
"\n",
"# We need this to be part of the imports. You can only restore known objects\n",
"# Once these are imported you can load these objects.\n",
"from adaptivemd.engine.openmm import OpenMMEngine\n",
"from adaptivemd.analysis.pyemma import PyEMMAAnalysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's open our `test` project by its name. If you completed the first examples this should all work out of the box."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"project = Project('test')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Open all connections to the `MongoDB` and `Session` so we can get started."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> An interesting thing to note here is, that since we use a DB in the back, data is synced between notebooks. If you want to see how this works, just run some tasks in the last example, go back here and check on the change of the contents of the project."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see where we are. These numbers will depend on whether you run this notebook for the first time or just continue again. Unless you delete your project it will accumulate models and files over time, as is our ultimate goal."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<StoredBundle with 122 file(s) @ 0x12057c790>\n",
"<StoredBundle with 2 file(s) @ 0x12057c750>\n",
"<StoredBundle with 10 file(s) @ 0x12057c710>\n"
]
}
],
"source": [
"print project.files\n",
"print project.generators\n",
"print project.models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now restore our old ways to generate tasks by loading the previously used generators."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"engine = project.generators['openmm']\n",
"modeller = project.generators['pyemma']\n",
"pdb_file = project.files['initial_pdb']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run simulations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we really start simulations. The general way to do so is to create a simulation task and then submit it to a cluster to be executed. A `Task` object is a general description of what should be done and boils down to staging some files to your working directory, executing a bash script and finally moving files back from your working directory to a shared storage. RP takes care of most of this very elegantly and hence a `Task` is designed somewhat to cover the capabilities but in a somehow simpler and more pythonic way.\n",
"\n",
"For example there is a RPC Python Call Task that allows you to execute a function remotely and pull back the results. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Functional Events"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want to first look into a way to run python code asynchroneously in the project. For this, write a function that should be executed. Start with opening a scheduler or using an existing one (in the latter case you need to make sure that when it is executed - which can take a while - the scheduler still exists).\n",
"\n",
"If the function should pause, write `yield {condition_to_continue}`. This will interrupt your script until the function you return will return `True` when called."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def strategy():\n",
" # create a new scheduler\n",
" with project.get_scheduler(cores=2) as local_scheduler:\n",
" for loop in range(10):\n",
" tasks = local_scheduler(project.new_ml_trajectory(\n",
" length=100, number=10))\n",
" yield tasks.is_done()\n",
"\n",
" task = local_scheduler(modeller.execute(list(project.trajectories)))\n",
" yield task.is_done"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"turn a generator of your function use add `strategy()` and not `strategy` to the `FunctionalEvent`"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"ev = FunctionalEvent(strategy())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and execute the event inside your project"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<adaptivemd.event.FunctionalEvent at 0x11ffc1290>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"project.add_event(ev)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"after some time you will have 10 more trajectories. Just like that."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's see how our project is growing"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import time\n",
"from IPython.display import clear_output"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# of files 169 : #########################################################################################################################################################################\n",
"# of models 20 : ####################\n"
]
}
],
"source": [
"try:\n",
" while True:\n",
" clear_output(wait=True)\n",
" print '# of files %8d : %s' % (len(project.trajectories), '#' * len(project.trajectories))\n",
" print '# of models %8d : %s' % (len(project.models), '#' * len(project.models))\n",
" sys.stdout.flush()\n",
" time.sleep(1)\n",
" \n",
"except KeyboardInterrupt:\n",
" pass"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And some analysis"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"trajs = project.trajectories\n",
"q = {}\n",
"ins = {}\n",
"for f in trajs:\n",
" source = f.frame if isinstance(f.frame, File) else f.frame.trajectory\n",
" ind = 0 if isinstance(f.frame, File) else f.frame.index\n",
" ins[source] = ins.get(source, []) + [ind]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Event"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scheduler = project.get_scheduler(cores=2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def strategy1():\n",
" for loop in range(10):\n",
" tasks = scheduler(project.new_ml_trajectory(\n",
" length=100, number=10))\n",
" yield tasks.is_done()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def strategy2():\n",
" for loop in range(10):\n",
" num = len(project.trajectories)\n",
" task = scheduler(modeller.execute(list(project.trajectories)))\n",
" yield task.is_done\n",
" yield project.on_ntraj(num + 5)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"project._events = []"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<adaptivemd.event.FunctionalEvent at 0x12151e190>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"project.add_event(FunctionalEvent(strategy1))\n",
"project.add_event(FunctionalEvent(strategy2))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"project.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tasks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To actually run simulations you need to have a scheduler (maybe a better name?). This instance can execute tasks or more precise you can use it to submit tasks which will be converted to ComputeUnitDescriptions and executed on the cluster previously chosen. "
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"scheduler = project.get_scheduler(cores=2) # get the default scheduler using 2 cores"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we are good to go and can run a first simulation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This works by creating a Trajectory object with a filename, a length and an initial frame. Then the engine will take this information and create a real trajectory with exactly this name, this initil frame and the given length.\n",
"\n",
"Since this is such a common task you can also submit just a `Trajectory` without the need tp convert it to a `Task` first (which the engine can also do)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Out project can create new names automatically and so we want 4 new trajectories of length 100 and starting at the existing pdb_file we use to initialize the engine."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"trajs = project.new_trajectory(pdb_file, 100, 4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's submit and see"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<adaptivemd.task.Task at 0x12184aa10>,\n",
" <adaptivemd.task.Task at 0x1219628d0>,\n",
" <adaptivemd.task.Task at 0x1216e6050>,\n",
" <adaptivemd.task.Task at 0x12151d950>]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scheduler.submit(trajs)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Once the trajectories exist these objects will be saved to the database. It might be a little confusing to have objects before they exist, but this way you can actually work with these trajectories like referencing even before they exist.\n",
"\n",
"This would allow to write now a function that triggers when the trajectory comes into existance. But we are not doing this right now."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wait is dangerous since it is blocking and you cannot do anything until all tasks are finished. Normally you do not need it. Especially in interactive sessions."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"scheduler.wait()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Look at all the files our project now contains."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# of files 18\n"
]
}
],
"source": [
"print '# of files', len(project.files)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Great! That was easy (I hope you agree). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we want to run a simple analysis."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"t = modeller.execute(list(project.trajectories))"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<adaptivemd.task.PythonTask at 0x12199a1d0>]"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scheduler(t)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scheduler.wait()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the model we generated"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['clustering', 'input', 'msm', 'input_trajectories', 'tica']\n"
]
}
],
"source": [
"print project.models.last.data.keys()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And pick some information"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 0.81400438 0.0147784 0.00405625 0.07977394 0.08738703]\n",
" [ 0.0129096 0.97701148 0. 0.00149321 0.00858571]\n",
" [ 0.02517036 0. 0.864 0.01026794 0.1005617 ]\n",
" [ 0.12381115 0.00265298 0.00256813 0.87096774 0. ]\n",
" [ 0.09210144 0.01035882 0.01707997 0. 0.88045977]]\n"
]
}
],
"source": [
"print project.models.last.data['msm']['P']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next example will demonstrate on how to write a full adaptive loop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Events"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A new concept. Tasks are great and do work for us. But so far we needed to submit tasks ourselves. In adaptive simulations we want this to happen automagically. To help with some of this events exist. This are basically a task_generator coupled with conditions on when to be executed."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's write a little task generator (in essence a function that returns tasks)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def task_generator():\n",
" return [\n",
" engine.task_run_trajectory(traj) for traj in\n",
" project.new_ml_trajectory(100, 4)]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<adaptivemd.task.Task at 0x121367a50>,\n",
" <adaptivemd.task.Task at 0x121435350>,\n",
" <adaptivemd.task.Task at 0x121435e50>,\n",
" <adaptivemd.task.Task at 0x121460190>]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"task_generator()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now create an event."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"ev = Event().on(project.on_ntraj(range(20,22,2))).do(task_generator)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`.on` specifies when something should be executed. In our case when the project has a number of 20 trajectories. This is not yet the case so this event will not do anything unless we simulation more trajectories.\n",
"\n",
"`.do` specifies the function to be called.\n",
"\n",
"The concept is borrowed from event based languages like often used in JavaScript. \n",
"\n",
"You can build quite complex execution patterns with this. An event for example also knows when it is finished and this can be used as another trigger."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"def hello():\n",
" print 'DONE!!!'\n",
" return [] # todo: allow for None here\n",
"\n",
"finished = Event().on(ev.on_done).do(hello)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DONE!!!\n"
]
},
{
"data": {
"text/plain": [
"<adaptivemd.event.Event at 0x12156d050>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"scheduler.add_event(ev)\n",
"scheduler.add_event(finished)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All events and tasks run parallel or at least get submitted and queue for execution in parallel. RP takes care of the actual execution."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# of files 34\n"
]
}
],
"source": [
"print '# of files', len(project.files)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So for now lets run more trajectories and schedule computation of models in regular intervals."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<adaptivemd.event.Event at 0x121528ad0>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ev1 = Event().on(project.on_ntraj(range(30, 70, 4))).do(task_generator)\n",
"ev2 = Event().on(project.on_ntraj(38)).do(lambda: modeller.execute(list(project.trajectories))).repeat().until(ev1.on_done)\n",
"scheduler.add_event(ev1)\n",
"scheduler.add_event(ev2)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"43"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(project.trajectories)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 94,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(project.models)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`.repeat` means to redo the same task when the last is finished (it will just append an infinite list of conditions to keep on running).\n",
"\n",
"`.until` specifies a termination condition. The event will not be executed once this condition is met. Makes most sense if you use `.repeat` or if the trigger condition and stopping should be independent. You might say, run 100 times unless you have a good enough model. "
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<StoredBundle with 70 file(s) @ 0x12056f3d0>\n"
]
}
],
"source": [
"print project.files"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Strategies (aka **the brain**)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The brain is just a collection of events. This makes it reuseable and easy to extend."
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": [
"project.close()"
]
},
{
"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": 1
}
| lgpl-2.1 |
tydickinson29/Impacts-Code | River and Flash Flooding/Rainfall Stuff/LCRA+Improved.ipynb | 1 | 8176 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"user_cols = ['Year', 'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sept', 'Oct', 'Nov', 'Dec', 'Annual']\n",
"Table710 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/710/precipitation-tabular.txt',\n",
" skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),\n",
" names=user_cols)\n",
"\n",
"Table709 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/709/precipitation-tabular.txt', \n",
" skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),\n",
" names=user_cols)\n",
"\n",
"Table807 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/807/precipitation-tabular.txt', \n",
" skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),\n",
" names=user_cols)\n",
"\n",
"Table808 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/808/precipitation-tabular.txt', \n",
" skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),\n",
" names=user_cols)\n",
"\n",
"Table809 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/809/precipitation-tabular.txt',\n",
" skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),\n",
" names=user_cols)\n",
"\n",
"Table810 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/810/precipitation-tabular.txt', \n",
" skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),\n",
" names=user_cols)\n",
"\n",
"Table908 = pd.read_table('http://midgewater.twdb.texas.gov/evaporation/quadrangle/908/precipitation-tabular.txt', \n",
" skiprows=44, header=None, delim_whitespace=True, usecols=range(1,15),\n",
" names=user_cols)\n",
"\n",
"Winter709 = []\n",
"Winter710 = []\n",
"Winter807 = []\n",
"Winter808 = []\n",
"Winter809 = []\n",
"Winter810 = []\n",
"Winter908 = []\n",
"\n",
"Spring709 = []\n",
"Spring710 = []\n",
"Spring807 = []\n",
"Spring808 = []\n",
"Spring809 = []\n",
"Spring810 = []\n",
"Spring908 = []\n",
"\n",
"Summer709 = []\n",
"Summer710 = []\n",
"Summer807 = []\n",
"Summer808 = []\n",
"Summer809 = []\n",
"Summer810 = []\n",
"Summer908 = []\n",
"\n",
"Fall709 = []\n",
"Fall710 = []\n",
"Fall807 = []\n",
"Fall808 = []\n",
"Fall809 = []\n",
"Fall810 = []\n",
"Fall908 = []\n",
"\n",
"WinterAvg = []\n",
"SpringAvg = []\n",
"SummerAvg = []\n",
"FallAvg = []\n",
"\n",
"#Combines the months into seasons from the Tables\n",
"for i in range(1,37):\n",
" a = Table709.Mar[i] + Table709.Apr[i] + Table709.May[i]\n",
" b = Table710.Mar[i] + Table710.Apr[i] + Table710.May[i]\n",
" c = Table807.Mar[i] + Table807.Apr[i] + Table807.May[i]\n",
" d = Table808.Mar[i] + Table808.Apr[i] + Table808.May[i]\n",
" e = Table809.Mar[i] + Table809.Apr[i] + Table809.May[i]\n",
" f = Table810.Mar[i] + Table810.Apr[i] + Table810.May[i]\n",
" g = Table908.Mar[i] + Table908.Apr[i] + Table908.May[i]\n",
" Spring709.insert(i-1,a)\n",
" Spring710.insert(i-1,b)\n",
" Spring807.insert(i-1,c)\n",
" Spring808.insert(i-1,d)\n",
" Spring809.insert(i-1,e)\n",
" Spring810.insert(i-1,f)\n",
" Spring908.insert(i-1,g)\n",
" i = i + 1\n",
"\n",
"for i in range(1,37):\n",
" h = Table709.Jun[i] + Table709.Jul[i] + Table709.Aug[i]\n",
" j = Table710.Jun[i] + Table710.Jul[i] + Table710.Aug[i]\n",
" k = Table807.Jun[i] + Table807.Jul[i] + Table807.Aug[i]\n",
" l = Table808.Jun[i] + Table808.Jul[i] + Table808.Aug[i]\n",
" m = Table809.Jun[i] + Table809.Jul[i] + Table809.Aug[i]\n",
" n = Table810.Jun[i] + Table810.Jul[i] + Table810.Aug[i]\n",
" cc = Table908.Jun[i] + Table908.Jul[i] + Table908.Aug[i]\n",
" Summer709.insert(i-1,h)\n",
" Summer710.insert(i-1,j)\n",
" Summer807.insert(i-1, k)\n",
" Summer808.insert(i-1, l)\n",
" Summer809.insert(i-1, m)\n",
" Summer810.insert(i-1, n)\n",
" Summer908.insert(i-1, cc)\n",
" i = i + 1\n",
"\n",
"for i in range(1,37):\n",
" o = Table709.Sept[i] + Table709.Oct[i] + Table709.Nov[i]\n",
" p = Table710.Sept[i] + Table710.Oct[i] + Table710.Nov[i]\n",
" q = Table807.Sept[i] + Table807.Oct[i] + Table807.Nov[i]\n",
" r = Table808.Sept[i] + Table808.Oct[i] + Table808.Nov[i]\n",
" s = Table809.Sept[i] + Table809.Oct[i] + Table809.Nov[i]\n",
" t = Table810.Sept[i] + Table810.Oct[i] + Table810.Nov[i]\n",
" u = Table908.Sept[i] + Table908.Oct[i] + Table908.Nov[i]\n",
" Fall709.insert(i-1,o)\n",
" Fall710.insert(i-1,p)\n",
" Fall807.insert(i-1,q)\n",
" Fall808.insert(i-1,r)\n",
" Fall809.insert(i-1,s)\n",
" Fall810.insert(i-1,t)\n",
" Fall908.insert(i-1,u)\n",
" i = i + 1\n",
"\n",
"#Uses i-1 since we want Dec. 1980 and 1980 is the first row\n",
"for i in range(1,37): \n",
" v = Table709.Dec[i-1] + Table709.Jan[i] + Table709.Feb[i]\n",
" w = Table710.Dec[i-1] + Table710.Jan[i] + Table710.Feb[i]\n",
" x = Table807.Dec[i-1] + Table807.Jan[i] + Table807.Feb[i]\n",
" y = Table808.Dec[i-1] + Table808.Jan[i] + Table808.Feb[i]\n",
" z = Table809.Dec[i-1] + Table809.Jan[i] + Table809.Feb[i]\n",
" aa = Table810.Dec[i-1] + Table810.Jan[i] + Table810.Feb[i]\n",
" bb = Table908.Dec[i-1] + Table908.Jan[i] + Table908.Feb[i]\n",
" Winter709.insert(i-1,v)\n",
" Winter710.insert(i-1,w)\n",
" Winter807.insert(i-1,x)\n",
" Winter808.insert(i-1,y)\n",
" Winter809.insert(i-1,z)\n",
" Winter810.insert(i-1, aa)\n",
" Winter908.insert(i-1, bb)\n",
" i = i + 1\n",
"\n",
"#Linear addition of the selected polygons and an average is taken to get rainfall for our CWA \n",
"for i in range(0,36):\n",
" wtotal = ((Winter709[i] + Winter710[i] + Winter807[i] + Winter808[i] + Winter809[i] + Winter810[i] + Winter908[i])/7.0)\n",
" sptotal = ((Spring709[i] + Spring710[i] + Spring807[i] + Spring808[i] + Spring809[i] + Spring810[i] + Spring908[i])/7.0)\n",
" sutotal = ((Summer709[i] + Summer710[i] + Summer807[i] + Summer808[i] + Summer809[i] + Summer810[i] + Summer908[i])/7.0)\n",
" ftotal = ((Fall709[i] + Fall710[i] + Fall807[i] + Fall808[i] + Fall809[i] + Fall810[i] + Fall908[i])/7.0)\n",
" WinterAvg.insert(i, wtotal)\n",
" SpringAvg.insert(i, sptotal)\n",
" SummerAvg.insert(i, sutotal)\n",
" FallAvg.insert(i, ftotal)\n",
" i = i + 1\n",
"\n",
"df1 = pd.DataFrame({'Winter': WinterAvg})\n",
"df2 = pd.DataFrame({'Spring': SpringAvg})\n",
"df3 = pd.DataFrame({'Summer': SummerAvg})\n",
"df4 = pd.DataFrame({'Fall': FallAvg})\n",
"\n",
"dftot = pd.concat([df1, df2, df3, df4], axis=1)\n",
"#dftot.to_csv('LCRA Rainfall.csv')"
]
},
{
"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 |
ceos-seo/data_cube_utilities | data_cube_utilities/transect/tests/Pytest+exectution.ipynb | 2 | 2617 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: pytest in /home/localuser/Datacube/datacube_env/lib/python3.4/site-packages\n",
"Requirement already satisfied: six>=1.10.0 in /home/localuser/Datacube/datacube_env/lib/python3.4/site-packages (from pytest)\n",
"Requirement already satisfied: attrs>=17.2.0 in /home/localuser/Datacube/datacube_env/lib/python3.4/site-packages (from pytest)\n",
"Requirement already satisfied: py>=1.5.0 in /home/localuser/Datacube/datacube_env/lib/python3.4/site-packages (from pytest)\n",
"Requirement already satisfied: setuptools in /home/localuser/Datacube/datacube_env/lib/python3.4/site-packages (from pytest)\n",
"Requirement already satisfied: pluggy<0.7,>=0.5 in /home/localuser/Datacube/datacube_env/lib/python3.4/site-packages (from pytest)\n"
]
}
],
"source": [
"!pip install pytest"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m============================= test session starts ==============================\u001b[0m\n",
"platform linux -- Python 3.4.3, pytest-3.3.2, py-1.5.2, pluggy-0.6.0\n",
"rootdir: /home/local/AMA-INC/owagner/work/data_cube_notebooks_transect/utils/data_cube_utilities/transect/tests, inifile:\n",
"collected 24 items \u001b[0m\u001b[1m\u001b[1m\n",
"\n",
"test_interpolate.py ........\u001b[36m [ 33%]\u001b[0m\n",
"test_linescan.py ................\u001b[36m [100%]\u001b[0m\n",
"\n",
"\u001b[1m\u001b[32m========================== 24 passed in 0.11 seconds ===========================\u001b[0m\n"
]
}
],
"source": [
"!pytest"
]
},
{
"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": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
andreicorneliusuciu/socialGraphs | Tree of Science.ipynb | 1 | 9168720 | null | apache-2.0 |
oscarmore2/deep-learning-study | intro-to-rnns/Anna_KaRNNa.ipynb | 1 | 352541 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"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": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import time\n",
"from collections import namedtuple\n",
"\n",
"import numpy as np\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"First we'll load the text file and convert it into integers for our network to use. Here I'm creating a couple dictionaries to convert the characters to and from integers. Encoding the characters as integers makes it easier to use as input in the network."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"with open('jinpingmei.txt', 'r') as f:\n",
" text=f.read()\n",
"vocab = sorted(set(text))\n",
"vocab_to_int = {c: i for i, c in enumerate(vocab)}\n",
"int_to_vocab = dict(enumerate(vocab))\n",
"encoded = np.array([vocab_to_int[c] for c in text], dtype=np.int32)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Let's check out the first 100 characters, make sure everything is peachy. According to the [American Book Review](http://americanbookreview.org/100bestlines.asp), this is the 6th best first line of a book ever."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'第一回\\u3000西门庆热结十弟兄\\u3000武二郎冷遇亲哥嫂 \\n \\n\\u3000\\u3000诗曰： \\n\\n\\u3000\\u3000\\u3000\\u3000豪华去后行人绝，箫筝不响歌喉咽。雄剑无威光彩沉，宝琴零落金星灭。 \\n\\u3000\\u3000\\u3000\\u3000玉阶寂寞坠秋露，月照当时歌舞处。当时歌舞人不回，化'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"text[:100]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"And we can see the characters encoded as integers."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([2831, 41, 773, 34, 3539, 4068, 1204, 2328, 2964, 484, 1249,\n",
" 308, 34, 2037, 109, 3951, 361, 3921, 130, 671, 979, 1,\n",
" 0, 1, 0, 34, 34, 3606, 1818, 4459, 1, 0, 0,\n",
" 34, 34, 34, 34, 3668, 490, 533, 583, 3468, 132, 2971,\n",
" 4456, 2861, 2844, 49, 662, 2032, 716, 654, 36, 4150, 424,\n",
" 1756, 946, 312, 1267, 2108, 4456, 1024, 2477, 4162, 3339, 3993,\n",
" 1780, 2291, 36, 1, 0, 34, 34, 34, 34, 2444, 4108,\n",
" 1044, 1054, 813, 2756, 4177, 4456, 1826, 2342, 1261, 1767, 2032,\n",
" 3221, 863, 36, 1261, 1767, 2032, 3221, 132, 49, 773, 4456,\n",
" 470])"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"encoded[:100]"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Since the network is working with individual characters, it's similar to a classification problem in which we are trying to predict the next character from the previous text. Here's how many 'classes' our network has to pick from."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"4464"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(vocab)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Making training mini-batches\n",
"\n",
"Here is where we'll make our mini-batches for training. Remember that we want our batches to be multiple sequences of some desired number of sequence steps. Considering a simple example, our batches would look like this:\n",
"\n",
"<img src=\"assets/[email protected]\" width=500px>\n",
"\n",
"\n",
"<br>\n",
"We have our text encoded as integers as one long array in `encoded`. Let's create a function that will give us an iterator for our batches. I like using [generator functions](https://jeffknupp.com/blog/2013/04/07/improve-your-python-yield-and-generators-explained/) to do this. Then we can pass `encoded` into this function and get our batch generator.\n",
"\n",
"The first thing we need to do is discard some of the text so we only have completely full batches. Each batch contains $N \\times M$ characters, where $N$ is the batch size (the number of sequences) and $M$ is the number of steps. Then, to get the number of batches we can make from some array `arr`, you divide the length of `arr` by the batch size. Once you know the number of batches and the batch size, you can get the total number of characters to keep.\n",
"\n",
"After that, we need to split `arr` into $N$ sequences. You can do this using `arr.reshape(size)` where `size` is a tuple containing the dimensions sizes of the reshaped array. We know we want $N$ sequences (`n_seqs` below), let's make that the size of the first dimension. For the second dimension, you can use `-1` as a placeholder in the size, it'll fill up the array with the appropriate data for you. After this, you should have an array that is $N \\times (M * K)$ where $K$ is the number of batches.\n",
"\n",
"Now that we have this array, we can iterate through it to get our batches. The idea is each batch is a $N \\times M$ window on the array. For each subsequent batch, the window moves over by `n_steps`. We also want to create both the input and target arrays. Remember that the targets are the inputs shifted over one character. You'll usually see the first input character used as the last target character, so something like this:\n",
"```python\n",
"y[:, :-1], y[:, -1] = x[:, 1:], x[:, 0]\n",
"```\n",
"where `x` is the input batch and `y` is the target batch.\n",
"\n",
"The way I like to do this window is use `range` to take steps of size `n_steps` from $0$ to `arr.shape[1]`, the total number of steps in each sequence. That way, the integers you get from `range` always point to the start of a batch, and each window is `n_steps` wide."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def get_batches(arr, n_seqs, n_steps):\n",
" '''Create a generator that returns batches of size\n",
" n_seqs x n_steps from arr.\n",
" \n",
" Arguments\n",
" ---------\n",
" arr: Array you want to make batches from\n",
" n_seqs: Batch size, the number of sequences per batch\n",
" n_steps: Number of sequence steps per batch\n",
" '''\n",
" # Get the number of characters per batch and number of batches we can make\n",
" characters_per_batch = n_seqs * n_steps\n",
" n_batches = len(arr)//characters_per_batch\n",
" \n",
" # Keep only enough characters to make full batches\n",
" arr = arr[:n_batches * characters_per_batch]\n",
" \n",
" # Reshape into n_seqs rows\n",
" arr = arr.reshape((n_seqs, -1))\n",
" \n",
" for n in range(0, arr.shape[1], n_steps):\n",
" # The features\n",
" x = arr[:, n:n+n_steps]\n",
" # The targets, shifted by one\n",
" y = np.zeros_like(x)\n",
" y[:, :-1], y[:, -1] = x[:, 1:], x[:, 0]\n",
" yield x, y"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Now I'll make my data sets and we can check out what's going on here. Here I'm going to use a batch size of 10 and 50 sequence steps."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"batches = get_batches(encoded, 10, 50)\n",
"x, y = next(batches)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x\n",
" [[2831 41 773 34 3539 4068 1204 2328 2964 484]\n",
" [3539 4068 1204 805 48 4456 4072 3925 4459 30]\n",
" [ 583 3856 1826 954 1451 3989 1464 105 506 304]\n",
" [ 533 3040 36 31 1 0 0 34 34 1194]\n",
" [ 149 868 2511 976 913 2057 875 932 4456 793]\n",
" [ 132 4456 1094 4050 3474 84 3094 4456 149 1038]\n",
" [1719 36 3539 4068 1204 773 3205 518 47 4456]\n",
" [4459 30 1438 1006 2510 347 100 50 2206 1016]\n",
" [1782 1943 1001 833 69 538 130 2632 2624 3543]\n",
" [2632 1775 4456 49 3570 2586 206 36 31 3870]]\n",
"\n",
"y\n",
" [[ 41 773 34 3539 4068 1204 2328 2964 484 1249]\n",
" [4068 1204 805 48 4456 4072 3925 4459 30 1323]\n",
" [3856 1826 954 1451 3989 1464 105 506 304 4456]\n",
" [3040 36 31 1 0 0 34 34 1194 1016]\n",
" [ 868 2511 976 913 2057 875 932 4456 793 4068]\n",
" [4456 1094 4050 3474 84 3094 4456 149 1038 2753]\n",
" [ 36 3539 4068 1204 773 3205 518 47 4456 1070]\n",
" [ 30 1438 1006 2510 347 100 50 2206 1016 397]\n",
" [1943 1001 833 69 538 130 2632 2624 3543 1854]\n",
" [1775 4456 49 3570 2586 206 36 31 3870 1720]]\n"
]
}
],
"source": [
"print('x\\n', x[:10, :10])\n",
"print('\\ny\\n', y[:10, :10])"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
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"source": [
"If you implemented `get_batches` correctly, the above output should look something like \n",
"```\n",
"x\n",
" [[55 63 69 22 6 76 45 5 16 35]\n",
" [ 5 69 1 5 12 52 6 5 56 52]\n",
" [48 29 12 61 35 35 8 64 76 78]\n",
" [12 5 24 39 45 29 12 56 5 63]\n",
" [ 5 29 6 5 29 78 28 5 78 29]\n",
" [ 5 13 6 5 36 69 78 35 52 12]\n",
" [63 76 12 5 18 52 1 76 5 58]\n",
" [34 5 73 39 6 5 12 52 36 5]\n",
" [ 6 5 29 78 12 79 6 61 5 59]\n",
" [ 5 78 69 29 24 5 6 52 5 63]]\n",
"\n",
"y\n",
" [[63 69 22 6 76 45 5 16 35 35]\n",
" [69 1 5 12 52 6 5 56 52 29]\n",
" [29 12 61 35 35 8 64 76 78 28]\n",
" [ 5 24 39 45 29 12 56 5 63 29]\n",
" [29 6 5 29 78 28 5 78 29 45]\n",
" [13 6 5 36 69 78 35 52 12 43]\n",
" [76 12 5 18 52 1 76 5 58 52]\n",
" [ 5 73 39 6 5 12 52 36 5 78]\n",
" [ 5 29 78 12 79 6 61 5 59 63]\n",
" [78 69 29 24 5 6 52 5 63 76]]\n",
" ```\n",
" although the exact numbers will be different. Check to make sure the data is shifted over one step for `y`."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Building the model\n",
"\n",
"Below is where you'll build the network. We'll break it up into parts so it's easier to reason about each bit. Then we can connect them up into the whole network.\n",
"\n",
"<img src=\"assets/charRNN.png\" width=500px>\n",
"\n",
"\n",
"### Inputs\n",
"\n",
"First off we'll create our input placeholders. As usual we need placeholders for the training data and the targets. We'll also create a placeholder for dropout layers called `keep_prob`."
]
},
{
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"metadata": {
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"source": [
"def build_inputs(batch_size, num_steps):\n",
" ''' Define placeholders for inputs, targets, and dropout \n",
" \n",
" Arguments\n",
" ---------\n",
" batch_size: Batch size, number of sequences per batch\n",
" num_steps: Number of sequence steps in a batch\n",
" \n",
" '''\n",
" # Declare placeholders we'll feed into the graph\n",
" inputs = tf.placeholder(tf.int32, [batch_size, num_steps], name='inputs')\n",
" targets = tf.placeholder(tf.int32, [batch_size, num_steps], name='targets')\n",
" \n",
" # Keep probability placeholder for drop out layers\n",
" keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n",
" \n",
" return inputs, targets, keep_prob"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### LSTM Cell\n",
"\n",
"Here we will create the LSTM cell we'll use in the hidden layer. We'll use this cell as a building block for the RNN. So we aren't actually defining the RNN here, just the type of cell we'll use in the hidden layer.\n",
"\n",
"We first create a basic LSTM cell with\n",
"\n",
"```python\n",
"lstm = tf.contrib.rnn.BasicLSTMCell(num_units)\n",
"```\n",
"\n",
"where `num_units` is the number of units in the hidden layers in the cell. Then we can add dropout by wrapping it with \n",
"\n",
"```python\n",
"tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)\n",
"```\n",
"You pass in a cell and it will automatically add dropout to the inputs or outputs. Finally, we can stack up the LSTM cells into layers with [`tf.contrib.rnn.MultiRNNCell`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/contrib/rnn/MultiRNNCell). With this, you pass in a list of cells and it will send the output of one cell into the next cell. Previously with TensorFlow 1.0, you could do this\n",
"\n",
"```python\n",
"tf.contrib.rnn.MultiRNNCell([cell]*num_layers)\n",
"```\n",
"\n",
"This might look a little weird if you know Python well because this will create a list of the same `cell` object. However, TensorFlow 1.0 will create different weight matrices for all `cell` objects. But, starting with TensorFlow 1.1 you actually need to create new cell objects in the list. To get it to work in TensorFlow 1.1, it should look like\n",
"\n",
"```python\n",
"def build_cell(num_units, keep_prob):\n",
" lstm = tf.contrib.rnn.BasicLSTMCell(num_units)\n",
" drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)\n",
" \n",
" return drop\n",
" \n",
"tf.contrib.rnn.MultiRNNCell([build_cell(num_units, keep_prob) for _ in range(num_layers)])\n",
"```\n",
"\n",
"Even though this is actually multiple LSTM cells stacked on each other, you can treat the multiple layers as one cell.\n",
"\n",
"We also need to create an initial cell state of all zeros. This can be done like so\n",
"\n",
"```python\n",
"initial_state = cell.zero_state(batch_size, tf.float32)\n",
"```\n",
"\n",
"Below, we implement the `build_lstm` function to create these LSTM cells and the initial state."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
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"source": [
"def build_lstm(lstm_size, num_layers, batch_size, keep_prob):\n",
" ''' Build LSTM cell.\n",
" \n",
" Arguments\n",
" ---------\n",
" keep_prob: Scalar tensor (tf.placeholder) for the dropout keep probability\n",
" lstm_size: Size of the hidden layers in the LSTM cells\n",
" num_layers: Number of LSTM layers\n",
" batch_size: Batch size\n",
"\n",
" '''\n",
" ### Build the LSTM Cell\n",
" \n",
" def build_cell(lstm_size, keep_prob):\n",
" # Use a basic LSTM cell\n",
" lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size)\n",
" \n",
" # Add dropout to the cell\n",
" drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob)\n",
" return drop\n",
" \n",
" \n",
" # Stack up multiple LSTM layers, for deep learning\n",
" cell = tf.contrib.rnn.MultiRNNCell([build_cell(lstm_size, keep_prob) for _ in range(num_layers)])\n",
" initial_state = cell.zero_state(batch_size, tf.float32)\n",
" \n",
" return cell, initial_state"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### RNN Output\n",
"\n",
"Here we'll create the output layer. We need to connect the output of the RNN cells to a full connected layer with a softmax output. The softmax output gives us a probability distribution we can use to predict the next character.\n",
"\n",
"If our input has batch size $N$, number of steps $M$, and the hidden layer has $L$ hidden units, then the output is a 3D tensor with size $N \\times M \\times L$. The output of each LSTM cell has size $L$, we have $M$ of them, one for each sequence step, and we have $N$ sequences. So the total size is $N \\times M \\times L$.\n",
"\n",
"We are using the same fully connected layer, the same weights, for each of the outputs. Then, to make things easier, we should reshape the outputs into a 2D tensor with shape $(M * N) \\times L$. That is, one row for each sequence and step, where the values of each row are the output from the LSTM cells.\n",
"\n",
"One we have the outputs reshaped, we can do the matrix multiplication with the weights. We need to wrap the weight and bias variables in a variable scope with `tf.variable_scope(scope_name)` because there are weights being created in the LSTM cells. TensorFlow will throw an error if the weights created here have the same names as the weights created in the LSTM cells, which they will be default. To avoid this, we wrap the variables in a variable scope so we can give them unique names."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true,
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"source": [
"def build_output(lstm_output, in_size, out_size):\n",
" ''' Build a softmax layer, return the softmax output and logits.\n",
" \n",
" Arguments\n",
" ---------\n",
" \n",
" x: Input tensor\n",
" in_size: Size of the input tensor, for example, size of the LSTM cells\n",
" out_size: Size of this softmax layer\n",
" \n",
" '''\n",
"\n",
" # Reshape output so it's a bunch of rows, one row for each step for each sequence.\n",
" # That is, the shape should be batch_size*num_steps rows by lstm_size columns\n",
" seq_output = tf.concat(lstm_output, axis=1)\n",
" x = tf.reshape(seq_output, [-1, in_size])\n",
" \n",
" # Connect the RNN outputs to a softmax layer\n",
" with tf.variable_scope('softmax'):\n",
" softmax_w = tf.Variable(tf.truncated_normal((in_size, out_size), stddev=0.1))\n",
" softmax_b = tf.Variable(tf.zeros(out_size))\n",
" \n",
" # Since output is a bunch of rows of RNN cell outputs, logits will be a bunch\n",
" # of rows of logit outputs, one for each step and sequence\n",
" logits = tf.matmul(x, softmax_w) + softmax_b\n",
" \n",
" # Use softmax to get the probabilities for predicted characters\n",
" out = tf.nn.softmax(logits, name='predictions')\n",
" \n",
" return out, logits"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Training loss\n",
"\n",
"Next up is the training loss. We get the logits and targets and calculate the softmax cross-entropy loss. First we need to one-hot encode the targets, we're getting them as encoded characters. Then, reshape the one-hot targets so it's a 2D tensor with size $(M*N) \\times C$ where $C$ is the number of classes/characters we have. Remember that we reshaped the LSTM outputs and ran them through a fully connected layer with $C$ units. So our logits will also have size $(M*N) \\times C$.\n",
"\n",
"Then we run the logits and targets through `tf.nn.softmax_cross_entropy_with_logits` and find the mean to get the loss."
]
},
{
"cell_type": "code",
"execution_count": 12,
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"outputs": [],
"source": [
"def build_loss(logits, targets, lstm_size, num_classes):\n",
" ''' Calculate the loss from the logits and the targets.\n",
" \n",
" Arguments\n",
" ---------\n",
" logits: Logits from final fully connected layer\n",
" targets: Targets for supervised learning\n",
" lstm_size: Number of LSTM hidden units\n",
" num_classes: Number of classes in targets\n",
" \n",
" '''\n",
" \n",
" # One-hot encode targets and reshape to match logits, one row per batch_size per step\n",
" y_one_hot = tf.one_hot(targets, num_classes)\n",
" y_reshaped = tf.reshape(y_one_hot, logits.get_shape())\n",
" \n",
" # Softmax cross entropy loss\n",
" loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_reshaped)\n",
" loss = tf.reduce_mean(loss)\n",
" return loss"
]
},
{
"cell_type": "markdown",
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"deletable": true,
"editable": true
},
"source": [
"### Optimizer\n",
"\n",
"Here we build the optimizer. Normal RNNs have have issues gradients exploding and disappearing. LSTMs fix the disappearance problem, but the gradients can still grow without bound. To fix this, we can clip the gradients above some threshold. That is, if a gradient is larger than that threshold, we set it to the threshold. This will ensure the gradients never grow overly large. Then we use an AdamOptimizer for the learning step."
]
},
{
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"source": [
"def build_optimizer(loss, learning_rate, grad_clip):\n",
" ''' Build optmizer for training, using gradient clipping.\n",
" \n",
" Arguments:\n",
" loss: Network loss\n",
" learning_rate: Learning rate for optimizer\n",
" \n",
" '''\n",
" \n",
" # Optimizer for training, using gradient clipping to control exploding gradients\n",
" tvars = tf.trainable_variables()\n",
" grads, _ = tf.clip_by_global_norm(tf.gradients(loss, tvars), grad_clip)\n",
" train_op = tf.train.AdamOptimizer(learning_rate)\n",
" optimizer = train_op.apply_gradients(zip(grads, tvars))\n",
" \n",
" return optimizer"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Build the network\n",
"\n",
"Now we can put all the pieces together and build a class for the network. To actually run data through the LSTM cells, we will use [`tf.nn.dynamic_rnn`](https://www.tensorflow.org/versions/r1.0/api_docs/python/tf/nn/dynamic_rnn). This function will pass the hidden and cell states across LSTM cells appropriately for us. It returns the outputs for each LSTM cell at each step for each sequence in the mini-batch. It also gives us the final LSTM state. We want to save this state as `final_state` so we can pass it to the first LSTM cell in the the next mini-batch run. For `tf.nn.dynamic_rnn`, we pass in the cell and initial state we get from `build_lstm`, as well as our input sequences. Also, we need to one-hot encode the inputs before going into the RNN. "
]
},
{
"cell_type": "code",
"execution_count": 14,
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"source": [
"class CharRNN:\n",
" \n",
" def __init__(self, num_classes, batch_size=64, num_steps=50, \n",
" lstm_size=128, num_layers=2, learning_rate=0.001, \n",
" grad_clip=5, sampling=False):\n",
" \n",
" # When we're using this network for sampling later, we'll be passing in\n",
" # one character at a time, so providing an option for that\n",
" if sampling == True:\n",
" batch_size, num_steps = 1, 1\n",
" else:\n",
" batch_size, num_steps = batch_size, num_steps\n",
"\n",
" tf.reset_default_graph()\n",
" \n",
" # Build the input placeholder tensors\n",
" self.inputs, self.targets, self.keep_prob = build_inputs(batch_size, num_steps)\n",
"\n",
" # Build the LSTM cell\n",
" cell, self.initial_state = build_lstm(lstm_size, num_layers, batch_size, self.keep_prob)\n",
"\n",
" ### Run the data through the RNN layers\n",
" # First, one-hot encode the input tokens\n",
" x_one_hot = tf.one_hot(self.inputs, num_classes)\n",
" \n",
" # Run each sequence step through the RNN and collect the outputs\n",
" outputs, state = tf.nn.dynamic_rnn(cell, x_one_hot, initial_state=self.initial_state)\n",
" self.final_state = state\n",
" \n",
" # Get softmax predictions and logits\n",
" self.prediction, self.logits = build_output(outputs, lstm_size, num_classes)\n",
" \n",
" # Loss and optimizer (with gradient clipping)\n",
" self.loss = build_loss(self.logits, self.targets, lstm_size, num_classes)\n",
" self.optimizer = build_optimizer(self.loss, learning_rate, grad_clip)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Hyperparameters\n",
"\n",
"Here I'm defining the hyperparameters for the network. \n",
"\n",
"* `batch_size` - Number of sequences running through the network in one pass.\n",
"* `num_steps` - Number of characters in the sequence the network is trained on. Larger is better typically, the network will learn more long range dependencies. But it takes longer to train. 100 is typically a good number here.\n",
"* `lstm_size` - The number of units in the hidden layers.\n",
"* `num_layers` - Number of hidden LSTM layers to use\n",
"* `learning_rate` - Learning rate for training\n",
"* `keep_prob` - The dropout keep probability when training. If you're network is overfitting, try decreasing this.\n",
"\n",
"Here's some good advice from Andrej Karpathy on training the network. I'm going to copy it in here for your benefit, but also link to [where it originally came from](https://github.com/karpathy/char-rnn#tips-and-tricks).\n",
"\n",
"> ## Tips and Tricks\n",
"\n",
">### Monitoring Validation Loss vs. Training Loss\n",
">If you're somewhat new to Machine Learning or Neural Networks it can take a bit of expertise to get good models. The most important quantity to keep track of is the difference between your training loss (printed during training) and the validation loss (printed once in a while when the RNN is run on the validation data (by default every 1000 iterations)). In particular:\n",
"\n",
"> - If your training loss is much lower than validation loss then this means the network might be **overfitting**. Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on.\n",
"> - If your training/validation loss are about equal then your model is **underfitting**. Increase the size of your model (either number of layers or the raw number of neurons per layer)\n",
"\n",
"> ### Approximate number of parameters\n",
"\n",
"> The two most important parameters that control the model are `lstm_size` and `num_layers`. I would advise that you always use `num_layers` of either 2/3. The `lstm_size` can be adjusted based on how much data you have. The two important quantities to keep track of here are:\n",
"\n",
"> - The number of parameters in your model. This is printed when you start training.\n",
"> - The size of your dataset. 1MB file is approximately 1 million characters.\n",
"\n",
">These two should be about the same order of magnitude. It's a little tricky to tell. Here are some examples:\n",
"\n",
"> - I have a 100MB dataset and I'm using the default parameter settings (which currently print 150K parameters). My data size is significantly larger (100 mil >> 0.15 mil), so I expect to heavily underfit. I am thinking I can comfortably afford to make `lstm_size` larger.\n",
"> - I have a 10MB dataset and running a 10 million parameter model. I'm slightly nervous and I'm carefully monitoring my validation loss. If it's larger than my training loss then I may want to try to increase dropout a bit and see if that helps the validation loss.\n",
"\n",
"> ### Best models strategy\n",
"\n",
">The winning strategy to obtaining very good models (if you have the compute time) is to always err on making the network larger (as large as you're willing to wait for it to compute) and then try different dropout values (between 0,1). Whatever model has the best validation performance (the loss, written in the checkpoint filename, low is good) is the one you should use in the end.\n",
"\n",
">It is very common in deep learning to run many different models with many different hyperparameter settings, and in the end take whatever checkpoint gave the best validation performance.\n",
"\n",
">By the way, the size of your training and validation splits are also parameters. Make sure you have a decent amount of data in your validation set or otherwise the validation performance will be noisy and not very informative.\n"
]
},
{
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"source": [
"batch_size = 128 # Sequences per batch\n",
"num_steps = 100 # Number of sequence steps per batch\n",
"lstm_size = 512 # Size of hidden layers in LSTMs\n",
"num_layers = 2 # Number of LSTM layers\n",
"learning_rate = 0.0003 # Learning rate\n",
"keep_prob = 0.5 # Dropout keep probability"
]
},
{
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"deletable": true,
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"source": [
"## Time for training\n",
"\n",
"This is typical training code, passing inputs and targets into the network, then running the optimizer. Here we also get back the final LSTM state for the mini-batch. Then, we pass that state back into the network so the next batch can continue the state from the previous batch. And every so often (set by `save_every_n`) I save a checkpoint.\n",
"\n",
"Here I'm saving checkpoints with the format\n",
"\n",
"`i{iteration number}_l{# hidden layer units}.ckpt`"
]
},
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"execution_count": 18,
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"collapsed": false,
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 1/50... Training Step: 1... Training loss: 8.4041... 1.4952 sec/batch\n",
"Epoch: 1/50... Training Step: 2... Training loss: 8.3998... 1.4524 sec/batch\n",
"Epoch: 1/50... Training Step: 3... Training loss: 8.3945... 1.3991 sec/batch\n",
"Epoch: 1/50... Training Step: 4... Training loss: 8.3873... 1.4213 sec/batch\n",
"Epoch: 1/50... Training Step: 5... Training loss: 8.3733... 1.4446 sec/batch\n",
"Epoch: 1/50... Training Step: 6... Training loss: 8.3482... 1.3928 sec/batch\n",
"Epoch: 1/50... Training Step: 7... Training loss: 8.2876... 1.4095 sec/batch\n",
"Epoch: 1/50... Training Step: 8... Training loss: 8.1361... 1.4096 sec/batch\n",
"Epoch: 1/50... Training Step: 9... Training loss: 7.8635... 1.3634 sec/batch\n",
"Epoch: 1/50... Training Step: 10... Training loss: 7.8812... 1.4309 sec/batch\n",
"Epoch: 1/50... Training Step: 11... Training loss: 7.7826... 1.4422 sec/batch\n",
"Epoch: 1/50... Training Step: 12... Training loss: 7.6051... 1.4290 sec/batch\n",
"Epoch: 1/50... Training Step: 13... Training loss: 7.5716... 1.4138 sec/batch\n",
"Epoch: 1/50... Training Step: 14... Training loss: 7.5146... 1.4320 sec/batch\n",
"Epoch: 1/50... Training Step: 15... Training loss: 7.4315... 1.4189 sec/batch\n",
"Epoch: 1/50... Training Step: 16... Training loss: 7.3902... 1.4191 sec/batch\n",
"Epoch: 1/50... Training Step: 17... Training loss: 7.3200... 1.4182 sec/batch\n",
"Epoch: 1/50... Training Step: 18... Training loss: 7.2669... 1.3586 sec/batch\n",
"Epoch: 1/50... Training Step: 19... Training loss: 7.1855... 1.4206 sec/batch\n",
"Epoch: 1/50... Training Step: 20... Training loss: 7.1204... 1.4203 sec/batch\n",
"Epoch: 1/50... Training Step: 21... Training loss: 7.0440... 1.4192 sec/batch\n",
"Epoch: 1/50... Training Step: 22... Training loss: 6.9846... 1.4362 sec/batch\n",
"Epoch: 1/50... Training Step: 23... Training loss: 6.9807... 1.4440 sec/batch\n",
"Epoch: 1/50... Training Step: 24... Training loss: 6.8857... 1.4185 sec/batch\n",
"Epoch: 1/50... Training Step: 25... Training loss: 6.8277... 1.4294 sec/batch\n",
"Epoch: 1/50... Training Step: 26... Training loss: 6.8140... 1.4275 sec/batch\n",
"Epoch: 1/50... Training Step: 27... Training loss: 6.7916... 1.3947 sec/batch\n",
"Epoch: 1/50... Training Step: 28... Training loss: 6.7677... 1.4322 sec/batch\n",
"Epoch: 1/50... Training Step: 29... Training loss: 6.7035... 1.4405 sec/batch\n",
"Epoch: 1/50... Training Step: 30... Training loss: 6.7070... 1.3937 sec/batch\n",
"Epoch: 1/50... Training Step: 31... Training loss: 6.6189... 1.4403 sec/batch\n",
"Epoch: 1/50... Training Step: 32... Training loss: 6.6099... 1.4299 sec/batch\n",
"Epoch: 1/50... Training Step: 33... Training loss: 6.5814... 1.3934 sec/batch\n",
"Epoch: 1/50... Training Step: 34... Training loss: 6.6172... 1.3255 sec/batch\n",
"Epoch: 1/50... Training Step: 35... Training loss: 6.5959... 1.2594 sec/batch\n",
"Epoch: 1/50... Training Step: 36... Training loss: 6.5343... 1.4601 sec/batch\n",
"Epoch: 1/50... Training Step: 37... Training loss: 6.5823... 1.2933 sec/batch\n",
"Epoch: 1/50... Training Step: 38... Training loss: 6.5858... 1.3944 sec/batch\n",
"Epoch: 1/50... Training Step: 39... Training loss: 6.6164... 1.4282 sec/batch\n",
"Epoch: 1/50... Training Step: 40... Training loss: 6.5207... 1.4261 sec/batch\n",
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"Epoch: 49/50... Training Step: 2959... Training loss: 3.7788... 1.3808 sec/batch\n",
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"Epoch: 49/50... Training Step: 2969... Training loss: 3.7978... 1.3903 sec/batch\n",
"Epoch: 49/50... Training Step: 2970... Training loss: 3.7944... 1.3440 sec/batch\n",
"Epoch: 49/50... Training Step: 2971... Training loss: 3.8236... 1.3879 sec/batch\n",
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"Epoch: 50/50... Training Step: 2990... Training loss: 3.9194... 1.4020 sec/batch\n",
"Epoch: 50/50... Training Step: 2991... Training loss: 3.8014... 1.3929 sec/batch\n",
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"Epoch: 50/50... Training Step: 3000... Training loss: 3.9521... 1.3879 sec/batch\n",
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"Epoch: 50/50... Training Step: 3049... Training loss: 3.7858... 1.3906 sec/batch\n",
"Epoch: 50/50... Training Step: 3050... Training loss: 3.7573... 1.3844 sec/batch\n"
]
}
],
"source": [
"epochs = 50\n",
"# Save every N iterations\n",
"save_every_n = 200\n",
"\n",
"model = CharRNN(len(vocab), batch_size=batch_size, num_steps=num_steps,\n",
" lstm_size=lstm_size, num_layers=num_layers, \n",
" learning_rate=learning_rate)\n",
"\n",
"saver = tf.train.Saver(max_to_keep=100)\n",
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" \n",
" # Use the line below to load a checkpoint and resume training\n",
" #saver.restore(sess, 'checkpoints/______.ckpt')\n",
" counter = 0\n",
" for e in range(epochs):\n",
" # Train network\n",
" new_state = sess.run(model.initial_state)\n",
" loss = 0\n",
" for x, y in get_batches(encoded, batch_size, num_steps):\n",
" counter += 1\n",
" start = time.time()\n",
" feed = {model.inputs: x,\n",
" model.targets: y,\n",
" model.keep_prob: keep_prob,\n",
" model.initial_state: new_state}\n",
" batch_loss, new_state, _ = sess.run([model.loss, \n",
" model.final_state, \n",
" model.optimizer], \n",
" feed_dict=feed)\n",
" \n",
" end = time.time()\n",
" print('Epoch: {}/{}... '.format(e+1, epochs),\n",
" 'Training Step: {}... '.format(counter),\n",
" 'Training loss: {:.4f}... '.format(batch_loss),\n",
" '{:.4f} sec/batch'.format((end-start)))\n",
" \n",
" if (counter % save_every_n == 0):\n",
" saver.save(sess, \"checkpoints/i{}_l{}.ckpt\".format(counter, lstm_size))\n",
" \n",
" saver.save(sess, \"checkpoints/i{}_l{}.ckpt\".format(counter, lstm_size))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"#### Saved checkpoints\n",
"\n",
"Read up on saving and loading checkpoints here: https://www.tensorflow.org/programmers_guide/variables"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"model_checkpoint_path: \"checkpoints\\\\i3050_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i200_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i400_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i600_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i800_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i1000_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i1200_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i1400_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i1600_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i1800_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i2000_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i2200_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i2400_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i2600_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i2800_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i3000_l512.ckpt\"\n",
"all_model_checkpoint_paths: \"checkpoints\\\\i3050_l512.ckpt\""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.train.get_checkpoint_state('checkpoints')"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"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": 20,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": 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": 21,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def sample(checkpoint, n_samples, lstm_size, vocab_size, prime=\"The \"):\n",
" samples = [c for c in prime]\n",
" model = CharRNN(len(vocab), 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.prediction, 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.prediction, 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": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Here, pass in the path to a checkpoint and sample from the network."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"'checkpoints\\\\i3050_l512.ckpt'"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.train.latest_checkpoint('checkpoints')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Restoring parameters from checkpoints\\i3050_l512.ckpt\n",
"浪。” \n",
"\n",
" 西门庆见了，只见西门庆进入门中，便道：“这里说了，只是一件儿。”西门庆道：“你说不知，我不敢说。”伯爵道：“这等你不知，我还不去。我说我不知你，你不知，你这个不好？”西门庆道：“我不得你。”西门庆道：“你休要说，你不要，我说你，你不要你，我也不要我去，你不肯你。他不肯来，你不知，我就不知我，不知你那里去了。”西门庆道：“你不知道，只是我不的。”西门庆道：“你的我这里，就是我的不好。你这一个小厮儿，不知道，你也要吃他。”西门庆道：“我的不说你，我说你不好，他不知道，不是这般说，我就不知道。”那妇人听了，只见他说话，一直往外边去。只见玳安进房里，一面走了一遍，金莲不见，说道：“他是不知，你这个好个儿！我不在这里，只是你不好。”西门庆道：“怪狗才，他这等你来。”西门庆道：“他不知，你这个不在我。”西门庆道：“我不要他，你不要，你还要我去，教我拿着他去了。”李瓶儿道：“你不好，只顾我来。”西门庆道：“我不知你，我不在家。那个不好不好？”妇人道：“我也不知，我也不是你这般，你不知道。”那妇人听着，笑道：“你的不是，说的是谁。”那妇人道：“我不知道，我不知道。”西门庆道：“他也不是，你说我，你也是个不知道！”于是打了一个，说道：“我不是你，我这里说的是谁的？”月娘道：“他这个不好，你就是我这个儿！不是你这个淫妇儿，我不知你，你怎不知道！”说道：“我的我不知，我怎的不知你？”妇人道：“你不知你，我不在他这里，你不知道。你不知怎的不得？我来家我也不要了，他也没了，我也不好。你说了一场，他也不知你的，我这个不是你的。” \n",
"\n",
" 不言语，只见了一回。 \n",
"\n",
" 西门庆在房里睡，只见他两个儿，不觉一阵风儿，只见一个不知。那日不见，不在西门庆房里，只见李瓶儿来了，说：“姐姐，他不在，我也不知你，你这个是他的。不知我，你不知他。他不是你，你这里，我不是他，他就是你的。”那玉楼听见了，说：“他，你还不在这屋里？”西门庆道：“你不好，我也不要你，不想来我，不要你看，他也要不出来，我也要他，你说他，你还不知道了。”一面走了一遍，又不在家，又问他：“我在那屋里？”金莲道：“你没了，我就是不得他。”月娘道：“你说道：“我，你这个不好。他来，你还不知道。”西门庆笑：“你不知道，你这等我说。”于是向前取出两个来与西门庆，说道：“我不的，他还不知道，不知你这里有事。”月娘道：“我不好不得，你只怕他。”于是走出来来，说道：“我不知，不知我的，不知怎的。”西门庆道：“我说，你也不是我的。”西门庆道：“既是你，不好不好，就要去了，你就要不去。我不要他，我也不要。你这个不知怎的？”李瓶儿道：“你不是，只怕他，只怕你也不得，我就是你的。不想你不在家，不知你家去了。”西门庆道：“你不知，你不好，不是你这里来！你说我的，你也不要吃，不好，就是你的人儿不好？”西门庆道：“你不说，我不知道。我说你不好，你也不知你，我不去。”西门庆道：“你这个不是，你也不好！”那春梅就是了他一块，一口里放着。 \n",
"\n",
" 那西门庆不在他房里，只见他不在话。西门庆不想他到后边，就在门前看看。 \n",
"\n",
" 西门庆看了，就往西门庆家去。 \n",
"\n",
" 正说： \n",
"\n",
" 花枝柳花花园，柳妆花草。 \n",
" \n",
"\n",
" 西门庆到后边，一个月娘，就是李瓶儿房中，一个金莲、金莲、金莲、李娇儿、孟玉楼、金莲、金莲、玉箫、兰香、玉箫、玉楼在李瓶儿房边，打着他睡着，不想他去，只怕不见他来。正是： \n",
"\n",
" 风不知是多少，少不好人？ \n",
"\n",
" 话说月娘众人，见了一回，只见西门庆来家，说道：“你的小的不知道。”那李瓶儿不敢说。西门庆道：“你这等不起来，你不知道，你就是我不去。”那西门庆不言，说道：“你不知，我不知你不知，我也不是你。你说他家，你还不要他。我若要我，不是你家人，他不肯说，我也不知他这里，你不知我的他，不知怎么！”西门庆道：“不好，只怕我的不知你。”那妇人道：“他的好不知，我也不好了。你这两日，你还不好，不要你去。”月娘道：“他是你的好！”那妇人道：“我不是你。你说我，你还不在他这里，我不好。你若是我，他不好，你这等不打我，只要教你吃了。”于是走到前边，西门庆道：“我不好，你不知，不知你去了。我不知，我不知道。”西门庆笑道：“你这里，他也有个儿。”西门庆道：“你这里说，我就要去。我不知你那里去了。”那李瓶儿不知笑了，只见李瓶儿在前边坐了。 \n",
"\n",
" 正说： \n",
"\n",
" 不觉心中，如意思不逢。 \n",
"\n",
" 话说西门庆自从，不知道了。 \n",
"\n",
" 且说西门庆在家，不觉心中，不知大姐。正是： \n",
"\n",
" 风不逢无，难为，人情难为。 \n",
"\n",
" 话说西门庆到后边，只见西门庆一个小厮儿，不敢走来。正是： \n",
"\n",
" 不知多少不成，无人无情。 \n",
"\n",
" 话说西门庆自从家，不知道：“这个是个人的，不得你不在他。”那妇人听着，把手子都是一条，说道：“你老人家不好，你不好！”西门庆道：“你这奴才，不是一般说的。”月娘道：“你的，你这里有个人儿，你不肯吃了？”西门庆道：“你说，我不要你这两银子。”那西门庆道：“你说不得，你这个不好。”西门庆道：“我不知道，你不知道，我就是你说的话儿。我说他不知道。”西门庆道：“我，你还不知我，你就来了。我就不去，我不知你这些事儿！你还不知道，我不是你这个儿。”西门庆道：“你不是这个人，就是了我。你若要不知，他是不好，你也不好，只顾说了。”于是走到前边，月娘说：“大娘，你不曾吃了，我也是不好？”玉楼道：“我的姐姐，只是我不得你，他这个不知你的。”月娘道：“你的，我不要你，你还要来，我也不去。”李瓶儿笑道：“我也不好，你说了一日，我还要来你看你。”西门庆道：“你这个说，我也没不出来。你若不在家，我不知怎的？”李瓶儿道：“你休说，我也不是我，你也有个他的。我不知道你，他也不是你这里，只是一个小的儿，不敢是他。”那西门庆道：“你不知，我这一日就是他。”西门庆道：“我不知怎么？我不知，你就往前边去了一夜，就是我一日。”西门庆道：“我，你不好。我不知道他，我怎的不知？”那玉箫道：“不知我的他。”西门庆道：“我这等不是。”那妇人道：“我不知，不知你这个是谁？你不知我，你还不知我，你这个不在家。”西门庆道：“我的，你也不知你的。”于是把他拿出来了，一直到到前边，说道：“你爹不知，你这个不知怎么！”说毕，只见他来了，只见西门庆来到，不知道了一日，说了他，说：“爹，你不知道了。”西门庆道：“我不好不去。”西门庆道：“我，你不知道。”西门庆道：“你不知道。”于是走到前边坐了。西门庆因问：“你那日，你怎的不知？”月娘道：“不的我，不想了他来。你爹来家，你也不知他，只顾我来了。”西门庆道：“你说，你不知，你不知你。”这李铭、吴银儿、韩道国、李娇儿、李娇儿、孟玉楼、潘姥姥，都是李瓶儿。李瓶儿在炕上坐，不在他，看了一个。西门庆见了，只顾说话，说：“你们不曾，他不来，我不知道。”西门庆道：“你还要说话儿。只是我说的，不知怎的不得。”那妇人便不肯，把手子拿出来，说道：“你不知，你这等我来。”西门庆便道：“你不知，你还不知你，你不知道我。”妇人笑道：“你不知，你怎得不的来？’他说：‘你不知，你也有不知道！你若是你不知，不是你的。”妇人道：“不敢，你不知道。”说毕，西门庆笑了一回，说道：“你不知道，我不知怎么，到明日，我不知道。”那妇人听了，只顾叫：“我的儿，你不知你，你这等我来，你不在你，我把奴才来了，你与他这个头缠！”西门庆听见，不觉他来了，就不觉心下不觉。 \n",
"\n",
" 正说： \n",
"\n",
" 一日无事不知时，一个无人情。不想一日不知，谁知道： \n",
"\n",
" 正在房中，忽有一阵风风，走到雪娥房里。那妇人见了一回，说道：“我的你，你还没去了！”西门庆道：“我说不是他。”西门庆便道：“我不知，我这一件事不知你，我不在他家，你怎得这般！”西门庆道：“我不得我，我也不得。”那妇人笑道：“怪狗，我的不知，不是那边，我只怕你不好？我就不知，你就不去。”妇人道：“我不知道，你也不吃了，我不知我怎的，你就不去？”西门庆道：“我的不知，你怎的不知？”那西门庆笑道：“你不知道，不是你说的，不知道，只怕我不的，他也不知，我不知怎的。你如今日不在，只是他这里。”那玉楼笑着不得，只说：“大姐姐，你这个小厮儿，我不知你这里，你就没了！”那玉箫道：“他是不知，我的不知怎么，不敢去了。”月娘道：“不知道了。”西门庆道：“你这个不好！”西门庆听了，说道：“我不的，你这个不在那里，不想他来家去。”月娘道：“你不知怎么？”西门庆道：“他不知道，不想你家人家不的，只是他家，不敢说，我就是不得你！”那李铭道：“不知你，我在那里，就是你去！你不在我家，我也不知。”月娘笑道：“不是你，我不得我。我这个说话，我还有个头儿，我说你怎的？”李瓶儿道：“我，不要你看，我说你不知，你也不好了。”于是走到后边，只见月娘说，不在那边，只见小玉拿着两盒子，一面都打了，一个小厮，不在家。西门庆便问：“你家，我怎不得？”月娘道：“你这个不知道，不知道了。”西门庆道：“你说道：‘我不知怎的不知？我怎的不知道？我也不知，你不知我，不是你的人，你不知，我就把你那个儿打了一个，把他一个儿也不得了，我也不好。”月娘道：“你的，不是你的。”那李瓶儿连忙走来了。月娘道：“我不知道，我不知你，他不好。”西门庆道：“你这等不知，只是我的不是你。你说我在这里，我不在我家。”说着一声。西门庆道：“我这个说不是，只是你老爹，我也不敢说。他不知怎的的不得？”西门庆道：“你休说。”西门庆道：“既是这里，我不知道！”说毕，又是一个人家，一个小优儿，拿着琵琶，唱了一套《黄吕》儿，又把花子儿来了。 \n",
"\n",
" 西门庆道：“你家，不是你家。你这里有甚么？”西门庆道：“我不要紧，你就去了。”西门庆道：“我也是我不的，不好不的。”月娘道：“不好，我不的。”那春梅道：“我的你不在那里，我和你说话，只怕他不知道！”那春梅笑道：“我说你好，你不要我，我不知你。”这西门庆笑道：“你不的你说了，他怎的好不的？”那玉楼道：“我这里没人。”月娘道：“你不的，你还没了哩！”那玉箫骂道：“怪囚根子，谁敢是你！那日没的，我来不知你。你来家不去，不想你去了。”妇人笑道：“我，你休怪我说了，你不知你，你不好，我不要他来！你说他怎的不好？我的我不要他！”金莲道：“我不好，我的你不知道！我不在这边，我不想我。”那妇人道：“我不知我，你休要说，我就不知你。我不知我，不知你，我不知你这奴才，你不好。”那西门庆听见，把手中放了一顿，说道：“你休怪淫妇，你不好说，你不是你家，我也不是你。”那妇人道：“他的，我也没了，你不在这里，你怎的不来？”西门庆道：“不知你这等我，你还不要来！”那妇人道：“不知，你休要说，我也不知你。你如今我这般，你也不是，你自从你去了！”西门庆道：“我不知你，不知我这些儿。你不好不去！”那玉箫便不言语，一面走到前边，月娘说：“你不知道，我不要我，你说道：‘他来不要你，我不知你，你休要他。我说我也不得，你只顾要我，我不知你，只要我这个不知道。”西门庆道：“我不要你，我不知你不知，怎的不来我，我和你说话儿。你说他，你不知我，你也不好。”西门庆道：“他的，你不好，我就要你这般去。”妇玉道：“你这里，你这等我不来。”于是把李瓶儿打发了，一回去了。月娘道：“你说，你不好不吃，你不好？”那妇人道：“你不知，我也不是。”那玉箫道：“你的好，你不在这里，我怎的不得？”妇人道：“你这等我说，我不是你。”西门庆道：他不是你，你也没的，不好不的。他不好，我也不是我。”那玉楼道：“你还没个，你说他这个好儿。”那玉楼道：“你这个不的，你不要他，只怕我不好！”月娘道：“我的你，只是你一个不好？我的你不好！你若不知，你不知道，我不好，我不好！”西门庆道：“不好，我也不好，我不好不知。我不想你那个不好！”西门庆道：“你不吃。你不好，你这般，你不在你屋里睡罢。”于是把银子打了一回来，把他打发，打发他去了。正是： \n",
"\n",
" 风无非，无非，难不可知。 \n",
"\n",
" 话说一夜不题。 \n",
"\n",
" 且说西门庆到家，早辰，早晨出来安儿，早晚来，只见他往后边去了。西门庆见来，只见他来，不想他说道：“你爹来，我这等我不知道了，你不知你，不知你这里有些。”那婆子听了，只顾说着，不知道：“你的我这般，说不是他的。”月娘道：“他不是你。我说我的他，不是你的他。他不在我，他就不在家，你不知道，不知你这个不是他。”西门庆笑笑道：“我的，我不是你，你说他的，不知你不知怎么，你不知怎么？”那妇人道：“我不得，你不知道。你是你家人，我不是这等我。你若不知，我不知你这奴才。”妇人道：“我也不知，他就要了。你这等不知，我不知怎的，只怕你不知道。我就要我说。他不好，他只怕你来。”西门庆问：“你不知，我就是他。”月娘道：“他的不好，我也不知道，你也不知你，不敢来，你就是了。我不知道，我也不得你，不知你不知，你不在那里，我不知你。我这里有些不好，我就来了我。你这等你来！”西门庆道：“你不知道，我这等你不去。我若是你这等我，你就不知道。我也没了。”西门庆道：“我也不吃你，就把你去罢。”西门庆便道：“我不知道，你也是我不得，只怕不好。”西门庆道：“你不的，你还不知，我不知怎么！”月娘道：“你不好，我不好，你也没了。我一个儿也有不的。”西门庆道：“你也有这等，我也不要吃了。”西门庆道：“我不知，我这等你。你若不知你，我把你我不知，我不知你这等你去。”那妇人道：“我不知你。你若不要我，你休要我，我不要吃你，我把你就在我上，你不知怎么？”李瓶儿道：“你不好，你我说我，我不知道了，你不知，你这里吃酒，我不知，我把他也没了。”妇人道：“你休要他，只顾你说，你也不要他。”那妇人笑嘻嘻道：“他的你说，只怕是他的，我不是你。”那西门庆不知道，只怕不的。 \n",
"\n",
" 且说月娘，不见话下。 \n",
"\n",
" 且说西门庆进来，只顾说着，不知道话。那日晚夕，月娘在炕上坐着，忽想出来，见西门庆在家，只是一日，他来到房中。西门庆便叫春梅：“我来，你不吃，我这里来？我和你说：‘你来，我还不知道。你那个淫妇！你也没有甚么？”那妇人道：“你说，我不知道。我是他这等他，我就不知他，你怎的不知他？”月娘道：“他不的，只怕你这里来。”月娘道：“我不好，不是他。你不知，你还不去！”西门庆道：“我说你，我就要了他，你这里有话。你这等说他来，就是他的，你不知道。我就是他的，我的不知，怎的不知道！”月娘道：“不敢说他。”月娘道：“我的不是，只要我一个不好。”西门庆道：“你不是，只怕这里，我就没了。”那春梅笑嘻嘻走到，只问：“你爹，他怎的不得？”西门庆道：“你不是我的，我不好了，我不去了。”那春梅道：“你还不去，只怕他去。”西门庆道：“我也不好，你就是我去了。你还不去罢，我就把你来家。”西门庆道：“我不知道，我不知你，我不在家，你就是我说。”于是把李瓶儿拿着，只是小厮，把他打开了。月娘道：“你的，不知道，怎的说了！”西门庆道：“我不知，你怎的不见？”西门庆道：“你这里说，我就是你，我说不知你这里来！你这等我不知你，不知你这里有个不的？”李瓶儿便道：“你不知道，我这里不知道，我不是那边去了。他不知你不的，不知我，只顾你这个不知。”西门庆道：“我的不是你，他不知道了。”那春梅道：“他是他的。我也不要他，他不知道，只是不知，你这个不知道！我也是不知的事，你也不好。”妇人道：“他不的，只要我说，你也不去。”西门庆道：“不是你，我不好。你这等你，你不知他罢，不知我的不是你。”西门庆道：“我不知，我也不是。你不知，只是我这一般，你不知道了。”于是把银簪儿，打发了一个。西门庆道：“我不要你，不是我。”西门庆道：“你不知道，我不敢说我？”于是拿过一钟酒来。伯爵道：“我的你不说。我一日儿没了，我也不吃。”西门道：“我不要，你这个说话。”那李瓶儿不肯，说道：“小的，你不好，只要吃了些酒。我就不在家，不是我说话。”于是打了他两个，说了一遍。西门庆因问：“你的甚么？”西门庆道：“我不知道。”说道：“我的你说话！”西门庆道：“我的不知，你还有个人儿。”西门大官人听了，又不肯出来。西门庆道：“我说你不是。”西门庆道：“我不知，我这等他去，你不知，我也不知。”那春梅便问：“我，你怎的不得你？”西门庆道：“他，我也不是你。”李瓶儿道：“我，我不要说。”那玉箫又道：“我的小淫妇儿，你怎的不去！”那玉楼笑嘻嘻笑道：“我的不是你，我的你也罢。”月娘道：“我不知你，不知你怎样的，只怕我不在这里，教我和你说，你这个不知怎的？”妇人道：“我说，我不知怎的？”那西门庆听了，便道：“你这里，我就没个人儿！”这妇人道：“你也不要，你说他。”那婆子笑道：“我不知，你也不是你。我不知道他，我也不知，你就不是。”西门庆笑道：“不的，你不在我，你说话儿。你说着你家去，我就是了。”西门庆道：“我不好，他不知道！”西门庆道：“你休不信他，只是我一般儿，他不是我的。”于是向袖中取出出来，递与月娘。妇人道：“我，不知你，你这等我来？”西门庆道：“我不知你，我也不是你，我也不好不得，你不好，不想我的我不知，你\n"
]
}
],
"source": [
"checkpoint = tf.train.latest_checkpoint('checkpoints')\n",
"samp = sample(checkpoint, 7000, lstm_size, len(vocab), prime=\"浪\")\n",
"print(samp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"checkpoint = 'checkpoints/i200_l512.ckpt'\n",
"samp = sample(checkpoint, 1000, lstm_size, len(vocab), prime=\"Far\")\n",
"print(samp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"checkpoint = 'checkpoints/i600_l512.ckpt'\n",
"samp = sample(checkpoint, 1000, lstm_size, len(vocab), prime=\"Far\")\n",
"print(samp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"checkpoint = 'checkpoints/i1200_l512.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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
MingChen0919/learning-apache-spark | notebooks/04-miscellaneous/add-python-files-to-spark-cluster.ipynb | 1 | 2949 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `SparkContext.addPyFiles()` function can be used to add py files. We can define objects and variables in these files and make them available to the Spark cluster."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a SparkContext object"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pyspark import SparkConf, SparkContext, SparkFiles\n",
"from pyspark.sql import SparkSession"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc = SparkContext(conf=SparkConf())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Add py files"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc.addPyFile('pyFiles/my_module.py')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'/private/var/folders/2_/kb60z5_j0k91tyh740s1zhn40000gn/T/spark-4f959e9f-4af6-490e-afce-02e1582aae8d/userFiles-8b1c073b-4c82-467a-b9ff-021aa3067abe/my_module.py'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"SparkFiles.get('my_module.py')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Use **my_module.py**\n",
"We can import `my_module` as a python module"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from my_module import *"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"addPyFiles_is_successfull()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sum_two_variables(4,5)"
]
}
],
"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": "3.5.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
Ledoux/ShareYourSystem | Pythonlogy/ShareYourSystem/Standards/Interfacers/Folderer/Readme.ipynb | 1 | 8131 | {
"nbformat": 3,
"worksheets": [
{
"cells": [
{
"source": "\n<!--\nFrozenIsBool False\n-->\n\n#Folderer\n\n##Doc\n----\n\n\n> \n> The Folderer is a quick object helping for getting the FolderedDirKeyStrsList\n> at a specified directory or in the current one by default\n> \n> \n\n----\n\n<small>\nView the Folderer notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Folderer.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 Folderer is a quick object helping for getting the FolderedDirKeyStrsList\nat a specified directory or in the current one by default\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Standards.Interfacers.Interfacer\"\nDecorationModuleStr=\"ShareYourSystem.Standards.Classors.Classer\"\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nimport collections\nimport json\nimport os\nimport sys\n#</ImportSpecificModules>\n\n#<DefineClass>\n@DecorationClass()\nclass FoldererClass(BaseClass):\n\t\"\"\"\n\t\tFoldererClass ...\n\n\t\"\"\"\n\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t\t\t'FolderingPathVariable',\n\t\t\t\t\t\t\t\t\t'FolderingMkdirBool',\n\t\t\t\t\t\t\t\t\t'FolderedDirKeyStrsList',\t\n\t\t\t\t\t\t\t\t\t'FolderedModuleStr',\n\t\t\t\t\t\t\t\t\t'FolderedParentModuleStr',\n\t\t\t\t\t\t\t\t\t'FolderedNameStr'\n\t\t\t\t\t\t\t\t]\n\n\tdef default_init(self,\n\t\t\t\t\t\t_FolderingPathVariable=\"\",\n\t\t\t\t\t\t_FolderingMkdirBool=False,\n\t\t\t\t\t\t_FolderedDirKeyStrsList=None,\t\n\t\t\t\t\t\t_FolderedModuleStr=\"\",\n\t\t\t\t\t\t_FolderedParentModuleStr=\"\",\n\t\t\t\t\t\t_FolderedNameStr=\"\",\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\t\n\tdef do_folder(self,**_KwargVariablesDict):\n\n\t\t#Get the current\n\t\tFolderedCurrentPathStr=os.getcwd()\n\n\t\t#set\n\t\tif self.FolderingPathVariable==\"\":\n\t\t\tself.FolderingPathVariable=FolderedCurrentPathStr+'/'\n\n\t\t#debug\n\t\t'''\n\t\tprint('self.FolderingPathVariable is '+self.FolderingPathVariable)\n\t\tprint('FolderedCurrentPathStr is '+FolderedCurrentPathStr)\n\t\tprint('')\n\t\t'''\n\t\t\t\n\t\t#Check\n\t\tif self.FolderingPathVariable!=\"\":\n\n\t\t\t#Add the '/' if not in the end\n\t\t\tif self.FolderingPathVariable[-1]!=\"/\":\n\t\t\t\tself.FolderingPathVariable+=\"/\"\n\n\t\t\t#Build intermediar pathes\n\t\t\tif os.path.isdir(self.FolderingPathVariable)==False:\n\n\t\t\t\t#Check\n\t\t\t\tif self.FolderingMkdirBool:\n\n\t\t\t\t\t#debug\n\t\t\t\t\t'''\n\t\t\t\t\tprint('We are going to build the intermediar folder')\n\t\t\t\t\tprint('self.FolderingPathVariable is ',self.FolderingPathVariable)\n\t\t\t\t\tprint('')\n\t\t\t\t\t'''\n\n\t\t\t\t\t#Definition\n\t\t\t\t\tFolderingPathVariablesList=self.FolderingPathVariable.split('/')\n\t\t\t\t\tFolderedRootPathStr=FolderingPathVariablesList[0]\n\t\t\t\t\tfor _PathStr in FolderingPathVariablesList[1:]:\n\n\t\t\t\t\t\t#debug\n\t\t\t\t\t\t'''\n\t\t\t\t\t\tprint('FolderedRootPathStr is ',FolderedRootPathStr)\n\t\t\t\t\t\tprint('')\n\t\t\t\t\t\t'''\n\n\t\t\t\t\t\t#Mkdir if it doesn't exist\n\t\t\t\t\t\tif FolderedRootPathStr!=\"\" and os.path.isdir(FolderedRootPathStr)==False:\n\t\t\t\t\t\t\tos.popen('mkdir '+FolderedRootPathStr)\n\n\t\t\t\t\t\t#Add the following\n\t\t\t\t\t\tFolderedRootPathStr+='/'+_PathStr\n\n\t\t\t\t\t#Mkdir if it doesn't exist\n\t\t\t\t\tif os.path.isdir(FolderedRootPathStr)==False:\n\t\t\t\t\t\tos.popen('mkdir '+FolderedRootPathStr)\n\n\t\t#Recheck\n\t\tif os.path.isdir(self.FolderingPathVariable):\n\n\t\t\t#set\n\t\t\tself.FolderedDirKeyStrsList=os.listdir(self.FolderingPathVariable)\n\n\t\t\t#Check\n\t\t\tif '__init__.py' in self.FolderedDirKeyStrsList:\n\n\t\t\t\t#set maybe FolderedModuleStr and FolderedParentModuleStr if we are located in the SYS path\n\t\t\t\tif 'ShareYourSystem' in self.FolderingPathVariable:\n\n\t\t\t\t\t#set\n\t\t\t\t\tself.FolderedModuleStr='ShareYourSystem'+self.FolderingPathVariable.split(\n\t\t\t\t\t\t'ShareYourSystem')[-1].replace('/','.')\n\n\t\t\t\t\t#Remove the ossibly last dot\n\t\t\t\t\tif self.FolderedModuleStr[-1]=='.':\n\t\t\t\t\t\tself.FolderedModuleStr=self.FolderedModuleStr[:-1]\n\n\t\t\t\t\t#set\n\t\t\t\t\tif '.' in self.FolderedModuleStr:\n\n\t\t\t\t\t\t#set\n\t\t\t\t\t\tself.FolderedNameStr=self.FolderedModuleStr.split('.')[-1]\n\n\t\t\t\t\t\t#debug\n\t\t\t\t\t\t'''\n\t\t\t\t\t\tself.debug(('self.',self,['FolderingPathVariable','FolderedNameStr']))\n\t\t\t\t\t\t'''\n\t\t\t\t\t\t\n\t\t\t\t\t\t#set the parent\n\t\t\t\t\t\tself.FolderedParentModuleStr=self.FolderedNameStr.join(\n\t\t\t\t\t\t\tself.FolderedModuleStr.split(self.FolderedNameStr)[:-1]\n\t\t\t\t\t\t)\n\t\t\t\t\t\tif len(self.FolderedParentModuleStr\n\t\t\t\t\t\t\t)>0 and self.FolderedParentModuleStr[-1]=='.':\n\t\t\t\t\t\t\tself.FolderedParentModuleStr=self.FolderedParentModuleStr[:-1]\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.FolderedModuleStr=self.FolderedModuleStr\n\n\t\t\telse:\n\n\t\t\t\t#set\n\t\t\t\tself.FolderedModuleStr=\"\"\n\t\t\t\tself.FolderedParentModuleStr=\"\"\n\n\t\t#Return self\n\t\t#return self\n\n#</DefineClass>\n\n```\n\n<small>\nView the Folderer sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Interfacers/Folderer\" 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.\n",
"cell_type": "markdown",
"prompt_number": 2,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
}
},
{
"source": "```python\n#ImportModules\nimport ShareYourSystem as SYS\nfrom ShareYourSystem.Standards.Interfacers import Folderer\n\n#Definition of an instance Folderer and make it find the current dir\nMyFolderer=Folderer.FoldererClass(\n ).folder(\n Folderer.LocalFolderPathStr\n )\n\n#If you don't have these folder, MyFolderer is going to create them for you\nMyFolderer=Folderer.FoldererClass(\n ).folder(\n MyFolderer.FolderingPathVariable+'TestFolder1/TestFolder2/',\n True\n )\n\n#Definition the AttestedStr\nSYS._attest(\n [\n 'MyFolderer is '+SYS._str(MyFolderer,\n **{'RepresentingAlineaIsBool':False})\n ]\n) \n\n#Print\n\n\n```\n",
"cell_type": "markdown",
"metadata": {}
},
{
"source": "```console\n>>>\n\n\n*****Start of the Attest *****\n\nMyFolderer is < (FoldererClass), 4540265104>\n /{ \n / '<New><Instance>IdInt' : 4540265104\n / '<Spe><Class>FolderedNameStr' : \n / '<Spe><Instance>FolderedDirKeyStrsList' : []\n / '<Spe><Instance>FolderedModuleStr' : \n / '<Spe><Instance>FolderedParentModuleStr' : \n / '<Spe><Instance>FolderingMkdirBool' : True\n / '<Spe><Instance>FolderingPathVariable' : /Users/ledoux/Documents/ShareYourSystem/Pythonlogy/ShareYourSystem/Interfacers/Folderer/TestFolder1/TestFolder2/\n /}\n\n*****End of the Attest *****\n\n\n\n```\n",
"cell_type": "markdown",
"metadata": {}
}
]
}
],
"metadata": {
"name": "",
"signature": ""
},
"nbformat_minor": 0
} | mit |
Ledoux/ShareYourSystem | Pythonlogy/ShareYourSystem/Standards/Classors/Tester/Presentation.ipynb | 1 | 14313 | {
"nbformat": 3,
"worksheets": [
{
"cells": [
{
"source": "\n<!--\nFrozenIsBool False\n-->\n\n#Tester\n\n##Doc\n----\n\n\n> \n> The Tester helps for defining asserts between \n> attested Strs stored in the Attests Folder and\n> new calls of the attest functions. Thanks here \n> to a wrap of the unitest python module :\n> https://docs.python.org/2/library/unittest.html\n> \n> \n\n----\n\n<small>\nView the Tester notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Tester.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 Tester helps for defining asserts between \nattested Strs stored in the Attests Folder and\nnew calls of the attest functions. Thanks here \nto a wrap of the unitest python module :\nhttps://docs.python.org/2/library/unittest.html\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Standards.Classors.Attester\"\nDecorationModuleStr=BaseModuleStr\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nimport collections\nimport copy\nimport os\nimport sys\nimport unittest\nimport ShareYourSystem as SYS\nfrom ShareYourSystem.Standards.Classors import Representer\nAttester=BaseModule\n#</ImportSpecificModules>\n\n#<DefineLocals>\nAttestingStr='attest'\n#</DefineLocals>\n\n#<DefineFunctions>\ndef setTestFunctionWithFolderPathStrAndAttestUnboundMethod(\n\t_FolderPathStr,_AttestUnboundMethod):\n\n\t#Definition\n\tAttestUnboundMethodStr=_AttestUnboundMethod.__name__\n\tTestUnboundMethodStr='at'.join(AttestUnboundMethodStr.split('at')[1:])\n\tTestModule=sys.modules[_AttestUnboundMethod.__module__]\n\n\t#Debug\n\t'''\n\tprint('l 50 Tester')\n\tprint('_AttestUnboundMethod is ',_AttestUnboundMethod)\n\tprint('_AttestUnboundMethod.__module__ is ',_AttestUnboundMethod.__module__)\n\tprint('')\n\t'''\n\n\t#Define\n\tdef test(_InstanceVariable):\n\n\t\t#Get the AssertedStr\n\t\tFile=open(_FolderPathStr+AttestUnboundMethodStr+'.txt','r')\n\t\tAttestStr=File.read()\n\t\tFile.close()\n\n\t\t#Call the attest function to get the TestVariable\n\t\tTestVariable=_AttestUnboundMethod(\n\t\t\t\t\t\tgetattr(\n\t\t\t\t\t\t\tSYS,\n\t\t\t\t\t\t\tSYS.getClassStrWithNameStr(\n\t\t\t\t\t\t\t\tTestModule.__name__.split('.')[-1]\n\t\t\t\t\t\t\t\tif '.' in TestModule.__name__ else TestModule\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t)()\n\t\t\t\t\t)\n\n\t\t#Bind with TestStr setting\n\t\tRepresenter.RepresentingIdBool=False\n\t\tTestStr=Representer.getRepresentedStrWithVariable(TestVariable)\n\t\tRepresenter.RepresentingIdBool=True\n\n\t\t#Represent maybe\n\t\tif TestModule.TestingPrintIsBool:\n\n\t\t\t#debug\n\t\t\tprint(\"\\n###########################################\")\n\t\t\tprint(\"\")\n\t\t\tprint('AttestStr is :')\n\t\t\tprint(AttestStr)\n\t\t\tprint(\"\")\n\n\t\t\t#debug\n\t\t\tprint('TestStr is :')\n\t\t\tprint(TestStr)\n\t\t\tprint(\"\")\n\n\t\t#Assert\n\t\t#print(\"a\",AssertingStr)\n\t\t#print(\"b\",_InstanceVariable.TestedPointer.TestStr)\n\n\t\t#assert\n\t\t_InstanceVariable.assertEqual(\n\t\t\t\t#1,1\n\t\t\t\tAttestStr,\n\t\t\t\tTestStr\n\t\t)\n\n\t#Copy a form of the test function and name it differently\n\ttest.__name__=TestUnboundMethodStr\n\n\t#Debug\n\t'''\n\tprint('l 96 Tester')\n\tprint('TestModule is',TestModule)\n\tprint('')\n\t'''\n\t\n\t#Append in the Test OrderedDict\n\tTestModule.TestedOrderedDict[test.__name__]=test\n#</DefineFunctions>\n\n#<DefineClass>\n@DecorationClass()\nclass TesterClass(BaseClass):\n\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t]\n\n\tdef default_init(self,**_KwargVariablesDict):\n\n\t\t#Call the parent init method\n\t\tBaseClass.__init__(self,**_KwargVariablesDict)\n\n\tdef __call__(self,_Class):\n\n\t\t#Call the parent init method\n\t\tBaseClass.__call__(self,_Class)\n\n\t\t#Represent\n\t\tself.test()\n\n\t\t#Return\n\t\treturn _Class\n\n\tdef do_test(self):\n\t\t\n\t\t#Init in the classed Module\n\t\tif hasattr(self.Module,'TestingPrintIsBool')==False:\n\t\t\tself.Module.TestingPrintIsBool=True\n\t\tself.Module.TestedOrderedDict=collections.OrderedDict()\n\n\t\t#set the tests for each asserting function\n\t\tmap(\n\t\t\t\tlambda __AttestedUnboundMethod:\n\t\t\t\tsetTestFunctionWithFolderPathStrAndAttestUnboundMethod(\n\t\t\t\t\tself.AttestingFolderPathStr,\n\t\t\t\t\t__AttestedUnboundMethod\n\t\t\t\t),\n\t\t\t\tself.AttestedUnboundMethodsList\n\t\t\t)\n\t\t\t\n\t\t#Definition the TestClass\n\t\tclass TestClass(unittest.TestCase):\t\t\t\t\n\n\t\t\t#Bind with the Tested object\n\t\t\tTestedPointer=self\n\n\t\t\t#Bound the setUp function\n\t\t\t#locals().__setitem__(setUp.__name__,setUp)\n\t\t\t\n\t\t\t#Bound each testing function\n\t\t\tfor __InstanceVariableedKeyStr,__InstanceVariableedMethod in self.Module.TestedOrderedDict.items():\n\t\t\t\tlocals().__setitem__(__InstanceVariableedKeyStr,__InstanceVariableedMethod)\n\n\t\t\ttry:\n\t\t\t\tdel TestedKeyStr,TestedMethod\n\t\t\texcept:\n\t\t\t\tpass\n\n\t\t#Give a name\n\t\tTestClass.__name__=SYS.getClassStrWithNameStr(self.NameStr+'Test')\n\n\t\t#set to the module of the classing class\n\t\tself.Module.TestedClass=TestClass\n\n\t\t#Definition\n\t\tdef test():\n\n\t\t\t#Call to the unittest runner\n\t\t\tTestLoader=unittest.TestLoader().loadTestsFromTestCase(\n\t\t\t\tself.Module.TestedClass\n\t\t\t)\n\t\t\tunittest.TextTestRunner(verbosity=2).run(TestLoader)\n\n\t\t#Link to the module of the classing class\n\t\tself.Module.test=test\n#</DefineClass>\n\n#Set\nTesterClass.DeriveClassor.AttestingFolderPathStr=Attester.AttesterClass.DeriveClassor.AttestingFolderPathStr\n\n```\n\n<small>\nView the Tester sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Classors/Tester\" 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\nThe Incrementer from the previous Attester Module is now tested from its corresponding attesting function\nattest_increment. Here a test_increment method is implicitely defined in a unittest class and when we call\nthe test method, a unittest.run is done.",
"cell_type": "markdown",
"prompt_number": 2,
"metadata": {
"slideshow": {
"slide_type": "-"
}
}
},
{
"cell_type": "code",
"prompt_number": 3,
"language": "python",
"input": [
"#ImportModules\n",
"import ShareYourSystem as SYS\n",
"\n",
"#Attest the module\n",
"SYS.DecrementerClass(\n",
" ).setAttest(\n",
" SYS.TesterClass.DeriveClassor.AttestingFolderPathStr\n",
" )\n",
"SYS.Decrementer.test()\n",
"\n",
"\n",
"\n"
],
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"###########################################\n",
"\n",
"AttestStr is :\n",
"-1\n",
"\n",
"TestStr is :\n",
"-1\n",
"\n"
]
}
],
"collapsed": false,
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
}
},
{
"source": "\n<!--\nFrozenIsBool False\n-->\n\n##Code\n\n----\n\n<ClassDocStr>\n\n<small>\nView the Tester sources on <a href=\"https://github.com/Ledoux/ShareYourSystem/tree/master/Pythonlogy/ShareYourSystem/Standards/Classors/Tester\" target=\"_blank\">Github</a>\n</small>\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 Tester helps for defining asserts between \nattested Strs stored in the Attests Folder and\nnew calls of the attest functions. Thanks here \nto a wrap of the unitest python module :\nhttps://docs.python.org/2/library/unittest.html\n\n\"\"\"\n\n#<DefineAugmentation>\nimport ShareYourSystem as SYS\nBaseModuleStr=\"ShareYourSystem.Standards.Classors.Attester\"\nDecorationModuleStr=BaseModuleStr\nSYS.setSubModule(globals())\n#</DefineAugmentation>\n\n#<ImportSpecificModules>\nimport collections\nimport copy\nimport os\nimport sys\nimport unittest\nimport ShareYourSystem as SYS\nfrom ShareYourSystem.Standards.Interfacers import Printer\nAttester=BaseModule\n#</ImportSpecificModules>\n\n#<DefineLocals>\nAttestingStr='attest'\n#</DefineLocals>\n\n#<DefineFunctions>\ndef setTestFunctionWithFolderPathStrAndAttestUnboundMethod(\n\t_FolderPathStr,_AttestUnboundMethod):\n\n\t#Definition\n\tAttestUnboundMethodStr=_AttestUnboundMethod.__name__\n\tTestUnboundMethodStr='at'.join(AttestUnboundMethodStr.split('at')[1:])\n\tTestModule=sys.modules[_AttestUnboundMethod.__module__]\n\n\t#Debug\n\t'''\n\tprint('l 50 Tester')\n\tprint('_AttestUnboundMethod is ',_AttestUnboundMethod)\n\tprint('_AttestUnboundMethod.__module__ is ',_AttestUnboundMethod.__module__)\n\tprint('')\n\t'''\n\n\t#Define\n\tdef test(_InstanceVariable):\n\n\t\t#Debug\n\t\t'''\n\t\tprint('Tester l 62')\n\t\tprint('_FolderPathStr is '+_FolderPathStr)\n\t\tprint('AttestUnboundMethodStr is '+AttestUnboundMethodStr)\n\t\tprint('')\n\t\t'''\n\t\t\n\t\t#Get the AssertedStr\n\t\tFile=open(_FolderPathStr+AttestUnboundMethodStr+'.txt','r')\n\t\tAttestStr=File.read()\n\t\tFile.close()\n\n\t\t#Call the attest function to get the TestVariable\n\t\tTestVariable=_AttestUnboundMethod(\n\t\t\t\t\t\tgetattr(\n\t\t\t\t\t\t\tSYS,\n\t\t\t\t\t\t\tSYS.getClassStrWithNameStr(\n\t\t\t\t\t\t\t\tTestModule.__name__.split('.')[-1]\n\t\t\t\t\t\t\t\tif '.' in TestModule.__name__ else TestModule\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t)()\n\t\t\t\t\t)\n\n\t\t#Bind with TestStr setting\n\t\tPrinter.RepresentingIdBool=False\n\t\tTestStr=Printer.getPrintStr(TestVariable)\n\t\tPrinter.RepresentingIdBool=True\n\n\t\t#Represent maybe\n\t\tif TestModule.TestingPrintIsBool:\n\n\t\t\t#debug\n\t\t\tprint(\"\\n###########################################\")\n\t\t\tprint(\"\")\n\t\t\tprint('AttestStr is :')\n\t\t\tprint(AttestStr)\n\t\t\tprint(\"\")\n\n\t\t\t#debug\n\t\t\tprint('TestStr is :')\n\t\t\tprint(TestStr)\n\t\t\tprint(\"\")\n\n\t\t#Assert\n\t\t#print(\"a\",AssertingStr)\n\t\t#print(\"b\",_InstanceVariable.TestedPointer.TestStr)\n\n\t\t#assert\n\t\t_InstanceVariable.assertEqual(\n\t\t\t\t#1,1\n\t\t\t\tAttestStr,\n\t\t\t\tTestStr\n\t\t)\n\n\t#Copy a form of the test function and name it differently\n\ttest.__name__=TestUnboundMethodStr\n\n\t#Debug\n\t'''\n\tprint('l 96 Tester')\n\tprint('TestModule is',TestModule)\n\tprint('')\n\t'''\n\t\n\t#Append in the Test OrderedDict\n\tTestModule.TestedOrderedDict[test.__name__]=test\n#</DefineFunctions>\n\n#<DefineClass>\n@DecorationClass()\nclass TesterClass(BaseClass):\n\n\t#Definition\n\tRepresentingKeyStrsList=[\n\t\t\t\t\t\t\t]\n\n\tdef default_init(self,**_KwargVariablesDict):\n\n\t\t#Call the parent init method\n\t\tBaseClass.__init__(self,**_KwargVariablesDict)\n\n\tdef __call__(self,_Class):\n\n\t\t#debug\n\t\t'''\n\t\tprint('Tester l.146 __call__ method')\n\t\tprint('_Class is ',_Class)\n\t\tprint('')\n\t\t'''\n\t\t\n\t\t#Call the parent init method\n\t\tBaseClass.__call__(self,_Class)\n\n\t\t#Represent\n\t\tself.test()\n\n\t\t#Return\n\t\treturn _Class\n\n\tdef do_test(self):\n\t\t\n\t\t#Init in the classed Module\n\t\tif hasattr(self.Module,'TestingPrintIsBool')==False:\n\t\t\tself.Module.TestingPrintIsBool=True\n\t\tself.Module.TestedOrderedDict=collections.OrderedDict()\n\n\t\t#Debug\n\t\t'''\n\t\tprint('Tester l 160')\n\t\tprint('self.AttestingFolderPathStr is '+self.AttestingFolderPathStr)\n\t\tprint('')\n\t\t'''\n\n\t\t#set the tests for each asserting function\n\t\tmap(\n\t\t\t\tlambda __AttestedUnboundMethod:\n\t\t\t\tsetTestFunctionWithFolderPathStrAndAttestUnboundMethod(\n\t\t\t\t\tself.AttestingFolderPathStr,\n\t\t\t\t\t__AttestedUnboundMethod\n\t\t\t\t),\n\t\t\t\tself.AttestedUnboundMethodsList\n\t\t\t)\n\t\t\t\n\t\t#Definition the TestClass\n\t\tclass TestClass(unittest.TestCase):\t\t\t\t\n\n\t\t\t#Bind with the Tested object\n\t\t\tTestedPointer=self\n\n\t\t\t#Bound the setUp function\n\t\t\t#locals().__setitem__(setUp.__name__,setUp)\n\t\t\t\n\t\t\t#Bound each testing function\n\t\t\tfor __InstanceVariableedKeyStr,__InstanceVariableedMethod in self.Module.TestedOrderedDict.items():\n\t\t\t\tlocals().__setitem__(__InstanceVariableedKeyStr,__InstanceVariableedMethod)\n\n\t\t\ttry:\n\t\t\t\tdel TestedKeyStr,TestedMethod\n\t\t\texcept:\n\t\t\t\tpass\n\n\t\t#Give a name\n\t\tTestClass.__name__=SYS.getClassStrWithNameStr(self.NameStr+'Test')\n\n\t\t#set to the module of the classing class\n\t\tself.Module.TestedClass=TestClass\n\n\t\t#Definition\n\t\tdef test():\n\n\t\t\t#Call to the unittest runner\n\t\t\tTestLoader=unittest.TestLoader().loadTestsFromTestCase(\n\t\t\t\tself.Module.TestedClass\n\t\t\t)\n\t\t\tunittest.TextTestRunner(verbosity=2).run(TestLoader)\n\n\t\t#Link to the module of the classing class\n\t\tself.Module.test=test\n#</DefineClass>\n\n#Set\nTesterClass.DeriveClassor.AttestingFolderPathStr=Attester.AttesterClass.DeriveClassor.AttestingFolderPathStr\n\n```\n\n",
"cell_type": "markdown",
"prompt_number": 4,
"metadata": {
"slideshow": {
"slide_type": null
}
}
}
]
}
],
"metadata": {
"name": "",
"signature": ""
},
"nbformat_minor": 0
} | mit |
googleinterns/new-semantic-parsing | notebooks/04_forward_data.ipynb | 1 | 6068 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"sys.path.append('..')\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"\n",
"import transformers\n",
"import tokenizers\n",
"\n",
"from new_semantic_parsing import EncoderDecoderWPointerModel\n",
"from new_semantic_parsing import TopSchemaTokenizer"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"ENCODER_NAME = 'distilbert-base-uncased'\n",
"HIDDEN = 768"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"9\n",
"9\n"
]
}
],
"source": [
"tokenizer = transformers.AutoTokenizer.from_pretrained(ENCODER_NAME, use_fast=True)\n",
"encoder = transformers.AutoModel.from_pretrained(ENCODER_NAME)\n",
"\n",
"vocab = {'[', ']', 'IN:', 'SL:', 'GET_DIRECTIONS', 'DESTINATION',\n",
" 'DATE_TIME_DEPARTURE', 'GET_ESTIMATED_ARRIVAL'}\n",
"schema_tokenizer = TopSchemaTokenizer(vocab, tokenizer)\n",
"\n",
"print(len(vocab) + 1) # plus padding\n",
"print(schema_tokenizer.vocab_size)\n",
"\n",
"# BERTConfig is a generic transformer and is only decoder Transformers support by now\n",
"decoder_config = transformers.BertConfig(\n",
" vocab_size=schema_tokenizer.vocab_size + encoder.config.vocab_size,\n",
" hidden_size=HIDDEN,\n",
" is_decoder=True, # adds cross-attention modules and enables causal masking\n",
")\n",
"\n",
"decoder = transformers.BertModel(decoder_config)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"model = EncoderDecoderWPointerModel(encoder, decoder)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[101, 7826, 2000, 15521, 102]\n",
"[2, 4, 5, 10, 11, 2, 6, 7, 12, 1, 1]\n"
]
}
],
"source": [
"source_text = 'Directions to Lowell'\n",
"schema_text = '[IN:GET_DIRECTIONS Directions to [SL:DESTINATION Lowell]]'\n",
"\n",
"source_ids = tokenizer.encode(source_text)\n",
"schema_ids = schema_tokenizer.encode(schema_text, source_ids)\n",
"\n",
"print(source_ids)\n",
"print(schema_ids)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[101, 7826, 2000, 15521, 102]\n",
"[2, 4, 5, 10, 11, 2, 6, 7, 12, 1, 1]\n"
]
}
],
"source": [
"source_text = 'Directions to Lowell'\n",
"schema_text = '[IN:GET_DIRECTIONS Directions to [SL:DESTINATION Lowell]]'\n",
"\n",
"source_ids = tokenizer.encode(source_text)\n",
"schema_ids = schema_tokenizer.encode(schema_text, source_ids)\n",
"\n",
"print(source_ids)\n",
"print(schema_ids)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[ 101, 7826, 2000, 15521, 102]]) torch.int64\n",
"tensor([[ 2, 4, 5, 10, 11, 2, 6, 7, 12, 1, 1]]) torch.int64\n"
]
}
],
"source": [
"x = torch.tensor([source_ids])\n",
"y = torch.tensor([schema_ids])\n",
"\n",
"mask = torch.ones_like(x)\n",
"mask[:, 0] = 0.\n",
"mask[source_ids == tokenizer.sep_token_id] = 0.\n",
"\n",
"print(x, x.dtype)\n",
"print(y, y.dtype)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(tensor([[0, 1, 1, 1, 1]]), torch.Size([1, 5]))"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mask, mask.shape"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source_ids == tokenizer.sep_token_id"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"combined_logits = model(input_ids=x, decoder_input_ids=y, pointer_attention_mask=mask)[0]"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"combined_logits.shape == (1, 11, 14)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"combined_logits.shape[2] == schema_tokenizer.vocab_size + len(source_ids)"
]
},
{
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| apache-2.0 |
Prooffreader/Prooffreader_snippets | matplotlib_examples/heatmap.ipynb | 1 | 131556 | {
"metadata": {
"name": "",
"signature": "sha256:84db97c8a65d63ee17891774a5a4c5594037cc8b316b33cd68aadca0114f0c89"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"'''\n",
"-------------------------------------------------------------------------------\n",
"Filename : heatmap.py\n",
"Date : 2013-04-19\n",
"Author : Joe Lotz\n",
"Description: My attempt at reproducing the FlowingData graphic in Python\n",
"Source : http://flowingdata.com/2010/01/21/how-to-make-a-heatmap-a-quick-and-easy-solution/\n",
"\n",
"Other Links:\n",
" http://stackoverflow.com/questions/14391959/heatmap-in-matplotlib-with-pcolor\n",
" \n",
"-------------------------------------------------------------------------------\n",
"'''"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"'\\n-------------------------------------------------------------------------------\\nFilename : heatmap.py\\nDate : 2013-04-19\\nAuthor : Joe Lotz\\nDescription: My attempt at reproducing the FlowingData graphic in Python\\nSource : http://flowingdata.com/2010/01/21/how-to-make-a-heatmap-a-quick-and-easy-solution/\\n\\nOther Links:\\n http://stackoverflow.com/questions/14391959/heatmap-in-matplotlib-with-pcolor\\n \\n-------------------------------------------------------------------------------\\n'"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import pandas as pd\n",
"from urllib2 import urlopen \n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named 'urllib2'",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-7c008141ba79>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmagic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0murllib2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0murlopen\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mImportError\u001b[0m: No module named 'urllib2'"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"page = urlopen(\"http://datasets.flowingdata.com/ppg2008.csv\")\n",
"nba = pd.read_csv(page, index_col=0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Normalize data columns\n",
"nba_norm = (nba - nba.mean()) / (nba.max() - nba.min())\n",
"# Sort data according to Points, lowest to highest\n",
"# This was just a design choice made by Yau\n",
"nba_sort = nba_norm.sort('PTS',ascending=True, inplace=True)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Plot it out\n",
"fig, ax = plt.subplots()\n",
"heatmap = ax.pcolor(nba_sort, cmap=plt.cm.Blues, alpha=0.8)\n",
"\n",
"##################################################\n",
"## FORMAT ##\n",
"##################################################\n",
"\n",
"fig = plt.gcf()\n",
"fig.set_size_inches(8,11)\n",
"\n",
"# turn off the frame\n",
"ax.set_frame_on(False)\n",
"\n",
"# put the major ticks at the middle of each cell\n",
"ax.set_yticks(np.arange(nba_sort.shape[0])+0.5, minor=False)\n",
"ax.set_xticks(np.arange(nba_sort.shape[1])+0.5, minor=False)\n",
"\n",
"# want a more natural, table-like display\n",
"ax.invert_yaxis()\n",
"ax.xaxis.tick_top()\n",
"\n",
"# Set the labels\n",
"\n",
"# label source:https://en.wikipedia.org/wiki/Basketball_statistics\n",
"labels = ['Games','Minutes','Points','Field goals made','Field goal attempts','Field goal percentage','Free throws made','Free throws attempts','Free throws percentage','Three-pointers made','Three-point attempt','Three-point percentage','Offensive rebounds','Defensive rebounds','Total rebounds','Assists','Steals','Blocks','Turnover','Personal foul']\n",
"\n",
"# note I could have used nba_sort.columns but made \"labels\" instead\n",
"ax.set_xticklabels(labels, minor=False) \n",
"ax.set_yticklabels(nba_sort.index, minor=False)\n",
"\n",
"# rotate the \n",
"plt.xticks(rotation=90)\n",
"\n",
"ax.grid(False)\n",
"\n",
"# Turn off all the ticks\n",
"ax = plt.gca()\n",
"\n",
"for t in ax.xaxis.get_major_ticks(): \n",
" t.tick1On = False \n",
" t.tick2On = False \n",
"for t in ax.yaxis.get_major_ticks(): \n",
" t.tick1On = False \n",
" t.tick2On = False "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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1tbXYsmULle5nn32G2NhYruz26tUrjB49mhsASqudnJzMGRiWl5dDS0uLSvtd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\nlXYAAAAASUVORK5CYII=\n"
}
],
"prompt_number": 23
}
],
"metadata": {}
}
]
} | mit |
abezuglov/ANN | Storm-Surge/notebooks/embedded_feedforward_separable_NN.ipynb | 1 | 28735 | {
"cells": [
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import os\n",
"import sys\n",
"import time\n",
"import tarfile\n",
"from IPython.display import display, Image\n",
"from scipy import ndimage\n",
"from sklearn.linear_model import LogisticRegression\n",
"from six.moves.urllib.request import urlretrieve\n",
"from six.moves import cPickle as pickle\n",
"import tensorflow as tf\n",
"import scipy"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"../data/ann_dataset1.tar\n",
"http://mrtee.europa.renci.org/~bblanton/ANN/ann_dataset1.tar\n",
"Found and verified ann_dataset1.tar\n"
]
}
],
"source": [
"# Download and save the archived data\n",
"\n",
"url = 'http://mrtee.europa.renci.org/~bblanton/ANN/'\n",
"to = \"../data\"\n",
"\n",
"def maybe_download(filename, expected_bytes, force=False):\n",
" \"\"\"Download a file if not present, and make sure it's the right size.\"\"\"\n",
" print(os.path.join(to,filename))\n",
" print(url+filename)\n",
" if force or not os.path.exists(os.path.join(to,filename)):\n",
" filename, _ = urlretrieve(url + filename, os.path.join(to,filename))\n",
" statinfo = os.stat(os.path.join(to,filename))\n",
" if statinfo.st_size == expected_bytes:\n",
" print('Found and verified', filename)\n",
" else:\n",
" raise Exception(\n",
" 'Failed to verify' + filename + '. Can you get to it with a browser?')\n",
" return filename\n",
"\n",
"data_filename = maybe_download('ann_dataset1.tar', 5642240)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"../data already present - Skipping extraction of ../data/ann_dataset_10points.tar.\n",
"Processed 0/324 \n",
"\n",
"Processed 100/324 \n",
"\n",
"Processed 200/324 \n",
"\n",
"Processed 300/324 \n",
"\n",
"(324, 193, 18)\n"
]
}
],
"source": [
"# Ten output data set\n",
"# Extract files from the archive\n",
"def maybe_extract(filename, force=False):\n",
" extract_folder = os.path.splitext(os.path.splitext(filename)[0])[0] # remove .tar.gz\n",
" root = os.path.dirname(filename)\n",
" if os.path.isdir(extract_folder) and not force:\n",
" # You may override by setting force=True.\n",
" print('%s already present - Skipping extraction of %s.' % (root, filename))\n",
" else:\n",
" print('Extracting data for %s. This may take a while. Please wait.' % root)\n",
" tar = tarfile.open(filename)\n",
" sys.stdout.flush()\n",
" tar.extractall(path = root)\n",
" tar.close()\n",
" data_files = [\n",
" os.path.join(extract_folder, d) for d in sorted(os.listdir(extract_folder))\n",
" if os.path.isdir(extract_folder)]\n",
" return data_files\n",
" \n",
"data_filename = \"../data/ann_dataset_10points.tar\"\n",
"data_files = maybe_extract(data_filename)\n",
"\n",
"# Load files and produce dataset\n",
"def maybe_load(file_names):\n",
" names = ('index','time', 'long', 'lat', 'param1', 'param2', 'param3', 'param4', 'out1', 'out2',\n",
" 'out3', 'out4','out5', 'out6','out7', 'out8','out9', 'out10',)\n",
" datafile_length = 193\n",
" dataset = np.ndarray(shape=(len(file_names), datafile_length, len(names)))\n",
" for i in range(0,len(file_names)):\n",
" a = np.loadtxt(file_names[i])\n",
" a = np.asarray([x for xs in a for x in xs],dtype='d').reshape([datafile_length,len(names)])\n",
" dataset[i,:,:] = a\n",
" if i%100 == 0:\n",
" print(\"Processed %d/%d \\n\"%(i,len(file_names)))\n",
" return dataset\n",
"\n",
"dataset = maybe_load(data_files)\n",
"print(dataset.shape)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1.00000000e+00 -3.00000000e+00 -7.90000000e+01 2.80900000e+01\n",
" 9.73400000e+02 2.71500000e+01 1.10000000e+00 1.01300000e+03\n",
" 5.40000000e-03 5.10000000e-03 5.60000000e-03 5.90000000e-03\n",
" 6.80000000e-03 7.10000000e-03 1.14000000e-02 1.22000000e-02\n",
" 1.34000000e-02 1.67000000e-02]\n",
" [ 2.00000000e+00 -2.97917000e+00 -7.90000000e+01 2.81300000e+01\n",
" 9.73400000e+02 2.71900000e+01 1.10000000e+00 1.01300000e+03\n",
" 4.10000000e-03 4.80000000e-03 5.60000000e-03 6.10000000e-03\n",
" 5.80000000e-03 7.10000000e-03 1.00000000e-02 1.08000000e-02\n",
" 1.38000000e-02 1.59000000e-02]]\n"
]
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test dataset: (48, 193, 18)\n",
"Validation dataset: (48, 193, 18)\n",
"Training dataset: (228, 193, 18)\n"
]
}
],
"source": [
"# train, validation, and test dataset percentages\n",
"train_percent = 70\n",
"valid_percent = 15\n",
"test_percent = 15\n",
"\n",
"# train, validation, and test dataset indices\n",
"# test: test_start : valid_start-1\n",
"# validation: valid_start : train_start-1\n",
"# training: train_start : dataset.shape[0]\n",
"test_start = 0 \n",
"valid_start = 48 #int(test_percent/100.0*dataset.shape[0])\n",
"train_start = 48 + 48 #int((test_percent+valid_percent)/100.0*dataset.shape[0])\n",
"\n",
"# Shuffle file indices\n",
"file_indices = range(dataset.shape[0])\n",
"np.random.shuffle(file_indices)\n",
"\n",
"# Assign datasets\n",
"test_dataset = np.array([dataset[j,:,:] for j in [file_indices[i] for i in range(test_start, valid_start)]])\n",
"valid_dataset = np.array([dataset[j,:,:] for j in [file_indices[i] for i in range(valid_start, train_start)]])\n",
"train_dataset = np.array([dataset[j,:,:] for j in [file_indices[i] for i in range(train_start, dataset.shape[0])]])\n",
"\n",
"# Save memory\n",
"#del(dataset)\n",
"print(\"Test dataset: \"+str(test_dataset.shape))\n",
"print(\"Validation dataset: \"+str(valid_dataset.shape))\n",
"print(\"Training dataset: \"+str(train_dataset.shape))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"train outputs: (228, 193, 18)\n",
"(44004, 6)\n",
"(44004, 10)\n"
]
}
],
"source": [
"def accuracy_mse(predictions, outputs):\n",
" err = predictions-outputs\n",
" return np.mean(err*err)\n",
"\n",
"def Normalize(x, means, stds):\n",
" return (x-means)/stds\n",
"\n",
"# Reshape the data and normalize\n",
"\n",
"train_dataset2 = train_dataset[:,:,1:7].reshape((-1, 6)).astype(np.float32)\n",
"train_output = train_dataset[:,:,8:18].reshape((-1, 10)).astype(np.float32)\n",
"print(\"train outputs: \",train_dataset.shape)\n",
"\n",
"# calculate means and stds for training dataset\n",
"input_means = [np.mean(train_dataset2[:,i]) for i in range(train_dataset2.shape[1])]\n",
"input_stds = [np.std(train_dataset2[:,i]) for i in range(train_dataset2.shape[1])]\n",
"output_means = [np.mean(train_output[:,i]) for i in range(train_output.shape[1])]\n",
"output_stds = [np.std(train_output[:,i]) for i in range(train_output.shape[1])]\n",
"\n",
"train_dataset2 = Normalize(train_dataset2, input_means, input_stds)\n",
"#train_output = Normalize(train_output, output_means, output_stds)\n",
"\n",
"print(train_dataset2.shape)\n",
"print(train_output.shape)\n",
"\n",
"#plt.plot(train_dataset2[:193,:],label=\"input\")\n",
"#plt.plot(train_output[:193,:],label=\"output\")\n",
"#plt.ylabel(\"training data\")\n",
"#plt.legend(loc='upper right', shadow=True)\n",
"#plt.show()\n",
"\n",
"valid_dataset2 = Normalize(valid_dataset[:,:,1:7].reshape((-1, 6)).astype(np.float32), input_means, input_stds)\n",
"valid_output = valid_dataset[:,:,8:18].reshape((-1, 10)).astype(np.float32)\n",
"#valid_output = Normalize(valid_dataset[:,:,8:18].reshape((-1, 2)).astype(np.float32), output_means, output_stds)\n",
"\n",
"test_dataset2 = Normalize(test_dataset[:,:,1:7].reshape((-1, 6)).astype(np.float32),input_means, input_stds)\n",
"test_output = test_dataset[:,:,8:18].reshape((-1, 10)).astype(np.float32)\n",
"#test_output = Normalize(test_dataset[:,:,8:18].reshape((-1, 2)).astype(np.float32), output_means, output_stds)\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"num_embeds = 2 # number of embeddings, i.e. 0 -- the original array\n",
"#print(train_dataset2[:-1,:])\n",
"#print(train_dataset2[1:,:])\n",
"\n",
"def get_embeddings(dataset, num_embeds = 1):\n",
" dataset_list = []\n",
" if num_embeds == 0:\n",
" return dataset\n",
" for i in range(num_embeds):\n",
" dataset_list.append(dataset[i:(-num_embeds+i),:])\n",
" #dataset_list.append(dataset[num_embeds:,:])\n",
" return np.hstack(dataset_list)\n",
"\n",
"train_dataset2 = get_embeddings(train_dataset2, num_embeds)\n",
"valid_dataset2 = get_embeddings(valid_dataset2, num_embeds)\n",
"test_dataset2 = get_embeddings(test_dataset2, num_embeds)\n",
"train_output = train_output[num_embeds:,:]\n",
"valid_output = valid_output[num_embeds:,:]\n",
"test_output = test_output[num_embeds:,:]\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def variance(x):\n",
" return tf.reduce_mean(tf.square(x-tf.reduce_mean(x)))\n",
"\n",
"def explained_var(y_true, y_predicted):\n",
" return 1 - tf.div(variance(tf.sub(y_true,y_predicted)),variance(y_true))\n",
"\n",
"input_size = train_dataset2.shape[1]\n",
"output_size = 10\n",
"\n",
"# Deep ANN\n",
"batch_size = 57*193\n",
"hidden_nodes = 32 #64\n",
"\n",
"num_steps = 20001\n",
"starter_learning_rate = 0.005\n",
"\n",
"graph = tf.Graph()\n",
"with graph.as_default():\n",
"\n",
" x = tf.placeholder(tf.float32, shape=(None, input_size)) #train_dataset2.shape(2)\n",
" y = tf.placeholder(tf.float32, shape=(None, output_size))\n",
" \n",
" y_list = tf.split(1, 10, y, name='split')\n",
" \n",
" yl_ = []\n",
" # Building graph\n",
" for o in range(10):\n",
" #o = 5\n",
" weights_0 = tf.Variable(tf.truncated_normal([input_size,hidden_nodes], stddev = 0.01, dtype = tf.float32))\n",
" biases_0 = tf.Variable(tf.zeros([hidden_nodes], dtype = tf.float32))\n",
" input_layer = tf.tanh(tf.matmul(x, weights_0) + biases_0)\n",
" weights_1 = tf.Variable(tf.truncated_normal([hidden_nodes, 1], stddev = 0.01, dtype = tf.float32))\n",
" biases_1 = tf.Variable(tf.zeros([1], dtype = tf.float32))\n",
" output_ = tf.matmul(input_layer, weights_1) + biases_1\n",
" yl_.append(output_)\n",
" tf.add_to_collection('losses',tf.reduce_mean(tf.square(output_-y_list[o])))\n",
" y_ = tf.pack(yl_)\n",
" #print(y_)\n",
" y_ = tf.transpose(tf.reshape(y_, shape=(10, -1)))\n",
" #print(y_)\n",
" \n",
" losses = tf.get_collection('losses')\n",
" loss = tf.add_n(losses, name='total_loss')/output_size\n",
" \n",
" global_step = tf.Variable(0.0, trainable=False)\n",
" learning_rate = tf.train.exponential_decay(starter_learning_rate, global_step, num_steps, 0.5, staircase=False)\n",
" optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step=global_step) \n",
"\n",
" \"\"\"\n",
" train_prediction = loss\n",
" valid_prediction = tf.tanh(tf.matmul(tf_valid_dataset, weights_0) + biases_0)\n",
" valid_prediction = tf.tanh(tf.matmul(valid_prediction, weights_1) + biases_1)\n",
" valid_prediction = tf.matmul(valid_prediction, weights_2) + biases_2\n",
" \n",
" test_prediction = tf.tanh(tf.matmul(tf_test_dataset, weights_0) + biases_0)\n",
" test_prediction = tf.tanh(tf.matmul(test_prediction, weights_1) + biases_1)\n",
" test_prediction = tf.matmul(test_prediction, weights_2) + biases_2\n",
" \"\"\""
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initialized\n",
"Loss at step 0 (19.20 op/sec): 0.081874; validation loss: 0.082135\n",
"Loss at step 500 (34.80 op/sec): 0.044962; validation loss: 0.033111\n",
"Loss at step 1000 (31.39 op/sec): 0.024669; validation loss: 0.022864\n",
"Loss at step 1500 (33.67 op/sec): 0.019150; validation loss: 0.019426\n",
"Loss at step 2000 (33.14 op/sec): 0.016856; validation loss: 0.017833\n",
"Loss at step 2500 (33.53 op/sec): 0.015503; validation loss: 0.016565\n",
"Loss at step 3000 (30.88 op/sec): 0.014549; validation loss: 0.015684\n",
"Loss at step 3500 (31.78 op/sec): 0.013825; validation loss: 0.014854\n",
"Loss at step 4000 (32.77 op/sec): 0.013179; validation loss: 0.014086\n",
"Loss at step 4500 (29.92 op/sec): 0.012590; validation loss: 0.013451\n",
"Loss at step 5000 (33.65 op/sec): 0.012078; validation loss: 0.012920\n",
"Loss at step 5500 (32.90 op/sec): 0.011700; validation loss: 0.012581\n",
"Loss at step 6000 (33.14 op/sec): 0.011395; validation loss: 0.012253\n",
"Loss at step 6500 (32.18 op/sec): 0.011128; validation loss: 0.011977\n",
"Loss at step 7000 (33.58 op/sec): 0.010894; validation loss: 0.011771\n",
"Loss at step 7500 (34.06 op/sec): 0.010679; validation loss: 0.011523\n",
"Loss at step 8000 (33.71 op/sec): 0.010498; validation loss: 0.011326\n",
"Loss at step 8500 (32.71 op/sec): 0.010332; validation loss: 0.011180\n",
"Loss at step 9000 (33.48 op/sec): 0.010162; validation loss: 0.010939\n",
"Loss at step 9500 (33.72 op/sec): 0.010009; validation loss: 0.010760\n",
"Loss at step 10000 (33.41 op/sec): 0.009873; validation loss: 0.010639\n",
"Loss at step 10500 (32.64 op/sec): 0.009745; validation loss: 0.010450\n",
"Loss at step 11000 (33.36 op/sec): 0.009637; validation loss: 0.010378\n",
"Loss at step 11500 (32.81 op/sec): 0.009547; validation loss: 0.010307\n",
"Loss at step 12000 (32.35 op/sec): 0.009461; validation loss: 0.010210\n",
"Loss at step 12500 (32.29 op/sec): 0.009386; validation loss: 0.010144\n",
"Loss at step 13000 (31.96 op/sec): 0.009321; validation loss: 0.010114\n",
"Loss at step 13500 (30.84 op/sec): 0.009252; validation loss: 0.010005\n",
"Loss at step 14000 (33.77 op/sec): 0.009190; validation loss: 0.009988\n",
"Loss at step 14500 (32.85 op/sec): 0.009138; validation loss: 0.009969\n",
"Loss at step 15000 (30.40 op/sec): 0.009084; validation loss: 0.009881\n",
"Loss at step 15500 (32.06 op/sec): 0.009034; validation loss: 0.009870\n",
"Loss at step 16000 (34.09 op/sec): 0.008996; validation loss: 0.009873\n",
"Loss at step 16500 (32.23 op/sec): 0.008952; validation loss: 0.009781\n",
"Loss at step 17000 (32.75 op/sec): 0.008912; validation loss: 0.009780\n",
"Loss at step 17500 (33.50 op/sec): 0.008881; validation loss: 0.009776\n",
"Loss at step 18000 (33.39 op/sec): 0.008843; validation loss: 0.009698\n",
"Loss at step 18500 (32.30 op/sec): 0.008810; validation loss: 0.009708\n",
"Loss at step 19000 (30.25 op/sec): 0.008785; validation loss: 0.009710\n",
"Loss at step 19500 (32.47 op/sec): 0.008751; validation loss: 0.009638\n",
"Loss at step 20000 (32.95 op/sec): 0.008723; validation loss: 0.009649\n",
"Test MSE: 0.0102\n"
]
}
],
"source": [
"def stop(data, n = 3):\n",
" assert len(data) > n*2\n",
" avg = sum(data)/len(data)\n",
" for x in (data-avg)[-n:]:\n",
" if x <= 0:\n",
" return False\n",
" return True\n",
"\n",
"from sklearn.metrics import explained_variance_score\n",
"\n",
"with tf.Session(graph=graph) as session:\n",
" tf.initialize_all_variables().run()\n",
" sum_l = 0\n",
" num_l = 0\n",
" #ev_l = [0,0,0,0,0,0,0]\n",
" print('Initialized')\n",
" for step in range(num_steps):\n",
" offset = (step * batch_size) % (train_output.shape[0] - batch_size)\n",
" batch_data = train_dataset2[offset:(offset + batch_size), :]\n",
" batch_output = train_output[offset:(offset + batch_size), :]\n",
" feed_dict = {x : batch_data, y : batch_output}\n",
" start_time = time.time()\n",
" _, l = session.run([optimizer, loss],feed_dict=feed_dict)\n",
" duration = time.time()-start_time\n",
" \n",
" sum_l += l\n",
" num_l += 1\n",
"\n",
" if (step % 500 == 0):\n",
" valid_loss = loss.eval(feed_dict = {x: valid_dataset2, y: valid_output})\n",
" #print(predictions)\n",
" #ev = explained_variance_score(y_.eval(feed_dict = {x: valid_dataset2, y: valid_output}), valid_output)\n",
" #ev_l.append(valid_loss)\n",
" #ev_l = ev_l[1:]\n",
" print('Loss at step %d (%.2f op/sec): %f; validation loss: %.6f' % (\n",
" step, 1.0/duration, sum_l/num_l, \n",
" #accuracy_mse(valid_prediction.eval(), valid_output)))\n",
" valid_loss))\n",
" sum_l = 0\n",
" num_l = 0\n",
" #if stop(ev_l):\n",
" # print(\"Non decreasing scores, so stopping early\")\n",
" # break\n",
" \n",
" feed_dict = {x: test_dataset2, y: test_output}\n",
" predictions, test_loss = session.run([y_, loss],feed_dict=feed_dict)\n",
" #test_loss = loss.eval(feed_dict = {x: test_dataset2, y: test_output})\n",
" print('Test MSE: %.4f' % test_loss)\n",
" #print('Test losses:', test_losses)\n",
" predicted_vs_actual = np.hstack((predictions, test_output))\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Point 0: Max loss: 1.240468, MSE: 0.028800 CC: 0.895165 \n",
"Point 1: Max loss: 1.165016, MSE: 0.025457 CC: 0.921425 \n",
"Point 2: Max loss: 0.863533, MSE: 0.019804 CC: 0.931636 \n",
"Point 3: Max loss: 0.732300, MSE: 0.014674 CC: 0.949629 \n",
"Point 4: Max loss: 0.956068, MSE: 0.023755 CC: 0.947756 \n",
"Point 5: Max loss: 1.324671, MSE: 0.041982 CC: 0.907157 \n",
"Point 6: Max loss: 2.073830, MSE: 0.076835 CC: 0.930429 \n",
"Point 7: Max loss: 1.744426, MSE: 0.086797 CC: 0.922877 \n",
"Point 8: Max loss: 1.519407, MSE: 0.066029 CC: 0.903478 \n",
"Point 9: Max loss: 1.887906, MSE: 0.094386 CC: 0.865807 \n",
"2275\n",
"[0.084737197, 0.080382437, 0.034475878, 0.041143581, 0.13712037, 1.3246714, 0.20506975, 0.15680939, 0.077375188, 0.27674031]\n",
"(9264, 20)\n"
]
}
],
"source": [
"over_95 = 0\n",
"for i in range(10):\n",
" cc = np.corrcoef(predicted_vs_actual[:,i],predicted_vs_actual[:,i+10])[0,1]\n",
" m = np.max(np.abs(predicted_vs_actual[:,i]-predicted_vs_actual[:,i+10]))\n",
" mse = np.mean(np.sqrt(np.square(predicted_vs_actual[:,i]-predicted_vs_actual[:,i+10])))\n",
" \n",
" if cc >= 0.95:\n",
" over_95+=1\n",
" print('Point %d: Max loss: %.6f, MSE: %.6f CC: %.6f %c' % (i,m, mse, cc, 'v' if cc >= 0.95 else ' '))\n",
"\n",
"i = 5\n",
"k = np.argmax(np.abs(predicted_vs_actual[:,i]-predicted_vs_actual[:,i+10]))\n",
"print(k)\n",
"max_error_case = [np.abs(predicted_vs_actual[k,i]-predicted_vs_actual[k,i+10]) for i in range(10)]\n",
"print(max_error_case)\n",
"start = (k // 193)*193\n",
"stop = start + 193\n",
"#start = 0\n",
"#stop = 20*193\n",
"\n",
"print(predicted_vs_actual.shape)\n",
"fig = plt.figure(figsize=(10, 6), dpi=80)\n",
"\n",
"for i in range(10):\n",
" sp = fig.add_subplot(10,1,i+1)\n",
" sp.plot(predicted_vs_actual[start:stop,i],color=\"blue\", linewidth=1.0, linestyle=\"-\", label=\"ANN\")\n",
" sp.plot(predicted_vs_actual[start:stop,i+10],color=\"red\", linewidth=1.0, linestyle=\"-\", label=\"actual\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 2. 1.]\n"
]
},
{
"ename": "AssertionError",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-231-59addae8a709>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m<ipython-input-231-59addae8a709>\u001b[0m in \u001b[0;36mstop\u001b[1;34m(data, n)\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mstop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;32massert\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[0mavg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mavg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAssertionError\u001b[0m: "
]
}
],
"source": [
"d = np.asarray([])\n",
"\n",
"d = np.append(d,3)\n",
"d = np.append(d,2)\n",
"d = np.append(d,1)[1:]\n",
"print(d)\n",
"\n",
"def stop(data, n = 2):\n",
" assert len(data) > n*2\n",
" avg = sum(data)/len(data)\n",
" for x in (data-avg)[-n:]:\n",
" if x <= 0:\n",
" return False\n",
" return True\n",
"\n",
"print(stop(d))"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Explained variance: [0.81146699190139771, 0.85759055614471436, 0.86870527267456055, 0.89061909914016724, 0.88695210218429565, 0.74401175975799561, 0.8059995174407959, 0.80061358213424683, 0.45739859342575073, 0.35861217975616455]\n",
"(1.4245720507513746, -0.030474568797174348, 0.062562678013771522)\n"
]
},
{
"data": {
"text/plain": [
"\"\\nnum_bins = 100\\n# the histogram of the errors\\nn, bins, patches = plt.hist(diff, num_bins, normed=1, facecolor='green', alpha=0.5)\\n\\n# add a normal PDF\\nmu = 0\\nsigma = .05\\ny = mlab.normpdf(bins, mu, sigma)\\nplt.plot(bins, y, 'r-')\\nplt.xlabel('Smarts')\\nplt.ylabel('Probability')\\n\\n# add Cauchy PDF\\nparams = cauchy.fit(diff)\\nprint(params)\\ndist = cauchy(params[0], params[1])\\nx = np.linspace(-2, 2, num_bins)\\nplt.plot(x, dist.pdf(x), 'b-', alpha=0.5, label='cauchy pdf')\\n\\n\\n# Tweak spacing to prevent clipping of ylabel\\n#plt.subplots_adjust(left=0.15)\\n#plt.show()\\n\\nfig = plt.figure(figsize=(10,6),dpi=80)\\nplt.hist(diff, bins = 100, alpha=0.5)\\nplt.show()\\n\""
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.metrics import explained_variance_score\n",
"import scipy.stats as stats\n",
"import pylab\n",
"\n",
"ev = []\n",
"for i in range(10):\n",
" y_true = predicted_vs_actual[:,i]\n",
" y_pred = predicted_vs_actual[:,i+10]\n",
" ev.append(explained_variance_score(y_true, y_pred))\n",
" \n",
"print(\"Explained variance: \",ev)\n",
"diff = y_true-y_pred\n",
"\n",
"#stats.probplot(diff, dist=\"norm\", plot=pylab)\n",
"stats.probplot(diff, dist=\"t\", sparams=(2), plot=pylab)\n",
"pylab.show()\n",
"\n",
"num_bins = 100\n",
"# the histogram of the errors\n",
"n, bins, patches = plt.hist(diff, num_bins, normed=1, facecolor='green', alpha=0.5)\n",
"\n",
"params = stats.t.fit(diff)\n",
"dist = stats.t(params[0], params[1], params[2])\n",
"x = np.linspace(-2, 2, num_bins)\n",
"plt.plot(x, dist.pdf(x), 'b-', lw = 3, alpha=0.5, label='t pdf')\n",
"plt.show()\n",
"print(params)\n",
"\n",
"\"\"\"\n",
"num_bins = 100\n",
"# the histogram of the errors\n",
"n, bins, patches = plt.hist(diff, num_bins, normed=1, facecolor='green', alpha=0.5)\n",
"\n",
"# add a normal PDF\n",
"mu = 0\n",
"sigma = .05\n",
"y = mlab.normpdf(bins, mu, sigma)\n",
"plt.plot(bins, y, 'r-')\n",
"plt.xlabel('Smarts')\n",
"plt.ylabel('Probability')\n",
"\n",
"# add Cauchy PDF\n",
"params = cauchy.fit(diff)\n",
"print(params)\n",
"dist = cauchy(params[0], params[1])\n",
"x = np.linspace(-2, 2, num_bins)\n",
"plt.plot(x, dist.pdf(x), 'b-', alpha=0.5, label='cauchy pdf')\n",
"\n",
"\n",
"# Tweak spacing to prevent clipping of ylabel\n",
"#plt.subplots_adjust(left=0.15)\n",
"#plt.show()\n",
"\n",
"fig = plt.figure(figsize=(10,6),dpi=80)\n",
"plt.hist(diff, bins = 100, alpha=0.5)\n",
"plt.show()\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": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/dev/.ipynb_checkpoints/n14_simulation_multiprocessing-checkpoint.ipynb | 1 | 6208 | {
"cells": [
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n",
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/miguel/anaconda3/envs/cap_env/lib/python3.6/site-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['f']\n",
"`%matplotlib` prevents importing * from pylab and numpy\n",
" \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
]
}
],
"source": [
"# Basic imports\n",
"import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import datetime as dt\n",
"import scipy.optimize as spo\n",
"import sys\n",
"from time import time\n",
"from sklearn.metrics import r2_score, median_absolute_error\n",
"\n",
"%matplotlib inline\n",
"\n",
"%pylab inline\n",
"pylab.rcParams['figure.figsize'] = (20.0, 10.0)\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"sys.path.append('../../')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from multiprocessing import Pool\n",
"\n",
"NUM_THREADS = 4\n",
"\n",
"p = Pool(NUM_THREADS)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 2, 3, 4, 5, 6, 7]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_list = np.arange(8).tolist()\n",
"num_list"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def fun(x):\n",
" return x**2"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[0, 1, 4, 9, 16, 25, 36, 49]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p.map(fun, num_list)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p.close()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"index = np.arange(NUM_THREADS).tolist()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Get data_df and symbol\n",
"total_data_df = pd.read_pickle('../../data/data_df.pkl')\n",
"SYMBOL = 'AAPL'\n",
"data_df = total_data_df[SYMBOL].unstack()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'initialize_env' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-33-d4b7da20a64b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Create many agents\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0menv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_states\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_actions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minitialize_env\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msymbol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;31m#for...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m agents = [Agent(num_states=num_states, \n\u001b[1;32m 5\u001b[0m \u001b[0mnum_actions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_actions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'initialize_env' is not defined"
]
}
],
"source": [
"# Create many agents\n",
"env, num_states, num_actions = initialize_env(data_df, symbol)\n",
"#for...\n",
"agents = [Agent(num_states=num_states, \n",
" num_actions=num_actions, \n",
" random_actions_rate=0.98, \n",
" random_actions_decrease=0.999,\n",
" dyna_iterations=20) for i in index]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"simulate_period(data_df, symbol, agent)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Simulate (with new envs, each time) in parallel\n",
"p.map(partial(simulate_period, data_df, symbol), agents)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "cap_env",
"language": "python",
"name": "cap_env"
},
"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 |
queirozfcom/python-sandbox | python3/notebooks/pandas-concepts/pandas-concepts.ipynb | 1 | 2772 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('0.23.1', '1.14.5')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.__version__, np.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## index"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([0, 1, 2, 3], dtype='int64')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"i = pd.Index([0,1,2,3])\n",
"i"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['a', 'b', 'c', 'd'], dtype='object')"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"string_i = pd.Index(['a','b','c','d'])\n",
"string_i"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## series"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 10\n",
"1 20\n",
"2 30\n",
"3 40\n",
"dtype: int64\n"
]
}
],
"source": [
"s = pd.Series([10,20,30,40],index=i)\n",
"print(s)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([0, 1, 2, 3], dtype='int64')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s.index"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Global TF Kernel (Python 3)",
"language": "python",
"name": "global-tf-python-3"
},
"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 |
cougarTech2228/Scouting-2016 | notebooks/robo_0.ipynb | 1 | 1298 | {
"cells": [
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class Robot(object):\n",
" \n",
" def __init__(self, points):\n",
" self.points = points\n",
" def points_per_sec(self):\n",
" return self.points / 150\n",
"\n",
"my_bot = Robot(300)\n",
"his_robot = Robot(5550)\n",
"\n",
"points_over_time2 = his_robot.points_per_sec()\n",
"points_over_time = my_bot.points_per_sec()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"37\n"
]
}
],
"source": [
"print points_over_time\n",
"print points_over_time2"
]
}
],
"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 |
European-XFEL/h5tools-py | docs/Demo.ipynb | 1 | 51301 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Reading data with `karabo_data`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This command creates the sample data files used in the rest of this example. These files contain no real data, but they have the same structure as European XFEL's HDF5 data files."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Written examples.\r\n"
]
}
],
"source": [
"!python3 -m karabo_data.tests.make_examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Single files"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CONTROL Group\r\n",
"INDEX Group\r\n",
"INSTRUMENT Group\r\n",
"METADATA Group\r\n",
"RUN Group\r\n"
]
}
],
"source": [
"!h5ls fxe_control_example.h5"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from karabo_data import H5File\n",
"f = H5File('fxe_control_example.h5')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"frozenset({'FXE_XAD_GEC/CAM/CAMERA',\n",
" 'SA1_XTD2_XGM/DOOCS/MAIN',\n",
" 'SPB_XTD9_XGM/DOOCS/MAIN'})"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.control_sources"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"frozenset({'FXE_XAD_GEC/CAM/CAMERA:daqOutput',\n",
" 'SA1_XTD2_XGM/DOOCS/MAIN:output',\n",
" 'SPB_XTD9_XGM/DOOCS/MAIN:output'})"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f.instrument_sources"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Get data by train"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing train 10000\n",
"beam iyPos: 0.0\n"
]
}
],
"source": [
"for tid, data in f.trains():\n",
" print(\"Processing train\", tid)\n",
" print(\"beam iyPos:\", data['SA1_XTD2_XGM/DOOCS/MAIN']['beamPosition.iyPos.value'])\n",
" \n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1024, 255], dtype=uint64)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tid, data = f.train_from_id(10005)\n",
"data['FXE_XAD_GEC/CAM/CAMERA:daqOutput']['data.image.dims']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are just a few of the ways to access data. The attributes and methods described below for run directories also work with individual files. We expect that it will normally make sense to access a run directory as a single object, rather than working with the files separately."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run directories\n",
"\n",
"An experimental run is recorded as a collection of files in a directory.\n",
"\n",
"Another dummy example:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RAW-R0450-DA01-S00000.h5 RAW-R0450-LPD04-S00000.h5 RAW-R0450-LPD10-S00000.h5\r\n",
"RAW-R0450-DA01-S00001.h5 RAW-R0450-LPD05-S00000.h5 RAW-R0450-LPD11-S00000.h5\r\n",
"RAW-R0450-LPD00-S00000.h5 RAW-R0450-LPD06-S00000.h5 RAW-R0450-LPD12-S00000.h5\r\n",
"RAW-R0450-LPD01-S00000.h5 RAW-R0450-LPD07-S00000.h5 RAW-R0450-LPD13-S00000.h5\r\n",
"RAW-R0450-LPD02-S00000.h5 RAW-R0450-LPD08-S00000.h5 RAW-R0450-LPD14-S00000.h5\r\n",
"RAW-R0450-LPD03-S00000.h5 RAW-R0450-LPD09-S00000.h5 RAW-R0450-LPD15-S00000.h5\r\n"
]
}
],
"source": [
"!ls fxe_example_run/"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from karabo_data import RunDirectory\n",
"run = RunDirectory('fxe_example_run/')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[FileAccess(<HDF5 file \"RAW-R0450-LPD04-S00000.h5\" (mode r)>),\n",
" FileAccess(<HDF5 file \"RAW-R0450-LPD11-S00000.h5\" (mode r)>),\n",
" FileAccess(<HDF5 file \"RAW-R0450-LPD15-S00000.h5\" (mode r)>)]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"run.files[:3] # The objects for the individual files (see above)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What devices were recording in this run?\n",
"\n",
"*Control* devices are slow data, recording once per train. *Instrument* devices includes detector data, but also some other data sources such as cameras. They can have more than one reading per train."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"frozenset({'FXE_XAD_GEC/CAM/CAMERA',\n",
" 'FXE_XAD_GEC/CAM/CAMERA_NODATA',\n",
" 'SA1_XTD2_XGM/DOOCS/MAIN',\n",
" 'SPB_XTD9_XGM/DOOCS/MAIN'})"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"run.control_sources"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"frozenset({'FXE_DET_LPD1M-1/DET/0CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/10CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/11CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/12CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/13CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/14CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/15CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/1CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/2CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/3CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/4CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/5CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/6CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/7CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/8CH0:xtdf',\n",
" 'FXE_DET_LPD1M-1/DET/9CH0:xtdf',\n",
" 'FXE_XAD_GEC/CAM/CAMERA:daqOutput',\n",
" 'FXE_XAD_GEC/CAM/CAMERA_NODATA:daqOutput',\n",
" 'SA1_XTD2_XGM/DOOCS/MAIN:output',\n",
" 'SPB_XTD9_XGM/DOOCS/MAIN:output'})"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"run.instrument_sources"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which trains are in this run?"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[10000, 10001, 10002, 10003, 10004, 10005, 10006, 10007, 10008, 10009]\n"
]
}
],
"source": [
"print(run.train_ids[:10])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"See the available keys for a given source:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'data.intensityAUXTD',\n",
" 'data.intensitySigma.x_data',\n",
" 'data.intensitySigma.y_data',\n",
" 'data.intensityTD',\n",
" 'data.trainId',\n",
" 'data.xTD',\n",
" 'data.yTD'}"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"run.keys_for_source('SPB_XTD9_XGM/DOOCS/MAIN:output')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This collects data from across files, including detector data:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing train 10000\n",
"Detctor data module 0 shape: (128, 1, 256, 256)\n"
]
}
],
"source": [
"for tid, data in run.trains():\n",
" print(\"Processing train\", tid)\n",
" print(\"Detctor data module 0 shape:\", data['FXE_DET_LPD1M-1/DET/0CH0:xtdf']['image.data'].shape)\n",
"\n",
" break # Stop after the first train to keep the demo short"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Train IDs are meant to be globally unique (although there were some glitches with this in the past). A train index is only within this run."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"tid, data = run.train_from_id(10005)\n",
"tid, data = run.train_from_index(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Series data to pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data which holds a single number per train (or per pulse) can be extracted to as *series* (individual columns) and *dataframes* (tables) for [pandas](http://pandas.pydata.org/pandas-docs/stable/), a widely-used tool for data manipulation.\n",
"\n",
"`karabo_data` chains sequence files, which contain successive data from the same source. In this example, trains 10000–10399 are in one sequence file (`...DA01-S00000.h5`), and 10400–10479 are in another (`...DA01-S00001.h5`). They are concatenated into one series:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"trainId\n",
"10470 0.0\n",
"10471 0.0\n",
"10472 0.0\n",
"10473 0.0\n",
"10474 0.0\n",
"10475 0.0\n",
"10476 0.0\n",
"10477 0.0\n",
"10478 0.0\n",
"10479 0.0\n",
"Name: SA1_XTD2_XGM/DOOCS/MAIN/beamPosition.ixPos, dtype: float32"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ixPos = run.get_series('SA1_XTD2_XGM/DOOCS/MAIN', 'beamPosition.ixPos.value')\n",
"ixPos.tail(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To extract a dataframe, you can select interesting data fields with *glob* syntax, as often used for selecting files on Unix platforms.\n",
"\n",
"* `[abc]`: one character, a/b/c\n",
"* `?`: any one character\n",
"* `*`: any sequence of characters"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"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>SA1_XTD2_XGM/DOOCS/MAIN/beamPosition.ixPos</th>\n",
" <th>SA1_XTD2_XGM/DOOCS/MAIN/beamPosition.iyPos</th>\n",
" <th>SPB_XTD9_XGM/DOOCS/MAIN/beamPosition.ixPos</th>\n",
" <th>SPB_XTD9_XGM/DOOCS/MAIN/beamPosition.iyPos</th>\n",
" </tr>\n",
" <tr>\n",
" <th>trainId</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>10000</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10001</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10002</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>10003</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10004</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10005</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10006</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10007</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10008</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10009</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10010</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10011</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10012</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10013</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10014</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10015</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10016</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10017</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10018</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10019</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10020</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10021</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10022</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10023</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10024</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10025</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10026</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10027</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10028</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10029</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10450</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10451</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10452</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10453</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10454</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10455</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10456</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10457</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10458</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10459</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10460</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10461</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10462</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10463</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10464</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10465</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10466</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10467</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10468</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10469</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10470</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10471</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10472</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10473</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10474</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10475</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10476</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10477</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10478</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10479</th>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>480 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" SA1_XTD2_XGM/DOOCS/MAIN/beamPosition.ixPos \\\n",
"trainId \n",
"10000 0.0 \n",
"10001 0.0 \n",
"10002 0.0 \n",
"10003 0.0 \n",
"10004 0.0 \n",
"10005 0.0 \n",
"10006 0.0 \n",
"10007 0.0 \n",
"10008 0.0 \n",
"10009 0.0 \n",
"10010 0.0 \n",
"10011 0.0 \n",
"10012 0.0 \n",
"10013 0.0 \n",
"10014 0.0 \n",
"10015 0.0 \n",
"10016 0.0 \n",
"10017 0.0 \n",
"10018 0.0 \n",
"10019 0.0 \n",
"10020 0.0 \n",
"10021 0.0 \n",
"10022 0.0 \n",
"10023 0.0 \n",
"10024 0.0 \n",
"10025 0.0 \n",
"10026 0.0 \n",
"10027 0.0 \n",
"10028 0.0 \n",
"10029 0.0 \n",
"... ... \n",
"10450 0.0 \n",
"10451 0.0 \n",
"10452 0.0 \n",
"10453 0.0 \n",
"10454 0.0 \n",
"10455 0.0 \n",
"10456 0.0 \n",
"10457 0.0 \n",
"10458 0.0 \n",
"10459 0.0 \n",
"10460 0.0 \n",
"10461 0.0 \n",
"10462 0.0 \n",
"10463 0.0 \n",
"10464 0.0 \n",
"10465 0.0 \n",
"10466 0.0 \n",
"10467 0.0 \n",
"10468 0.0 \n",
"10469 0.0 \n",
"10470 0.0 \n",
"10471 0.0 \n",
"10472 0.0 \n",
"10473 0.0 \n",
"10474 0.0 \n",
"10475 0.0 \n",
"10476 0.0 \n",
"10477 0.0 \n",
"10478 0.0 \n",
"10479 0.0 \n",
"\n",
" SA1_XTD2_XGM/DOOCS/MAIN/beamPosition.iyPos \\\n",
"trainId \n",
"10000 0.0 \n",
"10001 0.0 \n",
"10002 0.0 \n",
"10003 0.0 \n",
"10004 0.0 \n",
"10005 0.0 \n",
"10006 0.0 \n",
"10007 0.0 \n",
"10008 0.0 \n",
"10009 0.0 \n",
"10010 0.0 \n",
"10011 0.0 \n",
"10012 0.0 \n",
"10013 0.0 \n",
"10014 0.0 \n",
"10015 0.0 \n",
"10016 0.0 \n",
"10017 0.0 \n",
"10018 0.0 \n",
"10019 0.0 \n",
"10020 0.0 \n",
"10021 0.0 \n",
"10022 0.0 \n",
"10023 0.0 \n",
"10024 0.0 \n",
"10025 0.0 \n",
"10026 0.0 \n",
"10027 0.0 \n",
"10028 0.0 \n",
"10029 0.0 \n",
"... ... \n",
"10450 0.0 \n",
"10451 0.0 \n",
"10452 0.0 \n",
"10453 0.0 \n",
"10454 0.0 \n",
"10455 0.0 \n",
"10456 0.0 \n",
"10457 0.0 \n",
"10458 0.0 \n",
"10459 0.0 \n",
"10460 0.0 \n",
"10461 0.0 \n",
"10462 0.0 \n",
"10463 0.0 \n",
"10464 0.0 \n",
"10465 0.0 \n",
"10466 0.0 \n",
"10467 0.0 \n",
"10468 0.0 \n",
"10469 0.0 \n",
"10470 0.0 \n",
"10471 0.0 \n",
"10472 0.0 \n",
"10473 0.0 \n",
"10474 0.0 \n",
"10475 0.0 \n",
"10476 0.0 \n",
"10477 0.0 \n",
"10478 0.0 \n",
"10479 0.0 \n",
"\n",
" SPB_XTD9_XGM/DOOCS/MAIN/beamPosition.ixPos \\\n",
"trainId \n",
"10000 0.0 \n",
"10001 0.0 \n",
"10002 0.0 \n",
"10003 0.0 \n",
"10004 0.0 \n",
"10005 0.0 \n",
"10006 0.0 \n",
"10007 0.0 \n",
"10008 0.0 \n",
"10009 0.0 \n",
"10010 0.0 \n",
"10011 0.0 \n",
"10012 0.0 \n",
"10013 0.0 \n",
"10014 0.0 \n",
"10015 0.0 \n",
"10016 0.0 \n",
"10017 0.0 \n",
"10018 0.0 \n",
"10019 0.0 \n",
"10020 0.0 \n",
"10021 0.0 \n",
"10022 0.0 \n",
"10023 0.0 \n",
"10024 0.0 \n",
"10025 0.0 \n",
"10026 0.0 \n",
"10027 0.0 \n",
"10028 0.0 \n",
"10029 0.0 \n",
"... ... \n",
"10450 0.0 \n",
"10451 0.0 \n",
"10452 0.0 \n",
"10453 0.0 \n",
"10454 0.0 \n",
"10455 0.0 \n",
"10456 0.0 \n",
"10457 0.0 \n",
"10458 0.0 \n",
"10459 0.0 \n",
"10460 0.0 \n",
"10461 0.0 \n",
"10462 0.0 \n",
"10463 0.0 \n",
"10464 0.0 \n",
"10465 0.0 \n",
"10466 0.0 \n",
"10467 0.0 \n",
"10468 0.0 \n",
"10469 0.0 \n",
"10470 0.0 \n",
"10471 0.0 \n",
"10472 0.0 \n",
"10473 0.0 \n",
"10474 0.0 \n",
"10475 0.0 \n",
"10476 0.0 \n",
"10477 0.0 \n",
"10478 0.0 \n",
"10479 0.0 \n",
"\n",
" SPB_XTD9_XGM/DOOCS/MAIN/beamPosition.iyPos \n",
"trainId \n",
"10000 0.0 \n",
"10001 0.0 \n",
"10002 0.0 \n",
"10003 0.0 \n",
"10004 0.0 \n",
"10005 0.0 \n",
"10006 0.0 \n",
"10007 0.0 \n",
"10008 0.0 \n",
"10009 0.0 \n",
"10010 0.0 \n",
"10011 0.0 \n",
"10012 0.0 \n",
"10013 0.0 \n",
"10014 0.0 \n",
"10015 0.0 \n",
"10016 0.0 \n",
"10017 0.0 \n",
"10018 0.0 \n",
"10019 0.0 \n",
"10020 0.0 \n",
"10021 0.0 \n",
"10022 0.0 \n",
"10023 0.0 \n",
"10024 0.0 \n",
"10025 0.0 \n",
"10026 0.0 \n",
"10027 0.0 \n",
"10028 0.0 \n",
"10029 0.0 \n",
"... ... \n",
"10450 0.0 \n",
"10451 0.0 \n",
"10452 0.0 \n",
"10453 0.0 \n",
"10454 0.0 \n",
"10455 0.0 \n",
"10456 0.0 \n",
"10457 0.0 \n",
"10458 0.0 \n",
"10459 0.0 \n",
"10460 0.0 \n",
"10461 0.0 \n",
"10462 0.0 \n",
"10463 0.0 \n",
"10464 0.0 \n",
"10465 0.0 \n",
"10466 0.0 \n",
"10467 0.0 \n",
"10468 0.0 \n",
"10469 0.0 \n",
"10470 0.0 \n",
"10471 0.0 \n",
"10472 0.0 \n",
"10473 0.0 \n",
"10474 0.0 \n",
"10475 0.0 \n",
"10476 0.0 \n",
"10477 0.0 \n",
"10478 0.0 \n",
"10479 0.0 \n",
"\n",
"[480 rows x 4 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"run.get_dataframe(fields=[(\"*_XGM/*\", \"*.i[xy]Pos\")])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Labelled arrays\n",
"\n",
"Data with extra dimensions can be handled as [xarray](https://xarray.pydata.org/en/stable/) labelled arrays.\n",
"These are a wrapper around Numpy arrays with indexes which can be used to align them and select data."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray (trainId: 480, pulseID: 1000)>\n",
"array([[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)\n",
"Coordinates:\n",
" * trainId (trainId) uint64 10000 10001 10002 10003 ... 10477 10478 10479\n",
"Dimensions without coordinates: pulseID"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"xtd2_intensity = run.get_array('SA1_XTD2_XGM/DOOCS/MAIN:output', 'data.intensityTD', extra_dims=['pulseID'])\n",
"xtd2_intensity"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's a brief example of using xarray to align the data and select by train ID. See the [examples in the xarray docs](https://xarray.pydata.org/en/stable/examples.html) for more on what it can do.\n",
"\n",
"In this example data, all the data sources have the same range of train IDs, so aligning them doesn't change anything. In real data, devices may miss some trains that other devices did record."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<xarray.DataArray (trainId: 3, pulseID: 1000)>\n",
"array([[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)\n",
"Coordinates:\n",
" * trainId (trainId) uint64 10004 10005 10006\n",
"Dimensions without coordinates: pulseID"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import xarray as xr\n",
"xtd9_intensity = run.get_array('SPB_XTD9_XGM/DOOCS/MAIN:output', 'data.intensityTD', extra_dims=['pulseID'])\n",
"\n",
"# Align two arrays, keep only trains which they both have data for:\n",
"xtd2_intensity, xtd9_intensity = xr.align(xtd2_intensity, xtd9_intensity, join='inner')\n",
"\n",
"# Select data for a single train by train ID:\n",
"xtd2_intensity.sel(trainId=10004)\n",
"\n",
"# Select data from a range of train IDs.\n",
"# This includes the end value, unlike normal Python indexing\n",
"xtd2_intensity.loc[10004:10006]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also specify a region of interest from an array to load only part of the data:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Whole array shape: (5, 255, 1024)\n",
"ROI array shape: (5, 100, 512)\n"
]
}
],
"source": [
"from karabo_data import by_index\n",
"\n",
"# Select the first 5 trains in this run:\n",
"sel = run.select_trains(by_index[:5])\n",
"\n",
"# Get the whole of this array:\n",
"arr = sel.get_array('FXE_XAD_GEC/CAM/CAMERA:daqOutput', 'data.image.pixels')\n",
"print(\"Whole array shape:\", arr.shape)\n",
"\n",
"# Get a region of interest\n",
"arr2 = sel.get_array('FXE_XAD_GEC/CAM/CAMERA:daqOutput', 'data.image.pixels', roi=by_index[100:200, :512])\n",
"print(\"ROI array shape:\", arr2.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## General information"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`karabo_data` provides a few ways to get general information about what's in data files. First, from Python code:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"# of trains: 480\n",
"Duration: 0:00:47.900000\n",
"First train ID: 10000\n",
"Last train ID: 10479\n",
"\n",
"16 detector modules (FXE_DET_LPD1M-1)\n",
" e.g. module FXE_DET_LPD1M-1 0 : 256 x 256 pixels\n",
" 128 frames per train, 61440 total frames\n",
"\n",
"4 instrument sources (excluding detectors):\n",
" - FXE_XAD_GEC/CAM/CAMERA:daqOutput\n",
" - FXE_XAD_GEC/CAM/CAMERA_NODATA:daqOutput\n",
" - SA1_XTD2_XGM/DOOCS/MAIN:output\n",
" - SPB_XTD9_XGM/DOOCS/MAIN:output\n",
"\n",
"4 control sources:\n",
" - FXE_XAD_GEC/CAM/CAMERA\n",
" - FXE_XAD_GEC/CAM/CAMERA_NODATA\n",
" - SA1_XTD2_XGM/DOOCS/MAIN\n",
" - SPB_XTD9_XGM/DOOCS/MAIN\n",
"\n"
]
}
],
"source": [
"run.info()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'dims': (256, 256), 'frames_per_train': 128, 'total_frames': 61440}"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"run.detector_info('FXE_DET_LPD1M-1/DET/0CH0:xtdf')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `lsxfel` command provides similar information at the command line:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RAW-R0450-LPD00-S00000.h5 : Raw detector data from LPD module 00\r\n",
"480 trains\r\n",
"\r\n",
"256 × 256 pixels\r\n",
"128 frames per train, 61440 total\r\n"
]
}
],
"source": [
"!lsxfel fxe_example_run/RAW-R0450-LPD00-S00000.h5"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RAW-R0450-DA01-S00000.h5 : Aggregated data\n",
"400 trains\n",
"\n",
"4 instrument sources\n",
" - FXE_XAD_GEC/CAM/CAMERA:daqOutput\n",
" - FXE_XAD_GEC/CAM/CAMERA_NODATA:daqOutput\n",
" - SA1_XTD2_XGM/DOOCS/MAIN:output\n",
" - SPB_XTD9_XGM/DOOCS/MAIN:output\n",
"\n",
"4 control sources\n",
" - FXE_XAD_GEC/CAM/CAMERA\n",
" - FXE_XAD_GEC/CAM/CAMERA_NODATA\n",
" - SA1_XTD2_XGM/DOOCS/MAIN\n",
" - SPB_XTD9_XGM/DOOCS/MAIN\n",
"\n"
]
}
],
"source": [
"!lsxfel fxe_example_run/RAW-R0450-DA01-S00000.h5"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fxe_example_run : Run directory\n",
"\n",
"# of trains: 480\n",
"Duration: 0:00:47.900000\n",
"First train ID: 10000\n",
"Last train ID: 10479\n",
"\n",
"16 detector modules (FXE_DET_LPD1M-1)\n",
" e.g. module FXE_DET_LPD1M-1 0 : 256 x 256 pixels\n",
" 128 frames per train, 61440 total frames\n",
"\n",
"4 instrument sources (excluding detectors):\n",
" - FXE_XAD_GEC/CAM/CAMERA:daqOutput\n",
" - FXE_XAD_GEC/CAM/CAMERA_NODATA:daqOutput\n",
" - SA1_XTD2_XGM/DOOCS/MAIN:output\n",
" - SPB_XTD9_XGM/DOOCS/MAIN:output\n",
"\n",
"4 control sources:\n",
" - FXE_XAD_GEC/CAM/CAMERA\n",
" - FXE_XAD_GEC/CAM/CAMERA_NODATA\n",
" - SA1_XTD2_XGM/DOOCS/MAIN\n",
" - SPB_XTD9_XGM/DOOCS/MAIN\n",
"\n"
]
}
],
"source": [
"!lsxfel fxe_example_run"
]
}
],
"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": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| bsd-3-clause |
tuxfux-hlp-notes/python-batches | archieves/batch-57/files/Learning_files.ipynb | 1 | 28991 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# files - peristant storage\n",
"# .txt,.xls,.html,.png\n",
"# files - read,write,append\n",
"# anything more than this - editor.\n",
"# DBs - relational or non-relational dbs. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# open a file.\n",
"f = open(\"file1.txt\")\n",
"# or\n",
"f = open(\"file1.txt\",\"rb\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<type 'file'>\n",
"<open file 'file1.txt', mode 'rb' at 0x7fa210042030>\n"
]
}
],
"source": [
"print type(f)\n",
"print f"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# modes\n",
"# r - read mode - reading the contents of the file.\n",
"# w - write mode - you can write into a file.\n",
"# - if file does not exits , it create a file.\n",
"# - if it exits it will overwrite.\n",
"# a - append the line to the end of the file.\n",
"# r+ - read and write.\n",
"# b - binary. (rb,wb,ab)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['__class__', '__delattr__', '__doc__', '__enter__', '__exit__', '__format__', '__getattribute__', '__hash__', '__init__', '__iter__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'close', 'closed', 'encoding', 'errors', 'fileno', 'flush', 'isatty', 'mode', 'name', 'newlines', 'next', 'read', 'readinto', 'readline', 'readlines', 'seek', 'softspace', 'tell', 'truncate', 'write', 'writelines', 'xreadlines']\n"
]
}
],
"source": [
"print dir(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# f.softspace,f.newlines,f.errors,f.encoding"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function readinto:\n",
"\n",
"readinto(...)\n",
" readinto() -> Undocumented. Don't use this; it may go away.\n",
"\n",
"None\n"
]
}
],
"source": [
"# f.readinto\n",
"print help(f.readinto)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function xreadlines:\n",
"\n",
"xreadlines(...)\n",
" xreadlines() -> returns self.\n",
" \n",
" For backward compatibility. File objects now include the performance\n",
" optimizations previously implemented in the xreadlines module.\n",
"\n",
"None\n"
]
}
],
"source": [
"# f.xreadlines\n",
"print help(f.xreadlines)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"file1.txt\n"
]
}
],
"source": [
"# f.name\n",
"print f.name"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"rb\n"
]
}
],
"source": [
"# f.mode\n",
"print f.mode"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function read:\n",
"\n",
"read(...)\n",
" read([size]) -> read at most size bytes, returned as a string.\n",
" \n",
" If the size argument is negative or omitted, read until EOF is reached.\n",
" Notice that when in non-blocking mode, less data than what was requested\n",
" may be returned, even if no size parameter was given.\n",
"\n",
"None\n"
]
}
],
"source": [
"# f.read\n",
"print help(f.read)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Th\n"
]
}
],
"source": [
"print f.read(2)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"is\n"
]
}
],
"source": [
"print f.read(2)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" is my line1.\n",
"This is my line2.\n",
"This is my line3.\n",
"This is my line4.\n"
]
}
],
"source": [
"print f.read()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"print f.read()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function tell:\n",
"\n",
"tell(...)\n",
" tell() -> current file position, an integer (may be a long integer).\n",
"\n",
"None\n",
"71\n"
]
}
],
"source": [
"# f.tell\n",
"print help(f.tell)\n",
"print f.tell()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function seek:\n",
"\n",
"seek(...)\n",
" seek(offset[, whence]) -> None. Move to new file position.\n",
" \n",
" Argument offset is a byte count. Optional argument whence defaults to\n",
" 0 (offset from start of file, offset should be >= 0); other values are 1\n",
" (move relative to current position, positive or negative), and 2 (move\n",
" relative to end of file, usually negative, although many platforms allow\n",
" seeking beyond the end of a file). If the file is opened in text mode,\n",
" only offsets returned by tell() are legal. Use of other offsets causes\n",
" undefined behavior.\n",
" Note that not all file objects are seekable.\n",
"\n",
"None\n"
]
}
],
"source": [
"# f.seek\n",
"print help(f.seek)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f.seek(0)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
}
],
"source": [
"print f.tell()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is my line1.\n",
"This is my line2.\n",
"This is my line3.\n",
"This is my line4.\n"
]
}
],
"source": [
"print f.read()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# f.next\n",
"# file handle is a iterator by default"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f.seek(0)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is my line1.\n",
"\n"
]
}
],
"source": [
"print f.next()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is my line2.\n",
"\n"
]
}
],
"source": [
"print f.next()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is my line3.\n",
"\n"
]
}
],
"source": [
"print f.next()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is my line4.\n"
]
}
],
"source": [
"print f.next()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "StopIteration",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mStopIteration\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-28-1386fc0a282d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mprint\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mStopIteration\u001b[0m: "
]
}
],
"source": [
"print f.next()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function readline:\n",
"\n",
"readline(...)\n",
" readline([size]) -> next line from the file, as a string.\n",
" \n",
" Retain newline. A non-negative size argument limits the maximum\n",
" number of bytes to return (an incomplete line may be returned then).\n",
" Return an empty string at EOF.\n",
"\n",
"None\n"
]
}
],
"source": [
"# f.readline\n",
"print help(f.readline)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f.seek(0)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is my line1.\n",
"\n"
]
}
],
"source": [
"print f.readline()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is my line2.\n",
"\n"
]
}
],
"source": [
"print f.readline()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is my line3.\n",
"\n"
]
}
],
"source": [
"print f.readline()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"This is my line4.\n"
]
}
],
"source": [
"print f.readline()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"print f.readline()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# f.readlines"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function readlines:\n",
"\n",
"readlines(...)\n",
" readlines([size]) -> list of strings, each a line from the file.\n",
" \n",
" Call readline() repeatedly and return a list of the lines so read.\n",
" The optional size argument, if given, is an approximate bound on the\n",
" total number of bytes in the lines returned.\n",
"\n",
"None\n"
]
}
],
"source": [
"print help(f.readlines)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f.seek(0)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"my_lines = f.readlines()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['This is my line1.\\n', 'This is my line2.\\n', 'This is my line3.\\n', 'This is my line4.']\n"
]
}
],
"source": [
"print my_lines"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# write"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"g = open(\"newfile.txt\",\"wb\")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function write:\n",
"\n",
"write(...)\n",
" write(str) -> None. Write string str to file.\n",
" \n",
" Note that due to buffering, flush() or close() may be needed before\n",
" the file on disk reflects the data written.\n",
"\n",
"None\n"
]
}
],
"source": [
"print help(g.write)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"g.write(\"This is line one.\\n This is line two.\\n This is line three \\n. This is line four.\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# input => I/O buffers => cpu => I/O buffers => output"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function flush:\n",
"\n",
"flush(...)\n",
" flush() -> None. Flush the internal I/O buffer.\n",
"\n",
"None\n"
]
}
],
"source": [
"# f.close and f.flush\n",
"print help(g.flush)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"g.flush()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function close:\n",
"\n",
"close(...)\n",
" close() -> None or (perhaps) an integer. Close the file.\n",
" \n",
" Sets data attribute .closed to True. A closed file cannot be used for\n",
" further I/O operations. close() may be called more than once without\n",
" error. Some kinds of file objects (for example, opened by popen())\n",
" may return an exit status upon closing.\n",
"\n",
"None\n"
]
}
],
"source": [
"print help(g.close)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"g.close()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ValueError",
"evalue": "I/O operation on closed file",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-51-0223a80baa2c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"writing a new line\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mValueError\u001b[0m: I/O operation on closed file"
]
}
],
"source": [
"g.write(\"writing a new line\")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"<closed file 'newfile.txt', mode 'wb' at 0x7fa210042810>\n"
]
}
],
"source": [
"# g.closed\n",
"print g.closed\n",
"print g"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n",
"<open file 'file1.txt', mode 'rb' at 0x7fa210042030>\n"
]
}
],
"source": [
"print f.closed\n",
"print f "
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the file is closed , please open it\n"
]
}
],
"source": [
"# conditional operations\n",
"if g.closed:\n",
" print \"the file is closed , please open it\"\n",
"else:\n",
" g.write(\"writing a new line\")"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"buddy pleae open the file\n"
]
}
],
"source": [
"# exceptions\n",
"\n",
"try:\n",
" g.write(\"writing a new line\")\n",
"except ValueError:\n",
" print \"buddy pleae open the file\"\n",
"else:\n",
" print \"you are able to write into the file.\"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<closed file 'newfile.txt', mode 'wb' at 0x7fa210042810>\n",
"True\n",
"<closed file 'newfile.txt', mode 'ab' at 0x7fa2100429c0>\n",
"True\n"
]
}
],
"source": [
"# with\n",
"print g\n",
"print g.closed\n",
"with open('newfile.txt','ab') as g:\n",
" g.write(\"\\n writing a new line \\n\")\n",
"print g\n",
"print g.closed"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on built-in function writelines:\n",
"\n",
"writelines(...)\n",
" writelines(sequence_of_strings) -> None. Write the strings to the file.\n",
" \n",
" Note that newlines are not added. The sequence can be any iterable object\n",
" producing strings. This is equivalent to calling write() for each string.\n",
"\n",
"None\n"
]
}
],
"source": [
"# writelines\n",
"print help(g.writelines)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['This is my line1.\\n', 'This is my line2.\\n', 'This is my line3.\\n', 'This is my line4.']\n"
]
}
],
"source": [
"print my_lines"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"h = open(\"newfile.txt\",\"ab\")\n",
"h.writelines(my_lines)\n",
"h.flush()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# isatty\n",
"tcloudost@tcloudost-VirtualBox ~ $ tty\n",
"/dev/pts/2\n",
"tcloudost@tcloudost-VirtualBox ~ $ \n"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"i = open('/dev/pts/2',\"w\")\n",
"i.write(\"\\n lets go for breakfast \\n\")"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n"
]
}
],
"source": [
"print i.isatty()"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"i.close()"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<open file 'newfile.txt', mode 'ab' at 0x7fa210042ae0>\n"
]
}
],
"source": [
"print h"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"print h.isatty()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# pickle,excel,\n",
"# json,yaml,xml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# pickle"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"my_trainings = [\"linux\",\"python\",\"django\",\"shell\"]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f = open(\"my_train.txt\",\"wb\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function dump in module pickle:\n",
"\n",
"dump(obj, file, protocol=None)\n",
"\n",
"None\n"
]
}
],
"source": [
"import pickle as p\n",
"# pickling\n",
"print help(p.dump)\n",
"p.dump(my_trainings,f)\n",
"f.close()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function load in module pickle:\n",
"\n",
"load(file)\n",
"\n",
"None\n"
]
}
],
"source": [
"# unpickling\n",
"print help(p.load)\n",
"\n",
"g = open(\"my_train.txt\",\"rb\")\n",
"new_trainings = p.load(g)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['linux', 'python', 'django', 'devops']\n"
]
}
],
"source": [
"print new_trainings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# json"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# installation of another module excel"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"tcloudost@tcloudost-VirtualBox ~/Documents/git_repositories/python-batches/batch-57/files $ ls\n",
"file1.txt Learning_files.ipynb my_train.txt names.xls newfile.txt\n",
"tcloudost@tcloudost-VirtualBox ~/Documents/git_repositories/python-batches/batch-57/files $ virtualenv sheets\n",
"New python executable in sheets/bin/python\n",
"Installing setuptools, pip...done.\n",
"tcloudost@tcloudost-VirtualBox ~/Documents/git_repositories/python-batches/batch-57/files $ ls\n",
"file1.txt Learning_files.ipynb my_train.txt names.xls newfile.txt sheets\n",
"tcloudost@tcloudost-VirtualBox ~/Documents/git_repositories/python-batches/batch-57/files $ source sheets/bin/activate\n",
"(sheets)tcloudost@tcloudost-VirtualBox ~/Documents/git_repositories/python-batches/batch-57/files $ pip freeze\n",
"argparse==1.2.1\n",
"wsgiref==0.1.2\n",
"(sheets)tcloudost@tcloudost-VirtualBox ~/Documents/git_repositories/python-batches/batch-57/files $ pip install excel\n",
"Downloading/unpacking excel\n",
" Downloading excel-1.0.0.tar.gz\n",
" Running setup.py (path:/home/tcloudost/Documents/git_repositories/python-batches/batch-57/files/sheets/build/excel/setup.py) egg_info for package excel\n",
" \n",
"Downloading/unpacking xlrd (from excel)\n",
" Downloading xlrd-1.0.0.tar.gz (2.6MB): 2.6MB downloaded\n",
" Running setup.py (path:/home/tcloudost/Documents/git_repositories/python-batches/batch-57/files/sheets/build/xlrd/setup.py) egg_info for package xlrd\n",
" \n",
" warning: no files found matching 'README.html'\n",
"Installing collected packages: excel, xlrd\n",
" Running setup.py install for excel\n",
" \n",
" Running setup.py install for xlrd\n",
" changing mode of build/scripts-2.7/runxlrd.py from 644 to 755\n",
" \n",
" warning: no files found matching 'README.html'\n",
" changing mode of /home/tcloudost/Documents/git_repositories/python-batches/batch-57/files/sheets/bin/runxlrd.py to 755\n",
"Successfully installed excel xlrd\n",
"Cleaning up...\n",
"(sheets)tcloudost@tcloudost-VirtualBox ~/Documents/git_repositories/python-batches/batch-57/files $ pip freeze\n",
"argparse==1.2.1\n",
"excel==1.0.0\n",
"wsgiref==0.1.2\n",
"xlrd==1.0.0\n",
"(sheets)tcloudost@tcloudost-VirtualBox ~/Documents/git_repositories/python-batches"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# xml parsing : https://www.tutorialspoint.com/python/python_xml_processing.htm"
]
}
],
"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": 1
}
| gpl-3.0 |
rohinkumar/galsurveystudy | DR12G/.ipynb_checkpoints/DR12G_CMASS1_correl_V01_LCf-checkpoint.ipynb | 1 | 326433 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Correlation function of DR12G SDSS CMASS Catalog"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First import all the modules such as healpy and astropy needed for analyzing the structure"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import healpix_util as hu\n",
"import astropy as ap\n",
"import numpy as np\n",
"from astropy.io import fits\n",
"from astropy.table import Table\n",
"import astropy.io.ascii as ascii\n",
"from astropy.io import fits\n",
"from astropy.constants import c\n",
"import matplotlib.pyplot as plt\n",
"import math as m\n",
"from math import pi\n",
"#from scipy.constants import c\n",
"import scipy.special as sp\n",
"from astroML.decorators import pickle_results\n",
"from scipy import integrate\n",
"import warnings\n",
"from sklearn.neighbors import BallTree\n",
"import pickle\n",
"import multiprocessing as mp\n",
"import time\n",
"from cython_metric import *\n",
"from lcck0metric import *\n",
"from progressbar import *\n",
"from tqdm import *\n",
"from functools import partial\n",
"import pymangle\n",
"#from astroML.datasets import fetch_sdss_specgals\n",
"#from astroML.correlation import bootstrap_two_point_angular\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read the data file (taken from http://cosmo.nyu.edu/~eak306/SDSS-LRG.html ) converted to ascii with comoving distance etc. in V01 reading from pkl files for faster read"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data=ascii.read(\"./output/dr12gcmnsrarfLC.dat\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<Table length=618806>\n",
"<table id=\"table4337971152\" class=\"table-striped table-bordered table-condensed\">\n",
"<thead><tr><th>z</th><th>ra</th><th>dec</th><th>s</th><th>rar</th><th>decr</th></tr></thead>\n",
"<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>0.54253</td><td>129.176188</td><td>48.946499</td><td>0.433424</td><td>2.25455</td><td>0.854278</td></tr>\n",
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"<tr><td>0.526395</td><td>213.817287</td><td>64.462038</td><td>0.422909</td><td>3.731816</td><td>1.125075</td></tr>\n",
"<tr><td>0.479835</td><td>215.483846</td><td>63.98142</td><td>0.391931</td><td>3.760903</td><td>1.116686</td></tr>\n",
"<tr><td>0.448259</td><td>218.302727</td><td>63.158747</td><td>0.370362</td><td>3.810101</td><td>1.102328</td></tr>\n",
"<tr><td>0.473205</td><td>216.816829</td><td>64.036989</td><td>0.38744</td><td>3.784168</td><td>1.117656</td></tr>\n",
"<tr><td>0.485066</td><td>215.166779</td><td>65.041442</td><td>0.395459</td><td>3.755369</td><td>1.135187</td></tr>\n",
"</table>"
],
"text/plain": [
"<Table length=618806>\n",
" z ra dec s rar decr \n",
"float64 float64 float64 float64 float64 float64 \n",
"-------- ---------- --------- -------- -------- --------\n",
" 0.54253 129.176188 48.946499 0.433424 2.25455 0.854278\n",
"0.399682 117.416963 39.276759 0.336245 2.049313 0.685509\n",
"0.537702 116.912724 39.443311 0.430289 2.040512 0.688416\n",
"0.519172 116.950172 39.490769 0.418165 2.041166 0.689244\n",
"0.543191 117.528471 40.176493 0.433852 2.051259 0.701212\n",
"0.589608 123.816159 46.636784 0.463487 2.161 0.813965\n",
"0.548197 127.601277 49.775937 0.437091 2.227062 0.868754\n",
"0.555224 122.816418 43.940864 0.44162 2.143551 0.766913\n",
"0.380021 122.937804 44.033184 0.322099 2.145669 0.768524\n",
"0.562535 123.132378 44.200812 0.44631 2.149065 0.77145\n",
" ... ... ... ... ... ...\n",
"0.482233 225.499984 58.361607 0.39355 3.935717 1.018602\n",
"0.476738 172.852073 60.085187 0.389836 3.016838 1.048684\n",
"0.474484 173.448708 60.332544 0.388308 3.027251 1.053002\n",
"0.456862 173.667282 60.181836 0.376285 3.031066 1.050371\n",
"0.471463 174.140124 60.81971 0.386257 3.039319 1.061504\n",
"0.526395 213.817287 64.462038 0.422909 3.731816 1.125075\n",
"0.479835 215.483846 63.98142 0.391931 3.760903 1.116686\n",
"0.448259 218.302727 63.158747 0.370362 3.810101 1.102328\n",
"0.473205 216.816829 64.036989 0.38744 3.784168 1.117656\n",
"0.485066 215.166779 65.041442 0.395459 3.755369 1.135187"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data.remove_column('z')\n",
"data.remove_column('ra')\n",
"data.remove_column('dec')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<Table length=618806>\n",
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"<thead><tr><th>float64</th><th>float64</th><th>float64</th></tr></thead>\n",
"<tr><td>0.433424</td><td>2.25455</td><td>0.854278</td></tr>\n",
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"</table>"
],
"text/plain": [
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" s rar decr \n",
"float64 float64 float64 \n",
"-------- -------- --------\n",
"0.433424 2.25455 0.854278\n",
"0.336245 2.049313 0.685509\n",
"0.430289 2.040512 0.688416\n",
"0.418165 2.041166 0.689244\n",
"0.433852 2.051259 0.701212\n",
"0.463487 2.161 0.813965\n",
"0.437091 2.227062 0.868754\n",
" 0.44162 2.143551 0.766913\n",
"0.322099 2.145669 0.768524\n",
" 0.44631 2.149065 0.77145\n",
" ... ... ...\n",
" 0.39355 3.935717 1.018602\n",
"0.389836 3.016838 1.048684\n",
"0.388308 3.027251 1.053002\n",
"0.376285 3.031066 1.050371\n",
"0.386257 3.039319 1.061504\n",
"0.422909 3.731816 1.125075\n",
"0.391931 3.760903 1.116686\n",
"0.370362 3.810101 1.102328\n",
" 0.38744 3.784168 1.117656\n",
"0.395459 3.755369 1.135187"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rs=np.array(data['s'])\n",
"rrar=np.array(data['rar'])\n",
"rdecr=np.array(data['decr'])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dat=np.array([rs,rrar,rdecr])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.433424, 0.336245, 0.430289, ..., 0.370362, 0.38744 ,\n",
" 0.395459],\n",
" [ 2.25455 , 2.049313, 2.040512, ..., 3.810101, 3.784168,\n",
" 3.755369],\n",
" [ 0.854278, 0.685509, 0.688416, ..., 1.102328, 1.117656,\n",
" 1.135187]])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dat"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.433424, 0.336245, 0.430289, ..., 0.370362, 0.38744 ,\n",
" 0.395459],\n",
" [ 2.25455 , 2.049313, 2.040512, ..., 3.810101, 3.784168,\n",
" 3.755369],\n",
" [ 0.854278, 0.685509, 0.688416, ..., 1.102328, 1.117656,\n",
" 1.135187]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dat.reshape(3,len(data))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dat=dat.transpose()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.433424, 2.25455 , 0.854278],\n",
" [ 0.336245, 2.049313, 0.685509],\n",
" [ 0.430289, 2.040512, 0.688416],\n",
" ..., \n",
" [ 0.370362, 3.810101, 1.102328],\n",
" [ 0.38744 , 3.784168, 1.117656],\n",
" [ 0.395459, 3.755369, 1.135187]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dat"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Saving the objects:\n",
"with open('dr12gcmnLC.pkl', 'w') as f: # Python 3: open(..., 'wb')\n",
" pickle.dump(dat, f)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.433424, 2.25455 , 0.854278],\n",
" [ 0.336245, 2.049313, 0.685509],\n",
" [ 0.430289, 2.040512, 0.688416],\n",
" ..., \n",
" [ 0.370362, 3.810101, 1.102328],\n",
" [ 0.38744 , 3.784168, 1.117656],\n",
" [ 0.395459, 3.755369, 1.135187]])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Getting back the objects:\n",
"with open('dr12gcmnLC.pkl') as f: # Python 3: open(..., 'rb')\n",
" dat = pickle.load(f)\n",
"dat"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8.181494949043856e-07"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from lcck0metric import *\n",
"LCck0metric(dat[0],dat[1])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 32.2 s, sys: 326 ms, total: 32.6 s\n",
"Wall time: 33 s\n"
]
}
],
"source": [
"%%time\n",
"BT_D = BallTree(dat,metric='pyfunc',func=LCck0metric) \n",
"\n",
"with open('BTDdr12gcmnsLCf.pkl', 'w') as f:\n",
" pickle.dump(BT_D,f)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<sklearn.neighbors.ball_tree.BinaryTree at 0x7fcff0e58ff0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open('BTDdr12gcmnsLCf.pkl') as f:\n",
" BTD = pickle.load(f)\n",
" \n",
"BTD"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008,\n",
" 0.009, 0.01 , 0.011, 0.012, 0.013, 0.014, 0.015, 0.016,\n",
" 0.017, 0.018, 0.019, 0.02 ])"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bins=np.arange(0.004,0.084,0.004)\n",
"bins"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.00000000e-06, 4.00000000e-06, 9.00000000e-06,\n",
" 1.60000000e-05, 2.50000000e-05, 3.60000000e-05,\n",
" 4.90000000e-05, 6.40000000e-05, 8.10000000e-05,\n",
" 1.00000000e-04, 1.21000000e-04, 1.44000000e-04,\n",
" 1.69000000e-04, 1.96000000e-04, 2.25000000e-04,\n",
" 2.56000000e-04, 2.89000000e-04, 3.24000000e-04,\n",
" 3.61000000e-04, 4.00000000e-04])"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"binsq=(bins*0.007)**2\n",
"binsq"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 38109750510 151178922283 271882047064 348256898894 379493862537\n",
" 382919358899 382920865636 382920865636 382920865636 382920865636\n",
" 382920865636 382920865636 382920865636 382920865636 382920865636\n",
" 382920865636 382920865636 382920865636 382920865636 382920865636]\n",
"Total run time:\n",
"20195.2507951\n",
"CPU times: user 5h 31min 28s, sys: 1min 7s, total: 5h 32min 35s\n",
"Wall time: 5h 36min 35s\n"
]
}
],
"source": [
"%%time\n",
"start_time=time.time()\n",
"counts_DD=BTD.two_point_correlation(dat,binsq)\n",
"print counts_DD\n",
"end_time=time.time()\n",
"tottime=end_time-start_time\n",
"print \"Total run time:\"\n",
"print tottime\n",
"\n",
"with open('BTDdr12gcmnsDDLCf.pkl', 'w') as f:\n",
" pickle.dump(counts_DD,f)\n",
"\n",
"with open('BTDdr12gcmnsDDLCf.pkl') as f:\n",
" counts_DD = pickle.load(f)\n",
" \n",
"counts_DD"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 38109750510, 151178922283, 271882047064, 348256898894,\n",
" 379493862537, 382919358899, 382920865636, 382920865636,\n",
" 382920865636, 382920865636, 382920865636, 382920865636,\n",
" 382920865636, 382920865636, 382920865636, 382920865636,\n",
" 382920865636, 382920865636, 382920865636, 382920865636])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts_DD"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DD=np.diff(counts_DD)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([113069171773, 120703124781, 76374851830, 31236963643,\n",
" 3425496362, 1506737, 0, 0,\n",
" 0, 0, 0, 0,\n",
" 0, 0, 0, 0,\n",
" 0, 0, 0])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DD"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11235d110>]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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JiDS6suFuZk3AbcBFwGzgKjObPUy7bwH/nnSRjaK9Hfbvh/XrY1ciIo2ukj33+cAWd9/q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\nXgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bf5d4d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[1:len(bins)],DD,'bo-')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"RR_zero = (RR == 0)\n",
"RR[RR_zero] = 1"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"correl=4.0*DD/RR-1.0"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.50498415, 0.26478836, 0.08084476, 0.03974161, 0.02578027,\n",
" 0.02243945, 0.02628759, 0.02791146, 0.02888038, 0.03076744,\n",
" 0.03112052, 0.03297988, 0.03271199, 0.03416311, 0.0350347 ])"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"correl"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x113c4c5d0>]"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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wCvATd99uZqvMbFWhCyy0ZHWS9468R+cHnWGXIiISmHxG7rj7JmBTzr51o7S9bvJlTZ3s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\nIkjhLiISQf8fiJO8HAgondUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113b56f50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[1:len(bins)],correl,'go-')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x113d5fed0>]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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0rPsPcj8IIRqpTZRcRGowPetewqLkIlJD5e/J58j6R5ZZp2fdS2VTchGpgRas\nX8Avxv2C73d/T906+15a1bPupSoouYjUIHsK93DP3Hs4c+KZ/LjnR+YMnsPT/Z/Ws+6lymm2mEgN\nsfKblVz792tZuGEh13a9ltEpo2nWsBmAkolUOSUXkWrO3Xli4RP8bvbvOKLeEbxwxQsM6Dwg7LCk\nlovptJiZ9TazlWaWY2Yjyqg3Mxsd1C8xs+7R+prZUWY228y+CF6bR9TdGbRfaWYXRZTPDco+CZaj\ng/IGZvZ80OcjM+twaB+HSPWyYccGUjJTuOWNWzi3w7ksu3mZEovEhajJxcwSgDFACtAZuNrMOpdq\nlgJ0CpZ0YGwMfUcAWe7eCcgKtgnqBwInA72BJ4L9lBjk7t2CZXNQNgz41t2PBx4B/hL7RyBSPU1f\nPp1TnjiF93PfZ2zfsbz+n6/rjsYSN2IZuZwB5Lj7anffDUwDUku1SQWmeLH5QDMzaxOlbyowOVif\nDPSPKJ/m7rvc/UsgJ9hPeSL39SLQ03RPC6mhvtv5Hde8fA1XvXgVnVp0YvGNi7kp+SbdxkXiSizJ\npS2wNmJ7XVAWS5vy+rZ2943B+tdA6xiPNzk4JXZXRALZ28fdC4A8oEUM702kWslanUWXsV2Ytmwa\n9513H/Oum8cJLU4IOyyR/cTFVGR3d8BjaDrI3U8GfhUs1x7Mccws3cyyzSx7y5YthxCpSDh+3PMj\nt//jdi6ceiFH1juS+dfP565z79rvNywi8SKW5LIeaB+x3S4oi6VNeX03BafOCF5Lrp8csI+7l7zu\nAJ7lp9Nle/uYWV0gEdha+o24+3h3T3b35FatWpX7pkXixccbP+a08afx6EePcusZt/LxjR+TfExy\n2GGJlCuW5LIQ6GRmHc2sPsUX22eWajMTGBzMGusB5AWnvMrrOxNIC9bTgBkR5QODGWAdKZ4ksMDM\n6ppZSwAzqwdcDCwrY18DgDnBaEik2iooKmDkeyP55YRfkrcrj7eueYvRKaNpVK9R2KGJRBV1TO3u\nBWZ2C/AmkABMcvflZnZTUP8k8DrQh+KL7/nA0PL6Brt+AJhuZsOANcCVQZ/lZjYd+AwoAH7t7oVm\ndiTwZpBYEoC3gb8F+5oITDWzHGAbxUlMpFqJfO5KmyZtaFS3ETnf5jDwlIGM6TOGo444KuwQRWJm\ntfULfnJysmdnZ4cdhgiw/3NXSgxPHs6YvmNCikpkf2a2yN2jnpeNiwv6IrXdgZ678toXr4UQjcjh\nU3IRCdl062MAAAAOB0lEQVSugl2syVtTZp2euyLVlZKLSIje/epdTn3y1APW67krUl0puYiEYGv+\nVq6bcR3nTT6P3YW7uePMO/abBabnrkh1pl9giVQhd2fqkqn8z1v/w3c7v+POs+/kf8/5XxrVa0TX\nn3XdO1vs2MRjGdlzpG6VL9WWZosdhMipovqfXw7Wqq2ruPm1m5nz5RzObH8m4y4exylHnxJ2WCIH\nJdbZYhq5xKj0VNE1eWtIn5UO6EFMUr5dBbt4cN6DjHx/JA3rNuTJvk9yw2k3UMd0VlpqLv3rjlFZ\nU0Xz9+STkZURUkRSHby35j26jevG3XPv5tJ/u5QVt6zgxuQblVikxtPIJUYHmhKqqaJSlq35W7lj\n9h1M+mQSHZt15I1Bb9D7+N5hhyVSZfT1KUYHmhKaUCeBWStnUVuvXcm+3J2pn07lpDEnMWXJFEac\nNYJlw5cpsUito+QSo5E9R+43VbRBQgNaHtGSftP60XNKTxZvXBxSdBIPVm1dxYVTL2TwK4PpdFQn\nPk7/mPsvvF83mpRaScklRoO6DGL8JeNJSkzCMJISk5iYOpHc3+TyWMpjLNm0hNPGn8bQGUNZv730\nEwmkpslcmkmHUR2oc28dkh5JYsD0AXQd25VFGxYxtu9YPrjuA7q07hJ2mCKh0VTkCvLdzu8Y+d5I\nRi8YTd06dfntv/+W3531OxrXb1xhx5D4cKCbTPZo24OXr3pZz7GXGk03rqxizRo246FeD7Hi1yu4\n+ISLue+9+zjhsROYtHgShUWFYYcnFehAN5nc+P1GJRaRgJJLBevYvCPPD3ieedfNI6lZEsNmDqP7\n+O68vfrtsEOTClBYVKibTIrEQMmlkpzZ/kw+vO5Dpl0+je27tvMfU/+Dvs/25bMtn4UdmhyihesX\n0mNijwPW6yaTIj9RcqlEZsZVp1zF57/+nAcvfJB5ufPoOrYrw18bzuYfNocdnsRoa/5Wbpx1I7+c\n8EvWb1/P8OThusmkSBRKLlWgYd2G/O6s35FzWw43J9/M+EXjOX708TzwwQPsLNi5z8yjDqM6kLk0\nM+yQBSjyIv626G+c8PgJTFw8kd/0+A0rblnBmL5j9ps5OP6S8boNkEiEmGaLmVlv4FGKn10/wd0f\nKFVvQX0fIB8Y4u4fl9fXzI4Cngc6AF8BV7r7t0HdncAwoBC4zd3fNLNGwAvAz4PyWe4+Img/BHgI\nKJkD/Li7TyjvPYX5mOMV36zgjtl3MGvVLI464ii+3/09uwt3761vVK+R/liFbNGGRQx/fTgL1i/g\nnKRzGNNnjG4yKUIFzhYzswRgDJACdAauNrPOpZqlAJ2CJR0YG0PfEUCWu3cCsoJtgvqBwMlAb+CJ\nYD8Af3X3k4BfAGeZWUpEDM+7e7dgKTexhO2klicx8+qZzBk8hx27duyTWED3LAvTth+3Mfy14Zz+\nt9NZ890apl46lblpc5VYRA5SLKfFzgBy3H21u+8GpgGppdqkAlO82HygmZm1idI3FZgcrE8G+keU\nT3P3Xe7+JZADnOHu+e7+DkCwr4+BdofwnuPG+R3Pp6CooMw6zTyqWkVexKTFkzjx8RMZt2gct/3y\nNlbespJrul5D8cBcRA5GLMmlLbA2YntdUBZLm/L6tnb3jcH610DrWI9nZs2ASyge8ZS43MyWmtmL\nZtY+hvcVF8qbYTT8teEs27ysCqOpnRZvXMzZk85m2MxhnNDiBD5O/5hRvUeR2DAx7NBEqq24uKDv\nxRd+YrpVgJnVBZ4DRrv76qB4FtDB3bsAs/lpRFS6b7qZZZtZ9pYtWyog8sNX1j3LGiY05Oz2ZzNp\n8SS6jO3CuU+fy/PLnt/v9Jkcnu92fsetr99K8t+SydmWw9OpT/P+0Pc59WcHfqa9iMQmluSyHogc\nCbTjpwvn0dqU13dTcOqM4LVkbm60440HvnD3USUF7r7V3XcFmxOA08p6I+4+3t2T3T25VatWZTWp\ncmXds2xC6gTeu+491v/3eh688EHW5q1l4EsDSRqVxN3v3M267evCDrtac3emfDqFEx8/kSeyn+Dm\n5JtZectK0rql6TkrIhXF3ctdKH7my2qgI1Af+BQ4uVSbvsAbgAE9gAXR+lI8u2tEsD4CeDBYPzlo\n1yDotxpICOr+BLwE1Cl1/DYR65cC86O9r9NOO82ri8KiQn9t1WveN7Ov2z3mCfcm+GXPX+ZZq7O8\nqKgo7PDi2jNLnvGkR5Lc7jFPeiTJ//z+n/3sSWc79+A9JvTwRRsWhR2iSLUCZHuUv6/uHvNU5D7A\nKIqnE09y95FmdlOQnJ4MpiI/TvHsrnxgqLtnH6hvUN4CmA4cC6yheCrytqAuA7gOKABud/c3zKwd\nxddiVgAlo5TH3X2Cmd0P9AvabwNudvcV5b2nMKciH47V365mXPY4Ji6eyNYft3JSy5MYnjycwacO\n1jWCUg50g8nG9RrzaMqjDOk2RCMVkYMU61Rk3RW5mtpZsJPpy6czZuEYFqxfwJH1juSartcw/PTh\ndG3dFSj+45qRlUFuXi7HJh7LyJ4ja9VvZ4595FjWbl+7X3m7pu1Y+5v9y0UkOiWXKKp7comUvSGb\nJxY+wXPLnmNnwU7Oan8Wp7Y+lac/fXqfb+01+ceZOwt28unXn5K9IZuFGxaSvSGb5VuWl9nWMIr+\nUFTFEYrUDEouUdSk5FJi24/beGrxU4zNHsu/vv1XmW2SEpP46vavqjawCrancA/Ltyxn4fqFe5PJ\n0s1L9/5mqFWjVpze9nTm5c4jb1fefv1rwmcgEhYllyhqYnIpUeRF1L2vLn6A2d3d23SnfdP2HJt4\n7E+vicWvbRq3IaFOQpn9SlTG6bYD7bOwqJCVW1fuk0g++foTdhUWX3Zr1rAZycckc/oxp5N8TDLJ\nxyTTvml7zKzMay41efQmUhWUXKKoyckFoMOoDmU+d6Rxvcb8KulX5OblkpuXy47dO/apT7AE2jZt\nS/um7YsTTtOfEk/7pu1ZsH4B//3Wfx/WH2x3p8iLKPRCCosKeW7Zc9zy+i38WPDj3jZ169Tl581/\nzvod6/l+9/cAHFnvSE475jSS2yRzetviZPLz5j8v9xf0tf26k0hFU3KJoqYnl1i/teftzGPt9rXk\n5uWyNi943b52b9m67eti+vFmgiXQunHr4qRRVLg3cUQmkZLXA42oSqufUJ/07unFI5O2p3NiixOj\njqpEpHLFmlzqVkUwUvVKEki0b+2JDRNJbJh4wBszFnkRm3/YvDfxDHhhQJntCr2Q3j/vTUKdBBIs\ngTpWZ+965Gsdq7Nf2Z1Zd5a5zz2Fe3isz2OH8SmISFg0cpGDcqDTbYdzkbwy9ikilaPCbrkvEqms\ne6Ed7lMYK2OfIhIuJRc5KGXdC+1wZ19Vxj5FJFw6LSYiIjHTaTEREQmNkouIiFQ4JRcREalwSi4i\nIlLhlFxERKTC1drZYma2heKHlMWTlsA3YQdxEKpTvIq18lSneKtTrBCf8Sa5e9TnxNfa5BKPzCw7\nlil+8aI6xatYK091irc6xQrVL95IOi0mIiIVTslFREQqnJJLfBkfdgAHqTrFq1grT3WKtzrFCtUv\n3r10zUVERCqcRi4iIlLhlFyqgJn1NrOVZpZjZiPKqDczGx3ULzGz7hF1k8xss5kti/d4zay9mb1j\nZp+Z2XIz+684jrWhmS0ws0+DWO+t7FgPJ96I+gQzW2xmr8ZzrGb2lZktNbNPzKxK7hB7mPE2M7MX\nzWyFmX1uZv8ej7Ga2YnBZ1qybDez2ysz1kPm7loqcQESgH8BxwH1gU+BzqXa9AHeAAzoAXwUUXcO\n0B1YFu/xAm2A7sF6E2BV6b5xFKsBjYP1esBHQI94/Wwj6v8beBZ4NZ5jBb4CWlbFv9kKincycH2w\nXh9oFq+xltrP1xT/7qRKPueDWTRyqXxnADnuvtrddwPTgNRSbVKBKV5sPtDMzNoAuPt7wLbqEK+7\nb3T3j4O4dwCfA23jNFZ39++DNvWCpbIvQB7WvwUzawf0BSZUcpyHHWsIDjleM0uk+EvcRAB33+3u\n38VjrKXa9AT+5e7x9mNwQKfFqkJbYG3E9jr2/4MbS5uqUiHxmlkH4BcUjwgqy2HFGpxi+gTYDMx2\n98qMtdxYYmwzCrgDKKqsAGOMI5Y2DrxtZovMLL3SoowtlmhtOgJbgKeCU44TzOzIOI010kDguQqP\nroIouUiFM7PGwEvA7e6+Pex4DsTdC929G9AOOMPMTgk7pgMxs4uBze6+KOxYYnR28NmmAL82s3PC\nDqgcdSk+9TzW3X8B/ADsdx0knphZfaAf8ELYsRyIkkvlWw+0j9huF5QdbJuqcljxmlk9ihNLpru/\nXIlxlhvHwbQJToG8A/SuhBgPKpZy2pwF9DOzryg+jXKBmT1TeaEe3mfr7iWvm4G/U3wqqDIdTrzr\ngHURI9cXKU42laUi/t2mAB+7+6ZKibAihH3Rp6YvFH8rWk3x0Lvk4t3Jpdr0Zd+LdwtK1Xeg6i7o\nH3K8wfYUYFQ1iLUVwUVb4AjgfeDieI23VJvzqPwL+ofz2R4JNIlY/xDoHa/xBnXvAycG6/cAD8Vr\nrEH9NGBoZX6mh/0+ww6gNiwUz/xYRfEMkYyg7CbgpmDdgDFB/VIgOaLvc8BGYA/F37CGxWu8wNkU\nn2tfAnwSLH3iNNauwOIg1mXA3fH+byFiH+dRycnlMD/b44I/mJ8Cy0v6xmu8QV03IDv49/AK0DyO\nYz0S2AokVsXneqiLfqEvIiIVTtdcRESkwim5iIhIhVNyERGRCqfkIiIiFU7JRUREKpySi4iIVDgl\nFxERqXBKLiIiUuH+P3lKOdGYn3JTAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11057d390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[1:len(bins)],bins[1:len(bins)]*bins[1:len(bins)]*correl,'go-')"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x113e8ba90>]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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DsTWP5b3B73HOieeEHZJUQ7qgL1KFPLPoGbqldiMxIZFPbvxEiUVCo5GLSIyK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\nsl9SCw6oAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113d86fd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[2:len(bins)],bins[2:len(bins)]*bins[2:len(bins)]*correl[1:len(bins)],'go-')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11054ac90>]"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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28MymZ1heudzvSCIimNl251y9l3V1xD9NobwQd3/gbgD+5od/w3B02OdEIiLTo+KfgdqK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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x113eeaf90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[2:len(bins)],correl[1:len(bins)],'go-')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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28MymZ1heudzvSCIimNl251y9l3V1xD9NobwQd3/gbgD+5od/w3B02OdEIiLTo+KfgdqK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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114fe6110>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[2:len(bins)],correl[1:len(bins)],'go-')\n",
"plt.savefig(\"correl2x.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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+Kmluhm98Ax55BD7+8aSrERE5TcFfRW9+M6xeDZ//PPziF0lXIyISKPir7NZbYfFieO97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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114498b50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[2:len(bins)]*c/1e5,correl[1:len(bins)],'bo-')\n",
"plt.savefig(\"correl2x1.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x10f65f690>]"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10f65f6d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.yscale('log')\n",
"plt.plot(bins[1:len(bins)],correl,'bo-')"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1142e1f10>]"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SARAHr74qnXyyNGuW9OijLY9SgELbt09as6ZlZ79mTXNHXlEhTZwoTZgg3Xtv5rWv0wc7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\nEQAAEFP/D8seeYihiDWjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1142e1f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.yscale('log')\n",
"plt.plot(bins[2:len(bins)]*c/1e5,correl[1:len(bins)],'ro-')"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 0 236152 1835676 6024256 13930163 26635863 45156095\n",
" 70428809 103344608 144719046 195306049 255767295 326645884 408522145\n",
" 501808240 606822439]\n",
"Total run time:\n",
"5362.83386302\n",
"CPU times: user 1h 29min 5s, sys: 7.79 s, total: 1h 29min 13s\n",
"Wall time: 1h 29min 23s\n"
]
}
],
"source": [
"%%time\n",
"start_time=time.time()\n",
"counts_DR=BTR.two_point_correlation(dat,bins)\n",
"print counts_DR\n",
"end_time=time.time()\n",
"tottime=end_time-start_time\n",
"print \"Total run time:\"\n",
"print tottime\n",
"\n",
"with open('BTR200kcDRLCDM.pkl', 'w') as f:\n",
" pickle.dump(counts_DR,f)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0, 236152, 1835676, 6024256, 13930163, 26635863,\n",
" 45156095, 70428809, 103344608, 144719046, 195306049, 255767295,\n",
" 326645884, 408522145, 501808240, 606822439])"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"with open('BTR200kcDRLCDM.pkl') as f:\n",
" counts_DR = pickle.load(f)\n",
" \n",
"counts_DR"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DR=np.diff(counts_DR)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"corrells=(4.0 * DD - 4.0 * DR + RR) / RR"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 236152, 1599524, 4188580, 7905907, 12705700, 18520232,\n",
" 25272714, 32915799, 41374438, 50587003, 60461246, 70878589,\n",
" 81876261, 93286095, 105014199])"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DR"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.7052646 , 0.29978504, 0.10500558, 0.05797354, 0.03719729,\n",
" 0.02859312, 0.02948462, 0.02686032, 0.02325382, 0.02064449,\n",
" 0.01721195, 0.01703756, 0.01286895, 0.01164703, 0.01089049])"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corrells"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x114929c10>]"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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pzxcEbYPc/bC7Hw0erwfiZjY7m2VT1tHs7g3u3jBnzpwxvIXcU/CLSDHLJvifB04zs3oz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\niEjEKPhFRCJGwS8iEjH/H1Xca+zvbxadAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1144a3e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[1:len(bins)],corrells,'go-')"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1143fea50>]"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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UtRwDbHDbfaOrza+ESAjXnH0N83+Zb903AeTD1R+yee9muwhrTCV4VOhVtURV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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11494f190>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[1:len(bins)],bins[1:len(bins)]*bins[1:len(bins)]*corrells*(c*1e-5)**2,'go-')"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x113f16490>]"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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k4eIEalap6XYcYyosbwY1C0ppmWkkzksk71geAFn7s0iclwjA0A5D3YxmHC+u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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1144dd290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[2:len(bins)],bins[2:len(bins)]*bins[2:len(bins)]*corrells[1:len(bins)]*(c*1e-5)**2,'go-')"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1144d4690>]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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BmxnkWkdpWLu7+/vxPvnxn1SeUBrV34yZVQKfAO5LYc3Hjar2EI24bjMro/sA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\nxPx/xbOjFAFp/V0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114fafe10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[2:len(bins)],corrells[1:len(bins)],'go-')"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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BmxnkWkdpWLu7+/vxPvnxn1SeUBrV34yZVQKfAO5LYc3Hjar2EI24bjMro/sA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\nxPx/xbOjFAFp/V0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115337d50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[2:len(bins)],corrells[1:len(bins)],'go-')\n",
"plt.savefig(\"correl2xls.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWRNWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11543b810>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(bins[2:len(bins)]*c/1e5,corrells[1:len(bins)],'bo-')\n",
"plt.savefig(\"correl2x1ls.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x11535a210>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.yscale('log')\n",
"plt.plot(bins[1:len(bins)]*c/1e5,corrells,'bo-')\n",
"plt.savefig(\"correllsfiglog.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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NgTBxIgwb1vL158zxayOUlPhQSX5dkTyjU0CSW+nupLvy+h3dSb//vh/I1hQIy5bBxx/7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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1150b4e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.yscale('log')\n",
"plt.plot(bins[2:len(bins)]*c/1e5,corrells[1:len(bins)],'ro-')\n",
"plt.savefig(\"correllslog2x.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1155bd610>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.yscale('log')\n",
"plt.xscale('log')\n",
"plt.plot(bins[1:len(bins)]*c/1e5,corrells,'bo-')\n",
"plt.savefig(\"correllsloglog.pdf\")"
]
},
{
"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": [
"from functools import partial\n",
"\n",
"def harvester(text, case):\n",
" X = case[0]\n",
" return text + str(X)\n",
"\n",
"\n",
"partial_harvester = partial(harvester, case=RAW_DATASET)\n",
"\n",
"partial_qr=partial(BTD.query_radius,count_only=True)\n",
"\n",
"if __name__ == '__main__':\n",
" pool = multiprocessing.Pool(processes=6)\n",
" case_data = RAW_DATASET\n",
" pool.map(partial_harvester, case_data, 1)\n",
" pool.close()\n",
" pool.join()\n",
"\n",
"mapfunc = partial(BTD.query_radius, count_only=True)\n",
"map(mapfunc, volume_ids)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#ascii.write(\"DR72DDbinned.dat\",(bins[1:len(bins)],DDresult))\n",
"start_time=time.time()\n",
"@pickle_results(\"DR72DDmp1.pkl\")\n",
"def ddcal(BTD,dat,bins,Nbins):\n",
" counts_DD=np.zeros(Nbins)\n",
" for i in tqdm(range(Nbins)):\n",
" counts_DD[i]=np.sum(BTD.query_radius(dat, bins[i],count_only=True))\n",
" DD = np.diff(counts_DD)\n",
" print counts_DD\n",
" print DD\n",
" return DD\n",
"\n",
"def mf_wrap(args):\n",
" return ddcal(*args)\n",
"\n",
"pool=mp.Pool(8)\n",
"\n",
"arg=[(BTD,dat,bins,Nbins)]\n",
"%timeit DDresult=pool.map(mf_wrap,arg) \n",
"#DDresult = ddcal(BTD,dat,bins,Nbins)\n",
"end_time=time.time()\n",
"tottime=end_time-start_time\n",
"print \"Total run time:\"\n",
"print tottime"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%timeit dat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DDresult[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"DDresult[1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.plot(bins[1:len(bins)],DDresult[0],'ro')"
]
},
{
"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": [
"def myfun(a,b):\n",
" print a + b\n",
" return a+b\n",
"\n",
"def mf_wrap(args):\n",
" return myfun(*args)\n",
"\n",
"p = mp.Pool(4)\n",
"\n",
"fl = [(a,b) for a in range(3) for b in range(2)]\n",
"\n",
"p.map(mf_wrap, fl)"
]
},
{
"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": [
"counts_DD=np.zeros(Nbins)\n",
"\n",
"for i in range(Nbins):\n",
" counts_DD[i]=np.sum(BTD.query_radius(dat, bins[i],count_only=True))\n",
"DD = np.diff(counts_DD)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"print counts_DD\n",
"print DD"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.plot(bins[1:len(bins)],DD,'ro')"
]
},
{
"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": [
"dataR=fits.open(\"/Users/rohin/Downloads/random-DR7-Full.fits\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataR=dataR[1].data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"len(dataR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tdata=np.array(data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"type(tdata[4])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tdata.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tdata.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tdata=np.atleast_d(tdata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tdata.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tdata.reshape(len(tdata),3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tdata=np.asarray(data)\n",
"tdata=tdata.transpose()"
]
},
{
"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": [
"tdata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"len(tdata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"BTD.two_point_correlationpoint_correlationpoint_correlationpoint_correlationtime\n",
"stime=time.time()\n",
"tpcf=BTD.two_point_correlation(dat,bins)\n",
"print time.time()-stime\n",
"print tpcf\n",
"plt.plot(bins,tpcf)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"stime=time.time()\n",
"tpcfd=BTD.two_point_correlation(dat,bins,dualtree=True)\n",
"print time.time()-stime\n",
"print tpcfd\n",
"plt.plot(bins,tpcfd)"
]
},
{
"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": [
"X"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"np.random.seed(0)\n",
"X = np.random.random((30,3))\n",
"r = np.linspace(0, 1, 10)\n",
"tree = BallTree(X,metric='pyfunc',func=LCDMmetric) \n",
"s = pickle.dumps(tree) \n",
"treedump = pickle.loads(s) \n",
"treedump.two_point_correlation(X,r)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"BT_D = BallTree(data)\n",
" BT_R = BallTree(data_R)\n",
"\n",
" counts_DD = np.zeros(Nbins + 1)\n",
" counts_RR = np.zeros(Nbins + 1)\n",
"\n",
" for i in range(Nbins + 1):\n",
" counts_DD[i] = np.sum(BT_D.query_radius(data, bins[i],\n",
" count_only=True))\n",
" counts_RR[i] = np.sum(BT_R.query_radius(data_R, bins[i],\n",
" count_only=True))\n",
"\n",
" DD = np.diff(counts_DD)\n",
" RR = np.diff(counts_RR)\n",
"\n",
" # check for zero in the denominator\n",
" RR_zero = (RR == 0)\n",
" RR[RR_zero] = 1\n",
"\n",
" if method == 'standard':\n",
" corr = factor ** 2 * DD / RR - 1\n",
" elif method == 'landy-szalay':\n",
" if sklearn_has_two_point:\n",
" counts_DR = KDT_R.two_point_correlation(data, bins)\n",
" else:\n",
" counts_DR = np.zeros(Nbins + 1)\n",
" for i in range(Nbins + 1):\n",
" counts_DR[i] = np.sum(BT_R.query_radius(data, bins[i],\n",
" count_only=True))\n",
" DR = np.diff(counts_DR)\n",
"\n",
" corr = (factor ** 2 * DD - 2 * factor * DR + RR) / RR\n",
"\n",
" corr[RR_zero] = np.nan\n",
"\n",
" return corr"
]
},
{
"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": [
"dr7fdat=np.array([data['s'][0:300] data['rar'][0:300] data['decr'][0:300]])\n",
"dr7fdat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dr7fdat[2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def LCDMmetric(p1,p2):\n",
" costheta=m.sin(dec1)*m.sin(dec2)+m.cos(dec1)*m.cos(dec2)*m.cos(ra1-ra2)\n",
" s1=DC_LCDM(z1)\n",
" s2=DC_LCDM(z2)\n",
" return np.sqrt(s1**2+s2**2-2.0*s1*s2*costheta)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#fdata=fits.open(\"/Users/rohin/Downloads/DR7-Full.fits\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#fdata.writeto(\"./output/DR7fulltrim.fits\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdata=fits.open(\"./output/DR7fulltrim.fits\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cols=fdata[1].columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cols.del_col('ZTYPE')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cols.del_col('SECTOR')\n",
"cols.del_col('FGOTMAIN')\n",
"cols.del_col('QUALITY')\n",
"cols.del_col('ISBAD')\n",
"cols.del_col('M')\n",
"cols.del_col('MMAX')\n",
"cols.del_col('ILSS')\n",
"cols.del_col('ICOMB')\n",
"cols.del_col('VAGC_SELECT')\n",
"cols.del_col('LSS_INDEX')\n",
"cols.del_col('FIBERWEIGHT')\n",
"cols.del_col('PRIMTARGET')\n",
"cols.del_col('MG')\n",
"cols.del_col('SECTOR_COMPLETENESS')\n",
"cols.del_col('COMOV_DENSITY')\n",
"cols.del_col('RADIAL_WEIGHT')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdata[1].columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdata.writeto(\"./output/DR7fullzradec.fits\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat=fits.open(\"./output/DR7fullzradec.fits\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].data['Z']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].data['RA']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"comovlcdm=DC_LCDM(fdat[1].data['Z'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].data['Z']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"comovlcdm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"comovlcdm.dtype"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#cols=fdat[1].columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nc=fits.Column(name='COMOV',format='D',array=comovlcdm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nc1=fits.Column(name='COMOV',format='D')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdata[1].data['Z']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdata[1].data['RA']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nc"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nc.dtype"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#cols.add_col(nc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].columns.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].columns.add_col(nc1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].data['COMOV']=comovlcdm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"comovlcdm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].data['Z']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].data['COMOV']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].data['RA']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fdat[1].data['RA']=fdat[1].data['RA']*pi/180.0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"comovlcdm=DC_LCDM(fdat[1].data['Z'])\n",
"comovlcdm"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Random catalog created based on the survey limitations also taken from http://cosmo.nyu.edu/~eak306/SDSS-LRG.html"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataR=fits.open(\"/Users/rohin/Downloads/random-DR7-Full.fits\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataR"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataR=dataR[1].data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"len(dataR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"NSIDE=512\n",
"dr72hpix=hu.HealPix(\"ring\",NSIDE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = open(\"./output/pixdatadr72VAGCfullrand.dat\",'w')\n",
"pixdata.write(\"z\\t pix \\n\")\n",
"\n",
"for i in range(0,len(data)-1):\n",
" pixdata.write(\"%f\\t\" %data['z'][i])\n",
" pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(dataR['ra'][i],dataR['dec'][i]))\n",
"pixdata.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = ascii.read(\"./output/pixdatadr72VAGCfullrand.dat\")\n",
"hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n",
"for j in range(len(pixdata)):\n",
" hpixdata[pixdata[j]['pix']]+=1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hpixdata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.mollview(hpixdata,rot=180)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.orthview(hpixdata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\"\"\"\n",
"Tools for computing two-point correlation functions.\n",
"\"\"\"\n",
"\n",
"#from .utils import check_random_state\n",
"# From scikit-learn utilities:\n",
"def check_random_state(seed):\n",
" \"\"\"Turn seed into a np.random.RandomState instance\n",
"\n",
" If seed is None, return the RandomState singleton used by np.random.\n",
" If seed is an int, return a new RandomState instance seeded with seed.\n",
" If seed is already a RandomState instance, return it.\n",
" Otherwise raise ValueError.\n",
" \"\"\"\n",
" if seed is None or seed is np.random:\n",
" return np.random.mtrand._rand\n",
" if isinstance(seed, (int, np.integer)):\n",
" return np.random.RandomState(seed)\n",
" if isinstance(seed, np.random.RandomState):\n",
" return seed\n",
" raise ValueError('%r cannot be used to seed a numpy.random.RandomState'\n",
" ' instance' % seed)\n",
"\n",
"# Check if scikit-learn's two-point functionality is available.\n",
"# This was added in scikit-learn version 0.14\n",
"try:\n",
" from sklearn.neighbors import KDTree\n",
" sklearn_has_two_point = True\n",
"except ImportError:\n",
" import warnings\n",
" sklearn_has_two_point = False\n",
"\n",
"\n",
"def uniform_sphere(RAlim, DEClim, size=1):\n",
" \"\"\"Draw a uniform sample on a sphere\n",
"\n",
" Parameters\n",
" ----------\n",
" RAlim : tuple\n",
" select Right Ascension between RAlim[0] and RAlim[1]\n",
" units are degrees\n",
" DEClim : tuple\n",
" select Declination between DEClim[0] and DEClim[1]\n",
" size : int (optional)\n",
" the size of the random arrays to return (default = 1)\n",
"\n",
" Returns\n",
" -------\n",
" RA, DEC : ndarray\n",
" the random sample on the sphere within the given limits.\n",
" arrays have shape equal to size.\n",
" \"\"\"\n",
" zlim = np.sin(np.pi * np.asarray(DEClim) / 180.)\n",
"\n",
" z = zlim[0] + (zlim[1] - zlim[0]) * np.random.random(size)\n",
" DEC = (180. / np.pi) * np.arcsin(z)\n",
" RA = RAlim[0] + (RAlim[1] - RAlim[0]) * np.random.random(size)\n",
"\n",
" return RA, DEC\n",
"\n",
"\n",
"def ra_dec_to_xyz(ra, dec):\n",
" \"\"\"Convert ra & dec to Euclidean points\n",
"\n",
" Parameters\n",
" ----------\n",
" ra, dec : ndarrays\n",
"\n",
" Returns\n",
" x, y, z : ndarrays\n",
" \"\"\"\n",
" sin_ra = np.sin(ra * np.pi / 180.)\n",
" cos_ra = np.cos(ra * np.pi / 180.)\n",
"\n",
" sin_dec = np.sin(np.pi / 2. - dec * np.pi / 180.)\n",
" cos_dec = np.cos(np.pi / 2. - dec * np.pi / 180.)\n",
"\n",
" return (cos_ra * sin_dec,\n",
" sin_ra * sin_dec,\n",
" cos_dec)\n",
"\n",
"\n",
"def angular_dist_to_euclidean_dist(D, r=1):\n",
" \"\"\"convert angular distances to euclidean distances\"\"\"\n",
" return 2 * r * np.sin(0.5 * D * np.pi / 180.)\n",
"\n",
"\n",
"def two_point(data, bins, method='standard',\n",
" data_R=None, random_state=None):\n",
" \"\"\"Two-point correlation function\n",
"\n",
" Parameters\n",
" ----------\n",
" data : array_like\n",
" input data, shape = [n_samples, n_features]\n",
" bins : array_like\n",
" bins within which to compute the 2-point correlation.\n",
" shape = Nbins + 1\n",
" method : string\n",
" \"standard\" or \"landy-szalay\".\n",
" data_R : array_like (optional)\n",
" if specified, use this as the random comparison sample\n",
" random_state : integer, np.random.RandomState, or None\n",
" specify the random state to use for generating background\n",
"\n",
" Returns\n",
" -------\n",
" corr : ndarray\n",
" the estimate of the correlation function within each bin\n",
" shape = Nbins\n",
" \"\"\"\n",
" data = np.asarray(data)\n",
" bins = np.asarray(bins)\n",
" rng = check_random_state(random_state)\n",
"\n",
" if method not in ['standard', 'landy-szalay']:\n",
" raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n",
"\n",
" if bins.ndim != 1:\n",
" raise ValueError(\"bins must be a 1D array\")\n",
"\n",
" if data.ndim == 1:\n",
" data = data[:, np.newaxis]\n",
" elif data.ndim != 2:\n",
" raise ValueError(\"data should be 1D or 2D\")\n",
"\n",
" n_samples, n_features = data.shape\n",
" Nbins = len(bins) - 1\n",
"\n",
" # shuffle all but one axis to get background distribution\n",
" if data_R is None:\n",
" data_R = data.copy()\n",
" for i in range(n_features - 1):\n",
" rng.shuffle(data_R[:, i])\n",
" else:\n",
" data_R = np.asarray(data_R)\n",
" if (data_R.ndim != 2) or (data_R.shape[-1] != n_features):\n",
" raise ValueError('data_R must have same n_features as data')\n",
"\n",
" factor = len(data_R) * 1. / len(data)\n",
"\n",
" if sklearn_has_two_point:\n",
" # Fast two-point correlation functions added in scikit-learn v. 0.14\n",
" KDT_D = KDTree(data)\n",
" KDT_R = KDTree(data_R)\n",
"\n",
" counts_DD = KDT_D.two_point_correlation(data, bins)\n",
" counts_RR = KDT_R.two_point_correlation(data_R, bins)\n",
"\n",
" else:\n",
" warnings.warn(\"Version 0.3 of astroML will require scikit-learn \"\n",
" \"version 0.14 or higher for correlation function \"\n",
" \"calculations. Upgrade to sklearn 0.14+ now for much \"\n",
" \"faster correlation function calculations.\")\n",
"\n",
" BT_D = BallTree(data)\n",
" BT_R = BallTree(data_R)\n",
"\n",
" counts_DD = np.zeros(Nbins + 1)\n",
" counts_RR = np.zeros(Nbins + 1)\n",
"\n",
" for i in range(Nbins + 1):\n",
" counts_DD[i] = np.sum(BT_D.query_radius(data, bins[i],\n",
" count_only=True))\n",
" counts_RR[i] = np.sum(BT_R.query_radius(data_R, bins[i],\n",
" count_only=True))\n",
"\n",
" DD = np.diff(counts_DD)\n",
" RR = np.diff(counts_RR)\n",
"\n",
" # check for zero in the denominator\n",
" RR_zero = (RR == 0)\n",
" RR[RR_zero] = 1\n",
"\n",
" if method == 'standard':\n",
" corr = factor ** 2 * DD / RR - 1\n",
" elif method == 'landy-szalay':\n",
" if sklearn_has_two_point:\n",
" counts_DR = KDT_R.two_point_correlation(data, bins)\n",
" else:\n",
" counts_DR = np.zeros(Nbins + 1)\n",
" for i in range(Nbins + 1):\n",
" counts_DR[i] = np.sum(BT_R.query_radius(data, bins[i],\n",
" count_only=True))\n",
" DR = np.diff(counts_DR)\n",
"\n",
" corr = (factor ** 2 * DD - 2 * factor * DR + RR) / RR\n",
"\n",
" corr[RR_zero] = np.nan\n",
"\n",
" return corr\n",
"\n",
"\n",
"def bootstrap_two_point(data, bins, Nbootstrap=10,\n",
" method='standard', return_bootstraps=False,\n",
" random_state=None):\n",
" \"\"\"Bootstrapped two-point correlation function\n",
"\n",
" Parameters\n",
" ----------\n",
" data : array_like\n",
" input data, shape = [n_samples, n_features]\n",
" bins : array_like\n",
" bins within which to compute the 2-point correlation.\n",
" shape = Nbins + 1\n",
" Nbootstrap : integer\n",
" number of bootstrap resamples to perform (default = 10)\n",
" method : string\n",
" \"standard\" or \"landy-szalay\".\n",
" return_bootstraps: bool\n",
" if True, return full bootstrapped samples\n",
" random_state : integer, np.random.RandomState, or None\n",
" specify the random state to use for generating background\n",
"\n",
" Returns\n",
" -------\n",
" corr, corr_err : ndarrays\n",
" the estimate of the correlation function and the bootstrap\n",
" error within each bin. shape = Nbins\n",
" \"\"\"\n",
" data = np.asarray(data)\n",
" bins = np.asarray(bins)\n",
" rng = check_random_state(random_state)\n",
"\n",
" if method not in ['standard', 'landy-szalay']:\n",
" raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n",
"\n",
" if bins.ndim != 1:\n",
" raise ValueError(\"bins must be a 1D array\")\n",
"\n",
" if data.ndim == 1:\n",
" data = data[:, np.newaxis]\n",
" elif data.ndim != 2:\n",
" raise ValueError(\"data should be 1D or 2D\")\n",
"\n",
" if Nbootstrap < 2:\n",
" raise ValueError(\"Nbootstrap must be greater than 1\")\n",
"\n",
" n_samples, n_features = data.shape\n",
"\n",
" # get the baseline estimate\n",
" corr = two_point(data, bins, method=method, random_state=rng)\n",
"\n",
" bootstraps = np.zeros((Nbootstrap, len(corr)))\n",
"\n",
" for i in range(Nbootstrap):\n",
" indices = rng.randint(0, n_samples, n_samples)\n",
" bootstraps[i] = two_point(data[indices, :], bins, method=method,\n",
" random_state=rng)\n",
"\n",
" # use masked std dev in case of NaNs\n",
" corr_err = np.asarray(np.ma.masked_invalid(bootstraps).std(0, ddof=1))\n",
"\n",
" if return_bootstraps:\n",
" return corr, corr_err, bootstraps\n",
" else:\n",
" return corr, corr_err\n",
"\n",
"\n",
"def two_point_angular(ra, dec, bins, method='standard', random_state=None):\n",
" \"\"\"Angular two-point correlation function\n",
"\n",
" A separate function is needed because angular distances are not\n",
" euclidean, and random sampling needs to take into account the\n",
" spherical volume element.\n",
"\n",
" Parameters\n",
" ----------\n",
" ra : array_like\n",
" input right ascention, shape = (n_samples,)\n",
" dec : array_like\n",
" input declination\n",
" bins : array_like\n",
" bins within which to compute the 2-point correlation.\n",
" shape = Nbins + 1\n",
" method : string\n",
" \"standard\" or \"landy-szalay\".\n",
" random_state : integer, np.random.RandomState, or None\n",
" specify the random state to use for generating background\n",
"\n",
" Returns\n",
" -------\n",
" corr : ndarray\n",
" the estimate of the correlation function within each bin\n",
" shape = Nbins\n",
" \"\"\"\n",
" ra = np.asarray(ra)\n",
" dec = np.asarray(dec)\n",
" rng = check_random_state(random_state)\n",
"\n",
" if method not in ['standard', 'landy-szalay']:\n",
" raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n",
"\n",
" if bins.ndim != 1:\n",
" raise ValueError(\"bins must be a 1D array\")\n",
"\n",
" if (ra.ndim != 1) or (dec.ndim != 1) or (ra.shape != dec.shape):\n",
" raise ValueError('ra and dec must be 1-dimensional '\n",
" 'arrays of the same length')\n",
"\n",
" n_features = len(ra)\n",
" Nbins = len(bins) - 1\n",
"\n",
" # draw a random sample with N points\n",
" ra_R, dec_R = uniform_sphere((min(ra), max(ra)),\n",
" (min(dec), max(dec)),\n",
" 2 * len(ra))\n",
"\n",
" data = np.asarray(ra_dec_to_xyz(ra, dec), order='F').T\n",
" data_R = np.asarray(ra_dec_to_xyz(ra_R, dec_R), order='F').T\n",
"\n",
" # convert spherical bins to cartesian bins\n",
" bins_transform = angular_dist_to_euclidean_dist(bins)\n",
"\n",
" return two_point(data, bins_transform, method=method,\n",
" data_R=data_R, random_state=rng)\n",
"\n",
"\n",
"def bootstrap_two_point_angular(ra, dec, bins, method='standard',\n",
" Nbootstraps=10, random_state=None):\n",
" # type: (object, object, object, object, object, object) -> object\n",
" \"\"\"Angular two-point correlation function\n",
"\n",
" A separate function is needed because angular distances are not\n",
" euclidean, and random sampling needs to take into account the\n",
" spherical volume element.\n",
"\n",
" Parameters\n",
" ----------\n",
" ra : array_like\n",
" input right ascention, shape = (n_samples,)\n",
" dec : array_like\n",
" input declination\n",
" bins : array_like\n",
" bins within which to compute the 2-point correlation.\n",
" shape = Nbins + 1\n",
" method : string\n",
" \"standard\" or \"landy-szalay\".\n",
" Nbootstraps : int\n",
" number of bootstrap resamples\n",
" random_state : integer, np.random.RandomState, or None\n",
" specify the random state to use for generating background\n",
"\n",
" Returns\n",
" -------\n",
" corr : ndarray\n",
" the estimate of the correlation function within each bin\n",
" shape = Nbins\n",
" dcorr : ndarray\n",
" error estimate on dcorr (sample standard deviation of\n",
" bootstrap resamples)\n",
" bootstraps : ndarray\n",
" The full sample of bootstraps used to compute corr and dcorr\n",
" \"\"\"\n",
" ra = np.asarray(ra)\n",
" dec = np.asarray(dec)\n",
" rng = check_random_state(random_state)\n",
"\n",
" if method not in ['standard', 'landy-szalay']:\n",
" raise ValueError(\"method must be 'standard' or 'landy-szalay'\")\n",
"\n",
" if bins.ndim != 1:\n",
" raise ValueError(\"bins must be a 1D array\")\n",
"\n",
" if (ra.ndim != 1) or (dec.ndim != 1) or (ra.shape != dec.shape):\n",
" raise ValueError('ra and dec must be 1-dimensional '\n",
" 'arrays of the same length')\n",
"\n",
" n_features = len(ra)\n",
" Nbins = len(bins) - 1\n",
" data = np.asarray(ra_dec_to_xyz(ra, dec), order='F').T\n",
"\n",
" # convert spherical bins to cartesian bins\n",
" bins_transform = angular_dist_to_euclidean_dist(bins)\n",
"\n",
" bootstraps = []\n",
"\n",
" for i in range(Nbootstraps):\n",
" # draw a random sample with N points\n",
" ra_R, dec_R = uniform_sphere((min(ra), max(ra)),\n",
" (min(dec), max(dec)),\n",
" 2 * len(ra))\n",
"\n",
" data_R = np.asarray(ra_dec_to_xyz(ra_R, dec_R), order='F').T\n",
"\n",
" if i > 0:\n",
" # random sample of the data\n",
" ind = np.random.randint(0, data.shape[0], data.shape[0])\n",
" data_b = data[ind]\n",
" else:\n",
" data_b = data\n",
"\n",
" bootstraps.append(two_point(data_b, bins_transform, method=method,\n",
" data_R=data_R, random_state=rng))\n",
"\n",
" bootstraps = np.asarray(bootstraps)\n",
" corr = np.mean(bootstraps, 0)\n",
" corr_err = np.std(bootstraps, 0, ddof=1)\n",
"\n",
" return corr, corr_err, bootstraps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sklearn_has_two_point"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"help(KDTree)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyz=ra_dec_to_xyz(data['ra'],data['dec'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyz=np.asarray(dataxyz)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyz=dataxyz.transpose()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyz"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyzR=ra_dec_to_xyz(dataR['ra'],dataR['dec'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyzR=np.asarray(dataxyzR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyzR=dataxyzR.transpose()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyzR"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins=np.arange(0.0,1.05,0.05)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#@pickle_results(\"tpcf_std.pkl\")\n",
"tpcf=two_point(dataxyz,bins,method='standard',data_R=dataxyzR, random_state=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tpcf "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.plot(bins[1:],tpcf,'ro')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tpcfam=two_point(dataxyz,bins,method='standard',data_R=None, random_state=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tpcfam"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.plot(bins[1:],tpcfam,'bo')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins2=np.arange(0.2,0.6,0.02)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tpcfamb2=two_point(dataxyz,bins2,method='standard',data_R=None, random_state=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.plot(bins2[1:],tpcfamb2,'go')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above doesn't show any BAO feature... It used inbuilt astroML method to generate random catalog... by shuffling the original data's content... That way all of the random points fall in the same survey area and will adhere to all the filtering criteria... the factor or ratio of data pts vs. random pts will be 1... instead of large no. in case if we take existing random catalog or create one"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rng = check_random_state(None)\n",
"\n",
"n_samples, n_features = dataxyz.shape\n",
"Nbins = len(bins) - 1\n",
"\n",
"# shuffle all but one axis to get background distribution\n",
"data_Rxyz = dataxyz.copy()\n",
"print data_Rxyz\n",
"for i in range(n_features - 1):\n",
" rng.shuffle(data_Rxyz[:, i])\n",
"print data_Rxyz"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets see how it looks with a healpix map"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"NSIDE=512\n",
"dr72hpix=hu.HealPix(\"ring\",NSIDE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import math as m\n",
"\n",
"def cart2sph(x,y,z):\n",
" XsqPlusYsq = x**2 + y**2\n",
" r = m.sqrt(XsqPlusYsq + z**2) # r\n",
" elev = m.atan2(z,m.sqrt(XsqPlusYsq)) # theta\n",
" az = m.atan2(y,x) # phi\n",
" return r, elev, az\n",
"\n",
"def cart2sphA(pts):\n",
" return np.array([cart2sph(x,y,z) for x,y,z in pts])\n",
"\n",
"def appendSpherical(xyz):\n",
" np.hstack((xyz, cart2sphA(xyz)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ang=cart2sphA(data_Rxyz)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ang"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ang.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#ang.resize((105831, 2))\n",
"np.squeeze(ang, axis=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"help(ang.squeeze)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ang2=ang[:,1:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ang2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ang2.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ang2[2,0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = open(\"./output/pixdatadr72VAGCfullrandam.dat\",'w')\n",
"pixdata.write(\"pix \\n\")\n",
"for i in range(0,len(ang2)-1):\n",
" #pixdata.write(\"%f\\t\" %data['z'][i])\n",
" pixdata.write(\"%d\\n\" %dr72hpix.ang2pix(ang2[i,0],ang2[i,1]))\n",
"pixdata.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = ascii.read(\"./output/pixdatadr72VAGCfullrandam.dat\")\n",
"hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n",
"for j in range(len(pixdata)):\n",
" hpixdata[pixdata[j]['pix']]+=1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hpixdata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.mollview(hpixdata,rot=180)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.orthview(hpixdata)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This method doesnt seem to produce right random catalogs...doing it with ra and dec as follows"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['z'],data['ra'],data['dec']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"datzradec=np.array([data['z'], data['ra'], data['dec']])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"datzradec"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"rng = check_random_state(None)\n",
"\n",
"n_features, n_samples = datzradec.shape\n",
"\n",
"# shuffle all but one axis to get background distribution\n",
"data_Rzradec = datzradec.copy()\n",
"print data_Rzradec\n",
"for i in range(1,n_features):\n",
" rng.shuffle(data_Rzradec[:, i])\n",
"print data_Rzradec"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"min(data_Rzradec[:, 1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"max(data_Rzradec[:, 1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"min(data_Rzradec[:, 2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"max(data_Rzradec[:, 2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"min(datzradec[:, 1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"max(datzradec[:, 1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"min(datzradec[:, 2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"max(datzradec[:, 2])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"range(1,3)"
]
},
{
"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": [
"help(rng.shuffle)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_samples"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n_features"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_Rzradec"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_Rzradec[0][2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"len(data_Rzradec[0][:])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_Rzradec[0][:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = open(\"./output/pixdatadr72VAGCfullrandamrd.dat\",'w')\n",
"pixdata.write(\"z\\t pix \\n\")\n",
"for i in range(0,len(data_Rzradec[0][:])-1):\n",
" pixdata.write(\"%f\\t\" %data_Rzradec[0][i])\n",
" pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data_Rzradec[1][i],data_Rzradec[2][i]))\n",
"pixdata.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = ascii.read(\"./output/pixdatadr72VAGCfullrandamrd.dat\")\n",
"hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n",
"for j in range(len(pixdata)):\n",
" hpixdata[pixdata[j]['pix']]+=1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hpixdata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.mollview(hpixdata,rot=180)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.orthview(hpixdata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyz"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyzR1=ra_dec_to_xyz(data_Rzradec[1][:],data_Rzradec[2][:])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_Rzradec[1][:]"
]
},
{
"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": [
"dataxyzR1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyzR1=np.asarray(dataxyzR1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyzR1=dataxyzR1.transpose()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataxyzR1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins=np.arange(0.025,1.025,0.025)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#@pickle_results(\"tpcf_std.pkl\")\n",
"tpcf=two_point(dataxyz,bins,method='standard',data_R=dataxyzR1, random_state=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tpcf "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.plot(bins[1:],tpcf,'ro')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins=np.arange(0.0,1.05,0.05)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#@pickle_results(\"tpcf_std.pkl\")\n",
"tpcf=two_point(dataxyz,bins,method='standard',data_R=dataxyzR1, random_state=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tpcf "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.plot(bins[1:],tpcf,'ro')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"btpcf=bootstrap_two_point(dataxyz, bins, Nbootstrap=10,\n",
" method='standard', return_bootstraps=False,\n",
" random_state=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"btpcf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.errorbar(bins[1:],btpcf[0],yerr=btpcf[1],fmt='ro-')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"help(plt.errorbar)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"@pickle_results(\"tpcf_ls.pkl\")\n",
"tpcfls=two_point(dataxyz,bins,method='landy-szalay',\n",
" data_R=dataxyzR, random_state=None)"
]
},
{
"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": [
"#------------------------------------------------------------\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_functionsdr72.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]:\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) = compute_results()\n",
"\n",
"bin_centers = 0.5 * (bins[1:] + bins[:-1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_corr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_corr_err"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_bootstraps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#------------------------------------------------------------\n",
"# Plot the results\n",
"\n",
"label = '$0.15<z<0.25$\\n$N=33813$' \n",
"\n",
"fig = plt.figure(figsize=(6, 6))\n",
"plt.xscale('log')\n",
"plt.yscale('log')\n",
"plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n",
"fig.text(0.8, 0.8, label, ha='right', va='top')\n",
"plt.xlabel(r'$\\theta\\ (deg)$')\n",
"plt.ylabel(r'$w(\\theta)$')\n",
"plt.show()\n",
"\n",
"plt.show()\n",
"fig.savefig(\"wth_dr72015025.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data=ascii.read('./input/sdssdr72_sorted_z.dat')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#m_max = 19\n",
"\n",
"# redshift and magnitude cuts\n",
"data = data[data['z'] > 0.05]\n",
"data = data[data['z'] < 0.15]\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",
"#datag \n",
"\n",
"print \"data size:\"\n",
"print \" total gals: \", len(data)\n",
"#print \" blue gals:\", len(data_blue)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"NSIDE=512\n",
"dr72hpix=hu.HealPix(\"ring\",NSIDE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = open(\"./output/pixdatadr72005015.dat\",'w')\n",
"pixdata.write(\"z\\t pix \\n\")\n",
"\n",
"for i in range(0,len(data)-1):\n",
" pixdata.write(\"%f\\t\" %data['z'][i])\n",
" pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data['ra'][i],data['dec'][i]))\n",
"pixdata.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = ascii.read(\"./output/pixdatadr72005015.dat\")\n",
"hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n",
"for j in range(len(pixdata)):\n",
" hpixdata[pixdata[j]['pix']]+=1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hpixdata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.mollview(hpixdata,rot=180)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.orthview(hpixdata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"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_functionsdr720515.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]:\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) = compute_results()\n",
"\n",
"bin_centers = 0.5 * (bins[1:] + bins[:-1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_corr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_corr_err"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_bootstraps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#------------------------------------------------------------\n",
"# Plot the results\n",
"label = '$0.05<z<0.15$\\n$N=138051$'\n",
"\n",
"fig = plt.figure(figsize=(6, 6))\n",
"plt.xscale('log')\n",
"plt.yscale('log')\n",
"plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n",
"fig.text(0.8, 0.8, label, ha='right', va='top')\n",
"plt.xlabel(r'$\\theta\\ (deg)$')\n",
"plt.ylabel(r'$w(\\theta)$')\n",
"plt.show()\n",
"fig.savefig(\"wth_dr72005015.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.errorbar(bins[0:len(bins)-1],r_corr,r_corr_err)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data=ascii.read('./input/sdssdr72_sorted_z.dat')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['z']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#m_max = 19\n",
"\n",
"# redshift and magnitude cuts\n",
"data = data[data['z'] > 0.05]\n",
"data = data[data['z'] <= 0.10]\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",
"#datag \n",
"\n",
"print \"data size:\"\n",
"print \" total gals: \", len(data)\n",
"#print \" blue gals:\", len(data_blue)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"NSIDE=512\n",
"dr72hpix=hu.HealPix(\"ring\",NSIDE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = open(\"./output/pixdatadr7200501.dat\",'w')\n",
"pixdata.write(\"z\\t pix \\n\")\n",
"\n",
"for i in range(0,len(data)-1):\n",
" pixdata.write(\"%f\\t\" %data['z'][i])\n",
" pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data['ra'][i],data['dec'][i]))\n",
"pixdata.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = ascii.read(\"./output/pixdatadr7200501.dat\")\n",
"hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n",
"for j in range(len(pixdata)):\n",
" hpixdata[pixdata[j]['pix']]+=1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hpixdata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.mollview(hpixdata,rot=180)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.orthview(hpixdata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"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_functionsdr720501.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]:\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) = compute_results()\n",
"\n",
"bin_centers = 0.5 * (bins[1:] + bins[:-1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_corr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_corr_err"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_bootstraps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#------------------------------------------------------------\n",
"# Plot the results\n",
"\n",
"label = '$0.05<z<0.10$\\n$N=78939$'\n",
"\n",
"fig = plt.figure(figsize=(6, 6))\n",
"plt.xscale('log')\n",
"plt.yscale('log')\n",
"plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n",
"fig.text(0.8, 0.8, label, ha='right', va='top')\n",
"plt.xlabel(r'$\\theta\\ (deg)$')\n",
"plt.ylabel(r'$w(\\theta)$')\n",
"plt.show()\n",
"fig.savefig(\"wth_dr720501.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.errorbar(bins[0:len(bins)-1],r_corr,r_corr_err)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data=ascii.read('./input/sdssdr72_sorted_z.dat')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data['z']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#m_max = 19\n",
"\n",
"# redshift and magnitude cuts\n",
"data = data[data['z'] > 0.10]\n",
"data = data[data['z'] <= 0.15]\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",
"#datag \n",
"\n",
"print \"data size:\"\n",
"print \" total gals: \", len(data)\n",
"#print \" blue gals:\", len(data_blue)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"NSIDE=512\n",
"dr72hpix=hu.HealPix(\"ring\",NSIDE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = open(\"./output/pixdatadr72001015.dat\",'w')\n",
"pixdata.write(\"z\\t pix \\n\")\n",
"\n",
"for i in range(0,len(data)-1):\n",
" pixdata.write(\"%f\\t\" %data['z'][i])\n",
" pixdata.write(\"%d\\n\" %dr72hpix.eq2pix(data['ra'][i],data['dec'][i]))\n",
"pixdata.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = ascii.read(\"./output/pixdatadr72001015.dat\")\n",
"hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n",
"for j in range(len(pixdata)):\n",
" hpixdata[pixdata[j]['pix']]+=1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hpixdata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.mollview(hpixdata,rot=180)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.orthview(hpixdata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"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_functionsdr72001015.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]:\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) = compute_results()\n",
"\n",
"bin_centers = 0.5 * (bins[1:] + bins[:-1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"bins"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_corr"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_corr_err"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r_bootstraps"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#------------------------------------------------------------\n",
"# Plot the results\n",
"\n",
"label = '$0.10<z<0.15$\\n$N=59112$'\n",
"fig = plt.figure(figsize=(6, 6))\n",
"plt.xscale('log')\n",
"plt.yscale('log')\n",
"plt.errorbar(bin_centers, r_corr, r_corr_err,fmt='.k', ecolor='gray', lw=1)\n",
"fig.text(0.8, 0.8, label, ha='right', va='top')\n",
"plt.xlabel(r'$\\theta\\ (deg)$')\n",
"plt.ylabel(r'$w(\\theta)$')\n",
"plt.show()\n",
"fig.savefig(\"wth_dr7201015.pdf\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.errorbar(bins[0:len(bins)-1],r_corr,r_corr_err)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.mollview(hpixdatab,rot=180)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.orthview(hpixdatab)"
]
},
{
"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": [
"help(hu.mollview)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"\n",
"from astroML.datasets import fetch_sdss_specgals\n",
"from astroML.correlation import bootstrap_two_point_angular"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"help(bootstrap_two_point_angular)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"help(astroML.correlation)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import astroML.correlation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sklearn.neighbors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"help(sklearn.neighbors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sorted and reduced column set data can now be 'read' to reduce RAM requirements of the table reading. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sdssdr72=ascii.read('./input/dssdr72_sorted_z.dat')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create a healpix map with NSIDE=64 (no. of pixels = 49152 as $NPIX=12\\times NSIDE^2$) because the no. of galaxies in the survey are less. For higher resolution (later for dr12) we will consider NSIDE=512 or even 1024. For now, we will create a 64 NSIDE map."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"NSIDE=64\n",
"dt72hpix=hu.HealPix(\"ring\",NSIDE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We have data of galaxies with redshifts between 0 and 0.5 ($0<z<0.5$). To look at a time slice/at a certain epoch we need to choose the list of galaxies within a redshift window. As, measurement of redshift has $\\pm 0.05$ error. We can bin all the data into redshifts with range limited to 0.05 variation each. So, we have 10 databins with almost identical redshifts. We save each databin in a different file. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"j=0\n",
"for i in range(1,17):\n",
" pixdata = open(\"/home/rohin/Desktop/healpix/binned1/pixdata%d_%d.dat\"%(NSIDE,i),'w')\n",
" pixdata.write(\"ra\\t dec\\t z\\t pix \\n\")\n",
" #for j in range(len(sdssdr72)):\n",
" try:\n",
" while sdssdr72[j]['z']<0.03*i:\n",
" pixdata.write(\"%f\\t\" %sdssdr72[j]['ra'])\n",
" pixdata.write(\"%f\\t\" %sdssdr72[j]['dec'])\n",
" pixdata.write(\"%f\\t\" %sdssdr72[j]['z'])\n",
" pixdata.write(\"%d\\n\" %dt72hpix.eq2pix(sdssdr72[j]['ra'],sdssdr72[j]['dec']))\n",
" #print dt72hpix.eq2pix(sdssdr72[j]['ra'],sdssdr72[j]['dec'])\n",
" j=j+1\n",
" except:\n",
" pass\n",
" pixdata.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for i in range(1,17):\n",
" pixdata = ascii.read(\"/home/rohin/Desktop/healpix/binned1/pixdata%d_%d.dat\"%(NSIDE,i))\n",
" mpixdata = open(\"/home/rohin/Desktop/healpix/binned1/masked/pixdata%d_%d.dat\"%(NSIDE,i),'w')\n",
" mpixdata.write(\"ra\\t dec\\t z\\t pix \\n\")\n",
" for j in range((len(pixdata)-1)):\n",
" if 100<pixdata[j]['ra']<250:\n",
" mpixdata.write(\"%f\\t\" %pixdata[j]['ra'])\n",
" mpixdata.write(\"%f\\t\" %pixdata[j]['dec'])\n",
" mpixdata.write(\"%f\\t\" %pixdata[j]['z'])\n",
" mpixdata.write(\"%d\\n\" %pixdata[j]['pix'])\n",
" #pixdata.write(\"/home/rohin/Desktop/healpix/binned1/masked/pixdata_%d.dat\"%i,format='ascii')\n",
" \n",
" \n",
" #print dt72hpix.eq2pix(sdssdr72[j]['ra'],sdssdr72[j]['dec'])\n",
" mpixdata.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now, take each databin and assign the total no. of galaxies as the value of each pixel. The following routine will calculate the no. of galaxies by couting the occurence of pixel numbers in the file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixdata = ascii.read(\"/home/rohin/Desktop/healpix/binned1/masked/pixdata%d_2.dat\"%NSIDE)\n",
"hpixdata=np.array(np.zeros(hu.nside2npix(NSIDE)))\n",
"for j in range(len(pixdata)):\n",
" hpixdata[pixdata[j]['pix']]+=1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hpixdata"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.orthview(hpixdata,rot=180)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixcl=hu.anafast(hpixdata,lmax=300)\n",
"ell = np.arange(len(pixcl))\n",
"plt.figure()\n",
"plt.plot(ell,np.log(pixcl))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pixcl=hu.anafast(hpixdata,lmax=300)\n",
"ell = np.arange(len(pixcl))\n",
"plt.figure()\n",
"plt.plot(ell,np.sqrt(ell*(ell+1)*pixcl/(4*math.pi)))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"theta=np.arange(0,np.pi,0.001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"correldat = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(pixcl))/(4*math.pi)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.figure()\n",
"plt.plot(theta[0:600]*180/math.pi,correldat[0:600])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"plt.figure()\n",
"plt.plot(theta*180/math.pi,correldat)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"randra,randdec=hu.randsphere(2200000)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"randhp=hu.HealPix(\"RING\",NSIDE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"randhppix=randhp.eq2pix(randra,randdec)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"randpixdat=np.array(np.zeros(hu.nside2npix(NSIDE)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for j in range(len(randhppix)):\n",
" randpixdat[randhppix[j]]+=1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"randmaphp=hu.mollview(randpixdat)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"randcl=hu.anafast(randpixdat,lmax=300)\n",
"ell = np.arange(len(randcl))\n",
"plt.figure()\n",
"plt.plot(ell,np.sqrt(ell*(ell+1)*randcl/(4*math.pi)))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"correlrand = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(randcl))/(4*math.pi)\n",
"plt.figure()\n",
"plt.plot(theta[0:600]*180/math.pi,correlrand[0:600])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"finalcorrel=correldat-correlrand\n",
"plt.figure()\n",
"plt.plot(theta[0:600]*180/math.pi,finalcorrel[0:600])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"finalpix=hpixdata-randpixdat"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hu.mollview(finalpix,rot=180)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cl=hu.anafast(finalpix,lmax=300)\n",
"ell = np.arange(len(cl))\n",
"plt.figure()\n",
"plt.plot(ell,np.sqrt(ell*(ell+1)*cl/(4*math.pi)))\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"correlrand = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(cl))/(4*math.pi)\n",
"plt.figure()\n",
"plt.plot(theta[0:600]*180/math.pi,correlrand[0:600])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"finalcl=pixcl-randcl\n",
"correlrand = np.polynomial.legendre.legval(np.cos(theta),(2*ell+1)*np.absolute(finalcl))/(4*math.pi)\n",
"plt.figure()\n",
"plt.plot(theta[0:600]*180/math.pi,correlrand[0:600])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"help(fits)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data[1].data['z']"
]
},
{
"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": 1
}
| mit |
tensorflow/docs-l10n | site/en-snapshot/hub/tutorials/tf2_object_detection.ipynb | 1 | 27384 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "98rds-2OU-Rd"
},
"source": [
"##### Copyright 2020 The TensorFlow Hub Authors.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "1c95xMGcU5_Z"
},
"outputs": [],
"source": [
"#@title Copyright 2020 The TensorFlow Hub Authors. All Rights Reserved.\n",
"#\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",
"# http://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.\n",
"# =============================================================================="
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V1UUX8SUUiMO"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/hub/tutorials/tf2_object_detection\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_object_detection.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/hub/blob/master/examples/colab/tf2_object_detection.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View on GitHub</a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://storage.googleapis.com/tensorflow_docs/hub/examples/colab/tf2_object_detection.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://tfhub.dev/tensorflow/collections/object_detection/1\"><img src=\"https://www.tensorflow.org/images/hub_logo_32px.png\" />See TF Hub models</a>\n",
" </td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rOvvWAVTkMR7"
},
"source": [
"# TensorFlow Hub Object Detection Colab\n",
"\n",
"Welcome to the TensorFlow Hub Object Detection Colab! This notebook will take you through the steps of running an \"out-of-the-box\" object detection model on images."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IRImnk_7WOq1"
},
"source": [
"### More models\n",
"[This](https://tfhub.dev/tensorflow/collections/object_detection/1) collection contains TF2 object detection models that have been trained on the COCO 2017 dataset. [Here](https://tfhub.dev/s?module-type=image-object-detection) you can find all object detection models that are currently hosted on [tfhub.dev](https://tfhub.dev/)."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vPs64QA1Zdov"
},
"source": [
"## Imports and Setup\n",
"\n",
"Let's start with the base imports."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Xk4FU-jx9kc3"
},
"outputs": [],
"source": [
"# This Colab requires TF 2.5.\n",
"!pip install -U \"tensorflow>=2.5\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yn5_uV1HLvaz"
},
"outputs": [],
"source": [
"import os\n",
"import pathlib\n",
"\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import io\n",
"import scipy.misc\n",
"import numpy as np\n",
"from six import BytesIO\n",
"from PIL import Image, ImageDraw, ImageFont\n",
"from six.moves.urllib.request import urlopen\n",
"\n",
"import tensorflow as tf\n",
"import tensorflow_hub as hub\n",
"\n",
"tf.get_logger().setLevel('ERROR')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IogyryF2lFBL"
},
"source": [
"## Utilities\n",
"\n",
"Run the following cell to create some utils that will be needed later:\n",
"\n",
"- Helper method to load an image\n",
"- Map of Model Name to TF Hub handle\n",
"- List of tuples with Human Keypoints for the COCO 2017 dataset. This is needed for models with keypoints."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "-y9R0Xllefec"
},
"outputs": [],
"source": [
"# @title Run this!!\n",
"\n",
"def load_image_into_numpy_array(path):\n",
" \"\"\"Load an image from file into a numpy array.\n",
"\n",
" Puts image into numpy array to feed into tensorflow graph.\n",
" Note that by convention we put it into a numpy array with shape\n",
" (height, width, channels), where channels=3 for RGB.\n",
"\n",
" Args:\n",
" path: the file path to the image\n",
"\n",
" Returns:\n",
" uint8 numpy array with shape (img_height, img_width, 3)\n",
" \"\"\"\n",
" image = None\n",
" if(path.startswith('http')):\n",
" response = urlopen(path)\n",
" image_data = response.read()\n",
" image_data = BytesIO(image_data)\n",
" image = Image.open(image_data)\n",
" else:\n",
" image_data = tf.io.gfile.GFile(path, 'rb').read()\n",
" image = Image.open(BytesIO(image_data))\n",
"\n",
" (im_width, im_height) = image.size\n",
" return np.array(image.getdata()).reshape(\n",
" (1, im_height, im_width, 3)).astype(np.uint8)\n",
"\n",
"\n",
"ALL_MODELS = {\n",
"'CenterNet HourGlass104 512x512' : 'https://tfhub.dev/tensorflow/centernet/hourglass_512x512/1',\n",
"'CenterNet HourGlass104 Keypoints 512x512' : 'https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1',\n",
"'CenterNet HourGlass104 1024x1024' : 'https://tfhub.dev/tensorflow/centernet/hourglass_1024x1024/1',\n",
"'CenterNet HourGlass104 Keypoints 1024x1024' : 'https://tfhub.dev/tensorflow/centernet/hourglass_1024x1024_kpts/1',\n",
"'CenterNet Resnet50 V1 FPN 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1',\n",
"'CenterNet Resnet50 V1 FPN Keypoints 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512_kpts/1',\n",
"'CenterNet Resnet101 V1 FPN 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet101v1_fpn_512x512/1',\n",
"'CenterNet Resnet50 V2 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v2_512x512/1',\n",
"'CenterNet Resnet50 V2 Keypoints 512x512' : 'https://tfhub.dev/tensorflow/centernet/resnet50v2_512x512_kpts/1',\n",
"'EfficientDet D0 512x512' : 'https://tfhub.dev/tensorflow/efficientdet/d0/1',\n",
"'EfficientDet D1 640x640' : 'https://tfhub.dev/tensorflow/efficientdet/d1/1',\n",
"'EfficientDet D2 768x768' : 'https://tfhub.dev/tensorflow/efficientdet/d2/1',\n",
"'EfficientDet D3 896x896' : 'https://tfhub.dev/tensorflow/efficientdet/d3/1',\n",
"'EfficientDet D4 1024x1024' : 'https://tfhub.dev/tensorflow/efficientdet/d4/1',\n",
"'EfficientDet D5 1280x1280' : 'https://tfhub.dev/tensorflow/efficientdet/d5/1',\n",
"'EfficientDet D6 1280x1280' : 'https://tfhub.dev/tensorflow/efficientdet/d6/1',\n",
"'EfficientDet D7 1536x1536' : 'https://tfhub.dev/tensorflow/efficientdet/d7/1',\n",
"'SSD MobileNet v2 320x320' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2',\n",
"'SSD MobileNet V1 FPN 640x640' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v1/fpn_640x640/1',\n",
"'SSD MobileNet V2 FPNLite 320x320' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_320x320/1',\n",
"'SSD MobileNet V2 FPNLite 640x640' : 'https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_640x640/1',\n",
"'SSD ResNet50 V1 FPN 640x640 (RetinaNet50)' : 'https://tfhub.dev/tensorflow/retinanet/resnet50_v1_fpn_640x640/1',\n",
"'SSD ResNet50 V1 FPN 1024x1024 (RetinaNet50)' : 'https://tfhub.dev/tensorflow/retinanet/resnet50_v1_fpn_1024x1024/1',\n",
"'SSD ResNet101 V1 FPN 640x640 (RetinaNet101)' : 'https://tfhub.dev/tensorflow/retinanet/resnet101_v1_fpn_640x640/1',\n",
"'SSD ResNet101 V1 FPN 1024x1024 (RetinaNet101)' : 'https://tfhub.dev/tensorflow/retinanet/resnet101_v1_fpn_1024x1024/1',\n",
"'SSD ResNet152 V1 FPN 640x640 (RetinaNet152)' : 'https://tfhub.dev/tensorflow/retinanet/resnet152_v1_fpn_640x640/1',\n",
"'SSD ResNet152 V1 FPN 1024x1024 (RetinaNet152)' : 'https://tfhub.dev/tensorflow/retinanet/resnet152_v1_fpn_1024x1024/1',\n",
"'Faster R-CNN ResNet50 V1 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_640x640/1',\n",
"'Faster R-CNN ResNet50 V1 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_1024x1024/1',\n",
"'Faster R-CNN ResNet50 V1 800x1333' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet50_v1_800x1333/1',\n",
"'Faster R-CNN ResNet101 V1 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_640x640/1',\n",
"'Faster R-CNN ResNet101 V1 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_1024x1024/1',\n",
"'Faster R-CNN ResNet101 V1 800x1333' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet101_v1_800x1333/1',\n",
"'Faster R-CNN ResNet152 V1 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_640x640/1',\n",
"'Faster R-CNN ResNet152 V1 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_1024x1024/1',\n",
"'Faster R-CNN ResNet152 V1 800x1333' : 'https://tfhub.dev/tensorflow/faster_rcnn/resnet152_v1_800x1333/1',\n",
"'Faster R-CNN Inception ResNet V2 640x640' : 'https://tfhub.dev/tensorflow/faster_rcnn/inception_resnet_v2_640x640/1',\n",
"'Faster R-CNN Inception ResNet V2 1024x1024' : 'https://tfhub.dev/tensorflow/faster_rcnn/inception_resnet_v2_1024x1024/1',\n",
"'Mask R-CNN Inception ResNet V2 1024x1024' : 'https://tfhub.dev/tensorflow/mask_rcnn/inception_resnet_v2_1024x1024/1'\n",
"}\n",
"\n",
"IMAGES_FOR_TEST = {\n",
" 'Beach' : 'models/research/object_detection/test_images/image2.jpg',\n",
" 'Dogs' : 'models/research/object_detection/test_images/image1.jpg',\n",
" # By Heiko Gorski, Source: https://commons.wikimedia.org/wiki/File:Naxos_Taverna.jpg\n",
" 'Naxos Taverna' : 'https://upload.wikimedia.org/wikipedia/commons/6/60/Naxos_Taverna.jpg',\n",
" # Source: https://commons.wikimedia.org/wiki/File:The_Coleoptera_of_the_British_islands_(Plate_125)_(8592917784).jpg\n",
" 'Beatles' : 'https://upload.wikimedia.org/wikipedia/commons/1/1b/The_Coleoptera_of_the_British_islands_%28Plate_125%29_%288592917784%29.jpg',\n",
" # By Américo Toledano, Source: https://commons.wikimedia.org/wiki/File:Biblioteca_Maim%C3%B3nides,_Campus_Universitario_de_Rabanales_007.jpg\n",
" 'Phones' : 'https://upload.wikimedia.org/wikipedia/commons/thumb/0/0d/Biblioteca_Maim%C3%B3nides%2C_Campus_Universitario_de_Rabanales_007.jpg/1024px-Biblioteca_Maim%C3%B3nides%2C_Campus_Universitario_de_Rabanales_007.jpg',\n",
" # Source: https://commons.wikimedia.org/wiki/File:The_smaller_British_birds_(8053836633).jpg\n",
" 'Birds' : 'https://upload.wikimedia.org/wikipedia/commons/0/09/The_smaller_British_birds_%288053836633%29.jpg',\n",
"}\n",
"\n",
"COCO17_HUMAN_POSE_KEYPOINTS = [(0, 1),\n",
" (0, 2),\n",
" (1, 3),\n",
" (2, 4),\n",
" (0, 5),\n",
" (0, 6),\n",
" (5, 7),\n",
" (7, 9),\n",
" (6, 8),\n",
" (8, 10),\n",
" (5, 6),\n",
" (5, 11),\n",
" (6, 12),\n",
" (11, 12),\n",
" (11, 13),\n",
" (13, 15),\n",
" (12, 14),\n",
" (14, 16)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "14bNk1gzh0TN"
},
"source": [
"## Visualization tools\n",
"\n",
"To visualize the images with the proper detected boxes, keypoints and segmentation, we will use the TensorFlow Object Detection API. To install it we will clone the repo."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "oi28cqGGFWnY"
},
"outputs": [],
"source": [
"# Clone the tensorflow models repository\n",
"!git clone --depth 1 https://github.com/tensorflow/models"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yX3pb_pXDjYA"
},
"source": [
"Intalling the Object Detection API"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NwdsBdGhFanc"
},
"outputs": [],
"source": [
"%%bash\n",
"sudo apt install -y protobuf-compiler\n",
"cd models/research/\n",
"protoc object_detection/protos/*.proto --python_out=.\n",
"cp object_detection/packages/tf2/setup.py .\n",
"python -m pip install .\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3yDNgIx-kV7X"
},
"source": [
"Now we can import the dependencies we will need later"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2JCeQU3fkayh"
},
"outputs": [],
"source": [
"from object_detection.utils import label_map_util\n",
"from object_detection.utils import visualization_utils as viz_utils\n",
"from object_detection.utils import ops as utils_ops\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NKtD0IeclbL5"
},
"source": [
"### Load label map data (for plotting).\n",
"\n",
"Label maps correspond index numbers to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine.\n",
"\n",
"We are going, for simplicity, to load from the repository that we loaded the Object Detection API code"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "5mucYUS6exUJ"
},
"outputs": [],
"source": [
"PATH_TO_LABELS = './models/research/object_detection/data/mscoco_label_map.pbtxt'\n",
"category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6917xnUSlp9x"
},
"source": [
"## Build a detection model and load pre-trained model weights\n",
"\n",
"Here we will choose which Object Detection model we will use.\n",
"Select the architecture and it will be loaded automatically.\n",
"If you want to change the model to try other architectures later, just change the next cell and execute following ones.\n",
"\n",
"**Tip:** if you want to read more details about the selected model, you can follow the link (model handle) and read additional documentation on TF Hub. After you select a model, we will print the handle to make it easier."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "HtwrSqvakTNn"
},
"outputs": [],
"source": [
"#@title Model Selection { display-mode: \"form\", run: \"auto\" }\n",
"model_display_name = 'CenterNet HourGlass104 Keypoints 512x512' # @param ['CenterNet HourGlass104 512x512','CenterNet HourGlass104 Keypoints 512x512','CenterNet HourGlass104 1024x1024','CenterNet HourGlass104 Keypoints 1024x1024','CenterNet Resnet50 V1 FPN 512x512','CenterNet Resnet50 V1 FPN Keypoints 512x512','CenterNet Resnet101 V1 FPN 512x512','CenterNet Resnet50 V2 512x512','CenterNet Resnet50 V2 Keypoints 512x512','EfficientDet D0 512x512','EfficientDet D1 640x640','EfficientDet D2 768x768','EfficientDet D3 896x896','EfficientDet D4 1024x1024','EfficientDet D5 1280x1280','EfficientDet D6 1280x1280','EfficientDet D7 1536x1536','SSD MobileNet v2 320x320','SSD MobileNet V1 FPN 640x640','SSD MobileNet V2 FPNLite 320x320','SSD MobileNet V2 FPNLite 640x640','SSD ResNet50 V1 FPN 640x640 (RetinaNet50)','SSD ResNet50 V1 FPN 1024x1024 (RetinaNet50)','SSD ResNet101 V1 FPN 640x640 (RetinaNet101)','SSD ResNet101 V1 FPN 1024x1024 (RetinaNet101)','SSD ResNet152 V1 FPN 640x640 (RetinaNet152)','SSD ResNet152 V1 FPN 1024x1024 (RetinaNet152)','Faster R-CNN ResNet50 V1 640x640','Faster R-CNN ResNet50 V1 1024x1024','Faster R-CNN ResNet50 V1 800x1333','Faster R-CNN ResNet101 V1 640x640','Faster R-CNN ResNet101 V1 1024x1024','Faster R-CNN ResNet101 V1 800x1333','Faster R-CNN ResNet152 V1 640x640','Faster R-CNN ResNet152 V1 1024x1024','Faster R-CNN ResNet152 V1 800x1333','Faster R-CNN Inception ResNet V2 640x640','Faster R-CNN Inception ResNet V2 1024x1024','Mask R-CNN Inception ResNet V2 1024x1024']\n",
"model_handle = ALL_MODELS[model_display_name]\n",
"\n",
"print('Selected model:'+ model_display_name)\n",
"print('Model Handle at TensorFlow Hub: {}'.format(model_handle))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "muhUt-wWL582"
},
"source": [
"## Loading the selected model from TensorFlow Hub\n",
"\n",
"Here we just need the model handle that was selected and use the Tensorflow Hub library to load it to memory.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "rBuD07fLlcEO"
},
"outputs": [],
"source": [
"print('loading model...')\n",
"hub_model = hub.load(model_handle)\n",
"print('model loaded!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GIawRDKPPnd4"
},
"source": [
"## Loading an image\n",
"\n",
"Let's try the model on a simple image. To help with this, we provide a list of test images.\n",
"\n",
"Here are some simple things to try out if you are curious:\n",
"* Try running inference on your own images, just upload them to colab and load the same way it's done in the cell below.\n",
"* Modify some of the input images and see if detection still works. Some simple things to try out here include flipping the image horizontally, or converting to grayscale (note that we still expect the input image to have 3 channels).\n",
"\n",
"**Be careful:** when using images with an alpha channel, the model expect 3 channels images and the alpha will count as a 4th.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hX-AWUQ1wIEr"
},
"outputs": [],
"source": [
"#@title Image Selection (don't forget to execute the cell!) { display-mode: \"form\"}\n",
"selected_image = 'Beach' # @param ['Beach', 'Dogs', 'Naxos Taverna', 'Beatles', 'Phones', 'Birds']\n",
"flip_image_horizontally = False #@param {type:\"boolean\"}\n",
"convert_image_to_grayscale = False #@param {type:\"boolean\"}\n",
"\n",
"image_path = IMAGES_FOR_TEST[selected_image]\n",
"image_np = load_image_into_numpy_array(image_path)\n",
"\n",
"# Flip horizontally\n",
"if(flip_image_horizontally):\n",
" image_np[0] = np.fliplr(image_np[0]).copy()\n",
"\n",
"# Convert image to grayscale\n",
"if(convert_image_to_grayscale):\n",
" image_np[0] = np.tile(\n",
" np.mean(image_np[0], 2, keepdims=True), (1, 1, 3)).astype(np.uint8)\n",
"\n",
"plt.figure(figsize=(24,32))\n",
"plt.imshow(image_np[0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FTHsFjR6HNwb"
},
"source": [
"## Doing the inference\n",
"\n",
"To do the inference we just need to call our TF Hub loaded model.\n",
"\n",
"Things you can try:\n",
"* Print out `result['detection_boxes']` and try to match the box locations to the boxes in the image. Notice that coordinates are given in normalized form (i.e., in the interval [0, 1]).\n",
"* inspect other output keys present in the result. A full documentation can be seen on the models documentation page (pointing your browser to the model handle printed earlier)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Gb_siXKcnnGC"
},
"outputs": [],
"source": [
"# running inference\n",
"results = hub_model(image_np)\n",
"\n",
"# different object detection models have additional results\n",
"# all of them are explained in the documentation\n",
"result = {key:value.numpy() for key,value in results.items()}\n",
"print(result.keys())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IZ5VYaBoeeFM"
},
"source": [
"## Visualizing the results\n",
"\n",
"Here is where we will need the TensorFlow Object Detection API to show the squares from the inference step (and the keypoints when available).\n",
"\n",
"the full documentation of this method can be seen [here](https://github.com/tensorflow/models/blob/master/research/object_detection/utils/visualization_utils.py)\n",
"\n",
"Here you can, for example, set `min_score_thresh` to other values (between 0 and 1) to allow more detections in or to filter out more detections."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2O7rV8g9s8Bz"
},
"outputs": [],
"source": [
"label_id_offset = 0\n",
"image_np_with_detections = image_np.copy()\n",
"\n",
"# Use keypoints if available in detections\n",
"keypoints, keypoint_scores = None, None\n",
"if 'detection_keypoints' in result:\n",
" keypoints = result['detection_keypoints'][0]\n",
" keypoint_scores = result['detection_keypoint_scores'][0]\n",
"\n",
"viz_utils.visualize_boxes_and_labels_on_image_array(\n",
" image_np_with_detections[0],\n",
" result['detection_boxes'][0],\n",
" (result['detection_classes'][0] + label_id_offset).astype(int),\n",
" result['detection_scores'][0],\n",
" category_index,\n",
" use_normalized_coordinates=True,\n",
" max_boxes_to_draw=200,\n",
" min_score_thresh=.30,\n",
" agnostic_mode=False,\n",
" keypoints=keypoints,\n",
" keypoint_scores=keypoint_scores,\n",
" keypoint_edges=COCO17_HUMAN_POSE_KEYPOINTS)\n",
"\n",
"plt.figure(figsize=(24,32))\n",
"plt.imshow(image_np_with_detections[0])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Qaw6Xi08NpEP"
},
"source": [
"## [Optional]\n",
"\n",
"Among the available object detection models there's Mask R-CNN and the output of this model allows instance segmentation.\n",
"\n",
"To visualize it we will use the same method we did before but adding an aditional parameter: `instance_masks=output_dict.get('detection_masks_reframed', None)`\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zl3qdtR1OvM_"
},
"outputs": [],
"source": [
"# Handle models with masks:\n",
"image_np_with_mask = image_np.copy()\n",
"\n",
"if 'detection_masks' in result:\n",
" # we need to convert np.arrays to tensors\n",
" detection_masks = tf.convert_to_tensor(result['detection_masks'][0])\n",
" detection_boxes = tf.convert_to_tensor(result['detection_boxes'][0])\n",
"\n",
" # Reframe the bbox mask to the image size.\n",
" detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(\n",
" detection_masks, detection_boxes,\n",
" image_np.shape[1], image_np.shape[2])\n",
" detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5,\n",
" tf.uint8)\n",
" result['detection_masks_reframed'] = detection_masks_reframed.numpy()\n",
"\n",
"viz_utils.visualize_boxes_and_labels_on_image_array(\n",
" image_np_with_mask[0],\n",
" result['detection_boxes'][0],\n",
" (result['detection_classes'][0] + label_id_offset).astype(int),\n",
" result['detection_scores'][0],\n",
" category_index,\n",
" use_normalized_coordinates=True,\n",
" max_boxes_to_draw=200,\n",
" min_score_thresh=.30,\n",
" agnostic_mode=False,\n",
" instance_masks=result.get('detection_masks_reframed', None),\n",
" line_thickness=8)\n",
"\n",
"plt.figure(figsize=(24,32))\n",
"plt.imshow(image_np_with_mask[0])\n",
"plt.show()"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "tf2_object_detection.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
kandy-koblenz/people-networks | wikipedia-crawl/df_to_edge_list.ipynb | 1 | 36524 | {
"cells": [
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import time\n",
"import pandas as pd\n",
"import numpy as np\n",
"from mwclient import Site\n",
"import pickle\n",
"import csv\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#df = pickle.load(open('findf.p','rb'))\n",
"df = pickle.load(open('consolidated-profile','rb'))"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2016.0</td>\n",
" <td>3.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>162</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2010.0</td>\n",
" <td>7.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>163</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2012.0</td>\n",
" <td>8.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>164</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2008.0</td>\n",
" <td>5.0</td>\n",
" <td>[Template:Prime Ministers of Andorra, Template...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>165</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2016.0</td>\n",
" <td>2.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2009.0</td>\n",
" <td>3.0</td>\n",
" <td>[Template:Prime Ministers of Andorra, Template...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>167</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2011.0</td>\n",
" <td>10.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>168</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2011.0</td>\n",
" <td>8.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>169</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2013.0</td>\n",
" <td>7.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>170</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2008.0</td>\n",
" <td>6.0</td>\n",
" <td>[Template:Prime Ministers of Andorra, Template...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>171</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2007.0</td>\n",
" <td>7.0</td>\n",
" <td>[Template:Europe-politician-stub, Marc Forne M...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>172</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2015.0</td>\n",
" <td>6.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>173</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2016.0</td>\n",
" <td>6.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>174</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2007.0</td>\n",
" <td>10.0</td>\n",
" <td>[Template:Andorra-politician-stub, 1981, Decem...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>175</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2011.0</td>\n",
" <td>12.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>176</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2013.0</td>\n",
" <td>11.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>177</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2009.0</td>\n",
" <td>12.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>178</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2010.0</td>\n",
" <td>10.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>179</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2010.0</td>\n",
" <td>4.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>180</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2009.0</td>\n",
" <td>2.0</td>\n",
" <td>[Template:Prime Ministers of Andorra, Template...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2016.0</td>\n",
" <td>7.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2013.0</td>\n",
" <td>3.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2016.0</td>\n",
" <td>12.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>184</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2014.0</td>\n",
" <td>6.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>185</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2011.0</td>\n",
" <td>9.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>186</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2015.0</td>\n",
" <td>10.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td>Òscar_Ribas_Reig</td>\n",
" <td>2009.0</td>\n",
" <td>11.0</td>\n",
" <td>[Template:ISO 639 name es, Template:Prime Mini...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>188 rows × 4 columns</p>\n",
"</div>"
],
"text/plain": [
" page year month \\\n",
"0 Peder_E._Vorum 2016.0 1.0 \n",
"1 Peder_E._Vorum 2012.0 7.0 \n",
"2 Peder_E._Vorum 2011.0 8.0 \n",
"3 Peder_E._Vorum 2013.0 8.0 \n",
"4 Peder_E._Vorum 2011.0 1.0 \n",
"5 Peder_E._Vorum 2010.0 11.0 \n",
"6 Peder_E._Vorum 2014.0 12.0 \n",
"7 Peder_E._Vorum 2012.0 3.0 \n",
"8 Peder_E._Vorum 2015.0 8.0 \n",
"9 Peder_E._Vorum 2013.0 6.0 \n",
"10 Peder_E._Vorum 2016.0 8.0 \n",
"11 Peder_E._Vorum 2012.0 9.0 \n",
"12 Peder_E._Vorum 2011.0 2.0 \n",
"13 Peder_E._Vorum 2015.0 2.0 \n",
"14 Peder_E._Vorum 2015.0 7.0 \n",
"15 Peder_E._Vorum 2012.0 1.0 \n",
"16 Peder_E._Vorum 2011.0 4.0 \n",
"17 Peder_E._Vorum 2015.0 9.0 \n",
"18 Peder_E._Vorum 2012.0 12.0 \n",
"19 Peder_E._Vorum 2013.0 1.0 \n",
"20 Peder_E._Vorum 2012.0 10.0 \n",
"21 Peder_E._Vorum 2016.0 4.0 \n",
"22 Peder_E._Vorum 2011.0 11.0 \n",
"23 Peder_E._Vorum 2015.0 4.0 \n",
"24 Peder_E._Vorum 2013.0 10.0 \n",
"25 Peder_E._Vorum 2011.0 12.0 \n",
"26 Peder_E._Vorum 2015.0 11.0 \n",
"27 Peder_E._Vorum 2015.0 1.0 \n",
"28 Peder_E._Vorum 2014.0 9.0 \n",
"29 Peder_E._Vorum 2015.0 12.0 \n",
".. ... ... ... \n",
"158 Òscar_Ribas_Reig 2013.0 9.0 \n",
"159 Òscar_Ribas_Reig 2008.0 1.0 \n",
"160 Òscar_Ribas_Reig 2014.0 8.0 \n",
"161 Òscar_Ribas_Reig 2016.0 3.0 \n",
"162 Òscar_Ribas_Reig 2010.0 7.0 \n",
"163 Òscar_Ribas_Reig 2012.0 8.0 \n",
"164 Òscar_Ribas_Reig 2008.0 5.0 \n",
"165 Òscar_Ribas_Reig 2016.0 2.0 \n",
"166 Òscar_Ribas_Reig 2009.0 3.0 \n",
"167 Òscar_Ribas_Reig 2011.0 10.0 \n",
"168 Òscar_Ribas_Reig 2011.0 8.0 \n",
"169 Òscar_Ribas_Reig 2013.0 7.0 \n",
"170 Òscar_Ribas_Reig 2008.0 6.0 \n",
"171 Òscar_Ribas_Reig 2007.0 7.0 \n",
"172 Òscar_Ribas_Reig 2015.0 6.0 \n",
"173 Òscar_Ribas_Reig 2016.0 6.0 \n",
"174 Òscar_Ribas_Reig 2007.0 10.0 \n",
"175 Òscar_Ribas_Reig 2011.0 12.0 \n",
"176 Òscar_Ribas_Reig 2013.0 11.0 \n",
"177 Òscar_Ribas_Reig 2009.0 12.0 \n",
"178 Òscar_Ribas_Reig 2010.0 10.0 \n",
"179 Òscar_Ribas_Reig 2010.0 4.0 \n",
"180 Òscar_Ribas_Reig 2009.0 2.0 \n",
"181 Òscar_Ribas_Reig 2016.0 7.0 \n",
"182 Òscar_Ribas_Reig 2013.0 3.0 \n",
"183 Òscar_Ribas_Reig 2016.0 12.0 \n",
"184 Òscar_Ribas_Reig 2014.0 6.0 \n",
"185 Òscar_Ribas_Reig 2011.0 9.0 \n",
"186 Òscar_Ribas_Reig 2015.0 10.0 \n",
"187 Òscar_Ribas_Reig 2009.0 11.0 \n",
"\n",
" links_to \n",
"0 [Category:Use dmy dates from May 2011, Andenes... \n",
"1 [Category:Use dmy dates from May 2011, Andenes... \n",
"2 [Category:Use dmy dates from May 2011, Andenes... \n",
"3 [Category:Use dmy dates from May 2011, Andenes... \n",
"4 [Andenes, County council (Norway), Egge, Germa... \n",
"5 [Andenes, County council (Norway), Egge, Germa... \n",
"6 [Category:Use dmy dates from May 2011, Andenes... \n",
"7 [Category:Use dmy dates from May 2011, Andenes... \n",
"8 [Category:Use dmy dates from May 2011, Andenes... \n",
"9 [Category:Use dmy dates from May 2011, Andenes... \n",
"10 [Category:Use dmy dates from May 2011, Andenes... \n",
"11 [Category:Use dmy dates from May 2011, Andenes... \n",
"12 [Andenes, County council (Norway), Egge, Germa... \n",
"13 [Category:Use dmy dates from May 2011, Andenes... \n",
"14 [Category:Use dmy dates from May 2011, Andenes... \n",
"15 [Category:Use dmy dates from May 2011, Andenes... \n",
"16 [Andenes, County council (Norway), Egge, Germa... \n",
"17 [Category:Use dmy dates from May 2011, Andenes... \n",
"18 [Category:Use dmy dates from May 2011, Andenes... \n",
"19 [Category:Use dmy dates from May 2011, Andenes... \n",
"20 [Category:Use dmy dates from May 2011, Andenes... \n",
"21 [Category:Use dmy dates from May 2011, Andenes... \n",
"22 [Category:Use dmy dates from May 2011, Andenes... \n",
"23 [Category:Use dmy dates from May 2011, Andenes... \n",
"24 [Category:Use dmy dates from May 2011, Andenes... \n",
"25 [Category:Use dmy dates from May 2011, Andenes... \n",
"26 [Category:Use dmy dates from May 2011, Andenes... \n",
"27 [Category:Use dmy dates from May 2011, Andenes... \n",
"28 [Category:Use dmy dates from May 2011, Andenes... \n",
"29 [Category:Use dmy dates from May 2011, Andenes... \n",
".. ... \n",
"158 [Template:ISO 639 name es, Template:Prime Mini... \n",
"159 [Template:Prime Ministers of Andorra, Template... \n",
"160 [Template:ISO 639 name es, Template:Prime Mini... \n",
"161 [Template:ISO 639 name es, Template:Prime Mini... \n",
"162 [Template:ISO 639 name es, Template:Prime Mini... \n",
"163 [Template:ISO 639 name es, Template:Prime Mini... \n",
"164 [Template:Prime Ministers of Andorra, Template... \n",
"165 [Template:ISO 639 name es, Template:Prime Mini... \n",
"166 [Template:Prime Ministers of Andorra, Template... \n",
"167 [Template:ISO 639 name es, Template:Prime Mini... \n",
"168 [Template:ISO 639 name es, Template:Prime Mini... \n",
"169 [Template:ISO 639 name es, Template:Prime Mini... \n",
"170 [Template:Prime Ministers of Andorra, Template... \n",
"171 [Template:Europe-politician-stub, Marc Forne M... \n",
"172 [Template:ISO 639 name es, Template:Prime Mini... \n",
"173 [Template:ISO 639 name es, Template:Prime Mini... \n",
"174 [Template:Andorra-politician-stub, 1981, Decem... \n",
"175 [Template:ISO 639 name es, Template:Prime Mini... \n",
"176 [Template:ISO 639 name es, Template:Prime Mini... \n",
"177 [Template:ISO 639 name es, Template:Prime Mini... \n",
"178 [Template:ISO 639 name es, Template:Prime Mini... \n",
"179 [Template:ISO 639 name es, Template:Prime Mini... \n",
"180 [Template:Prime Ministers of Andorra, Template... \n",
"181 [Template:ISO 639 name es, Template:Prime Mini... \n",
"182 [Template:ISO 639 name es, Template:Prime Mini... \n",
"183 [Template:ISO 639 name es, Template:Prime Mini... \n",
"184 [Template:ISO 639 name es, Template:Prime Mini... \n",
"185 [Template:ISO 639 name es, Template:Prime Mini... \n",
"186 [Template:ISO 639 name es, Template:Prime Mini... \n",
"187 [Template:ISO 639 name es, Template:Prime Mini... \n",
"\n",
"[188 rows x 4 columns]"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ymg = df.groupby(['year','month'])\n",
"ymg.head()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pageset = set(df.page.values)\n",
"len(pageset)"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(2007.0, 7.0)\n",
"(2007.0, 8.0)\n",
"(2007.0, 9.0)\n",
"(2007.0, 10.0)\n",
"(2007.0, 11.0)\n",
"(2007.0, 12.0)\n",
"(2008.0, 1.0)\n",
"(2008.0, 2.0)\n",
"(2008.0, 3.0)\n",
"(2008.0, 4.0)\n",
"(2008.0, 5.0)\n",
"(2008.0, 6.0)\n",
"(2008.0, 7.0)\n",
"(2008.0, 8.0)\n",
"(2008.0, 9.0)\n",
"(2008.0, 10.0)\n",
"(2008.0, 11.0)\n",
"(2008.0, 12.0)\n",
"(2009.0, 1.0)\n",
"(2009.0, 2.0)\n",
"(2009.0, 3.0)\n",
"(2009.0, 4.0)\n",
"(2009.0, 5.0)\n",
"(2009.0, 6.0)\n",
"(2009.0, 7.0)\n",
"(2009.0, 8.0)\n",
"(2009.0, 9.0)\n",
"(2009.0, 10.0)\n",
"(2009.0, 11.0)\n",
"(2009.0, 12.0)\n",
"(2010.0, 1.0)\n",
"(2010.0, 2.0)\n",
"(2010.0, 3.0)\n",
"(2010.0, 4.0)\n",
"(2010.0, 5.0)\n",
"(2010.0, 6.0)\n",
"(2010.0, 7.0)\n",
"(2010.0, 8.0)\n",
"(2010.0, 9.0)\n",
"(2010.0, 10.0)\n",
"(2010.0, 11.0)\n",
"(2010.0, 12.0)\n",
"(2011.0, 1.0)\n",
"(2011.0, 2.0)\n",
"(2011.0, 3.0)\n",
"(2011.0, 4.0)\n",
"(2011.0, 5.0)\n",
"(2011.0, 6.0)\n",
"(2011.0, 7.0)\n",
"(2011.0, 8.0)\n",
"(2011.0, 9.0)\n",
"(2011.0, 10.0)\n",
"(2011.0, 11.0)\n",
"(2011.0, 12.0)\n",
"(2012.0, 1.0)\n",
"(2012.0, 2.0)\n",
"(2012.0, 3.0)\n",
"(2012.0, 4.0)\n",
"(2012.0, 5.0)\n",
"(2012.0, 6.0)\n",
"(2012.0, 7.0)\n",
"(2012.0, 8.0)\n",
"(2012.0, 9.0)\n",
"(2012.0, 10.0)\n",
"(2012.0, 11.0)\n",
"(2012.0, 12.0)\n",
"(2013.0, 1.0)\n",
"(2013.0, 2.0)\n",
"(2013.0, 3.0)\n",
"(2013.0, 4.0)\n",
"(2013.0, 5.0)\n",
"(2013.0, 6.0)\n",
"(2013.0, 7.0)\n",
"(2013.0, 8.0)\n",
"(2013.0, 9.0)\n",
"(2013.0, 10.0)\n",
"(2013.0, 11.0)\n",
"(2013.0, 12.0)\n",
"(2014.0, 1.0)\n",
"(2014.0, 2.0)\n",
"(2014.0, 3.0)\n",
"(2014.0, 4.0)\n",
"(2014.0, 5.0)\n",
"(2014.0, 6.0)\n",
"(2014.0, 7.0)\n",
"(2014.0, 8.0)\n",
"(2014.0, 9.0)\n",
"(2014.0, 10.0)\n",
"(2014.0, 11.0)\n",
"(2014.0, 12.0)\n",
"(2015.0, 1.0)\n",
"(2015.0, 2.0)\n",
"(2015.0, 3.0)\n",
"(2015.0, 4.0)\n",
"(2015.0, 5.0)\n",
"(2015.0, 6.0)\n",
"(2015.0, 7.0)\n",
"(2015.0, 8.0)\n",
"(2015.0, 9.0)\n",
"(2015.0, 10.0)\n",
"(2015.0, 11.0)\n",
"(2015.0, 12.0)\n",
"(2016.0, 1.0)\n",
"(2016.0, 2.0)\n",
"(2016.0, 3.0)\n",
"(2016.0, 4.0)\n",
"(2016.0, 5.0)\n",
"(2016.0, 6.0)\n",
"(2016.0, 7.0)\n",
"(2016.0, 8.0)\n",
"(2016.0, 9.0)\n",
"(2016.0, 10.0)\n",
"(2016.0, 11.0)\n",
"(2016.0, 12.0)\n"
]
}
],
"source": [
"korn = \"\" # Base path\n",
"name_d = {}\n",
"name_l = []\n",
"for name, grp in ymg: #Iterating over groups, each group - one month\n",
" conn = str(int(name[0])) + \"_\" + str(int(name[1])).zfill(2) #Constructed name \n",
" cpth = korn + conn+'.csv' #Constructing pathname from base path and year_month\n",
" name_l.append(conn)\n",
" name_d[conn]=cpth #Storing names and paths\n",
" f = open(cpth,'w',encoding='utf-8') #Opening a file to store an edge list\n",
" writer = csv.writer(f, delimiter=',',lineterminator='\\n') # Creating csv writer instance, to write into a file row by row\n",
" for ind,row in grp.iterrows(): #Iterating over articles with revision in given month \n",
" for lk in row['links_to']: \n",
" lkr = lk.replace(' ','_')\n",
" if lkr in pageset:\n",
" writer.writerow((row['page'],lkr)) # If article links to one of our articles we add the edge pait\n",
" f.close()\n",
" print(name)\n",
" #print(grp.head())"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2007_07\n",
"2007_08\n",
"2007_09\n",
"2007_10\n",
"2007_11\n",
"2007_12\n",
"2008_01\n",
"2008_02\n",
"2008_03\n",
"2008_04\n",
"2008_05\n",
"2008_06\n",
"2008_07\n",
"2008_08\n",
"2008_09\n",
"2008_10\n",
"2008_11\n",
"2008_12\n",
"2009_01\n",
"2009_02\n",
"2009_03\n",
"2009_04\n",
"2009_05\n",
"2009_06\n",
"2009_07\n",
"2009_08\n",
"2009_09\n",
"2009_10\n",
"2009_11\n",
"2009_12\n",
"2010_01\n",
"2010_02\n",
"2010_03\n",
"2010_04\n",
"2010_05\n",
"2010_06\n",
"2010_07\n",
"2010_08\n",
"2010_09\n",
"2010_10\n",
"2010_11\n",
"2010_12\n",
"2011_01\n",
"2011_02\n",
"2011_03\n",
"2011_04\n",
"2011_05\n",
"2011_06\n",
"2011_07\n",
"2011_08\n",
"2011_09\n",
"2011_10\n",
"2011_11\n",
"2011_12\n",
"2012_01\n",
"2012_02\n",
"2012_03\n",
"2012_04\n",
"2012_05\n",
"2012_06\n",
"2012_07\n",
"2012_08\n",
"2012_09\n",
"2012_10\n",
"2012_11\n",
"2012_12\n",
"2013_01\n",
"2013_02\n",
"2013_03\n",
"2013_04\n",
"2013_05\n",
"2013_06\n",
"2013_07\n",
"2013_08\n",
"2013_09\n",
"2013_10\n",
"2013_11\n",
"2013_12\n",
"2014_01\n",
"2014_02\n",
"2014_03\n",
"2014_04\n",
"2014_05\n",
"2014_06\n",
"2014_07\n",
"2014_08\n",
"2014_09\n",
"2014_10\n",
"2014_11\n",
"2014_12\n",
"2015_01\n",
"2015_02\n",
"2015_03\n",
"2015_04\n",
"2015_05\n",
"2015_06\n",
"2015_07\n",
"2015_08\n",
"2015_09\n",
"2015_10\n",
"2015_11\n",
"2015_12\n",
"2016_01\n",
"2016_02\n",
"2016_03\n",
"2016_04\n",
"2016_05\n",
"2016_06\n",
"2016_07\n",
"2016_08\n",
"2016_09\n",
"2016_10\n",
"2016_11\n",
"2016_12\n"
]
}
],
"source": [
"temp_d = {}\n",
"for n in name_l: #Iterating over month\n",
" print(n)\n",
" cdf = pd.read_csv(name_d[n], header=None,names=['from','to'])\n",
" cart = set(cdf['from'].values) # Set of articles that have revision in this month\n",
" tst = set(temp_d.keys()) # Set of already existing articles\n",
" per = cart.intersection(tst) # Set of articles that have revision in this month and already existed\n",
" sch = tst - per # Set of article that don't have revisions in this month\n",
" for upd in cart: # Iterating over articles\n",
" #if upd not in temp_d.keys(): # Checking if page already exists\n",
" temp_d[upd] = []\n",
" for index_s , rww in cdf[cdf['from']==upd].iterrows(): # Iterating over rows with given article in 'from' column\n",
" temp_d[upd].append(rww['to']) #Adding link to temp_dictionary\n",
" for rt in sch: #Iterating over articles that don't have revisions this month\n",
" if rt in temp_d.keys():\n",
" for clk in temp_d[rt]:\n",
" cdf.loc[cdf.shape[0]+1] = [rt,clk] #Adding row to the edgelist if the article had prev revisions, but doesnt have this month\n",
" cdf.sort_values('from',inplace=True) #Sorting table\n",
" cdf.to_csv(name_d[n],index=False) #Saving it to the same file"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
lalonica/PhD | vehicles/VehiclesPortionDataSet.ipynb | 1 | 135310 | {
"cells": [
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#%matplotlib inline\n",
"\n",
"from pandas import Series, DataFrame\n",
"import pandas as pd\n",
"import numpy as np\n",
"import csv\n",
"import matplotlib as plt"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"c_names = ['vID', 'frID', 'tFr','Timestamp', 'localX', 'localY', 'globalX','globalY', 'vLenght', 'vWidth', 'vType', \n",
" 'veloc','accel', 'line', 'pred', 'foll', 'spac', 'headway', 'dateTime']\n"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = pd.read_table('D:\\\\zzzLola\\\\PhD\\\\DataSet\\\\US101\\\\test\\\\portion1Set2DT.txt', sep='\\t', header=None, names=c_names)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>2</td>\n",
" <td>34</td>\n",
" <td>437</td>\n",
" <td>1118846982300</td>\n",
" <td>16.478</td>\n",
" <td>117.118</td>\n",
" <td>6451192.757</td>\n",
" <td>1873284.247</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>35.08</td>\n",
" <td>-1.89</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>2</td>\n",
" <td>35</td>\n",
" <td>437</td>\n",
" <td>1118846982400</td>\n",
" <td>16.520</td>\n",
" <td>120.600</td>\n",
" <td>6451195.109</td>\n",
" <td>1873281.656</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>34.96</td>\n",
" <td>0.18</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>437</td>\n",
" <td>1118846982500</td>\n",
" <td>16.562</td>\n",
" <td>124.096</td>\n",
" <td>6451197.462</td>\n",
" <td>1873279.065</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>34.98</td>\n",
" <td>0.25</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>2</td>\n",
" <td>37</td>\n",
" <td>437</td>\n",
" <td>1118846982600</td>\n",
" <td>16.605</td>\n",
" <td>127.597</td>\n",
" <td>6451199.814</td>\n",
" <td>1873276.473</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>35.00</td>\n",
" <td>0.04</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>2</td>\n",
" <td>38</td>\n",
" <td>437</td>\n",
" <td>1118846982700</td>\n",
" <td>16.647</td>\n",
" <td>131.099</td>\n",
" <td>6451202.167</td>\n",
" <td>1873273.882</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>34.99</td>\n",
" <td>-0.20</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>2</td>\n",
" <td>39</td>\n",
" <td>437</td>\n",
" <td>1118846982800</td>\n",
" <td>16.691</td>\n",
" <td>134.595</td>\n",
" <td>6451204.519</td>\n",
" <td>1873271.290</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>34.98</td>\n",
" <td>-0.02</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2</td>\n",
" <td>40</td>\n",
" <td>437</td>\n",
" <td>1118846982900</td>\n",
" <td>16.727</td>\n",
" <td>138.081</td>\n",
" <td>6451206.879</td>\n",
" <td>1873268.700</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>35.10</td>\n",
" <td>1.95</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2</td>\n",
" <td>41</td>\n",
" <td>437</td>\n",
" <td>1118846983000</td>\n",
" <td>16.796</td>\n",
" <td>141.578</td>\n",
" <td>6451209.191</td>\n",
" <td>1873266.113</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>35.49</td>\n",
" <td>5.55</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2</td>\n",
" <td>42</td>\n",
" <td>437</td>\n",
" <td>1118846983100</td>\n",
" <td>16.795</td>\n",
" <td>145.131</td>\n",
" <td>6451211.610</td>\n",
" <td>1873263.514</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>36.20</td>\n",
" <td>8.99</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2</td>\n",
" <td>43</td>\n",
" <td>437</td>\n",
" <td>1118846983200</td>\n",
" <td>16.724</td>\n",
" <td>148.784</td>\n",
" <td>6451214.156</td>\n",
" <td>1873260.882</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>37.15</td>\n",
" <td>10.44</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2</td>\n",
" <td>44</td>\n",
" <td>437</td>\n",
" <td>1118846983300</td>\n",
" <td>16.588</td>\n",
" <td>152.559</td>\n",
" <td>6451216.824</td>\n",
" <td>1873258.213</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>38.12</td>\n",
" <td>9.30</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>2</td>\n",
" <td>45</td>\n",
" <td>437</td>\n",
" <td>1118846983400</td>\n",
" <td>16.376</td>\n",
" <td>156.449</td>\n",
" <td>6451219.616</td>\n",
" <td>1873255.522</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>38.76</td>\n",
" <td>4.36</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>2</td>\n",
" <td>46</td>\n",
" <td>437</td>\n",
" <td>1118846983500</td>\n",
" <td>16.064</td>\n",
" <td>160.379</td>\n",
" <td>6451222.548</td>\n",
" <td>1873252.829</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>38.95</td>\n",
" <td>-0.73</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>2</td>\n",
" <td>47</td>\n",
" <td>437</td>\n",
" <td>1118846983600</td>\n",
" <td>15.763</td>\n",
" <td>164.277</td>\n",
" <td>6451225.462</td>\n",
" <td>1873250.139</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>38.95</td>\n",
" <td>-1.15</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>2</td>\n",
" <td>48</td>\n",
" <td>437</td>\n",
" <td>1118846983700</td>\n",
" <td>15.471</td>\n",
" <td>168.150</td>\n",
" <td>6451228.376</td>\n",
" <td>1873247.450</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>38.99</td>\n",
" <td>1.90</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>2</td>\n",
" <td>49</td>\n",
" <td>437</td>\n",
" <td>1118846983800</td>\n",
" <td>15.226</td>\n",
" <td>172.044</td>\n",
" <td>6451231.290</td>\n",
" <td>1873244.760</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>39.18</td>\n",
" <td>3.47</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>2</td>\n",
" <td>50</td>\n",
" <td>437</td>\n",
" <td>1118846983900</td>\n",
" <td>14.979</td>\n",
" <td>176.000</td>\n",
" <td>6451234.204</td>\n",
" <td>1873242.071</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>39.34</td>\n",
" <td>0.02</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>2</td>\n",
" <td>51</td>\n",
" <td>437</td>\n",
" <td>1118846984000</td>\n",
" <td>14.720</td>\n",
" <td>179.959</td>\n",
" <td>6451237.144</td>\n",
" <td>1873239.374</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>39.20</td>\n",
" <td>-3.52</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>2</td>\n",
" <td>52</td>\n",
" <td>437</td>\n",
" <td>1118846984100</td>\n",
" <td>14.508</td>\n",
" <td>183.862</td>\n",
" <td>6451239.988</td>\n",
" <td>1873236.708</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>38.89</td>\n",
" <td>-3.28</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>2</td>\n",
" <td>53</td>\n",
" <td>437</td>\n",
" <td>1118846984200</td>\n",
" <td>14.331</td>\n",
" <td>187.716</td>\n",
" <td>6451242.770</td>\n",
" <td>1873234.057</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>38.73</td>\n",
" <td>-0.33</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>2</td>\n",
" <td>54</td>\n",
" <td>437</td>\n",
" <td>1118846984300</td>\n",
" <td>14.240</td>\n",
" <td>191.561</td>\n",
" <td>6451245.501</td>\n",
" <td>1873231.336</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>38.88</td>\n",
" <td>3.49</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>2</td>\n",
" <td>55</td>\n",
" <td>437</td>\n",
" <td>1118846984400</td>\n",
" <td>14.309</td>\n",
" <td>195.455</td>\n",
" <td>6451248.125</td>\n",
" <td>1873228.494</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>39.28</td>\n",
" <td>5.00</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>2</td>\n",
" <td>56</td>\n",
" <td>437</td>\n",
" <td>1118846984500</td>\n",
" <td>14.428</td>\n",
" <td>199.414</td>\n",
" <td>6451250.788</td>\n",
" <td>1873225.539</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>39.68</td>\n",
" <td>3.76</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>2</td>\n",
" <td>57</td>\n",
" <td>437</td>\n",
" <td>1118846984600</td>\n",
" <td>14.540</td>\n",
" <td>203.417</td>\n",
" <td>6451253.489</td>\n",
" <td>1873222.554</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>39.94</td>\n",
" <td>1.29</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>2</td>\n",
" <td>58</td>\n",
" <td>437</td>\n",
" <td>1118846984700</td>\n",
" <td>14.646</td>\n",
" <td>207.430</td>\n",
" <td>6451256.177</td>\n",
" <td>1873219.592</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>40.02</td>\n",
" <td>-0.22</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>2</td>\n",
" <td>59</td>\n",
" <td>437</td>\n",
" <td>1118846984800</td>\n",
" <td>14.751</td>\n",
" <td>211.431</td>\n",
" <td>6451258.866</td>\n",
" <td>1873216.630</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>40.00</td>\n",
" <td>-0.21</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>2</td>\n",
" <td>60</td>\n",
" <td>437</td>\n",
" <td>1118846984900</td>\n",
" <td>14.856</td>\n",
" <td>215.428</td>\n",
" <td>6451261.554</td>\n",
" <td>1873213.669</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>39.99</td>\n",
" <td>0.00</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>2</td>\n",
" <td>61</td>\n",
" <td>437</td>\n",
" <td>1118846985000</td>\n",
" <td>14.962</td>\n",
" <td>219.427</td>\n",
" <td>6451264.243</td>\n",
" <td>1873210.707</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>39.99</td>\n",
" <td>0.00</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>2</td>\n",
" <td>62</td>\n",
" <td>437</td>\n",
" <td>1118846985100</td>\n",
" <td>15.067</td>\n",
" <td>223.462</td>\n",
" <td>6451266.932</td>\n",
" <td>1873207.745</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>39.65</td>\n",
" <td>-5.35</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2005-06-15 14:49:45</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" vID frID tFr Timestamp localX localY globalX globalY \\\n",
"0 2 13 437 1118846980200 16.467 35.381 6451137.641 1873344.962 \n",
"1 2 14 437 1118846980300 16.447 39.381 6451140.329 1873342.000 \n",
"2 2 15 437 1118846980400 16.426 43.381 6451143.018 1873339.038 \n",
"3 2 16 437 1118846980500 16.405 47.380 6451145.706 1873336.077 \n",
"4 2 17 437 1118846980600 16.385 51.381 6451148.395 1873333.115 \n",
"5 2 18 437 1118846980700 16.364 55.381 6451151.084 1873330.153 \n",
"6 2 19 437 1118846980800 16.344 59.381 6451153.772 1873327.192 \n",
"7 2 20 437 1118846980900 16.323 63.379 6451156.461 1873324.230 \n",
"8 2 21 437 1118846981000 16.303 67.383 6451159.149 1873321.268 \n",
"9 2 22 437 1118846981100 16.282 71.398 6451161.838 1873318.307 \n",
"10 2 23 437 1118846981200 16.262 75.401 6451164.546 1873315.323 \n",
"11 2 24 437 1118846981300 16.254 79.349 6451167.199 1873312.382 \n",
"12 2 25 437 1118846981400 16.221 83.233 6451169.802 1873309.533 \n",
"13 2 26 437 1118846981500 16.201 87.043 6451172.358 1873306.719 \n",
"14 2 27 437 1118846981600 16.169 90.829 6451174.961 1873303.870 \n",
"15 2 28 437 1118846981700 16.204 94.683 6451177.613 1873300.929 \n",
"16 2 29 437 1118846981800 16.252 98.611 6451180.342 1873297.924 \n",
"17 2 30 437 1118846981900 16.339 102.560 6451182.980 1873294.961 \n",
"18 2 31 437 1118846982000 16.400 106.385 6451185.537 1873292.122 \n",
"19 2 32 437 1118846982100 16.430 110.079 6451188.021 1873289.408 \n",
"20 2 33 437 1118846982200 16.435 113.628 6451190.424 1873286.817 \n",
"21 2 34 437 1118846982300 16.478 117.118 6451192.757 1873284.247 \n",
"22 2 35 437 1118846982400 16.520 120.600 6451195.109 1873281.656 \n",
"23 2 36 437 1118846982500 16.562 124.096 6451197.462 1873279.065 \n",
"24 2 37 437 1118846982600 16.605 127.597 6451199.814 1873276.473 \n",
"25 2 38 437 1118846982700 16.647 131.099 6451202.167 1873273.882 \n",
"26 2 39 437 1118846982800 16.691 134.595 6451204.519 1873271.290 \n",
"27 2 40 437 1118846982900 16.727 138.081 6451206.879 1873268.700 \n",
"28 2 41 437 1118846983000 16.796 141.578 6451209.191 1873266.113 \n",
"29 2 42 437 1118846983100 16.795 145.131 6451211.610 1873263.514 \n",
"30 2 43 437 1118846983200 16.724 148.784 6451214.156 1873260.882 \n",
"31 2 44 437 1118846983300 16.588 152.559 6451216.824 1873258.213 \n",
"32 2 45 437 1118846983400 16.376 156.449 6451219.616 1873255.522 \n",
"33 2 46 437 1118846983500 16.064 160.379 6451222.548 1873252.829 \n",
"34 2 47 437 1118846983600 15.763 164.277 6451225.462 1873250.139 \n",
"35 2 48 437 1118846983700 15.471 168.150 6451228.376 1873247.450 \n",
"36 2 49 437 1118846983800 15.226 172.044 6451231.290 1873244.760 \n",
"37 2 50 437 1118846983900 14.979 176.000 6451234.204 1873242.071 \n",
"38 2 51 437 1118846984000 14.720 179.959 6451237.144 1873239.374 \n",
"39 2 52 437 1118846984100 14.508 183.862 6451239.988 1873236.708 \n",
"40 2 53 437 1118846984200 14.331 187.716 6451242.770 1873234.057 \n",
"41 2 54 437 1118846984300 14.240 191.561 6451245.501 1873231.336 \n",
"42 2 55 437 1118846984400 14.309 195.455 6451248.125 1873228.494 \n",
"43 2 56 437 1118846984500 14.428 199.414 6451250.788 1873225.539 \n",
"44 2 57 437 1118846984600 14.540 203.417 6451253.489 1873222.554 \n",
"45 2 58 437 1118846984700 14.646 207.430 6451256.177 1873219.592 \n",
"46 2 59 437 1118846984800 14.751 211.431 6451258.866 1873216.630 \n",
"47 2 60 437 1118846984900 14.856 215.428 6451261.554 1873213.669 \n",
"48 2 61 437 1118846985000 14.962 219.427 6451264.243 1873210.707 \n",
"49 2 62 437 1118846985100 15.067 223.462 6451266.932 1873207.745 \n",
"\n",
" vLenght vWidth vType veloc accel line pred foll spac headway \\\n",
"0 14.5 4.9 2 40.00 0.00 2 0 0 0 0 \n",
"1 14.5 4.9 2 40.00 0.00 2 0 0 0 0 \n",
"2 14.5 4.9 2 40.00 0.00 2 0 0 0 0 \n",
"3 14.5 4.9 2 40.00 0.00 2 0 0 0 0 \n",
"4 14.5 4.9 2 40.00 0.00 2 0 0 0 0 \n",
"5 14.5 4.9 2 40.00 0.00 2 0 0 0 0 \n",
"6 14.5 4.9 2 40.00 0.00 2 0 0 0 0 \n",
"7 14.5 4.9 2 40.02 0.25 2 0 0 0 0 \n",
"8 14.5 4.9 2 40.03 0.13 2 0 0 0 0 \n",
"9 14.5 4.9 2 39.93 -1.63 2 0 13 0 0 \n",
"10 14.5 4.9 2 39.61 -4.54 2 0 13 0 0 \n",
"11 14.5 4.9 2 39.14 -5.73 2 0 13 0 0 \n",
"12 14.5 4.9 2 38.61 -5.15 2 0 13 0 0 \n",
"13 14.5 4.9 2 38.28 -1.61 2 0 13 0 0 \n",
"14 14.5 4.9 2 38.42 3.73 2 0 13 0 0 \n",
"15 14.5 4.9 2 38.78 4.86 2 0 13 0 0 \n",
"16 14.5 4.9 2 38.92 0.00 2 0 13 0 0 \n",
"17 14.5 4.9 2 38.54 -8.59 2 0 13 0 0 \n",
"18 14.5 4.9 2 37.51 -11.20 2 0 13 0 0 \n",
"19 14.5 4.9 2 36.34 -10.86 2 0 13 0 0 \n",
"20 14.5 4.9 2 35.50 -6.20 2 0 13 0 0 \n",
"21 14.5 4.9 2 35.08 -1.89 2 0 13 0 0 \n",
"22 14.5 4.9 2 34.96 0.18 2 0 13 0 0 \n",
"23 14.5 4.9 2 34.98 0.25 2 0 13 0 0 \n",
"24 14.5 4.9 2 35.00 0.04 2 0 13 0 0 \n",
"25 14.5 4.9 2 34.99 -0.20 2 0 13 0 0 \n",
"26 14.5 4.9 2 34.98 -0.02 2 0 13 0 0 \n",
"27 14.5 4.9 2 35.10 1.95 2 0 13 0 0 \n",
"28 14.5 4.9 2 35.49 5.55 2 0 13 0 0 \n",
"29 14.5 4.9 2 36.20 8.99 2 0 13 0 0 \n",
"30 14.5 4.9 2 37.15 10.44 2 0 13 0 0 \n",
"31 14.5 4.9 2 38.12 9.30 2 0 13 0 0 \n",
"32 14.5 4.9 2 38.76 4.36 2 0 13 0 0 \n",
"33 14.5 4.9 2 38.95 -0.73 2 0 13 0 0 \n",
"34 14.5 4.9 2 38.95 -1.15 2 0 13 0 0 \n",
"35 14.5 4.9 2 38.99 1.90 2 0 13 0 0 \n",
"36 14.5 4.9 2 39.18 3.47 2 0 13 0 0 \n",
"37 14.5 4.9 2 39.34 0.02 2 0 13 0 0 \n",
"38 14.5 4.9 2 39.20 -3.52 2 0 13 0 0 \n",
"39 14.5 4.9 2 38.89 -3.28 2 0 13 0 0 \n",
"40 14.5 4.9 2 38.73 -0.33 2 0 13 0 0 \n",
"41 14.5 4.9 2 38.88 3.49 2 0 13 0 0 \n",
"42 14.5 4.9 2 39.28 5.00 2 0 13 0 0 \n",
"43 14.5 4.9 2 39.68 3.76 2 0 13 0 0 \n",
"44 14.5 4.9 2 39.94 1.29 2 0 13 0 0 \n",
"45 14.5 4.9 2 40.02 -0.22 2 0 13 0 0 \n",
"46 14.5 4.9 2 40.00 -0.21 2 0 13 0 0 \n",
"47 14.5 4.9 2 39.99 0.00 2 0 13 0 0 \n",
"48 14.5 4.9 2 39.99 0.00 2 0 13 0 0 \n",
"49 14.5 4.9 2 39.65 -5.35 2 0 13 0 0 \n",
"\n",
" dateTime \n",
"0 2005-06-15 14:49:40 \n",
"1 2005-06-15 14:49:40 \n",
"2 2005-06-15 14:49:40 \n",
"3 2005-06-15 14:49:40 \n",
"4 2005-06-15 14:49:40 \n",
"5 2005-06-15 14:49:40 \n",
"6 2005-06-15 14:49:40 \n",
"7 2005-06-15 14:49:40 \n",
"8 2005-06-15 14:49:41 \n",
"9 2005-06-15 14:49:41 \n",
"10 2005-06-15 14:49:41 \n",
"11 2005-06-15 14:49:41 \n",
"12 2005-06-15 14:49:41 \n",
"13 2005-06-15 14:49:41 \n",
"14 2005-06-15 14:49:41 \n",
"15 2005-06-15 14:49:41 \n",
"16 2005-06-15 14:49:41 \n",
"17 2005-06-15 14:49:41 \n",
"18 2005-06-15 14:49:42 \n",
"19 2005-06-15 14:49:42 \n",
"20 2005-06-15 14:49:42 \n",
"21 2005-06-15 14:49:42 \n",
"22 2005-06-15 14:49:42 \n",
"23 2005-06-15 14:49:42 \n",
"24 2005-06-15 14:49:42 \n",
"25 2005-06-15 14:49:42 \n",
"26 2005-06-15 14:49:42 \n",
"27 2005-06-15 14:49:42 \n",
"28 2005-06-15 14:49:43 \n",
"29 2005-06-15 14:49:43 \n",
"30 2005-06-15 14:49:43 \n",
"31 2005-06-15 14:49:43 \n",
"32 2005-06-15 14:49:43 \n",
"33 2005-06-15 14:49:43 \n",
"34 2005-06-15 14:49:43 \n",
"35 2005-06-15 14:49:43 \n",
"36 2005-06-15 14:49:43 \n",
"37 2005-06-15 14:49:43 \n",
"38 2005-06-15 14:49:44 \n",
"39 2005-06-15 14:49:44 \n",
"40 2005-06-15 14:49:44 \n",
"41 2005-06-15 14:49:44 \n",
"42 2005-06-15 14:49:44 \n",
"43 2005-06-15 14:49:44 \n",
"44 2005-06-15 14:49:44 \n",
"45 2005-06-15 14:49:44 \n",
"46 2005-06-15 14:49:44 \n",
"47 2005-06-15 14:49:44 \n",
"48 2005-06-15 14:49:45 \n",
"49 2005-06-15 14:49:45 "
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[:50]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"v_ts = DataFrame(data, columns=['vID', 'Timestamp'])"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"v_dt = DataFrame(data, columns=['vID', 'dateTime'])"
]
},
{
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"execution_count": 48,
"metadata": {
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"text/plain": [
" vID Timestamp\n",
"0 2 1118846980200\n",
"1 2 1118846980300\n",
"2 2 1118846980400\n",
"3 2 1118846980500\n",
"4 2 1118846980600\n",
"5 2 1118846980700\n",
"6 2 1118846980800\n",
"7 2 1118846980900\n",
"8 2 1118846981000\n",
"9 2 1118846981100"
]
},
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],
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"v_ts[:10]"
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{
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"metadata": {
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"text/plain": [
" vID dateTime\n",
"0 2 2005-06-15 14:49:40\n",
"1 2 2005-06-15 14:49:40\n",
"2 2 2005-06-15 14:49:40\n",
"3 2 2005-06-15 14:49:40\n",
"4 2 2005-06-15 14:49:40\n",
"5 2 2005-06-15 14:49:40\n",
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"7 2 2005-06-15 14:49:40\n",
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},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v_dt[:10]"
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},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp\n",
"1118846979700 1\n",
"1118846979800 1\n",
"1118846979900 1\n",
"1118846980000 1\n",
"1118846980100 1\n",
"1118846980200 2\n",
"1118846980300 2\n",
"1118846980400 2\n",
"1118846980500 2\n",
"1118846980600 2\n",
"1118846980700 2\n",
"1118846980800 2\n",
"1118846980900 2\n",
"1118846981000 2\n",
"1118846981100 2\n",
"1118846981200 2\n",
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"1118846981400 2\n",
"1118846981500 2\n",
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"1118846981700 2\n",
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"1118846982000 2\n",
"1118846982100 2\n",
"1118846982200 2\n",
"1118846982300 2\n",
"1118846982400 2\n",
"1118846982500 2\n",
"1118846982600 2\n",
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"1118847025700 2\n",
"1118847025800 2\n",
"1118847025900 2\n",
"1118847026000 2\n",
"1118847026100 2\n",
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"1118847026800 1\n",
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"1118847027000 1\n",
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"1118847027300 1\n",
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"dtype: int64"
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},
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],
"source": [
"v_ts_gr = v_ts.groupby(['Timestamp']).size()\n",
"v_ts_gr"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
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},
"outputs": [
{
"data": {
"text/plain": [
"1"
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},
"execution_count": 52,
"metadata": {},
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],
"source": [
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},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"dateTime\n",
"2005-06-15 14:49:39 3\n",
"2005-06-15 14:49:40 18\n",
"2005-06-15 14:49:41 20\n",
"2005-06-15 14:49:42 20\n",
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"2005-06-15 14:50:13 40\n",
"2005-06-15 14:50:14 40\n",
"2005-06-15 14:50:15 40\n",
"2005-06-15 14:50:16 40\n",
"2005-06-15 14:50:17 40\n",
"2005-06-15 14:50:18 40\n",
"2005-06-15 14:50:19 40\n",
"2005-06-15 14:50:20 40\n",
"2005-06-15 14:50:21 40\n",
"2005-06-15 14:50:22 40\n",
"2005-06-15 14:50:23 39\n",
"2005-06-15 14:50:24 29\n",
"2005-06-15 14:50:25 20\n",
"2005-06-15 14:50:26 18\n",
"2005-06-15 14:50:27 10\n",
"2005-06-15 14:50:28 7\n",
"dtype: int64"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v_dt_gr = v_dt.groupby(['dateTime']).size()\n",
"v_dt_gr"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"40"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v_dt_gr.max()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v_dt_gr.min()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_mean = data.groupby(['vID', 'dateTime']).mean()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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" <th></th>\n",
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" <th>localX</th>\n",
" <th>localY</th>\n",
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" <th>line</th>\n",
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" <th rowspan=\"30\" valign=\"top\">2</th>\n",
" <th>2005-06-15 14:49:40</th>\n",
" <td>16.5</td>\n",
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" <th>2005-06-15 14:49:41</th>\n",
" <td>25.5</td>\n",
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" <th>2005-06-15 14:49:42</th>\n",
" <td>35.5</td>\n",
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" <th>2005-06-15 14:49:43</th>\n",
" <td>45.5</td>\n",
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" <th>2005-06-15 14:49:44</th>\n",
" <td>55.5</td>\n",
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" <th>2005-06-15 14:49:45</th>\n",
" <td>65.5</td>\n",
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" <td>1.118847e+12</td>\n",
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" <th>2005-06-15 14:49:46</th>\n",
" <td>75.5</td>\n",
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" <th>2005-06-15 14:49:47</th>\n",
" <td>85.5</td>\n",
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" <td>16.130900</td>\n",
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" <th>2005-06-15 14:49:48</th>\n",
" <td>95.5</td>\n",
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" <td>0.000</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:49:49</th>\n",
" <td>105.5</td>\n",
" <td>437</td>\n",
" <td>1.118847e+12</td>\n",
" <td>17.410300</td>\n",
" <td>413.588400</td>\n",
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" <td>0.000</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:49:50</th>\n",
" <td>115.5</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:49:51</th>\n",
" <td>125.5</td>\n",
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" <td>1.118847e+12</td>\n",
" <td>17.591300</td>\n",
" <td>499.134600</td>\n",
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" <td>1873015.865700</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:49:52</th>\n",
" <td>135.5</td>\n",
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" <td>17.668100</td>\n",
" <td>535.880900</td>\n",
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" <td>35.005000</td>\n",
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" <td>0.000</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:49:53</th>\n",
" <td>145.5</td>\n",
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" <td>18.431600</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:49:54</th>\n",
" <td>155.5</td>\n",
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" <td>1.118847e+12</td>\n",
" <td>18.072300</td>\n",
" <td>607.279700</td>\n",
" <td>6451546.814900</td>\n",
" <td>1872943.332100</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:49:55</th>\n",
" <td>165.5</td>\n",
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" <td>1.118847e+12</td>\n",
" <td>17.601300</td>\n",
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" <td>6451576.569800</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:49:56</th>\n",
" <td>175.5</td>\n",
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" <td>17.327600</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:49:57</th>\n",
" <td>185.5</td>\n",
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" <td>17.056600</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:49:58</th>\n",
" <td>195.5</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:49:59</th>\n",
" <td>205.5</td>\n",
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" <th>2005-06-15 14:50:00</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:01</th>\n",
" <td>225.5</td>\n",
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" <td>11.653000</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:50:02</th>\n",
" <td>235.5</td>\n",
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" <tr>\n",
" <th>2005-06-15 14:50:03</th>\n",
" <td>245.5</td>\n",
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" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:04</th>\n",
" <td>255.5</td>\n",
" <td>437</td>\n",
" <td>1.118847e+12</td>\n",
" <td>10.852600</td>\n",
" <td>1092.173700</td>\n",
" <td>6451916.415900</td>\n",
" <td>1872629.039300</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>57.238000</td>\n",
" <td>-3.704000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:05</th>\n",
" <td>265.5</td>\n",
" <td>437</td>\n",
" <td>1.118847e+12</td>\n",
" <td>10.917100</td>\n",
" <td>1147.714700</td>\n",
" <td>6451958.253000</td>\n",
" <td>1872592.503000</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>53.398000</td>\n",
" <td>-5.018000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:06</th>\n",
" <td>275.5</td>\n",
" <td>437</td>\n",
" <td>1.118847e+12</td>\n",
" <td>9.421900</td>\n",
" <td>1198.508000</td>\n",
" <td>6451997.439200</td>\n",
" <td>1872560.216400</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>49.199000</td>\n",
" <td>-1.014000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:07</th>\n",
" <td>285.5</td>\n",
" <td>437</td>\n",
" <td>1.118847e+12</td>\n",
" <td>7.389700</td>\n",
" <td>1246.675700</td>\n",
" <td>6452035.007800</td>\n",
" <td>1872529.999700</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>47.242000</td>\n",
" <td>-0.035000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:08</th>\n",
" <td>295.5</td>\n",
" <td>437</td>\n",
" <td>1.118847e+12</td>\n",
" <td>6.532700</td>\n",
" <td>1292.783100</td>\n",
" <td>6452070.273700</td>\n",
" <td>1872500.239700</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>43.434000</td>\n",
" <td>-4.889000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:09</th>\n",
" <td>305.5</td>\n",
" <td>437</td>\n",
" <td>1.118847e+12</td>\n",
" <td>7.086300</td>\n",
" <td>1334.235300</td>\n",
" <td>6452101.140700</td>\n",
" <td>1872472.575300</td>\n",
" <td>14.5</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>40.679000</td>\n",
" <td>-1.014000</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>10.0</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"30\" valign=\"top\">6</th>\n",
" <th>2005-06-15 14:49:59</th>\n",
" <td>205.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>60.513200</td>\n",
" <td>777.939700</td>\n",
" <td>6451647.011100</td>\n",
" <td>1872798.569800</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>44.811000</td>\n",
" <td>-4.032000</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>10.8</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:00</th>\n",
" <td>215.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>58.874100</td>\n",
" <td>821.153600</td>\n",
" <td>6451680.516600</td>\n",
" <td>1872771.288000</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>41.970000</td>\n",
" <td>-1.509000</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>18.0</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:01</th>\n",
" <td>225.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>57.563100</td>\n",
" <td>862.637900</td>\n",
" <td>6451712.530000</td>\n",
" <td>1872744.872800</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>41.707000</td>\n",
" <td>1.206000</td>\n",
" <td>5.5</td>\n",
" <td>2.0</td>\n",
" <td>16.0</td>\n",
" <td>27.492</td>\n",
" <td>0.661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:02</th>\n",
" <td>235.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>56.480500</td>\n",
" <td>903.853500</td>\n",
" <td>6451744.306900</td>\n",
" <td>1872718.384400</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>40.061000</td>\n",
" <td>-1.939000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>54.721</td>\n",
" <td>1.367</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:03</th>\n",
" <td>245.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>55.872400</td>\n",
" <td>943.986400</td>\n",
" <td>6451775.113500</td>\n",
" <td>1872692.352600</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>41.150000</td>\n",
" <td>1.830000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>56.747</td>\n",
" <td>1.381</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:04</th>\n",
" <td>255.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>55.715400</td>\n",
" <td>985.958300</td>\n",
" <td>6451806.879000</td>\n",
" <td>1872664.945800</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>42.401000</td>\n",
" <td>-0.024000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>55.683</td>\n",
" <td>1.313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:05</th>\n",
" <td>265.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>55.637200</td>\n",
" <td>1028.288200</td>\n",
" <td>6451838.825100</td>\n",
" <td>1872637.237100</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>42.047000</td>\n",
" <td>-0.855000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>54.945</td>\n",
" <td>1.308</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:06</th>\n",
" <td>275.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>55.248700</td>\n",
" <td>1069.928900</td>\n",
" <td>6451870.478300</td>\n",
" <td>1872610.178000</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>41.295000</td>\n",
" <td>-0.583000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>57.046</td>\n",
" <td>1.380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:07</th>\n",
" <td>285.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>54.545200</td>\n",
" <td>1111.026700</td>\n",
" <td>6451901.928700</td>\n",
" <td>1872583.713300</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>41.423000</td>\n",
" <td>2.062000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>60.047</td>\n",
" <td>1.449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:08</th>\n",
" <td>295.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>54.876400</td>\n",
" <td>1153.791000</td>\n",
" <td>6451933.917100</td>\n",
" <td>1872555.400300</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>43.778000</td>\n",
" <td>-0.235000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>59.052</td>\n",
" <td>1.350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:09</th>\n",
" <td>305.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>54.569000</td>\n",
" <td>1196.751600</td>\n",
" <td>6451966.367100</td>\n",
" <td>1872527.416200</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>42.155000</td>\n",
" <td>0.572000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>60.828</td>\n",
" <td>1.442</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:10</th>\n",
" <td>315.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>54.747300</td>\n",
" <td>1240.391200</td>\n",
" <td>6451999.070600</td>\n",
" <td>1872498.523400</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>45.431000</td>\n",
" <td>3.154000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>62.434</td>\n",
" <td>1.376</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:11</th>\n",
" <td>325.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>55.769200</td>\n",
" <td>1286.133500</td>\n",
" <td>6452032.807100</td>\n",
" <td>1872467.603700</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>45.293000</td>\n",
" <td>-2.087000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>62.630</td>\n",
" <td>1.384</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:12</th>\n",
" <td>335.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>55.889600</td>\n",
" <td>1330.994600</td>\n",
" <td>6452066.657600</td>\n",
" <td>1872437.888100</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>44.902000</td>\n",
" <td>0.632000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>64.403</td>\n",
" <td>1.433</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:13</th>\n",
" <td>345.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>56.038600</td>\n",
" <td>1375.986300</td>\n",
" <td>6452100.505100</td>\n",
" <td>1872408.238700</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>45.042000</td>\n",
" <td>0.586000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>66.039</td>\n",
" <td>1.468</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:14</th>\n",
" <td>355.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>56.309800</td>\n",
" <td>1422.056000</td>\n",
" <td>6452134.949700</td>\n",
" <td>1872377.870500</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>48.007000</td>\n",
" <td>4.407000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>67.357</td>\n",
" <td>1.405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:15</th>\n",
" <td>365.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>56.590300</td>\n",
" <td>1471.490000</td>\n",
" <td>6452171.797400</td>\n",
" <td>1872345.135700</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>49.465000</td>\n",
" <td>-2.606000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>65.482</td>\n",
" <td>1.324</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:16</th>\n",
" <td>375.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>55.896100</td>\n",
" <td>1519.680400</td>\n",
" <td>6452208.437900</td>\n",
" <td>1872313.835700</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>47.163000</td>\n",
" <td>-0.904000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>65.844</td>\n",
" <td>1.397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:17</th>\n",
" <td>385.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>55.658900</td>\n",
" <td>1567.237500</td>\n",
" <td>6452244.675200</td>\n",
" <td>1872282.472800</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>48.566000</td>\n",
" <td>2.585000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>66.604</td>\n",
" <td>1.370</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:18</th>\n",
" <td>395.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>55.151300</td>\n",
" <td>1616.241500</td>\n",
" <td>6452282.068200</td>\n",
" <td>1872250.729600</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>49.060000</td>\n",
" <td>0.694000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>68.158</td>\n",
" <td>1.390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:19</th>\n",
" <td>405.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>54.399400</td>\n",
" <td>1665.899000</td>\n",
" <td>6452319.387700</td>\n",
" <td>1872219.059000</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>49.930000</td>\n",
" <td>1.253000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>67.749</td>\n",
" <td>1.357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:20</th>\n",
" <td>415.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>53.987300</td>\n",
" <td>1715.876600</td>\n",
" <td>6452357.065800</td>\n",
" <td>1872185.975100</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>49.773000</td>\n",
" <td>-0.221000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>68.493</td>\n",
" <td>1.378</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:21</th>\n",
" <td>425.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>54.792600</td>\n",
" <td>1765.726300</td>\n",
" <td>6452394.768300</td>\n",
" <td>1872152.202300</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>50.669000</td>\n",
" <td>1.756000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>72.646</td>\n",
" <td>1.435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:22</th>\n",
" <td>435.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>55.325800</td>\n",
" <td>1817.404600</td>\n",
" <td>6452433.272900</td>\n",
" <td>1872118.231400</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>53.269000</td>\n",
" <td>2.632000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>77.489</td>\n",
" <td>1.456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:23</th>\n",
" <td>445.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>55.419000</td>\n",
" <td>1872.069200</td>\n",
" <td>6452474.516100</td>\n",
" <td>1872082.188200</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>55.457000</td>\n",
" <td>1.730000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>77.553</td>\n",
" <td>1.397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:24</th>\n",
" <td>455.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>56.863900</td>\n",
" <td>1928.320000</td>\n",
" <td>6452516.763900</td>\n",
" <td>1872044.113300</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>56.300000</td>\n",
" <td>-1.801000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>75.212</td>\n",
" <td>1.336</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:25</th>\n",
" <td>465.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>56.953100</td>\n",
" <td>1983.358600</td>\n",
" <td>6452559.296300</td>\n",
" <td>1872008.186900</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>55.266000</td>\n",
" <td>1.124000</td>\n",
" <td>5.0</td>\n",
" <td>4.0</td>\n",
" <td>14.0</td>\n",
" <td>74.823</td>\n",
" <td>1.356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:26</th>\n",
" <td>475.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>58.597600</td>\n",
" <td>2040.118800</td>\n",
" <td>6452602.654000</td>\n",
" <td>1871970.617000</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>56.359000</td>\n",
" <td>-2.856000</td>\n",
" <td>5.0</td>\n",
" <td>3.2</td>\n",
" <td>14.0</td>\n",
" <td>58.260</td>\n",
" <td>1.024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:27</th>\n",
" <td>485.5</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>58.766600</td>\n",
" <td>2095.723300</td>\n",
" <td>6452646.343000</td>\n",
" <td>1871935.321900</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>56.380000</td>\n",
" <td>3.739000</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2005-06-15 14:50:28</th>\n",
" <td>494.0</td>\n",
" <td>357</td>\n",
" <td>1.118847e+12</td>\n",
" <td>58.119143</td>\n",
" <td>2144.347000</td>\n",
" <td>6452685.030286</td>\n",
" <td>1871905.380000</td>\n",
" <td>14.0</td>\n",
" <td>4.9</td>\n",
" <td>2</td>\n",
" <td>57.262857</td>\n",
" <td>-3.014286</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>14.0</td>\n",
" <td>0.000</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>162 rows × 17 columns</p>\n",
"</div>"
],
"text/plain": [
" frID tFr Timestamp localX localY \\\n",
"vID dateTime \n",
"2 2005-06-15 14:49:40 16.5 437 1.118847e+12 16.395125 49.380625 \n",
" 2005-06-15 14:49:41 25.5 437 1.118847e+12 16.248700 85.049000 \n",
" 2005-06-15 14:49:42 35.5 437 1.118847e+12 16.549500 122.327800 \n",
" 2005-06-15 14:49:43 45.5 437 1.118847e+12 16.078200 158.535100 \n",
" 2005-06-15 14:49:44 55.5 437 1.118847e+12 14.532900 197.567300 \n",
" 2005-06-15 14:49:45 65.5 437 1.118847e+12 15.562200 237.208600 \n",
" 2005-06-15 14:49:46 75.5 437 1.118847e+12 16.657900 278.598900 \n",
" 2005-06-15 14:49:47 85.5 437 1.118847e+12 16.130900 322.998400 \n",
" 2005-06-15 14:49:48 95.5 437 1.118847e+12 16.430600 367.761600 \n",
" 2005-06-15 14:49:49 105.5 437 1.118847e+12 17.410300 413.588400 \n",
" 2005-06-15 14:49:50 115.5 437 1.118847e+12 18.355500 458.081900 \n",
" 2005-06-15 14:49:51 125.5 437 1.118847e+12 17.591300 499.134600 \n",
" 2005-06-15 14:49:52 135.5 437 1.118847e+12 17.668100 535.880900 \n",
" 2005-06-15 14:49:53 145.5 437 1.118847e+12 18.431600 571.199700 \n",
" 2005-06-15 14:49:54 155.5 437 1.118847e+12 18.072300 607.279700 \n",
" 2005-06-15 14:49:55 165.5 437 1.118847e+12 17.601300 646.529900 \n",
" 2005-06-15 14:49:56 175.5 437 1.118847e+12 17.327600 687.665400 \n",
" 2005-06-15 14:49:57 185.5 437 1.118847e+12 17.056600 732.249800 \n",
" 2005-06-15 14:49:58 195.5 437 1.118847e+12 16.456900 777.691900 \n",
" 2005-06-15 14:49:59 205.5 437 1.118847e+12 12.999600 827.392800 \n",
" 2005-06-15 14:50:00 215.5 437 1.118847e+12 11.227900 876.678400 \n",
" 2005-06-15 14:50:01 225.5 437 1.118847e+12 11.653000 926.757400 \n",
" 2005-06-15 14:50:02 235.5 437 1.118847e+12 11.286600 978.255700 \n",
" 2005-06-15 14:50:03 245.5 437 1.118847e+12 10.747200 1034.549600 \n",
" 2005-06-15 14:50:04 255.5 437 1.118847e+12 10.852600 1092.173700 \n",
" 2005-06-15 14:50:05 265.5 437 1.118847e+12 10.917100 1147.714700 \n",
" 2005-06-15 14:50:06 275.5 437 1.118847e+12 9.421900 1198.508000 \n",
" 2005-06-15 14:50:07 285.5 437 1.118847e+12 7.389700 1246.675700 \n",
" 2005-06-15 14:50:08 295.5 437 1.118847e+12 6.532700 1292.783100 \n",
" 2005-06-15 14:50:09 305.5 437 1.118847e+12 7.086300 1334.235300 \n",
"... ... ... ... ... ... \n",
"6 2005-06-15 14:49:59 205.5 357 1.118847e+12 60.513200 777.939700 \n",
" 2005-06-15 14:50:00 215.5 357 1.118847e+12 58.874100 821.153600 \n",
" 2005-06-15 14:50:01 225.5 357 1.118847e+12 57.563100 862.637900 \n",
" 2005-06-15 14:50:02 235.5 357 1.118847e+12 56.480500 903.853500 \n",
" 2005-06-15 14:50:03 245.5 357 1.118847e+12 55.872400 943.986400 \n",
" 2005-06-15 14:50:04 255.5 357 1.118847e+12 55.715400 985.958300 \n",
" 2005-06-15 14:50:05 265.5 357 1.118847e+12 55.637200 1028.288200 \n",
" 2005-06-15 14:50:06 275.5 357 1.118847e+12 55.248700 1069.928900 \n",
" 2005-06-15 14:50:07 285.5 357 1.118847e+12 54.545200 1111.026700 \n",
" 2005-06-15 14:50:08 295.5 357 1.118847e+12 54.876400 1153.791000 \n",
" 2005-06-15 14:50:09 305.5 357 1.118847e+12 54.569000 1196.751600 \n",
" 2005-06-15 14:50:10 315.5 357 1.118847e+12 54.747300 1240.391200 \n",
" 2005-06-15 14:50:11 325.5 357 1.118847e+12 55.769200 1286.133500 \n",
" 2005-06-15 14:50:12 335.5 357 1.118847e+12 55.889600 1330.994600 \n",
" 2005-06-15 14:50:13 345.5 357 1.118847e+12 56.038600 1375.986300 \n",
" 2005-06-15 14:50:14 355.5 357 1.118847e+12 56.309800 1422.056000 \n",
" 2005-06-15 14:50:15 365.5 357 1.118847e+12 56.590300 1471.490000 \n",
" 2005-06-15 14:50:16 375.5 357 1.118847e+12 55.896100 1519.680400 \n",
" 2005-06-15 14:50:17 385.5 357 1.118847e+12 55.658900 1567.237500 \n",
" 2005-06-15 14:50:18 395.5 357 1.118847e+12 55.151300 1616.241500 \n",
" 2005-06-15 14:50:19 405.5 357 1.118847e+12 54.399400 1665.899000 \n",
" 2005-06-15 14:50:20 415.5 357 1.118847e+12 53.987300 1715.876600 \n",
" 2005-06-15 14:50:21 425.5 357 1.118847e+12 54.792600 1765.726300 \n",
" 2005-06-15 14:50:22 435.5 357 1.118847e+12 55.325800 1817.404600 \n",
" 2005-06-15 14:50:23 445.5 357 1.118847e+12 55.419000 1872.069200 \n",
" 2005-06-15 14:50:24 455.5 357 1.118847e+12 56.863900 1928.320000 \n",
" 2005-06-15 14:50:25 465.5 357 1.118847e+12 56.953100 1983.358600 \n",
" 2005-06-15 14:50:26 475.5 357 1.118847e+12 58.597600 2040.118800 \n",
" 2005-06-15 14:50:27 485.5 357 1.118847e+12 58.766600 2095.723300 \n",
" 2005-06-15 14:50:28 494.0 357 1.118847e+12 58.119143 2144.347000 \n",
"\n",
" globalX globalY vLenght vWidth \\\n",
"vID dateTime \n",
"2 2005-06-15 14:49:40 6451147.050750 1873334.595875 14.5 4.9 \n",
" 2005-06-15 14:49:41 6451171.078800 1873308.121600 14.5 4.9 \n",
" 2005-06-15 14:49:42 6451196.268900 1873280.366000 14.5 4.9 \n",
" 2005-06-15 14:49:43 6451221.327700 1873254.149300 14.5 4.9 \n",
" 2005-06-15 14:49:44 6451249.440200 1873226.795300 14.5 4.9 \n",
" 2005-06-15 14:49:45 6451276.553600 1873197.369400 14.5 4.9 \n",
" 2005-06-15 14:49:46 6451305.625800 1873167.561400 14.5 4.9 \n",
" 2005-06-15 14:49:47 6451337.918800 1873137.082800 14.5 4.9 \n",
" 2005-06-15 14:49:48 6451370.431600 1873105.807000 14.5 4.9 \n",
" 2005-06-15 14:49:49 6451403.488500 1873073.998600 14.5 4.9 \n",
" 2005-06-15 14:49:50 6451435.566900 1873043.150300 14.5 4.9 \n",
" 2005-06-15 14:49:51 6451466.487100 1873015.865700 14.5 4.9 \n",
" 2005-06-15 14:49:52 6451493.863000 1872991.230200 14.5 4.9 \n",
" 2005-06-15 14:49:53 6451519.682800 1872967.114800 14.5 4.9 \n",
" 2005-06-15 14:49:54 6451546.814900 1872943.332100 14.5 4.9 \n",
" 2005-06-15 14:49:55 6451576.569800 1872917.526900 14.5 4.9 \n",
" 2005-06-15 14:49:56 6451607.682900 1872890.569600 14.5 4.9 \n",
" 2005-06-15 14:49:57 6451641.366700 1872861.358500 14.5 4.9 \n",
" 2005-06-15 14:49:58 6451675.894900 1872831.836900 14.5 4.9 \n",
" 2005-06-15 14:49:59 6451715.504000 1872801.608200 14.5 4.9 \n",
" 2005-06-15 14:50:00 6451753.675600 1872770.389600 14.5 4.9 \n",
" 2005-06-15 14:50:01 6451791.118300 1872737.034000 14.5 4.9 \n",
" 2005-06-15 14:50:02 6451830.213800 1872703.530700 14.5 4.9 \n",
" 2005-06-15 14:50:03 6451873.033300 1872666.971500 14.5 4.9 \n",
" 2005-06-15 14:50:04 6451916.415900 1872629.039300 14.5 4.9 \n",
" 2005-06-15 14:50:05 6451958.253000 1872592.503000 14.5 4.9 \n",
" 2005-06-15 14:50:06 6451997.439200 1872560.216400 14.5 4.9 \n",
" 2005-06-15 14:50:07 6452035.007800 1872529.999700 14.5 4.9 \n",
" 2005-06-15 14:50:08 6452070.273700 1872500.239700 14.5 4.9 \n",
" 2005-06-15 14:50:09 6452101.140700 1872472.575300 14.5 4.9 \n",
"... ... ... ... ... \n",
"6 2005-06-15 14:49:59 6451647.011100 1872798.569800 14.0 4.9 \n",
" 2005-06-15 14:50:00 6451680.516600 1872771.288000 14.0 4.9 \n",
" 2005-06-15 14:50:01 6451712.530000 1872744.872800 14.0 4.9 \n",
" 2005-06-15 14:50:02 6451744.306900 1872718.384400 14.0 4.9 \n",
" 2005-06-15 14:50:03 6451775.113500 1872692.352600 14.0 4.9 \n",
" 2005-06-15 14:50:04 6451806.879000 1872664.945800 14.0 4.9 \n",
" 2005-06-15 14:50:05 6451838.825100 1872637.237100 14.0 4.9 \n",
" 2005-06-15 14:50:06 6451870.478300 1872610.178000 14.0 4.9 \n",
" 2005-06-15 14:50:07 6451901.928700 1872583.713300 14.0 4.9 \n",
" 2005-06-15 14:50:08 6451933.917100 1872555.400300 14.0 4.9 \n",
" 2005-06-15 14:50:09 6451966.367100 1872527.416200 14.0 4.9 \n",
" 2005-06-15 14:50:10 6451999.070600 1872498.523400 14.0 4.9 \n",
" 2005-06-15 14:50:11 6452032.807100 1872467.603700 14.0 4.9 \n",
" 2005-06-15 14:50:12 6452066.657600 1872437.888100 14.0 4.9 \n",
" 2005-06-15 14:50:13 6452100.505100 1872408.238700 14.0 4.9 \n",
" 2005-06-15 14:50:14 6452134.949700 1872377.870500 14.0 4.9 \n",
" 2005-06-15 14:50:15 6452171.797400 1872345.135700 14.0 4.9 \n",
" 2005-06-15 14:50:16 6452208.437900 1872313.835700 14.0 4.9 \n",
" 2005-06-15 14:50:17 6452244.675200 1872282.472800 14.0 4.9 \n",
" 2005-06-15 14:50:18 6452282.068200 1872250.729600 14.0 4.9 \n",
" 2005-06-15 14:50:19 6452319.387700 1872219.059000 14.0 4.9 \n",
" 2005-06-15 14:50:20 6452357.065800 1872185.975100 14.0 4.9 \n",
" 2005-06-15 14:50:21 6452394.768300 1872152.202300 14.0 4.9 \n",
" 2005-06-15 14:50:22 6452433.272900 1872118.231400 14.0 4.9 \n",
" 2005-06-15 14:50:23 6452474.516100 1872082.188200 14.0 4.9 \n",
" 2005-06-15 14:50:24 6452516.763900 1872044.113300 14.0 4.9 \n",
" 2005-06-15 14:50:25 6452559.296300 1872008.186900 14.0 4.9 \n",
" 2005-06-15 14:50:26 6452602.654000 1871970.617000 14.0 4.9 \n",
" 2005-06-15 14:50:27 6452646.343000 1871935.321900 14.0 4.9 \n",
" 2005-06-15 14:50:28 6452685.030286 1871905.380000 14.0 4.9 \n",
"\n",
" vType veloc accel line pred foll spac \\\n",
"vID dateTime \n",
"2 2005-06-15 14:49:40 2 40.002500 0.031250 2.0 0.0 0.0 0.000 \n",
" 2005-06-15 14:49:41 2 39.026000 -1.853000 2.0 0.0 11.7 0.000 \n",
" 2005-06-15 14:49:42 2 35.444000 -2.795000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:43 2 38.113000 4.215000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:44 2 39.461000 0.598000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:45 2 39.882000 0.205000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:46 2 43.238000 3.943000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:47 2 44.858000 -0.376000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:48 2 45.404000 2.916000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:49 2 45.283000 -1.972000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:50 2 42.870000 -4.140000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:51 2 39.434000 -4.553000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:52 2 35.005000 0.267000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:53 2 35.510000 -0.446000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:54 2 37.734000 4.918000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:55 2 39.891000 0.670000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:56 2 43.209000 4.440000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:57 2 45.000000 0.000000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:58 2 47.360000 6.275000 2.0 0.0 13.0 0.000 \n",
" 2005-06-15 14:49:59 2 49.574000 -4.008000 1.6 0.0 11.8 0.000 \n",
" 2005-06-15 14:50:00 2 50.124000 1.663000 1.0 0.0 10.0 0.000 \n",
" 2005-06-15 14:50:01 2 50.024000 1.104000 1.0 0.0 10.0 0.000 \n",
" 2005-06-15 14:50:02 2 53.912000 6.474000 1.0 0.0 10.0 0.000 \n",
" 2005-06-15 14:50:03 2 57.466000 0.426000 1.0 0.0 10.0 0.000 \n",
" 2005-06-15 14:50:04 2 57.238000 -3.704000 1.0 0.0 10.0 0.000 \n",
" 2005-06-15 14:50:05 2 53.398000 -5.018000 1.0 0.0 10.0 0.000 \n",
" 2005-06-15 14:50:06 2 49.199000 -1.014000 1.0 0.0 10.0 0.000 \n",
" 2005-06-15 14:50:07 2 47.242000 -0.035000 1.0 0.0 10.0 0.000 \n",
" 2005-06-15 14:50:08 2 43.434000 -4.889000 1.0 0.0 10.0 0.000 \n",
" 2005-06-15 14:50:09 2 40.679000 -1.014000 1.0 0.0 10.0 0.000 \n",
"... ... ... ... ... ... ... ... \n",
"6 2005-06-15 14:49:59 2 44.811000 -4.032000 6.0 0.0 10.8 0.000 \n",
" 2005-06-15 14:50:00 2 41.970000 -1.509000 6.0 0.0 18.0 0.000 \n",
" 2005-06-15 14:50:01 2 41.707000 1.206000 5.5 2.0 16.0 27.492 \n",
" 2005-06-15 14:50:02 2 40.061000 -1.939000 5.0 4.0 14.0 54.721 \n",
" 2005-06-15 14:50:03 2 41.150000 1.830000 5.0 4.0 14.0 56.747 \n",
" 2005-06-15 14:50:04 2 42.401000 -0.024000 5.0 4.0 14.0 55.683 \n",
" 2005-06-15 14:50:05 2 42.047000 -0.855000 5.0 4.0 14.0 54.945 \n",
" 2005-06-15 14:50:06 2 41.295000 -0.583000 5.0 4.0 14.0 57.046 \n",
" 2005-06-15 14:50:07 2 41.423000 2.062000 5.0 4.0 14.0 60.047 \n",
" 2005-06-15 14:50:08 2 43.778000 -0.235000 5.0 4.0 14.0 59.052 \n",
" 2005-06-15 14:50:09 2 42.155000 0.572000 5.0 4.0 14.0 60.828 \n",
" 2005-06-15 14:50:10 2 45.431000 3.154000 5.0 4.0 14.0 62.434 \n",
" 2005-06-15 14:50:11 2 45.293000 -2.087000 5.0 4.0 14.0 62.630 \n",
" 2005-06-15 14:50:12 2 44.902000 0.632000 5.0 4.0 14.0 64.403 \n",
" 2005-06-15 14:50:13 2 45.042000 0.586000 5.0 4.0 14.0 66.039 \n",
" 2005-06-15 14:50:14 2 48.007000 4.407000 5.0 4.0 14.0 67.357 \n",
" 2005-06-15 14:50:15 2 49.465000 -2.606000 5.0 4.0 14.0 65.482 \n",
" 2005-06-15 14:50:16 2 47.163000 -0.904000 5.0 4.0 14.0 65.844 \n",
" 2005-06-15 14:50:17 2 48.566000 2.585000 5.0 4.0 14.0 66.604 \n",
" 2005-06-15 14:50:18 2 49.060000 0.694000 5.0 4.0 14.0 68.158 \n",
" 2005-06-15 14:50:19 2 49.930000 1.253000 5.0 4.0 14.0 67.749 \n",
" 2005-06-15 14:50:20 2 49.773000 -0.221000 5.0 4.0 14.0 68.493 \n",
" 2005-06-15 14:50:21 2 50.669000 1.756000 5.0 4.0 14.0 72.646 \n",
" 2005-06-15 14:50:22 2 53.269000 2.632000 5.0 4.0 14.0 77.489 \n",
" 2005-06-15 14:50:23 2 55.457000 1.730000 5.0 4.0 14.0 77.553 \n",
" 2005-06-15 14:50:24 2 56.300000 -1.801000 5.0 4.0 14.0 75.212 \n",
" 2005-06-15 14:50:25 2 55.266000 1.124000 5.0 4.0 14.0 74.823 \n",
" 2005-06-15 14:50:26 2 56.359000 -2.856000 5.0 3.2 14.0 58.260 \n",
" 2005-06-15 14:50:27 2 56.380000 3.739000 5.0 0.0 14.0 0.000 \n",
" 2005-06-15 14:50:28 2 57.262857 -3.014286 5.0 0.0 14.0 0.000 \n",
"\n",
" headway \n",
"vID dateTime \n",
"2 2005-06-15 14:49:40 0.000 \n",
" 2005-06-15 14:49:41 0.000 \n",
" 2005-06-15 14:49:42 0.000 \n",
" 2005-06-15 14:49:43 0.000 \n",
" 2005-06-15 14:49:44 0.000 \n",
" 2005-06-15 14:49:45 0.000 \n",
" 2005-06-15 14:49:46 0.000 \n",
" 2005-06-15 14:49:47 0.000 \n",
" 2005-06-15 14:49:48 0.000 \n",
" 2005-06-15 14:49:49 0.000 \n",
" 2005-06-15 14:49:50 0.000 \n",
" 2005-06-15 14:49:51 0.000 \n",
" 2005-06-15 14:49:52 0.000 \n",
" 2005-06-15 14:49:53 0.000 \n",
" 2005-06-15 14:49:54 0.000 \n",
" 2005-06-15 14:49:55 0.000 \n",
" 2005-06-15 14:49:56 0.000 \n",
" 2005-06-15 14:49:57 0.000 \n",
" 2005-06-15 14:49:58 0.000 \n",
" 2005-06-15 14:49:59 0.000 \n",
" 2005-06-15 14:50:00 0.000 \n",
" 2005-06-15 14:50:01 0.000 \n",
" 2005-06-15 14:50:02 0.000 \n",
" 2005-06-15 14:50:03 0.000 \n",
" 2005-06-15 14:50:04 0.000 \n",
" 2005-06-15 14:50:05 0.000 \n",
" 2005-06-15 14:50:06 0.000 \n",
" 2005-06-15 14:50:07 0.000 \n",
" 2005-06-15 14:50:08 0.000 \n",
" 2005-06-15 14:50:09 0.000 \n",
"... ... \n",
"6 2005-06-15 14:49:59 0.000 \n",
" 2005-06-15 14:50:00 0.000 \n",
" 2005-06-15 14:50:01 0.661 \n",
" 2005-06-15 14:50:02 1.367 \n",
" 2005-06-15 14:50:03 1.381 \n",
" 2005-06-15 14:50:04 1.313 \n",
" 2005-06-15 14:50:05 1.308 \n",
" 2005-06-15 14:50:06 1.380 \n",
" 2005-06-15 14:50:07 1.449 \n",
" 2005-06-15 14:50:08 1.350 \n",
" 2005-06-15 14:50:09 1.442 \n",
" 2005-06-15 14:50:10 1.376 \n",
" 2005-06-15 14:50:11 1.384 \n",
" 2005-06-15 14:50:12 1.433 \n",
" 2005-06-15 14:50:13 1.468 \n",
" 2005-06-15 14:50:14 1.405 \n",
" 2005-06-15 14:50:15 1.324 \n",
" 2005-06-15 14:50:16 1.397 \n",
" 2005-06-15 14:50:17 1.370 \n",
" 2005-06-15 14:50:18 1.390 \n",
" 2005-06-15 14:50:19 1.357 \n",
" 2005-06-15 14:50:20 1.378 \n",
" 2005-06-15 14:50:21 1.435 \n",
" 2005-06-15 14:50:22 1.456 \n",
" 2005-06-15 14:50:23 1.397 \n",
" 2005-06-15 14:50:24 1.336 \n",
" 2005-06-15 14:50:25 1.356 \n",
" 2005-06-15 14:50:26 1.024 \n",
" 2005-06-15 14:50:27 0.000 \n",
" 2005-06-15 14:50:28 0.000 \n",
"\n",
"[162 rows x 17 columns]"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_mean #Mean values for the data grouped by vID and dateTime"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"num_v = data.groupby(['vID']).size()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"vID\n",
"2 437\n",
"4 351\n",
"5 452\n",
"6 357\n",
"dtype: int64"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_v #foe vID how many times it appears"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_v.count() #num of different vehicles"
]
},
{
"cell_type": "code",
"execution_count": 61,
"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>vID</th>\n",
" <th>frID</th>\n",
" <th>tFr</th>\n",
" <th>Timestamp</th>\n",
" <th>localX</th>\n",
" <th>localY</th>\n",
" <th>globalX</th>\n",
" <th>globalY</th>\n",
" <th>vLenght</th>\n",
" <th>vWidth</th>\n",
" <th>vType</th>\n",
" <th>veloc</th>\n",
" <th>accel</th>\n",
" <th>line</th>\n",
" <th>pred</th>\n",
" <th>foll</th>\n",
" <th>spac</th>\n",
" <th>headway</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1.597000e+03</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1597</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" <td>1597.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>4.182843</td>\n",
" <td>267.204133</td>\n",
" <td>404.460238</td>\n",
" <td>1.118847e+12</td>\n",
" <td>40.166214</td>\n",
" <td>1114.140043</td>\n",
" <td>6451915.031800</td>\n",
" <td>1872594.324443</td>\n",
" <td>15.425485</td>\n",
" <td>5.749092</td>\n",
" <td>2</td>\n",
" <td>46.853181</td>\n",
" <td>0.427790</td>\n",
" <td>3.865373</td>\n",
" <td>0.706324</td>\n",
" <td>9.244208</td>\n",
" <td>11.302549</td>\n",
" <td>0.241935</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.496573</td>\n",
" <td>123.996664</td>\n",
" <td>45.413270</td>\n",
" <td>1.239967e+04</td>\n",
" <td>18.506289</td>\n",
" <td>559.918912</td>\n",
" <td>419.122022</td>\n",
" <td>372.802068</td>\n",
" <td>1.209852</td>\n",
" <td>1.351837</td>\n",
" <td>0</td>\n",
" <td>7.709628</td>\n",
" <td>4.989681</td>\n",
" <td>1.647519</td>\n",
" <td>1.525733</td>\n",
" <td>3.987194</td>\n",
" <td>24.613803</td>\n",
" <td>0.523286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>2.000000</td>\n",
" <td>8.000000</td>\n",
" <td>351.000000</td>\n",
" <td>1.118847e+12</td>\n",
" <td>6.263000</td>\n",
" <td>35.381000</td>\n",
" <td>6451122.815000</td>\n",
" <td>1871894.687000</td>\n",
" <td>14.000000</td>\n",
" <td>4.900000</td>\n",
" <td>2</td>\n",
" <td>32.640000</td>\n",
" <td>-11.200000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</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>2.000000</td>\n",
" <td>172.000000</td>\n",
" <td>357.000000</td>\n",
" <td>1.118847e+12</td>\n",
" <td>17.813000</td>\n",
" <td>664.639000</td>\n",
" <td>6451572.038000</td>\n",
" <td>1872291.603000</td>\n",
" <td>14.500000</td>\n",
" <td>4.900000</td>\n",
" <td>2</td>\n",
" <td>40.710000</td>\n",
" <td>-1.460000</td>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>6.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>5.000000</td>\n",
" <td>272.000000</td>\n",
" <td>437.000000</td>\n",
" <td>1.118847e+12</td>\n",
" <td>42.818000</td>\n",
" <td>1102.629000</td>\n",
" <td>6451903.487000</td>\n",
" <td>1872598.226000</td>\n",
" <td>16.000000</td>\n",
" <td>4.900000</td>\n",
" <td>2</td>\n",
" <td>45.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.000000</td>\n",
" <td>0.000000</td>\n",
" <td>8.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>5.000000</td>\n",
" <td>371.000000</td>\n",
" <td>452.000000</td>\n",
" <td>1.118847e+12</td>\n",
" <td>54.753000</td>\n",
" <td>1572.348000</td>\n",
" <td>6452258.323000</td>\n",
" <td>1872883.465000</td>\n",
" <td>17.000000</td>\n",
" <td>7.900000</td>\n",
" <td>2</td>\n",
" <td>51.360000</td>\n",
" <td>2.660000</td>\n",
" <td>5.000000</td>\n",
" <td>0.000000</td>\n",
" <td>13.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>6.000000</td>\n",
" <td>497.000000</td>\n",
" <td>452.000000</td>\n",
" <td>1.118847e+12</td>\n",
" <td>71.498000</td>\n",
" <td>2161.090000</td>\n",
" <td>6452704.608000</td>\n",
" <td>1873344.962000</td>\n",
" <td>17.000000</td>\n",
" <td>7.900000</td>\n",
" <td>2</td>\n",
" <td>70.030000</td>\n",
" <td>11.200000</td>\n",
" <td>7.000000</td>\n",
" <td>4.000000</td>\n",
" <td>18.000000</td>\n",
" <td>78.580000</td>\n",
" <td>1.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" vID frID tFr Timestamp localX \\\n",
"count 1597.000000 1597.000000 1597.000000 1.597000e+03 1597.000000 \n",
"mean 4.182843 267.204133 404.460238 1.118847e+12 40.166214 \n",
"std 1.496573 123.996664 45.413270 1.239967e+04 18.506289 \n",
"min 2.000000 8.000000 351.000000 1.118847e+12 6.263000 \n",
"25% 2.000000 172.000000 357.000000 1.118847e+12 17.813000 \n",
"50% 5.000000 272.000000 437.000000 1.118847e+12 42.818000 \n",
"75% 5.000000 371.000000 452.000000 1.118847e+12 54.753000 \n",
"max 6.000000 497.000000 452.000000 1.118847e+12 71.498000 \n",
"\n",
" localY globalX globalY vLenght vWidth \\\n",
"count 1597.000000 1597.000000 1597.000000 1597.000000 1597.000000 \n",
"mean 1114.140043 6451915.031800 1872594.324443 15.425485 5.749092 \n",
"std 559.918912 419.122022 372.802068 1.209852 1.351837 \n",
"min 35.381000 6451122.815000 1871894.687000 14.000000 4.900000 \n",
"25% 664.639000 6451572.038000 1872291.603000 14.500000 4.900000 \n",
"50% 1102.629000 6451903.487000 1872598.226000 16.000000 4.900000 \n",
"75% 1572.348000 6452258.323000 1872883.465000 17.000000 7.900000 \n",
"max 2161.090000 6452704.608000 1873344.962000 17.000000 7.900000 \n",
"\n",
" vType veloc accel line pred foll \\\n",
"count 1597 1597.000000 1597.000000 1597.000000 1597.000000 1597.000000 \n",
"mean 2 46.853181 0.427790 3.865373 0.706324 9.244208 \n",
"std 0 7.709628 4.989681 1.647519 1.525733 3.987194 \n",
"min 2 32.640000 -11.200000 1.000000 0.000000 0.000000 \n",
"25% 2 40.710000 -1.460000 2.000000 0.000000 6.000000 \n",
"50% 2 45.000000 0.000000 4.000000 0.000000 8.000000 \n",
"75% 2 51.360000 2.660000 5.000000 0.000000 13.000000 \n",
"max 2 70.030000 11.200000 7.000000 4.000000 18.000000 \n",
"\n",
" spac headway \n",
"count 1597.000000 1597.000000 \n",
"mean 11.302549 0.241935 \n",
"std 24.613803 0.523286 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 0.000000 \n",
"50% 0.000000 0.000000 \n",
"75% 0.000000 0.000000 \n",
"max 78.580000 1.500000 "
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"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
}
| gpl-3.0 |
sgkang/DamGeophysics | notebook/DCinversionCCdam.ipynb | 1 | 20059 | {
"cells": [
{
"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"
]
}
],
"source": [
"import sys\n",
"sys.path.append(\"../codes/\")\n",
"from Readfiles import getFnames\n",
"from DCdata import readReservoirDC\n",
"%pylab inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from SimPEG.EM.Static import DC\n",
"from SimPEG import EM\n",
"from SimPEG import Mesh"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read DC data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "plot_pseudoSection() got an unexpected keyword argument 'dtype'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-3-8b6282311090>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdobsAppres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msurvey\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEM\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStatic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUtils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStaticUtils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_pseudoSection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msurvey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'volt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msameratio\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[0m\u001b[1;32m 6\u001b[0m \u001b[0mcb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mcb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Apprent resistivity (ohm-m)\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: plot_pseudoSection() got an unexpected keyword argument 'dtype'"
]
},
{
"data": {
"image/png": 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zZn2f/s9M8rSq+s0iY44D2LNnz4DlSYObnZ1lZmZmpctQCzjXNCrONY2Kc02jMC8THDes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\nhiBJkiRJrWIIkiRJktQq/wdeeXXM7CZkKAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1045a83d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fname = \"../data/ChungCheonDC/201507210000001.apr\"\n",
"survey = readReservoirDC(fname)\n",
"dobsAppres = survey.dobs\n",
"fig, ax = plt.subplots(1,1, figsize = (10, 2))\n",
"dat = EM.Static.Utils.StaticUtils.plot_pseudoSection(survey, ax, dtype='volt', sameratio=False)\n",
"cb = dat[2]\n",
"cb.set_label(\"Apprent resistivity (ohm-m)\")\n",
"geom = np.hstack(dat[3])\n",
"dobsDC = dobsAppres * geom "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# problem = DC.Problem2D_CC(mesh)\n",
"cs = 2.5\n",
"npad = 6\n",
"hx = [(cs,npad, -1.3),(cs,160),(cs,npad, 1.3)]\n",
"hy = [(cs,npad, -1.3),(cs,20)]\n",
"mesh = Mesh.TensorMesh([hx, hy])\n",
"mesh = Mesh.TensorMesh([hx, hy],x0=[-mesh.hx[:6].sum()-0.25, -mesh.hy.sum()])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def from3Dto2Dsurvey(survey):\n",
" srcLists2D = []\n",
" nSrc = len(survey.srcList)\n",
"\n",
" for iSrc in range (nSrc):\n",
" src = survey.srcList[iSrc]\n",
" locsM = np.c_[src.rxList[0].locs[0][:,0], np.ones_like(src.rxList[0].locs[0][:,0])*-0.75] \n",
" locsN = np.c_[src.rxList[0].locs[1][:,0], np.ones_like(src.rxList[0].locs[1][:,0])*-0.75] \n",
" rx = DC.Rx.Dipole_ky(locsM, locsN)\n",
" locA = np.r_[src.loc[0][0], -0.75]\n",
" locB = np.r_[src.loc[1][0], -0.75]\n",
" src = DC.Src.Dipole([rx], locA, locB)\n",
" srcLists2D.append(src)\n",
" survey2D = DC.Survey_ky(srcLists2D)\n",
" return survey2D"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from SimPEG import (Mesh, Maps, Utils, DataMisfit, Regularization,\n",
" Optimization, Inversion, InvProblem, Directives)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"mapping = Maps.ExpMap(mesh)\n",
"survey2D = from3Dto2Dsurvey(survey)\n",
"problem = DC.Problem2D_N(mesh, mapping=mapping)\n",
"problem.pair(survey2D)\n",
"m0 = np.ones(mesh.nC)*np.log(1e-2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from ipywidgets import interact\n",
"nSrc = len(survey2D.srcList)\n",
"def foo(isrc):\n",
" figsize(10, 5)\n",
" mesh.plotImage(np.ones(mesh.nC)*np.nan, gridOpts={\"color\":\"k\", \"alpha\":0.5}, grid=True)\n",
"# isrc=0\n",
" src = survey2D.srcList[isrc]\n",
" plt.plot(src.loc[0][0], src.loc[0][1], 'bo')\n",
" plt.plot(src.loc[1][0], src.loc[1][1], 'ro')\n",
" locsM = src.rxList[0].locs[0]\n",
" locsN = src.rxList[0].locs[1]\n",
" plt.plot(locsM[:,0], locsM[:,1], 'ko')\n",
" plt.plot(locsN[:,0], locsN[:,1], 'go')\n",
" plt.gca().set_aspect('equal', adjustable='box')\n",
" \n",
"interact(foo, isrc=(0, nSrc-1, 1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pred = survey2D.dpred(m0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# data_anal = []\n",
"# nSrc = len(survey.srcList)\n",
"# for isrc in range(nSrc):\n",
"# src = survey.srcList[isrc] \n",
"# locA = src.loc[0]\n",
"# locB = src.loc[1]\n",
"# locsM = src.rxList[0].locs[0]\n",
"# locsN = src.rxList[0].locs[1]\n",
"# rxloc=[locsM, locsN]\n",
"# a = EM.Analytics.DCAnalyticHalf(locA, rxloc, 1e-3, earth_type=\"halfspace\")\n",
"# b = EM.Analytics.DCAnalyticHalf(locB, rxloc, 1e-3, earth_type=\"halfspace\")\n",
"# data_anal.append(a-b)\n",
"# data_anal = np.hstack(data_anal)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"survey.dobs = pred\n",
"fig, ax = plt.subplots(1,1, figsize = (10, 2))\n",
"dat = EM.Static.Utils.StaticUtils.plot_pseudoSection(survey, ax, dtype='appr', sameratio=False, scale=\"linear\", clim=(0, 200))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"out = hist(np.log10(abs(dobsDC)), bins = 100)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print np.log10(abs(dobsDC))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"weight = 1./abs(mesh.gridCC[:,1])**1.5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mesh.plotImage(np.log10(weight))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"survey2D.dobs = dobsDC\n",
"survey2D.eps = 10**(-2.3)\n",
"survey2D.std = 0.02\n",
"dmisfit = DataMisfit.l2_DataMisfit(survey2D)\n",
"regmap = Maps.IdentityMap(nP=int(mesh.nC))\n",
"reg = Regularization.Simple(mesh,mapping=regmap,cell_weights=weight)\n",
"# ITERATION OF inversion\n",
"opt = Optimization.InexactGaussNewton(maxIter=15)\n",
"\n",
"invProb = InvProblem.BaseInvProblem(dmisfit, reg, opt)\n",
"# Create an inversion object\n",
"beta = Directives.BetaSchedule(coolingFactor=5, coolingRate=2)\n",
"betaest = Directives.BetaEstimate_ByEig(beta0_ratio=1e0)\n",
"inv = Inversion.BaseInversion(invProb, directiveList=[beta, betaest])\n",
"problem.counter = opt.counter = Utils.Counter()\n",
"opt.LSshorten = 0.5\n",
"opt.remember('xc')\n",
"mopt = inv.run(m0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xc = opt.recall(\"xc\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax = plt.subplots(1,1, figsize = (10, 1.5))\n",
"iteration = 8\n",
"sigma = mapping*xc[iteration]\n",
"dat = mesh.plotImage(1./sigma, clim=(10, 250),grid=False, ax=ax, pcolorOpts={\"cmap\":\"jet\"})\n",
"ax.set_ylim(-50, 0)\n",
"ax.set_xlim(-10, 290)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print np.log10(sigma).min(), np.log10(sigma).max()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"survey.dobs = invProb.dpred\n",
"fig, ax = plt.subplots(1,1, figsize = (10, 2))\n",
"dat = EM.Static.Utils.StaticUtils.plot_pseudoSection(survey, ax, dtype='appr', sameratio=False, clim=(40, 170))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"survey.dobs = dobsDC\n",
"fig, ax = plt.subplots(1,1, figsize = (10, 2))\n",
"dat = EM.Static.Utils.StaticUtils.plot_pseudoSection(survey, ax, dtype='appr', sameratio=False, clim=(40, 170))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"survey.dobs = abs(dmisfit.Wd*(dobsDC-invProb.dpred))\n",
"fig, ax = plt.subplots(1,1, figsize = (10, 2))\n",
"dat = EM.Static.Utils.StaticUtils.plot_pseudoSection(survey, ax, dtype='volt', sameratio=False, clim=(0, 2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# sigma = np.ones(mesh.nC)\n",
"modelname = \"sigma0721re.npy\"\n",
"np.save(modelname, sigma)"
]
},
{
"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 [default]",
"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 |
ppyht2/tf-exercise | 008. RNN for MNIST/.ipynb_checkpoints/MNIST RNN-checkpoint.ipynb | 1 | 6322 | {
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import tensorflow as tf \n",
"import matplotlib.pyplot as plt \n",
"import numpy as np\n",
"import tensorflow.contrib.rnn as rnn\n",
"\n",
"% matplotlib inline\n",
"plt.style.use('ggplot')\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
"Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
"Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
]
}
],
"source": [
"from tensorflow.examples.tutorials.mnist import input_data\n",
"mnist = input_data.read_data_sets(\"MNIST_data\", one_hot = True)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"learning_rate = 1e-4\n",
"n_epoch = 10\n",
"epoch_size = 100\n",
"n_iter = n_epoch * epoch_size\n",
"\n",
"batch_size = 100\n",
"\n",
"n_input = 28\n",
"n_output = 10\n",
"n_steps = 28\n",
"n_hidden = 128"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"\n",
"x = tf.placeholder(tf.float32, [None, n_steps, n_input])\n",
"y = tf.placeholder(tf.float32, [None, n_output])\n",
"\n",
"W = tf.Variable(tf.truncated_normal([n_hidden, n_output]),dtype=tf.float32)\n",
"b = tf.Variable(tf.truncated_normal([n_output]), dtype=tf.float32)\n",
"\n",
"x_unstack = tf.unstack(x, n_steps, 1)\n",
"\n",
"\n",
"lstm_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)\n",
"\n",
"outputs, states = rnn.static_rnn(lstm_cell, x_unstack, dtype=tf.float32)\n",
"\n",
"\n",
"h = tf.matmul(outputs[-1], W) + b\n",
"\n",
"cost =tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=h))\n",
"training_steps = tf.train.AdamOptimizer(learning_rate).minimize(cost)\n",
"\n",
"correct_pred = tf.equal(tf.argmax(y, 1), tf.argmax(h,1))\n",
"accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 0 Batch Accuracy: 6.00%\n",
"Epoch: 1 Batch Accuracy: 52.00%\n",
"Epoch: 2 Batch Accuracy: 64.00%\n",
"Epoch: 3 Batch Accuracy: 74.00%\n",
"Epoch: 4 Batch Accuracy: 71.00%\n",
"Epoch: 5 Batch Accuracy: 79.00%\n",
"Epoch: 6 Batch Accuracy: 76.00%\n",
"Epoch: 7 Batch Accuracy: 89.00%\n",
"Epoch: 8 Batch Accuracy: 84.00%\n",
"Epoch: 9 Batch Accuracy: 93.00%\n",
"Epoch: 10 Batch Accuracy: 93.00%\n",
"CPU times: user 16.9 s, sys: 784 ms, total: 17.7 s\n",
"Wall time: 15.1 s\n"
]
}
],
"source": [
"%%time\n",
"sess = tf.InteractiveSession()\n",
"tf.global_variables_initializer().run()\n",
"\n",
"\n",
"for iter in range(n_iter+1):\n",
" xs, ys = mnist.train.next_batch(batch_size)\n",
" xs = xs.reshape((batch_size, n_steps, n_input))\n",
" sess.run(training_steps, feed_dict={x:xs, y:ys})\n",
" \n",
" if iter % epoch_size == 0:\n",
" print('Epoch: {} Batch Accuracy: {:4.2f}%'\n",
" .format(int(iter/epoch_size),\n",
" 100*accuracy.eval(feed_dict={x:xs, y:ys})))\n"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IndexError",
"evalue": "tuple index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-41-8be648660221>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maccuracy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtest_y\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Test Accuracy: {4.2f}%'\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\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: tuple index out of range"
]
}
],
"source": [
"test_x = mnist.test.images\n",
"test_y = mnist.test.labels\n",
"\n",
"test_x = test_x.reshape((len(test_y), n_steps, n_input))\n",
"a = accuracy.eval(feed_dict={x:test_x, y:test_y})\n",
"\n",
"print('Test Accuracy: {:4.2f}%' .format(a))"
]
},
{
"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
}
| gpl-3.0 |
emmatoday/PyClimateGraphs | notebooks/SeaIce_Antarctic.ipynb | 1 | 128540 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Global Sea Ice Extent Graph\n",
"\n",
"This code will download and render a current graph of the global sea ice extent. Beginning in 1979 until yesterday (based on availability of the raw data from the NSIDC).\n",
"\n",
"### Location of the requisite data files (in case you need to manually download them):\n",
"\n",
"[ftp://sidads.colorado.edu:21/DATASETS/NOAA/G02135/south/daily/data/S_seaice_extent_daily_v2.1.csv](ftp://sidads.colorado.edu:21/DATASETS/NOAA/G02135/south/daily/data/S_seaice_extent_daily_v2.1.csv)\n",
"\n",
"[ftp://sidads.colorado.edu:21/DATASETS/NOAA/G02135/north/daily/data/N_seaice_extent_daily_v2.1.csv](ftp://sidads.colorado.edu:21/DATASETS/NOAA/G02135/north/daily/data/N_seaice_extent_daily_v2.1.csv)\n"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Load all needed libraries\n",
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.dates as mdates\n",
"import matplotlib.pyplot as plt\n",
"from scipy import stats\n",
"import datetime\n",
"import os\n",
"import math\n",
"import calendar\n",
"import urllib\n",
"import shutil\n",
"\n",
"# Set up some constants\n",
"LOCAL_DATA_PATH = './data'\n",
"LOCAL_OUTPUT_PATH = './output'\n",
"DATA_URLS = {\n",
" 's': 'ftp://sidads.colorado.edu:21/DATASETS/NOAA/G02135/south/daily/data/S_seaice_extent_daily_v2.1.csv',\n",
" 'n': 'ftp://sidads.colorado.edu:21/DATASETS/NOAA/G02135/north/daily/data/N_seaice_extent_daily_v2.1.csv'\n",
"}\n",
"\n",
"pd.set_option('display.max_rows', 5)\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Check if necessary directories exist\n",
"def mkdir_if_necessary(dir):\n",
" try:\n",
" os.stat(dir)\n",
" except:\n",
" os.mkdir(dir) \n",
"\n",
"# Check if our data files exist already\n",
"def data_files_exist():\n",
" for key, url in DATA_URLS.items():\n",
" filename = os.path.split(urllib.parse.urlsplit(url).path)[-1]\n",
" if os.path.isfile(os.path.join(LOCAL_DATA_PATH, filename)):\n",
" return True\n",
" else:\n",
" return False\n",
"\n",
"# Load the data from disk and return the dataframes\n",
"def load_data_files():\n",
" sea_ice_indexes = {}\n",
" for key, url in DATA_URLS.items():\n",
" filename = os.path.split(urllib.parse.urlsplit(url).path)[-1]\n",
" sea_ice_indexes[key] = pd.read_csv(os.path.join(LOCAL_DATA_PATH, filename), skiprows=[1])\n",
" for key in sea_ice_indexes.keys():\n",
" sea_ice_indexes[key].rename(columns=lambda x: x.strip(), inplace=True)\n",
" sea_ice_indexes[key]['Date'] = sea_ice_indexes[key].apply(lambda row: datetime.date(\n",
" row['Year'], row['Month'], row['Day']), axis=1)\n",
" sea_ice_indexes[key]['Day of Year'] = sea_ice_indexes[key].apply(lambda row: row['Date'].timetuple().tm_yday, axis=1)\n",
" sea_ice_indexes[key]['Date'] = pd.to_datetime(sea_ice_indexes[key]['Date'])\n",
" sea_ice_indexes['n'] = sea_ice_indexes['n'][['Extent', 'Date']]\n",
" sea_ice_indexes['s'].rename(columns={'Extent': 'S Extent'}, inplace=True)\n",
" sea_ice_indexes['n'].rename(columns={'Extent': 'N Extent'}, inplace=True)\n",
" return sea_ice_indexes\n",
" \n",
"# Check if our data appears to be out of date\n",
"def data_is_fresh(sea_ice_indexes):\n",
" for key, sea_ice_index in sea_ice_indexes.items():\n",
" today = datetime.date.today()\n",
" print('Data is {0} day(s) old'.format((datetime.datetime.now() - sea_ice_index['Date'].iloc[-1]).days))\n",
" if (datetime.datetime.now() - sea_ice_index['Date'].iloc[-1]).days >= 2:\n",
" return False\n",
" else:\n",
" return True\n",
"\n",
"# Update to latest data files\n",
"def refresh_data_files():\n",
" for key, url in DATA_URLS.items():\n",
" filename = os.path.join(LOCAL_DATA_PATH, os.path.split(urllib.parse.urlsplit(url).path)[-1])\n",
" with urllib.request.urlopen(url) as response, open(filename, 'wb') as out_file:\n",
" shutil.copyfileobj(response, out_file)\n",
"\n",
"# Prepare and load the data files downloading and updating as necessary\n",
"def prep_data_files():\n",
" mkdir_if_necessary(LOCAL_DATA_PATH)\n",
" mkdir_if_necessary(LOCAL_OUTPUT_PATH)\n",
"\n",
" if data_files_exist():\n",
" print('Data files exist')\n",
" sea_ice_indexes = load_data_files()\n",
" if data_is_fresh(sea_ice_indexes):\n",
" print('Data files are up to date')\n",
" else:\n",
" print('Data files are outdated')\n",
" refresh_data_files()\n",
" sea_ice_indexes = load_data_files()\n",
" print('Data files have been updated')\n",
" else:\n",
" print('No data files found')\n",
" refresh_data_files()\n",
" sea_ice_indexes = load_data_files()\n",
" print('Data files have been downloaded')\n",
" return sea_ice_indexes\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data files exist\n",
"Data is 1 day(s) old\n",
"Data files are up to date\n"
]
}
],
"source": [
"# Prepare and load the data files downloading and updating as necessary\n",
"sea_ice_indexes = prep_data_files()\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"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>Year</th>\n",
" <th>Month</th>\n",
" <th>Day</th>\n",
" <th>S Extent</th>\n",
" <th>Missing</th>\n",
" <th>Source Data</th>\n",
" <th>Date</th>\n",
" <th>Day of Year</th>\n",
" <th>N Extent</th>\n",
" <th>Total Extent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1978</td>\n",
" <td>10</td>\n",
" <td>26</td>\n",
" <td>17.624</td>\n",
" <td>0.0</td>\n",
" <td>['ftp://sidads.colorado.edu/pub/DATASETS/nsid...</td>\n",
" <td>1978-10-26</td>\n",
" <td>299</td>\n",
" <td>10.231</td>\n",
" <td>27.855</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1978</td>\n",
" <td>10</td>\n",
" <td>28</td>\n",
" <td>17.803</td>\n",
" <td>0.0</td>\n",
" <td>['ftp://sidads.colorado.edu/pub/DATASETS/nsid...</td>\n",
" <td>1978-10-28</td>\n",
" <td>301</td>\n",
" <td>10.420</td>\n",
" <td>28.223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12326</th>\n",
" <td>2017</td>\n",
" <td>1</td>\n",
" <td>31</td>\n",
" <td>2.488</td>\n",
" <td>0.0</td>\n",
" <td>['ftp://sidads.colorado.edu/pub/DATASETS/nsidc...</td>\n",
" <td>2017-01-31</td>\n",
" <td>31</td>\n",
" <td>13.774</td>\n",
" <td>16.262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12327</th>\n",
" <td>2017</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>2.475</td>\n",
" <td>0.0</td>\n",
" <td>['ftp://sidads.colorado.edu/pub/DATASETS/nsidc...</td>\n",
" <td>2017-02-01</td>\n",
" <td>32</td>\n",
" <td>13.821</td>\n",
" <td>16.296</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>12328 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" Year Month Day S Extent Missing \\\n",
"0 1978 10 26 17.624 0.0 \n",
"1 1978 10 28 17.803 0.0 \n",
"... ... ... ... ... ... \n",
"12326 2017 1 31 2.488 0.0 \n",
"12327 2017 2 1 2.475 0.0 \n",
"\n",
" Source Data Date \\\n",
"0 ['ftp://sidads.colorado.edu/pub/DATASETS/nsid... 1978-10-26 \n",
"1 ['ftp://sidads.colorado.edu/pub/DATASETS/nsid... 1978-10-28 \n",
"... ... ... \n",
"12326 ['ftp://sidads.colorado.edu/pub/DATASETS/nsidc... 2017-01-31 \n",
"12327 ['ftp://sidads.colorado.edu/pub/DATASETS/nsidc... 2017-02-01 \n",
"\n",
" Day of Year N Extent Total Extent \n",
"0 299 10.231 27.855 \n",
"1 301 10.420 28.223 \n",
"... ... ... ... \n",
"12326 31 13.774 16.262 \n",
"12327 32 13.821 16.296 \n",
"\n",
"[12328 rows x 10 columns]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_sea_ice_index = pd.merge(left=sea_ice_indexes['s'],\n",
" right=sea_ice_indexes['n'],\n",
" on='Date')\n",
"\n",
"global_sea_ice_index['Total Extent'] = global_sea_ice_index['S Extent'] + global_sea_ice_index['N Extent']\n",
"\n",
"global_sea_ice_index\n"
]
},
{
"cell_type": "code",
"execution_count": 49,
"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>Year</th>\n",
" <th>S Extent</th>\n",
" <th>Day of Year</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Day of Year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>299</th>\n",
" <td>1978</td>\n",
" <td>17.624</td>\n",
" <td>299</td>\n",
" </tr>\n",
" <tr>\n",
" <th>301</th>\n",
" <td>1978</td>\n",
" <td>17.803</td>\n",
" <td>301</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2017</td>\n",
" <td>2.488</td>\n",
" <td>31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>2017</td>\n",
" <td>2.475</td>\n",
" <td>32</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>12328 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" Year S Extent Day of Year\n",
"Day of Year \n",
"299 1978 17.624 299\n",
"301 1978 17.803 301\n",
"... ... ... ...\n",
"31 2017 2.488 31\n",
"32 2017 2.475 32\n",
"\n",
"[12328 rows x 3 columns]"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"global_sea_ice_index.index = global_sea_ice_index['Day of Year']\n",
"global_sea_ice_index.drop(['Missing','Source Data','Day','Date','Month','N Extent','Total Extent'], axis=1, inplace=True)\n",
"global_sea_ice_index.index = global_sea_ice_index.index.astype(datetime.datetime)\n",
"\n",
"global_sea_ice_index\n"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
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9iiWwxhhjjDFmv5IV68CKyETgN8BJQBx4Cvi6qjaLyCnAz3B3olgL/ExV/zRs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wMXSSnLs203AncAEeAIqmBBSggxB7WirAMe4E4p5cNCiFxUTvyibPvzUsp/nPjZTBzZfU3XA2mU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0xB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\n7fxhwn0gnMsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fbd000846d8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for key, grp in global_sea_ice_index.groupby(['Year']):\n",
" if (key >= 1979 and key <= 2018) or key >= 2016:\n",
" if key == 2017:\n",
" plt.plot(grp['S Extent'], label=key, color=\"#ff00bb\")\n",
" elif key == 2016:\n",
" plt.plot(grp['S Extent'], label=key, color=\"#cc00ff\")\n",
" else:\n",
" plt.plot(grp['S Extent'], label=key)\n",
"\n",
"plt.title('Antarctic Sea Ice Extent (Updated {0})'.format(datetime.date.today().isoformat()))\n",
"plt.xlabel(\"Month\")\n",
"plt.xlim(1,366)\n",
"plt.xticks(np.linspace(1,366,13), calendar.month_abbr[1:13], rotation=0)\n",
"plt.legend(loc='best')\n",
"plt.legend(bbox_to_anchor=(1.35, 1), ncol=2, fontsize=8)\n",
"plt.ylabel(\"Sea Ice Extent (10^6 sq. km)\")\n",
"\n",
"plt.savefig('output/antarctic_sea_ice_{0}.png'.format(datetime.date.today().isoformat()),\n",
" dpi=300, facecolor='w', edgecolor='w',\n",
" orientation='portrait', papertype=None, format='png',\n",
" transparent=False, bbox_inches='tight', pad_inches=0.1,\n",
" frameon=None)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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": 1
}
| mit |
RoebideBruijn/datascience-intensive-course | exercises/statistics project 2/sliderule_dsi_inferential_statistics_exercise_2.ipynb | 1 | 52570 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Examining racial discrimination in the US job market\n",
"\n",
"#### Background\n",
"Racial discrimination continues to be pervasive in cultures throughout the world. Researchers examined the level of racial discrimination in the United States labor market by randomly assigning identical résumés black-sounding or white-sounding names and observing the impact on requests for interviews from employers.\n",
"\n",
"#### Data\n",
"In the dataset provided, each row represents a resume. The 'race' column has two values, 'b' and 'w', indicating black-sounding and white-sounding. The column 'call' has two values, 1 and 0, indicating whether the resume received a call from employers or not.\n",
"\n",
"Note that the 'b' and 'w' values in race are assigned randomly to the resumes.\n",
"\n",
"#### Exercise\n",
"You will perform a statistical analysis to establish whether race has a significant impact on the rate of callbacks for resumes.\n",
"\n",
"Answer the following questions **in this notebook below and submit to your Github account**. \n",
"\n",
" 1. What test is appropriate for this problem? Does CLT apply?\n",
" 2. What are the null and alternate hypotheses?\n",
" 3. Compute margin of error, confidence interval, and p-value.\n",
" 4. Discuss statistical significance.\n",
"\n",
"You can include written notes in notebook cells using Markdown: \n",
" - In the control panel at the top, choose Cell > Cell Type > Markdown\n",
" - Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n",
"\n",
"\n",
"#### Resources\n",
"+ Experiment information and data source: http://www.povertyactionlab.org/evaluation/discrimination-job-market-united-states\n",
"+ Scipy statistical methods: http://docs.scipy.org/doc/scipy/reference/stats.html \n",
"+ Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n",
"\n",
"****"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline \n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"from scipy import stats\n",
"import seaborn as sns\n",
"from matplotlib import pyplot as plt\n",
"\n",
"sns.set_style('white')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = pd.io.stata.read_stata('data/us_job_market_discrimination.dta')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"157.0\n",
"235.0\n"
]
},
{
"data": {
"text/plain": [
"78.0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# number of callbacks for black-sounding names\n",
"print(sum(data[data.race=='b'].call))\n",
"# number of callbacks for white-sounding names\n",
"print(sum(data[data.race=='w'].call))\n",
"# difference\n",
"sum(data[data.race=='w'].call) - sum(data[data.race=='b'].call)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1109bb908>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x110bec390>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"2435\n",
"2435\n"
]
}
],
"source": [
"sns.countplot(data.race)\n",
"plt.show()\n",
"sns.countplot(data.call)\n",
"plt.show()\n",
"print(sum(data.race == 'w'))\n",
"print(sum(data.race == 'b'))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 1. A permutation test to see whether the difference can be based on coincidence. \n",
"# No, CLT does not apply, there are only 2 values, not multiple values from which you extract a mean and std.\n",
"# We can use the permuted distribution which will be normally distributed and CLT will apply there. (more than 30 samples)\n",
"# On the other hand we could see it as a proportion of callbacks for two populations with n=2435 and k=#calls\n",
"# In that way CLT does apply. n>30, hence assume normal distribution. So do Z-test.\n",
"# Can't find a package that made a Z-test, hence I'll be using the T-test instead (gives similar results with many samples)\n",
"\n",
"# 2. H0: Race has no effect on callbask. H1: race has an effect on callback.\n",
"# The question is whether race has a significant impact, not whether being black has a significant impact,\n",
"# hence test is two-sided."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Permutation"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from numpy.random import permutation\n",
"\n",
"def permutate(X):\n",
" new_array = permutation(X)\n",
" return sum(new_array[0:2435]) - sum(new_array[2435::]) # calculate difference between first group and second\n",
"\n",
"difference = [] \n",
"for i in range(0,100000):\n",
" difference.append(permutate(data.call))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-38. 38.]\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ro.d.bruijn/anaconda/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n",
" y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x110f7b550>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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zjqcRRD42cj09xQehPlNtVuVEq1Ehe2EM/lrVhpZOC+ZyuVuaRRjqRD7kcoto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\nfnjUc9u3bxdLS0tFURTFX/7yl+Jnn30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"text/plain": [
"<matplotlib.figure.Figure at 0x110c27eb8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Confidence interval 95%, \n",
"# our result is very much outside the confidence interval of the difference between two groups\n",
"print(np.percentile(difference, [2.5, 97.5]))\n",
"sns.distplot(difference) # permuted data, normally distributed (CLT applies on this)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"37.0668740963\n"
]
},
{
"data": {
"text/plain": [
"37.969659999999998"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Margin of error with Z-table\n",
"# Critical value is 1.96 in the Z-statistic for 0.95% (more than 30 samples and not skewed, hence normally distributed)\n",
"print(1.96 * np.std(difference)) # hence our value is outside the margin of error\n",
"# margin of error is the difference between the border of the confidence interval and the mean,\n",
"# which is 0 in this case, hence margin of error that calculated that way is 38.\n",
"np.percentile(difference, [2.5, 97.5])[1] - np.mean(difference)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"0.0\n"
]
}
],
"source": [
"diff = sum(data[data.race=='w'].call) - sum(data[data.race=='b'].call)\n",
"times = sum(difference > diff) + sum(difference < -diff) # times the difference is bigger than the found difference\n",
"print(times)\n",
"print(times / 100000) # p-value, hence clearly significant"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Alle measurements lead to the conclusion that it's very unlikely that our value would come from the permuted distribution.\n",
"# Therefore race is concluded to have an effect on callback."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### T-test"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.032032854209445585"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nw = sum(data.race == 'w')\n",
"nb = sum(data.race == 'b')\n",
"kw = sum(data[data.race=='w'].call)\n",
"kb = sum(data[data.race=='b'].call)\n",
"pw = kw/nw\n",
"pb = kb/nb\n",
"pw - pb # difference in means"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"Ttest_indResult(statistic=4.1147052908617514, pvalue=3.9408021031288859e-05)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# You should actually use the Z-test, since it's normally distributed and over 30 samples, \n",
"# but T-test gives similar results.\n",
"from scipy.stats import ttest_ind\n",
"\n",
"ttest_ind(data[data.race=='w'].call, data[data.race=='b'].call) # p-value clearly significant"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.0167774478596\n",
"0.0472882605593\n"
]
}
],
"source": [
"# 95% confidence interval\n",
"print((pw - pb) - 1.96 * np.sqrt(((pw*(1-pw))/nw) + ((pb*(1-pb))/nb))) # lower limit\n",
"print((pw - pb) + 1.96 * np.sqrt(((pw*(1-pw))/nw) + ((pb*(1-pb))/nb))) # upper limit\n",
"# No difference: 0, lies outside the confidence interval, hence race seems to have an effect"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.015255406349886438"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# margin of error\n",
"(pw - pb) + 1.96 * np.sqrt(((pw*(1-pw))/nw) + ((pb*(1-pb))/nb)) - (pw - pb)\n",
"# Our mean is 0.03, hence outside the margin of error, if the true mean would be 0."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Also from this calculation it's clear that it's very likely that race has an effect on callback."
]
}
],
"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 |
alfkjartan/control-computarizado | polynomial-design/notebooks/HW5-controller-design-by-pole-placement.ipynb | 1 | 4512 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import sympy as sy\n",
"import control.matlab as cm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"z = sy.symbols('z', real=False)\n",
"hh,a,r1,s0,s1 = sy.symbols('h,a,r1,s0,s1')\n",
"tau = 0.262\n",
"pc1 = -5-1j*5\n",
"pc2 = np.conjugate(pc1)\n",
"h = 0.04\n",
"hpt = h/tau\n",
"Km = 0.746*41.8\n",
"A2p = sy.poly((z-np.exp(h*pc1))*(z-np.exp(h*pc2)), z)\n",
"Acp = sy.poly((z-np.exp(h*pc1))*(z-np.exp(h*pc2))*(z - sy.exp(h*a)), z)\n",
"Ap = sy.poly((z-1)*(z-np.exp(-hpt)), z)\n",
"Bp = sy.poly(Km*tau*(hpt-1+np.exp(-hpt))*z + Km*tau*(1-np.exp(-hpt)-hpt*np.exp(-hpt)), z)\n",
"Rp = sy.poly(z+r1, z)\n",
"Sp = sy.poly(s0*z + s1, z)\n",
"dioph=(Ap*Rp+Bp*Sp-Acp).all_coeffs()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-0.878368264722988*exp(0.04*a) + 0.115597706410771\n",
"-1.34327624390048*exp(0.04*a) + 1.52396058689998\n",
"0.972399357505391*exp(0.04*a) - 1.15308370050492\n",
"0.370876886395060\n"
]
}
],
"source": [
"sol=sy.solve(dioph, (r1,s0,s1))\n",
"print sol[r1]\n",
"print sol[s0]\n",
"print sol[s1]\n",
"\n",
"t0 = A2p.evalf(subs={z:1})/Bp.evalf(subs={z:1,})\n",
"print t0"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
" 0.001339 z + 0.001247\n",
"----------------------\n",
"z^2 - 1.807 z + 0.8071\n",
"\n",
"dt = 0.03\n",
"\n",
"\n",
" 0.001339 z + 0.001247\n",
"----------------------\n",
"z^2 - 1.807 z + 0.8071\n",
"\n",
"dt = 0.03\n",
"\n"
]
}
],
"source": [
"\n",
"G = Km * cm.tf([1], [tau, 1, 0])\n",
"Gd = Km * cm.tf([tau*(hpt-1+np.exp(-hpt)), tau*(1-(1+hpt)*np.exp(-hpt))], [1, -(1+np.exp(-hpt)), np.exp(-hpt)], h)\n",
"Gd2 = cm.c2d(G, h)\n",
"print Gd\n",
"print Gd2"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Poly(z**2 - 1.58555290309441*z - (-0.792776451547206 + 0.168974310731771*I)*(0.792776451547206 + 0.168974310731771*I), z, domain='EX')\n",
"0.0714939167206446\n",
"Poly(0.00133942860759726*z + 0.00124712240506047, z, domain='RR')\n",
"0.00258655101265772\n"
]
}
],
"source": [
"print A2p\n",
"print A2p.evalf(subs={z:1})\n",
"print Bp\n",
"print Bp.evalf(subs={z:1})\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.042426406871192847"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0.3/(5*np.sqrt(2))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.16897431073177133"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.exp(-0.21)*np.sin(0.21)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.65704681981505675"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.exp(0.03*(-14))"
]
},
{
"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": 1
}
| mit |
ffpenaloza/AstroExp | tarea5/.ipynb_checkpoints/tarea5-checkpoint.ipynb | 1 | 74749 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tarea 5\n",
"\n",
"Luego de descargar las imágenes en los filtros F475W y F850LP del objeto VCC1316 (M87) se siguen los pasos de la primera tarea para generar el catálogo.\n",
"De [Sirianni et. al (2005)](http://adsabs.harvard.edu/abs/2005PASP..117.1049S]) se obtiene la escala de WFC (0.05''/px) y los zeropoint (según la tabla 10) y se ejecuta Sextractor.\n",
"Se corrige por apertura según la tabla 6 (2 pixeles de radio a escala de 0.05''/px corresponden a 0.1'') para longitudes de onda efectivas iguales a las longitudes de onda centrales.\n",
"Se corrige por reddening según la tabla 14 y el valor E(B-V) de 0.023 de [Côté et al. (2006)](http://iopscience.iop.org/article/10.1086/504042/meta), tabla 1.\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from astropy.io import fits\n",
"import numpy as np\n",
"\n",
"f475 = fits.open('hst_9401_02_acs_wfc_f475w_drz.fits')\n",
"f850 = fits.open('hst_9401_02_acs_wfc_f850lp_drz.fits')\n",
"\n",
"f475[1].writeto('sci_f475w_m87.fits',clobber=True)\n",
"f475[2].writeto('invvar_f475w_m87.fits',clobber=True)\n",
"\n",
"f850[1].writeto('sci_f850lp_m87.fits',clobber=True)\n",
"f850[2].writeto('invvar_f850lp_m87.fits',clobber=True)\n",
"\n",
"f475w.close()\n",
"f850lp.close()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1M> \n",
"\u001b[1A----- SExtractor 2.19.5 started on 2016-11-24 at 18:32:27 with 1 thread\n",
"\n",
"\u001b[1M> Setting catalog parameters\n",
"\u001b[1A\u001b[1M> Reading detection filter\n",
"\u001b[1A\u001b[1M> Initializing catalog\n",
"\u001b[1A\u001b[1M> Looking for sci_f475w_m87.fits\n",
"\u001b[1A----- Measuring from: sci_f475w_m87.fits [1/1]\n",
" \"Unnamed\" / no ext. header / 4238x4213 / 32 bits (floats)\n",
"\u001b[1M> Looking for invvar_f475w_m87.fits\n",
"\u001b[1A----- Weighting from: invvar_f475w_m87.fits [1/1]\n",
" \"Unnamed\" / no ext. header / 4238x4213 / 32 bits (floats)\n",
"Detection+Measurement image: \u001b[1M> Setting up background maps\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 64\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 128\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 192\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 256\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 320\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 384\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 448\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 512\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 576\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 640\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 704\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 768\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 832\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 896\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 960\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1024\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1088\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1152\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1216\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1280\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1344\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1408\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1472\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1536\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1600\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1664\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1728\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1792\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1856\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1920\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1984\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2048\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2112\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2176\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2240\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2304\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2368\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2432\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2496\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2560\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2624\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2688\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2752\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2816\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2880\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2944\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3008\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3072\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3136\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3200\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3264\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3328\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3392\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3456\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3520\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3584\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3648\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3712\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3776\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3840\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3904\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3968\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 4032\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 4096\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 4160\n",
"\u001b[1A\u001b[1M> Filtering background map(s)\n",
"\u001b[1A\u001b[1M> Computing background d-map\n",
"\u001b[1A\u001b[1M> Computing background-noise d-map\n",
"\u001b[1A(M+D) Background: 0.167588 RMS: 0.0131859 / Threshold: 0.0395578 \n",
"\u001b[1M> Scanning image\n",
"\u001b[1A\u001b[1M> Line: 25 Objects: 0 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 50 Objects: 3 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 75 Objects: 6 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 100 Objects: 13 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 125 Objects: 28 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 150 Objects: 39 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 175 Objects: 48 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 200 Objects: 60 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 225 Objects: 69 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 250 Objects: 74 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 275 Objects: 82 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 300 Objects: 96 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 325 Objects: 106 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 350 Objects: 120 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 375 Objects: 137 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 400 Objects: 152 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 425 Objects: 165 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 450 Objects: 177 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 475 Objects: 191 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 500 Objects: 205 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 525 Objects: 215 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 550 Objects: 225 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 575 Objects: 241 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 600 Objects: 267 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 625 Objects: 279 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 650 Objects: 290 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 675 Objects: 303 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 700 Objects: 314 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 725 Objects: 325 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 750 Objects: 338 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 775 Objects: 347 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 800 Objects: 355 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 825 Objects: 365 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 850 Objects: 381 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 875 Objects: 390 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 900 Objects: 400 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 925 Objects: 415 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 950 Objects: 426 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 975 Objects: 443 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1000 Objects: 457 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1025 Objects: 473 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1026 Objects: 473 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1050 Objects: 496 detected / 4 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1075 Objects: 508 detected / 10 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1100 Objects: 523 detected / 19 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1125 Objects: 533 detected / 30 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1150 Objects: 540 detected / 38 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1175 Objects: 563 detected / 50 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1200 Objects: 581 detected / 61 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1225 Objects: 598 detected / 69 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1250 Objects: 620 detected / 74 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1275 Objects: 639 detected / 81 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1300 Objects: 661 detected / 88 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1325 Objects: 683 detected / 95 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1350 Objects: 699 detected / 106 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1375 Objects: 717 detected / 112 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1400 Objects: 743 detected / 124 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1425 Objects: 759 detected / 135 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1450 Objects: 788 detected / 147 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1475 Objects: 816 detected / 163 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1500 Objects: 839 detected / 174 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1525 Objects: 857 detected / 185 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1550 Objects: 878 detected / 195 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1575 Objects: 897 detected / 214 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1600 Objects: 921 detected / 236 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1625 Objects: 945 detected / 248 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1650 Objects: 964 detected / 257 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1675 Objects: 995 detected / 270 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1700 Objects: 1027 detected / 281 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1725 Objects: 1062 detected / 290 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1750 Objects: 1112 detected / 302 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1775 Objects: 1159 detected / 311 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1800 Objects: 1245 detected / 321 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1825 Objects: 1382 detected / 331 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1850 Objects: 1559 detected / 342 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1875 Objects: 1705 detected / 352 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1900 Objects: 1815 detected / 363 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1925 Objects: 1895 detected / 373 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1950 Objects: 1972 detected / 386 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1968 Objects: 2031 detected / 400 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1975 Objects: 2045 detected / 405 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2000 Objects: 2126 detected / 420 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2025 Objects: 2212 detected / 436 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2050 Objects: 2289 detected / 450 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2075 Objects: 2391 detected / 464 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2100 Objects: 2471 detected / 476 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2125 Objects: 2547 detected / 488 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2150 Objects: 2659 detected / 496 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2175 Objects: 2735 detected / 518 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2200 Objects: 2794 detected / 537 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2225 Objects: 2848 detected / 555 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2250 Objects: 2895 detected / 574 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2275 Objects: 2933 detected / 593 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2300 Objects: 2986 detected / 611 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2325 Objects: 3051 detected / 633 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2350 Objects: 3154 detected / 654 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2375 Objects: 3303 detected / 672 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2400 Objects: 3420 detected / 692 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2425 Objects: 3519 detected / 710 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2450 Objects: 3586 detected / 735 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2475 Objects: 3637 detected / 759 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2500 Objects: 3680 detected / 784 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2525 Objects: 3720 detected / 800 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2527 Objects: 3723 detected / 800 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2550 Objects: 3779 detected / 821 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2575 Objects: 3812 detected / 835 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2600 Objects: 3840 detected / 860 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2625 Objects: 3864 detected / 880 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2650 Objects: 3889 detected / 898 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2675 Objects: 3906 detected / 923 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2700 Objects: 3927 detected / 952 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2725 Objects: 3953 detected / 977 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2750 Objects: 3972 detected / 1006 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2775 Objects: 3997 detected / 1040 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2800 Objects: 4021 detected / 1093 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2825 Objects: 4042 detected / 1167 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2840 Objects: 4051 detected / 1200 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2850 Objects: 4066 detected / 1238 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2875 Objects: 4094 detected / 1302 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2900 Objects: 4116 detected / 1357 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2925 Objects: 4140 detected / 1398 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2950 Objects: 4159 detected / 1446 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2975 Objects: 4182 detected / 1480 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3000 Objects: 4200 detected / 1517 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3025 Objects: 4218 detected / 1546 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3050 Objects: 4233 detected / 1574 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3075 Objects: 4251 detected / 1593 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3083 Objects: 4258 detected / 1600 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3100 Objects: 4277 detected / 1617 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3125 Objects: 4286 detected / 1646 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3150 Objects: 4306 detected / 1679 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3175 Objects: 4327 detected / 1702 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3200 Objects: 4345 detected / 1730 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3225 Objects: 4365 detected / 1753 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3250 Objects: 4381 detected / 1776 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3275 Objects: 4398 detected / 1797 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3300 Objects: 4411 detected / 1823 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3325 Objects: 4425 detected / 1860 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3350 Objects: 4443 detected / 1908 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3375 Objects: 4459 detected / 1963 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3396 Objects: 4476 detected / 2000 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3400 Objects: 4479 detected / 2013 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3425 Objects: 4493 detected / 2091 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3450 Objects: 4510 detected / 2133 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3475 Objects: 4523 detected / 2173 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3500 Objects: 4544 detected / 2212 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3525 Objects: 4557 detected / 2247 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3550 Objects: 4569 detected / 2280 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3575 Objects: 4581 detected / 2321 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3600 Objects: 4595 detected / 2348 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3625 Objects: 4606 detected / 2371 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3650 Objects: 4618 detected / 2392 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3667 Objects: 4625 detected / 2400 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3675 Objects: 4633 detected / 2407 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3700 Objects: 4645 detected / 2426 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3725 Objects: 4665 detected / 2449 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3750 Objects: 4684 detected / 2473 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3775 Objects: 4695 detected / 2498 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3800 Objects: 4700 detected / 2516 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3825 Objects: 4713 detected / 2533 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3850 Objects: 4727 detected / 2559 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3875 Objects: 4737 detected / 2589 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3900 Objects: 4747 detected / 2615 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3925 Objects: 4763 detected / 2630 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3950 Objects: 4771 detected / 2656 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3975 Objects: 4785 detected / 2678 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4000 Objects: 4796 detected / 2690 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4025 Objects: 4809 detected / 2710 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4050 Objects: 4813 detected / 2725 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4075 Objects: 4819 detected / 2745 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4100 Objects: 4823 detected / 2762 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4125 Objects: 4827 detected / 2775 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4150 Objects: 4829 detected / 2797 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4156 Objects: 4830 detected / 2800 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4175 Objects: 4830 detected / 2814 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4200 Objects: 4833 detected / 2834 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4213 Objects: 4833 detected / 3200 sextracted\n",
"\u001b[1A Objects: detected 4833 / sextracted 3306 \n",
"\n",
"\u001b[1M> Closing files\n",
"\u001b[1A\u001b[1M> \n",
"\u001b[1A> All done (in 2.6 s: 1646.3 lines/s , 1291.8 detections/s)\n",
"\u001b[1M> \n",
"\u001b[1A----- SExtractor 2.19.5 started on 2016-11-24 at 18:32:30 with 1 thread\n",
"\n",
"\u001b[1M> Setting catalog parameters\n",
"\u001b[1A\u001b[1M> Reading detection filter\n",
"\u001b[1A\u001b[1M> Initializing catalog\n",
"\u001b[1A\u001b[1M> Looking for sci_f850lp_m87.fits\n",
"\u001b[1A----- Measuring from: sci_f850lp_m87.fits [1/1]\n",
" \"Unnamed\" / no ext. header / 4238x4213 / 32 bits (floats)\n",
"\u001b[1M> Looking for invvar_f850lp_m87.fits\n",
"\u001b[1A----- Weighting from: invvar_f850lp_m87.fits [1/1]\n",
" \"Unnamed\" / no ext. header / 4238x4213 / 32 bits (floats)\n",
"Detection+Measurement image: \u001b[1M> Setting up background maps\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 64\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 128\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 192\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 256\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 320\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 384\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 448\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 512\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 576\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 640\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 704\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 768\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 832\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 896\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 960\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1024\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1088\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1152\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1216\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1280\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1344\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1408\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1472\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1536\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1600\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1664\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1728\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1792\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1856\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1920\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 1984\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2048\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2112\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2176\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2240\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2304\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2368\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2432\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2496\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2560\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2624\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2688\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2752\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2816\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2880\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 2944\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3008\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3072\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3136\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3200\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3264\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3328\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3392\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3456\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3520\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3584\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3648\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3712\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3776\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3840\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3904\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 3968\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 4032\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 4096\n",
"\u001b[1A\u001b[1M> Setting up background map at line: 4160\n",
"\u001b[1A\u001b[1M> Filtering background map(s)\n",
"\u001b[1A\u001b[1M> Computing background d-map\n",
"\u001b[1A\u001b[1M> Computing background-noise d-map\n",
"\u001b[1A(M+D) Background: 0.175354 RMS: 0.0133454 / Threshold: 0.0400361 \n",
"\u001b[1M> Scanning image\n",
"\u001b[1A\u001b[1M> Line: 25 Objects: 0 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 50 Objects: 2 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 75 Objects: 4 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 100 Objects: 8 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 125 Objects: 18 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 150 Objects: 27 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 175 Objects: 37 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 200 Objects: 49 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 225 Objects: 56 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 250 Objects: 60 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 275 Objects: 71 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 300 Objects: 81 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 325 Objects: 89 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 350 Objects: 101 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 375 Objects: 112 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 400 Objects: 122 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 425 Objects: 137 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 450 Objects: 145 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 475 Objects: 157 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 500 Objects: 166 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 525 Objects: 174 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 550 Objects: 181 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 575 Objects: 195 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 600 Objects: 214 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 625 Objects: 224 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 650 Objects: 233 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 675 Objects: 241 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 700 Objects: 253 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 725 Objects: 264 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 750 Objects: 272 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 775 Objects: 282 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 800 Objects: 292 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 825 Objects: 299 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 850 Objects: 311 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 875 Objects: 321 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 900 Objects: 328 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 925 Objects: 338 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 950 Objects: 348 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 975 Objects: 360 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1000 Objects: 372 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1022 Objects: 387 detected / 0 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1025 Objects: 390 detected / 3 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1050 Objects: 405 detected / 4 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1075 Objects: 414 detected / 9 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1100 Objects: 428 detected / 14 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1125 Objects: 436 detected / 22 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1150 Objects: 445 detected / 27 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1175 Objects: 462 detected / 38 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1200 Objects: 485 detected / 48 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1225 Objects: 502 detected / 54 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1250 Objects: 528 detected / 60 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1275 Objects: 553 detected / 67 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1300 Objects: 586 detected / 69 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1325 Objects: 603 detected / 75 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1350 Objects: 623 detected / 86 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1375 Objects: 644 detected / 91 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1400 Objects: 676 detected / 99 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1425 Objects: 694 detected / 107 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1450 Objects: 726 detected / 118 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1475 Objects: 755 detected / 130 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1500 Objects: 798 detected / 137 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1525 Objects: 835 detected / 146 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1550 Objects: 888 detected / 155 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1575 Objects: 943 detected / 172 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1600 Objects: 991 detected / 189 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1625 Objects: 1050 detected / 195 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1650 Objects: 1114 detected / 203 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1675 Objects: 1178 detected / 212 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1700 Objects: 1267 detected / 225 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1725 Objects: 1386 detected / 236 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1750 Objects: 1539 detected / 246 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1775 Objects: 1714 detected / 254 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1800 Objects: 1889 detected / 266 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1825 Objects: 2035 detected / 274 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1850 Objects: 2166 detected / 283 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1875 Objects: 2282 detected / 292 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1900 Objects: 2380 detected / 299 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1925 Objects: 2497 detected / 306 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1950 Objects: 2583 detected / 316 sextracted\n",
"\u001b[1A\u001b[1M> Line: 1975 Objects: 2682 detected / 329 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2000 Objects: 2790 detected / 342 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2025 Objects: 2889 detected / 357 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2050 Objects: 3009 detected / 372 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2075 Objects: 3100 detected / 381 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2100 Objects: 3214 detected / 388 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2125 Objects: 3350 detected / 399 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2132 Objects: 3383 detected / 400 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2150 Objects: 3486 detected / 411 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2175 Objects: 3595 detected / 427 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2200 Objects: 3703 detected / 451 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2225 Objects: 3810 detected / 466 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2250 Objects: 3883 detected / 488 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2275 Objects: 3958 detected / 507 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2300 Objects: 4040 detected / 528 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2325 Objects: 4136 detected / 554 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2350 Objects: 4263 detected / 576 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2375 Objects: 4407 detected / 595 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2400 Objects: 4601 detected / 617 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2425 Objects: 4812 detected / 636 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2450 Objects: 5026 detected / 657 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2475 Objects: 5192 detected / 681 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2500 Objects: 5332 detected / 708 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2525 Objects: 5432 detected / 737 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2550 Objects: 5539 detected / 763 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2575 Objects: 5626 detected / 786 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2588 Objects: 5659 detected / 800 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2600 Objects: 5694 detected / 814 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2625 Objects: 5774 detected / 847 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2650 Objects: 5833 detected / 873 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2675 Objects: 5875 detected / 909 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2700 Objects: 5923 detected / 947 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2725 Objects: 5963 detected / 991 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2750 Objects: 6004 detected / 1059 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2775 Objects: 6048 detected / 1135 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2797 Objects: 6076 detected / 1200 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2800 Objects: 6086 detected / 1213 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2825 Objects: 6106 detected / 1279 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2850 Objects: 6136 detected / 1329 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2875 Objects: 6154 detected / 1372 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2900 Objects: 6173 detected / 1403 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2925 Objects: 6197 detected / 1444 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2950 Objects: 6215 detected / 1480 sextracted\n",
"\u001b[1A\u001b[1M> Line: 2975 Objects: 6237 detected / 1529 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3000 Objects: 6257 detected / 1565 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3019 Objects: 6269 detected / 1600 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3025 Objects: 6273 detected / 1608 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3050 Objects: 6288 detected / 1657 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3075 Objects: 6302 detected / 1697 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3100 Objects: 6321 detected / 1752 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3125 Objects: 6332 detected / 1800 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3150 Objects: 6351 detected / 1854 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3175 Objects: 6368 detected / 1906 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3200 Objects: 6381 detected / 1942 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3225 Objects: 6399 detected / 1976 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3247 Objects: 6411 detected / 2000 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3250 Objects: 6414 detected / 2002 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3275 Objects: 6428 detected / 2033 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3300 Objects: 6440 detected / 2068 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3325 Objects: 6452 detected / 2108 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3350 Objects: 6465 detected / 2160 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3375 Objects: 6481 detected / 2229 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3400 Objects: 6497 detected / 2314 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3417 Objects: 6503 detected / 2400 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3425 Objects: 6505 detected / 2449 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3450 Objects: 6520 detected / 2563 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3475 Objects: 6532 detected / 2662 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3500 Objects: 6545 detected / 2753 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3518 Objects: 6552 detected / 2800 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3525 Objects: 6555 detected / 2820 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3550 Objects: 6564 detected / 2890 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3575 Objects: 6573 detected / 2955 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3600 Objects: 6581 detected / 3011 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3625 Objects: 6592 detected / 3058 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3650 Objects: 6601 detected / 3102 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3675 Objects: 6613 detected / 3138 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3700 Objects: 6622 detected / 3176 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3716 Objects: 6631 detected / 3200 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3725 Objects: 6632 detected / 3211 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3750 Objects: 6650 detected / 3256 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3775 Objects: 6661 detected / 3295 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3800 Objects: 6667 detected / 3320 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3825 Objects: 6678 detected / 3344 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3850 Objects: 6688 detected / 3370 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3875 Objects: 6694 detected / 3390 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3900 Objects: 6699 detected / 3412 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3925 Objects: 6711 detected / 3431 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3950 Objects: 6714 detected / 3452 sextracted\n",
"\u001b[1A\u001b[1M> Line: 3975 Objects: 6720 detected / 3468 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4000 Objects: 6728 detected / 3484 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4025 Objects: 6738 detected / 3503 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4050 Objects: 6744 detected / 3518 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4075 Objects: 6748 detected / 3530 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4100 Objects: 6752 detected / 3544 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4125 Objects: 6756 detected / 3556 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4150 Objects: 6757 detected / 3572 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4175 Objects: 6759 detected / 3591 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4192 Objects: 6759 detected / 3600 sextracted\n",
"\u001b[1A\u001b[1M> Line: 4200 Objects: 6760 detected / 3604 sextracted\n",
"\u001b[1A Objects: detected 6760 / sextracted 3971 \n",
"\n",
"\u001b[1M> Closing files\n",
"\u001b[1A\u001b[1M> \n",
"\u001b[1A> All done (in 2.9 s: 1437.4 lines/s , 1354.8 detections/s)\n"
]
}
],
"source": [
"!sextractor sci_f475w_m87.fits -c f475w.sex\n",
"!sextractor sci_f850lp_m87.fits -c f850lp.sex"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from astropy import units as u\n",
"from astropy.coordinates import SkyCoord\n",
"\n",
"# Se cargan listas con RA y DEC para cada imagen\n",
"RA475 = np.loadtxt('f475w.cat',usecols=(3,))\n",
"DE475 = np.loadtxt('f475w.cat',usecols=(4,))\n",
"\n",
"RA850 = np.loadtxt('f850lp.cat',usecols=(3,))\n",
"DE850 = np.loadtxt('f850lp.cat',usecols=(4,))\n",
"\n",
"# Match por parte de astropy. El catalogo del filtro f850lp contiene mas objetos\n",
"catalog = SkyCoord(ra=RA475*u.degree, dec=DE475*u.degree) \n",
"c = SkyCoord(ra=RA850*u.degree, dec=DE850*u.degree) \n",
"idx = c.match_to_catalog_sky(catalog)\n",
"\n",
"# Del catalogo f475w.cat se extraen las filas que indica el match\n",
"matches = list(idx[0])\n",
"f475w = np.loadtxt('f475w.cat')\n",
"f850lp = np.loadtxt('f850lp.cat')\n",
"out = []\n",
"\n",
"j = 0\n",
"for i in matches:\n",
"\tout.append(np.concatenate([f475w[i]- 0.454- 0.023*3.773,f850lp[j]- 0.584- 0.023*1.498]))\n",
"\tj = j+1\n",
"\n",
"# Salida a archivo\n",
"np.savetxt('m87_match_f475w_f850lp.cat',out,\n",
"\tfmt='%d\\t%.4f\\t%.4f\\t%.7f\\t%.7f\\t%d\\t%.4f\\t%.4f\\t%.7f\\t%.7f',\n",
"\theader='f475wN\\tf475wMAG\\tf475wMAGERR\\tf475wALPHA\\tf475wDELTA\\tf850lpN\\tf850lpMAG\\tf850lpMAGERR\\tf850lpALPHA\\tf814wDELTA')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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OVtX1S8+yLqrqvKq6qqo+UlU3VNUrlp5pHVTVQ6vqg6u/PzdU1eVLz7QuqupB\nVfWhqnr70rOsi6q6dUuvXHPC9Q/inrqqOqu7717d/tEkT+vuH1l4rMVV1bckuaq7762qn8nmSSaX\nLT3X0qrqKUnuTfIvk/zP3f2hhUdaxOri3h/N5ntW/yjJtUle1N3/cdHB1kBVfUOSu5O8sbu/eul5\n1sHqP5YPdfd1VXVWkt9Lcol/X5Kq+mvd/ZnV+8D/XZJXdPcJ/2BPV1X/KMkzk3xJdz9/6XnWQVX9\nYZJndvcnt7P+gdxTd1/QrTwim3+wD7zufl933/dafCDJeUvOsy66++buviWbnzB+kLm493F09/uT\nbOuX7kHR3Ue6+7rV7buT3JTNa4keeN39mdXNh2bzve0Hb+/K/VTVeUm+PcmvLj3LmqnsoNUOZNQl\nSVX9L1V1e5LvSvLPlp5nDf1AknctPQRr5VgX9/ZHmhOqqvOTXJjkg8tOsh5Whxk/nORIkvd297VL\nz7QG/vck/yQC9/46yXur6tqq+h9PtPLYqKuq91bV9Vu+blj98+8nSXf/eHc/LslvJPnRZafdPyd6\nXVbr/NMk93T3mxYcdV9t53UBdm516PWtSV55v6MkB1Z339vdT8/m0ZCvq6qnLT3TkqrqeUmOrvbs\nVhwV2erZ3f2MbO7FfNnqrR7HNeqSJlt197duc9U3JXln9usS1gs70etSVd+XzX95vnlfBloTO/j3\n5SC7K8njtnx/3moZHFNVnZHNoPv17n7b0vOsm+7+s6q6Oslzkty49DwLenaS51fVtyd5eJKzq+qN\n3f3ihedaXHd/fPXPP6mqf5PNt8G8/3jrj91T90Cq6klbvn1BNt/rceBV1XOyufv7+d39l0vPs6YO\n8n9BXpvkSVX1+Kp6SJIXJXGW2l+xh+GLvTbJjd39C0sPsi6q6kur6pGr2w9P8q1JDvTJI939qu5+\nXHc/MZu/V64SdJsn1Kz2dKeqHpHk4iR/8ED3OZBRl+RnVofWrkvyLUleufRAa+KXkpyVzeP3H6qq\n1yw90DqoqhdU1R1Jvj7Jb1XVgXyvYXd/Psl9F/f+SJI3u7j3pqp6U5J/n+TJVXV7VX3/0jMtraqe\nneS7k3zz6nIMH1r9h+NB9+VJrl79/flgkvd09zsXnon1dE6S96/ef/mBJO/o7isf6A4H8pImAADT\nHNQ9dQAAo4g6AIABRB0AwACiDgBgAFEHADCAqAMAGEDUAQAMIOoAAAYQdQAAA4g6AIABRB1w4FXV\nmVV1eVU0U1WQAAACPElEQVT9elX9XFX9UFW9taq+Z5+e/wer6o+r6ver6itXy/55VV2wH88PzHDG\n0gMALKmqHpbk3Uk+3t3fuVp2aZJLkjzgh2fv0vN/Q5LvSfJLSR6e5FVV9bgkv93dN+318wNziDrg\noPvZJE9N8rwtyz6czSMZV+/D8x9J8s3dfW+SVNV/n+SS7v6ZfXhuYBBRBxxYVXVekh9O8kvd/Rdb\nfvSN2dxzd8sx7vPVSV6/g6f5cHe/5Hg/7O6PbXnslyQ5nOTFO3h8gCSiDjjYviPJg7N5+HWr/zbJ\nxrHu0N3XJ3nGbg9SVf8kyRO6+3/Y7ccGDgYnSgAH2VNW//zgfQuq6qFJvjbHibq9UFU/meRLuvul\nW5b99f16fmAGUQccZJ9K8ufd/Wdblh1O8pDsz/vpUlWXJ0l3/8SWZU9N8qr9eH5gjurupWcAWERV\nPTPJ7yb5m939idVZp1cnObO7H7cPz39Jkh9K8o4kX53k9iSPTfKtSQ539117PQMwh/fUAQdWd/9e\nVf1IktdW1Y1JPpvkT5Ps+aVEquqsbJ71+u2r738syWVJPpLkuYIO2Cl76gBWqurh2Twk+8Pd/bql\n5wHYCe+pAw6kqvrSqvr791v8vGz+Xtzziw4D7DZRBxxUv5TkN1efKJGq+vIkr07yTx36BE5H3lMH\nHFT/NsnZSX6iqh6S5PFJXt7d71p2LICT4z11AAADOPwKADCAqAMAGEDUAQAMIOoAAAYQdQAAA4g6\nAIABRB0AwACiDgBggP8fSxsgVQCQQJ4AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4a9463c890>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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waeb9Ltrr99QgAABsDt9FCwAwmHm/quwx6922u2+YfxwAAHbXvK/Buy7Lb6B4IL2BfQMA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\nAAAGI/AAAAYj8AAABiPwAAAG8/8D4kJXbRaGOsIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f4a94992a50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from astropy.io import ascii\n",
"\n",
"tbl = ascii.read('m87_match_f475w_f850lp.cat')\n",
"\n",
"plt.figure(figsize=(10,10))\n",
"plt.hist(tbl[\"f475wMAG\"] - tbl[\"f850lpMAG\"])\n",
"plt.xlabel(\"$g-z$\", fontsize=20)\n",
"plt.ylabel(\"Counts\", fontsize=20)\n",
"\n",
"plt.show()\n",
"plt.close()\n",
"\n",
"plt.figure(figsize=(10,10))\n",
"plt.hist(tbl[\"f475wMAG\"], histtype = 'step', color='b')\n",
"plt.hist(tbl[\"f850lpMAG\"], histtype = 'step', color='r')\n",
"#plt.xlim(-0.5, 1.5)\n",
"#plt.ylim(24,16)\n",
"plt.xlabel(\"$g, z$\", fontsize=20)\n",
"plt.ylabel(\"Counts\", fontsize=20)\n",
"\n",
"plt.show()\n",
"plt.close()"
]
},
{
"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
}
| gpl-3.0 |
alexmojaki/flower | docs/api.ipynb | 15 | 17026 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# flower REST API\n",
"\n",
"This document shows how to use the flower [REST API](https://github.com/mher/flower#api). \n",
"\n",
"We will use [requests](http://www.python-requests.org/en/latest/) for accessing the API. (See [here](http://www.python-requests.org/en/latest/user/install/) on how to install it.) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Code\n",
"We'll use the following code throughout the documentation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## tasks.py"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from celery import Celery\n",
"from time import sleep\n",
"\n",
"celery = Celery()\n",
"celery.config_from_object({\n",
" 'BROKER_URL': 'amqp://localhost',\n",
" 'CELERY_RESULT_BACKEND': 'amqp://',\n",
" 'CELERYD_POOL_RESTARTS': True, # Required for /worker/pool/restart API\n",
"})\n",
"\n",
"\n",
"@celery.task\n",
"def add(x, y):\n",
" return x + y\n",
"\n",
"\n",
"@celery.task\n",
"def sub(x, y):\n",
" sleep(30) # Simulate work\n",
" return x - y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Running\n",
"You'll need a celery worker instance and a flower instance running. In one terminal window run\n",
"\n",
" celery worker --loglevel INFO -A proj -E --autoscale 10,3\n",
"\n",
"and in another terminal run\n",
"\n",
" celery flower -A proj"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tasks API\n",
"The tasks API is *async*, meaning calls will return immediatly and you'll need to poll on task status."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Done once for the whole docs\n",
"import requests, json\n",
"api_root = 'http://localhost:5555/api'\n",
"task_api = '{}/task'.format(api_root)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## async-apply"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/task/async-apply/tasks.add\n"
]
},
{
"data": {
"text/plain": [
"{u'state': u'PENDING', u'task-id': u'f4a53407-30f3-42af-869f-b7f8f4fbd684'}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"args = {'args': [1, 2]}\n",
"url = '{}/async-apply/tasks.add'.format(task_api)\n",
"print(url)\n",
"resp = requests.post(url, data=json.dumps(args))\n",
"reply = resp.json()\n",
"reply"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that we created a new task and it's pending. Note that the API is *async*, meaning it won't wait until the task finish."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## apply"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For create task and wait results you can use 'apply' API."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/task/apply/tasks.add\n"
]
},
{
"data": {
"text/plain": [
"{u'result': 3,\n",
" u'state': u'SUCCESS',\n",
" u'task-id': u'ced6fd57-419e-4b8e-8d99-0770be717cb4'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"args = {'args': [1, 2]}\n",
"url = '{}/apply/tasks.add'.format(task_api)\n",
"print(url)\n",
"resp = requests.post(url, data=json.dumps(args))\n",
"reply = resp.json()\n",
"reply"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## result\n",
"Gets the task result. This is *async* and will return immediatly even if the task didn't finish (with state 'PENDING')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/task/result/ced6fd57-419e-4b8e-8d99-0770be717cb4\n"
]
},
{
"data": {
"text/plain": [
"{u'result': 3,\n",
" u'state': u'SUCCESS',\n",
" u'task-id': u'ced6fd57-419e-4b8e-8d99-0770be717cb4'}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = '{}/result/{}'.format(task_api, reply['task-id'])\n",
"print(url)\n",
"resp = requests.get(url)\n",
"resp.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## revoke\n",
"Revoke a running task."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/task/revoke/bcb4ac2e-cb2d-4a4b-a402-8eb3a3b0c8e8\n"
]
},
{
"data": {
"text/plain": [
"{u'message': u\"Revoked 'bcb4ac2e-cb2d-4a4b-a402-8eb3a3b0c8e8'\"}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Run a task\n",
"args = {'args': [1, 2]}\n",
"resp = requests.post('{}/async-apply/tasks.sub'.format(task_api), data=json.dumps(args))\n",
"reply = resp.json()\n",
"\n",
"# Now revoke it\n",
"url = '{}/revoke/{}'.format(task_api, reply['task-id'])\n",
"print(url)\n",
"resp = requests.post(url, data='terminate=True')\n",
"resp.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## rate-limit\n",
"Update [rate limit](http://docs.celeryproject.org/en/latest/userguide/tasks.html#Task.rate_limit) for a task."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/task/rate-limit/miki-manjaro\n"
]
},
{
"data": {
"text/plain": [
"{u'message': u'new rate limit set successfully'}"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"worker = 'miki-manjaro' # You'll need to get the worker name from the worker API (seel below)\n",
"url = '{}/rate-limit/{}'.format(task_api, worker)\n",
"print(url)\n",
"resp = requests.post(url, params={'taskname': 'tasks.add', 'ratelimit': '10'})\n",
"resp.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## timeout\n",
"Set timeout (both [hard](http://docs.celeryproject.org/en/latest/userguide/tasks.html#Task.time_limit) and [soft](http://docs.celeryproject.org/en/latest/userguide/tasks.html#Task.soft_time_limit)) for a task."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/task/timeout/miki-manjaro\n"
]
},
{
"data": {
"text/plain": [
"{u'message': u'time limits set successfully'}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = '{}/timeout/{}'.format(task_api, worker)\n",
"print(url)\n",
"resp = requests.post(url, params={'taskname': 'tasks.add', 'hard': '3.14', 'soft': '3'}) # You can omit soft or hard\n",
"resp.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Worker API"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Once for the documentation\n",
"worker_api = '{}/worker'.format(api_root)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## workers\n",
"List workers."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/workers\n"
]
},
{
"data": {
"text/plain": [
"{u'miki-manjaro': {u'completed_tasks': 0,\n",
" u'concurrency': 1,\n",
" u'queues': [u'celery'],\n",
" u'running_tasks': 0,\n",
" u'status': True}}"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = '{}/workers'.format(api_root) # Only one not under /worker\n",
"print(url)\n",
"resp = requests.get(url)\n",
"workers = resp.json()\n",
"workers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## pool/shutdown\n",
"Shutdown a worker."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/worker/shutdown/miki-manjaro\n"
]
},
{
"data": {
"text/plain": [
"{u'message': u'Shutting down!'}"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"worker = workers.keys()[0]\n",
"url = '{}/shutdown/{}'.format(worker_api, worker)\n",
"print(url)\n",
"resp = requests.post(url)\n",
"resp.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## pool/restart\n",
"Restart a worker pool, you need to have [CELERYD_POOL_RESTARTS](http://docs.celeryproject.org/en/latest/configuration.html#std:setting-CELERYD_POOL_RESTARTS) enabled in your configuration)."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/worker/pool/restart/miki-manjaro\n"
]
},
{
"data": {
"text/plain": [
"{u'message': u\"Restarting 'miki-manjaro' worker's pool\"}"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pool_api = '{}/pool'.format(worker_api)\n",
"url = '{}/restart/{}'.format(pool_api, worker)\n",
"print(url)\n",
"resp = requests.post(url)\n",
"resp.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## pool/grow\n",
"Grows worker pool."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/worker/pool/grow/miki-manjaro\n"
]
},
{
"data": {
"text/plain": [
"{u'message': u\"Growing 'miki-manjaro' worker's pool\"}"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = '{}/grow/{}'.format(pool_api, worker)\n",
"print(url)\n",
"resp = requests.post(url, params={'n': '10'})\n",
"resp.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## pool/shrink\n",
"Shrink worker pool."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/worker/pool/shrink/miki-manjaro\n"
]
},
{
"data": {
"text/plain": [
"{u'message': u\"Shrinking 'miki-manjaro' worker's pool\"}"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = '{}/shrink/{}'.format(pool_api, worker)\n",
"print(url)\n",
"resp = requests.post(url, params={'n': '3'})\n",
"resp.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## pool/autoscale\n",
"[Autoscale](http://docs.celeryproject.org/en/latest/userguide/workers.html#autoscaling) a pool."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/worker/pool/autoscale/miki-manjaro\n"
]
},
{
"data": {
"text/plain": [
"{u'message': u\"Autoscaling 'miki-manjaro' worker\"}"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = '{}/autoscale/{}'.format(pool_api, worker)\n",
"print(url)\n",
"resp = requests.post(url, params={'min': '3', 'max': '10'})\n",
"resp.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## queue/add-consumer\n",
"[Add a consumer](http://docs.celeryproject.org/en/latest/userguide/workers.html#std:control-add_consumer) to a queue."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/worker/queue/add-consumer/miki-manjaro\n"
]
},
{
"data": {
"text/plain": [
"{u'message': u\"add consumer u'jokes'\"}"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"queue_api = '{}/queue'.format(worker_api)\n",
"url = '{}/add-consumer/{}'.format(queue_api, worker)\n",
"print(url)\n",
"resp = requests.post(url, params={'queue': 'jokes'})\n",
"resp.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## queue/cancel-consumer\n",
"[Cancel a consumer](http://docs.celeryproject.org/en/latest/userguide/workers.html#queues-cancelling-consumers) queue."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/worker/queue/cancel-consumer/miki-manjaro\n"
]
},
{
"data": {
"text/plain": [
"{u'message': u'no longer consuming from jokes'}"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = '{}/cancel-consumer/{}'.format(queue_api, worker)\n",
"print(url)\n",
"resp = requests.post(url, params={'queue': 'jokes'})\n",
"resp.json()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Queue API\n",
"\n",
"We assume that we've two queues; the default one 'celery' and 'all'"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://localhost:5555/api/queues/length\n"
]
},
{
"data": {
"text/plain": [
"{u'active_queues': [{u'messages': 2, u'name': u'all'},\n",
" {u'messages': 1, u'name': u'celery'}]}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"url = '{}/queues/length'.format(api_root)\n",
"print(url)\n",
"resp = requests.get(url)\n",
"resp.json()"
]
}
],
"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
}
| bsd-3-clause |
lyokka/lyokka.github.io | graphs/Untitled1.ipynb | 1 | 418759 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"img = mpimg.imread('chameleon.png')[:,:,1]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f61cbd42dd0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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8DbbsJC9t7cWwFYy+xs1Ggb6tcN/gOspbR/wMT+GV2p7dxJFqnhvBAAfy2xi2\nQj7eZa2cJprq47oSS400nWqEvRNlBCHgofFlbtULGLbCTHaHhqWjSh4LZoGJwSpNO8wfzZ/iaGGT\nr1SOcTp9i+e39iGqHn5bJjnQpgkIVZWxcI1zTzbQnk5iboZQZlpcbO7msB89sMrWyUn0UoBsBhgD\nMmorQHQEuicMHh5bZaObJDrSZr2S4t+deJb/5Us/zj//yPN8Zv4koRWN7wTTfODdF8ioXea7ec7O\nz6CMtThRXKeejuD6IqmJBsmwybVL42gdEa1+h7KT4G21nrfDXWFhg0AgG+uxZ2SHI4lNnikdwvJk\nVpspooqFJPpcqgxiuTJr3RTf2pxmJlqmYkf5X1ef4kJvnBv9QabCZTTRJa92OFFcJxPqcShW4lhk\njY8OXyGvdbhUHkKTXJZaWT6Yu8Jn3niYrqHRczV8X2SzkyCumPxk8U2eLNxkPFonplq7e7KBiCT6\nFMNtUrpBwwzTcTUsX6Zthfib9WNcbI6w1MgwEm1wcniVwWgb1xd5aGKZ7U6MU8UV9k9t0nZDHE1u\n0HAj9NzdLaV2PYLnigxE2gDcrA3gBCKvrk1Qt3Q+MfQqT2RuEgQC7x27yU4/hutKeL6IIEC3Gcbs\nqvSWEqQLbVYrKTTFZa2TpnMuR1h1uLg6wvr5IQajLQ6FN8iFu1iezIfGr1HQOtyq5gmJDpVmFFV1\nEdJvuZsNFa0u8pfz92HcTOLEBKIbAp4nktF63J9bo+eqSHaAr0BzRsDJuNgxASMfEHgiZ87PsDZb\nwPdFHptc4D+88QF+96f+nNcbE/RX4/gHunz8+Dm+Pr+fp1cPcW5lnMJ0hQO5Mq/M72WtmaRuRqhv\nJ8iEekRG2/hKgNq9c3EYPxBuq71d3BUWVhE9RmMNmnaYih1DFAK6roau2czXcpiWgqKIxEMWpiuT\n0Xt0XY2GrTMZq1G3depWZNeNA17Y3ocmuSzvZNgXK/PZ0knSWp/RcJ3Hhhd2xxQ8fn/hXZw+dAsn\nEOl5KvtyZYb1JjtmjKob4/MrxzFthYHE7raGJu5mT7WcELlwl3DUISLZ1J0Icc3k5uYAlXocWXFp\n22FMT6Zu6MQ0i4tbQ3xwcvf8w6nMEmfrE9xYLaKGHSZzNfbGKswl8yR1g66j0alG6AQCa6rNx6Yu\nU7FjXOqN0fdVnhq/geGpHEht4wcC66tZkALksIsgBFB06fZDqKpHpxciotmYww7mZhLEgIMPLxOW\nHLbdBKWp337vAAAgAElEQVRugu1GjKKeYSqyw2/s+wZbToqf2neRz559GKkj0egqKHkD/apOy1Jw\nEx5yX0arwUiqyVCoyVeWDyOLPv6AiOCD2gLlcA/5UhJ1Bcxpk+PTm2z2EmxUUry6NsHoQJ3f/OIv\noO1rMXpwi1pP54vPPcIn3v8ic708heEOX7xwH+8uzlEbirDVijMaq+OMiFzbLnKgsM3iIZ/mjLxb\n0+QH5EfBwt4VCtt3FdxA5LHsHK83JjiQ2ma2tRuxTSUNKlaUnNbF8ncfN66YPLt0kOF0k+VmBumt\nrYez5XEGo20a/TCJsMmTe2+RkPu0rd2Mptdr47TNEDPpHc5/4wCZk9tUrCjvysxxtjnBqdQSz+/s\nJ6pYXGyPkAybeJpNz1YxNZmlbpaOoWFHZA7H1yhZCXJqh56RQRY8YlGDvekqg+EWL2/u4VRxheve\nAPWeTi7Wo25HaNhhDsa3GNKbLEfTSJLPRitBVLGYzu4QVSxsXyaW7WHbMork8fTKIfKxLmLcRxNd\njkXXUASXkrOb9Z4O9bk8u5vAIIoBMX33zK3rifRaEbaDBDgC8cEO7WqE/fFtDE/hQnuUrVqCD89c\nYbWf5s/OvIPBiSrbt/KMHyohx208VwPZZyTXYKsYwXNEpmdKbK6OggCrtRS2L9FrhTkwXuLWaJLc\neegPiPRW46hZEHyBsOpws5ZnJl3BSCqUN1Ks9lWCtMtgpE/PVkmGTdazDp9fOo5pqAyk2wwO1/nS\n3zyKEwsYPrrF2ZUJPFckHjcISS6y5NMsh++IHAYIeHeH0/lduSsUNiLbNK0wn5k/yfvHb/B6ZZxf\nGDnLxe4YLyxPY7U19k6U2Z/Y/nulzMW7SIJPOtzH9iVEISCr92jZIR4srhGRLUpGAsNT0GSXvrsb\nmKjVo5y/kEE50qRv7b73hfXj9CyVrqMxHd/B8FRqlk4m1KPvquy0o6z6aU4PzfFYdg7Hl6k5ETb7\nSa7Xi2xVExweKfGvp7/JF8v3c7Y8ju3KfO3WAdSQSzrWI6kZnC2NcXxgg8VejsOxTWr5CDUzQky1\nuFXN023qRJN9HEfG9wXiEZNCuIPjSfQdhbOlcWTJ46vXjjA40KDWiWD1FbLZDkLIQxACzFoYez0C\nOQs9apEebpKL9AgNu8xXs4yM1PADgaoV5X2Zq0hCwIubU3xo7BoD93e41iiSm65S6UYYzjVYtbPQ\nl1iaH0COBNBSyOztsZgNkPsCRwZLTERqmK7MfDlH7kAF7YU0bkhAOtlHvxnBSgl0rmZwUh5mokFW\n79HPK3ieyEP7VimbMRxfZKuW4Kmj19g2YlycG2NwtEXfVSm+Z47Zap6PDl3iT/sPIwoBhrU7yUc1\ni6p356zi2+nu3g53xXQSEh1OpNbJx7rU7d3znq+0pphv58gnukyO77DTiaKJLj8+egk3EKm0o4xE\nmsytDOB4Eimtv3sQINSjZunsmDHWO0k6rkZCNXg8N8fJzArJZI9j754lG+2RjvQZiTVpdnV6y7v5\nxX4gEpZsRCGgZkaIKyZDqRYZvcd8J0fDiXCuMc6F+gghaTft8Z17F/AReKFxAFV0SWgmQQBqyGV/\nYZuhaIvRSJ0vnfhDfqv4HLP1PH1fRRQCPjZ4CcuV6fc1srk2xViHB4ZXSUQN4iGTrNalVEtwKr/M\nQKyDYam8/+A1TuZWODq4ibylcX9+HVl1cV0JMeqgT7SJxExsW+aJ4Tm6tkbTCiNLPi0jRF5tc25l\nnN9++cd4eWEvHxu/zGIvx5X6IL8wcpZ0uM+fHv0vHE6VGBxoICVtBieqAAiewGw9R3wRZCPgzZVR\nztdGOZlf5WBxi+2NNP2CTHMfOC0NNyTQG3fJnyiTHGyz0kzvpoHG2/yPh55lqZNhtZFiq5zkY/sv\nUVDbHE6U+NWHXmQg1KZu6Lj+bi2q/3z9UYwbSWIhC9tUSCgG9Z6OcIcUNkDADqTbam8Xd4XC9jyN\nhGzw7vwsY+Eaa70UpV6CR3MLnMytoIgePzl5kbza5rX6JNV+BLMeIq32kJoyJ7Lr6LLNTwxd4GB8\niw/krhKWHDLhPsvNDKVuAl20WepnaWzHsT2JmGqRC3e5WSlwcGCLkw/O0jTDrPVTnC2Pc7U0SEyx\ndi2gYtKxNVp2mC/cPIHp7aYxbvXiXK8XuVYtUunvVpdIKCYLpRz9doiQ6rBYz9KwdCbDFaaVCN/u\nT9O8keHZ9YM0LJ3XmpN0nd3aS7XZDNW+znwzx750GVnwaTlhDgxu83plnM1WAmNH55vP3EfbDXPx\n2zOE9jUp9RN88tArTBRqEEB/NU48ZDGerZOWe2TDPVY3srQrUQxT4QurJ/AsiXSxhd+T+cLScWxf\n4ki6xFd2jnEsuUHHD7Fjxdiux/HaKvVOhNBMCz/hAuCrArIRIGxrmK6M4Slc3yoyMlolsu2idIXd\nWk8qRFZk7L8qMJGq4fkiJ3MrpLU+v3Plg3xq/Jv8/N43uH/PKjtWjJDo8LnrD/BqbS/PzB6i8maB\npa/soVFKEMxHGXqgxKH0FlNDO7yyPrkbk7hDMafdEjHibbW3i7vCJe67Cv/53GMMDdWRRZ/NapKp\n4g5OINF0dPLhDim5x5Xu7rG3fz5xBnXS5auVI/z46bNYvsybpRE+krlE31f5i42T2J7EvuQOW50Y\nvzT+Gi/U9nOrUiA72GJYb/Ls7CEODZcQhYDr20Wm8hV6lkrPUmmtJZBzBmutJI4nEVYdXE+k2Quj\n6xaVXoR4yCKhmazU0kRCNpYjc2FplGjCIJfZ3Qf1AgFRCJiKV3ilvhdF8Pjc2gO4WYdGK0LPVHET\nIl1To9qKEqQdJDHgSKbEi4vTaCGH7U6MXl8jHjV4YHCNw9O7ZX8UwWPwg02+vHwEgCUjR8sMIZQ1\nvLRLrROhZCTRZJeeozI5usPyRg6nHkJPt/nQkcuUjASdaxmMhMKYXufphUN8aO81vjh7jOci+4hp\nNpRCyIMmji0TUh0kzUMSA2wVuiMinu5SjLT5xo0DnJpe5MzCJCOyQP68w9YhDycWoG+B6AZcO7MX\nt2jxwsY0rXYEv6nyma2HuXhznLGJCo8X5rjeLZJNdTA9mdF8HS8rsnZzgHcdu0nNitA0w7ywMMNI\nrsG7x+bwAxHBuXNu7N0edLorLKwmucQzPUQhoNyKUUi3CUkOX14+QssJsdLOsGZldisehLq81trD\nf1l/mK1enGeWD3K5PsSp4RW+3jiMLtocTpUwHZmk0udTU9/iz9dOstTIEFYdplIVrjYGeWhiGdOT\naW7FsfoKVxeH6cwnMW2FDz58AVn2sV0ZVXaxHJlWS8cyVWIhi2TYZKcdpdqPMJRq4Xoi7xhcYs9w\nhSAQaPXCKJJH31JxPImbzQIAL9ZmuC+7zuToDqcmF/GvxwnLDofzW9gtjey3VcYTdS5Xh5BvROiW\no0RDFmP5OkEgIAs+f7ZwkqhkMm/k+YsrD3I4v0Xd1Dm3s1uTKrynTSrbQZJ8fvLwBWTB43CqxPL1\nQQJPIDXcom1qXG/ulsB97PQVfmLmEovdLJGwxZdeeZBwyCEZNnm8MMeBB1YI6xYIAYmwideTqayn\nMHMBog1KezeL7NcfeIGnMlfZM1QhkMCJSbi2hGQKiDa0x0VOPDrL1PAOR3JbDOca5Cdq3NopIEVd\n1rbS/MXXH+PMKweZSlbQZZullTy6YhPeFjm/NcLN1yawPYnH98zhBwKHIpuc2RpH7t0hlzgQ8ALx\nttrbxV1hYQMEZt5K8DcchclEFT8QGUs1WO/snnqp2xE2+wmWqxkeGF4lotisbhUZLjSodiNMxmqc\nTt3kje4EZ8vj/NzEeQaVBktWnoRmcjRTYiJc4fnyASKKzZmFSf7liW/z7vwt1s00VxuDdPMauUiX\nr80dJJvqUGnEKMQ7KKJHN2SRDfeIKhZdR2PNSjOYbFPuROk0dL7lTTOZqRFSXFzRJyw7PDl6C0Xw\neHF7ikvrw8QiJvvGynQsjdfOH8TLeKzU0qQH+4i6i+DJzP31DB//lW/wB8unSRbbtPphOqJPvxdC\nFHw+NfMif7J6ily4x+RgleV2GgFwXInuaoJA89HSBo4t84WrJwhcka1inMR4k2YlimGpuI5EayHF\nigjvfOg6K/0MohBwMLsN2W3issVkuMLTW4epdiMYhkrgCURVC7Er8YF3vskz37mPQBRQ2gKa7PLp\nuUdQZZfmQpropEioGuB3FEQHtE6AnRS4vjNASjf49pV9xG8qdPZ4BLpHNNWnW42QP+/zz/790zTc\nCJ9bvJ+PHb/Aci+Dc7TLULTH1qTE6eIca0aanq3yv114L4PZJndIXwHw73ILe1corCjsLkKurA6R\nzXS4ujNIMd5ms5XgxMAG880cb5aH0VWHsGZzZnkSz5QRVY9KO0ou3uVWM8/VWhHXE+n2NZ7f2Y/j\nS2zWE+wr7CAS8GJlhgfSq0QlC122EQWf651hjLcKiUuiT0hymCru0LU19g2W6TkqUcWiY2t0HI0r\nK0NIis/DE0vYvowk+AwNrHOpMogu20RUm76jYHkyV5uDzG/meWLmFlt6AlnweGb1IMV4m+pgjGh8\ndxvoJ3Ln2RfdZmdfjMu/c5wXf/FBwu+TEK6mMB4x0cIOesSkbMR5xjyM40ls92I8VFh5KyItoci7\nx+cAJMlH1ncTHh4eWmEsXKPlhllMZZmv5VAUl3c+epOnUpc519tDUWnyfPUA59bHcGyZ//TQX/Ll\n2n38s5HX+KOVd2CZCo6hMBppcCNT5KtnTkDaRlwL4UmwuZJlZKLCh4au8J34FFfDo0S2RJLXZHrD\nAV4JfBn6GzGCIYGfuP882oMuL25PkQv3eFd2lvXRNJcmhtm0U0Qlk04rzJXYEKeyS5T7MQp6hxPp\ndVJKjy8vHsEyFPyOQmWuSOaWz8IdkMPdoNNdoRLflbvCJQ4CgevlAaJxY1fheiHWm0nCqkPZiNEy\nQnT7GpVWlCAQuH9sjf0TJXLpDoeLJbYbMUxHxvVEXF8kFHIwXYWdThQ9ZKPLNucrI7SsEE4gcatX\noG2H+ErpKAejJWaiZR4fmOPB/CqD4TYL2zlGYw2SqsHD2WUyWo+jmdJu9fu4yWi+zlwjz3oniS7b\nvLo2wSPFZfJaB12xOZzZIqX1qfUjHB9fp2zEeTQzT8/RsByZtNYnleoyGG8zFavwP8++H9NXeObW\nIcIbPcTFdWQDnPe2mPgTAceWcV2JkOzgByLNts50aoeVbmY3UQKoLqYZSHVw36rre3JwlclMjW/N\nT3OrO8DTC4dYbaUwDZVMpM9KL82vf/G/4u9WDvOlrROMRho8Nr7AzFCZPy49yo1GgRGlRs9SiegW\nguZRtSLQURAdAUnx8TSQDIgWuoRlhz+5eQqRAMSAUN1Fa/k4BQe155NY8gg0n2KizRffuJ/PvXqK\nnXqcyzfH+OO5Uxi+ymY9wcXmCNtWggNjW/zyyMuk5B6fmvwmbTvEppnkRHiFRMRAXguhZQ2SD5Wp\nHr9zJWLu9qDTXaGwlicTC1tIb62TQmGbeNjkYHqbkLRb1XAs12Aw3eL08BxNK0zd2M3VPb80xki2\nSTJsIks+khCwL7vDZjWJKrvsy+zw2tUpqs0oXVPjaysH8AOR5UqGiGKzZqV5sTzN5+dO8MLKDKLg\nI8k+xVCLph3mWnuQjNJjsZ3F9UWMvsbB5BYnchvcl13H9BT25qsYnsqV+hBtK8RsM898LceRbImf\nyL/J/alV1s00lV6EaNji3NooogA73SgPRxcI/1GStNxj5n9qYgxHKH92gKM/fY1E2KT9Gx2U6zru\nXIzLpSGW/2YPT+ydxfZlDFdBV5zdwNi2xNblASTZxzNkFttZeo4KOxpnzs/gL0URBYhGTAxH4Vhy\ng3/30S+Q1A0+PHCFjX6Sbz93jLYVYrGe5b7sOp+6/DM0K1Eimg1dhfMX91LcWyEoWJwcX8FXILbh\nIwoBghCQifXY7sUYG68AUD0mgCXiqSKtSYmZTxusVVMURnZrdIUu6Jw+doPuVhTDUzgwsI3lymii\ny+nsLG/2Jti0Uvze4hNUelGyao9/dfHjbK9kkHu7E9n2cgZfuXOpiV4g3FZ7u/i+CvuPfW2eIAhp\nQRC+IQjC/Fu/U//g2n//1lfozQqC8N7beYik2mc8UUeWfIJgdz1rOjIvLU5R6iZQRJ9qN4LpypzZ\nmSAb6jEca5KO9gkMiaYRQhb8/4e9N4+W7KrOPH93jhvz+OZ5yJcvM5WDMpVSSgIpNSAwYGaKwTbY\nmLaN7aLLVTaeytXdLrfLNMbGZWNctN1AYTMLEEgCCQkpNaZyUs75Xr55jhfzHHc89UekvSiXDTKV\n3Wi5vde6692478aJWCv2ufucvb/9ffSFqyRDTQrtEOlEjdA1dgY1aiN8idalOL4vUbSCGIZDqW2y\n1Y7iC4m7R2bZ1b1FWLG4dXCRV8fOAXBnapYTxWGWCwlGokV+cs9xLld6MGWbuVqGUtskoDjMlLtw\nfZlq22AslidmtjmxNciqk+SRzWmeWJtAkQVJs0k4aFGpB6jOJFl3Enz9P/8xPxE7D6UK5lqD2Mci\nLP5f00SNNvff8P+QvujiD7fRnovQ7BV8+8o0s4UMVbtTUtFVl/H7FjAmqrh1DT1ss3auh1w9RCAn\nI/SOQ1fOpSjnwtzRO8d6O853y9PckNjgS2s38rrMOd7yumfoCVWJBCzOFfuJBVv81E3PkS1GkaI2\n8eEy2UIMBBStIK4paHTLtFo6TUen3DRRZZ+2q+JrEua2RCDTwg1IaA3wdQXPVZhOZnFslfHXz5Nt\nR7jrxks8cW4n280I86cHefgvb+fPXryD08VBvnziEFtrSdpPpjnz4f20N0MEtlT67l5FUXy0hEVs\nx/fTnHrp9rdIp5dy/KjspXzyp/gfZfN+HXhMCDEJPHbtNdckJd8B7L72no9fk578vlZxTC5u93Bb\n7wINW2e+mGY4VmKwq8Oju5btgNjfOHCOTLDBmc1+rhYybJciDI7kkSXYE9+g2A6Sq4fIViNUmwEM\n1WW2lMEMWmSSNRhrIEkCy1PxfZmNzQTzpTSa4nEqP4Aq+9S9Dpj/ucYk/27w28y1upiObbGnd5O0\nUedqo4uRcJEL5T42q1EqDZNbEwuYqsO9vVfQFI/VeoJa2+B1wxf5+PNH2Z9a4x1jp7mtdwEhJHTV\nIxy0ePerjjGpb/H6iz/BO2feSfeDDuofFVm9Vyf6whq83+Dnbn4rN/770/zft3yaj3zgkwQKEqEz\nJtW6SX+4Qn+4wnCs1KG4kX3SfRXsYgAv2amXjt63iBR0CU2X8EwBnsRDS7t4cmaSzWaU57MjVFoB\nPj5/BwdDS5w+N46pOSyvpjs8yq5BPNrEdxTK5RCy7KMtmGzVIiiWhFnwkeaCrC6ledfESfan1rEc\nlVZSxQlDu2ZgR65FJAFHJ2ZZqiUZ7clzcb2XhqPz+PlpAok2e1MbHLp1hvJeB3IGK88OoG+rxM5p\neDpsvhJEyMOO+SxmU2QSNXb1bVGphl6CG78084X8ko4flf3AT/5HZPPeAHz62vmngTd+z/XPCyEs\nIcQiHVbzwz/wWwh469iLLNTT7M+sM5oo0HAMHK/TiRKJtrile4kH1m/AclWiwTaJYItQ0GKrGKVS\nM7lc7cFQXQZiFRTZR1V8HK/zrAhoLsVqkEiozUC8QsJooikequHhejLrxRilWpDVWpyGa+AKmS8u\nHOBP1u9mwCgxV8twYbOX49vDjIdyyJLPeDRPf6zCu3ac5Gvr+9iqRWj6Oh+cfBzPl1EVnyezExzY\nsUzeCvO11b0o+GzXw+QKEcrlEK+KnOdT2duJvbcB/2eGmNZib2ydrn1Zlj4WZ+WtvbzriRM88PxB\n3vfce7jbtDj0lvPUJl0Sj5r0BGqcXhlkuxmh4ei02xr5tTjxvirdPWVaTYPVchxV81AVH6nLQova\n1EtBhK2wXQ8zFi8ggNcOXORDJ97CL93xKMvbSabHNmhYOlfrXRQKYfDBMB3i0SbOWAtDc2kP29hh\nqbOnjTh85sphHjy1r7NsNDo9sdq2hpXs/MZ2QkeWfJav9LBRjhIwbVa3k3T3lxhKlqg6AU49PQWy\nILwiY3e57L5jjka/4PVvfZZgfx1Z93jznceRgJatcWmjh72Daz/Y01+CdcD/L+8I+8OmxLq/Rwdk\nC+i+dt4PPP899/2thN7/YN+rXmd0RXhsa4qb0stYvtaR0ghW6TY6XTJZK0LNCfCq3iucKA0T1m3W\nSzEGE2W0WAXLU9kd2+TF0gBxvUU43SFj22x0msx1xSMTr9OyNXwkruS6GUkWmW0a1K7G8VIOIwN5\nRiJFlupJFla60EM2m40oz/pjhDWLVwwv0G1U6dKq1F2DE/lhBsJltu0IN6ZXWainiShtPr95mLRZ\n50Kxjz39G8wV02TCDfK5KM9IY5Q3o6QHyuxNbxCQXAq3laj8xC0c/l9PsdJIMJvv4h0Tp3ihNMIH\n3/95Ppu7lYm/bqMWGkz6P8v9r/hzjrV2U94JD13cw+hAjrVCHCFAuRTGS3Z4mLa3YwDUcjHUrhal\nK0lEj8VYd565rQx+ySATanA4tsSgWeILV29kvCfHX84cYbiryNHMDIvhDFutCKrhgi7RLpikIg3I\nG2SLGdRMGy+go9WhXdXZNb3CgprqdAcFJNQWtCUQCug1wfIboLE9iJy0GU8XSBkNnji3k2Rfk3Sg\nzguP7kaMt9FWAyhHC4Q9haarE8hLPLc9iu9LSFsG34lMderSio8RcDgzO/xDuvF/bwLp7zq+Xq72\nP/2ouCY58E/e9Qsh/osQ4pAQ4tBIt8WbB86Q0hqUHZPbk3O8IdUp5muSx0CgzJBZZK2d4GBiBVN1\niIVaHEyusFGN8v7BY+wOrrM/sYapOKiST80JoMk+h7uWCWo2xVqIat1ERrAzkyWo2mQSNWRHIpmu\nYV+TAlkrxNFDNneOXmU8ViCut4hpbU5v95Ozw3zk2Gu4UO6j1jZYqSWouQGOrY+zN7bOI5udUpIr\nFP788GcZCpUIGTYZs05fTwlfdCJRfjvKG1Kn+WrlIPK+aXhXp+6c/8NR5GdiPJmbpOHo/OF9P86p\nL93Arv98kfyRDENfUPjt5TcSyMuElyWCVwzWizHsaqeX1yiDUAS1iylES0HRO/Q5TsnANwWK4rOY\nSyEvm/Q8JTG72MM3Nm/ga1f2YS+FaTg60WCbm5LLXKr34QmJM/ND/NjkJVLxOggwVBchgzlQwwxa\n+GpnQqpRm4PxFeLBFqVakMq0Bz4ENyVcU+CakDytUn0xhax4tFyNJy5OcXB6kdVynGcXxtlz9Cqy\n4uF0Oeiqh666zCz0MvjaJeKBFrIsUCwJTfWQFY/cZox0uMHU2A/UkHqJPsnLHjjxw35y9m/Voq/9\n3b52/YeS0Cu4YUpuiLpnkGt1uH8ut/t4R88LPJmdIGdHiKlNTm4NMqQXuCWxyNGeq5yr9PO+iWe5\nP3eQNTvJ8dzI3xW+b0ouc6RrkZrbYS3MROsMdxWZXejl1OwIp1cG8XyZrgNZ6s0AiiQob0ewSwHs\nqsGx5QmePTXFvugqhuzSH62y3oxjJFsUW0EaLZ0b06uc2hogGrD461M3sz+1xki4iC8kfufqGzhb\n7Kc7WKftaliuSki3UTWXI1PzfPCxn+CvX7iFld+Ref/o01Rdg/DTc/R97AUsV2W7FkZyPQa+kaXf\nKDHwM3MUfrbB5ReHcYMCrSFQWiBmw0htGb9kIO4qISVsPF3wvtuO0Z2sIgIeSsxhdGqTVLyOXQrg\n9NjU/lUV2fDI1cLcMLDOu+99ir2pDd42eIYvXznAE2emabgG0USTmhNgazmFbMmsFeKQsGmWOwkm\nX4Pwuo/bVDlVHqJpaziWSnywTKDsI9sgBlskZizKUwI77eF7CnVbp7u3TNPVO9xMPpxdHWAwXUbd\n7qhAH+lZRs9qzJ4cZvbJUfRHo9hJj9xWDKcYYGI0y9qpPmYvDPyQbvz3TcJ/icePyn7YCfsA8J5r\n5+8Bvv49198hSZIhSdIoMAm88IMGa7kaT+fGeS4/iqk6fH1jH45QaPoGmuwT15ostDJ0R2qcbQwS\nVCw02SNpNLja6uaXe79DWq0R1i0uF7t5bfocjlD46pV9FKwQh+IrlFsBUoEG0UwdudpJ0HSF6qiy\nj6p6rM5nUEMOctgBRbCvb507brrEV1YO8OjCFNuNMLObXezt22A8kUfXPc4V+3ll/wIbxSg7Rrb4\n9sOHGDXzlNsmYc0mW4kgSz5Xsl0YqstaLsFIushaPc5d+y9hxNv8/t6v8pWtG3nuqd1Iuo68Y4yb\nM0uEAxai0QLg4y8cJaZ3um8mP10jcUVQG5GoTrnYfQ6BrIKQBdVcGGUtgOxIGLJDtW0gNxSEDx8a\neZj6d7uRbIlooollqUSjLWxb4cVzY3zm1BHqrs6DW3vQdJcDuxcpWkFqFZPnH+jglcMjFfqSFQzT\nAUdmJF7EDQs8XULLadRsgwNd66i6h+MptFIyiiXwGhrbBwN0HwdzVWUgU2JvagNF9js8xKaNZri8\nduoCG08NYO4sk11O8t2VCSZvWyIxXUC2JdTX5ekbzSPVVV5x4AqpQAOjJEHU/SHd+L83wT+DCPsP\nyeYB/wm4V5Kkq8A9115zTVLyi8Al4FvALwrxg8U0Q6pNvt6hIJ2MbON4ClfrXXx56yC+kJivp5mr\nZfCEzKOLOzlWmGRIL3A228+J3BBXrD6eLk+yP77GmwbPcq45SExt8f4bnmYgWOa/zhzG92VKVpBE\nsEVyssj+gXXOXx4iVwsRNByUmMOd41d54/RZgrEWuux2OKVcBeFLZJeTKKpPRLXYqMcYiJdZXuji\nwQt7CJk265UYb3ndM3xrcxcj0SKz8720SiZXCxl2dm8jALelMjPbz78aPMnWu9I8feQT7NRz7I+v\nMfHZEiIe4ep7U+TtMJlgA+uGISr7M+z8aJ1nlkYJmRZdH18leXyb8aOLjHzdJzSr40QE0VkVZMGO\nW6aMZJIAACAASURBVJZQ2hJnq4M4LyZIXJTwWyq/8PxPYEcF6VMy1WwYp6WhKj7v2/0cqZES7zzw\nAt1GjYlojkSoRb4VZuP+EXYNb/IrP3k/I+NZAJaWMxzoWyPZX+5o2OYlmt0SfcdcWo7Gia1BnLzJ\nKwYWMAs+tREIpxvYUWhlZCIrAl9IHFueoNI02S5G+eDUd3nt+EUeuf8wTrRT2lOiDm8YO89mLYKu\nurQnOjQ91VYApSVxZmuAy7lufB3GB7e/r3/9U+zlnnR6KVnidwoheoUQmhBiQAjxl0KIghDibiHE\npBDiHiFE8Xvu/z0hxLgQYkoI8fBL+RINV+eOgTk2G1Gu1rooNUzieos70rNMJ7Y4t9bPDfENAHTN\nJaC4PJzfw9HBq+xLbXCm3kk6yJLgry4d4fH1HbR9jYob5JXRGaa6tgkaNprssTe5zrtHX2CrEeXA\nrkV01SOk2+zs3+Lk1iCPrk4RD7Ww/Y6yebOt422ZIAuc5RBPPXEDyUCTmat9BDMN9o2tUd6OIMs+\nCa1BT6jKM5cm2De1wpFdcwwnSsw8Ps5mNk4k1eCbr/4YX/rVVyMqVY5bKX5/89V89Wu3I1caVHcn\niV+GC/le7stcRLY9og9fxE2YJB4KMZYocPKbe0AIKn8yxNI7BFodBh538BUw1nQuLffS94zNTLGL\nyJJAEvCaA+cJnzSJzkP+oI9kySi6T7UR4HOfvJfBaIm6Z3CyMMSYmWcgUiZqtKmN+qiSz8dmjnJD\nYgPHVVCCLqrsUcxHUCWP1s111CYUdmuU6yaNpRgi4JHRa0SW24RXQT4WJzYnSF1oY8c6qnw3DSxz\nY+8qQ11FvrhxiIe/egutIYf0dJ6jg1f5yT3H+fyzR/B9ma1LXVBTCagu9VIQN+PgugqK7KPcWGZu\npeuH8/6/7+u8ND6nH2WT+8sC6eQLmaVGCl9INFwdWRY4vsJfXrqVkh3kTTvPElHaZGthptLbjAQL\n7I5uslRPcanUA3T4lnJ2mIFUmTv7rqJJHi+WB3ggv5+hUEezpdes8vjKDp7ITzEQLqPKPuPJPLla\nqFObFRJH+pYYjRZZqiQxVYeuWB0/6iKHXHxT4JqC8yt9aFEbWRboikskUycZbPFIdhdVO8CR6Xls\nX2GmmGGzFsHZ0UJYCgd71hhVFVopleyn0uzS8owHc6QueCBJRK5UyDy1RdtR+dzKTah1GyloohUa\nxGcbnHtqEqGBv7RK9Lll4skGelXQ7NYIbfrEZ33knA6+oNHWSZ6v0spIPLE8QfeJJo0BidBgjchQ\nFXk1gF0xCP/YFufX+vn2/DQ7Ytv8xRN3UXcMZEnwK69+kIFgmZBhk9IaDCbKsBngzNYAfb0lZvNd\n7OrNIvkdGlNDd5G62kyMZFlpJamMmZiFDoSxuAe0fBM30IFR2r7KM8d3UbMMFrdThG7J0zNUJLuS\nZKMV43hxBLklUakE+ZVXP4hWkYkbLeKpzpZG01xu7V1iOFFCLmnXxQ87NKfqSzp+VPayYP4PjPeL\n4Q//HMGARTEbBVfuUJ6oPqFwm0y4QbERpFo1iUZbjCYKLJY65YOfHn6WFTvFqdIQAFORLDO1bgKK\nw82JRRxfpeKZTJsbrNlJKq5JWLF4Oj9OJlDncr6bqXRnSWV7CuPhPF85fwAt4GKXDcbGs0Q0i/Fw\njsvVHmY3uskkq7iegu0qWLaKVeqIDSthF89SCEQsPFfGaasYIRvTcDC0zj4rYljoPyvz5oeO80eX\n7+azB/6Kj2zex/kv7KL34yeRo2HK9+xAvDeH5ajULyRJXhQkzxRxE0G2bg7S6hZ4YZ+dv3WF5q07\ncMIKQoZ6v0wrI1BbEtaoRfSUQW3MJzYr0bizQSZWJ3eqm9gslKZBErDz1kUWi0nCAYvBSJmNeoyG\npXPXwCzfWZ0iErAoNUwykUZHIOx4gOotLW4aW+b4+QnC3XX8F+LIHjiHakgSBHSnA+NciNJ3zKc8\noWLFBV1nfIo7Fbz9NcRsGHW6ww5pLUWgx2Lv4BpnlwfQAy7tUgDZdPEthX2Tq4Q1i3PbvdSKIcxo\nm6FkibVynMZ2iOQZhRc/8W//p5n4B/fExAe/dMtLuvdXdz3y/1/mf+HKmIZNQHNRgi5avE0gbNGT\nrlAvB3E8BV9ISIqgnAtzYaOXeiNAoRbiLxZewdO5cRYLSWY2upmvZ8g3Qx25jFaG5XaSZ7Jj/Ne1\nW7hS7+FcqZ+SG6Q3WGEkWOCewRmGg52IGtYsHl7ahaz5ZGJ1tJiFobg0XJ3H13ZQtw08Wyabi9F2\nVBpNA9+TkYMuZqqF78gg6HA4RZvoQQdDd5lM5QioLq/pv0T2wUGEprJipzjSv8R7/vjfcHNskcE3\nLSKPDTH761M4P1lEkQRCSNxz7xne8ZvfQv54DddUGHhgk+CWhNlTR0rEaXapaDWPVlJGrwq6TgvU\nJgTmDLwA6CWZ6oRAOx1G/0iCnuc9WhkJvSrhxD3OL/RzoGeNV/VdYemTO3h9/3nuGZxhMFDEdlRM\nzaFVC7CylcQMWVhHq4hSh/8K1afZMLBjAtmC0HfC3Dt6BetkkmbdQMjgqxKuCd5YGysiEygIIg+H\nsdMeoYDNYKJMejrPcHeB889OMNaXx26r3LbnKkfGF4mmG6xU4ixVkxiqh6x5dMdqpAMNbhtYILCl\nYub96+OH/DNAOv1/Yj5EAxa1toFnKezs3UaSBJvZOIcmltgsxEgEW6iqBxK41zRThYBGW2c1H6fV\nMFA1j5VKnHw5TLYVYa0ZJ6xYtK4JG9cdg1vTC2y1o9wQWadXryBLgheLA1iOSr4dZlfXFsGg1aEx\niTbZqkWoWQYB3aHWNhgbzHHL+CJ90SpeU0VRfVTdo1UOMNBbBEtGk31y2Ri67iKAcxt9rOXjHAot\nwCtLzL6/i1eGr/D8l/fR/2iej3/ptRxMrBD9qyKZPdtU6gGaX+um74NNLv/2Hp4tjdMdqNHo05n7\n3Sj63XkO96/gra5TGQc3KCP5gvIOsGJSZ6JWoNUl0KsQyMkMf3oeI9+isFvFTgh8vcNmCHCl2M1n\njt/Ka37lGA9u7OGBqzdw/9oBumM1tmthDk8uououo6kiAd2hezJPvhkCISE8qUMdo4Mdlbhc6eGe\nN5zgp/YeR+tv4AYkQhsCkTOwoxJCBjckYaabtB2VjQeHaVg627UwviGoWQbC67jls3NjtNoa6v1J\nhiIljvQsoupeR7JzeZjZShehNUG97/qBHf5FquMlmBpwMRQXRRKMDOTZrEVpVkwSyTonF4YJmDaF\nRhCrEsCIWB2IXLLOSLrIdFeWkGkTj3dwwvVGAK+lsryd5MJSHy+WBkgHm4xFCqiyR90zuCW+wNfW\n9pF1ojy6OoUi+/zMxHNMRzvdQc1GgFOFIXwh4XgKpVoQ11OIBCyGwiWeuzCBobrEM3XCpoUkCaSW\nwupChsxwibV8HCyZZt2gvhLFbmmYps2F1iB9vyszuH+Dq1YP/U/U8C7NMn3XVb66uJczawMEPxJn\n+E9leh7fxi+UCM7msD2V4xvDtN5cxvdlKvUA228K4bxyH0O3rhHcsug6XmXqz9bp+uosRgGke4qY\nExW8ACRmPEQqTmF/DMkD2ZGwkh5GV5NEqk75XBo13OFSGoqUcLZNhiIlkoEGlq1yV/IKyWiTvbF1\nqnWT8vFutq+miWbqCE9mNFnEjnceAnPL3SzU0zybH8NZC1HYK6HXfOS2RPVQm/TZJkKGVi6IoXoE\n7spRL5sc7l1hYu8a+YsZuroq5NshtBUDVfUpvqrFc5fHeWF7GCdv4vkyewfW2SxFafRLhDd/YCHi\nJZkQ0nWNsJIkvfpaE8ycJEm//g/8PyZJ0jckSTorSdJFSZJ++geN+bLo1v3bjN+rhq6w2kywSZT7\nbjyO5atYvSphxcLyVR72dnHzwDKOr1B1AlxY7kO4Mn19RQrVED3xDpSxEdEpVYMIV+7IU+oWq40O\nc8VXnjvM6NQmW/kYC5E0Y4kCPYEaa3YCVfYZDRYI77BxfIXvPn0DSlPi9nvP8+zqKPm1OI1+Dcnw\nyTVDhAyb9YU0UthFqALJl8htxsCRSQ+WyWejjExvIYTERjHKQitN9uYYu0PrXGgMkP1th5g5wlRw\nnaOpWR74wF3oC1nQVOy+OLXDGSpjMnK+QsS0yK4kCS6rtLt8/EoVLzDIxrFBRrOb+LEgyDLOzkHc\nMNTzYcxYG6MO2wdlQisqsiNo9gqknjaa4uN5EhPJPP7tBfbH1vjK4n6ePTnFv7772zQ9g4ITopQI\n8vvPv4a7pmf46tw+IqE21VgAoyDTtadOdTtMrhnCDQr0soRUV1gsJGnVAsjdFm5VQ2sIwisKrTGH\n0s4wQgKtrMAg5JYT9IwWaHg6uUaI33jdV/m9Ez9GcT2DN9ImpDu8fvwCD33uVmrzXcgpn61yhOWV\nNIGYheRBM3N94k4n6XR9ovW1ppc/A+6lA9E9IUnSA0KIS99z2y8Cl4QQr5ckKQPMSJL010KIf1Td\n62URYbtCVd7ee5KaG+BQbJmIbrHHXGN/aAVTceg3OmWHjx/4G26OLuAKmTd3n+Zn9j/LPbsvc0/v\nDD3xGrlaiOWFLlq2xnBXkXi6Trke5OpaJ+0/8+IQhFwSRpNMsoahuGw2ovQYFbatCOutOE9mJ+kz\nyrw7/RxvOnqcf/2Wb2L5KqPpAvHeKrrqcWhiCdtV2Jjpome0gGa4pAfLRPqrICTifVXy6zHMWJuI\nbrGWj+M5CoOBEnpNUPj5Hp7ZGOVd4ycYCJf5xov7OF0bQl8u4G1tIzSVyT+6Qui9G3i76kym89Tb\nBsFMg9bONjs/vISkqrimjBPxEWETX1dpTqZp9AdwA4At412JEHn9JsENCcn1EYqEXpHxmipOW8XN\nm5w8O8HpM+P85dOvpLoQ59ZDM1TcIJ984RV87anD3JDYgLbCd2d3kIw0uGNgDpGyafe6zC12E76q\noUgd/RylDWpdJhFqIak+b9l1hjccPk07qeAGwVsMk7+ps63RqhL+N1KMTGZRJMG5b+3kFyef5D99\n403E4w3e/pqnEZ7EG4bP0/I0fAXe9+5v4cU8ehNVUARWQye0LkifbV4nT7yunE6HgTkhxMK1Cfh5\nOs0x32sCiEiSJAFhOk023xcF8rKYsKrk4wmZq9UMY8Y2AcXhj+fv5ndO/DiWr/KNrb28PfUCj1T3\n8M3tvfQHynyrsIfT5UG6jSoVt8P8fqB3nURfhWY1wNJmivFkHk1zMYIOuUYIpaeDHMq3wh2R50YM\nz5d5LDtFxQ7Qb5Y7QI1mho9vHsURCk+VJnnm0gSW11mM9IaqXNruwXZVlEybsG7j5E3yG52xzBUN\n57lkR58VuHB++O8ocDTJI/G5E2y9MkngC3G+/OFX8ezVMe7cM8Mzi2Os/XGI7a+McuuXLhBR2yws\nddGTqHFhrY/mepiRX20w/VtZvJ4UUiyKUDp7KXFhlmZ/AK3uIglQLFBrCnbaI18LUdnt4cYDGBUP\nvQxKRYWKhuRIyE0ZyZMw0i1i4yXqjsGX5g8gNVR8w+fBYwfpGSkgWgr5U928Onae/SOrKDEbI2oh\n31ZiYyvBDbfMgQRmTuIPdnwZgKu1DIbsUhuUUZsdnHP8goprQmzB58afOUd/qML6UhrjUJGsE+PI\nKy6SCjV5cHk3wYjFp8/ewrceOYS1p8X9a/s5MLVEf6hCMl3j9qmr1EYkiruvlwL7P0lbJy1J0snv\nOf6XvzdcP7D6Pa//oUaYPwWmgQ3gPPBBIcT3zaC9LJbEbV/jseI0Q6ESj1emuSm+xP7oGsvpJG+M\nn+ZpbQef3r6NHaFt7krPEJQtLlc79dcrtW7uSV1mJZDghsg6huwyMFziS1cPcG69D6epI+sesuzj\ntDRC8RbZSoSx7g6EUJIEg+ESq/UE81KaWtugFjTYakSZjmyRl8JMjGYxVYdNJ8rlbA9DyRJzmxl6\n0xU02UNLt0hGG+TPduGOWZgRi7DqkQk3yMoC21YIBG2KbojbT9f51sYW4d8y4cIc6e9m2EwPMVks\n4nXFqQ8H+XrkKHrdZ9dTSxA0maIErTYIgbBsZj80gFqLojYkghMllN4eAnmHVrcBQmDHBUKCnidl\ntu4OoMRtnLCK2vAIbcnYMZn2sE0w0aK1EsHXfeymzqGBVfrNMqrsMT56kYZrUHUNnj45za/c9S0M\n2aHsBTm31o9X1fGDLnv7Njhr9VNsh6iN+Gh1iffc/wGC41VKVpAHsjfQHnJxSgqyK1He4xJcUakN\nyRz/4j7aN9XpG+mQlH/ma3cROlAgE+qQvK/m44z351ibH+TQyBLPn9qBPCXYOtNDz4Etnjo3RTwL\nWuM6Mk689BiWvw5lnfuAF4G7gHHgUUmSnhJCVP+xN7wsImzZMmm6OpdK3bxYGGBYz3O6MkhKa3C8\nOY4jFDJ6nXPVfk5Xh7ja6ubtvSd5bfd5hoNF1uwktyfnyTthBswSi80Ug4ky+wfWOTi5hG8ptBoG\niXSNWLBFMtJgZqGX7HqCQiXEibVhVNlnJt/FO8ZOsTOS5cb0Kg+t7qbuGBSbJrlmiGiwTSZax0di\nqLtI1GizVomRiDTJlyKo43UC4Q6gwvdlVnMJkqEmgYBDKGDzyMpO9phrFGohln9dZuk3D7L+xiHy\nB6OIRhNfV4gdXyfzfJ7YsUVQVfxoEKcnBoZO9dYRNj/VTfJFGX+4zeBjdQZ+egsvHetEt802kqBz\nnpUwyh7hVJNgqE1xp0Zgs47a9vGCHd4lq60RHqmgxG16e0oMmUVmqt0slFIcCc9xPDtMXGvxa3d/\nk3P1AT56/m7+93Ovw3dklIiDqnmc3+zDsVSylQh+wsEoSvQ+K+iLVrm3+wqOrfKxez5L8qJAaUnI\nEQe9AsnLLrVJF7etkTEb3JRZIXU4i+/LzMz2s5xNsX9gndd0X2T86CIXcz3s2beM7SmEpku8beA0\nExNb1IcFtaHrtYe9rkinl9II89PA/aJjc8AisPP7DfqymLC+rXBpo4dCOYyhuvzV2u0d1n0nxHRg\nnadz48zXM8S0NnXHYL0dZ81Osm4leFfyec5V+im6oY6CgNIio3dIwXaEt1muJBkezCMrgmZbxxcS\nLVsDW0aPWniOQirSYK0Ypy9a5UR5mD3mGnkrTNJs0m1W6Q7X6Q7W0ZXOElORfLarYdYqMSIBi3rb\nwG1oyHInkdOsGUTMNoeGVqi1DZpNg3wuSsvSONMc5t/teRSrqWEN2FT2OBQOedReMYE6uw6KjFRt\nIJot3LV1xIVZqmMmb374BbYPyrhPJbFjEtqsidx2kaJhJMdDaTg0BgLETm1hFCTaGUH1F6o0qgEc\nR6U+5OMkTNoJBbUuIZc0PEfGslWEJ5EtxHh8cwfZZhjHU/izlaPkV+LMVrsoumG+c2GaQwOr7Otb\n5217TyNyBm4+gLURQlgK7YKJJAsCBYETlMg3gzyZm8RrqPyHSz+OXvcxSuDXNeq3N9k+pBJcUUES\nuELm8dVJ9iQ3iZltpIDH4ZElcq0wf/rEvSzkU9TWowDsSGxTrZp8auFmsrUwwY0O7/F188XrR8J2\nApiUJGlUkiSdDhPLA3/vnhXgbgBJkrqBKWDh+w36spiwKIJkrIHnycyvdjG71k3BCjFXzfBrZ97C\nKzNzJIwmNdfg1ZkLyAjyThhN9vhi+TC9ZoVH1nfSrVfJOlGObU5wW2qOry/ewLtHXyBfD3HH+FX2\n9m1QKIdxfZlgd4NktIGieZQaJp4rs1JMcG69j6/lDyBLgpViguFAR41gNpehbumETYt9iXWa+SC9\nkRr1tkEjF2TH2CaeJ+M0dXq7y2S3Y6iyR8xss39wDc10UBSfJ7OT/OHFexj/K8E7b3yBsfEs3UNF\ndv3GOVb/S4ZLv9XN1Y9kuPIH0yz8wRFmPrGf8g6JP5k5yuB3LBS703/aHrRZfn0crysOCysodQsn\nJNPY2YVREigW1JsG8raBuxhGRB0UyyM238bMddBQwpGRZUEgaKMbDtVmgK2tOI1aAFN16BktUPvE\nIJ+dvYkjO+d59vQUxy+NU3ZMgsNVJE9CBD0O7lxEiTiYVwLkbvIpTUuUFpKMR/N0D5Toj1VYe7NL\nMOsTWlRxGxqRJYGZE4iGyh3pWUKGzZNLE1TbBsKTOLE83GEMiTrIskBLt7hwfpgLuV6CYYtKNYQQ\nEvWbWgSul6CzAMeXX9Lxg8cSLvBLwLeBy8AXhRAXJUn6eUmSfv7abb8L3CpJ0nk6VEsfEkLkv9+4\nL4sJq6gejicTjbRAFvR2lbmw3MdmOYqq+pTcILl2mIlQDkeo3J28zMNzu/j8lYOktRpjZp6dySwH\ngkuMB7aZTOQ4Xhrl3+9+kIv1fm7q62Sbb03Ms39wjdZMHNdV2FpO4bQ0Wpsd+lS7reI5CkuVJEvV\nJLFQi09fvpmo1iYabPNrU4/QsjUeWtrFnfuusJRP4gsJPd6pxfq+hBpw2S5GUQ2XtqexP7VGvhUm\nHetke1cv9tCqGSR+b4Wnt8d5c98Z3jR4lh6jiuMoaBGLO8bm2DG1QXx3gZ+66TnsYYv2+TjG7BbB\nbZ/6lE3PYyqJGR95cQNv3yQA4TWb6rCKkEFMNdA0Dy9t42ZssBTshEGjz8CoCIIbElpew7ZVmoUg\ntq0SC7WQVZ94vMHMVhe2q1B/V4VYqMXxE1OM7tzkg7c+yqMn9tIfq6A0JSRL5tTFMZSlAK3pNnLK\nxjUFsi1xItvpj43rLe6dvowVk/jbqolrQnDbQy8q/MW376VpdTDkO1IdxsVErMF0Ious+niezEi6\niJAF5ZU4bx17kcMjS8iSwJgxaXVdPzGs61mHFUI8JITYca0Z5veuXfuEEOIT1843hBCvEkLcIITY\nI4T47A8a82UxYVXZp1wJ4XgKXekqm9k4ZsiiP1GhtRzhodndlNsmS80UX1q7kUeLuzgwsMbrJi7g\n+CoJtcHu8CbfKB7g8eJO3pg5zY9nXuSTq69Ekz1ezPZTdkwu1vs5cXWE4FSZvmQFpdFRLtcyLTxb\nIZlokEw0yM2m6QtXKFaD3Dq8yEotwaHMKn+6eJT37XgW11WYr6RJRJr0xyocGFhjZq6PWLgNKyZi\nM0As3Ga9HuN8qY9sJYLny1zNpxEJh3C8xWotTvOLPXx5/UY+8cxR7l/Yh5gJ877dz1GyTWbneiks\nJfji/XegLwXQ91SY+8AwjimhFjTaKZl6v4xotdFW8zipEMgSkg/JmTbOtknsSxF6HtFIHNfB8FBa\nHoGCgx2SsJLgK6BpHvgSvisTUF3euOss9w7OoJwPUyqGGYyXGY8ViI2VeHPfGT729L38+C2nmN/K\n4GuABGMTW2QOZjvKgEOrhFdlIosStdMpfF+m2+jkUEo3+CRmPYYekCjfYiH5kLgiSE/nee3wRQYT\nZU4/OcUNE2toisd3Tu/Gq+hYW0EWTg2iNGWE6fGVxX1sNGIEvxDDMwRq/fr54r8gnV6CRVSLVKJO\nJlJnX3qd23fMMd2VZW6+B3NLhqUg2WKUZ09PsTqfoWyZTIWz3Bc7z4VaHwo+d4SuUHEC/If+b3Ki\nPsZ/vP9t1G2dk9uD3Na3SFxr4QqZP3/FZ2k2DY6kFxFdFvFkHVkWDPfnqTYCVOoBpIyF7ak45QBP\nLYxjqC4PX9lFsR6k6XVgio4vE9JtFnMplipJdoxvks9FcLttRJdFPhtlMxunYesMp4rYroKhuagB\nB6ut0bB0Yks2Pzd0jHhvlZBhM/EXK3znA7dz5tQEctDFyCsMP1Rj9MNncc7HGLpljfI0THyuQu9n\nL9L/SAFhO+C62DGNVkolsuoiZAnJlWi8o0LtbTVKNzmYcwaS51Mb1DFqAtkBpQ1WNogStZGKOisX\nenng0Zv54smb2HHvPIrmc3GuH19I/NjQJT529i7kkMs3Z/bwC3uP8TP3PQ5hh4WVLkKaTb0RYLmS\nxLmtM0HVpsRbxl/kdHGQjF4nMVIicqVCvU8h8x2D7YMq1VGZ4tkM31jaw9pjQzDaJGU02FhPggQ9\nIwXiw2W692bxDYFS1Gg2Ddbycdx3FwnsKdMYuk5LYv5JZZ0fib0sJmzJ7tTR+kMVzhX6GA0W2GxE\nQfPh5gpeQHSSOm2J9FCZmYVePvPcbfybs29nT2SDRSvDf1x5HfekLvN4c4qgYjN2ywp7klskzSYt\nT2NveJXZcoaTzVFS8TqOULhv52UqlSCuoxDSOtldz1Hwyzrnzo/w3iNPc3h4mZje4raJeRTF52/m\nDhHQHQzFw1Bc+pIVsitJWq5GJNEES8GvaYQSLRASpVqQ9UqMnkgNz5dxSgFkxSeoOyy/Wuc3Hnsb\nLUvjLUNn8OMRZMdjZM8GwpMJHCii5KvI8Rg33n2F3NcHyZwWSEvrbL9lF2JxFXliGL8nRWi+RDDr\n0E4pNHp10mckqhud5m8tZOPEBL6ukJht4QQlzGwHSkjEQVk0EapA628guyDZMvvia7xnz/NMjGap\nuwZ/ffJmnLbK66fPY54J0vY1TlcGQQK5rDK3mSFg2pSrQdpNneqET2hD8JlTR1ia62apmcJ2VWY/\nZBLa8nBNsOM+SguMokTUbDN+3wLv3HWSZxbHyPRU2L1zleFoCUNzWV9P8pbbXkAdruM7Mn2pCr6A\n+mIMJ359oIlc5yXx/xv2sqjDSghu7VlkV3CDG6MB/uzMHRwcXeE1By/x8MYunD4FRfFJDjZZX04R\n6a5Ty4cI6A6PZndyY3KVUtvkuco4r0meQ5M8fnlonj9Zvod39r/AM5VJPrN8CxsrKe6399FoGcyH\n0/hCZqIvx+x8L1dOjOAlHJAAzSfZW+G5wigrxQS98Srn1vs4OnaVghVivpjGlhVKTZPqZoTBsRxp\ns05XsMapUhBsmcZ2CDVq47RV0rE6uuzh+TJyxCERabK1Feetdz/PmX97gOTvbvA38zcR+2ib9w8/\nyrHKDhpdOqUzGZY+ouDYKhwLooXBbkhgGLTTElJfN1K1AbEw7cEYeq5Fc79BbcohdlGj95jMNHwt\nygAAIABJREFU9k0JIosyalMg2x7aVoVQWKW4U8eNuWApeCNtzICNJMHOO65yQ3SDL37+TpqTNlgy\nrzl0jt69FY7GrvDl7YO8770PsdpOEtPaTPZvsxWNUN2MMDa4wdniIDgSUpdFs9uk+3GV2E+vkWuF\nabd0hnqKlAf78QxACHqfa9LsMajdZhDWbL67tQNJFpTPpSkMhgmGLJpLUdTuNl99/GZCExWioTbl\nVoD6SrTzc5WvH/j/X8SwXoKljfrfyR/OVruYGsiyI7zNkdBVNlJx6jGdsh3EFxLrQqJ9JU5iV5FG\ny8ByNL5dm0YI2DWwwWPlXdwdv8Tntm/B8lTm2t1UnAB3987wkLub8USeXCtMxTbZLEdJRxq8+eAp\nns+NUKyFcB2FaKRJpRpCU3xa+SDbis+OnhznCn28uu8yrq9wIL7K1UYXcvc6ca3FN87uQwl4aKaD\npykIV0aSfYygh+2qXN7qxq7rqGZHvlIzHb69spO+usPmRyeovtmmshbj/zj+dsRkA28zSDgn0ZKi\nROeh2SsRWhckL9aY/+VxJv58CQCvK4Hk+3iGTPZ/82jWW5iXTDwditMyww9ZeLqM+eRF8H38iRE8\nU0Z2Qc8p2H0OImvQjClIiuC808d6PcbofYsEFIe2p/HdpUmGU0V+8/jb+bk7H+dYYZLNRhRFEtzZ\nc5Vj7gSiF4rtIJIsCHd3Mv7tTADFkqmW4rx27CIb1SiFRpDaLpeJLzg01gw2bg+SOevgP5ugcqdD\ndiXJz9/2XT7p3cbBoVVOzg9jlGXkIYfJmzaZL6YoXUwzdnCVWS2CCHsMDuSZvw5+2MkS/zOnOb0e\nFpYtjmZmOV/qYyKSY0d0mye2JulTapRtE0P2eF3XOa5mMyg1Bc8QlDajWAUT71yMVlOn1TB4pjDB\n4cgif7FyB+e2e4kbLQb0ItORLdZaCe4buIztKR2aVL3F3t4N+sIVLF9jT3KTYMAiYNq0ba2jFreW\n6CzLAcdT0GSfT526lZTR4LGtKapOgHw7zLliP1Ojm0jrAXxPIRZtIusekgTSuQjFuSS+J6MEPKKR\nJpIkUBRBLRcm9zsW0RezaPMm4QWF8Y9ewTgdxtySsRLgBQTpT59g5EvbpL9+CSVbxkn44Puga3Bp\nDjlbxI7IVMpBvJpG3zNt4gse3SddXFNBKBLegR1Yt+/G6gtT71Uwyj6BvIRUVxEpG7mmIlwJu9hh\nSNxuhNkZyTIRyREJtsnWIqRGSzySncYVCrd2LbJVjFL3DHpCVQzVY7MQY3QgR2MpRqtu4KYdWhmB\n+UiER1Z2EjXb9EZq6Kk2tUGDRq+MlRLU+zpxYyKe51U3nmeu2YVp2pyYGeWWiUVG71xCVXzmiyla\nLR1trMZWLYKRaqEYHpZ3fSbZv1DEvERbbid5aGM3mtzZi/TqFRqWzi/NvQOAuNbkC+uH6I7X8EIe\nfsRFi1koTRkr7ZFK1BG2zHI5wYfPvYpfHn6cD00/wkIpSVxpstGOc1fiMjk7zEY9xsWFfnZFt6ja\nAU4+v4OFeoo+o8JUMke7pdPMhghoLka8jdRSCBk2kiRY3UjS3VOm4gSYjHXKD3Xb4GBqhaV8Ejfq\n4XsStquysy+Lrrukb99E6Wky1FVE1VxKiwmKWzE8T0KLWDiewupHTcY/uYxsgxQ0GfqbZYYeyCEU\nQSDf+YmEqdO8dQcr7xwiMqd0JqskIQ/2Mf+BMbYPgbZiEJ5XOyD/sgsCwhezBNfqaMv/jb33jpIk\nPct8f2EzIr0t7217O9093T3eO/lBGgESIMyCgMsiLkfsXVghLgLErkCsEayEhK4kBELSSBq58RrT\nPTM93dO+u7qqurzJyqz0NiLD7R856HLuvUCz23s1q6PnnO+cOllZWZmR7xcR3/c+ZhNXFVHzTYLr\n7eNsJjz0DRGhoOLJHmJNRo0b+OJNHFfgfLmXO8KXmYht8tah8+xJrVExNObzCR49v5cdvWkeu7iL\nG6ML5DJhBMFjNR9FH6gSjdV5y+6z6JsCtQHwfylC4ZUu5k/20x2rkL3RIzZroVQF6j3tpICXTk1i\nuxKLtTgR3eCuHVOceHWSsdAmTUPhYPcyTkXF2AhQ2QjhzgVxHYGNpcR1q8UfVpvT6wrHFakaPqK+\nJndEpvivr91CKR2mYSmcXBrkUrkby5HI1/z89NFj3LHjCorioGVFCFsUygF6+gpUKjruQoCPTD+A\n5Um8f+IF/mz+DqJKg+/md+ITbTLpKG/ZfRaA7ZE0/bvSvKPrNHeFLjJfTiDO6XQOFRAFD91nMTCR\noW6qXN1I4Qu02CyE6NBqWJ5Ip1ZFEl2+8b2D3Dc6RddgHj1g0lwOsVxqp+etznagqg4LqynCAQMv\nZCMF2mSAeLiBZcmMxArM/9wget5l8+5BvFoNb3WDocdqSAaIoVCb4J+SSVy06H6uzOUPdlPd2YHV\nHUXfENAzImOfXqP7pQZKpYVSNFCrFthOm4Mc8qNlGtSGgpRHZPK7BfxpaIy32n1bvwNJE8+DVlOh\nkAlzaaWbX/veT1CxNP6vF2/mRHqAHck09UyADx56nNHgJl5D5snsVgKxJrLiEAs12JLKUFyLkDVC\nVA82cTSPRpfIT7z9WX7uwadZycToHttEcECpgJFy0TMeaklkrpJkbrWdm3TiS7vRB6t889RepCtB\nnj2zDQQPsSmCz8GVYbC7raK6Hvih2CUWBKFfEITvCYJw+XWR7a+9/vh1S7DzyTY/M/4yAL/+4iMc\nmZhDDrcYDhewDRlVdBgK59nfs8K3V7fzwe4n0FQL5UihnRtzVWdjqoPx3izqeIVCKcCHj72ZOaOD\n27tmGfAVMBwZUfB4+q6P872Vcbbq63SoFX6q/2UO6IsEBIsbOxdp9bXIrMUo13VqUzHKTQ3TUHBq\nCn6tRSTc4PGX9nBqrS1w/6XB5+jf3Xaez+bDOBcjTO5eploI4Loiwb4Kotg2R4vrDZKpKk5VoZXx\nIwgezlyQ8+eG2H/PZfI7BYyEwMzvbEXw6winp3BVqNw5QemRGyhPwPp7TRp/1GDbR1ZZvVtAXdwk\neb5B9/EGbjSIJwuUJgNYMQ0rIOP5FDxFwkoEaHYHcBUBR2078ostEJoSSB5CTcJtyghXA4hZH3gC\n905OcduuK1xJd/Abd3yH2lKE1XoUPdXgv0zfysVSDz995Bjv7/8eR/vmCWgtcqUgp8+OIlck0o0w\nd01cQc+KCA781dO38e31HbhG2/Bu6ccdIks2nu5Q2OvQirmsF8J4RtsSqLbbwFgIEeio03PTKr5E\nk1RficB4CUEAJ9li6UoXvZHyf2/9/7/wRt8lvpb/bAO/4XneNuBG4JdfT6m7bgl2suDymdnDDPoL\nRBM1kr4aO/vWuLTZxQdv/C47Im2L0+3BNH2hEhtOgNt6rvLR7V/l4S1nMLtt1LLI9EI3zZoP/YKO\nWJH5zsI2ao6P5wvjRBSDLXqa480hZMnhEwu3smFGKNhBfnvprdQ9hQ0jTDJVZXJ0ne1daTzFQ1Mt\nEDwEX9sce2/HGsmxPN3RCqv1KP954Q5Cqsk2/zqHRxbYctsccy8NIlbkdlKeqZAK1tHHymSqIYpX\n4viTDTzdoWGqeANNvKDDiWNb6X3Owkh6uAmL6X8zwvK/OUjktg3En8+SPexh+z2shspGIczKu4bY\n8h9zVA70oq6XUBey4LrUenwkXlzDCkv454ogSbiKhNHhQ7RcHFVo28M44Ku4RC+KELQhYiHWJFqd\nNvQYqGGTl9eH2BdaJhGpc6I8zMDWDRJanZ+cOMm7R18D2pLBT6zcTkqtkVuJ4tQUPMnjrXe/wvKF\nbr73zB4a3S7NTo+eYx6rmRgHts6T8tdJJqoEZ8skX5aRKxJ9z3h4ywFwBYQVHWXVhzpYY0sqw+qJ\nXrzpICGfSVAzkTULoS6zbefy90PP/kfheQK2J17T+EHhWnyJ057nnX795yptXmQv1zHBLigZVNdD\n5Mwg5ZUIJzcHmM2n2jxcbZld/mUKZoBzlXYkw+nmEAB/MP8gvb4iQkPCGGghNCTEnIp0uIgXt/A8\ngacXJ5nJp7g5OsMnr97EA4El3jZ4vt0T9SReyI/TrZd5urqDxXIc14OZqT7OrvQhGe1bH9uU8RwR\nxxF5cWGUgx3LLGfjmI7MRiGMiMcfHnuQqNLk4moPu2+dITJSJOhr0aqr5GoBesIVGoaKaLdf0xc2\nqVU1fJrV/iIG6yhVi75nWoh5hcBQmdaWJhuXO1hZjyPVxfb7aYm4rkho1cWdXyK7T6S+JUVrrJO1\nu+JEv3YWqzeOljGpT8RpjMaoDuvYPgFXEVEaLkoNagMu67d7GEkBUXGR0z481UPJy7iWiHAlSKWq\n86en7qQ7UCFnBIn6moRkk2P5URTR5khynr+d30dYNXgmPdFuiUke8f4Ss9UOxC4DO+QidTXRsgK2\nJuCb0zi70sdMJkWp4mf2vXFk02tLBVfq+AoCwTkZt8dg6MYVmiWNM4v9tOIOj7z5BRbTCbKFMN5y\nAF9ng5l0B3OZ5DUV+7Xgf/lb4n8IQRCGgL3ACf7pBLt/TriLIAi/8Pfi382cgOBvb4Ts2LmEX7F4\nYOgyB5JLfCF/hFFlk3Q1xKYRZLEU579evpnjmRH2JFb5zNxhtN4ag305PL1Ns2s2VZKpCj7Fpln1\nUc0FeDK/nYeHzvKba/dxqjjIvb1TfPvF/RyMLbZ7q40kb+s/h6bY/PjRl/j5ncdpdVmYlsye4RWC\nsQbmaptz/PjsVnqTJfJ1P+/edopzs/2IdYlcK8DtYzNcynSxLZnBsGXC8ToesFyI4TgiruzRFy23\nJXimRHMxBC0Ra8PP3PtFVu9UcQIu0lMxkrEqqS05MCU6dmVgqAGih++iTvpWj4XfPUBwBar9MrYu\n0/etDGIs2g5O9svUuiWq/W3tqRUQqHdJFCYlWmFwonZ70y7u4hZUHM1DNATsbhMED9/uIrq/hdeU\nkUWXqZleLrw2zDNXJimbGjP1Lr61soOecIWLG90cSC1zx57LJDsrNE2Vc5cGScaqbdqnLRK8f4NW\nSMDRPBxHxCxpWGUfdtTG9gloBZj9yRDdxxrUxmx6O0rEtAZyTmGkJ0eou8qj87vpSpWxDRk7aiPL\nDj7NIuA3/yVl/I/ih2IN+/cQBCEIfBX41/9Pge1/T4LdP0yvkyJ+3rfvOFdKHUTVBr819B3icp3T\nhX6+M7Wd2VYnxUKQq4udFLMhzIyfatPHcj1Gta5xx+Aso+EcWsREtMDJ6JQqfiK6AZaI0JAIKQYj\nviynN/r41b6nSco13n37cb6+sovbE9NkmyF26Cv81OArhCSDC9VeOrtL1C7FOTs/QL2i4UUtXEfA\nqahkyiE0xWbdiDI0uMmH7vsKvVoJn2jTKOmcz3YzEs2jqxb1msZwMk9fsoR/rEyh6cdqySgBi+h4\nAV+8CYJHONQkuisHHlSPNMldTlJpaOjJBgJwcHCJsb+20XMePc9Cq7eFkWiHKm/cqCDUm9iDHfgW\nNjFjMo5PwNYF9Gw7/6aZEnB9Hp7kIaguTtBFy4p4Phc34EC3gdeQoaKgqxaNqg8l0o7I6BnMow9V\n8Royb+07x3Nz4+xIpsk3AtwzfIXTuX6ePbuNgNrCbCoInkAm23aOVDUbw5Ix7qmQOO8RfVEjMK+A\n6qKEW+T3uvizLlJTIL9TJ7Aos7KY5OT8IHZni40n+jncs4htS2iyzd6xJQRLpL4eopYJft/R43rg\njT5hr4k4IQiCQnuy/rXneY++/nBGEIRuz/PS/6MJdpYj8blLh7BNGQFYi8V4MrOVw8kFbuuY5Xvl\nLWwZ2CCqNgnILZ5fGOXOwRm61AqaZFOydObKCVqmTGBbCfmJKCVFY/N8APptPN3l6ZktLPXEeevw\neY7VJvn64i401SKuNxhSN7mnY4o/vPoAaysJlGCLD+55gpdmHmTi0DLT890oAZuOWJX0ZgQUF8+D\nck3j5dYQjaqPv3Rvxq+08DyBnWOrXFzopRLRcD0BSXJZzMcxmwrBkEHQZ5IzwnR2tdMHisUgw9vS\nrGzG2rugQQvPhcBYGfNCFGfEYH2tg3UhxZYrC3SctxB8Knq2B9GqsXEoiKO1i1Y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EAAAg\nAElEQVRt2s1fphju22SwP4cTdPFnHTyfi1IRKBUC2N9Jki2G8DxIX+7AcBRivga37Zi+TpUo4Lji\nNY0fFN4QEzYQMJAFF2MpxHuGTvBvd3+H2FCRA9vn0ZJNfvnuJ1mpxxiMFPjAzmeoz0ZxVejzlwir\nTYSROmO/U+H21DTf3tjJPQNX+K35d1BYjWLpr/ck53WKkyKu6uG5IkpFRAmZ31cJfXTxfn79xLv4\nP4a/jSrYPNhzgRtTi/z00Muk9BprZpQtySwPJM7jF1vUqxo+2Wa8N4vriXhZDVF06UmUqV6Js2LE\n2Nuxhqi4/O7Bxzid66dUDOCWVAxHoZnXySzHUQQHttTYGV3HVT3EuEllSMRVJUT7dfH8gIm0lmPo\nLwWElEm5qfGmwYuUbZ2Y0mA4VUCWXWxXxOqNk/jCa6hZmdJXuojOeCQuNCmc6kBPNrBS7d6sE3GI\nd7dVLpLkkgrUcS0Rf6qO53fYyEfwaRa1qoZgCdibGl6rrZ1tGQrhjhq1is573/0Ud43OsHtglS+f\n30fPvctIh4uoWZneZwRq6SBmQef5Z3ZxJDmPaSg0ZqKUqjq+mMHJ1UG2xTJkb7Oo9ksUv9rL+qlu\nDuyYY/1uFzzQswLago/yjQZ2TkN/nX89HMhzcnqY1Xr0utXij9aw14C4XGd/aJGR3Wu4nsjvffPH\nqJ9JIAoe1nKA//LUPWyPpFFFh4v1Xv7V/U9iBeFSqRtJ8PD5LIoHO7lQ7WVXdI2t+jrz6SQ/cfhl\nCtvB8QnoGQFhZwUtIzHUmceV27d0z+Um+ePpe3j/wHMEggYfvvpm/nrtEI9vbOfrzx/kk1dvYjKY\n4e7IJfaEV+lVinzh6gEkxeHqcgdzGynOXR5E7a3TmyyR1Gs4nSanMv3E1TqpWJWvZG7gXw29wGBP\nnvfd/DxxX51wZw01ZjDd6OJNYxd5emUS30ANp65g+z0cTUbZrJO45OCf0jC39iK2XBTVprYR5G+m\n9iMKHkcDM7y5+xytlsTsegf1vnYLp9VlkSuGcKU23Q/AWAsiGBJm3AXRo1wJUMsEsbM60wvdeK6A\n1ZLRogZOue1Z7LkC4e159N4aiB5evIUku1i2xP+2/1k+efYmxvQsZ8+OsGVgg6V8jHeNnOYdDx6n\n519f5eEbT/LwwZMcveMiz2XHEeb8iBZYTQXdZ+HOtHWuQkNCanpYQYHeA+vtz7ZjlsQJmeqhJqN3\nLPCe3SfYsmMFv6/F1n1L/N2Jg0STNTYqoetShz9UXOL/mRBx+fdPvImq6WPOSHHrzRfYc+c0Idnk\n5qOXEC2BY5kRtoXS9GsFJDzGbl0gUwlxeqMPo6kS/tlVXntsBydzg0w1e+jvKPLE6lYO33oJuekR\nWnMwGioIUGrquD4PuyEjCi6mJTOk5NiR2uDtfWfYHk1zT+cUBw7N8MDAZV7IjrFDzfNQ+Bx/unIP\niuTgbPjbu8UBA7EpYmb8rG7GWCrHuXPLdJtxJThspGMM+It8dOoePE/g84/dzvQnthP8YoTRjrbJ\n+4nNIcxX4/gfDxE5r5A87yJaLmZXiPCZNK2wh2g4KPMbxL4SQI602NGT5unVCUqun+PFMcY6c7g1\nhfC3LyD1dKJmFFjRqQ4KVG5uomcFkmcEPM35flfd2/SBz2kTHHQbyefguiK2JZEcKBEJNgmEDVTZ\nobEZIBBvovhshMX2CeDR1b3sHFjnMzOH2bd3jv5AkT29a3zq+K0AnH55gkdfOMSjLxzihvAiK5sx\ntt9yFWVrBRyBctlPcn8GtSAhV0Wkh/IE1l1KX+vlxOVRTry4lcIur22Bq9XYbLX9nf2KxdTZQSbG\n17mhawXhenGJvfY69lrGDwpviAlbcgLsveEqY9EcEi7PTk0Skk1OpAdYrUfp2JUhoTf49ImbiUt1\n1swo2XqQgViR8cQmqViVoVCe+BWH1XSctWaUnkCZ23pm6dYqWEEBVxZQVnzEpxwKxUCbAC97zBWS\npEJ13v3Kz/Py9CgzjS6yRoglI8Ed8Ss8FD7Lu/tO8heFw/y7pbcwe3wI05IJD5dwLRGjqeL6XRCg\nM17BtCVSapXVfJTlZpydo6tMVzoQAMOWcVWovbVC8Z01gopJTGmQ1Gu4KoSXLbpeqaIVbKoDPhxd\nxBxKEpuC/E4/5aODRK6UGf2YxeIXx+gOVXkstxddsjgQX8KfqlN+005wXEY/fhU77GAMtnAqKj3f\nTWP5BaJnVOR6W6bn66shiB44Ak5JxanLhILtHme+EKRS10gG6/gVC6ku0qyruI6IPFFFFNuWOZlG\n2w3x9PlRnp7ewnotwsTEOo+vbCW1I4uQMvnjB77IpXovBweX2B1ZIx5oIKguYsZHy5ZRt5XxhpoY\nlkzhTQ0kyyN6VkEZrRIbKSB/LsFzlyd56tm9rBSjbbbbvjlcBI6vDONXretWiz/aJb4GGI7Mkdg8\nSV+NtBFBkDwuFrroCVdYysbZEm1nxuqxJt/I7uHR0/t5z9CrVEyNmNokpjVpuTK53RLKmspLV0cI\nKQbPrE4Qkgyqg2DpAoEVqPZKiGmt7SJvSFRLfnbF17hv/DLv3HuK9UaEW2IzxJU6x0pjLNtxTpRH\nSCpVYr4GVr9JSDep1nS0YDuFSUs00TrrrC0msSyZL13aj73u59JmF9l6kKuz3ciSw9v6z/Gue44R\n9hs4jsh6LcK317aTbYSQDMBt3wqrr84Qf3KOwLl1HE0i+ewSqb96jcIjdVbuiyEVanT97WWmzgxy\nYnGILcE0frHF9s4NcrsFnM4obqVC14sisVMK0Qsy6Y/5sIOgFVzUUltzahoKniMi2O1UOcHnUC4G\ncFrtY2OWNDLlEAtrSRy/i+eI2BW1rTSSHPSQycZKnFikTmygCDkfG2e6aLkSzVMJdiXWed/Ol/iz\nhTt5em6Cfn+RVSPGylyKfcPLCL1NKnWN+nKYvQMr1FdDuK5IeawdIek4IqXZOJk3tSV/0e15emNl\nLq71cHZ+gNVClGZJI7MUvy516P1o0+na0LJlPnn5KFOlLl4+N4687uNdA68RVg129a3x/Nw4HVoN\n3ddisRhD3ZD5eno36xsxnp2ZIKwYPP/qNoxei+AyPLLzFD7RplQI0nBU1O1lRNsjmHYQrbZxtZ2w\nQPQQJJeSpfPi2iiFVoCWK/Gxs3fzt5f3M1tK8eELDzGgF/j8/EEWKgn0gMldPdPcOX6FnliZRLTG\nQLyI7muhFCVahozblPFUD6OlkFmO8+5DryB9JcG5Sh8ZMwyfT+EsB2haMvInk1Se6mLg2wUEF5SN\nMoIgIIQCOF0xfLkmbrHE1T/cR7Og4x6okLmzBxSV2CWB8e4sf3HmFtbMKGu1CEpVaPdte7rAg+J+\ni/pNdcorEayghxETUSse9WEHQfQQS3L75FVUEWUPz5CQVBdkDySPZlEnGGki1UXktIp/WUZY0nE8\ngWbVx+DQJv3hInF/kw/c+23sbpOjqXmMbpuX14e4WO1hZSnJjYOLPDq1h3O5HobGMxyILjHatYll\nyHh+h3NrvXgBB88RSO7K4iogXg6y/+AsQsaHIHoUZuLkagFkxaGnq0hXtEKso4qWuX5uvT+6Jb4G\nBFUTUfToCZTROxpoW0p86spR7kpMIYsuHz3wVR5/dTcB1UKRHFoph5BiMjGwwR0TM5xN9xIaqNDV\nV0BswdfndtHrKzHan+VypZugZmLGRETLo3FrDakJguy1M2XqCnPlJNtTGzx9eQu/2PccLOt85sbP\nMhrJ8+m9n6Pm+DjavUC2EuTuoWm+9PhNhGUDw5bZWEowM9uD7Ug4uodriwiqg7+7RlA3ObBjji9P\n7cN5OM/VUpIT6QEa7yyjDNUYj+cwf66Ar+DhqTLqS5dwF1dxGw28XAFpte3MKPh8CK5A8KqCNRci\ncb4GnkvyTAXz/+zGK6u4nsgj/a/RGG1RfPd+nNU0sadmSR5XGPt9g+Gv2bgy1Ac8Qqs2ckXEd8EP\nIsjDNbxA2/GxazCP6wjoCypa2MQXMTAMBam/gdNnEL45gzpZIe5vIpYUVs91M73ZwdXpbh5L7+bI\n+DzfWNgJHrx/4gVWa1GCqTpbAhnevvUsR7oW6A8W+cLn7mZmtROfv23Bo6o22pJKNFonXw7Q9ROL\nCDZkGyEm9rVVQGpF5I6+GTrCNdJXOqi31HY85Y7rY8IGP9olviY4nshEahNVtPmN7U9Tr2qEdJMv\nrd3AyflBPnLlfo7sneaDo9/lHYPn0Fdkzs31E1YNFmtxdnSluaFrBZ/k0IoKGOsB7g1eZH49ScnU\nOdKxQKPbw1UFHEekFQGhpCCFLJSiRLHm5/7EBYb6cnyzsJfk7izfq27jV7qe4WfPvJe7wxd5enGS\n39rxOEdCs9x95xm+dmU3D/ZcQgxaiA0Ry5Zw/Q7BaBOvKTMUL7A1scF0roOP3PA1JuPtyXdT7wI+\nxcavmYRkk2ZLoXxPg/zOEIwN4R7chrdvK/R3Y412IxVqFB6cRBioUx90UEaryJsV3FIZsWaivHCO\nrR/b4Mm5SRaNBLJuk7vHwNu/BUFRyO93mfrVCM2kgtwQGP/Py9h+kfglDz3n4UYsrJaMYEj4dIti\n1d/Wyh7Itz2p6iqS5OE6IuFwE0nw2NO1RleggtBh4h8v0TIV9I4GO6LrHD87gesKPHjgHMtmgq2x\nDW7um2fJiHMsM8K3ruzkxXNb0G7N8cDWS9w7MsXIUJZaNoA5bFLIhhFFj0wtBAJkXuqh1vLhNWTM\nhMN0tZPl6ba5Sbmms7SeIBZsXJc6bF89fzRh/1l0qWVUyabLV+GpwjbclkRPsMzqy70k4jWOdC9y\n+rvbOFUf4YXcGGbKZe/YEv16kfHwJnfEryCLDkHVpLK9RWBZ4m2P/yqHRhbZEUuTNiLE9m4itjy8\nrIZ/wyO4JBIONXDVdkzkuhVlIpIlLDfpDlT43IVD/MHKg4R0k1/5u5/Dngnxx599J64nstqIcufY\nNBerPYjrGv07Nnj72DneeeAkjaUwgimyXgmzVo/SEarxqZVbmMp1MhAuElfqvGPgLHtS62wNpGk2\nVay6Qu6wzeaNMQpbdGpDAcrbojiahNUdpdovYrdklGQTWXa48ntxrMd7MPvawoLmeIqRjjyy6DLZ\nk8ErqchLWVAVPF87k7XRKWL7PbxKlVZAJJC28JU9lLSK05SQ6iJGUcN128VYzIZwHBFFt+iMVBnq\nzFNvqqzNJ3np9CTn0z04DZnqegjblOiPldjmX0fwOzw8dpaK7eNvTh3i1fQgG80QeTPA+moc1xG4\nefcVGqbCpVIXy/UYKb2GEm6h+lv4VlVuHpwnlwljbmki12H9QieCIyBXRdbKETyfy0/e/iIDySKq\nbrElmv2nyutfhB+1da4BGStMRDGY0Da4vNnJ9tE1pjJdSFur+BWLlzcG+Zl3PYEmWoRVg8MHr3Bm\ndpBvXd2B6ch8au4op7P9WI7EzvFVep+r8Kd3fZG42iDXCtCwVXTFwtZFopcFWhEBf8alZcttIfSm\nn+P5MfaHlnj03D4eTF3gDw8+ygOpCzzUd5F9t06z7eg8j/z4szxV3E5QNglKJtOFFHbIYb0QJm1E\nSBthjh66THCwTKWmc0NimfVSmKsLnVRrOlc2O9FEi7945TZeeH4n/+nk7TimRChZJ3pOoX5vjeoI\nOApUhiWqfSrlUR35UBGhoJKM1DCuRBHXNOZnuvClq3i2zcpdCtOL3YxqWe5KTbWJDukNMFts/ZM2\nz7bZ4WH3tGgcncSIC+R2+ID2el70OdhhBxwBQQBRcxgcyNFqKFgVH+WmhuVIeMsBgt01CNqYq0GQ\nPEI9VRTdYr0S5m/XD3Df9kt84dmbeXFqAlG3UWSHgUCR2XyKkaEs471ZVmoxeqIVUnqNXDPITD7F\nYEeBVsbP4E3LbBpBunqKxKN1Ynel6TgFN+y+2pbqmQpKTubp9GRbMXQlyKvpgetWiz9aw14DGi2V\nq5Ukv/PEw9SWIgwEivza9mf5dzu/zUo2xl19M3x5aR/fTW/nLamzvPTaJIe3zOE4It1amWpdYzia\nZyBYpGTo5HeFONdof4mqaFMydXTZwp82MBICRqJ9xP2+FqIFnujRciUuN3rQwwY7tRUMV+FMbYAn\n01sZ8ecomTqfeeFWXlwcaStxLD+pQJ2uoTyOJdF0FC5udnNqbYAdqQ32DaxwLDNCR7jGoa3z3D42\ng3U5zGeeu5VYZwWns0VPd5FfvOEFHhk5TWlfC89r248acZH6oIPgguODpqEQnhXJXOpA31LCUzz2\n75zn6k8mkHt7GNi3xqHJeb6wfIiG40PuaSDIMm69Adk8ggvBFQid8bF6h0zfV5dITLUoD4sodQ9l\nXkcwReRIC88V6EhUWFpOougWgupSXoy2FTldJqahIMgu4eESim4R1Q10zUJXLZqWwsnMAK7mooXa\nsSeOK3Cu0MtgrMjy6V5mFrpYWk8QUZvIgsvq1Q5SgTojoRy4AlfP93E1n2RjPcZ4bJNbOq8S/8Ul\nruQ68GIWZlFD31Yid6qTZpeLPdaklglelzr0EHBd8ZrGDwpviAmb0OoElBY9E5vceMM0D8bO8rFv\nvpkVK04iVuOxqzvJXU1we+cMJ2vDjG9bY76cIBZucLbUxw39KyxXYkyXOljLRhEcMF2Z77yyh4at\nMhrOMbPeSSuiotTACbnYmkCxHEC0BBDgaibJSxvDxIINPr5+Dx+9dC9Pnt7Jei7KYws72ZNY5ab9\nU98nu6/Wo4yFcuxKrOPaba/aiG4Qe11ap4oO6WyUkVAew1ao2j4euv8Ev3X3NyluhHnbzjMIwJcW\n9jHXSNHTW8B1BdRdJco7LLTOOtUBgfxeh4muTbqfauejVjJB3GSLc8fH8W8IeC0LXbaYznVg2DJf\nW96N58H87x9g8QM7EcIh+p4qUe+F6n4DJ+iALFEaUUhctml0CTg+D6ItWNVRfTYb6zE6e0pomoUg\nuwhxk9wTvWj+Vluv6wiYloxttQ2/VdlBFDxylQAAW7es8qFd38Kn2Lxt8Dzd/grz+QTaeBmxKnP7\n5Aw7w+ucPLaF9938PGvlCK+sD9EzmUXoNGg2VMSqzLZQmvPlXvxymwoqaxaC7mCejxLbt8nw9nWO\nDM+zf/v8datF7xrHDwpviPQ6SXBZLkURgN/d+i1++eoj3H3HGa42OrBsieFknlq4xnPZcSKqwZu7\nz5GSK/z26bdQbfgw8zr7d8xTbWkk4jU8SePLTx3Fi9oMB/K8nB1msDNPpbsXf9alfthEcPS2P9Dr\n7YtI0ODBvktstkKcyfeyt3uNd+9+hY/O3c/vj3+N/7ByH2uVMIX1CB+cfIIT1VEKVoAOX5V7tl/m\nhaUxbhm8yovLo+0wYlsA1eX48jBmzYes2ZyojSJVJISOFk1XJanXEf0uZ7K9DERKpLNR0CyUiImx\nHkDc1kQCLl3pZzLSJPGaSOrYJoJl4yTDiFdXoKeT5ZLElmSWxXKc0ViOXQNrfKp5E+gWxUPdRL5+\nFrNnB/4pDdGC5R/rozZhYYUV9Aw0OwWkRQ2zt4WzEgKfS6mmI50JIYU97F6T2phNQPSobQaQghbN\nnJ9Uf5H1bBRR8ggFm7Q2/Pgm6siiy+9fegCjqfLVhT30RsoENZPMagwh0cJFYEzLIFowU++gXtAZ\nGtxkKFRgPLpJr1bir18+zGcv3ojfb1JbDiMmTexNHc/nsufOaVar7bDs4+lRDo0sXp9CfH3T6XpB\nEIT7gD8DJOAvPc/7o/+P59wGfBxQgJznebf+U6/5hrjCOp6ILLp8avfn+Jmp91A2NEKywUy5g8Fo\nkfnNBEm9RkKrI4sOLxbH2bTDjHTk2dGdRokZNGyVpXyMidgmrbDALzzwJB84+iS9viJhn8FyNo6R\nFFBrDrrfxL9pt2l5IshFmWrDx6Avx9l8L0GlRbdW5iu5A3QFKqxYCfr8JbYnN/jpG4+zYHZQsvzo\nkkXdbq8FVcXmYqEbczlIKFEH2UPRbLpjFY5uucr23jSCz+H2my+gB0zO5Hq5nO7k7LkRAJZKMaLR\nOj3hCnZeBxGkRY0jI3N0DhTgowXK9zS48m9jbNzb295VTsa58ssxPrj1SVaqUfLTCTabQb68sBeh\nLqOcD5LfIVB8eA9jn3foOGMRWnWxdYiflPFECGQcQoseZl8LuaAgxE1QXMyyhrWr/n9bycguguCh\nx9tB1YJuU6rqiJsqngeldBjREohqTa5uJtnWscFYd5bReA5VtJFFlw8cfZLhnhwnVgf53dfeRKvD\n5qboVQAe7j1NSDYQ8TiRH0JqiAjLOuHPhyFq8et7noGIhWCInFr8b+y9Z7RdZ3Xv/Vtt97732af3\nrl4syZabLNlyw3ZsCI4JcBNq2g2Bm5uENG4YuSkXQkJCCCEJ4UII1RhjMDayJNuyrGJ16Uin97bP\n2b2vtddaz/th+2VkMEZAjKv3xXcM5hjPh7PLWufDnPtZzyz/XydrGT9rM1G2di6yXLoxqonADdti\nX6dd/D1wP7ABeOJ1KsZ//EwI+DTwsBBiI/DzP+66b4iATWX83NYyzT8k9rM8G2NztN4nG3RUWSoE\n+aOtz2ILmbOTnZyfbWcuH+ZEtpfpRIzpTJQ7u6dYLfjpiydJVr04M4J+Z4JPHLuXr87vZGotxh09\nk8h6/YzocdSo+RQQEnJNwvRbBLxVPvrCozzUehlJEryS6OHYVB/n5tv5u6m78Co6EUe9fFC2Hbw0\n1U/WcJM2PLw810ex4CJxJY6rs0CDr1RP4MiClUyAxWKIwUCC5sYsppCxLwdp9eV4ZOAySNAXSXKw\nY5RSxcnkagPupiJqrIJrY5bxTJwP9R1iKVd3ypv7p7n1vWdI3mKy8HPNCEVwqtBDKufFsyqjSjad\noQxvuvkctc0lhm+fxnRKKK+O4Dx8kcBT56m21jA9Er7F+tGg5pNwzzqQdbALGpqnhuIxqZU06Csh\nSiqK26Jcqv842SkH5DRMXcWOGQT8FZSAgeW1yesuhJBYr/hYyQcYXW+kyV0gdaKJv/v2A9wUncfl\nqPFHO76DM1Tlz489CIbMv0zu5Tsnd3DkyhBvbj5HcCCNazDH6s0y7msuPvHcgwSD5TplT7bpjKeR\nahLd3hTzyzeOXncDyzq7gUkhxLQQwgC+Qp2K8R/tbcA3hRDz9XuLH5vufkMEbDyS4+jXdrFe9dHa\nlWSpHKRqaGR1N/e2XeMPX3qMyVSMXf2zPLThMkPhNcqmxqbWZRq8JV6a6eWJnjO0erJMLMVRDEHJ\ndtLQniHqLtMWzfLiZD9CAT2oUKw6Kcdl1DUNpLqwV81UCHVk6XMmGJ9vYu1aA7FwgXdtOsH/GHiG\nb1zcwblkO1+d2MG/Hb2dSKjI6ZODVC0VY8GLy2PQsjlBZyRDpuxGS6rUqiqKYjM/3kjBdFExNF4a\n62fvfZfo9qb41uhWPnjXc0xlokTUEh6XgdNZQ9c17uyeIuyp0OQt8HvP17GbH7/p61xcbWG+FOF3\nb3uW7oemecvu1yiYLoK+KsVuk8VskJGlZp6fGsZedjP+Qi/eNQuEDVsHqN69BYSE6YZcj4xWEQil\nzqFVdAn/lIppKDhdBu45B0FfBclj1rOjGQcxf4k7d19FChv4gxU6mtNIUr1hRM0qLE7E2d6yiFcz\nKBZd/MmmZ3j+2Db8u9dR+wv4lSqlixH+9PyDNIfztHcmuXvnCH8w9D3CnRk29i3xseceYkd8iY3x\nVTq3L3HnY+cQUYNS2UlTd4pQoMzUSAs7b5pgrhxBVG4UHxZsW7quBcT+X3LF6+t9P3S56yFgDABh\nSZJelCTprCRJ7/xx/+OPPcNKkuQCXgacr3/+G0KIj0iSFAG+CnQBs8BbhRCZ17/zYeDdgAX8phDi\n+R93n3e+83n+4dydqA6TfUMTTCzFwV/i6Go/H7j1EH/74r2cWe6je2iF4WCCFycHUZwWbQ0ZDvaO\n8ZnzdyCvuFA7SxTbZF4rdvPVTf/KA6d/FX3Ng9BstJKgEpUxTZlA0qbUVh+70+NQLLnY2TnPb7/4\nOAO9K/RuTNLnXiNnuflOZhsHN17l6JFt+OZgw9smuPhqP1bEZMifYOiuBPOVCEulILaQ2Nm4yAuJ\nANqyk/D2LKG+ChP5BgAc7hrHZnpRVJtYuMDfP/kA++8/jyXkOpFPtXi09xJfvnwTtq5QasoTuSCT\ntgMc7xrg0d5LXM038b9O34eU1pjuilKtONAcJnJFxuOscaBznMVyiAvVdhq3pFgINjMw1YO0kia1\nvwukGkKB+HkTyRbEzxpUGxws3l1/1tOWnRiKE9kpKFUdqA4Lv6+CEi6ymAhTMjS8/iqlkguASsUB\nop7Mw1djvhBmeaoBOazzkSsPYXst1leCNDTnGC02oW7IE/BU8Tt0dFOlxZXlMwt38o6e03xl7iYe\n23cKW0hEHCUGfQksISPKKrWKyobuKU4td3JgzxUmcg3kq87/XNPzJzUBXP8ZNimEuOn/8I4qsJO6\n+qgbOCFJ0kkhxPiP+sKPMx3YL4Qovk4AeEWSpO8Bj1Gn1/2FJEm/R51e97s/RK9rAV6QJGngR2kT\nJ4oBXkgMs6FjhUcbz/OXFw8SCFT4i74nma3F+PDRn2f75mlkSTCdiXIk289A5yodvgxuxUCWBJIM\norWKtejBmRWsVgN8YPYt6CUHzniZ7liK5KudKAhKloyi11sTFUOgFGVsn4Qq2bz75mO8kuwla7hJ\nqAG+fnkHyoqTzbdM0rdnjq4DaV6c68Nq1tG0enb0SyduQQtXiQTKzM41kGt1gWpjuQTZspv7u64y\nV47Q5UtjC4npQpSB4BovjA/Rc+sibtngn4/t4zfuPMSnDh/kBWmQhmiBO5sneWmlj1t/7RQWMgXT\nxYtzfbx94DWuLPfR880C5VYvD/3PwyRrfuxeiaVKiJOJLkJ/6qFPtnnosxf466UYyd1RnPkwlW1l\npHUXnd9OIzSFYrcP0+Vg7SYZz6KEHhZ1XqwENb/AWPOiBA1MSyGT8hCKFCmUXAXsgX4AACAASURB\nVMiywCpoVBSLWkUjECnha8ziUk1sIeFtKXBzyxwF08myN8i26CK2kHluYhg74+RNe0dYKId5rPEc\nf/Liz6EGDB7oG+GbynZeWBgkm/Kxb8MYx6b66G5KMji4xMyrHZxLtNERznA20YZTM8nlPURas8xf\nZ4T8OLuBNdbrIWAsAikhRAkoSZL0MrAV+E8D9nrodUIIUXz9T+31JbiB9DosiYqpYdoyn5vbS63s\n4MHOEY6VB/j07D62b5jhylILY8k4A9F1tjQv82jzeV64OsRiOcTz08P1y+gK4RGJzLBgtRRgIRvC\n5dPZ3LzM1FoMR9HGUbSp5ZwIBcygTalZQjbrmkfNrhwvrfczNt76g/OqJAt+9cHnuSM6wWrBT6Mj\nz+0dU7xr26uYNYVLuVbu3jGC84yPdM5LT9caiUQILAnxehfVVDHG1bUmjowP8NLxTcwtRzkyOcie\n7lnm1sN8b2YDN28b5/OTe4j1pVhP+fE7da7lm1Bkm4linNH3D3E+2cpvbjjKl6d2og3msVwqC28S\njBabOZdu5/DiAGXTwXt6jqNcnECdXOaTzz4AJZW2d02S2CUjLbpxZGWkRBr54xmEBMVWpf6kEbER\nmkDtKWL5LNTGMkpZxuEwKY+HcHoNcpNhahkXDoeJ7KvVa5K1emlHAjJlN4mcn7i/yJGJATb5l+kP\nrjOWa+S7FzejqhZvvuU0Xzu9i2vJRj43dyvOSB0G/a5r72DpWiPDsQQtLWlUyUaWbdYKPiZX4tQC\nNtlVP2MXO7APRdFrKv0ta3hv4HjdDazrvAb0S5LULUmSg/om9u0f+szTwG2SJKmSJHmAPdTpkP+p\nXS9bRwHOAn3A3wshTkmS9KPodSf/w9f/U3od8D4AZ9zP7/Z+j+ezm3luchipoPLM7CZsIVFa8hPZ\nXObOnkkuJlvwKgZeVWdej6I4LbYGl1gpBUhlfYTjJUx3DE93Dt1S0GsqD/VeIVtzI8uCSkTGlRU4\nI2UKbX5cdewsptcGS6LXtca+zmt8KPlWWpxZ/vn8rQx1rHIu30GXJ0V2JsznV28FQPGafHDnYf5x\n7DaIQrG/ht9tMD0b5+CWEQ4f24rlsakWnRAHQ1exTRlvd4HWYI69sWkypgcz5UaUZE4mBpDDOtu6\nlxhov8xXJndi6CpBf4WbwzN84XcaeKLlGi9mBnmk+zLpmpf5Pw3z4caLnMrVM816TcUUMl9dugmn\nv0rh1m7eevdxvjG2jbHn+jEbbXo3LTGTiHLtTztRT6l0pA1KTQq2BuGrEqltNnpZA6CWdyI56hM7\nRGo4FZtgX4aapWCaCq0NWRZmGoi1ZekN15tLsmU3lYKTxuYCCY+f7yxuIp334HbVuH3jOG6lxrdG\ntzI8sIRDtrh8tpv9t15GtxXmCxFsl83IehP5hI/luSg9vQmCjgq9HUlmSlEmUg0U5gN4H1wllfcy\np0eo1W6UzOmN6xMWQpiSJP0G8Dz1ss7nhBAjkiT9yuvvf0YIcU2SpOeAS4BNvfRz5Udd97oC9vXH\n2W2vp6GfkiRp0w+9L6SfcOxfCPFZ4LMAvZu94hvJXayUA5iGysDmBXK6i+5AGnfzPOfX6vHu0Woc\nuTiM5LYQusK79xzjX07fRqQxz0d2fIePj95Dw/kSM7c6CLirqKrFrb5xXshtxNBVfCYUW2R6GlIs\naH6EAs4MVDptsCX+fWE3B5uv0RgqMFpqRHVYzCSjTNTiHM8PoFUk/tcDX2bBiPJ3l/bxmdHbMHSN\nguEETVApO/mlXa/y9NxmGjeusbwcQdRkLi22YlVVNvYsIUuC9bKXE6luBgJr7NgyRarqpVLTWJuK\nckzvZ741gt9dRbgk7m+9il+p8vjgOTyKzqlLfZxSe+nqWmN2spGVgp/0egCprLB7+wT7I6OElBLf\nfmo7dwcO8U/XboVpL51fX6WwuYHFVDveHCi6YOcvX2Li2AZCUzWKrSq5flCqMlScyF0ljJIDYQsk\nXcbbWqBacSCcUE57cAR0qqb6A4nRsWScwmQI94qMc3eBi6stVMsOpJNBxLYyHqfBybku1Cs+zC7j\nB3CwQG+Ws4k2yuejbNo/Tq1LZmWiAWdGRigwW27B05Xn0mu9WH4L2WPibC6zMtWAGqmiqDa9jUlm\nfhLn+5GOeaMuBEKIZ4Fnf+i1z/zQ3x8DPna91/yJssRCiCxwlDpZPfE6tY7/U3pdquYlpXt5sPEK\nGzpWuCM2SbbowasavPrsFrILIeayYfY0zCKZMqKoonhrTJUb+NAthyhVnHx+cS+2kCg3u9jfN4bf\noWPbMn828QAnVrsJ+CtYTkCCa+OtBGbrs7FI9TqsZEgMhtYIqyX+vP9JTi90cmfPJNaUj/7mNRra\nMzxx/8t8fOognzh1D7s759CrDrRrHpJFLy6/zr3910jVvAxF1+o/NoEqsstEWBJ7BqfJVt04ZJPV\n2ShjY63MlyKMpxpYO9LKm9qu8On7P8+Dm66wuB4mW/SwOz7H/YGLfHF2N8vVEF+evgnJlpCcFiXD\ngZpXUGTBroEZhNfk7FwH305s5S9G7+NiooVPHbkHc8qHkMGamMb37EU6//wMelSQ2VXj3Oe24JnN\n414qIhvgWZZQSxK2Q2CUHGBD+KKCGqtQLjqxV11U5/1oPoNaRaNiaCQTAcq6Rj7vxo7WcOQFtSUv\nTcECtq7guD3JcOsqUXeZHe2LvOcXnmP/plHu776KXdIoTITY1TSPZMHl5RZqloKntcid913Asyzx\n7gNHaQ9l2XTTDH29q9w7eA2HZuKMl4mHC2xvWaRUc/wkbvwjHByELV3X+mnZ9dDrGl7fWZEkyQ3c\nA4xyA+l1QkhMPdfDp6/ewchEGycz3Xxy+1cIqBXe/Ngx/um+f0ZVbL52ajd/cc9X+Mj+b3Ggb4wX\nLw3xiVcOYlQ1dkXmyK/7yPYr9LiTvLftGJ/Y8jXi3iL7W8fJLQap+STKzQLnqoZWsjF9AlujLpkS\nqpEx3MzrUd7+3K9wS8csL1wdwmwyqNkKd7eM8dWxHaxl/HxozyEArJLK/3jHlwi6q1SLDpYrAZ4d\n28S5pTZOz3dgmjK2oYAEr0130h9a52qiCSVocPu2UZaLAYxLIbiprl74cmGQQ9ODtDVkeOvAOc4m\n26kJhe0Ni7xwbiMD0XV6v2Yw8O4rNLynwMBfT9Pw7jxTXxioy7wYCg83XqQnnOJAxzjBzhyb905i\neW3U9jakjlaUlkb0JhMlrdH03CK5jWFMv5NqVKLcIpBroDRVUJManmkH2WGbWtZVp9IJEOEauzvn\nfkC7U1wWuq6haBZKwoH8cAqhCpZebaWvO4FLMykYTkxbJuIo8+lLd3Dk8jBTxQYG+pe5/bYRvn95\nIx984lu4nDWGIgkqs36WyiGMQL0LLlH0YdgKUyMt1GyFu9on0BMeUieaOPviEGvnGn+Ue/2EJl3n\n+unY9eywzcBRSZIuUT9IHxJCfIcbSK/TTZXAHQlcjhqOgE6/b43ZWgPZmodzmXZOlfr47wPP8+DO\ni1wqd3Ct0sJdwVEGelf4xT0neXTjBSZLDXimNYyQ4IsTu/lyYjfrVl1gOm148cwreFYFoQ0phCKo\nhhUCW1MoOigGiLJK2XTw1OhWHtp9npfObGBX/2yd5K6YnEh2A3W1v6pQKZsaf3j7MxQsNx8f+DpU\nFOZyYZqiOfS0m5ZInqCvipbQQICiWbw80UdPLIVtyry22MlHBp8hujtBed3Lvxy/gyMrAzhP+0i8\n0kLOdNPpz1Cw3cy/r5uDOy/jVQxWPmAw/dGdWE1RjMEWjL5mGs4XGf7LNI3Pa/zb/B4uHe/n2YkN\nlK6FuXakH9+sgiiXqTUGWN/XRvSUiislYXTECB2fRynVCI+bSBYYQYGVcNfB2NsKIIHsreH2GFgB\nC1GTuZho4ZaNkzR5CwQDJSxdoVbR8AxmURULbIm+O2aZvtzKSiLE7GKM22JThNQy79h4GteCxnrF\ny/vaXyale9E8NaJKkVtbZ3htsZNfOXiIkalWHDeneXJ+GwGXTsRZxtlcZqEUwhYSWk5GbCzgnwW1\nfAMD6A3eTHw9WeJLQojtQogtQohNQoiPvv56SghxQAjRL4S4WwiR/g/f+Z9CiF4hxKAQ4ns/7h4+\nh87e+Az/uuV/IwRs9c7zV08+wpHzG3hnywn6XKv88YWH8ao6zy8OU7E0vpXczm92vkDedBHVStwc\nmqHcWQdB/ULfWabSMf5u6i7mMmFSuof4WR3LBWVdQ6lKWA4J05JRKgJnWoAkyFTdeD06a7qfB/Zc\n4N7oCA+2jbAlWE+Q/MrGYzy29RztWhpVtjmZ7+VyuY1/S+3lod3nafQVWVoJs2vTFLqlkCu4EV0V\nhCVjmQoknYyMtiMsCXHNx5cSt5A51lQnkecUtsaWKG6povdVuZBqY74Q5r8++S62/+sVFkph9gSn\nKaU8vOW+40y+3U/ud4pItkDOlpDKVYJfP8NiIkzPV/P0/3aK/s8s0fFsgUKfiTnYzo6/PU/p4Twt\n75xBSDD5DvUHXqCHZLRi/XFYLUoYjTX0ohNsCc1pUsq6QRL4YiWKKQ8nRvqwkSiWXbQ2Z2hpzuB3\n6ZiWgqu1yMhMC1JNQlIETU1ZrhaaWdGDfGV8J+6bUuyNz/BUcgdjq3FqJY2/nr6b748P433Ox9lc\nJ66AjqrYeB0Gd8QnOXFqCMuUmVxt4GyyncabVuGaH9MlIW5kR/wbPGDfEM3/6uuli+PuPu7qmeB0\noZedd40iS4KPjd/DtoZlooESR5f72RRb4WSii/W1AGXTQZsnyxfHdlPNuoieUUjtrfH5Q/u489Yr\nLJWDOJT65p4ZdKKVBOV1L27AlbVZWgziapKQLdDSKr98xwkmKo2M5Jopmw4upFpZWorw4b3P8iv9\nr7JuOXhN6uab6zvYG57i1UwvuZqLmWy0zr5xVlHSGjPZKF5HfbLFuBLEbLBQgyZmxGBr1xKGrTCZ\n6KRsOqgOVulrXUfuFHS60gRDZd7Tf5xjmX4ijjLTX3PzzEA9x/cXlx7Gtyjz/ZO3woEKXofBzMMx\nPKteWg+lsLrjtD6loaytIowakikjnbuG8oHNTD/qYeb5W1ArEmNqgL4vzCJCfhL3deJfMglMVZAs\nN5UmMGIWziUH3iVBepuFnnQjB2p4fVVC7io+l87qaojLIx2gCvLOGqapYJoytZKDvq4EObeLpBTA\n56uiSIKzi+0YRQfYEmrM4rvTG1FVi33dE7y22kGq4EXY0PLOGa6uN9IfX8e0ZWKuIkdXB2gYTFI6\nEidw9yrrWR/aRR9mRFC6pYLHo98YR/zJGid+KvaGCNhs1c2tkSm+OLuHbNFNT0OKPZFZvnBpD7au\ncEJ30BVNs5gLMp5twLIlWpszTCWjmBGZna0L0Arz3x0g1rXCe287xu+f/znu6JrizdEz/Na5t+IW\noJUFWlBHmVZRDBuhSHjWBHpQwu6scKnYzvenBnmo/wpxRwGnXENv1ciYXt498QQd3gxL5SDjI21c\niLXRGU+TLrspV51sbFqhWHPStHGNku5AkW3cjhq1wSLykpeaU8UfLrNcDPBg2wiN+wpYQkJWbRTJ\nZnNoGU02eVvva3zy4n7eu/kVehzrfP9DwzzScZF+d4IX44OMjQ9RbpbY0bHAbC6CGbCI71ph1t9O\n/EwNwyeDZYFtgd9L5oldbGmf5OpUH7cevMyRkSGaWjKIUglMk9TuAMGv2qzv9GIrEL0gKDWr1IKC\n7LBALSooVTArMrbHoFB1YpgK6poDy2PjbixRSHnRvAaWqaAmNebW2jB9NsJnYtsyZUNDlgVb+xao\nmBrpioctzcscm+3hYrKVtmCOVMXDrp55vj8zhBASl8fbiTXnSJa9JFN+/mDXs/zZ1vvp9xRYno5R\nazdxx8u8Z+hVCpaLH1kL+Qnspzmcfj32huglBvDIBu/tfoWAt0rIUeGrEzvqdcsJBz/ffx6HbPGm\nzhGavXkMU6UrkOZDGw5z7Vob6xUfx6/1UQ0p3Nc4giLZ1Coa04Uo58pdKIpNcK6GZAskWeDMCGTd\nJjBWn1hx5gSWrvDSYi/dDWlWqkE+e/E2vrGwg2Ytw2Q5zuRUE0fObaDTlybem8JadaNINs3+AkLU\nsSHz6TAl3UE24Wd6Lk624MY0FZTmMqrTQpEEW2PLHF4d5MVLQ4ymGxFrrh/U/l5IDPMPh+9hqCXB\nmhEgZflwe3ReXunj0+N3UPhFH42fPYO8I8fFpVa0/x1l+I8m0X7DRfOrOgv3KshvWwOPGyyLyT8P\nsHarxaVTfbS8bPDKXA+tzyroT8dZeO9GJE2j9ysWQpVQy4LoNYNSs0TNL6iFLNydBWSDOonAWx/b\nKUyEKK97MSMm3rYCtfEA1GRqaRd2SUXpLuIaziKFDEKREtWqxj3tY/zhlmeZy4Yp1hzkSy7uCY9g\nrrnRaypX5lp4W8drvLRYP+ObNQXNZxBwVYl7i4iyyhcXbkZednHptV4e2XMOZ6yCbUtMVuJ86equ\nG+eItnR966dkb4gdtsmd42/O7aevZZ02f5aTk90IW+KRLRdZG/DzhZN7+cBth1AQPLm8ja2tS4yl\n45yc7eLunSN4VZ3GDXkmD23gSzO78Tl1vnLnZ3gyu4uy7UAIiWpIwZ00MXLOusqEUm+Ad68L9JCM\nktKIt69jI3Hq1CCh/jSPt5/lYqmDw2ODhJvyPNR5hadmtuBULXbummC94iPmyvFI/2UatTyyLMgX\n6qr4gWgJVbEwLYXa2TCVboOqpnFkfAAhJA5uu8KlVAt2sMam0DIr1SDjc03IUZ0rF7pY6Arh7DQp\nLft54vYX+fzIzZSHfGQfaKO0ZqJlFEKnFkHTsJ0arsk1tHwbyUtx1n9dYPkb8KhllEWVaqPF7KMq\n0opGeriu7dS+exHxT0VMVwtq1UI2ITPgIDRlk+2VoVtnU+MK1WiSS3OtyLJACHB2F9CnA3j6s+iX\nQ7g3Z6llPLQ0Z+gOpHHKJi8d34TcXCWb9tLSnGGmFOWp52/BmZZg/yoOh8mfXHoTvo48xdEwNOsc\nWt9ANuEnm/LxxPbTOGWTb89vYi4fZevwHJcudtGxfYW52QaeubaZze3LpKsevnd2C66VG+fGNwoi\n8P+VvSF22Lzl5n3bXsGwFc5f6eam3jmwJdKGhxMX+/FNakSUIp+6cieNoQIPxC5Tqjq4q3cCt2LQ\n4Uxz6shGErvhia4zpEseXioNATBZaqBmqJguCVuTkSoKakVgepUfJA+UisDdm8erGbjVGu+++yj3\nto3yN8/fz6n1Lu4cmKB0McKXr97E+wde4Zd6TvCm2EUONI7x6ngvXz++h5pQeHPvBZpiOTS/gSLb\nCCHhc+lUug2QBNaUD2XBRXM8yysLPeyJz3Fw41W+M7mJHYF5tvfNEQ0X2bJthltaZjmT7kAyJC7k\n29AcJvP3KpgekKsy7buWmHxfG6kD3ZTbfSw93I4RFPgWJGwVsOv4SGNDmb07x+h41iY4JqEYELsk\nWDjdiuT11pEgIQ1bBdMF6WEZV0pgL7k5c2KAi9Nt+AMVhC1RTnoIeStYAZNK2Ylzc5aaqdQ1nZYi\nhLRKXV9JgFnUwJJYTQbp865TC5sE9q+yuh7koa4rbG5exrZlenfNc2BwrF5n3zjFE9tPc3S1n+8s\nbKI1kMdOO7g020q8L8XixWbckQp2ReXyuW4WlqLgtHDtSP/nzvWT2PUmnN7IWeL/PyyqFvnG3Hbu\naRxl44YFcrobh88goOo0dqX5q/f/Ey9kNiBJgiZvnmuVFmqGyquLXTxzbhvTlQaad63QcBbO5TsI\neyr846XbObHWzbX1Rja2rlALSNR8cl0nySGhVGz0iKAclyl2QnUiyL7oOLOZMD3ONV5Z68V221Rq\nKqOZOJv3TfDo0EU+fuw+vr2ylWO5QQ4nBnlg4xU2bZ5joRrhfLadXNmNmXbR5C+QSflYz/jBBjmr\nUQubiK4KyzMxyjk3z08PczHZimXJfPrSHWR0D8mUn5LpoGYryI8bNJ6CM1OdeF0G9952Ad+BBD2b\nl5g/04rZUSVxd41yg4J8Two5plO4pYJWkNg4tIDtsRn872sk7yiiFU0yO0xMN1RiMh2HdIy+RpSq\nhXtNRysLLCcIGdJ7agTHJaxoDSoKxoUwXn8VHDbrWR/OVQ1TV/C7dISQaAgV8UXKfPfSZqoVB1JL\nldaOFNRkFMXmVKoL2WuyM7bAYxsv8PziEFsCSxiGwkImhFM2KYyHSVW9lG0H6byXsl5vjxSaQFRU\nvA4DqbVCJe1muG8JoQkcXoN9w+OUqzeocQKpnnS6nvVTsjdEwK7oQbpDKV5a7+ftzScJu8p8eOtz\nhLQyHYEMH7r0Vs6ttPPOodPkdDeXsy3c1jNFSyjPX+77GhVLQ1MsFEMwk49wV+M4f3bTUwDkV/1c\nXW7Cu2LjyFvIuoQ7aSJbAkdOQiigVCXMgMUL60N8aOgwVaGxMBejs3eNfS2T5EpuMrqHr13YyRO7\nT7E/PsaAdxXDUjiV6GR/bJTtvjmuzLbQGswh1SQmVuJ4AlVCgTJSSUVpLuOO1pvcJa+JpNQb23Ml\nN3bGSXdjikSuTmEr1zRmixFqQ20EJos0xnNUDI3vndlCtuhh+mIrZshCWnWx4Y8TxA8tEH9sCqui\nEHvWSXACJo514Z9QqA42I4eC2KpM6LyGVqgLu6lZndSwi1pAQy7XUPS6TjGAmtIotYFUVHGvqFTb\nDWK+EpgyilJXp2htzmDZMkZVpcFdYrghQV9XAnnGzb7eCdYyfkIteVxug+n5OM2xenPIM5ObeFPH\nCCfT3UQCZYbjCQ7PDtC5fYlGd4Gnz2yntu5GkuDyRBtKUeYtu1/DFhI+bxVHUOfaZGt9hnnNzdnV\nthvbefSzHfbHW8hR4fbwJGtFH99JbWW94uOjJx8iopbYG57izb0XiPlK5Cw304lYXTBtrpu5tQi/\nf+ZRjp7ahIzAckqk8l4OrQxxOLuBx9vPgCRob8hg+CWMgIJ/DjID9V9vW6OOxyiCUpTxaTofG7mH\nz0zfQWNbhvWCl6hW4qHeK6wXvfzXXUe5km9huhLjKzM78Tt0vA6DT13cxxcXbibekGcuFWbXrnF6\nGutkOlkSRHvT2LZMddWLXtEgpyFMGb9bp1p0oJRkoq5SXRK0KU2rL1cn+T3uYOXWAIm1IJXJIMO/\nP0H3ry7TftgifEGh4bwATUUUS8i9XWhrGu5fWiE8VqL3U1PIBky/A+bfM8j0zysYwXqwakUBVyaI\njOqUG1Rkw3z9yCAhCdByEq6khNAERtBGcVksJUP1lsQ5L43NWUxbpsWXo7khx1w2jCwJZlZj7Nk/\nwsVkC7WShqrY6LrGI1svEPcUSBte3M4aX372Dq7ONZM514AtJPSKRoO7yMVECzs3zCCFDEpZN/5Y\nCWd3gW++vId2X4ZSxVGXqQlXkEMGaqzKnuZ5trT9yM7Xn8zs61w/JXtDJJ3yNReHk0M0+gucnOlG\nViw6W5O8mBrAoxrEnQXuahzn61PbuaV7Gk2yiThK6LbKhVQb65rFrugcLxdbiAZK7IgtsFgO8c38\ndvyNRYKOCkmPhDtlU+yUUIugVCy0AihVMIKg5WX6vOvcu2EEl1xjpNzKk+PbmKtGaHdl+P3h55jU\nG0lVPGwJLpHNeslmvdhllVhrjsFQgvPrbRhlB2PJOC5HvTaZznmp5ZzI3hqOlIK8olDbWEaSBBF3\nmZzPje2pj4flU15ub53GrRi0uHMojdM8xS5ERUU0V8ndPUDo9DKeiTSp4UaCMzWoVBHtjaS3hYmM\nCLRnQiijs9jFEvFzRWTLix4CFEG53QQZYpdB7mpDsgRqVbC+O4JWEVgOsBVBLWJjeWSwwfLZUFGQ\nfRZWTcHZVcQWEh2BDJsDyxwq13MFp6e6wJZ4ZbyP5sYsgVgJr6MOCyuZTtJVL9dyfowFLyJgI2oy\ndk+FNk+Wy8t9nLa7UVcdnC86+a0dR/jb7z5AKRdkcNs8E5MBXpnowznlwuiv4HIb+H0VAF6e7cXI\nOm+MI/6sDnt9pkg24+sNbGte4hc2nmGLZ55XC/08fWEbb9l+licv7WBD1zKbGlc4v9pGdySNjcRy\nKcjCUhRJFixVQ+TbVdpcFU6vdb5OGZNwaXXlxIVCH0IC74JE9iYd6YiNe12Q75HwzwryvfXdcKUW\n4qtTO9B1FVNXaXHmGHCt8J3UVgxbxe/QUSSbeCzPwZZRfEqVfx29haNTA3W4lMMi6K6yfKaZ6PY1\nciU3Td3rWEIiOe9C94t6Qmrey7VcG/6mAve0j/HySh/ecIUTq50AVA2NpmABAjU8Xp1q2cHyXQJH\noYnkpnoLpulVEJUKUtmNf0FHWy8jra4jhYLYG7rId7sJj+mopRrGWQdGUKUSlfGdn0OE/ChlA09C\nIrnZhVoVWC5w5CUkIaPHrbrwWklFyAJFESiKSSXjxrJkklqNa3ITlZpKLuMlGitg2RLFspPl5QgY\nMpHeMv+t/xCfnN5PpuDh1ze+zCdS99LVm2AlE6BmqHxvfCNaQSK2NUvK6cN30suTjdtxrUkU+yxG\nr7bTuGWN1dkoQhGEjrkotrnwbE9ROBdFtiTufuAin7tBvvizLPF1WNXUiPlLZA03k6UGPjZ+LxVL\n47dveZ6nn7uZN226RMRZYmStCeNKkC3BJWq2wtS5dvZvGKW5KcN6tT6VMnq2k75Qkmzew/v6XiE5\nE+FsqoPsINiqhBEADBk96qTUIqEVQKj1R8Uvnb6Zp+e30BLIE/JX+M2bjnA538Jfjt1LtydF3nAx\nudrACyuD+BwGX7ywh6/P7WBT0wr2spuuWBqroGELCTNgk5iOEfaVMSyFRDqA3V1Baa7AjBczYDE4\nsETUW+b5uWGinhKldQ/ppJ89TfNUF/wsnmpFVBWG4wnevuU0d+8cYe5BmcFHxpEHiywdgOK+Qcjk\nMAIq0uIKdjaHFfajR5xUozKOK3Moi+u45jKs/JyBJ2ljJdaYfjxCqdNHGSWOeAAAIABJREFUttdJ\nubl+nHAlBWoZ5JqEZEpoaxqEDaSqjGVJVDMulLyCUdboDSTJVD1kZsMoDotc0YWq1J8VA5ESgaYC\nyaKXP792HwdbRnms/yKf/N79KAWFBneR9204DkBv0zqyBd2BND5vFfOOHKsnWogeXEYJGKh5mdXV\nEO5lFTFYorCvjHdrGvGtKLYG5mCZy6nmG+eMPzvD/niTJIFhKSzm6ppI6ayXQ6PD/PX3HuRP3/Lv\nLFeCdHtSxP1F7n/gNa7kWijWnNhum/c3HsXv0MnrLmQTbE1w/Eo/HfE0K7UQjb1JbotPYbkEsiVw\nZuvSMHpQxj9vYzuoN+frgCoIuKr8Qfd3eLBthBeTg1xYaKM9kONspoOAo8oHtx0mmfOxKzIHwN6m\nGbq9KfbuvcqTg9/g7m1X2RWbQ7gtbt42jmXLpPMeLF3Bdc6DteKmFq+xfcMMH+3+Fh/oOsz9XVdJ\nlb0gQyhS4sRyF1CfInps51mSFR9TpQZeme8h1Jnl3Pne+lmuqUjH74wz9jedLD9uYA53obS1MP9A\nkKW76ljJ2fcPMvlr3Uy9s5HIUReJXTKrv7qb3n+cw3LUz6xmm041IlFukbA10AcruBIKlkuAgI6h\nBNKkl3BTHitSA1NmKh8jbzhpG1irk+xlQbbgBiFR1TUsW+bx3nPc2jrDTDnKN0a3c9+d52nZsspr\n493841P3ojlMJpbjOPamWC4Fyaz7qaz40NsMTFvGXnfRs2eeztYUlW6DjliGWkXDeDVKZpOArjKP\nbzhLxH1jYFj/N9gbImBtWyZ5Ncb+9gk2+lf45S0neNuW19i79yr/tnILe8PTvLzWhywJRrLN/HzT\nGcbW4nzmvs+xagY5EB8FQI+AM63wh7c/Q4O7iFMykSVBt3MdgjWqYRlXuu6EQpZQq4Jq3MYISlju\n+utOxeRTywf48thOVkt+trUvslQIcnNkBsNS+OTTb+KDmw/jU3R29Mzz3bFN/F7sVdK6l3/IbuTw\ntSGemdiE7LA4Nd1FYimMaai0NGeo7igjGxKPbTtHtzfF0eIGXswPkTR8GKbCLRsmaQ9lKV8KI8er\ntN89x7HVXnwOnZVygI1NKxxoGyfUleX2jilsW0aTLXb1zPFrW15m4r0a1/5bM9quDO+65yi5frjl\noUsoVYlau4Hhl+j4vk7zyxlEtUpgqkQ1JiGlHFhOqPls8kMmIudAMkE2JIQlkyp5qPkF2bSPUKRE\noKFI3FNAkQTJgpf29hQBb5WmcAG328C2ZDY1rvDC6hDfHx/m2NUBQoEyw95lDjZfwzPpYPO+CWq6\niiTBAx1XmZuPMdyzTHBUwX/VQfJ0I77OHBPLcRLHW3hk6wUmJ5pRUhpqGXo2L+E85+Xfj+9ldO7G\n7bCSuL7107I3xBlWCHj/fYfQJIvnEhuJuwsslUKEnWXaPFk+P7mHtmCOTNVNf2ida5UWFMXmD0Yf\nJbkW4MHNl1nP++qjYAZ8ceFmfq71AitGiA3hBE+vbQNZ4MzaFFsVJK8BQiHXrSAUC60oUCoSVrA+\niOBVDeLBIpmym4HmNS7Mt/Pv4zfREckQ2bpOlyPJZ//2YYbeMco9/aNMmyoRZ4l/OHsnD2y8wmQh\nxthkC2g2seYcjteFyTa3LXFg2yhfnN1D6nIDnlWJ/FC9Sd87o3Bpr4wxHsDs1BF5BzdtmCdb8/DS\nYi+twRxnr/Rwzu4l3p1iLNuIPeqDdphIxzhzYoDwQIbHd52rjxf+2wGcJpz//GZ8FUFgTsO7rGN6\nFOSqg9quXoQCNZ9ALUnIJmgFGbvBQF53oEfqnimVFYqWF6WhCkDEW6bBXeTUWA9On45R1TA8Vao1\nlSZfAbdaY3zNx6WVFrwuA81hYmccZHJePn7iXkKxIqZbkNE9NDXkeLBlhKvFZga6V5lMxKD1ddnZ\ngMVN8RXG1DhZt4tvX95KpDXLYGSdM/Md2EKi2F+rCxCs3rgB9p9m2+H12BsiYLFkdFvj6eWtlHQH\nkytx7uyd4MRiFzNOg0Lai+UvkFgPokiC6VyMoKfCR/uf5tnmrZxa76Kac+KpQWDOZnE9zN9fuJ/4\nlgT5ioveSIpQqIQkQjgzgqIiKLVIRMZMin1g+OtjZZgyDtnkL1uf48PciztS47tzG3nzhvOsVAMk\nqz6avAX+duEAjofXee2VIWJb1njr6PtpjWfxXHPxwX2HeXj+/WDXG/szeQ8H+sY4Nt9LYjLGlYZm\nPMd99B9JUWkPoFRUFB2ygwJ7zo8dNZGyGlLA5OmZzZRLTmRZMGdGGP5EEqlQIvU5P3OTceTuKpaQ\nqBoag381izHQzD/ffzfOgTxKFVqfXmDmne1UYzZKRSJyKoNU0SltbcG5XiWxx48eM5FMCdMrYXst\npKwDtSxRCwhahxNoisX8WgRJtmmJ1GeDq6aG6jIxVryojWUSKyFaW9P4VJ25TJiengQH4mN8YXQ3\nDodJx/ASc8fb2XjnNImyD3V7iumFBn57z/fRbY2VcoDFVIha2oXWVcbtNmjxVDgx3Y1dViFksbN3\njql0jJOT3TQ3Zmn25Jn1RAkGyuTz4Rvniz9LOl2PCcZLcQ40j3F76xQHB64x5FvhfcOvoCk2fZ11\nx/EHKjR58/xO73Pc1TTBH088wrdf2MNgaI2erjVqQUHNI+HxVrnjjst0B9J0R9JsDS3SEcyS7VXq\nwtmWRLnVwtIkJFddKkayQDIkijUnzxR7ObXcyem1DvwunYqlsVIO0uVLc221kYCjSmI+guWxSee9\niKyDhYUoig4fXngE9VgQSZdxumu8efgC1zJNxL7ooeVFcJ/yER3RkbIFct0q/kWLfC907VpEyCA5\nLBpPSCgOi2LCh13UCAdLbG5epjwQJfFgD9mim6buFHZZ5djVATzOGpk7u1jd5caMmnxw+DCOvED4\nPHhWBeERCfe6hGRaGL1x9KBCNe5Grgn8UypqUcZ22kgOG9kEvdFk500TyJJgKR3E5TZoCBWZW4yR\n012YQsbvqyA3VKll6moUZUNjvhAmv+6jwV3k1XQPG5pW8ThqaIpFfM8qYWeZgy2jJBdCbO5Z4jNj\nt/N3Jw6wkg1gz3gJt+XQHCZ+l85a3gdJJ8iC0GWVs5d7yCZ99Latk6+4ODnbhbxcx2BGhlI3zBPf\n6I/Eb5CArQfK4ZVBDs8NcGq1g08f38/fX9xHYj5CsyfH5FqM6kiIHcEFfuv4E5xKdbEztkB8awK3\nUkOTLazWKpIFhbQXv1alWHNye3SCoulkIhkjNGlhuiSUhBOtsV7HU1Yd2E7wzYNalsnpLo5mhvjN\n4aPEvUXe3nGKB0KXqFkKz48OY+SdVE2Ngzsu459WMFIu/O151KRG5IElVMlGskHRJZyayU7vDE7V\nxPDJOHImsgGKblHe1EKpFTIDKspQganFBt5253G8gSpru8DMO0AWSB6TvlCSYf8qLb8/Scd/meT2\nzjqtTcmpDHavYAtYvU1w8ImTaH6dj33tMaLnMkz+YoTweJWmZ2Zo/9IUVHX0sIZvQafUrCBbEB4z\ncSck5KqMP1TG0Vkk3JxnMh1jOR1AVeuZ3+XFCO2tKSqGxshYG5nlIAFfpa6WYkpkVgOsjsTxRsuc\nnevgztg4yYqPiLvM1dkWnKrJifkuvjWzhVh7lkZXASEk/A1FnJqJGa9Rrjoo59wsTzVgX/Ujx6sg\nw/53nUQO1Hhs2zlyVRfFhI+D/aM4MhJ6VaNQdt04V/xZlvg6zIILM+14NYP7uq7x5s6LyB4TseRm\neHCR45O96EUnUn+RQ4khhCGTLHq5nGlheTbG0fk+0hUPmzuWcRRtVJeJaStcXWnkW4tbkSVBWyhH\nYo+MK2MRHAOj4MBRsHHkJCwN1DLUQhbFqpODkSucLXRSMTW+tbqNc+Uu4p4Ce3pmefeeY/xC02ka\nHEUiDyzR0pMkv+YjOAnir+OceWkIoYAjK/Hm7gv84blHmJxuotwos3y7E++qhWTZCLneTRScsej6\nSI2hjlVOpbr49cH/h733jpLruu88P/elepVjd3XOjW5kEIEAE0hClESJipZkjy1ZDnLQOu5ox0E7\nnp3ds9aM01je2bEty2ctS7ZlRZsiqURSYg4gCJKIjQY65+7KuV68+0fBPvasZVGzmGN4ju85dbqq\nzjuvXlX/fu/e+/t9w1Pcc/tFlLBD+oxGYMFkuxUlZ0e5mOthOrrN46/upXCuGy/lENEt7umbQziC\nr371OCyG0WsgVrcJ5gT6uQWK947gN5pIx8GouLQzBqoFjT5Bfr+GVDsV6dp6DHcuSu1KCiEkhuHh\neQqepzA2ssPqYhflYoT906ugSEr5KNmhIkrIJdVbITxeIRFq8a7pc+zYMRq2zpWVHqKJJtu1CIPp\nMq2WQWEhybde2kc81MLUXRozSYKLBp6rMjWyyYlDV3GDoOkeasDjr04fZaSnwCNz+1AVHz3RZqsV\nxThRZE/fFieH525cLN7kCXtT7GGF31Grs32VM/lhNi5mCe4oNIc8PtT/LL+08l76+4qczM7xzbVp\nhOkxnChxeTPL8f1zjIXyKEJS9wJcnRpHnwnxpmMXeHThMJv9Gs/449zavczaeJz6XIxgwUetaNQG\nBFoL6iM+kfXOLNNuGXxy6SSpYJOTXXM8srqPP//iG4isSEp74HT/CLJkoNgC4Qhii8Ahj9owRNeg\n/2mXpfdJTu29wlfX9+I5KpGrOsGcJDXrYsU6GrqhuQLhVxsUT41id4Upfj5Feb/P77zajxf1OL5v\nnlf0AbJfCLJlDfD2D5zn5O5ZPrF8N/ccmuFobIk/unonA6EyD1/bR2CwTqti8tHbv8ZvPP52tr9/\nmsQ1l+IDu0md3kbEY8hqDTekorZ9muMqRhW0piSY99kcEigtgWIJokfyFOdSBAbqZOM1is0gS5tp\nItk69XyYC/MDiICHbGhUmyZdqRoJs0W5HWR9LcWXtxNQ0Tl6eI6rj2Zo3+Vg74TwNpIEjpXx/CDh\nkQq3dy/y/M4oSLjtgfNkA1X+8uXjdPWV8VIO0tKQviCQ01hLJwg+F6H/fRtsbSS5tNmL3dRpnUsy\nM9W4MXH4T7zcfT3j5phhFVADHlHD4t9PPMTooXWcw3XUpMWvPvh+TkwssrGTIKJajCaKHBhe52T6\nGv/x8IPcElvlSHgJXXg8PLMfxQE74fPXhSP0HNkiGm/ReLKbiNrRCDLqEive+drtjCCy7nWKLX5n\nH+tvmyTMFvtiG/QaHc1d81iB3G0egZIg+0iA7hcF2dOQPeNTG4HBsRzOsEV5TKe0S0epaDz1zH7k\nn3cx8qcK2TNtIhs2atsn9eQS4tVZnJ44frmC3vRZeN91Zooi+f43PE/XQJmLD0/jFoIUp1QGH6vy\nueWjfOIj76X09T6evDjFf3rsAXxf4cXtEbyNEHcMLqJUNf7j828luqDiq4LgVpPU4wv4S6sdFYre\nbrSmR6NHQ7WhMeDjBQS5Qwp6teOA4AUljRczfzuLrG4nqRXCpFN16sUQWtghELGQvkCJOrSKQVq2\nztxWF6VaCH1HR6iS8GCNV16cJPa2TeztEIcPztPq96gXQwgPGnWTZ7fH2L6Qxe2zWKilWWsn2L9r\nlX8/9TB62EZdNfmJQ89hDdjYdQPv3jLr9Ti7RrbwVkOoho+vQyBwA5X//4XA/jqGD2ItSGTY4iul\nw6wVE/zr/d/i92fvpvtwkdliF2+YmuXF0iiTkR0eX53i3NwpunoqDMVKfNueoitY57axRVa6klS+\n0ctTs5NQ1xmY2EF94zrP58a4e2COF8yjCK/TwmgMuQRKKmgu9QGBn+kEoqk6PL09wSPtvbx79DzX\n6t28VAvR2ueQOVViZSNNb0+JlqPhV8NsFuLEXzRJztkUpw2yL4GvSUI5FyPfRBoanL8KvkQGTdSu\nDEq+jndgkuiZNfqVQcoTgqlPNvj6hTvR2pLm3W2kp+BGFJr9IVq2R/rsCuajJVKnDhCa3UC22uBL\nUn6RuaO7yXQL2ikD92SFzKfC4EmIhlF1HRkNgRC4IRXhQ6AscYMKwgWpdnquehXae1q0ojqJviqN\nZgDDdHEUqDVNdo1ssV6Joyo+VjGI7yjgCuo1k0SiQcsyaCU8lFyAhi+4+85LXMz3Yu6o3J26xvlU\nP2I+hFTgxPg8z5ybZuTQBr2hKi+cn+QNd8xylW5+e+F+5EoYt8fmT796il9550P85pMPUC+GcFcS\n5Kaa9OzbQQLjY/O4vsqlGxSK/8PMsEIIVQjxqhDikeuvU0KIx4QQ167/Tf6dYz8qhJgTQswKId78\n3c4t1c6y2JUKj7xwmL5khRUrzcHsBouX+viZiafZFd7iykaWh2YPUC2GUU2PtqNRaIfZrEV5eW2Q\n8zu9rG2mqE65aBsB3n3iDLFAm6XlLu7ruULZCdLMCsJbDskZH7WhYhZ9QtcCOPsbKDkDWio12ySg\nufz0rmd5dGOatXqCrlSVD+4/TUB1oaHxjv4LFNcTeJaKZ6tY6Q6hoDrlUdqlEMy5SEUgWjbKwjpK\nNIISDiKiEZzhLqy+GFbaxB7tJrjVpv/bVeY+ECW+6BCo+oQvmmg7OpEV0Ose9YU4frXGwmd2s36v\nDo6L0HVkrYZXKBI6t0r6kSv0f/oSwz+xQWipitK0kBvbyGgINxHCD+oIDzSrw301C5J2VycKrIxH\nc8xG0z0wParVIIrq47kKXkVHUXzqjtHRWpaCSLb+txK9YsuktJKk3TDQEjYi20Y2NTZ/bphCIcK+\nN8/ymcXjdCdruCFJeG+JZ8/sRmkplJtBzm31QcDnL758ip1WlOXlLnxdYkZs5HCL3zrzZpIDFZSA\nhzPVxKvrrK+n2NhM8sz5Kc5v9/23xP53CMbX+fgnGt/LkvgX+ftGPb9Kx71uEvjW9df8V+519wN/\ncN2b5zsOrQlaTXDmyih3Hp1hYaWbh5f20XR1pg+scLnZR90z6c+UcVsaiXSdnnSFxkKcN2avsCud\nw5+PcKRnjT+561NEe2qYOUHRCXcc6W59lm69yk4ripWStDIaCPCSHdGyQFHitDWCOwqBnMbVpR6y\nwRq/88z9WI7GQKRMoRyh4IRZyqd4z20v8dWNfdxx4Cq7RzeQloKvS7SahQx4+Iak0aMTurSJXNsE\nz0NEI9DbjTPcBb5EsX08U6HdHWDnaJjtW2MkLwmqIxrxF1bJnLeJX4PYkoOV0PBNn/lfO8hETw7P\nkFz5N0Nc+9kh6m89yNzHT9DaN4A3OQC6gTADKI1WJ1kdFy8eRGoKbiyAG1KojCk4EUFlgg4ssyk6\njudtFW8ljBCgqBKrZHb4u56guR0mV47gWBqu2/l3amGHcLYB/S20VJvAvIlb0/HaGmpdYfZDIUTJ\n4OziEPntGBHD4uDReRTFZ3TPJpMHVxFCIqUgdM2g3e8wt9WFGnYIbil4roJb13nb3gsdd4DtAL6t\nooZdgvE2tFWMhEV7Mfo9hPE/Ml5nS+emRzoJIQaAB4CPAR+5/vY7gXuuP/808CTwK/wd9zpgUQjx\nN+51L3yn87tBsFM+t+xaJqB4ZLqr2K7KQKhMUm/y0NJ+ag2TZKyJGbNwfQVd9UjtKrJlxzh7aYz+\nW7apOQGeqO9GVz1cCa9t9/Ovxs7y6PZussEabVdH9rbxZky0io/QOy0YNyxQDR/FAlWDZFeNpmvw\n4dufpOkb/OXMEVLxBrdH57ja1Y3la7QcHVcqpAIdYrr5ahQ7FaT7KZ3YUhv9wgJevYGSTCL7Mvia\ngp0O0sjqqE4HBumEOvBIvSZxIh0cb/m4TWXXEJN/XiM0u4MsV2i/dTehbJ12zGD++WEISYQPTtah\nPGYw/HWbtVMGRiWA/a8VMg8Giay20HSN5fdl8Q/WsEoapw7O8OTz+5g4uEKuEYZ6EF/1cR2VeKxJ\naTOGH3OgqqN0OQjTQ9M8jGyT7nidfC2Mmw+iR9vUK0H0tQDtEUE2XaH1lSzl29oMZktkgnWWK0mk\nFNQbJk5L5y0HOhpWqUCTbKTO7HqW/q4y5XIY6Sgw5IKAEyNLXM5n8awQiuqDJvna7D5uH5vH3FHQ\n5wOE3rGF5ytEhixym3F69uRuSm+d/x7j9c6wvwf8Mn+fuvuPudd9N+dphBA/9Tfu1bLSQLiCixu9\nVBwTQ3NpNAMs1DMAHMquYxguU6kdhlIlGmtRNksxCktJHn75FpJ9FWrtAK6v8mp5kNJKEsWG3Zlt\nTpdGqLZNFippcrUwH9x/umNHYXekR1pdHZYKmwFQoDniUKmE2BPb5GxliIBwcWoBfnz0eR7M30J/\nqMJGK05XuE5cb3E8voh/OUrmQgvPVHDC0M4YtE7swj++j/rto+SOJSjuj5HfZ7Bzj0NtQMEzBI1+\nQXGvwE50ihieIQhfCoCE5QfitMe6kG2L+oCCpvpMfugyY1+sMPmrr9L9EiTSdQ5+32UAus/6DD24\nQ/aLJolXdlBsj4UPdHPorTPcN3qVBw6f5+XPHyC8rrAvscFYsoBQfOyCib5g8s7hC+zetc5AtoRi\nCexKoPP7XEng2BqVlolpdKCAta0oomAg3M51F2thGvc0yKRrOL7CuZUB0v8hSNvWiUdbnNpzhbVm\ngvWlDK8+tptjqWWUNRPbU5GuAhJCqxoISdEKEf7TBFpTEjBc3n/4NF5T4/TKCPF7t3DfWKbaMtne\njlOpBzk4uUrSbL3OMP7uQ/iv7/FPNV6Pt87bgB0p5dnvdIyU8nte2UspPymlPCqlPKpGwggf7hu/\nSliz2VhPcdfYPG/uukzb13n62gRSCtbqCRa2M8iwx4f2PM/bTryCXlQpLyb5ofGXGQoXqdkBukcL\nVCd8rhS6mc11Y7squdkMB3s2eHDpAO2UQPgSrapSn7axkgIv6mHHwdzoVGyf3p5gqZLiG5t7OLZ7\ngU9cu4vhUJFMoI6m+BxLLRNUHYaNHIotaGUDLL8bIu/eYueoQmlSp7gniB3tJGd1VHSI8js6zT6f\n2ojATvgorqDVJQkWfFrdEjcIqQsCvQEbdwUovu8W3BCEAjaNBw4hbBdlYoT4lQrdH9Mp/lgG85VF\nwl8+jT+/RGSugqg12Lg7hj1sUbaDPDo3zYHIKtUDFurJIk+sT3J+rZ/EN8IkLmpY3R6ffuYucp8Z\npvaVXoJbCuaGTnBNw+uz8Ks6rZZBoxVgfHqDnuECoU0FJ+bjNTXa+SC3Dy9SrITJl6IMdpdYeUuo\noxncDPDG5CXWKnHw4dibL/K5K0fQxuvkCtGOlYmt4AYlQpOkAk3W7vdxg4ITfUt84et3opU0YuE2\nW5e7AfBfSpDpquF5CtvNCEvfHvleQu+f9Xg9M+wdwDuEEEvA54BTQog/5wa610nRaaks1NJowufI\nriXemznDH87cxRefO04i0YBLUZYXu+hNV6Ct8IdP3cdrhQGi+wucODrLc8VxAopL29X48NjTmDsK\nzjNpmoUQhubxwF1nuZTroZyLUBvzqfdqdL8s6eqtkJx10CoavibxdYnYMtkpR9ib3uKBvot0B+r8\n2PiLfG1pDw03wIHoOi/mR/nKmcMdDeSoxFc7sMK6ZaDvrlKd8igc9tg5BvURiZ3xEB4gQa8LVAsC\nBYV2j4teE+QOgx8AxYXqGB11RAeshMA4VOL/mHyII//2LBv3ZWBjBz+goS5uQbGC7O9GGxzAP7YX\nJxVCxiK0uyRK3sBUHY4OrfAbj74DoUh8KahdThF4NUx8vk2g6tP7hEJ8RiW2bJOeaRPalmgNaA24\ndGeqJAcq7OvfIJuoMbfSTb4Upb7HQrgCI2aR7K/w4uoIfekKxoUQPzn8DAAvVUYwDJfPbd1KZSGJ\n2dViNFTArhmkow20FROnx6ZvLI+T8Emmazw3MwGeoDrtcqnYy9GTVzh2xxXKF9P4UY9G1cSJSvLb\nMZSVIPlz3R1Vyhs1bvKi03fdw0opPwp8FEAIcQ/wb6SUHxBC/DYd17rf4P/rXvdZIcTvAn28Dvc6\nxQUkLOVTRHSLV+aHaXs6rqtw79FLvPDwASK35Qn6gligzT2HZ1hrJJhbynLH7jmCqsOLc6M0+g1i\nhsUfzN9Ne2+Lgc/r/PyPf5W0WufLuSP88u5H+d2rb8DQPOzL3WhNn1wtiB5RiM1B8ahL4pxOdbIj\nRL5ST2J5Gq9uDLDWneCWnjXCmsUfnz6JGnI5vn+On3vqA0RyguqIirR8HE9FCEl8sIInBV2RBgtX\nexiZ3GYllkJdN3FDEnvYQrZVorM6zX6/42nTEFhJ2SEi1CBQElR3ebAe4/M9x7lU7GHoPQvMh3cT\nXfXZ+ZExtKpCdF8BXTUpnQ0x8alNcFwmfucqlXsnuNSaILgjMGOQnCpT/0YPYf+6EFvNojYYxKhA\n72PbLLw/i29IvIE2fl0nek2jvthNY8qmvJJAy7TQgw5CQCRhoySbKEIihCQaapOrRkjdvcWvPf8u\nTE9w6SvT1KdsLjZM9t+yyLm5QTbaCU7tu8JTCxN4aY9UpsZ0YoeddIw7ehf5xsWjqJbADUry1TAb\ns93IsAvdDj29JZJmi2ubwwhVok9WaebC4N6gvuj/4MCJG+Zep9qS8PU5eL0eR0q4tt2FUzZ5LddH\n7I4d8jsxGq0Ay6UkT87sotAIMTa8Q1izeGpxHNnSWN5J8csjX6dl65APYJQdLjb6eaa2C0VI/t3p\nd/KjY6fZ3okT3vJopVU8V6U+oOJEBSgSqXQq1tq2weJGhqIVol0LcGtyiZ1WlKLdCZBkvEF3oMbe\n8XXcELR6fMKZJo6j8lO7nqPRDFArhqm2TbSEzcpWisFsCTHaQPZYUDZQayq1CRcv6nU4uoAy2ABF\n4kYk7UyHbL/7N9apOCYD0TJb9SheUFJ9V53J6XXi+wu0bZ38TAa1LZCmgZ+K4ldqJM5ukznn0/up\nC3S/4hD69Rh9n3iF7rNNQtsSsbyJXofUFQu5vkXmgkf8Gh2LTNmRPNWaYGzqSMNHuxjB2wyhvRbB\nvhSnWgxjORrVWojSxQz6C1HyZ7I8fur/ot3vYB2u86Fjz9KTrnAHAZszAAAgAElEQVR5o4eungrP\nrY7yxEt78SoGetwiGug4EriWysPnDpK83HEaiCwLXFdFhjzUskbkstFhaj09gt/fhppGs2qi1hW0\n0g2EE9zkM+z3auj8pJTybdef3zD3Ol8TSFVgt3X2JLc5NrnEeHeeRG+VbKTOuwfPgYTjQ0tETAvV\n8OmJ1lhY7aLiBHFtDSPZ5m1TF/nole/rfDFbsPwzPo+cPsxSPU3VMXnj9Ax/unCcaLzF2ikFxZMo\nGyZWosOjFe1Ou8KodRA/k/07NB2Dwf4CzxfHUIQkprX40B1PU66GOJsf5NK1AdRDFUYPrWNoLj+7\n92leq3V2BPvH16g2TNyyQTBss5ZPwFwYTfeQAb8jcKZK1LCLYgu8iI+iSPSqgtQ6M22gr4Hbn+Ls\n3DCvnBun1jQRvqBVClJ3DCIBC3cmxsSvvEz3qw64Hl7YQBkZQFaqROfq+HtHCa7UaHcFYNcIm7eH\niC22oTtN37fyGPkGYmQAgPiihZbXmfpUi6HPrVAb9wkUBea6jhvqcFWbIw7BHYFoqviXYgjFxwv5\n1CZcpAbvO/ch9GiH4fNaZYCtYgzfVyhWwvzyvkcJDdRRIg7edpCmoyMXw4RnA+ghh9xxn+jiddme\nioGwFZSBZsetAbBH2wgF9IqCsW7gxbwbOyve5Al7UyCdxN/MvxWd2XI3tXaAd42epx4LUHTCfOK5\ne9ETbZ6bG2f34Ba9g1VqjsnukU0mwjlK/SF01SOj1zFUj5GuIhc9hdDXo7ghwcVEH7v6tlmoZTjU\ntcF2O0or0qAwN4CZAyslOx6xmsRO0KmFq5K5jS4e2H2RQbPIM4VJLp8fIjcWQVM9xHKQdP8W2+EY\noYBN3TYYSRR5aOsArq8Qj7ZIGE1i4TZlT6FRDiJUn9DeMvVKEAIeSIFa0vB8AXEPPWJjFYIoIYna\nVAhMVGlUTN7zqa/zG4+/HRRw5yNEtyXtftjcSSBbKvQ5IBTCl7eRmorScmmNpzHDJu3eEKG5IqJa\nJ7q5g7Qd+j/eAcv7gDAMlFQSGY+wc0TB7RXItsfaqSjhzQjmtqA+6hG7qtLoB6l2DLE9A8LLKq0e\nH69h8JYT5/Ck4MW/uIXWhIEvBePxPGc3BhFrQUK7ytTWY/zl+q00NyPoZYXgtsAdU3AyDm5QQ/EV\nul8QVEcEkdtzaJUwblMjFWvS0CNs5eJk0jW6w3WuKt2YQZuI6lEtp29MHPJPWwF+PeOmwBILKdFa\nErWhUKyHaFs639qc4smNCa6UuvnFux7FbeucnJxjbifDq/NDBDWH2fUsfzV3kPntDJcvDfFUbpIf\nGDxLX7CClILCsc7d94f3nWZmdoDj6SV+svsp3p19leXtNE6ETg804aG2O9rEblB29J0EDPUUUYTk\nSr2XhmNw4shVUsEmbVvH3F1mqZTknVPniZtttlZTvHZ+DF8Klla7sF2V9UaCtqOhGy7Gho5sq6RC\nLaTVsbcQDbWzXwWMHQ1vO9iZcXubyIEWquLz67c/SN0zGd29yTtOnCW5P4+vCTK9lY51Y0Xjloll\nVn/pKDIYwOmJo9SaBB5/FXnxKoGvnUFu7iAtu2NBaeiomTRKOoUwDJgYwk9EkULQ+7yHFnCJ9dTI\nvGED+QN5msMuelcLs+CTvAzCEVjdLuLOEo29FjJrgSJ5Zm2Mb12bJvtSg0jQYjRb4LXtfqy2gdtl\n43oKhw/MMx7LQ8RBbQsqB2zql1KYcYvEaAlV87CSCpE1SeW1DL4v+MXbHsfxFEI7Er+hk9tIMP/k\nKMPdRWqbUepNE2X4xoD/bzRwQghx/3W035wQ4lf/keOOCSFcIcR7v9s5b4qElWpHgT+4IxhKlQgH\nLXKVCG1bZzq5w58t3Ip0BYvVNLcNLaEFPEzVITAbZCxTIByyGN61xUC4TNM3WGsmePfEOQKpFuFN\nj89cOM5bjpxn24rxB9v38rHTD3BidJFAWaK4MDy+g1QhtKl0cLUeKE2FtVySh2f289T8JI6n0h8s\nk2uEua1viTcOznKsZxXL15lbznZUFo5dJBloct/eGe4fnuFN2RmO9q7Syodww5JId4OIYaFWNAzD\nQ68oCEcQyKm4QUl8rESmr4KuexhGpxc9oud4Z/Q8XcE6X/3WMcTnMtTGfN46eAmloPMLD3yNi+t9\nHT+dYgX90jLUGohgECUaRe3qQomEQfrIVgsyKfyeNDKbQunpxoua1HfFsbvDqJaPWzSp5iIMRYsU\nShH0uIVdDVB8e5P4QptAUUH4gj1d2yiGh9/SCMfb/P7BzxJ7xmTuwyrHs8tsVmIMJcp4dQ18QWsr\nwq7IDqe3hgiEHFq9HY3kPbcv0C6aHOtZYShTotUtKU9DcEug6R7/5dw9FPNR6u+sEpvV0CM22sEy\n82tdTE2tc7B/nWODKzcwGF/n47uM6+i+3wfeAuwBfvA6CvAfOu43gUdfz+XdFEtiJAi3U7kEeMPA\nVS5Vell6fITTr+3nLe96kYuhPnwEzy2N8kN7z/DoxjR6De7rmuE/n38z7azOBwZO80cLd1E9m+G9\nP3CWL1+7g8qo4JcOP8rn148S1BwyZp2fO/oED20cwEoIois+TUenOgpmEdy4R1OoRHeVaLYN3KrJ\nr939EJ9euY28HeGNg7O8VhzAlQprp/vpu3WD41MLLFVSXC5l2dhMIpoav3zvI/zm0w8QvaYx8dZV\nyq0g+Z0YlwohRMinuRrFnK6hSIG1HUJJdmYbt99C0z2MgItfCvCxN72H2v5uIt+8wLh9Bum6pL5o\n8nJigF3pEt/43b1M6iWcfgGuiwgYSMfBr9VQDkwj2g7S96nfOoTiSvDBDXXI9HrIoDIeRLUlbkgl\nuN6g/1sR1t4kuJjrRVs2cYfbTHzaARRW7jeJzUvuePtFnpifZNf/VuHOv7rMhpXgw3/yMwRUUDTJ\nt5cn8WaiXOoJIWyFHz7xLF+aO8QrpUHKlTBjvXnmt0MMD+a4st0NvuDJxw7hDFokDhXhaymkBpl4\nnXo7QH0rjpwLcP8Hn2fc3OGP5+8kmKkxf2YIN+kSTN044MQN3J/eCsxJKRcAhBCfo4MCvPxfHffz\nwJeB1+WZedMkbHjLpdGnc3Wlhzd1zzAjehh6wzKFZscuQwhJrW3yo3tO88fP3o3Z1WLo7SuoSIyS\nyvDeIr/+1DuI9dS4683n+Xdn34neEthJyTdy+9gqx2gXTd577GX+8Ktv5uDt18gJ0NqSrcsZQuUO\n4qk2LghtCppOEjvlMTiW40qrl3t7rvLw8j6EkDTbAbpidWIHC1TbAdYLcY4OrlJ1TEanivhS8GRp\nimhPjZoSwX9sECcqGTy6xdq1bsZ3b1BpmxSKEXxHRU3aSB8YaSJLARxFw4so6CUFUW8S2mzRPrkX\nO6oQ2rJRqm2k7eJkQhiNFng+8+8z2fWrTUQyAZqGNjwIpTpubxInZiB8qPdqBAs+kZUm9aEQVkqn\n0SsQnkBxFax4FMWFvm8LyuNpQjVoW0Eq4xKpdlQ5KrvoVOU9wZVf7Gbzk71E11zSwmP7VgWxZnLg\nzmWigxYvrI+gKD6Pb07RzIX5yMEv8HRqmkfXp5GapNIyGesqkP/qMM0ega/5lDbiRKIdgbXeQJvi\nCz2EbymhjkteKw7w1cdupzFpo2/reP0WxyeWOH1u4oaF4vdQwMoIIV7+O68/KaX85N95/Q8h/o7/\nvc8Soh94N3Av/5wSVirQTnVoX7Kl8snLd5CONbg7O0coY/PXKwc53L3KWnmMz84dJdzTQEqY2+zi\ntfAgh++b4YUr40xMbqIKn13hLfr2lvkz5zjqZoDziwP8+OHneCY3wd7QOs8f3GalmsQNSRRXorah\ntsvBqGooLYVGn8SoCrSqSqkZpGiH+fLLtzK9d5WqZXZoZI5OfiXB7t1rHO5eZ6aU5QeHzrBipam5\nJhvNOHf2L3Ix2Mt6KIFf11m7kkWaHnMr3Yi6RnyoQnUxgRcWCN1HuxrEGbM4tXuWlUaShZ1+WvsG\nMCo2tX6NzGfOUnv3YZSMQSujkHmtjj2cRj+/xNSvX0N0ZcD3EYA0A1j9cRp9OsIHxZGkLzbYORyh\nkY0QyvkYFRfhGdiJTlvNSiiEcj7tZKevKTxJfF5Sf08N71yc+LzPyMMtCvvCKC6004LqmE/9dptQ\nyOJD4y+T1SsM6gV+8tkfQdF9fFtlYLjCkaOv8lxjF3ONro6ki4RyKYztapgGNHdb3Dq8wvp/miS/\nH8x1nRl/gK5bc6iKT/58N+VYDD0uCSdbWAUdc87kFWMQpX0Dd3avP2HzUsqj/z8/7feAX5FS+kK8\nvl7yTZGwwu94vGgt0Esqp45e4+tnD/BQM0ijYvKTR57laxt7OZDdYLsVZbMcQ9c8/LZKzoqQDjTI\nZKu0HJ2haIk/vnAnd4wuEIpaNKs66UyNr6wcYHd6i//z5QfYPbDFpZlBRNLHCSuolgCv84PpdYGv\ndZA+aluQPNTitVwfWrpNsRViJxeDmo7TjPKWe19lvprhW7NT7Bve4Pcv3429FEEdbDKUKTEQKGFk\nXMotk5qlMjS2Q9vVqLcDEAdF8QkM1NnXs8mVfDeB4zUCnsLzq6O4jsrIgQ0Ct7gsFVK0Kg7dL4wQ\n2mijz6wQi0XxVtc5dsbm1R/eQ20qTvTrF5COCwd3ITUFXxdIReAGIFpwWbk/gnDBykgQCq19Kr0v\n2Kyd0qkPQWgD8vsFTsonflmlNtqBSWpPxrFONAm9oKEVGqQvQP5QCASYOYXEiybCC/D5rvtwYoKh\nP1sg+gMmdgzc6SbvyJ7j92fvJhNpsLTUjdJQ0WxBcqhE7aUurCRIV3D6wgTR0c5+PrQliB8voik+\nlZZJYm+Btw5e4svzh9BVj0bEI7O/QOtrWRoDN2gdK29olfj1IP6OAp+7nqwZ4K1CCFdK+eB3OulN\nUXTydYFe9wiUOsCFp1fHSfRWOdy7SiLV4K9XDlJtB3jh/CRH0ys4C1HiwTZm3GKhkCbXjvwtfWut\nnsAtmpxeG2a6a5vQmorlqtzWs8hqPcnugS0ur/Ry24FrJAbLaG1JaFsyNb2O8MANSqRORxs3Attn\neqjVg7gFk9IrXWiGh5ZpMX50hUev7sbyNMyQzWRkh0y0wTvvfQl3M8TdXdf43MJh1poJGotxhO6z\nnk9QbwdoVEwcR6WYjxI2bc6t9xPQPPLbMSqrcdyFCF+87Y/Q7lvht0e/jNXS6X1Mo3hLirkfMlj4\nhWmu/VQfVz9+hG99/A5q03F2jigoyQTKxDBqrkJlIkRtQO9Ya7pQ3GOgWOAFZQe2GRVIVbJ9xOhU\nxtuCRr9EawpkyCVY8Emfl+RO+Ngnq/R91sB8ZRHhuFQmQ/R8a4fB/+cKVkqSOL3OxjscEgsO7ZRk\n4afGqI/4WGmfbKrKb7/yJgzNZXkjjVrWEC7Er4Lz9S6GTq7Q7PHRd3T0RJtg7joTKQKq4lNuBmm1\nDGrNAJ+fPUw2VqO8GSNxWWNrOY3yxgJq+6a0mzwDTAohRoUQBh3K6UN/76OkHJVSjkgpR4AvAT/z\njyUr3CQJC529g2+AURFEg21q9SCjoQLlnSgtW+fU4DWO7Z/nQrkPX5Pc1r1Iu2yiPBtnsxYFCYPh\nMt2hGm+99TUmuvLMFzM097apl0NstOIsX+7lynoPP3f4SaqOyb6uTayogq/B1dUO2cioCJwu57oq\nAziJDon7x08+hTpd47eOfpkTw0vMLvbi2QobxRi7unJ884sniBgWjlSRCYc/eeIe6isxzl4cw4+7\nUNNJPB6ktRQlkmjhtHTC8TZNSydk2hTyUfAFUvcJTFZ5rNEpKH6pcgRjLkh8pkL8L14kfkXDTvhM\n/NYVpv/vPM0eQfSbl8me8ZHBANLQkLpGfK5JsOhTGdFp9Asa/Z2pQ6sLfAOshESxBW5Eona1sRM+\n/kCb5i6LyOUAm/f4FPcJonMq3pUoblBQv3Oc0rEeylOw/tYsM789xuhXWjh9KYJXTFZ+xCMx2xEn\n33vLEsGBGmPxPNIXRAN2x2JzVwk/41CZhPIel3wzhF4XMNpkuKtEcW+nW5C86rN9IUujFMRr6MTC\nbWLhNo6nEsw0ybx7FTPdonE+hbqvckPj8Ea0daSULvBzwDfp8Mi/IKW8JIT4sBDiw/+t13dTJKxn\ngtZwO76lQLEapidd4YvXbuG+A5eREop2iKhmsVRI4cddvvDyMVI9FeoH25TnUqghl9VGgm6zzmy1\nm6vbXbxj5AKq5pN+2qBmm2R35bhrfI6nC5P8RP8zLFQyqLYkttThYvp6Zz8t1M51uCbgg6r5vFYZ\nIBVp8r888gFeXB5B39YRAoKmw3gkhzheZjGfJqg6TA9vIlIWRw/PMTiWY//4Gt2jBfb91EUOHp2n\nXgxxYHQNACkFjZaBtmGQ6a2gNFQa+RBP5KbYfHA3f/bYSfQ6iK0CalcXwbzP0KMeaJ3djGLDyp8O\nUZxW8ZfXafVFcLNxCnvDOCGBkJLgjsSoKDhRSXPCxkr6OAkPK+OjtgXa1RBqS6AoEqSg1eOjRh1C\n6x2ObqAkaHarhNZb6A0f/M7Ndc+v5zEWd1DaLoOPVRn6tEr5jS2mjy1RtUwsS6fpGmS7KpQf6UO0\nVaqLCZS8jtYQoEBlNoXWFCjXQswtZek57eNEwYp2GFQjQzkO7lqh8lqGwmyaTLCOtRZhqxolZFr4\nYy1aW5EbF4w3EOkkpfyalHLXddTfx66/9wkp5Sf+gWN/VEr5pe92zpsiYVEkdlxH8Tp7CLtgsr6Z\npL0Z5lhskWYhxFCwRMvTaZVNFMNjYmyLvZkttI0AU4dWuHV4mdkr/cyUs6zkk9w/PsO1ejdD3UVU\nS1KxTKQUPDmzi5ar85FH34/rKwgffEOQTNbxAgKjCjQ0zILEDUmkIelJVbm01cvhzCoff9tnUFWJ\nMlHn6MQS8WCb1VaSxkYUz1X48uVbGIsU2D24RcUKUm6ZrJY7S+GwanPxuQlEW+XyRg/NtQjJSJNI\nyMIbapNfSeCHfETAY2apl4GfLjD18RUG/2Ke+Z8fZ+HnJ9i+Q6K2fYSh46YjxJdcIl+JAaBmuxC+\npN1l0MoKCvsFbkhQG+2YhDk9DkpVQ3Egdk3Dj7q0xizaWZfwmsBta9BWUGyBXzLwDfCPV1AcSMzZ\n+JpC+FqR+BwMPpyjeijL4o+NsPjeGLM/G6D5P5fxSgEKnxzG+lQPgYsharZJ8eVu+h9aAw/+1zc/\niJ9xCB4tICyFgQNbGJVOFfr4dEdvOXPepToOZrrF0moX5y+MYJQEB4/OM7Pdg1Qk7WtxiusJXEsl\nPVy6MXH4epP1nwuW+L/b8AWNHg2t6f+ti5xuuvyHN32BP5q7E3zBZ5+7nfPbffQNFJElg7VnBinb\noU7BCDizPMyP3vEsg5ESQkgqTpBMoM7qy/34uuDW7mUmEnmEKjmWXibcV0NTfHwN7IhCvWliVCTx\nRRfhCBRHojcESlNBVXw8T3AmN8Rnd06Qjjawd0KMhgsMRMrM5LLs2buCovr4nuDR+SnqdoBcI8z3\nj71KuRAhoDs8Oj/FrXfPEO6r0Z2soTiCzWtdVCohxFYAIQXJvgqBkMOp3bPUbxvB295BJmP89Lu+\niZXxCPfVWPw+jfmfHmbuB00CJYd2UjDy2VW83hR63UVerzhGVgXtTGfpixTgCoyyglFRsKMgdJ+x\nP4fgukb5gEsgYoGAQLGzNK/ttvEvxKkcsqmM6uwcC9OYTFGZAH9hBTui4AUlTkTS11tieymFVlVQ\nPrhDbVjBqMLOXw+h762y+nth/qdTj1P0wnBdFmbX/lWW19PoDXjL/WcotUNURlRyhzTciM9wugi2\nglFUiN23xfmXxrE2woQHa0SmSihhB6FIinOpGxKGgptfIubmSFilgzhqpVUUF4Sl0JOq8rtz9wEg\nbMH0nlWa69eXPgkHJ+JzYW4A41AJx1cZy+Z5cOkAz1zahab5vLAyQtEOI1xwwoL+QBnbV+nrLvMX\nZ4/zg+NncX2FZo9CoNJRXnCD4EQU/LCH6khCW51g3yzFcIomk4kcpy+NU2mZSNPjwasHWKymGE6W\n2BvfxN4K8a/2v8yunhx94QoHuzf4k1fuQCgSVZGcGF5ipZakng9jeyp+l018qEI42kYqEOmvAnD7\n4CLPLI1RGdYQ0xNc+ZUY+8xVdv3MS0S+ECM+o6K2BD9y9zNor8zRGPLJnxxAzVdBXmcctcCOdVYs\nvibpes1DtFTM/PU22oiNrGssvE+lNeQgvA75Qquo1PdYZPor9PSXsNIeSkWjOimpD0p2DmuEDxZZ\n/cgRrIQACVpTsPNaFjXu4KRdkmYLOy6pD0haJ2u4roJl6Xz66gk+8egbCcfapMJNrq5mEXWN8hS0\nPJ2k2cSJda5PapLlJ4cRjsAesqm1A+jDDZJjRQYTZXTNI/mUSeysSe/0zneKrO95/EvCvp7hCYr7\nIL7URgoI5FXK3+ylO1ynZRkoGQvL09h3YJmN5TSypeIHZaeV09ZZ3E4jhORQdp29E+v8wu4n8H3B\nXDnDkXuvoNcl39jaQ9U2iQXavP/Iab60dIixeAE3BHZMgYCPkODpAq2o0Y4r1Ps7/qlW2eT9t7/A\nc/Pj9AwWeefoBfZPrnFyZJ7j3cv0h8q8lB/G6Gky1+hiuZTk7NoguXaEycFt+npKRAMWz768m41C\nnOHhHIVihFvHl6iUwtSKYQJDdWxbo1IJcf6T+wk/G0G1JEvvTUFD499+7CdQk0kSF8r0fPoCigNP\nfPQOhKYx+b9fop0StMcyKJbbCXilsycPFATRZVh7s6TnBbBPVWj3ute5fKDHLdSIQ6i3TnDGJDhd\nRtF9woZNoRIGCX7C6fjCConWhvJSgr77VjEqEiYbJA7kcbM2bAZQmiqa8HGSHlMnlrC3QziWhl03\nSIWbTN2yQmM9ynY1ivQFRDtmXHPVLi7nsvS8YGOnPQKZFu0Ru2ODEnBpNq5X1tcTXDs9TOFamuob\nmlQPW1S+3XPjYvFflsTffSgOHeqI37l7SQWa16uaAd3lR/e9yOJ6hs1aDDXict+hy8iARz4XRdN8\n3rrrEtPxbQAuzffz0PZBouE2+9ObXCl04wU6xZ22qzNzrZ+cHaHRCrBWTxDalB3Fi6aKrwuCBRfV\nAs8UqDYgIdlT5ZXSIOFIm5M984QUm5FwgaV6im8u7KbLqPObE1/C9xTOrfcjpaA3WWWnESFpNgnr\nNqVmkPhQBT9nsrKRxjBdLueyoHTggq18CKtiIisG3/+RR+n94hw9j2+ROedh5FXiSxbu7iGk3qEA\n9v/eS6yf1Fj/4G7sY7sY+NISitWhPSlepy0S2pZ4JjQGBKFlje3jYFk6SkshdrGjyeRtBvELARo7\nYVp9HrVchMBskNJX+3DqnZulqOqIhkZyukh9xAUVJmM5MmcKiCsRSpUwqUwNL+IjuizOvzbK8f1z\nHIiv8xN3P8mbp2dQKh1dqJkrAwhX0CgFEQKkJ/ADknw9TG0zSn6/QeyqClciCNWnthYjHLLQ5k28\nloaZbiEH2yi2QNV87py6RvzU1o0Lxps8YW8O4IQHCHDDGsG8T6ur02rZrEUpb8S4nOmlL1tmMx9H\n0z1WGwm0kMtYNs9aOc5crYvFfGeWfcuBi+y0I0ym8pzdHqBWD2LGBSuzWaQuSfZVeGppgnikxepG\nimhMYJZ8ghsqwpN4AQWj0tF8Cu6A8AWl9TjakE9tI8oXCkd5+8FzbLVjTMZyvL//NBcaA/zkuQ/C\ncgg50uA946/xZ0/fyejuTVxfYeHlQRhsoV4LIfsctB2DdlzBbqmQcAglO1hY29LxGyaHg0s8ET+E\nm44QuVYh9nIdd3UNNdvRNJKeh398H74hURzB9rEA5ugwXWdKtAainb2l2YkqowrtDDSHr8+qZQNF\ngBMCo6hgD9mohkcmWaNhGZ2qtR3FSglwFLy2hkjaUNFxPQWzq4U3F6Foh9i6O40XlARDFsX1BLfs\nWeRaoQsx3Ga+lOGl+RGE6HCOdx9fYqMaQ7EUpCIRuo9saOgllWBOUIvGEBGHQFklea1NozeAe6RG\nNWhSLkSI5wVWt4K7GGH48Dorq/20yybPre9Bpm6QRMw/8XL39YybYoYVsrNnsRIqdlRgVMDMK3SF\nG3zk5Dd5YXacajtAKtEgGW2yUkzyCwef4L7sDM1SkOViEiEkYdPmm7O76TFrnFkcJh1ucnJsDist\nGZ7eQo06lEthPjB9hnI1xNhgDuGB4kp8A1QLpNqpzyAhUPMRHqh1laTZ4sj+ThXzkdOHMRSPlqfz\nV9uH+cqVg+zt2iK6t8DbJy9ytjSESNo0bINXlwYJTpUZ78lhJz0U00VxINVXwTd9pCdwHBWr3UkW\nqUq+ULiV5n/xqI4HufYjSarH+lGnJsjfP44/2M3Wjx1CaTtM/+cN4osOraxPZMtF2Smhtbz/l703\nD5Lsus78fvftL/fMyqzK2qururp6XwA0FgIgAEoEIVILqX0keURJtjQzCo9jNBGOsUw7PJrNMRGS\nZyxLYzvkUEjiSJRsUgtXgQCIvRtAo/e1tq59r9wz3/6u/3gtDmeskWCpJUEMnoiMqsqsysx+fW/e\nc77zne8j1qD6VkSQEXhFIAZjVyW9rNH3rkqcinDGg0SmJlDoL7XYvVmhN1fAu5VHKhJZDMgOttF2\ndWRHQ6qSxk6WeDZDUIi4+MoMueUQvSnwrhX4/offQVNieks5Ois53FfKSEdF1SOmzq7QDQzqu1my\niwpaVyGV8ZIhdlsSWiDtiP5KCyT4WZ36jEK7ZxGFKuk7Bq2ZCBEIxFiXjVdHsGaaScvNEUwP378a\n9v1+wr4vNqwUoHUUgpSgdKN3704IYpVfeuvDDA3VCC8U8UKVejvF8eomr9Sm+fT8w5QGWkyV9zlU\n2SVnuch7KG0cKHx39SqBTPSalpb6iXwFzYi42BhluNxgs5EYY/lZBSWA2nFJpCdu5J2JhDIpIogy\nEV6k0Wd2+aeP/wEHZja5td/PyzdmOFNY5eGJJS6sjAHw1ecwOwIAACAASURBVNUZ5l6b4GdPv0Kt\nlSKTdWnvZLhzd5DMWIu4o6POtGkslCgMtiASBA2LSqmFafnITMjz756g8fkhtp+IGTm1yeb3+Wx+\nWz/qD++w9vOSzrhk9iczrH/3CKmFGgd/r4e93Cau9qG1fZQAQkvBzycrS3NBaon0SuOoJHtHR69p\nWHMWoqeyOVfBnmwhVYmY6qJkAzJXE4mbyJKITEhxpInQY4J8THG4iXmkyfInILIk5okGX1g4zqWV\nUaQhOXR8je5EhFHwCLoGmhIzmGrx9LE7dEaT+eOBXBtVi0HC4DmXVN6h1bNoT0BzUsMdClDVmLBl\nEGQlqVUVY6BHHCei7eHlAijw0AdvJxnMfYq/9TKnfy0hQG8nG6YzZhMbCVtmabOP7z11ke3r/QQZ\niXO7wFCpyVKzxLt3JgDwQ42akyJnOGy3sjx39Caj5QanJtf4pfMf5vL2MLEOakvl7PQSz0zNstoq\nEkQqqhrTHlXQu5LMmqR0qEZkJkwgSQJAhWnufXgojFs1CmqPWjdFvZ7h6WN3+PTXnuTC6iinR9do\nNNKk/p885bPbXGiOI+bTeL5Gqq+HuWbQWc4zPrVDHAuIodWyQUJ5pIEiJL29FLoV8tJHf4mBX36T\nTz72OvvdFIf+4TL9v/omtYv9fOrYl4gHXQ5/apbh/3uRxpkKsa4iNncQQYRfsvCKAqmAtSco3onx\n8xK9JfCLMVQ93LIk6A9wJnxGDm+j+InPapwLYTZN7Gi0j/lUB+uYQ93ESX4ni24HaH0unZ5JFCmU\nqk2OPTVPt2OhaRGmFXD8+DJ35oZQih6GkaThJbPLudtTvHF3EnWsC0bM3TuDiMUU+TlBr2rw0QM3\neWh4BamRUCQbGuJ8YowdTTrEGhzs3yPyVIJRn1iTZG/pXHj1MMp9FE38Fkr8HkIJIbdyjzrnxtjb\nksyawJiz+dylBzh6domo6hMWIh6vLHK4tM3fPXuO1l6azn6K9bUSr12d4bHhJb586QR3bwxx9foE\nVs5jJN/EbEqeeeIaN3eqvDQ/wz+e/ipj2TrfP3kZJYLWmEqsgvdSGaciyC7HCV1OCGINtI7KgVyN\nT8+e5VdXn2GmvMO/eewz3KoNIAUEDYutbo4PTs8T/XCNw4Udbu/3kzu9j6rGVPNthh9fwxjssnqz\nir+TwmgpSCmYmNzB1u+5rwmIIoWNKMWDl2K+unkYxzGQjotiWfiDAd3YJHPBJqrXifsK+FnB4s+A\nyKQRrS5eQcNsSFoTCl4RnJIgvZbUsFrZJd43CAYSBUJChZ23qsh+jzhWUNoafjVM0GA7pO1YDOTb\nRF0dK++RSbkYZkhfvou7maa+VGT+D6eZHtqhnOmiKDE316sIO+LRiSV6d3P83bPnCGMVPIWgZZDP\nOEm50d/Dr4RElqA1ofC5Vx5hoVmm/92YwTcjIkvSHY3ITzTIvWbjTPosfO0A9ryJdFSkBrlnt1AP\ndjjw+H0aYP9bQJx4X4BOSLD2A1TPoNuvYrYkvarAHQoZHqkxv1vmA4cWAPj3lx6mXGmz3i1wanqV\nxXqJzlqO5x65wlKn9HUFPTnqMt5X487aAKkhhcu7wxzs28ONND63+wD9ZoffuPIoA6sxsSYwOjF7\nT0bYC0bCFuqPkPMCEUNkStxIYyDfZqVWZMfIcCc/yKMDS1xQYtYXy5wtLxOh0HFMBq0mL+0cJlV0\nODawRScwWXhznKAQ88yj13n11RO4I34iphbotHoWiiKxSw5u1+AnPvOzFGah9Ol3iH/xIdqfq6II\nSdFr889f+W76ehL52ClkEJHZCCn9O0m0vok6NkJ61cEvGHgFneaxgPS6ht6TlC6p1E9YpFdUIltl\nW82juAreYAhdPdGYyoXoaZ+gYRF2dcSSzfa0QqrUw1/I4U7FOGtZUgd9pCrBiGlPS+Y3+4kclWy5\ny6MTS1zZHuKtpQms8TaffvFJ4mKA1lLRpjrszfchAPIeRt7DqWhoXYE+3GVjsUx+SEXrJZNbIhQU\nUg7r40WK7+jUzwSUh5qkgXBIYf1uGa2tsqbexxz1mwF0EkIsCSGuCSEu/8nQ7v10rwvS4PbppFcT\nKp2IJPnFGK2hEsYKE301vr10k2s7g5yaXOPpoTkWV/q59u4BBrNtRN7ny5dOsFwrUjy+x8RDa5Ty\nXe7MDhN3knnQmdIOI6kG45kaW90cb25OkM/3qB8RBBnoDKkYaT95XVciIoHqSVKbEtUT7LtpThQ3\nCEOFH5t8h99eOMvXVqfZP1/l2NFVqmaTP3znAWb6d/jjtSNoqZAzg+vUvRS3V6qcefoOalvh6t4Q\nYSYiV07kTB1fJ44FQkiiSIGOTlAOaTzXZf43TnDoxCrW/1xgfafAwKcEh/7e2+w/GNEZt1GcgPWn\nNTojFkomTW+6jNtvIlVBdi2i74JGaxK2nohpPOFirydOB85wiKgbWKNt1IYGZoS2Y2CtGthWAGaE\n6KmIqS5BoNKX6SFGewR3cshMSPNSmbMnF0iXnAQ48lRKAy1cx6Dh20iZyMC6K1k+8uRlkILU4QYp\nyyO1qZDaVPA9jVKui70rCLKS752+AqrEaErS2xG5eUidqLPz0nCSvg8IFDukNlfC8XVaCwVysxph\nISS+lP+L74BvXOd8c6XEz0gpT3/D0O59c69DkzglgdGW+LlkkLo+oxCWA9qv97PWzPOV/eM8UF3j\nu/qvcGF/DKHFfMeTlwCo9LX5wbPv4M/liGLBRKbG7moRNZt8sgM8kr/LjpfhVr1K2zWRUuB4Onpb\nIIVAdSV+3ULzJE6fitYRRKbAbMVfr5FuNatoWswvn/8QjVqa7lyBIx+aYzjV4FfPP8PTp29x5cY4\nphZSyPUomx3mZwd5amaOk9l14mGX4WwTreBj6iFCSFq7GbyWSTXXxm+ZSDuiMtzgU6e/xEiljpQC\n7fWrHP7UHrOfLDBwLoeIBPkv3iC+s8ChX15FDSSiVCTIqnQGVYKMQmdIpXEooRmaeyqlr1mkNyXa\nng5CIgKBZSSECKFIIkviDgdkLA9igeILZCyIPJWurxM0TcIRj+pQHRHDUrNEFCkU8l0+dPQ2Hxqe\nI5tx2OkmbLQgVKke2eErb50CP7GorK0XcKox7gkH2TTYrWcJLdAcwe/eeJCfeuw1dh+L6JVV9s+G\naH947wwIBNXH1zHmbSoze/TaJqorcAYkVtElyN6/HSRi+Z5uf1Pxl6lhv4fEtY57Xz/+Dfd/Rkrp\nSSnvAn/iXvdnRqwLQhvc0YDegMDelZgbOkE24Z1udPK8fGOGf3XpOTQlRtFi3t0bYW69n+2NAhdr\no0ycXeM7x27wwvUjnDqyzMnRNcJchPdIBwA30pnI7fPw4DKN7SxezSa1LQkzCdkgV20TmgKzGSP1\nBHSK9aSOXVorM5Pb4eNTV8mWuwhVMnBim8uLY6x2iwwMNcjrDh964Cbry31kTI/nf/9hDh3aYN9L\n8zsLDyLrBk3folxss7teIPZVhK8wMb7L3a0yhyY3KVVatHsWv/Dud5H+CZ/ZxUGW//uHWfyJUXJT\nDV6fO4hMhdz+NzOs/9zD7H7bKH5GgZ6D6knK1xyUILEOUb1E4NysC2rPuOw+HKH1BOm7OrEd071Y\nBgHlchuKPkpXZefqAMQC40CbyYE9UjkXxzPQcj7qpsnWZhFvMOS/nHyDM0NrSCl4ZWGa5V6JxlwJ\n5+UK5UyXxn6Gs5UV/uWHfw9hh3SbdmKeVfawbJ/UqoppBth7kr7rEam0x299/hlytzRSuxHEyXXv\nTSQGXIYS4VZDWm/2k71kISIISiFDxSZh9s/UqX/v8beghn2vG1YCLwgh3hVC/PS9+/5S7nX/abQn\nY7JrIaULGloP9C7oLUGYifF9jZLVo1Jt8ujEEj879jWilsGBXI2BShM9HdALdBY3y3zm9oOoNZ3n\nKteZ+8I0udsacaTwby9/iJZn8cb8FF995yRPn7xNYbCFnxUoAaR2QlQlRir3+rKaxGxHiCiZJBGK\n5ItXT/DK9kE+efAtHppcJmt4aGZISvMp2T3+6PpJ2qHJJx99A0OJ+Pj3vc5eL8X1pcRwWBqSwVQL\nRUi0TICxoaO1FFquiaLErLw2Rn2pyIcP3IYtk4X/pQ99T8Md83EHQpwrRWQk0LcMlJZGYT6i70Id\nvRcj+wrYa11iI1Ga0BxJfj7h+cYqqOuJA4E7HOBWYtSSx/Dja4iOyu56ASvlow91KRzdJ1fp4G6n\nWdwu09tNo2kRUaAgFSAQjI7t8a/e/CjvLI/TWC4gVi0ur46geoLys+uU7Q5W1mO+XcFSArKXLZ46\nPIviKMSBQrdm0z3k06vbpLciOsNq4pmTj4nN5MMbVRJZgupYDavoMr9ZoXBDwz3o0TqZ0BdRJYuz\n1SSXvU/xzZISPyGlPE0i2fizQogPfuODfxH3um+0m4zrPdSqg+rGGG2ZTIw0I8ymxKipMJ9mtZWn\n0Urxxu2D/NzzP8rMoXUUIdlaKfH4gUWKlkPsq/z9468SD3i80zqAe7qHMyCJdyziQGF5pYyMBadP\nLPLaG8fw3iklpPv1ZNC70zMRMbTG7wFXSsIltvYFxrIJgcLGZpHj1iofKt1maa/EPzj5Cu/OTpDR\nk807XyvzG688CcB+kGaquI+iJVnCdz10iXNXptm5MkAUKATjHpljNRoLJcJ9C2svEed+4Q/OYu4p\nTP6DDZjoobQ1Bt5QUF3BoZ94lwP/3TkmP+exd1xF3p7H3vVhp0bjWBanrNMZUmhNwe5jIaENnZkA\n7WAbTY/QsgGxIZEbFl6oQSFAywR4axmC9TR7awU8PyFKGGbIwFii/0tHJ0pHpCs9Wl8Y5GMnr/E/\nPfh5ECDGe6h30vSf2WanleHaxhC26TOZ2eMfvfgjuCXJ3VYfUTYiNWuiWBH2XQO1rhOZAns3ZqRa\n56mHbuIVJUImS6kzHtP74wG8zRTqmsX4DywwNrTP+MgeWkclXXTQix6plW9ZdfxHIaVcv/d1B/h9\nkhT3L+Ve9412k5ZMkUm7eEWN1riCFOAWVJxKYrvo94eEkcpwuYHQYh44uUDZ6vLu+ihPnrxDJAXD\nqQYEgnWvyKNTd/ne8gWifZPUpmDyxDrZQo8jUxt89OgNrr49hQgEzpRHrENrTMGuxeh6hIghtxxi\n7isogSS0BF4hAaHS/V1wVe54Q7zVmiRte/zW4sPYeZetbo5nJudorBZAwNzqAHcaA1xaGcUwA4bz\nTUKpYpYdJh5aQ3oqdtpDSkFsxeTuqDj9ks5RH7cSM/7vbhDt7TPy6zqHf3GN/KfPM/Hba6jHZlCL\nRYzVfYLDPZRslrs/I6GUx96LaI+qeMVE8kXf1yjeAgJBOJtNiAgNA5mOyM/UyBgeph2QSbvoQ10y\nywrmjopXtzD2VEw9YHe2jNnnkB1uobdUgls52lNJOfI/vPD9KI5C4GpEhzvsNjMEvoamRTw6uMyX\nZo9x7PAqg48kiZixryIkKBsWkSGJKwk4sPsgRFLw8s0Z7J0EUxBWxMCxHVpHA0QosA43uPu5KTbf\nHsRUQ7KHa/i+yrMHbyMeev8pTvxVxXvxh00LIbJ/8j3wLHCdRJ/mx+/92o/zH7vX/bAQwhRCHOA9\nuNfFhqC5UKQ+o1K5EqA5sH9Gkl1JUlThKYSRghtqPHBghZvbVc69fZggUImlwrsbo6z3CqBKXlid\n4cZulV9aepbMXRURgamGHKtsMZGp8ZVXzyBVKF+VECVAlxJBrILTMdk/LggtASIZQkjthGRWBLEh\n+Y6JmyDgcxuneeWdoxhaRLOVxtm32WlmeG1liqnDG2RHW9hZl5zpYtk+w8UmWcNlpVukmO2xsFZB\nzQQo5/P0rhbRWir5pRBv1Edpakg7Yu5XD1D/8cew5/e4+alBwm97EGmbsL6FdBw2PjpC9XMmnaem\nqXzeQlo6UkBuOSK7nDCQglLE3uMBqqMQTzh0d9KgSXAV2l2LpdfHEFezBG+W8BoW7TMu3kgAkWD6\niSXKqR6yEDDeV0/KhTGH9IkaxnCXlB6gNxT+q+de4NTkGmnb50h1m/H+GmnL5/zmOFFXp+7aNB2L\n1Z0ixtEm3RkPOewS5GNEQ8fPKBgNhc07/RAn799sBOjLJruXBzDyHua+QsoIaB0N8MsRW18Yw/EM\nwu0Uz7/4AOH13F9s9f9p8U1wwg4ArwshrpBsvC9KKb/CfXSvQ0JqXSHISMKUQpAFWfQxmzGpLYna\nURACtlZKXLwyxXCxSWq8ha5HhFLh2MAWC7vlBN1UYjptiyhW6D7goAaS7U6Wt5cmeGF+BmLB9KlV\njv8311DaWrI5JQQZgWZEFG/LRF9Kg15FJTIVYj1hXn329YdJVbo0ejY/+dQrtB2L0f4aKBD4GhN9\nNb67epVWPUUh7XDt1hi9rkkv0LmxNQjA3tV+ZKTApkVvKMavhkSmxMspqHsGajUZBEi9k6J+FJoP\nDIACiz+gcuen+1j/8WN0P3KS9mTM+ndEpL5wEXs3QCxvoHcSqRvNTcTUtKaK6GjEpkSfTYGalBu5\nWQ3TDImne/hHergnHYQVkck7ZEtdKuN1tjtZlveLSFdlYbtMfSNPHAu8QMN3NdK6T/pEjVf2prly\nY5zHhpawtIDJ7B7+H1eob+YoXtRouyZTpT1ULaKzl0bbNkBI7E01mc6KEoe8o6eWEV2NSBe0R02C\nUozWTpDqMCtpdm2sdZ3KeZXW0YAwUDl0YhUmuijBfbSbfJ9TE9+LP+wicOpPuX8f+Lb/zN/8C+Bf\nvOd3IaB0J2T9hwNa+zb2tqQ7prF3XEP1QEhJHAtGJ/bY76TwQg0pBZ84eIWi3uX/uvkBChkHKeHn\nDr3IP734naxdq0K/R29AEHUtBvqaRLFCS4+QUvDS3KGEOytBa0vMVoxzPU13GIyGxGglKKvqxShh\nIs7mFyTT5T0AVt0iH5+8ys1WFXNqA0VIOr7JglvBznps3exn4sQmA6k2F1+b4eTjc2x2c4w9uM7d\ntQpSQGzGCD0GodCcVonyAXLTRpQCQhvGnvcxN9uk1yw4f5X6F6fZLeQwn22R+3yZztM+znMPkLm0\nRnB0gshWiVWBnxHonUR3KbQiUMA7ECCkwJo3aZ3yECs59GqPqKNj5Ty87RTeikmYkbRTUVKbmhHG\nvkroWpAJEzE126PXsMloHvWNPO2cjVrweW1tEqdnUsx3aT/sIEKF6CMNfuHY5/nHn/8xRNUFLcZo\nCXptPeE593u0Dti4gyELu2WKN5OpC70XQyagdAc2Bi0OP7LM/FvjyLTEzyaueQOj+8yuDpC+YX1d\nYO4vG3/Sh30/x/uC6SRiCNIK1b4mmyMmRlOALvHzEmtfYO0JAi/H6pjF95y6zMW9UR4bXiJC4Upr\nlL5cl7ZrEgYav7n2GM9N36T/WJu86vBv1Wew38lgPddgp51oKC1sVZCxID/epKHl6LuoEhkC/1gP\nZclOxuoiSe0EpDclRlsiFYF9qsnsboWPTd7gS3ePMl6qc/vGKM88dIOXzx/n7NlZrtWHeHJsgXhU\nYdvNktU8glzE1fUhQleHrgb37CVFrKDvaIl6iy1RuiqTp9ZZOT9C/zPrtB83qEcKnetpSgcfJf2/\nhvgzBn2vaHDnEgO/qVD/xElqPzpB340AJZAokaQ7LEAkc77pSo/43Tyaq9M+4uP1xZirBvqJJp39\nFIVKh9ZiAZmJCO4h5sJVoBCgmyFyKkQT4HcMaOp0swaKGWGrAdlqm0N9u1zfHMTSQ4L5ArWciVTA\nHOoigV9ZeQYqHtk3UsQGuH2SwjWdxqmAdMpHq9uYDY3OWJraQyHZWzpGWyJqBvVpBWkGzJ8bJyhE\nHJrZYLavipX3WF/tQ22p9E46KPcRJUa+v3fs+4JLjIDIEGzt57HH2ygBKG0Vsy7ILcfEOvi5GD3l\n87XVaT4+fIU3Vg/wB198jHMLByhaDp2WTTrtktY9vjR7jOc3j/DbKw8hY0H/RY+ddoZnRudI6Qkz\nRzdDms0U+VsayKQPKyNBUA0wWzEoiXCZU9HRe4kaRadr8bHJG3zu1UcYLTbYbOWwBzusdIuMHdvk\n8cICi6sVbDWgZHS5tjjMSqfI8OQe4b6NjATWQJfKiwZR1efo2SXcwYBgysFoCHKTDVbOjeAPBKxs\nl6jdLNNopJn8/Q75T58ndWWV7pAEKRHpFKgqZjOmMB9hr7ZRghg/o1C6JQlyMVFK4ixl8Spxgr66\nKoon8MoRznwe4ag0GymoeAg/qSXtLQUUyF2wiFbSAAQ7Ntm+LpnRFo5joOkR5754krFCg3dvTKJp\nMfv7Gfof3WT02Bbf/cQFnhhbJGUE3N3uQzdDGidCWodC/GpA47SfCAa8mwcBrQc8GHLJX9MTZ/he\nop2MAG1fIyhFKJ7C3PURCBTSz2dILeqoPUHsqyjK/ctR/9aDTn8dEavg5QVsmYShgtWMyM8JnONO\n0kN0QJYCgppFMeXwa7c/gK5F+MM+08M73Lw2BhLajRR7Toajw1uoSszWehHZNFh51kARkg0nz9J2\nH7lSl3AzxVB/g/ZUjBKB5sSIrcRCwt72UF3uiZUlLKgwBfGWxSubBzl2ZomHSit0bheJY4WVN0c4\nUtjmyzvHyBZ7XN4f4bM3z4CjEkqFrRv9KAWfbF8Xp2Xx9M+dQ3oK126PIjyF+J5KYxCp+OWkvxh7\nKnpbUP2iQXckBUC4tY021UGqKiKVQqRs7K9cRP70LsILCTIauSWP1rjyH0yhiwF6UyE64JIebhMN\neqQGusiqiyj4KLsG6poFMXjDPp1DAXrFoT0ZUz62C0tpFF/Q3s7g3SjAus1Yuc4/+y8+zUS6Rrq/\ni+sYGHaAqYWcLK3zR68/lPj8vtaPNm/jdUxQZGLO3NLQ93T0ag/vsJOIwdV1+r5s4zzeYeyLNZyK\ngnvUQYqkFFEyAdZYm+njawg7ojDvAaCEgmypS7Rl35+F+E1EnPgrjdgAqxaTmxd4NRu3oCIVQdxL\nQCG9K9E2DVIDXcayNYSQPDm8yN85/Q6RVFAdhZ888yb5YpetS1Xmd8us7pSoDteTdsTBNu2mjaFE\nzAxtJy7urmAiV8MabaN3Y7oDKva2INfXpTNq4faJRFxbT9QnrF1JbCdaRzndBcCeaTBWqpN/YI9W\nYHF7tYrr6qxdGUTTQ7SWSiyTfO3JqXnam1mUlsbvXThLebjJ6MQexQN11ExI99s7dPdSqNkA3QrJ\nlHp88ge+SuHSLl5eQUml6H7fI/jraQ7/2h2kqbP/7BTK1AR7bw8QZ0yMVoBUBKqXaAkjwFg38MsR\n+qxNt2mjbZj4cwmqKiOF7N3EUFnqEn1HR0mFSAkihO3VIrEuidIxxcEWyqEOuZkay7tFfuHmxwB4\nfGSR02MJT2bx6jBfuHECaUVs1bPIB1tox1ucObiM8BSkmUxBhYMezKdJXbFJbUmkJhn/mVkCV2P3\nbJH+SwnwVpyNiHWIXRV5Kc/C5REQkqWPGUQWDFwIaO+nOfXgwn1bi3/rQae/jpAqeHmF9HZEr+TQ\nHsti70j0moafSxae6kKvbvPazmEwYjYqOZ4YmOV3X/4ACvA7cw8yVGgRHRZ07+aRffdExAAhoPim\nyfV8FVUkJ1lQCVBEjOcaxHqyyDUH3EAjLaEwHxLrGr2qpDAvCVMCa1Njsy9Ho2eznsrzodE5Fjtl\ndjcKiVqEoxIpiVBZ4Gnokx32OmnkgMfLN2ewyg626XO2usKF7VGCWKHZSmNfsZNe75BPLttDU2Oa\nl8pc6R/h1j8pMvMrLWb/+UnS6wpSj3j1184SfaegMxGjuX1M/foG0tCJMgZ+QcNoSxqHQPjJtSMU\n6GfquHtpONAlDlQ0IyJestC+Yw+xUPq6llbc0Yl1FfIRBAk9UNghthHg+jqN1QLPPXyFEbPOr11+\nHBkJJkd3iSOFsw/P8tadScyii3IlC2daFFIONTdN5q5KaIMzHGKsmEkmgYqIBX0H6lzdGCJ9w0IN\nJKGlYswbbD8sic2Igwe2GTte55XXj6NtmITZmNSCZPn7Y/Rtg8bvjN23tfgtB/b3EIp77xsBXstE\n7yTCbOa+ILseUZwLSG1JRE/lxNEVFDMipQX86tLTyD6fsBQwVd5nu53Bv1pg8uQ6g/0NHp+4S9r2\nmerfQyqgCklzNY+zlAVgoVmmUmoRpJOhdc2RuLs2ei9GdaN7CHXy1qSS9DYPDezyUHWV/W6Ky/sj\nXFscRrFD9jbyqNmATMYlmnQ5MrpFsJZGSoGyYTExuouU4AUa72yNUUl3mS7sErV0uqMRYS7mqcOz\npIyAiXyNaNLh3LVptB2dvdMJ4b+wEHL4U7MM/PolRn53Eb0lyL80hzNVhu3dRMhOJrKulUvJG48M\nEH0e4YVi0jYJ1cT3NVAJ+wK8UEUb6JGZaCImuijpILm+pUT5QwmTf3+tncZzdKQec25jgoVeBVWL\n0e2Alps837vLY4ieitc1+KEffBmnabGxUGF9r0D7YIhXjhk/uENQjDl7YgG9LdA7EjfQ8B2d6ps9\nirc6SDUZayzeArXssd3O8PL54yi+ILMMiifoDSbZQ+m65O73/dmzJe85JAno9F5uf0PxvtiwcTom\ntRcT6QJrzaAzGWG2Jb3hmN1TCt2BBBhSewp3Xj2AnfJ5d32U3TcGka6K6GpcuzXGZLFGaEMYK+RN\nl7utPqb7dpk/P45TEbgXSxw+tkqciRgerrFzZQDX19n/gJ/0A12JkIL1pxTCjIoSSIKspDFlYDYk\nqi9Y3O/jxdszDGQ7LC/2J5aKbT1J+bYtpkp7xKFg9tzEPelSlajqsdvO4HVM4lhBfqWP9a+MY6sB\n1qaGVnGQqYiXrx1mv53m4soo5vUU1qbGgT/q0R0RjLwUkrm5j7BM0HXiTjchOZQKxLoCuoHUkhM1\ntKE1rtyzmQS5b+KVY4zBLmLTQj3chj0Tta7TXcoT7tq09tPIlXTSztm06NVSqAWf2JKJHClgWCEz\nBzdod2y+duEYYcsgk/LY28mh6RFCSMgGPHZokd96skgFSQAAIABJREFU/imUtsbY9DaW7SPuuQNu\nNbIoruCdOweS4YQBgesYCEUSZnTqhzP4WZUwLWlPQNjVic8ViY2Y6oNbOAOCKB+SOl1DCQS7D/H1\n574f8S3Q6T1EwXJwSgIlArMGZANCS9B3WSS0RFvQGUsACP1Yi0PlHaqFFkE+ZmhsH7XiYuypXL07\nwqGHlqnYHQ5md1le7Gejk0eOO5gNmHrmLnOb/VSGG+hqhD7VJm+7CAFWI0b1JFozIUpAkoZLQ95j\nQ0mMOjhLWbKXLD5QXmT0wC59xU5yWvQ7TJ5c59LCGM/MzBKPuTw2ssTJ4Q0MK6CU7lGqtHBrFo0z\nAc4ph1e/eIa+mxFBw8LY0FHsEO2tLFFHpzcS4pUjasdSlB7bojGtI9pdmh8YR2gazpOHGXhRY+eD\nA8Rm4r4e6wrIJLXvTSacYcS9RdbvEq6kie2YYCGLVCVRLiRORchMCKEgGvBgxyTKJz/bl1Nk51TM\nFZOU5RH4GmuNAj949F2kHmPsqnR6JoW+DrbtE+5afNvhO1zbGSTKRmSWFXbbaRzHwNpKEGpxI4ve\nURBaTGEuxCvFRDUToUpqMzqNw0nHQG8l6bjSUfH6JPlbGnuvD+IOJ3Yj9Z0s0eEuR04vYw927t9i\n/Bbo9OdHw0nh5xNwR3OSHhwyoQUKO0IJJaWbideN72lce/Mg280s5kSb7WsD2Ckv0UaqtLh1Z4TL\na8PseFkGxmqsbxZ5YnKB1nTE7Y2BxGtHibG1gEOVXXqBjqInp7sSJUSKhx6cQ28l6v/mjpogxFpi\n3iR1iXy6zvMbh1m9W6Fk97i2OYRpBuz+YUKhfvXVEyAkL107wnonj+/olO0O7Rt9fM9DlxgYrhPX\nDcKZHm5RwdhXkVM9Cvnu12c80SSi6ON/rMH+2wO0HvC4+8tlNj/hU/vYDPZam+KNFnpPkvriZeJ2\nm8hMJmqMZjKaaNQVwnRMnIqIGgZRPko0iY/uJyLiOR80iZEKUDsqMhZIQyavnQqRjzZxH+vQ/+gm\nrXaKdNrF91U+c/Uhzh5bRB5M0uZCyiGMFAoTDV68dRhnMYdd7uE80uEXTnwe0wwIj3cJKwHWAzXc\nER8ZKUSWILuoIFMRkaMx9LUaxZsSez9k4ILP0OthwtjqCZpnEhMvYcSonsDKe4S+yvIXDuCuZu/L\nOvxmG2D/qwsJzkBMaCY0QSoeqi8JMgqKFlO67aJ6EqMtCDoGj3zwFmGo4Hs6H33mAqqQHHh8hd3t\nPGpb5VOnv8TN3QG6nsGpyTUubI6Sv6MStQyO5LepXeznzpUxGp7NB6p3OTqyyd5pQa+ikp+Fd25M\nIjWB3pNovSSttGoRig+qo+DMFti9WeHxk7MYaoRlBHQX83zip15mdKjGj3zkVQ4PbSPMiOFME1WP\nubE5iLkv+OKLZ5nM79M3USeV8mhNgj8YEHR16ktF4gEPywgQRkw+1yNlBBz84BJD1Tr5z2aofNlk\n+4mY238vx/yP5Nn6cEj4+HGUyfFkWMEW9KoC+3RSHkg7xtjRMGoqSlfFrAlqqwWkHhN0dfAV/JZJ\n3BeApyLyPpliD6QgjgWGEbK6VAagVU8RujqHx7ZYqPcRtA2QgqLZo1e3+bHJd8gUekTZiDBQ0fWI\nF5tH8ZayyNUUal2jsZlDaWkQCurTKqon0bd1cn1dpKmj+pLNxzV2TxtsfdIl1hLsIHPLIL2uIBo6\nQT7CbZrIQMErS6r3y6pDvrfh9b+tA+z3LRQtJi6EWI0IESVIZWgLmlMqkaOx/JxFr3JvHjMUXFgb\nZbTcIGwZfGn2GEfK26zWC3z/6XcRww5f2T9Bu57C0EKu3BljtNCg/YEe2TmNL906hl8OSY23GEo3\neWF5htntCkHVJ9ITbafMgs7mowaQqOd3RgVBOpneySwLwr6A0uF93pybpOVZtGeLCAn//stPMVPY\n4bdvnOX2xgB22uPmdhV1LkXmpTQnPn6LyIrphQa1epp202bowU0ytw1Si8bXr0enayFdlfpmjvrF\nCgB7b1XJ/c55ip+9zCcfe52j/3qLqZ9/B4TEXNihN12iPXIP9BfQWM+hT3QwNzXCtERO9YjtiMgC\nFBCOmvzvqxLhKNDSMPYT9NhZzKGvGThNC/9GHn1fI6qbqPs6dtalFxh8YvwqT564Q7hrcbfeBwpE\nCMQrRVATx7/ebppzGxMJ4OeCMuyg11XiVIye80hvSupPeFhHGvC1Is3pNIXz61g7gtxSzPD/YVC8\nCfamQPGTbgIx945CSJccglLIdu1b5P+/1ohDBdFNyODprTDROioL7O2kN5jaSMAUvQVIGCy2iGKF\nymidh8ZXeOe1wxhaxOdefYQoUFjrFBACGnMliAR11yZqGph1yQcOLiKsiL50D1sN+PFD54lCFSvj\no3qS6N4pL4+3UZ2YyAC/EBOkBIUFH7cMetrHUCNUPebbq7fR24LiTA3VEbxw4wiTA3vEgULa8vGX\nMxx9Zo7a2YC3z88gdcnNzQHinka+0ONAbh+nIuHBZtILNUPiLQu1rVIaaqK6glur1YTzfGAcpZDn\n8//bU6x9YgR1bITcFRNUBaekkd6OiFWBvSMx91S8zYRwkZtPFAtFoOAXI9S2ilL2UM0IPeMjSj7S\nigkKMYGnoQw5RAdc7LyLX4oSGZ1USGxJTD1k/fIgI0aNxWaZscPbNGppcqUu//vzH6bzoIOV8xjL\n1slW2zSbKezxdoKyhwrRiAtmhHInQ2SAtmHyYHUNJYLOkEL3xCDOQOLqHqZU2uOJA5/ek6gOiKqL\nXlexch69jkmm0kW/k7pva/FbKfF7CNMM0PsdUKDXr0Ek8MqS9HYyfeIXkt/zCyACheXZKm3PwFAT\nTmtsSlptG7XaY3xon4zhMTJYQxvuJYCQEjN0YI/MeiInmsp6LC/289LNw7y4c5gnJhd4cHgVvScR\nMaQ3Yryazf4JnSAjiPMhQUbQmDIwGhDULNbvlpke3OE3bz6Md9AlZ7n88Pe+jJX1uLvTh3RV9nZz\npKeaXH/zIDOTm3zimbcwaipxrKBmQqZKe7x86QiqD+5KFsVVEtJC1SUqBtS2c0gNqpUm/kGH+Z8a\n4s4vDuGUBW5JsvfEIJUrLoQR+UUHpy9Z4L2qQHUT5FSEgu6oBFWSGuqQGulQPLKPYQawaRJ0dWQk\nIBJkhlukMh5RqCDjRHI1O9RGfaSOZkaoPYVO1+Lj336ef339WcJYYe1qlaHBOs+O3aZyeA9jziab\ncnnj6iGMLxSgpSc6UQJSV23UVQtCBa2XKEtEIy6vzh8k+12bCUnCEESjLt1jVfR2SGxKpAZSQPNY\niLJsIzVIvZBBeirVXBtvyv3PLa3/fyGBWL63299QvC82bFbz8BsmnSENsxWTWtEIChGdQY3BNyL8\nnMTPia+zTNLLKvVahvXlPl6dP4jWEXxoehakYHWnREb3qL8wyD888TXCWGEk0yCIVGpHDa78wVF8\nX2VoYg9ieKK8gILk/LnD7H9PDy+fzMOqXYXOREh+KcTY0HGqEtVPPl2zCxoIqLs2Bwf2eGJ6nsWV\nfu50BnDrFvmsQ7rSQ0rI2y5BJeDOcpXPvv4w1p4glfKI6wZFw+FDZ25SemCHjzxxGWO4SyHrELV1\ntJqe8G2PdGg5Fh+ZuUX+zB4Hf8mnNxYiZjoUf+ddulUDDB1tr4PRidG790ywTDDqKmFKEhkS4ajE\nsSAMFfZ2cniuTlQMUToa2rqJ8BWcOwW6O2nEdiJS59ct2jsZ2vfMqkfObHCwussfzZ3A1EMOFvaQ\nVY/NuQqfPfcwR0rbWA/U6E93mDy4xcM/cwkEbK+UCFNJiyxzvIbaUpM550giNi0ODu7iBHpipg0Y\nczZSFeyesQgKCciU3olQXIXyFUl6VVA/HpO6q7N4ZRhFvY8b6Fsp8Z8fe06aickdRAhOn0KYlggr\nIrIFzQmN2I6x9pOUKLOskN6IOTS6jfAUfv7BL1N8cJcX3j1G0NMZKje4cPEg4vE6v/j2s1x//SBX\nt4c4U1lLWEtzEUOlFhtrJUZH91noVVjrFkhPNQk6BiIGsy0xagqjU7sgE5qfiEg8YJSkz6kXXHKG\nR85wcSOdZ4/f4PJXjpDr7zCcbaLeI6Q/0b9A5o5Bpb+F1u9gf3iHtOmTHWmx0ilyu9FPs2vz5evH\nSH01w+5WHr2mIUccUiMdyvkOlh7yU+VX6b1aQVnbZfjF5PRU+8v0fqgJPYfWiT68nEKQIUnpNUlk\nykS+xU9UEN2ukUiqtjVkw4BAgbKX1LjZEG0yaY9EpRCaetIiqmkYfS6GFbD55jC37g7h1yzSpp9o\nD9s+YzPbnDy+xGsLB0mZPrNvTnCiuMHXlqbpP7CP4iY9YX/Mp1FPE/cFaD0w2pLxM+vsdtPs1zJo\nPUlqrYcSQmgrZNZjBl8RVC6H1A9q6E2FvZMCt5yQJ6LT7YQvHb4/+7BCiOfuSf3OCyH+yZ/y+I8K\nIa7ekxB+Uwjx/xlj/U/jfbFhNS1GU2LClEBzJfk56O9vIkJJaidG8RIlw9iEzHrE9uMxc+v9pEY6\nXOuNsL1cSpQUguQ0NQd7dDsWqhERlCJGCg0u7Y4gFdg9rbLz2hBKW2N1ucyN/Sp33x5FVWIKlU5i\nsOTF2LuS9d0CnUEVo5FIrlgNSXozeSzct9HViLfnJ1jv5Lm4M4r5UI1WPcWVO2N0OhZEghc2ZugO\nx3ygepdw32Z7vUjHNSmle0xk94mlIFjMJh65D0TgKUn/dMOiu5Vm+3o/Tw/N8UPnfprhZ1e4+/cP\nsv5sDLczOMeG8C8VQdMwa4kthupBb1Cg9gSqJwgcnXjMIcon86zxeor0WAul6GOvaxizNlpPIJSE\n5ZWqdMFTKE/WEOmQKBUTbKWIlhL5UmvJIDfYZvNWP8pCYhm5cXGQjO7BlknF7vKJ7zjH83/wMJoW\nsbOTJ7ZiVEcwPrxH9qKFbgf03fAQEjYbOXqugbppIiS4/Taac6+erSrsHxesf1AhMiGYcognHIJJ\nh+ySglezkZkIUTf+nBX23uN+ocT3pH1/hUQH7Sjwd+5JAH9j3AWeklKeAP4Z8H/+ec/7vtiwthow\nmGqS3o7QexLdkezMlYkskdQ1uqQ5pZJbimmNq6hdhdhT6XVMXlqdBgG5/g7FoSbn5w8w3b+L2DKZ\nqu6CFrPZyiVc30E3MW22JPmpepLWNtOIGBzPoNO1SP3gFm5BxWxK1DWLxrEYzZMUZyN2HgKvIAjt\nBJha3OuDlk6f3WOmtIN7qYSd9TA3dYQimRjfpXazzPTxNebbFfoP7JMq9XhqZIGub/D8peNsbRY5\n9sgiqh2RGeigeMmkjTraAytGCQXv/rcPcOhnl1H/6xSVSyHTv+4z/j+eozWmM/lrS8hsCgSYrSQT\nCVMSee+6iZ5K1ExOU9WIEZGgu55FUWLSj+zhTXqE6Tg5Ua2YXsPG2tTY28sie4nVZGa8SZgP0Xrg\nDUSEbxc59eAC/+j7/ghDC4nHHG7/+hHMPYXbL0+x7hRwyzG9rsnD03exNjQefOY2G28P0TrlJ20k\nXcGsRzh1G25liYdc8nddRAwDb3Up3Q4o3fYx64I4FeMc8LFu2IgVG2XDIsiAXe5x5MBGMhJ4P+K9\npsPv7YR9GJiXUi5KKX3gMyQSwP/h5aR8U0pZv/fjeRL9sz8z3hcb1gl1FpplsrNNQvOe8v9EA6OZ\nuMmVrir4BYnmxJhNiRIKhKcm2rXnE8HwKFbouQbZvMPNtUGy04nBVGWwSbtlJwsrUDBroHUEzsU+\nzh5Z5JGJJQ48toJbtzg1usbqYoXeYMKySa0LpJYMr++dUJEq6N1k1C69ouLUbcqTNa7fHebNuUnc\nUf//Ze/NYyxLz/u85zv7uftWVbf2var3dXo2znCZGXI4XDQKRUaQEi2hFVlRoshwAhkJHAnIZthI\nFFhwItvQQiWSLMuMRFMkh8N1OOQsPVv39N5VXV171a26t27d/dyzfvnjtARFiDUjuWWNFb7AAboL\ntwuncb7vnvd739/7/MgmHT7wzCWiQKHSyPDZp7/F1jcm6PoGJ4u7lLNtXtufQBGSh06skMr3uL0/\niKYHOCuZWEhwvM3JkR3oK0hg8yMG9Y8tgpSkL66jr8YGxslKCGGIOGyh9kNakyp+Kh5C75cD9E58\n/0MvKZj7GlHdiCWYHQW/q9NoxdXV9KqKTAeoZohqhfQnPcpfMUgOdpF1k/Z+CntLJ/WBfYQr8LKS\nm3tlfmP1UT46fhN2LNpPdXGO9nn2B17mdn2Q6eM7aHrIaytTBCnJq3emUR2BehCDzLtlDaekYW3q\nKMdbTPyORvW0zcExHRFEdEY01j6u0RuSFF9XSS0Z9AcipC7RHIEzGOE6OrcvTfD0Z169L+swFk7I\nd3UBpT+mft67fvrP/Lq/KO73bwHPvdM9vic2bBAq7F0fxC2n6A4ruNm4Gll7JECq0JwnTutMQagL\nQktib6ssTFToFyUDhRalVJenppfwX88Tegqur7G0M8RUts70SA1DDSmXG9jViNxKRH/S5fKLC9xp\nlKh2k7zvxDLLBwPo+T75pZiemN4O0doqyR2P9LqMuVNJQXYldgMQekR1J4dQI37q7EvMTO6TNft8\n/cYxPnHsKu+fvMN6v8D4h9c5mtvjxbVZhhMt/t788+zt5ri0NcZHJm5xdKiC/kYabaKLfPKQxyfv\ncmltHKyIxJEGig9TP7vE6n84gAwj2g9Pok2Oo/gRmz86C0Kg7xySrEQQxSxio6bSm/VQegqtKQXF\nA60dSxcloDY1wrqJUjXoXuhhplxCNxbRK22Nw0930dUQaUQQCgJbMpZuECUi5LjDWKHBwXKR33v7\nAmOnKkShgpXw+IPnH6H/Qgk30BjIdrCWLaQq0cyA5I7EnGnFhS43RsFEhqTfNQhNQWo7pPxql7VP\npmjNQHJTIRjwqZ+LbSmlETOio6MdFE9g2j7vf/Q6X/juO3Lq331E7/KC2h9TP+9d75jO/ptCCPEh\n4g37997ps++JDZu1+igjDq1xPUaUhBB0dIQX357eiuFq7VE17sc2FZyRkNt3hwkzAZXNAh8fucrN\n5hDyTJufvfACzn6Cnzz5CgBuoLG+XeJsaZvOmIJZ9yFQ+MiH3+KBgU2GUh1euj5P/3oOv2Ow81gs\noLCqHsah4OCEid6VRDqEpiC545LaiZ+aYgX88Ik3Sat9tutZer7Bh47cRhGS6/VhXlidZ7uZ5Ztf\nPctieZ+Ndp5/sv4h8BUyyT5OZHB5bRzFh/Fig3bL5rXfO43YM0FInL7O7KPr3PncIgOP7nLzH05S\neVhl89PjHBw3SO1EBOMDdE4M0RlVcIYk3ckArxSiHeiIcp/+cIjmQJiILTnCERc56JKbaJCYbwAQ\nbCQhEHG7J4L+gU2jlgJVomU90icPuLxxb+C+ZgKQmmoyMXLAxvJQ/CWgRsw/tE7nqEc52UJX44kn\ne6ZFcGDTmolhdaW3BE5JoXZaQXXiHnukC1pTKvWjCVCIpaj3CmikfYIkmDWVKB1gvJkiGusjpeC7\nd2cZWKjdt7X4F3jDvlO8K9yvEOIU8GvAs/c4aX9uvCfmYZuOjd02YthZKxbaEwrSYy3MF7OkdgK2\n00m8PGSX43OdiFSChMLUA1vcvTLK76+dB+CzR1/mj7ZPcfbEKr/13IcQk12eml2i5+lxJVaDxrzB\n5OQuX33hHEcurHHn4iTClsjZLouDB+x3UrTuFEmaguSupPlMF+OrSVLbkv3HA9KbBp0RBfu2iTMS\n8uLeHIddm2AziZJr8623j6KmAnQjIJ/uEUYK0bEWlurjhSm6PRu71KPZtnlu9yRGxsV5qMPqpVGE\nBu25EDXvooQCy/JZfWEK/0hE9IUyR79V5fZ/l2bs/+wi17ZQBorIhIWSN0lvRGTWIXOrxf4jeRqL\ncdvEbCp0zzroZoDXM1C1WHCfLddpOhalXIe26dPfTONECUTJA09BaWoMHKmxv5+l0UhiJTzOP7jC\ndjfH9mGWMFTYWM9ijnRxHR23r3N7awjVDliplwiiuDWmLWVJzLfwmhn8Q5NeWaC3JfaeYPBTG7Q8\nEyhhNCWtp3ooywl6gwoDlwI2x0DfNNE60DnqkVw26Cx6GOsW/VJAYk2ndvI+LcT727J5HZi/h/rd\nJvab+tE//QEhxATwB8CPSSmX3s0vfU+8YZEwN1u5x24SWAch+qGKIiStCYXmlE5QDNC68Xxmaxq8\nfERoR2wfZlHLDgfreQ7u5vna3jEql8rs9dIE2ZCk7fHNuwt4gUbylhmbO7uwc5Dlxz7yHXZaGczF\nJvZYmzNj2+y0MswVaqieRPElViPENGN/FzcryAx0qDysYLQkkQGkAyTxmyMqeVTbSX7ufd/kZ09/\nB287yWenXyZnx7aTt2pxC6dTS+JUEyhqRHqgw4dnbnNseI9o0EMqEqlK5K5F1NZpV9IEiz0effQG\nw3+0gWi20e9aSC1+dLLVgUjSL2r0hhRCA+5+Jk97EvSOIMwFiFMtZKjgtkzMVZPA0VAK8chfaznP\n/nIJp22hDjmkhjoYlo/QJOqwQ72ZRLf9+N+vpXlzZ5zNah711QyKEg8KSAnSUQlqFlFfwzACujfy\nlDNtMkvxaKT51QxM9xCBQO2DVZcESVi6M0xlvYgIoVcW+H0NqybojkcogSRzzYC5LsFDbcxtHRHE\n68Urhnzy/GV6s14s/LhPC/F+VYmllAHwXwDPAzeB35dSXhdC/IwQ4mfufewXgSKxYdyfOEP+efFu\n7SZzQojPCyFuCSFuCiEeuZ92kwiIpMD9eJPEfoTqxemmqQeEdnyXakvFy8Xi9tSmwKwqGOUePzh7\nhU8uXMUc7GGNdJlJ1/CHPHqugVV0SJgeU6V6LBQwITLjiaAPzNzhixsnCCKF0WwTz9V5c22CjOVy\nfa9M/2MtpCIwD3zau2nMVoSXg9Z+Cr0tMDqS9LpE3zI47CTw2wZmwqdXTXK9M8Jv3XmIqRM7/IOX\nP8ZwoknVSdFbzuF2DY7ObTMwcYh7aKGrIa9UJrm9P4iiRchUGPc/OwrcqxwHjsZrm5P0F8pE5SJ+\nJl4w4dkFonYb0XfRuxGhCYdHBd6YR2hLjIbA3NYJltIINcLMuPTHPaYmqkR1E2cjTZiMUAb6KEZI\nGCp0NzLI2ymMhEe0lcDv6vj7NigSORizlJIJl8GPbnG8vEtiuIPbsPj5x77Oo+dvc/7IKtH1DKMP\n7LDx8lhM7pfgPdNEu5EkO9kksx7gDCiofZibqzA6VWPvhx36gyGJTD/WjEeCvQs65qEk8WIKt6cT\nGaB3JIXXdLSmyleWjqPXdFTtfvrDyvs2wC6l/IqUckFKOXsP/YuU8p9KKf/pvT//lJQyf88V8k87\nQ/4b492+Yf8x8FUp5RFiRvFN7qPdpKaH9AON6UIdvRfRHdbJ34S9vWz8jQqkVxTSa8TObAaEtsRt\nWnx9+whXGqMYesB4vsFScxBclcZBiv/m5HOEkUIgFTQ9xF90MA4FvUGF5f/+GI1mkihSWNooMzV0\nQC7b5aMjN9DUiOBqln5e4eC4xeArKjuf9NG6YG3rOCMBUsRvhNAkFiM4KinbRVghV2ojdO5mqXWS\nGCmPW/UhDCUkTIfMj+9RdxIU7B5KIiBjuTivlfhPj75E6KoIR0VJ+bhDQTz2FyoQCkYLTeo/38X7\nXzpEVsTOEwUaczYIhf7sIH5CQesS95FrBvaeQnciInGmHr/ZFBDX0+AprN8qo/YUIiNCbavIfYuw\nr1HIdTGGu8j5Lp6jk1msoyUCpBVh3rWwEh79noEXqHj/+zCXX5sjuJbhU+ff5Fdef5K3dsZYqZd4\n39NX2PvOKObJBsJXCG1Jr2MiAmgv50jsOKR2QrpjkqlUbBmS+2qSwhWF6EqWws0YKOBOubRmQfUk\nsqsR6RL5zCHNRYkcd1A2bPxsiFi5T1pi+d5nOr0bq44s8H7g1wGklJ6UssF9tJtMaB47OwVarkXt\nhIaXFoQGqHUd/3SHSIt1xAcXAkJDYNUkiicovqbhBip77RTnylusvDVOoxc3/8vDh/zSK8+yt5Vn\nu56lX7fQ9IDOgk/xpodT0EhcsSkke9DSWNkaoLFc4HPPfwjlnpzFzQukKuiMCoaHGnHhow4iGdAd\njtNPqcq4gZ8I+c/nXuATJ67Q7Rt88NFrdO7G57zqdo7tZpbHT99idb9I/e0BlrcHyWZ7rN8sI0+1\n+Y3f/ijjI3X0ukLU1zALDsqWRXQ9AwJ2DrP0PR3zv7QoXVRjSHgC1NEyWscjsOOetdEUce/ygRZR\nKiBlevgNE90IcAfC2PIxHaBOddBbKumFQ06fW2Fuco9GK0G4msJvmah6xGE1TdAwEK5CuBjTEedG\nqvieRvMnWoSpkP/4B7/NH7x1Hn3bILqewTJ8XnjlBNmViHYlTWpFRW8qFPLd2JFwsoszZMdpcl3w\n1v4o21+ZpD0pGPzXdxAR1I8ZmIcC2VfxChEHD/skNjXCbOzgp090iQ7N2DnAu1e4ul/xNwARMw1U\ngd8UQlwSQvzaPY+dfyu7yT/tXteshYyPHjCbqdFbcAlNQXbVi8v6robelmhdEFaIVEB3IvQOdMbB\n6Zn0eiYvLs8RJSJcX0NTI/aqWXQ7dmZbGKpSGGlyZGifZKlH5afjtKv8ioMfKRw5ucn0aA2l3Eed\n6DKdPyC0oDccISLJwGWfg1YSZzTAPoigpePmIb0hSewqKH0FPe3yy7ee5I8uncHdSPHS86fIzddB\nCrSUz9OTN3lzZ5yZoRr20QbjQ4ecHdzm4fNL/P1TX8E73WVzdQDjRBMkKG+n0WY7aDFAkPBOiuhW\nCnarFH7zFSZ+d52BS13aZ8pITSFRDZEqSA3cfOyUoO/rbO4USK5piCtppBZbZAgtQtMi/JJPYy/N\npTuT3L0yilAiwiGP0kgz3qiOirWnceT4JuqtJPMj+6zVCiQTLt2OBcAXN0+CL/BTkvT5GvO5KgOL\nNWqf7JNY13AGJZEpOWwlaB4JCO6maE1oqF6tQxwGAAAgAElEQVREd8ZnKNXBT0P2TkTzgzNEusRP\nxnWG3FUNe1slddMgsx6hWCGJM3WC1RQi41E4U40llRcO/+zy+svH3wAtsQacA35VSnkW6HIv/f3j\n+MvYTf5p9zqRTNFxDTa6+Xhw2rgnEQtB9jTMdnzupKkjIjg4FmfYUoWJwToPTG6g7FpIIQl8lU7b\nQt+MWw9RJLh6d5T6bpYrV6fobaXo7yRxhgSNOYvKbp6V/RIPFDeYGKzzQwuX+VBxidkHNohseQ8b\nGuHWbAZeVUnueqhdBaMJ9kFIpMUUilTCpV1Jc3RuG1Huc+6pm0SRwkipwVAhVrUHtzIsbw/y+Ohd\ndg6y+FLhjY0JfunNTzJSbGIWHOTFHKklnTARb7ruvIcwQ0JTwkIXYZmoi3MQBGi7h0SaIDJUvJSC\nW5T0i/HEkVdJEFoSRY9If3APZ8bFqmg4iy6qGtHbSqGnPNR7PrUIYkCbGdDqWqRH2igFFz8dcWt9\nGH+xx1Yjh6aFfHbuFWQkeOL0Tbp9g+JbKmR9atU0lz9/grTp8thMjB5VPUjsCBJvJNBbKqEVP8v2\nmAaRYOXlSQCcQQU/oeAOBX/iqJ6shJSuBiT2JZXHIszbNo1GkuKJKqbtx1/OVR31G/k/u7z+0iGi\n6F1df13xbjbsFrAlpbx47++fJ97A/1Z2k/+vCAVBpKAgGSo3kAK8rEZyLyI30qIzoqD1wd5RCS3u\nsWzBK4Rs1nJcemERMdElPdKGtQSLY3v4ox65tMOPn7yImfTis6EbI1BGFqrkn9ylNywY/9cKSdvF\nUnxszecLK6f4g50zHM/ucuLEeoxgzWvkr6i0P96hNWFiNgSdqZBIi2dPtaZC39PJjzRjDvG2TVLz\neHB4naZjUbk+yDc/9zDeiIeqRXx16Ri6EfDqC8cZzLeRkWC/lSIMVIY/vElvNMIbCIhChUyxi6rF\nEytTpTpL/2uZzWcHaT0yRTCcJ7XSoTdkxGOAauxgoPVisb891WaqfEDXjaWJ/kIPdc+Iz8UqBAc2\nUd3En3CJsj6ZdPw692s2nY0M9uUEYSpC2zGwbY8wVOjtJ/nfvvlRaOh8e2mBnzv6Aq0ne2TzXayU\nR+HpHSIpeOmFE3jZuHfdWgzpnOljVWO/3dRuQGY9QE37+JmIwo2I4lWXZMUnc0MnMiTKh+rUj6l/\nUpW19jWcGZdEymVvN0d0PUPndh7Fh97j94npJPmLCCf+WuIdN6yUsgJsCiEW7/3oSWJnuvtmN6mZ\nAZ1GAiEkhhri5iWhETOe3DcKdKYi+gVBaiseME/uhejtmDs0PVDn+ON3yKUdfmzuNSYf3KJkdRCH\nOvWbRT73ymMsDFWRGZ9jD6wxN7/LzsoAoRREZ2MOUhAp/N6t8xTMLqeGd9j7zih/tHSS1XoBPwnd\nIRW9C4Ye4BYExWtBfBYU4GUFQVLiHNr8nYVvcqcyQFj0uXE4xHe/eJb2dowobZ70OTq1y+PTKySS\nfZzdFKnjdXZrWaiZOE2L85MbuIGG4gv0jIsQkk7bwk64SAWW1sqkUw6dWZ+9CwpKx0Ott0jsebHw\n34tll/0plzAR0dtMs1Ypon8lB0E81hfaUQydq6gkhjuUpuvInoZu+zT200Q7NjIRkJ1s0pn3KU8e\n4A/6dBo24UoqZj0lQo6d2iCdcfjlK08iFEmzmaDfMdi+PMz2S2OkjtUREoKEhEBAW4/tQgb7NKc0\naid0lA0LaUYElqB22qR61sA+iNC6guClAv35Pus/JPF/uI5VA6Wp060kwVfwCiFh2cUbCND1P98c\n8d2G4N2JJt6lcOKvJN5tlfjngN8RQlwBzgD/M/fTbhLIFTrstdNs3h0gyMZvLxFBf9ZF68T0//aU\nwGhK/GR824kbFnffGKcXGBwsF/nVNz5I2zV56eIxcrcE9r6C2lW4tTMEnkLdSbB5kEPNu3T6Jinb\nZe8h6K5mWSzv0w91ru+X8Y/1EHcSeDeymI17YDgp6fVM7P0IP6mQXlNwigrJSkTxskCYIV+qnebZ\nxSvkS2121koEx7uQ9hETXeyCQ7Ubn20BSAUcHqRIpvrMnNzGXjXo+Cb7zRQiAL9pEvgq9jWb/q0c\nYdnlwuIqKdMjUeox9xsVhB8QDOdpzpg4AwKpxtaN2r6BnndRSi6yaSAVsHc1uh0LEQrCpo7RjAmQ\nUgqUpI/v6OgpL8aweArN9Swj4wdU1oskl+M39LFH7iJUiZ7yuH5rnPZGhjBQkRLUXRPNDAntiMnH\nNuhdLsBUD3IeKBKtpaAsdhgqtLAakuJNHznpoNc1jB/Zw96PhxY6Y/FmdIYirGULo6LTbCUZ/fQq\nYrCPNeCgJH2kHSK7Guaehu/fJy5xvID/vS86IaW8fO+8eUpK+YNSykMp5YGU8kkp5byU8ikpZf1P\nff5/utd7WpRSvqOgOfBUTg/u8JnpS4zPVFH6Col9H70TQlNH7QuyKx6BLaldCHGzgn4xPt+Nnt1l\nt5Xh6Jl1Tk1v0XUNElsKv/Rf/xadoy6/8LEvEuzb6BmP7dUSbsfk6fmbeL5GynSxKwqFq4Jr6yO8\nuTZBt22h6SHhrIM/7tI4EeHlBMkdH+tSAjen4GYFvbKkX4itHZsLIAOFty7Os9wZRMrYrd3v6qQz\nDtMDddzNFAerefxsxPtH76KZAamcg3stR/UL4/z0j3yFmVSNpO1SPl9BeArmXQu3KPFzIboVkNZc\n7KdXsZ/L4I3lCUoplJ5HcjcgMmMHvM50iLXQxLR80imH2SM7HJ4KCWxJ5CtIQ6K34gWudQW1vbgK\nLboa0VYiHstL+xgNhUoti1lw6B3rQyR4e3mc/Hcsgn2b1FCHzzx+kcXRPWSk8Hc++SXOT26QHm+R\n1F3cUZ/A1ZCeikwFmIcCbyvJwWtD+AmBtecQNA2SRw+JfnOQzEYfxRf4aYm9E4tQvFyEstCh+LxF\nJAXacoLgbuqebBNGviXwMxLjzdRfegP8fyz2f/837F91mJbPW5UxfuPqo2xulJg7tYWb1xChJLWh\n4B5xcAsaekcgpIhtM2oSvaazUSnQXcmy205z+5uzlFJdOkc8/v71Z/m7D36Df/DqMyAkD06uMzRZ\nRzVDrh0O4+4nWLszRPdEPNKVy3WJOjrmioW3meTU2DaaHqI4AqMh8XIanemA7KpPeivAqsU92CAp\nCE1JYtkgTIesH+Y5Wtojl4iRN5oa8onyVf6TJ18ACcZol0o/jWEGjGabiIUOzXMud3pDNHybtOmx\nc3UIEQq8OYfQilCSPlOlOhcyq2gzUzgDgr0HLPTteqz7LapoXVA9gdQl7s0s3bZFu2OzfnEMvaXi\nF0Iy+R4js7FDgJuDzNkDlLZG5CtoJQdlrEfhTJXJwXo81ugruAd23IOtaMxN79Gcg+GFKm5f5/Mv\nPMztSxMIJeLbB4vc/PwR2lsZ9nppEKDtGggjJPeGiXOmx8ypbYI5ByWA0NZBlfT6BnbVJ1LjSaq5\nX91A60HpWyZShWA1RaTCyvcmiXSJ0YwzidHROtUfckhNNe+bP+zfiDPsv4tIay4SCJs6wgxZ3hxC\n60V4GS32dqmYBJbAaIDwBKoTi/AjNU6LM/OHNBpJPvGDr+CFKqVyi/9o9g1++btPoxzqjC3uE8jY\nDeADs8vsHGQZmqlx+tg6J6Z2qB8H/7tFPv3g6+ht+PEnX+TS7SmmBw9i9/KeJFJBb6lUHtJR+xFW\nXeLlIkQA+RuC/kDE+GSN1n6KV27Ost9IIToq9a0cv7b8KH+4fgrFE3x4+hZXd0YwtIClq+MIAQsT\ne3z97iIXN6boerHoQesKtFULa6SLPDRY2hjiH37vY/T+maQ3GaD1Qeoa7kiKfl6hMxMPsBv5Pn4h\nxFyxiHwFfbGFPxSnpe2WTeXqEEFSMv2hNZKGR5QOyBc7HBnex7Y9/FBhfb+AmOkiVImS9hl63w79\ncsDHy1d55P3Xmc7UiXZsBt6MCSB+0+TNpSlaR33+g0deZ+/6IEozfna0dBonAnLfsql+YZyx39aR\nArojJmauj9fT6Rc1usM6vbLk1n81jtaTHDzVR6qSia96MeVjqo/ZEPTGAyIzYu/yEH7PoFVL3lco\n2nu9SvyeEP+3A5PPTl/hxeQc6ztF7HSfbjmD2YowG5Lhj2xTuztOZMZQMSWI+Up6O0a3HO5kKU8e\n8L29GepvDuKPu9wqlbFLPTKJPvvNFNvVHIoiubs1Hsvx8m2ubo6QTPVRPRh42+PL5iO4Zx3+1cpZ\n9JTH0sowqbE2fjJL8WqX2ukU7ohPc8YgUQuRukDrSYyOJFFR2JFDiKKPlXJ5YnKZLx+e5oNnbzKf\n2Odz1x8mKvl86cZJaOr0DpMkTsQuBpduTCPsgGOTuyy9OkVw3EWoEZGjoXoaihc7+aVWVNa0AdSO\nQmBB+8RAbJMZQnJdozcaIhsmdqmHm9ShZdDzEggzRK1rKKFGYlfQOB2wWisS3UqRPtnACzQafZv2\nehajoRAVQ6QnwJBohwoVI42W8fiV159EMULkvhXDvH98l7lUg61Ojn6gUU62+fKXH2L8kR06ron4\nfBE/pdJcDKmfjUiPtjh4QpD53TSaI3E7JqdnN7n6yDSFa3HxLrsUi1W09fi83ZhXSFZCDnoaWg/U\njI+yblG4Ial+NJZx5oZa92kl/vWmu+8m3hNv2FDGVVpNiTgxtYMQMbw7NASRJqj3bPqlmGYIMQIl\n0uP2juLFmzhr9mn1LGbft87EcJ3XticYzTdpdm0SlscT80sMFVpEw33Soy2qh2kyaYeM5RIkJPUj\nBvoDh0gpODG0y0C+TXqwQ6eaJLkXEJoqVlWQLPbQ+pL2iIZS8LCaEU5RwctK7JkW6VyPXzjxNbZ6\nOfSGiiYifuuLTxDs23zixBUenbuLtEPcwRCnZ7BUG6Qw2qBUanP99hj+oI+2a6Bux161YdMgsiXZ\nsSbjv7/OkX/cZeGf7ZPZiMi8vYefVOLWU06itxTMPY1+12BxbA9rqIuxd+8cOeFgHGvSOB5g5x0m\nCofY+4IoUnA20mzuFFCduNCHAL2toHYUzJMNuJUi9FQ+c/pNNC1k/FiF//ZT/zdbV8pstPOYaoCm\nRFzbGuGnP/U8G9eGqW3m6I4IGicCzAMVra0QvJZnPNdAqveUSa7C29cnSewq6F0JAy6NoxEilAy/\nEuBNxiKazR+IuHD8Ls6gRLtj4xcDoh85QDYM9NU49b8vIfn+GfbdROBoJG2XjVqerm/gebE1YT+v\nYNcibD3Az8Tlf6Ml8DMR1mHseSM1mFyscPvuML1agpX9ErVOknK2zeZBjn7L5LGRu5hKwPZOgWeO\n3ODB4Q38hkkoBa2+SWq6ift4GwkoVYOLV+bY2Sji9EyIBK1xjdBSUQLoOwZ6TxLaIKsmTlFBCSXJ\nLejsJ1GViKvdMd6+OUkw4vKtl09iHG/yP3z0X/Gli+d47cWjZEpdjh/ZjAkYuk+7YzOVjVVRoqPG\nG3W0T3qog95QSa6pNPbTeLODKActRLtL7qUNwkIKPxl7AYWJCG/YJ0hKlKrBzZURgkAhSMUT61Gg\nIIRkcXGbMFRoeSadR3r4NzPInI9u+6SP1dHONCDtw9E22myHKFIwTzWQgeBrG0c4Vq5gaT7/6P/6\nNMqow2y2xtJaGUVIjoxW+NWvPM2PP/EiRqFPkJBYFQ1/3oEInNGQu9+dpFtW8NIKicEuIozrEdmb\nDbT1uM0j1Vj4n7hl0poLURsal7dG4UgntqkU0LxUQloh4ZyDdj15/xbj98+w7xz5TBfX1xBCUusk\nOTe+FStk9uNu0P5bQ/GUTV+i+DGrKFJjel2kwdalEcqjh4xOxvO/Ts+g4Vg8Mb3MhcVV3EjjG6sL\nzEzsc71R5oWVeQDa9SSFhEP0ch7X0WkfJggL8SidkXUJHI3TR9dJ7kV0Rg2SlZDIV6gvqqgOmFWF\nfkHEvqp63JdtrOd482ACva4iDg300S691Qz/45WP8eCZZfx8QHAxT8u1KA20MbSQkWKTt7+9gF6P\nq7czD2+gbVicL28RWhL5SJPiaxrKL1bZ/OFJdj41S3TYwC3ZeGlBZzaMJ5rq8bkxTMYrKjiwKS4c\noDbjk497Pcft1WGC7QSVSg79dgI50yOd6+E7Ou1bBTqHCR6bv8NwvsVE4ZCxXINez+SpkzfxXymw\nelhko54nOt1Gbib4zq0F8BU2N0rcvDTJ2NkdPvfSY4hbKUQEWgdK+XbccmopKL4gWYmINEF4PYNZ\n7mEfhvQmMuRug5IIaB4N8ZMStyDRmwrpFQXteopwNYXiCJREgHqkjZYISL2cwC3evx30N6UP+1ca\npvDxPQ3TCAhChV5gYDYjOmMqds0nSMXwMKlCej2K5YtazA+OdAgKPpXtPCW7yw/MX+X4+C6jmRZJ\n1eX1K7Ncqw9TynRZvTnMVLqOWLN56tx1kjmHrVoO91wHGQoyV0z0ioGRj8HUf+vC97i1N4ibVVA9\nSaQJxobr9/jEErsmiWIFJG4O7I1Y4re+PoCccogyAeFqigsPLTFROOT27x5B2CFP/tDreKFKbS9D\n7cogW1fKqJ7ggQ/cQoSC8WQDbzDgjc+fJLGr0K0l6I4I7lwdIzSgdKWH9Dy0blxoSq6pyGRAmAuw\nx9qk7moILU6jD5tJwmwQD68XQvAUomQ8xuYOhmh6SKueRD3QCYo+BIKlw0G23xhh+doYS5tDiDWb\n767PMPDkNu2OjdvXcbsGw6crTI9VY8ZzKHjkoVtU2ylGZ2r4sw5eIaI3ElG9XUJ14y/a4vWQwBQk\nqgHRfA+vr5HY6qH1QoZ/6i4cmFh7KmofcrfAGwzofaCDM+0RJiPCXEDqTRunHc/1No+EKEP3CSQO\n7/mU+D1RdKq0s4xDbEtoe/EPBaiORAqQWpzWqS60x1TQQkQkcHMC60ASmjqJU4ccy+zypfXjtCtp\nRCC4ao6TG23R6Zu0akmOntjkO7fnkcWAtU6B7qHN6blN3l4eR21qtM/0MWwft2FhZF1MxccwAnpl\ngVmPlUTNToLegk/xjXgw28tE2BWB3o3Nk9PLKsH7O3EzPxBMPbDFxeuzCCtEmZLk8x2+fPMEsmkg\nAKsm6I1K1HMNLj1/lGja5dsr82iHGpEep5HCCpn555uE1RreB0+j77VwnjpLc0qjOyExawLc2OrE\nX8rgzwaYCY92xyZ01djHxnbxvQTSClHTPqYe4PYU+ntJUCXRgAeuir2h4w5pSAWUgT7sWEgtxr8Y\nSkg20yWKFB4ZWaPlW1xcmyKsmaTG26y1CihKRKNnE3V01Hz8LBPJPu3DBFrVoDWpkNyN6OdVdCMg\ncC2q59Lk7nhcWR0js6LQedjBumJz+KTDUL7N/t0ix09scOPaBOa2Qeecg1I1iRIRuYkGPFe4PwtR\nSgjf2xbs74k3LILYEXzfZqFY5dbOEMnVTpxSOSH2thaPsWViZZHUI1rTcbW4XxQxRc/V2erncK/l\nQIt48OwyiUIPRYkYzrR49uxllnYHkb7Cpy+8wd3dEj/5wMvc2htEuCpiuM/EcJ2Pz15ndOKAMFBZ\nNHdp78dN+cgQNBZgMNNBSQbkll20viRKRLG7XU1Sfs2lMx1xuryD37BIDXSJpCBfblEqtvnbz3yN\nnN0n6uooeQ+pxkUzxRPkEw4Pf+wqoqtxbmKTSJd4WQlpn4lynbt/e4bOs+c5XDDpTxeQCnTHYeQ7\nAZ2ZAKWvYFY1Qkuipn1UNSIKBIoR4jdNDC1EFON5XblvoSoyTv8laA0VtWIijAj/WI/OzTxBISDs\n6EgVlMkuzm6KtZfHUQT4rxR47vJJ3tweR9VC0hMtum2L7a0CPzl3kbFcg7/7+PMkX7dR1ywEkLpu\nonjQng4xmiF6L6K/nmZ+fI/uGJiVNhP/UqE3LDFu2HTnfMKmTtsxkZqk+SsTCC82uxX7JuZEBxEI\n/O8VcXPfH6/7dxsSZCg4c3aF3W6GSApaC2mydz1CU8Vsxh9z8/E5UW1q9Cc9Ik2gt6E36TNebPBY\n7g5Tj2yiJQIufXsR19Vpd2w2D3NcbwwzV66yMF3hG5uLyFDwysE0uZTD8GyVwNHYeWOY577wMJ8a\nu0zY1fiFf/ETzM1W8LIS1ZGkNqDyvVFkoLD7qIWbVRB28Cc0+MqDJjLn8+rKNIsL2zw8ss7ma6Mc\n1tIcLBX59RuPUu8msIoOD0+vYtRUvCwE+YCJ9CEXtyaRdsjr12bR24IgH5DLd1lfGWT8+W48EvjS\nIZ1hnfaYhtEQ7J/XSa9oyIKHP+OgDPUxr9n09pKYKxZHxyqIvsrhmwOwb6JvmUTJkGbH4vjsNmR8\nBs/sMXZuh2KpTeippI/VIRTo6bhPO1ZsMDxXRT/e4qCWpn+qhzBDxOU03n6CXs9EHBgQKPze+nmW\nNsr8y83zdEclqiMIIwXNAfNEA2tPxd7t4icVEhWF1dfGKb0d0ZnPsfm0ir0v8HISe0OncFmlV00y\nNFHn4LhKek1BCsgv1uPMIAJnMGLxmeX7uBa/v2HfOQTkix0iqdDs2RhGgJcSoAj6JR0RSLSMF1tk\ndKLYOqOvojlxlRhgq57jC5Uz7HdSpJJ9mO8S1Uz8no7r6hx0YyqBEJJmI0Eu32WtVmC/mqHeTmKm\nXY697y72gzX+j69+BCLB5Ps2iaQgdxsKN3pk1wIGLgfkCh2ydyPsg+geCQEy631SWxLR0NHNgNvr\nZV782ikiHYQWcfr8CqYRkDQ93L0Eb37tGKULe6QerGFm+7z+raM4bRPRVRl4VWXwoQrliTqaGoEq\n0TeqpP/wTSI79lAN7FjG5xYiwkebEAmUXQtlxSa1JVGzPotPrhBECrnpQ5RAoLcVFE+gWAF+z6DW\nS6IZAQLwQhVDCyiW2jQO4zTZ72ssTFeodZKkdA/P1fixs68yOViHlo7qAxE8NLWGWnZYmN9hLldj\neqzK+4fuIIf7OKMB3lKGzvt65H89zcRXm7QWMogQSh/eRnVjI+3eQFxwizRI7sT2kmc+e5WRqRp7\nW3n8lKT8Shv9eIvxzCGTCxUefeA2qiu49fz8/VmHkve8GdZ74gybT/bIJxyWqgM8MLrBq+tTuEVB\nLWcwcMnFKRoEjgZpieLHTGAk+Mm4Qkso8P0Y2tbYzTA+XaXv6ZD1WRjbZ2l5hEShxeZhDkMLUfZN\nzMEmKdNj28txdmSL1793BKcYD0IPHK/Sdiw6vsF+PcPwYUh7yo7T0FGFbj3FcCix6gFCanRHIbFv\n0JyPZ2O9Q4u5+V2GFtu8dGsOwwy4fGUGo67QGvdAj3CnAsJIodmx8NsmMw9vM5xosdvLcDc9wKgU\nVOsZwqZOZqSNPzWI1nNojdm4OUF2LWC/oMFAH3ctjSKJ6fyHGvVnHPKZHsu1Ev31NGZdwc9FKKMO\nyYRLwfRo902qt0ow4FJrJXEbFqgSIgFaRKLQw3N1tptZunWbViWNkvL52s4RpjN17mplxKOHTCZ7\nvLo6jWH6rH9vgpVkRKTDeqlI5GhgRIye32N9u0T1J3rIq1msA/AyUIwUTn/4Fsu7i/QLgvGvB7TH\nBL0hQZiQvHBnHhkKBl7W4DM11p0B+pWAy1szSDOisjNKmInwBoP7tBIlyO+fYd8xGn2b1ZvDzJQO\nuFQZY6Fc5eSzN0ltRfRLOpEmSBe7hOmIzoiOkKA48XnWqkm0topp+hz2bQbGDylYPcJQcG5mg0Aq\nPHTyDpV6hg9MrNA4SPGh919h/+YAHdcgclXW23kSFcHytTEaywVaLw4h38iS0j2emLvN3gMqoSnQ\nHYlVleSLbXoDanxfd1TcUoifFKiOIHtTRWuo3NkY5JW705gpF69t8ItP/SHJswfgKZj5Psemdtjb\nyxKFcX9q9fYwt3/tKHmzh1Y1SBkuYtPi2YfeYuznO9SP2jBYpDukIhXB7iMqkSlJpFwmT+9gTrcR\nroI608FOuDTbNq6rYx4qODMe+mQXy/boOgZ79QwCSM40iRwN8+U0asoHRWLl+uh7Bt5yBsP0CQIl\n3sh6hGEGVK8N8nZlBLQIKQVPDt1mcuiApBWD345dWCO5qTA+WEc/0EisGGxcH0ap6Zwa3sFPxTRK\ntQ+b20XeenGRwq0+5ddctp5QSVQjQktiHgiijo5SNfCTAj9Q4XgbLechk3HhzJ3tI3zB0Oh9Ik5I\n4qLTu7n+muI9sWEB0uMtDpwEUSTYbOR45fpczClSY1Jedy2LXlfQ+pL87Xj6A8BqRkhNkkn0+bGJ\ni7R7FvV+gvFSg9VGgc1qnruNIh+aXea5N06Bf883VcKJgV2EHjGaapJ6uoKQcPTcOoNPbHP64zdZ\nWh7h6xdPYTbi+3CKCporcX2d3qikO6yTqESIIG4zmQ1JYBF7oS6ZLI7u4TZjP9Qv7J1lKNVhdDIu\naNV6Sey0i6qF6FWdwiWFzrhgNlVDjvbxIxXtnsdr48ERBl5v4EzlkCo0HnAJy/H0Uq9rUmmmcTrx\nBEsUCbptizPjWyhCxmowPURR4g0mIwWxZeP5GkEQizTasyH6UoLR4UOK6S7qbAc53sfZSpNLOYiO\nxjOnrlHOtcgeOeBkeZcfv/AKi6V9/sXyeUaTDWq7WX706RcxlID8h3dp9U2GLkbklkOkiNVoN/7g\nCFEiFr1EOhg7On42ZP+szc7jJtNnttl5XBBkY7mlsAMyC4d0H+/Q7Zn4G0nCQzOWR+5ZSE8hTEdU\nD9L3byF+/wz7zmFoAdP5OkGo4rk6mhoiPAU/ERP4pQpRKiCadQgsQXtcxctI3JzAySsxIdA1+Edv\nPI3bjZ3Mtg5yZCwX0/JptG32+mnGZ6p8/NwVvrs+Q5gKuVUfQtkz6QUGO6slokzAgZNgs5rnjY0J\nUkMxyaA3EpGqBEQa2Ps+vd0UWkfEetisQFoRbk6g9eL/T5AN6U373K0WEa6CvaVx9dI0Hc9k53Y8\nfre/n8Xt6wgh0Tr3/HDGPV6rTTJQaLG6NcDHPvkqS61BRARBxsIpaSieJHnbJJHpM318B9PymSwc\nMjTYxB7p4Ds62WyPtzbGUW8mOTwWLwMlT7AAAB0vSURBVC7fV+m2LHQjIBxyCXyV8GYava6RXFcx\nmrBze5B230RVIzQ9QOoRadMlNd7ie9vTmGrAYqHK2cwmv3PtQZYOBpgv1cjpDs+eu8SXNo7z5rUZ\n9i+W4Y+KNGdUqmcVMisKQS6kd76H8AVI+Sd2KPZgD8WD5Kak8tw4qiMoXI65z4omOdzL4DcsLNtD\nbyvkripEvkpyuona1DAGenHqfb/i+xv2ncMLNOr9BD1Xp5Rv03uzxJMXrtGZkgSWoF8SMdhaCzFb\nEZoDuSXoF+4JJyzo3cnygYVlpseqKELitQ3Wt0v88OxbCBGn3ZtrJb7y8lmiuymeOXeVIFSQ5bjp\nnr2hMf5Fhf3bAzz/vn/CXLlKFAkSox30pkI/rxIagtBWkXpE/5hDpEN6K0TpqLgFiZ+MN625p6G2\nVHQ9RHEUxNkm5x9YZnOthCi4qGsWStVA00N+5vh3eeAT16g+7nPhyCp7zTTnS1sAfCz3NutfnyL7\n2jbrH7PYeywi/+ltvJxEV0P22ymkFNzeGqJ2dRB3LY25adB1TBIJl8SFGtZEG1WLKRPSU3FqCfQN\nk6CnUXhgHyR05n065x2SEy2Ci3nCt7OkEy5K2me1UsJ1dXorWY5nd3ltfZJ/fvkxokCQ/P0sXqTy\nldsn+PLXL1Dfz5AZbiMV8HLxl1CQimhfiNEzkadC0cWu+mRe38Id89G1kNKVHomDkNI1H9WLHeSz\nqx5UTJ44eRPhC2bydbQOnP7Ja9BX6K1kCQs+Xs/A2tbv00p8l5v1/+/CiQG7w34zhXtg4wQplIUe\n33j7GCIR0c8rpNcjAtvEHVFozqiUX+nRG7HoFwTWoSRICvxFl5LR4Ts3jzE4e8DZhXX2e2l++9YF\n/KrNqZnbOL5OECr4ocq3v3SOgUd3ae8UyMz0cYYkzTMRZtrh4xf/M8JAJQwU9E0TqwVSCBLVCK0X\nkloycc7FPczusIrWg/xNSa8cTw9JTTJ3bpOmaxFMdVDViKtfX4RywES5zlp7iLnFXaZSdX7lGx9l\naKGKaod8evANvPBhvvHcOY48vs4vP/UJCmdCpGVgNATBSMBeK0042qd7Ix+PrykSrSdixVXZJZdv\n4wUqA8kuS0sjsRKq3GWqUGfTzAHQTVukk30qGwVENiJR6mHqAVm7z+ZJg7BlELQSHB2vsNnI4fka\n0hN86bmHEAaERZ8Li6vc+HSZ/UsTyJKHzAfgKTw1fpsr6VHWXxvDLwUgJKl0n7avcmZmgztfmGfv\nAvSeHcfYFrRkkkISEtsO7ekkah+68x5reR0GXa4eDGPvqPQ/VyL6CHSD/6e9Mw+O5Lrv++f1PfcM\nZgY3FljsRSxB7sFjl4ckUxRJSZRk01ZsK5GSKGISl60quSopO3Gq/KdL+SflpCopWUkclxSVJCsS\nrUiWSNEiRXIlHntxF7tcYBfHAlgAA2AGc09PTx8vfzSobKkscWlBwi6FT1VXYbq7pvsB76Ff/97v\n+/sa9I8WsTQPU/WwP9vPwsftremIEthG6dyNcFM8YYvtGB/eewE900bp6vDovknG9i2RmFFDC4ce\nJayyX9KwewJquyNYJRffhE5CYNQkFExOFod55Nh5emN1eiN1Vs/1MJStEBuo8/SVMe7pWaBSjNOX\nquEdaFG1LQaOrNAJVLzhNl2v6tzWs8bhgSU8WyMSc/BH2tjHw9rIwgc7ryM1UJSATiJ8xxz6vkPx\niCA162HUwuysqdk+1ssJ9uSLNCpRrBKM7F1FSoFZVFkspbF9Hc0WFJbDqn//ffHdXDoxSrCvxexL\nw+B6JC5XuPRHWbQWPzYuHu0vouxpkLqtRJDv4EclYqjF+OAyMaNDZSNO1bFI99dQEi6KEuD6KrXl\nBLVylETcDrOsutqIZAd3Kkl1OkOzYyB9gZ5us69vjcsr3XivZug44Zqvu8tBGW4yNrrMTDlLEIiw\nB1V00CWJKxqrTpLFUprIqiDTU0Mr6XTOp9k/XOD1iVEah9rkJlz2fakRqoJWdfyIwuqxRFiu54BD\nfNJA7QjURYtiMYFybwUnH6U16jLxvQMUT/WwcHqA6VeHmf8nAYeGr21dZ7zJn7A3xYDNmC2enh9D\nVSW6EaYNAkgFUnOd0DlOAzfv4kcDrIqPk9ZoHwi1rIEWZgvNL2XxApWM2WK2nkUEUGzEiBguHzpw\ngadfOYSVdJi51I9fNrFtg/n5PJPrPYiChd0jmPubPZTbUe45MMeDQ7MoSsDu/AZmPUB1JYonceOS\n7nSDaDEIXd0PWQR9bVo5NXzqlwSReQO/qVNpR9CWDYL3lVlcz5A2bZwej4/uf52XZ3Zz9F1TjA6v\nIYRk/uQgUoUHds/SyQZ0dndTG0uTe1mjkwaZDqO2V08PIgRUL3ehagH946v0Z6vMVzJhETfDR1WC\nsIKjCEUOM6s5hC+IpdpUlpLYbT10LFCA0SZBzMf11LBQeMtgtRFHNzyM4xto8xbphwr0d1d4ePQy\nly4P0BNvoGs+gRWQ2FUjMhN6z15c78VdjuFboU5ZBGDeWWHu5BBaXSGWbLN6l055LEFggByxWblP\npd0F3a+BuRjWoOp9xQ/jBKZHYyXO2lGdwe8oGDUwapuWo7dVQud4fwunxDtR4rem0onQXE7w8Mhl\nnth7jpVaEtvTadzhUDhu4sZEuKwS66AkXYQvafaom2uxhBXlawIhYKaW4+TyLhY2MjDSoidRZ30t\nyTdPHyG7u0w+Gb6T3n1omuMjc1hLOu8emuHgvXPYQy6RR9bQ1TDb6NlX7mQgF9oxLj3u4UYE0aU2\nyRnQlAAnEf76tJaEkklszcfdDFiqDpipNkmzjTlWRVUC5KrF+QsjIOBrf/sgI30lZso5MmYLr2bw\n4UdepfdIAUPx2HPbMnO/B4X7BO0PV7FHOgg1IGp1uOuBKRLRNsl9ZYSQ1NsmtbaJEJKlhSx+S6NY\njVOvRHnswCViaRvf1iDpErccRCBgLkanGlpaphM2WkWjNpvm8nwvQgsol+O01mMEMpzmj2VWKWwk\neebEYfqHS/RFahzvv8qer3i0WibOmE1zUDKWW0XtbYXR/Upogl1fShKo4CZ9gkBg1MOZkR8JGOkp\nITWJVYRmr0KggVUK9cZuQuJWTTACOmmJ2gmwjzdQ7i8jEx6/sfs87913melCfms6ogQpgxvatoub\nYsAqQvLE8ZOcLQ3w5VPHqNUjLK51oRR1UrMBXZc6ZC538No6uuHRGNDQmxKxERoBAxh1iKVs5me7\nefLAD/m14WnchsHlxR4SmRaPHzlP0mqzuJTlkUfPcDh5jcvlbiJHS3x3YpxLS71YmTbyK3neuLgL\n9ABrTSWiucyu5tBMH92WeDENqcHS2T4awyLUmmqCwAroJMIC40YtXGd0ihEmF3oJTqZ5aOAKI3cs\no28o9AyW8UfaLJ4aoC9R4/TFUe4Zn8FUwhTFexNzfKDnIu/aM43f5fI7e86gxzqIgkXH0zj/3dvo\nPJOncaELw/RotswfOx4IR0GPuXTKFjQ1zqwPIoREj7pE4g7lehQ9b3Pw/tnQ1LmhU21EGDy6jNQk\nQpEoWoCqBRiZNk5H4/DDU7yyPIxctcifgnrb5PmTt3PiqSNc/VcBvzn2OiO9JQ7eO8dr88NoF+OI\no1WG77lG+mAJraaQu32dxIwGp1Lkz7RQXUn8qsrVU4NkzwmkBvFln4EXXEr3u7gJkLts1IRLdMbA\n7+6w70/fwN2waDYtFN3nCyfv47WvHMLrbGHVxJs80+lGvHUObFrhvbnVhBB/uJXudX6g8O3vHKPl\nGOgJh1SyhRAS2eMQX2xj5zW8qBqm/b2WCGcuJlhr4e0LP1z/TEXa5AYrvFjax6VKD129Vajr2FNp\nCu0EGbMFnuDpF4/wvy7cx3opQX+yhhELEyQiZodOUmDkW0RnDO770HmWqil8V0FcjeAkFFQ3ID3t\nIFVJZ49NYskjMECrqniWQG+EgoROEg7dPk8228C53eYbZ4/ieBqxQxvsTRd5Yux1EreXOJ6Z4z++\n96s8lr3IqpMkZzb44uJxPnfhQU7M7mF0aJ2/mrgPt2LRdbCI7yu0e31q+328VEBnKsy9tpfieL5C\nYqiGUCRqsgM+tDo6zcUEw90bZOMtOlWTwFeYr2RQhprEci1cR+PqYh6SLjIQBB2VoGDhrkVwmgaF\nZhJ3IoXUJasPe9RLMdLDFTLvKaDORHjqmfuYne5l4sog//rOl/AiEkWRpAwb5/kcXjJgfTJH87BN\n8t2ruAkd1QmrZHg5l+IjYdAvMd1g7S6D+CWD/DmX8YEVgrKJ1gYaGi9e3cP+A8v4TQ1V90nkmtQP\nOcj2TpnTHyOlnHrTDg+4C2gBT7GF7nVBIOg/vkylGMetmrx7YAavEa4ndpKhK7viSqQiae72UTzQ\nm6Eetb47wI2FgSdFSOyOzsWVPgZiVVptkz9931MMHFnh7Om9zGzkUGMeqi0IChafPPQyXqDw6fEX\n0EVAZTFNq0fy0f2v084FTJT6eHz4ItFLFm6vi1XxKRyLUN1tEttbJeiouDE1DDRpknZO4Buba6q9\nPhML/ZiaR3+uAqokabbZn13nI7mzNH2T3ekNvjB5L3984h/xSm0Pz505yAvX9uIGCv9y/If4ZZOr\nF/qJvRZBq6nEjE64RlpXePCuS0gjIHdOcnCgAEB5JUn7YhoxGQ/tQZIe/ckaKDA930NEc7EybfyS\nSWU5SXAtSmspjigZaEUd6ahEUzZW3CE2WuXeo1eITZksXOkhcbQUWmA6Ch8+dA73xSwdX+Xh959F\n3dPAytrEsy0+9+wjuHmPZjXCpbUeuh5bJrKkQt6BkkmjbVK6Q8eLQNcliWirBJ5CcibUGLdGwmDi\n/OMK5y+MEO1rULvTITNcplMxuXJxAFSJEFBfi5M6ZXJk7Oo/eAD8RGcPo8Q3sm0Tb3dK/DAwI6Wc\nZwvd65CCYiPGo3dc5PcfeI6qG0FPOjQnunAyKp24wFqzkYZEscPyoooHwgPR5dDOCvSWZPlCD+/d\ndZnb+1a41khj6B5/9je/FV4j51C/nAn/JqM2+mCTby7ciaYE/PmzH+Bvz93B8cOXyV6QfPkHD5CY\nU9ioxPnaMw9g3F8imrJpdatY65L6sKBWSKCWNZo9Cr4JftynORRWfpACMsNlZCDQlIDlYpr+vjKX\nz+zi1Ym9/Mk3/zEvL4/Q8gx+58AZRFNlf6yA8AV3dC/zvr4p/tvL70UKyQceOEvqqodREVxdyONM\nJ/n1x17hxGsHUSyP9cOCqRO7SY9UyP9Iw1oXoT/NV3pQDZ/lWpL7j04htIBCPRG+w9fC8izDh5eJ\n9DeQ2Q5+NGDvngLpmM1QpkJ9JUGtY6EeLzM+Pk/jtRwDdy3zz+8/gS583KSkPJHjXKmfO/qWGeyq\n4E6kiI7UUOoqZiy0prw20Us7H6AtWAgPWg0Ta11S3S9xIwJ9QyGTbaC3JHO/mUJpqKGssqkgVYn6\nQgo6CrV6FGNNQ68qiLaKnIkxtm8J/31lXj8/+ja78c/qi7f4E/Yn+F3gy5s/b5l7XdQpcXffIt87\nN85ko48fTNzG/t51Or0uhYc93ISgNRhFOApBdyecTqXCgmFShkEMvSkxygoDZoXf6D7L8qk+IoYL\nI02SZhtZNgi6HfRZC/VKFMPwqNYjLFTSyK4O+b4qF74+RuEBibmuUL3Txa/rsMvGCxTslThmVeLG\nBclZSWRJCzv+eljuRC9rpCbDQJjTJWmezdKdrzEYr2BeiLC6kUSvKYwduEZkb5WmbWAoPl++dBeR\nZZUerYqWt1GF5Itnj/OpYy/xriOTnC0NUPxEC+PYBorpw6DNt6bHkWZA9FwEvS7YdfwaqUibZr/A\n2Xwxyf2zeaxIh/panFdfGkPTfQIpWC6mYW8TU/dYq8dJx2zMiIvS5TB9pY+WYzC3lqVrsMJovBTW\nqJodIHt/gflLvXxh4hjnKwP0Hl8hPlYmG2lx6sxeZicGUG+vYU+nQhfBQhT1moXiCNReG+EJIqsK\nQoHqAUhNCdQOdHI+pu7RTgvUdhgLaB+0iRTC2ETtoEss3yKdbBHssTHLgsyuMqlpKP3lMP6rGaze\n5tvsxj8NifT9G9q2ixsesEIIA/gI8LWfPPbzute1oxnOFAZJd9d5fmo/sWyLN672Q0dheKiI1pJo\nTZ/4vII5bdFJChKLHr4V6mj1enhpPyL5i+ce5i+uvpv0oXBxXQYKkyvdyJjPYG+Z8YeuYBwqczC/\nipiJUi/GyGQalC/kaN1lY62qtA+0ieeadA1U8GyNVtNi/I55Wt0KZlWiOZLYUliXuLpbQUhIzIbT\n9k4KVCeUvpUm8kys9eGM2/gtDeHB7HqW7kSD37/jRSzN5d8e+jus+4v8n9W7eWj0CpoIuG14hRfW\n93Huq+NhMbiNCLfnC2GAKxCoZxPEZzQi6xJ70KPYiFFpRWgNe+h3VjC7bK5VUwykqozsXsPr8rAi\nHezZJGxmgdXfCOs3bdRj+J6CabrEexo0mhbMRSnPdvH8U3dRmcuQ765Ra5vsum2VWLyN66tE9Q7t\ns11cmBgGAUMHC3zqwMth+ZyIx/13TeH2hEXh1MkYTp9LY6xD4AmsdUFywUPxJBgBluaRnnXpPuNi\nbKgkXovQSYdxAeGEherSERvP1lDb0Dqdo9UbVhyxB3zs4tYZOt/yQafr+ABwRkq5uvl5y9zronqH\nrliL+/qvMj6yTDpqo0dc0v01yq0IbkJQH9RxspLEsXWagxIvqpCc9xB1jdqowM4qEIDW02J5MUvD\nNlm80ItpuXhtnXimRcW2aPs62ViLsz84QKfPJXbFYGM1ibq7gW54xO4rIm2VoXSFai2GlXToSjeY\nXs/R7JeojiSy7gIQJD0iaxK9LjGrEqdLEF2RofVFAH6fQ3Mmhd9R6B0o0/NrSxzsLbD84iDfLYyj\nIPnGyhGO9S7wwfwE358+wO3xJUbiG8xM9lPb7xGd0xnYVeJaIx0aPU9GiK2EUeiNceh9SaGyHlbF\nSPbWEULiz8ZpXUmHckLFJ9NTo16KIXscUokWsS4bL+2TPmOwJ18M9a1AYzWOMhdBcQUoEr0BqJL1\n5TT1QoL5pSzNusVCoYuVWpL43UWIezz5nh+wsJzlmdWDeLvaSFvj7MoAya4mMurTSYXT7URXk8ik\nRaBDK6/RGFTo7S+TMBzWDxn4liC+AK1+SddFn66LoNoKG8spslaTxEWD+mhAYk7iJiS1vQFaQwmN\nybYKGdzYtk28nQH7Mf7/dBi20L0ukApXF/LM1rNMrnSHO6dj9CVr+IGCuZl+GKhQmcjhRwNUR6LX\nXaw1FT8SFhaXOnSnG+wdLSBOJ5GaxGnr7N9VwGnr2C2TgWiFpVKKhx89ixbxsHsDhKPCZBxD9yiu\nhwnxS9UUgavQLkZwvpfHWYwTWRfUdiu0uzTMaoBoajiZ0M+2PqSQnAtQvDCRXXgCRZXce3wKpazz\nyZGXWavFuVLKc+SxS1ye72VqI09U6zAWXeHF8n6iUQeA56/uI9LbwCireIcb+FKwuJbhXeNT+Jak\nug/sHglCYpU8Mt11omYHQ/Npt3WCwTZ+zMd7I8l8KUOjZdLTV0H6AlWReBeSKHEX5bEiV5/eTaGW\noFWzMLtskGH+r3AFjeMtZMxD6AFaskO6K5x6Pnn4h+iaT3E9gWyrzLTyRCdNFp/bxfAXVVBlONXW\nPZIXdIyaQuGZIYxvpdEb4StDqyfUMjcdg/HkMno99OKNrfpIYP2Iwvq9AXvuWWBopMhrp/fhZMIK\nFuv3+STmQCZd9KrAWtmaKLEEZCBvaLsRhBDv31wpmRZC/Lu/57gQQvyXzePnhRBH3/I75Q28QG86\nri8Ao1LK6ua+LKFL3S5gHvjtNw2xhBD/AfgXgAf84VsZYll7BuSjf/UEcd2hz6ry9Zfv5YnjJ3ll\nfYRG26S+kKTvJWj0qyg+ofXDeY30TIfGgE7xHp++HygUDwsiYxVM3UNVAnQlYGktjQwEsWSbVsNE\n1QIe2nOZ7525g9G9BeqOSeVcDjfrYa3oYS7riBdqS9uC9O0lklY7fDp3dCLfTaJ4YWCpeMxHbSjo\nNQUU6OyxMScjYWJACuzdHdSSjp/00Dc0gl1tUskm7xu8zFOTodMdwLemx0nHbVaX04i2irGhoNoC\n+4ADdY2evUXK9SiuE6Z+x09HqB9tk3rVovlgk72961wtdeF5CsG1KNFlBf2hIvVGhPgLUfQWrD3g\nke6t07QNhAB3PYLV28SuWAhNohUMAj2s8xRP2ziOxkC2ykYzGmpRgVyiiaoELJ7rgz6HTx9+njO1\nXVyp5HG+2U1qzmXh436o0c2UmS9naC4miF1TSV/xWXrCRVk3CCyJtaxibUgqxzrk8jVUJaD5bA+K\nB/XRgMAI2PelDu2cwdJ7FPS6Qt/9S8wv5YhMmYh7qrRnE9DnkEk1OfP4n52WUt79lp35Z5AUXfK4\n9ugNnfus99Wfeb3NlZHLwCOEcZyTwMeklG9cd84HCZ0hPwgcA/6zlPLYz7rujbrXNaWU2TcH6+a+\nLXOvk77g8nqemOZwYnUUs7vFVL2H5WtddCcaaA2FQBPEV8JUwEiuhd0rQ7mZC8ZGuAZqVAVxy0FX\nfQrXuhiMV9AMnyeP/JBm3UI3PZQrUZ69NEbvcInlcorKRI7UNFjLOm4ioP+lFnpZ5ejRaUbuvka9\nZVJ4bpBaPYo8m8JoSJp9gk5ShEocT5CeDshMBqEe1ZR0kqGTHG0FZahJoqfBb3/gBL6j0rRNAin4\nzKHn+PbMON+aHmegq8qh3BJGooNZVOlkAtr5gP7eMqRc9M9lAQhaGuZUhAMfnSJyKRys8VibQAqG\nMhVUVaIOteh7fAFLD99bd31slvJB0FMOrbaBvBrDLUTJjpTpODrpfCMs1Jb08aMBxjWD5nKC3fkN\nFgpd5OJN/KkE79o1w+J8juWNFIl9FX7r4Fn+bn2MH/3oIGuTedyk4NpDOoGj0ppLcunMMHbLRO+x\naQ77XPuIR+77JkpHEJ9VSc4HNAdAOgotx6BUidPcFWCWJWpbEJvX8GIaq/eE/yzMQ2WWimkiUyax\nB9eRJ1PIHofAF5SKW6eH3cKg073AtJRyVkrZAb5CuIJyPb8OfEGGvAKk33zN/Gnc0BP2F40Qog5M\nbfd9/JLJAcXtvolfIr/o9g5LKX+uHEUhxNOE93kjWMD1BZE/L6X8/HXf9VHg/VLKJzc/fwI4JqX8\n9HXnfBv4rJTyxObn7wN/LKU89dMuelPI64Cpn3c6c6shhDj1q9TmW6G9Usr3b/c9vBU3RS7xDju8\nA7mR1ZK3vaKyM2B32OEXw0lgnxBi92YOw+8SrqBcz/8F/ulmtPg4UL0uGenv5WaZEn/+rU95x/Gr\n1uZfqfZKKT0hxKeBZwAV+Esp5UUhxO9tHv8c8B3CCPE0YY7+J9/qe2+KoNMOO+xwY+xMiXfY4RZi\nZ8DusMMtxLYP2LdK37oVEUIMCSGeF0K8IYS4KIT4zOb+LRP934wIIVQhxNnN9cV3fHu3g20dsJvp\nW/+VUFhwEPjYpgD+VscD/o2U8iBwHPiDzXZtmej/JuUzwKXrPr/T2/tLZ7ufsDeSvnXLIaVckVKe\n2fy5TtiJB9hK0f9NhhBiEHgc+B/X7X7Htne72O4Be0Ni91sZIcQIcAR4lZ9T9H+T8+fAHwHXa8/e\nye3dFrZ7wL6jEULEga8TKpZq1x/7h4j+b1aEEB8C1qSUp3/aOe+k9m4n25048bZTs24VhBA64WD9\nkpTyG5u7V4UQfVLKlZ9X9H+T8QDwkU25mAUkhRD/m3due7eN7X7C3kj61i2HEEIA/xO4JKX8T9cd\n2jLR/82ElPLfSykHpZQjhH/D56SUH+cd2t7tZFufsD8tfWs772mLeAD4BDAhhHh9c9+fAJ8F/loI\n8Sk2Rf8Amylrfw28QRhh/gMp5fZV+to6ftXa+wtnJzVxhx1uIbZ7SrzDDju8DXYG7A473ELsDNgd\ndriF2BmwO+xwC7EzYHfY4RZiZ8DusMMtxM6A3WGHW4j/B/nY0R4CeSl5AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f61cf908390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"imgplot = plt.imshow(img)\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f61ca337a50>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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PoohnbJjiemOQZCyC4hsRsuZVSgr74YEnWb9EqGAarYGF8IRlJpUBi8rEVC5G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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f61cf9083d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(img, cmap=\"hot\")\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f61f680f510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(img.ravel(), bins=256, range=(0.0, 1.), fc='k', ec='k');"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.colorbar.Colorbar at 0x7f61c9b72390>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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iqM+BKZw978a4bidQCgpWdf6arVUdGFUwlHmaUILeiWe3TonPgSkELolncZkf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NAAAg\nAElEQVQdc+MdXuDihXZYacV04VqYHQcqVXz2fDh1EwT8z47G8rkW7PpQwfh3xAJq8Q5v3u30I65K\nK1zrr0CtIM6kryprZxR1GGbxnZIrqDuvYmL4TfzP9if17FYyaqtYfiOUOsvHY3wgVtD+e1hqj4JH\nycFrgAGCIFTUJ/7HZDLZPiAcSGrkLjoXMLiLjgV8qHcXlclkv+kumpVvDzRwyN12TqXX4Ev4mJjj\nY6IhZHwESYlipTSiYKDYipIGQ8TKus5CIP/9QDwjjN0+LE9YkDMfcvqvJ+yn+gKZxviCkP9yDpMQ\nsUd8NNa4GPrU59G0WZps1Af3Pj4exRxrKl3ErxQWPoHsiWbM7JPAd/98jmkLt1NpL8dSrmD+pRfq\nIw+FmIbUj5+6Kq3wXh9N+vVVgILBoWPYmxjPYB8fVh2rJuqlPUxXX62/4Bv62ob81pCH2w7NJtWw\nIOoJeAbXTieScZ1tRcn0IOn5uUsD8fjoEs39MYRzGUb/VqjVOH2kIvcFOfn37Tj6lT+9Xk5n1+mu\nyCzq6LC/mgt2rtidk+M8IZdXI5MZYH6DckGPZpmWHF/xb5SypsGB1WNTFB5flzIxsYFdGDpmErnh\nona6v3kuY/NCufyFF6ffi8XryyguTlxNwvavCXP2Y2zeAFK7bSWiIJg+LbNJ3u9L6NJUDj+crPeH\n8AQEtI8Vj6KLLgiCYChfq+p/BB6ju6hMUyn1r7dV2GCXqpCqwinVOpI2xeG+fRrB6eHEuR6TWFMG\n7rhBpti6Ed06o7aKsXkDqOlbTk7/jRIpBqBDvLjLNzbws8kV/7JaQSdVrQF05kgL29DSyuy9iYtv\nxmBRKF60Cdu/xjPyFCt+Fskcn615EdN7AgrvjlRUiXul/9nRdF4TTZizH0M/e5sO8ZFkTo3Bc1cU\n3sfHszcxntDMYQQfKSL2uyFEthS/jLNKbHGGOftJem/B6eGSz5ehGm4wZgSaGEM4H7ojfQf3OSck\nGajm8nMDN50AXzI/9ICUdJTuFdT0u8H9wEquBVTgubGaDm1ucS3EHOU9BVZFdWKrTF7F7OsDCTsZ\nxS6fbyQ2oGFxB86KROdUw97EePpHTGFs3gBSqnVcmSJD7lDNgUoVi5/qwxa3Q7wzR6S6alvoGXl5\nCF5fRqF0dODGPzvwXMcgrgVU8MtdTzKnxvCSbUqT7/Gn8Qf6ZP8NeKQqej3t7gzQEVgjCMJJmUz2\nW+6ijc/4Q91FgakAzm3kBJjZAYi+z9svMXzCIEqrLLCZLjqO5oava2J6UF4vhz2/216WlL7AwDdO\ncCZOzv397qRWF3G+qI10M1CaN4ToV6bI8PilwSZH2c4F03viDUYlU5D7khim+X4aTebs/8femYdF\nWf/r//UwDDsooiLIIsgikhOKC+CaZigulVZqpmkoCm4nta+mv9Lyq1m5lBq4keaaLVbuaJYLsogo\noYiAgoCCqCyOMCyz/f54mAdQVCrPuex7zn1dXAyzPLPxeT7v5X7fd52BfeFgOX36iBX7ou4yMmaJ\nYgdJXb6jenA32vsUEPKv2aSsFPNObYAC01NW0BOKr9vSvFhPyQEvUrvUHdNn7T20aRmE+I7i+vgW\nLBi3nwXTMuh2fgzF120Z3TOekVapZG3rwgQbcUFKApMFUF8xJemWM7ggaciBuIjd9nWXxBN+ufQr\nHXdMxz5JR3BDJyQAjGysazXQy3E+LH42dnssUI4JoLlNCU4JVmSUlVNc0pwt49eycHIYPVYk1XmU\n3XXE/LgVtj0tsOp6l7Wlrqw6HUwnnzyJZdh+z1SuRYuXgx39yS4Qi6NpNZVo/L0ZnmWDcrkza4Hk\njavFMN0TmCgy4rQXrJmUcon3El7DM9sD8s2Bp1Fo+2cNkjQFTVrgteG1Xy139idBEJ574Ha9IAh/\n2l0U2Ahikc1wvWWOMYfSfpdM5zlZJ6n0sk0K/UJnclchp/k1LVlrogicM5Vuc5LJHrGhtudaQmF6\na6zdq3BqUVb3RjPrWRG9+DXB1PWz7/s5YFqilphgu1/YAMgodxc1w3RFZrjvrcao9IIUwrvvnYLi\n7BgqcpqRpyin3NGY0tS2XFu5XhRbzEzlk08DsSoUpYVbZCLx56X3hkjBzNOU88v9UoQQBV1ywtk+\nfxXTPE5w381cKvIZOgTBI8ZT4WTOrlUrmfxGBN7r0omL6krrU0WknhTzc4MaDYjRjdfUWkfS0lKG\ntvXHHTGNMRQI60NzqwiHVUVkRnfFLk6GqUtLbP64g3It3E5vTZJGhs0OG6oGa/mtvKMkWTw6pz+l\nPUtoQSZZ27qgODsGTZItB4baYrTaiBH253HbF4bX1LO0HgM+TuMQUqxRnLkiFSVjClKYGv0jXw/s\ni2lu7XFde1NxyBXLQdlUvtydO52N0axSEf3qYAiXQ4tqssZFIfvXn/nvewz+QbtzU/Cn2mR6vb4M\n+B0YxFN0F60PQx6+9LzYFQh29KMwtC2jc/rja2KORZ4Sp6P3KOomvvT4les5cLkTar0WlUZkf10b\ntV4cnaw0Z22pKwuKFDw/8Ir0HP1CG/K4zX85i0lWgRRKBpjJxJ76iA24v5nCtVHrJV+woyo5XT8I\nJ3vEBqrTmuPxbgJvXh7PvQGVUisu2iWWkVZKKgaVU+ItQ64UOLckShJ22D1hkCRrNDB9GJNdejHD\nNpeD8fs5/2EUI3bOZun5EGlxGyKX0LxeyO5XU9Zexo4yf0hIpY2JknNLorg1wF4SfwjN60Wv1BEM\n6RIseYY9iLLxgQ8tbglGMuzi5MhVeoxOXyB9TktKTrfB490EHEZkUtAPmqWYcKjAl+Ore3Ig15fU\nQx0krrvnhD846R9Ns2wd3ufkXBu1XmoLxhSkiA6xPbfT9lQlpT1LpBap2+FJZFQ5cPsr8zrJrhvJ\nxCr2ElOQwvFIkQTj0/YWWQvNWT/oa7L6bW2yYMYToQe9TmjSzz8FTXEXbVW7cyMIgjkwELjCU3QX\nBXEhe56YIMkyGbjoMQUpHDq2B3eLuwwcNZFDx/Zw5OBOssZF0Sc8jH6hk7FIM0MuyChZ27BfndTl\nO2bY5rLMPhVlWCvpetPDD5NVct9uL10eNGQs2S9+TZePwymaEYRar2VBywzcf32HqUfekXZi+7Mi\n4+5Dj/1YnxKLfd5bwnH/9R3c9oVRVWyOdb4evVxs3xlIINmvWhKzdxvL7npjNCBfyqdB/CffO3ZV\nAyPDBS0zCB4xnmiXWLRpGbT9NI6TCvH5TirMCXb0w/SeHuts8euMdoklVrEX6x/VxBSkNHAhBVFS\n2dD3bgxZa7piFx2Pze4EjNvY47FTjb52DRVN60H2iA3Y5GkY5ZxMmTdUZjSn0lmD/do4/C/oMHZ2\nZLRzEKdWfEVfm4wGDETP7eHSd2zwkesxP5yYghQ6edxgQcuMBi1MqPOHe8VfPOnv8zyC5yKl1Ftv\nqmBG0yA08eefgabs4A7A77WN9STgmF6vP8BTdhcdc6UAf9c8bg5u/K7L7FMlZ0sDTkVt5ET0JmnX\nN7CzDOSK+jDk2wZhvwcv258Taavue6dw5OBOQvqO4PyHUbwx+bhkEQTQ4kLdR2YI11+yUNM6UcnM\ngm6sHbUZt28EDgz6kjE9Emi+LR5BDZXOGjp+FUF6z+2Ylgh4npjAgpYZhGVm02qtBcEjxrO21BWv\n0HPMbieKNhgYdcGOftJ0XMkBiarcADa7E7BfG0dClZZgRz/c9oWJiqWOfpL5gQEGA4RHwftf4hht\n1rYuYGrCzT7mWHW9S8VrPbAqFCfHTkVtZIZtLsHB57DOEVVSTU+2wcv8FnqlGG4P9+zNR+vf4pVl\n70nHzhoXheUPiQ3UZ+50F7+HfZ5H8DwxQVr0tmdasOyuN3Nbi+21zFUO0mO0WdkPubk8FfyHFdma\nUkVP1ev1nWvdRZ/T6/Uf117/1NxFO1kXM8Hmtlg5rx2UCHb0k8JYQJJ0MoyFNgaDsEFSl+8kPbQH\njQ0O5Nad7bNVLaXLWhMjtipb071zFoBE1jBUpF2Mrch+8etH6rYdObiTNY5JzF8xSbRPqnLl2zOB\nXF0dgIkSzG4Z4/xJIgPGhYoc9drQeaOXO/famUBCqugWOjuIm/OCSKup5KhK3oD0AaJ3meHvxtRg\nJm0Qh20MeTcgDZjEFKRQ+LNPnftnYzPoRjJ0KhXKMQG0OGmKXllOlXcV+p/s0MoFmHQH7Tet6RMu\nFhv3//E8y+duZuHsnUxve5zMyjbkbHSiy8fh6FQq1DYw879+lA7veWICTgl11kNuhydhf0bgqEp0\nEzV8LiWDKnG3uMtbzZNFBRgnf7QlpnhuD+fHchs+yk7Gc/z5Rr+Lv4X/sAX+zDDZzsY4N3qbgd1U\nn6O+7K43P0T2lzzBPE9MwH6vKZY/JFIcGohddLz0T2wonBnyzfosrvojmfBw2+ioSs4HH0+qHVt8\nNBYUKXAwuSfuaI5++F/QMdY2kSlXxmI5KBvNAH9yXjXGKlvWYJLKQO65MHOtJFwxOqe/1CI03M9A\n2bQZUyq1uLI/DeT4mM8fIrE0hutLA2m38NEheX0YWVhQE+gjiUQYyC+5LzfDqXc+S9x+5kqNA2u+\nGEnrRCUqF0tMZxZyo6Q5VcXmtI6TEfvJOj4v7sh3mwbQ6pV83nM9wpSTb9P6pBy5SgeT7lB83xKn\nFmUc89lPaF4vTmV7IE+z4PK0SAamD2tgMpFQpSWp0p0DvraEZWYz97fR0gns6uoAfLtc52CfdX+f\nydbOSe+waGaT7pv7zrx/BJPtmeCiX7wvtsjWlrpKOzWIeWvSSn98zoxjpJWS3+948WO5DbdqbFg+\nd7Mkp5TVb6tk/HduSZTYGqrtexsKZgZcG2UiXTbwuKFOO7y++udLFmppcdd3zxgSOAz3vVNQ67XM\nLOjG9+ldpILYnKtpZKta4ijTE+KYRta2Lhj/nkLzS0bYD8mXCmaGk0l1C30DVZpv3X5rYMN0fWkg\nsYq9xCr2SqO0IIa6O8r8ETr71hpGPBoGz7CYghTKxgc2kKR6kJeuU6nqFGCMZOgvpKG/kIbL4jjK\nKs35onAgq64MoNX6eGQlSsom3kf5jRNVxeaY3TKm+bZ4hrb158wwL4zUesoqzdlztwfG5hoSl0cR\nu2YDfeyvkt5zO1ezHFhb6kq0SyzLu+6V9PWuF9mJyrlFCpbd9WbShhnMsM0lf2EQI62UeG+oQOjs\nS973nbg2ar3kTvM08H+ii/+NmGGbyyQ/ccbb58w4zIr12OxOkHrZx3z2M9JKyfm7zrxkoSa1+24G\nDRkLIBnWGdAsxUTqB1vZ1IWyevO6hVpfysg6R3QsMXDafTZGNKjODogIl55r0KFUPGeeQy6IGnFa\njZH0mr8u6k3BZx4MWjiHTXF9kRWakhnpzz1vPXZmFSxomUFClZY+4eL8uUFCyoAQ3xcko0OAVv5F\nLCgSFUoNUUjJAVGF9KTCHP2FNIa2FVMTIwuLBos3M7ormgH+0uPcDk+i+bZ4dneoa4A3VnAEMd0p\n2VdXeNT17gxAac8S7FeaUfFaD+5tkOPwSjpWN2vw3lCBfaKarG1d0B13JuctZ7zGZVCWZke0SyxZ\n/bYyOqc/P5bbsPu8mBp0XJzLmbL2PPelKI09Oqc/6T23oysyo6AfvNdSHOF1iKskraYS56Xi0MmR\ngzvJfNdUktqqf0L829AJTfv5h+CZWOCdrIulud4FLTNQrIjA/LgV5z8Ud+P6bS23w5OIVexlQZEC\nz+3hkrifYaddUKQgT1NOyvuREo9Zra5r9zvva/wtt1ofz37fHaj1YpFq4NAkabLsuS8j8Hz/MkcO\n7mRg+jAKa5oRcyNZKrwZetRW5tX8cUw0N7APzWFY1wv4BOTgNfUstpcEknNd6PhVBJM2zOBU1EYc\nTUopGv+89Bq6fhBOziwfQnxfkOaobeaIXubX0usWpc1q60YHSXQqlbR4K1/ujrVdBcbHkzF2dUbm\n602HGZel+z6o6fYgcl41JqnLd8QUpHBzXhA3BphTrZFR+LMPRqcvYB5egOWgbExPtqHU04TJe/aL\nXnIvfo3RgHyqW+rI3O7N8lfF7yfY0Y/SnmKZpm3bEjxPTKBmp4l0HUDBZx50/SCca6PWkz1iA4uK\n+uK9JZxje7bwr4BXWHU9nsqXu+OzMYLsF79mV69NDBw1URppfRoQ9E37+afgmVjgAJeniVpdg4aM\nJXVuJPJaMtagIWOlXcbt8KRaEgr8dPV5SfgQ6hRgltmnMj3ntQbHrrlhKV22OpXV6PNXHHHHVmaB\nXBAlggxTTUdVci7NiuRGQDnue6dwzGe/xNpyMbbC75MIQvqKo5xJXb7j32/u4MZAgVam5VRoTHnb\nIY7i0EB0ctF2yHlpHB+F7kBxdgwTbG5z/sMoKZ04tySKcSOOcyjtdykP16ZfFV+3kxKMxFDeJD69\n0QUqs7WVfpuWqHF4JZ3sTwM5GL8fbVpGAz260c6Pt9z1nJ4o6ca5h2Qjq4QWG6xY1PEg1YO7kZ3a\nFt1xZ7Jut6LUT8tn/xajG8NYa9sTOkzv6fl/u94CxFQgLDObD755i1jFXrL6baWsUux3X5oVyeic\n/pyK2si5JVFsVbZmQZGCNY5JeGy8gWJFBIOOX2F2u0DuvFWJ3SUtwU7+TF86nWJfs4dNHf4qmlpg\n+78F/uexoEiBi7GVtCPf7ivunkN2xEr5qiDXSWSUmhuWKM6Okaq5q67XFZE+cf0Jz+3hJFRpJdli\nA7T3GlfgtBwkcr8NYv0JVVoIUHCgTOSAxxSkkD1iQ4NowhApGCruAMszB9Ehqoxol1gubO3Ee0fH\nIFfpabXxLNWDu3F1dQAbx7+Cyf7meJ6YwNpSV4zvyvHcHk6eppxdOwdI+X6wox/otChWRDDa/Twx\nN5IZc6WAfVmnGe0cRP7CIO5MFWsHTglWXJvbgetLA9GWlnJszxZkvt64z4uXTn4Pqrg8CgZt9TtT\nA2mdqKS67y2av3ALpasxc38bjenhJJplCEx1PolGLWNF/2+Rv1lEj/nhFKa35urqAP7fyi3Y5Kio\nbqmlVKuiqIcopHF5WqTU1757o7lURyntWSK974/ODJe6HTUuLbFPquQtm3Surg6g1Q5zYtdsQNbe\nleIgNec/jCLxysPGFn8NgjhN1pSffwieiSp6B4Wp/vJRl0febuhrPzgPrtZrpQKVWq9laFt/wjKz\npRZMQpWWD96exLVRJk8M43S9Ozfos/cJDyPqyy/xNTHH58w44gI2MGDpHKlyD+JgTHJFO2lHV6yI\nIHVuJIM9gshcqsDS7R6j3c8T29eR9E88MbOrRKOWYfqHhSg20aeUSpUpnksr+eXoLhRxE5Cds+aj\n0B0MtyyVcusxVwrYfL0XloutyJ5phPubKY3OdCvHBDyk3PJ3YOzqjCY3n7LxgXz/7895c/YcikZU\n0+KIOUs+3ExGtSNRu4ZweZrYGTB8BzEFKbjvnUL3zlkUV1lSusuJZe9v5iULdT11HiQxC88TEzjb\nW0yp+oVOxuJqMXdWG2O8swU2uxOIKUghT1POv/KHk3jFnbaHZdiklyGUq6j5Gq6lO5I77b2/X0V3\nddY7vD/ryXcEcsP//vP9T+CZ2MGtBPGMaMg7S7Uq/D6pG7xP6vIdW58TB9fa75mK274w3A5Pwvun\nCDy3hxPi+4K00N9LeE0qjgWYyVBbGZP88uonvgbDYlGcHSMKOi6LYUyKWH1P77mdNzJHNVjcMwu6\nMdJKyVjbRDy3h5NWU8nk0IO4//oOh6/GSVGDtawKv9+KMb4nw21BBaZ/WOD6bT7md/VM9ozD+pQ5\n2rQM5IIMz9Z32BO+kpFWStaXuSPzFHemVRtfEyOMhFSpT9yYYIPN7oRa6906GHZ4Q5Hsz6DKQ1TO\nKfOGfvvmUNBPTDPemneIjGpHZtjm8sXETdL39tzW6ZLQhv0ZAXeLu1y9bs+5JVFSPaN7++sEO/qh\nWBHBAV8xpTD9wwJbmQW9UkdwInoTOWPboP/JDqtJNyn82YfBHqIdUuJFDwS5juJRKq692YL09xw5\n5rO/QVHyb0PXxJ9/CJ4ZTTYQW0TeW8KpsdNy8r3PqK/B5mtiTrfzbzQIt0Hcpcc51413fh7wg/TP\nVKpVIZ97Syq2NQX11VJWFQ+ULhv6sj+W2zDSSsmvP3WjI91ofV7N68vO4Gtizi9KM2ySzBjdvj+D\n7C5xv9iS1TcG0X5PDfI+AtqsbNp+ms2V1QHo5Tpm2Oaypk8lpd26AikURbvhu1ykoM6wzWXGyVxC\n+o6g1R/V5C0OkhRpHocH++Kt1oupS6W9CVadfdFamTxWzQXAuI09mltFyEuq0PbuTItLem711WOe\nLzqL3teebTCSCmIh0lwlRjGLpu4gYep1xtomSrpsaTWVvBInEllkvt5YFuq4ujoASKHtp3H4qUQB\ni44LI0ifVuvrvkVG6o1kglWizZFB/03uV0nHfuLzux2ehOsPAjDviZ/NE/EfKPjwTOzgUOcnljEx\nipzhG3ExtpJaX4bdPKnLdw+J3E9fOh3L+LoFPNJKKQ2YzL35Ei+0ashrhrpd7VHolToCt31hWKeZ\nPHSbIfz/17gfuDwtkhPRm/jp6vO4753CpqTeVDjp+aDtQXZ3cGRRz32Y2qtY8s1mlry9QzqGzlyH\n5/REBo6aSDv7YnIGb2ZBkQLdG8XMLOjGzIJuKM6OoVSrIm9EG+7MUCGo60gnfwWWPySiv5D2xMUN\n4kSZrndn7nWwRn7pOnKVDmObGtS+Kuz2WLApqbekM3egzI8r33Zg+OhY/Memct9LwyefjqXy/zlg\nbaRlmX0qW5WtGXZqGln9tqIZ4M/6w9EUDa7B5J6RNCDzX9N+IGtdD5yXxknH1rzgR8jAUYy5UkDb\nk2LHwmVxHA6vpONucZfSniV4hZ7jRPSmv/y5PIinWUX/q9ZFj3usIAgtBEE4JghCVu1v28e9hmdi\ngV+pbM4W/63S3wY6auLyKIId/Uh5X8zxeqWOkIphIBahzi2J4oMZOxpcN8M2lz0ngsShi5G+D6lu\n2uRqaAx9wsNYUKQgVrGXyUEnJdaZoSgESFTJsdaFKFZEsOyuN+k9t+M5PZGcwZtpe0LDfZ0JMk93\nvhv5Av9+/heSKt2Z/9NYrq4WnT69pp5FM8CfY3u2EO25m27n32CZfSqWXzUjdnNX1jgm0fZN0bqp\nxRUNqd13c3laJEcO7sTY1Vks+O3yayBY0RTkL3x85bw+5LfuYbM7gbwwH6wTc9EoTXDYY4LlD4nY\nJskljfprI9tw313H7sQA2lvcAcBxfA7m/y7kzcvjGZ41iAk2t8URXUc/jI8nS7RfQIrIJtjc5oWu\naRT+7MPsdoGsuh5P3ksmaNMyWPPFSEhIJTO6K1nreuCUYEVyZ/Ff9+rqgEZFMf8ynlIV/e9YFz3h\nsfMRlZQ8geO1fz8Sz8QCtzBWk1TpLlWwf/qlF899GfEQD3u/744Gfxty1JFWSkkRxriNPX3Cwzj3\nmigq9sb+WHxNzKUWEoBpaZ2DSH2oWsp4r6VYpDKEoJ7bwyUfMRAZZHNPvyHl/Ib7GV7XiehNBJjJ\nKA6059CxPRSobVl1OhjrHNG11HN6ImXjAyWxfhdjK5K6fMeCIgUaSyO8xmU08PM6FbVROkF5nphA\nmz2lDOkSjLFcy42lRsQUpJC/MEhi4j0OzksbD/Fvzmu48PMWB0lupEZq+CzhZ7ymnsXz/cvcmRqI\nxV0daTWVbFW25oPff6b1WRAsNGyK64ugEbh41YnC+zbEKvZKLDP3vVOIKUjho+xkun4QTrCjH9Xu\nVfhsjMApwYq0mkouRXbC6b1qysYHclNjg+fyK8QUpLB9/iqKQwPJHLSBFheMuBFQjpGFBVnrenBt\n1HqJRfiM4S9bFz3hsY9SUmoUz8QCv1dhwQFfWynPTQ+L5NKsyAaLOU9TjpWRqdRK8TwxoUF7ygDN\nrSJORW2U8m5royoACt6qG8kUquvYbEYWdeG9TF0n8uf+6zusLXUla1xUgwGXYEc/cgZvBkSrXEOI\naYDfJxG47QvjvrNAx68i2LNoEJY5xiycvbM256RRbvslpSOxazbwrdtvlPYsIXOpQipY+ZqYU6pV\nkdVvK9EusbT5uYL0ntupVIlyUJenRZI1Lori0EBRneZkm7p56trfj/IlA2j7eV2HQde7M27fF0t/\nm9zTM+XKWGS2tiTvVOA1LoMWM3LxNTHH0biUickTWL30Kyb5nWH3S1E81ykXI6VIkkmrqcR7Szg9\n5oczqc8JQCx8GmYFzDLM0Mn1RLvEMun9d0lcHoU2K5vm2+JZ6eGL+gdRt252u0DOLYkiZPQk7KLF\nmsK+rNN4Tk8kZOCoBgXZv4s/EaK3FAThXL2fsAcO1Zh10YPKRl6ArSAIJwRBSK61I3rSYx+lpNQo\nnokFjv7hQY/6skMD04fhYmzFzvsO0s7Z3EbFgiKFZLo3LqGu0FY/JDfkzJp6dbaCF5pJl41a2dVd\nVtfFXrt6bWJPvtim+vFQT6lGYHApNfSW6xf9FCsiSHk/kpzhG2l5SYNZsZ4+C+P5KHQH93VmXBu1\nXjQyKHUVp+Uc/egXOpmQgaOo7nuLbuffINjRj5vzgmh3QCwULmiZQa/UEdjKxJHSkIGj8LPOJ62m\nUqqozyzoRrCjH89PvsjB+P0U3reRXp/h96N8yQDQ1Z3wirqZNxjAaX2qCDa3Imt+Byqc9ES5HGCf\n5xG6fhDO1LhxyM5Zk1Tpzu93vAgwk7HP8whtT+jwOTOOnaU90LlVYhdfRPTxF+gxP7xBp2TJ2zsk\nnnz8yvUN5gCEzr4c89nPqy+LXQG/TyLwWX2JmIIUstb1oFxXTeXL3VH6NH/8e/sz0PNnqKr/7dZF\nT3y5Yo/7sQnDM7HAzW6L/8xHVXJG5/THe0s4tjILfM6MY2ZBN0p3OQGwYovYAjuqkjPf6wjL7FMl\npZSW+82k443YOZtgRz9+LLeRinI9Xk2VblfXjSJT41I3Mmp1oy50N/haD88aRBFkrV4AACAASURB\nVMbEKIkPbwgHh6aVSkKIHb+KED2say2FFGfHYFqipspO4PujPRlppeTTi8EMCRyG274wqT0EIhfc\nsKBaDL9GxWs9cP3mGsUdTemVOgKfjREUprcWhSUTUqX7znl1kmSve+KGBwA3AsobVWmpn548CKGz\nL8Zt7MFIRuXL3XHaIloPGVlY8FF2Mjt/20FhTwFNMy0/j1olRTilz+npMCuHCjcNOz4N4T1XMRQf\nnjWIX9Z9QXrP7Xx7sSvt7Is5dHIvHu8mkLg8ikF2l/C/oGPA0jnSybdUqxLbnhsqyFrXA9OTbVj5\n02Z8NkYQuzgAXe/OpLwfye1qaxQrIvj8pd3Yyiy409kY77lpjb+xv4qnx2T7O9ZFj3vso5SUGsUz\nscD1cnFXfslCzbduv0ln9fSe2/E0v038x+sAcA/J5iULNS9ZqCUPsWBHP16yUHPoszqHF5eAG8QU\npDDSSikJFBpkgQDkSjFXB7jjZy5db5JVN6jR7fwbLLvrzfS2oteZITUw0Er3Te2PsaszH50ZjtuO\nfN5dOE16rMMr6cgvXUfvd5+scVEkVGlxef0imtz8hnPa9XDgZjKyZjYoXWRobhVx312H+dJmOB2v\nJHTA75LCDcDq5AGM3n2MVhc09JgfzlDXun/yitd6PEQIMoyYNvrZX0hDc6sI4fkOKF2M0ZaWUj24\nG4evxmFtVIP/79Nx8LnNiv7f8vaS2ZJQQ7/AS2TN74DX1iq0cthztwdHVXJK1rpKJ4HsF7+WXGQM\nNsATbG6THO7H+Q+j6JU6gl4zpxCUMAWv0HO0jcrFyknJ9LbHeS0pjHafpaC2MMLo9AUGDRlLzgZv\nkuesZXX2i3huD8dlcRwZZa0bnYv/q3iKVXTJukgQBBNEKfF9D9znF6CXIAjGgiBYIFoXpT/hsY9S\nUmoUz8QCF9Ti4jmqkqM4O4aZBd3oFzqZ9numsm9qf+SCOPJZ3Vc0tvf7JAK1XstIKyVhmWIxyFZm\nIVWJDTt+fRgokSAK+19eKp4g64d3mltFki1SUpfvWNAyQ+qpZ6pr6PhVBG0PiyejY3u2oMnNx3td\nJbJt6odIJtrSUp53FE+6/o25DNRD3ved8F85g0Npv2Pzkvge2x1QU91CTmGQeYNCXkxBilSBNv/l\nLInLoyjXmiLzdBcHQ17UP1pr7THQX0jD5bVs8r7vRPtF6YTm9WLokVnYtRDbUysWvcm6hes4VKKg\nX+hk8t7zpE/fi2gs5Vjc1ZL1SUdWevhSPaFUqkskVGmZYZuL3ycR+JuKdY3hWYPwj6odldUYYx5e\nwPOONykODeT3TC9M9jfni5dH4PL6RTKXKlBbCMQUpJD7cjMSl0fxeXFHTD+3leYQCq62YsulJxcY\nm/5BNPHnSYf5G9ZFj3ps7aEbVVJ6FJ4JqqqN0ELvMucDqS0FDS2IOn4VgcUtPSV9qznRbw1vzZiN\n47+u8pnzPiZOnMWdGSpGu58Xtcsc/bi6OoBro8ScbkX/bxtIHxnQGK1T17szlQvvSbu+wTbnUcjT\nlJNU5ciX741uVAZJlCwOo+2vwkNmf43dtz711vD+weD/bSEJIQwcNRGj0xcoGx8oFeyCnfwb5NJ/\nBfUFMLzPyYm/5UaZ0gJjuZa4gA288eY07viZk/J+JH3Cw1C1lPH27ENEfj+EgUOTuF5hx/S2x6WT\n4syCbpzY1Y12L2dTqZFzqMPPeP8Ugc+/r4OpCe8cO0lGlYOkL2f43kD8rrJ3+WGUY47bD0p279tE\nl6MzWdRzHx+deIWc4RsbiGPIHK7+faqqs7PeadZjrb4kZL835x9BVX1mmGzGKj15mnIKNOYscvfH\nmbjaPm8KxpVIXOaZBS9Q0sGYU26/4bZvNjnbH65teLybAKNE2aKPZr9FQajoElL4sw8Or4g5Zv3F\nbWRhIQodJGdQmK7gqIfoj2YrsxA9vzrXBTp5i4Nw35LfQOfMPKCqwfMbFt5WZWvM842x/OHJRaBg\nRz9pgVUP7sbl6EjcnEWZ4dHOQcQUpHDMZz9pNZUSZ35guhiFuO0Lw2xPJTb7rUhcHsWQwGEP6bA9\nCUYWFg106y70lFP+jSk/B0XxX9feoOf6ubRYeAurzSZ0/SCcmvYCjr/fI/JyHyJer3NhWfRBKHMd\njHDedY2D52MIXqXmkl8XOiwpY2iWP45H7qBT3kez305Ms4CTuEv2T71mTiHkwxOUjQ/EOFNAVilG\nF12OzsQr9BwTCm4TFSfDTT4J2yQ5wetLyIzuyhPawU3CP20UtCl4JkJ0EE0MXIytCDCTceBmMpnR\nXcmYZEuwox+pcyOZvUmc4lrjmCSJLBrEGQwwOGkY8FF2Mu1ezpaKWu1sG89F74d0AsR5ao85dXPg\nBjglWEl6by6L4x5aPB/t2tKgl9x8mzjBtbuD4yN7z/VhIKxorU2JKUjhpU9PEezoh8/ae9J9DJX3\n2e0CCZwzlRDfFyRZaK+pZ0nvuZ3mO87SY374n17chvduQHJnI3RVYsHx9U1zUH7jxFujjqPeZU9h\nT4GSvtXiCTmkGa12mHPsrg+e28NZlTAQm90JqG3g3lZLgh39+Cg7GZ8P7nDo5F5kvt6od9mjU6m4\ncdqZrcrWoiPr7CBptj52zQYWtMzgdpAWl8VxXJoVSdn4QFqfFN1Gtypbk7g8Cq/Qc1TUlqE82hX9\n6ff76A/i/wQf/lvgPq9u3LPjyVByBm/Gwec23+bHMbOgm7SoQazU9pgf/pDTSXpYpFTMUayI4ONh\nb7LA+SCb8mJJq6l8SNrHQP1UtRQ/hsLZQdwJE9VGjqrkDEwfxjJ70bP7Rn8T8hYHsep6PEUzggjL\nzMb7nJzrSwMZ+8s02n7611s1UlsqIZVgRz9OKswpnB3E7YC6Oedjd+v6+Da7E9CWltJiaKZ08jKM\nljbfFt9AhvnPIm9xEAQosD3djOoiC9S+Ksq8YcslMSpp4VnCc64F2KVV0fKSBou8Cq4edWfwgHOY\n2VQjdPbF5eNEyn4Xe/H77nWWTjhXwptLcs3pYZHsnDyETXmxpM6NZPIbdSfntJpKvKae5c5UUbs9\ncXkUtheVqHfZs7uDI57bw5lzNY1lr++UIpunhf80wYcm5+C19LlzwE29Xj/0abqL2ggt9D2EAcQU\npDAkcBg7YvdIuW+I7wuSFpn73ikIagHbSwL33cVqebTnbkKzxnDtZivat73DnZ+daZVSidHpC1Jv\nPXDOVMk251EFKIPZvdDZlyMHd0r591ZlazqYFHKlxkFyIzEYBQ4YF8q9diZoLASpWPegkOPThuF1\nPm3IPN0p7dqaWy9qsE2SU2UnUOVdBUo5XtHlFCzSsajjQd5LeA3H1rWOMZtbYXmjEhJSJWfRCmcd\nRjUCLkdrMD6eTOHsIFLnRrKgSEE/63RWevhie6YF90fK0dy+i+3pZly+Y4/J/uYSAabXzClY7j1H\n9o5O6IrMsMw3otsbqcQdUaAx1+MQp6W6mQy1hUg7rph2j/NDlv3tnNjMyVnvPG12k+57dcHsf0QO\n/md28FmIFT0DGuXEPuAuOgiIrD05PBGBc6ZyMH4/AxfPkfrX9YUGTwxfScsLAh7vZLB21GaMBuTj\nYmzFMZ/9eI4/zzGf/SJvffHdBsc1LO4HoRwTIF1ufkn8KPQX0kRN7trZ5NNlIoHj419fBcQ22aFj\neyRTxHNLomh+tS6kL1U8lvv/l2GINqTRSKN6H6lRkz7ex0KblY3tudt4hZ5DJxeobqnDZbcMU3sV\nshIljh8ZMfe30ZhlmFFUIrbKCnsKlPy/KrI/DeRKeHMcVsXhfFRLs8xavbsABW3Xi0XS46t78pKF\naMTQ2vQ+O5J+IuZGMpayGtRqY+yiRTkmz+3homuNTou2xJS2J3Skzo0k2iUWl8VxWN4QKOouQzms\nnFYpFeS+pn/IKOEvo4m79z9pB2/SAhcEwQmRbbO53tVPzV3UAMNCfGn6GVqY15MXyumP5/Zw1hcH\nUTKokhv3m9PBpFQyLhg4aiJzrtb1gn2a3ZIeB3WsuLSaSokcAlDhUPf2K+rRCp6LEIX/T0RvItol\nFrd9Ybz7ghjeG2iq++7VzVcXvVNXZPu7ggtFM4IaXbAFi3TkLQ4SIxAjGei0FIfWtofqVc+z1j2s\n2pK1rQvVg7s1oK86JVhxdXUAmdFdyVschFOCFSoPO27OC8LxyC10NhpKfOQ4tSgjfUkrFv+4DTO7\nStp9d4t29sUoj4pWRso/7Fj+6k48pydyc14Qy79aL9r+npPj9EU2GZ91wuKWnsTlddZNaxyTCIqe\nS0KVlsSfFNjst4IABTq5IDrW9L1Y+2GqUbWU4b2lTlDTfm0cy17fSUTHU/TdmEibX40bCE3+bfwv\nlWz6AvgXDUfdH+cu+iQOLoIghBl4vGrEgo6BquhveZ30BDfpvt+6/YaxeznL7FPJ6reVZlPU9Dsx\nk5Q3PBg4aiIun2ex7uYA6f5X33aXHgfw+V1x4mhMyjukh9Xl8vZJdQQJg2545cvdid/f0Fyh7a/C\nQwMNBhUXAJfXLz749h4JgxzSo2C/Nq7Rdlc729K6efDa2z3eyXiI4vtg+G7om5+I3tQgPYl2iUVn\nrsN7XSUui+OIdonlRPQmNk9ZizYrG+d9RrT6oxqjAfmYZpuxLH8IQc45YCJHt6y1xNoDmH9uBJnR\nXXlz7HGu1DjgszGCjK5q4vLdGN0zXjKL+HziW9JrcFkcR1KlO2Z39SJFOCFVNFZAZOQBPOdawLkl\nUbS6oMPt8CTCMrOpOOLO6uwX2bI2hE1JvYlfuV5KnZ4GBF3Tfv4paIo32VDgtl6vf6T4dlM4sY08\nZqOBxytHZIIYquKf/XtsA0HFjl9FoFHLSKuppP2eqRS96ET2i19z6ORejE5foHCwXCqg5WnKGfxd\nAhjJCM3rheLsGC4pHTnQqSWp3Xc3mNQyOn1BCtMN+uCGfvZRlZxld71JqNISu2aD5L39OFS81kMq\n8j0Kj/MEexxG2D/s4mGwJnoUCmeL0YpBVvjO1EBsz7QgNK8X7nun4DX1rMhkG+BPSN8RBDv6schd\n7BaY/3IW4+PJ3JwXhOsv97h00ZXf0jrwy9FdjPuqtqhlJKPVBR3ua3TkDN7MlsP9WXJ4BD9MWAkg\n0VWh1jixvEbSrM/e5UfUriHYRcfT7Mp9ACpri50Gx5V9nkcIdvTD8odExnQ5y0Yv8cRtOSibQVPO\nYHxXzlZl679E7Pnfgqbs4D2B4YIgXEccW+svCMIO/pvcRaFu2srnzDhC+o7gxVeTcH8zhWnhM9n5\n8le0PK8kZOAoEqq0eJ+TN8jTf1O5i7utTsuNgHKab7HmOZsCYm4kMzxrELnrvBpws4e9Lz62vj64\n89I4pvw6kQUtMySRx2iX2IfEJgaOmtjgb6WLjE23+1JywEscZQ1Q1CmdPmJ2uylSSqYn2zDB5vZD\nu/WTYNhls0dsIMT3BTQWAiqNCTcCyhvs9MbHk6Xx0PrQDPBnyviDtI3KJXvEBna/sIEeS6az5PAI\nUTRDp+XmYC1OX2TjeWIC219fh8e7CdKsOED7tuKMeEF/HdfeE0P0Nr8a81X3nTgvjePO1EAm79mP\nsaszfd5Oosf8cMyLqiBAwVGVXGLvJXc2ouK1HiiPtgHg+OqeuM8TNd6lVOVp4H9biK7X69/X6/VO\ner2+HWLx7De9Xv8WT9ldFOr8xwzjf1XF5hw6uZeMrmr8L+hovyidADMZRw7u5Jeju5i1eLo0q61Y\nEUGpVsUEm9ssu+st5dqnojZK4fQ+zyN0m5PMG/F1+fqmuL7SZcPOYWRhgW2KTMrdPU9MAJAYbr1m\nTpH0uusbLjS/piGjrDWmxhpstpahW1rKztSDCJ19WX84uoHgQuHsIKoHd5MUVuo7gNY/ARm7OqOJ\nEKff1pa6NpyHrz1peJ+TS1NuD8IwtaYtLWXR1B0S3fdxyF8otsqMjydzwNeWPzZ1IqTvCMYcDafU\nT4vORoPFK0WYnmxDjw7ZWBpXM/G5eMYlhBKWmU2Xj8Ol51Z+44T3lnAOX42jxRFx4cevXM+0Hyeh\n692ZXpPOMdJKyZX/assaxyQxwklIhYRUpsaNI8T3BQLnTKU4NJBJ//5JYjtWNxMo/NkHXe/OWNz9\neww+Cf+BRba/w2RbDnwnCEIokAu8AaK7qCAIBndRDU1wF62PrcrWGKn1uB2ehMdONTO7dmNo2m2+\nWRVCcZCaXmWtUf1sT+cJFzmydCVggdu+MDySKum+aw5Z46I4VOBLu89SHtLGe+7LCC7NikSxIgIH\nxN3N7FbdR6C2MMIckfTRan08vZvN5dKsSJodN4d+dcexvFFJn4XiQktcHkXwNjFErJxcRvHt5mS/\n+DXue6fQ4oIRn08PIGu8NYM2/IvvJ69kqPsMzPJNcPsyHW1pKUJnX1Qullgnijl+5vrudPykEKPq\nanQqFXplOdraPvIM21yp95/3fSfSe25nQZGCA7keZLzSkJzTGAwh7pNgna9HqNZKG5VddDzlL3cX\nB2VqC3wABaGBVAwqp+aGJR7vJuBOChtx53xBFH0Kw8gfDDnDxeKa+94pTJr7Gz5nxuE+4zbu7SrI\nesucL1qewu3wDGwzBHrMD6c5dSmMrNAU5QAvqXDp+L44wZe/MAi3HfkUqp0wOh2P+YNv4O/gH7R4\nm4I/RXTR6/Un9Hr90NrLT81d1ACLNDPJDCBn8GbslueyxjGJGba5yNRiBXtSu1gqnMWQecDSOQBY\n5hhzdYKMrHFR0gLQqVRkretByMBR0vEvzRI1udu9nC3tkvWFDG3P1RVrZLa2XJoVSUKVlnUL1zV4\nnTF7t7HnRJA4jjpzCiUHvDhwMxlFywJpEAREI4Pd57vjue0+loF3eWXPbLxCz1HlXIP6uXZoBvhT\n1caCou7iBJnM0x3BQoMmNx+hrRiKZn3lCkCXj8MJ6TtCGqyw22PBj+U2TLWLk+i3TwvNt8WjvyBG\nOQZBDENtwsjMVKpXtD5VhMvrF9GZ6zB2dZZaeaF5vbgxUGBF/28BmPbjJOT3jNhyWLQmur25GfkD\nrPD+10V+UfrhFXqOVuvjudNdJ6n0ALj/VIFNehmZ68XI6r7OnJvzgtBa6NHk5tNqfXzjDql/B//b\nQvT/Sbh+m89RlVyy/DVUwQeOmsjtIC0dv4pggs1tMiZGodZrJRnjS7MipfZVV4tsYhV7yfu+E57T\nE9GmZUhtFrVeyxrHJPZ5HmFn6sGHnr/apV4Pu6UtHb+KIMBMJuXhQ7oEs6BIwVZlazzeTWCklZKi\nbka86vIHO+87EO0Si1qvJU9Tzuie4k40pstZssZbo//JDvMiAd1xZ6zTTCj2NeNeOxNMDyfRbn8F\nFa/14MqsVnhGasBIhjYrm5IDXlifEt0/HA7fABO5JPIAsCj6LcZPefep5qBFM4IkWi6IJ8r6i86o\nlZ1UrzDk7a3jZByM3y+ZVpzK9kBWYcRIKyWKs2NwnxePd68cPLeJe0CreQLOS+PQqVRcU7VCOSYA\nma0t8ntG0jH7plZydZQFpQpbZvcWeVIffPMWb449TsbEKLLW9UB33Bm/D54sItlUCPwvrKL/T0KT\nm8+sC6O5eNWJoyo5Mwu6sVXZmmN7ttAszZjL0yJx//UdunwcjlyQ4bMxAp8z40Rt9IGjUOu1Eo9c\no5ZJBayMiVEMHDWxgYtnwLY5dU9c659d34wQEzlHpnwm/VmqVXHwfAzL7FM5UvwcuuPODEwfRta4\nKN6zu8zX814hpO8IynXVjPjoPS4pHSnVqjhV5IGJUwV20fG0SFfzRfvvaH5NQ6mfFvtfb0i5t+UP\niXj/6yKV9mZkRvqT/WkgDtZK6SS2I3YPh47tkWaf+yyMx1glFgcf5Vn+V6D01mL54U1WXY+nb2ol\nut6dSZ9fR5nV5OaLPevjzhTNCCJvcRBqC1EQ03Aifd3nPJffWsfA9GFUpzXnztRArpfacujYHgBu\nBIsqOsoxAdwIKOfusCocDqupaVOXZpxUmGNyz4j4levJqhRHeNuequTQx/0IdvQj49VIjBbasv+P\n52uHTZ4C/gNz8GdqgYPYWunve4UpJ9/mxA0PJtjcZmZBN8xrhf6yX/yae97iJ+yyOI70ntvxCchh\n/eHoBgs4q99Wrk0W+6o+GyMauJb4fRIhiUoArPr2YeF8bVoGL/w4F5+NEXhvCSdDLbbyQvN68a3b\nbxzz2S9xoAM/nM4v677g0Mm92MosOLckin2eR7CVWRCr2IvTGmMwkqGZVYyviTnmv5ylpVMZmtza\nqbQEsQjo+JsRvRYnYN3mPsbu5Q24ALYyC4mnjpGMZfap2K+Nk1xEnhY6RJVRstYVXxNztu8dwLVR\nJrQ9LJPCZADZ/WqO+eynwkmP3SUtsqHFxCr20m5hPIWzg1hmn0rQhTEc89mPQ6wGuQqcForb3syC\nblR2VSF09pVmvdtulxPtEkvmoIbfQ5VzDZ7bw4nd3FUyPSzoJ/rI+a+cQczebeQM3ixFb08F/2Eh\n+jMzD95DEIkq9avEhv6m4Tq1XovX/nByhm9kq7I1uzs4ivz1LsFcXupMzuDNLLvrzUmFGNbWf3ye\nphwX4zojhbSaSma3E0PbvqmV0kyyZoA/xseTsT3TgsQr7rjv0nN8ezQJVVopVAdxRx/tHMSmvFhc\njK1YW+qKo7yUpavG0uZ0CYeO7ZHmtg/cTGbR7c4cXdcTuUqPbWopFNwmZ6MTGrUMz0VKrk2wp93C\neG7OC0LlrMWuXSmt5gn/rbz2R0EzwJ92SzO4EVDeqOFCTEEKntvDcZ8XL303hs/6+tJAjLzKcXn9\nIhWv9aBZUgEH4/ejODuG1O678dkYQerktbzc73XWH99GvxMzxbQkIZWb84Lo8WqqRHQJy8zmm8Ig\nqfJveK4FRQqsZNWcVJhT+XJ37rxVSeZri/42N9zcwVnvNrFpXPT0T/7zuOj/I6ivkBlTkCLRUQHW\nl7mTM3wjg4aMZen5EOkL19wqImfwZgaMCyWh1I2YgpSH1E4HxE6XjAODHf0a9GoNixvANE9sN5X2\nLMFrUw1LN4nkmwAzkTgzs6AbnicmkKE2xf+CjhW3X8BtXxgzbHNF7/IPo6RQlMV3qXy5O0Pb+pPc\n2Yh1C9dhe+42vxzdRa+TBQgp1jQ7bs61CfbIVIKo+X30Hm4/aSg/1/JPLe766rB/F8bHk6VF1pib\nitu+MORKAWNXZ6kNZ0C7hfGk99xOTEEK1RNKqfladIOxXylq5qWHRfJ5cUfUbZrxUcEgsShZG8FU\nttFxfaFYJK18uTsjrUTjQ91xZ8lBNNjRj+Ore0oTd6eiNuK67Oklxf9pIfozt4MToCArwpjPA36Q\nlFgWFClYZp8qmRACVGtkqFNsJeppj/nhxH6yrkGYrlgRgcOqOIpmBEnmCSD+w9VXEgGRyVaf7GJA\nxRF3YhV7Cek7gkMn93JUJWfWhdG86vEH59/pJBWWjqrkD82RgyhbNHHbDBxPV2N6PhtVgAdKV2Mc\nDt/AelcF9wZWcT+kE+UOMuzXisSP8x9GSUZ+zwJKDng9dqDj6uoAfLtcbzCOq1gRQaW9HssbQoPP\nHkRegfsaHYVBVpj2v0uLoZnSd1T/ZKHr3ZkXvorD0aRU9D0PUEBCaoPXY5iu+1X/w1PZwd3fbtoO\nfvnT/9vB/xoSUsl+8Wsiw1+n2/k36PJxOMvsUzmqkpPU5TuOPr+V8kpT2tmW0jyj7sw94N0z/F4p\n7hKGnNS0/12MXZ0JeSdW0jb32RjBSCslJ8N6NGCXmd1S0RhWeH3PzIJuXJnVirWlrsyNnIzbggqS\nOxtJi/u5LyN4yUKNz5lxBDv64XZ4kiTSGGAmw6l3PuVtTfj8wiFGrjiKbGgxt78yp7Xpfa5/057Y\nNRto9Uo+/hd0eI3LwPPEBDp8N41v88UcO+/7To/ksP9Zdhs8ebd/kHXXYmimpOneGDzeTaC67y1p\nNw929MNhVRzu8+K57y5+R71SR5CnKafXzCl4TMqEhFRGTzguLVTzuzoGjAttcNzh63+jq0U2m6/3\nwv+Cjpi92yg54EV727sInX0Zc6Xg6Y7O6p9uFf1J1kW1tkX3aq2LUgRB+LD2eu9616UIgqAUBOG/\nam9bLAjCzXq3hTzuNTx7CxxR0bTVxzkkdfkOr3EZLChS8HnuIIJHjMdWZoHL6xdZ4HyQu8OqpF3d\nMI6YUKWlQ1QZPmfGkdTlOzS5+SSVuLLMPpU8TTnpYZE892UEMXu34RBdx6A1LNYHscjdnwMJXejw\n5R0O+NpS7q4lfU5LYgpS8N4iuopqu4pc6i3+W4kpSKHDjMvIBZnk1GI0sIAKB4HhP73LtpweaA/Y\ncbfIhtjNXXnV4w/cDk+iW4tcjq/uiXJCc9zfTOHaqPWMdhaZb0KKtcRhr+9PZgiR/yzqq7fAw7LK\nt3rXVc1lvt7kfd9JlMF6DAy8dwOMLCyQ2doSNSwan40RdGmZz9Av/oXlD4nS8xtqJSBO4WW/3nCK\nzlFeysIrr1L0hz3fXuyK+6/vcPT5rRTPd0V/IY3t04bhlGDFU8VTKrI1xbqoFqf1er1f7c/HAHq9\nPsNwHaJuugr4qd5jVtd7zKHHvY5ncoFP8zjBhRPiLnJjtSfHbnbgmM9+YvZuk+4TYCbDfq8pLYZm\nMrOgm2RaEGAm49CxPaT33M7onP7cnBfEnZ/FVtTU54fhvSVcUoepL6WsWFGX+9ffLWWe7pgVyjh0\nUuytZ4/YQM7wjfSYH45lPgw9Mov0ntsJdvSTinCFoX6E+L6AydgagkeMp3hid1wOFOPxbgIthmZi\n8UoRzVJMkA0t5kCuLz06ZHPo617IVTpUHnaUHGiofV9f9kl/IU1akH9FmqkxZHzoDUYy8hYHcWdq\nIK3Wx0stRm1axiOn5er33x1WxXF9ad3fh6/GcWNLG16yUCOrhCktT2F+V4/tmRaSy4rQ2Vfyfcta\n14PMYQ3bfRu93DH6zg77JB2T/M4wye8MGWpTbszUcOBmMnmh4rzB31GwslHh3wAAIABJREFUeRBP\nMQdvinVRUzAAuKbX6/+SP9MzscAFWcMz90dnhrN21GZ8zowjds0GqjV1t/ucGUfZ+EB6zZxCl/nn\nKZwdREZ3HXf96nSy1Hot7r++w7duv1HZRieprbwRn9agPWaYTwbQ1V2kulndsYT7FVQ5iOH2l52/\nJdjJH7d9YTTfFo9OLtDJJw8Qc3UDZz3l/UiuR/hw8HwMH+3awrklUQz+LkEanFDvspfyUrXamMSL\nHtivjaPPwnhMDydhvqm5+JqON75DP6hz/mf+wcvGByKztUXo7CuF6h7vJoBOS00zHUa1ZYS8QWaP\nOYoIg42QAe0WxnN1dQAxBSmk1VRSmSG+j7kTf2B2u0Dskku4N7BK+j60ViZkdBWf0HN6Ir3en97g\neLrenfn+358Tu2YDJxXmnFSYE2Amo93b13jh4usM9krj6uoAqQX3VND0HfxpWBcBBAmCkCoIwmFB\nEBqzjx0N7H7guhm1j/n6H+EuqrVuKBzuva6S+Ssm4T5XlAZqvsVauq35XkvuOwv8/mUkaxyTcFgV\nR8yNZLLGRTFoyFjyNOV4HZkiUUYNJoRrS125rzWXBiFCBo4io9pROm7/1+sKbI6/1XlzVfk6sSsk\nEs8TE0RFkhvJGNvUkLWtCynvR7LAWWTE2cyRoy0xlcgel6eJC/iDnFcYndOfbTmiEEOwox92ySW4\n751C60n3+Pfzv9Df7zJ5i4M4/04ndL07U9bemC4fh2M04OEdurGptKZSVYtDA2m+4yza0lL0F9Io\nee15cSqsluhje0mg5XmxsNl+qyhk2NSTx52pgQxNK8V7c6kkDmnQ2dvdQfycheIyDl8Vi55OCVbI\nL11n1fW6k8SDnm3Xw/WM+Og9QvqKU3wxBSl4npiAkY01loOySZunoFmGgN+Op0TVberiFhf407Au\nOg+46PV6BbAW+Ln+jbWmB8OB7+tdHQW4A35AIbDycU/wTCxwmbKh7LDKxZKy53QcjN/PzIJumJao\nCRk4im7n32DuR7twi772UIW5/Z6pHDm4ExdjKwR53RndkMfOsM1lhm0uNbW7s3KlGm/TOiWQ/X88\nL13W59QtLOPjyTgaV6JRmkimCF9130mHJeLJJ8BMRq/UEWJrzEaNXCngti+MtJpKQvN6ccxnP1e/\n9iawTQ5u+8KIKUhBuVLN+kFfo7lVxNcD+xK/X4HL4jiMajS4fJ5FZVeVxGAzIKYgBaGzL8qVammR\nG9xZmgqLu9oGYhIVDgKt1ovTW7renSnpW43eVIyWVB52KMcE4PBK+hOpsDJbWxbO3snBt3qhTcuQ\nzChAPKGVHPBCd9yZg+dFyqlODhVaEw6l/c645bOl91cfxm3sRXGP6zVos7IJy8wm2NEPz2m53Ntq\nie2ZFhQGmKKxELjQ8+nk4QJPNUR/4ti0Xq9X6vX68trLhwC5IAgt691lMHBer9cX1XtMkV6v1+r1\neh2wiSeoJT0TC1xj23AeyDqlkF0hkdLgiElWAYeO7aH1NJGmmft2e0BsjZmebINarxWLUjn9CZwz\nFbsW5dLo6YNYNXmT2FJT7G2wg3uvq1N3McgoG9DvxEy8N1QwweY2fcLD+PjqMEq7tsbt8CS2KltL\nY6Q+/7op8uKHb8TXxJxol1j8PolAYyHQxkRJzvCNos3x5lacU7lzc14Q1rsqaNtfPKGo14jFp6x+\nWxsY8RlyVv2FNCwHZUv9ccOCaSqsTmVJl4tDA3E6eg/NAH8qX+6OvESFRZoZJKRyZ2ogQcsSmbJo\nL8auzthtaTjt+2BhS1taykYvd3Jes5H61wZkbetCcYkVV6/bS+aKNi/dIsrlgNjGPHyj0deqdWhJ\nsKMfZldvUzQjiPX5fcne5YfHsXJiFXtRWN/EeWkcDqvieDf14fbmX8X/pHWRIAhtBEEQai93R1yP\nxfXuMoYHwnODBkMtXgUuPe5FPBPGB7LiCvH0WQtNbj6L3P2JKUjB58w4NO9aiVzw+P30C51MS8Rd\nbMC7Z9idGEDHhOlkjYsSh1NWigMqbvvCpPnpYEc/icn2koWal5ZHEZrXi2iXWA4gpjClnWxoXju3\n8KALidOPxhw5+DU+GyOY9+kPokTQA4FRnqac9M/EFKtX6ghu3mxBzuDNpLwfyVGVnG6m9xgSOIrM\nCCd+XrGK1zfN4fKsSPw+icBYpafi++ZU35RzM8OZ4KXleNUboXf8rZjgtQ/n4n+mgq4Z4A/Hkyn8\n2YdWay2QqcUcOOdVY2QVRjSfrKft0Dh0x52pPC0w1jaRoUdm0bE6V5KSbrVeDKdvvWLJgZsnGdrW\nn8zorkzudpofIvvTbmG8lNfrenfm2J4tDAl0JuctZ6zz9Xy5eDcBBTJC+rrz0uoJGKv03BzujP3a\nfEq1Kt7IHIUR+cg83bnyriktTgbiOD4H5UUtlQlOYAzpUc8RfFpd6w/eCZsMGSs9oKEe6N/AU6KF\n6PV6jSAIBvshGfC1wbqo9vb1wGtAuCAIGqASGF2rjoQgCJbAQOBBHvJngiD41b7S643c3gDPxA7+\nODzveJOscVH0XT2XBUUKCv8/e2ceFmW5///XbOwgAyiboIwCIooogoCWFhlqLh21TEuzUANSO9li\n6a+0OlqW2UkNcaFMyyWXytzQSE0DFBckEWF0UFYRWQWGZZbfHw/zwLiUled7eZb3dXEBwwzzzDNz\nP/d9fz7vZZCcw0lrqQpSsn/1QKyctWb2TiYDhlH9zzBLeUW0WrphECyfHowTZsafsnsIs2krXI7c\nfiYB8H3zPACdHyg08/9qMerFwpq33A5qFfjsm0aTTs7uqBXiCuRRmxZeLX4U+031+H5wgac/foWW\nwAaG5oxCZwOKBiOdl8vx2KWg6zdXb1mu3gu6qjzllJAgmuuI/FAmjhvSUFytwXfmcfwSinBcYou8\nixfaVR54L0xlTtcI3I5IyXtZhdvWC7illBFyxoCkbyBGZ0eRUOQXc5KklIcYNO0kuqgQNPP7MCLw\nIZH7vyftB86/mIDjhjQWFz5G9NgpGIuvIv3GGYNCgmWNMKKe1YxFOl+42OrVGnynnKa5gwTdNBvC\n+qrpOj8Np2xhX355UYSgcnNqIvPNBDPDzb+Me8hFNxqNe41Go1+rdHpR622JrYMbo9G40mg0BhqN\nxj5GozHcaDSmtntsvdFodDYajTU3/c/JRqOxt9FoDDIajaPb+SLeFvftAJcplUJ08Cut0cHxa/n2\n+0Gi4uhaGJx+exUKhQ4Q2FEBv0wWCzXLPTKIKRjE4bReALw++Elmx8zE983zqH58numhR4n/sK1q\n+1stp6K/q1h83R/Zqw5mDi4KiUxM/RytHiaQLlqkZPT7htcHP0mcUlgSRHsEczitF1UDK6mN8sN1\nRSqeGxXIR1Ugb4D6CTWUvdJI6UAJxuLfd1z5M9BFhUBYIN221CK1EoqaerWGslmRXPi7J0+v3UPz\n58C0cmGPL5XhkN9AhzwomBGApLkFTYMLl15T3HLBUQUVkz03iJSNSXQMKaNlux3zyoLo9Wk8PT+L\nJ9ojmOSSTDpa1iHNuojUwZ56dwmuGXVif18X34HSSPOl/1NTU7jwliMXPxculHbFzagmZZI+RVg+\nue60ZHj3SDH15i/jf2qy/zvoq6pY7pFB8s4N9H8rjo+uDONvY44xJ/wgAb9MFkPqbLd2IOjEROQK\nPX/rflaMLwpYE0+i1xHxfquObqboYQsyvglC88jnrPt5CCnzf7MA2Yb0LOa55IpkmPYmEgD+X8Sx\ny3c//icVonGk5jkvvqoNoOdn8RhSvLg0IRG5myuV/sLMF/ReJs0RAVjWGLlx1Z6mbEfkDRKkHQUp\nZfvq8r2APOUUpGdhPJNN/dBeaMeEUTonErtSPR8+tonNPTyQRhUKe3x7S8peHIBhURXOSWl0PlCD\n3smBok98BT16eJDZ9kAaVUjKxiQAbEdcweLpZk71leKS1YLWSyeKfc4l9MbQ0IDuahnWD1ynMMpO\ntIi+Fu7E9Bhzjf4X5yKQVFjgnJSGLNAfq4vXyEsM4ymvSOr3q5DXGzA0NJil3vxl/Iepye7bAQ6C\nYqv/W3G8OfdrpFECI21Z+lByBm4EhFkz9JVTZIVtJmfgRl5zSUdRK+y/ra8azXjpk+a8gm0h1KmE\nZfnh0R8TmX6XMsvwILKbhSLc8Q9W8fX+L5hdEirO5rnPrRLpsUNzRhHtEUzOjASWHY2m+6Mampa7\nM+CNOJq/tsArpY6Cbb1RP6ig47v5WNboUW3T8+a4HVgGVlM6vDPRY6cwp2sEuqgQll1OEwkkEy+U\niCSYO5k43g2svz+B9fcncF+WikNKHp8PHUz9fsHUIS+pPwXD7HBdkUrVps7IlEqKHu2A56or1LnL\nkPmq6PxPwb64frzQ+jOkeOF7eCrdtsYiC+jOntPJyAL9uTLeSKdU4T3wltu1tcHCgzjQZ73AL2it\n6jePqibh/IPiMTYND8XRoYGuu4UVmz47l5z3OiKvEf6f7TANOlupmWrwXuB/hg//hxiY+Con31sl\neolJowo5MvSfLL7uT8Qrglrs9HUvkbDy6NmpZL2awOnH/snpt1cx4I04kS5qu/04p99ehWbsauaV\nBXGhWYmzfb3Z87VXrrWHpEnPhea2llRcwUh+2hbK8Q9WMbx7JD67ZqAZu5ofvw1FGlUoton8Yk9w\n7ooH1c/dEPa84+tYtmU1TVoFhoYGsvb2QNpsZMDSDOyljTittqO6l0Fk7KVsTOKFC0+Lnu2be3iQ\n0e8bCudH/ql9ubyLl3ixqIiJoHZiOG+f+hHdlUJKLgrOKn4xJ3E+p6d6SgQzX9uB+rMuNDkZOf9p\nL2qCm7nwUkd+yuxJxQ1b+r1xmuSSTIqOeuHqVIuTryCTHRIzHfUUJzw9K0WG4YA34lDtfIG8pP5U\n+9ny9LDnzHjkpou0CZb7MnAamYflaQ2aJcIxax75HPv8tq6C7fbj99wy+X9L9P9DeC1KJeKVWJJL\nMkUrIW+5HfNcckn7OJHyxK7ov+zEgrdiGBIznYx+3/Bg3AyeDnqMEUMnUDlMK5oyJJdkEnr6Sbpt\njWWxaxaP2rSI7S0T2scEt4fxTDbj7GoJ+GUyIFhJnXspgRVVXZBYWqLapie9Uc/5FxMIOWNgnF0t\nMQWCQMLOQcuRkCS2FKZy4VMfXu0ZJfbQpTrYsPoTRncQ9uoGCwmW12Xi8R5oUFB2tu3CYupH35xY\n2j6t5U7QRYWQ85oH3VdoqIgRBq9dURMLVCEUzo9EUSPFYWsG9eMHoFdIuN7XyGcXh/Bd5CpcMwxc\nH9XIqD5nce5axYIh36F6tZpL49zE1cqmnhtwtNbiuzGO60EK1JNXYTtMI6bVKH+txXuPgemhR5G2\nGFHPtzbzU4vuHMKOOgfxd5lSibyLFw67JajmplERIwQRdkxMwzVDkLK+cjEbzabgPyW4uS3+GNHl\n3wL3n1y0HQwP9OW1L77ipTNPiVf3fu/GMeyFX3jNJR2lzIahOaNQxCrYe0QYrKYrumZJBOrJq+j5\nWTxei1IJOWMwSyMBQQXWPhW0IiZCpF/K3VzRXRX4BTKlkr3ZhyjQ1RF9PI5Ir3yRx36gQcGyJ54U\n9+e+h6eiHrKeiFdiqZ9QQ1bYZvG4ZIH+7D24VbATnhiO1kVKTOweEs4/iJ11E3VaSxRp9mapITKl\nkr9nHMNB2sjaa4MpeM1XtFr+PdwsgZX5qkTPs9I5kXgmZnJjRG+sy5qpCLRC0QDXIvV0/7oFrasF\nttuPo9kUjLVNE+6P51D6XQCdn7vKoCMl/Pxsf2p62GNX1ETBMCuczhmxm1bMwYAfGJQ1ltKcTtj6\n1OD+eI7Ib9dsEogqIJhJ6rRy/GJOCr9v6IffJ02i2aMJawuOMd1b6IRox4QxaKEgetE0uAgF2Fb5\n6L0IH7Tp6GXsMfbu5KJn1vwHyUUlEslliUTya6s87WTrbU4SieSgRCJRt35Xtrv/m60SuVyJRBL9\npw/u6BlymzzM9tyn317FYtcshi58hWiPYA4G/MDlJ914MG4GQScmMjhLy+7iUzw3/CeiJsfglSJc\n7dsP7vRGPTEFg+g1+oLZ81VEtum5ddfaAgz1VVViH72xQiCwPJX/MKPVw/i4e6A4uKMmx6Aesp55\nZUFcCxOWnS1GPUFL40kuyUSfLSjjJH0D6Rh7GZ2NEAvcpFVgKdfRWGtJ5+QKtGPCyEsM4/KiCPZm\nH+Lj7oE8nzSLovA6pEfPmJki/hZMg1vSN1BY1qs11E4MRxcVgmdiJsWbumC7/TgVgVZ0TEzDcUMa\nvQMKqAi0wiElT/A6K7RmTdBXqFcOEFhto3pwJMia/Xu+Rvd0JUVR1thrIPSVU1zKEYhDTTo5MVGH\ncB+bR3JJJtIWIbxhuF82+qoq9FVVyPNsWDCwjfeheeRzGt3MZazKX5yY7j1IXLlc7yXnVIiCg8U9\nqBpYCelZaMeE3bPwwXvMZLsv8EeW6A+1ytNMV617li6qd7K97e21E8PZHagU99Ht44kaXSTi0ixu\n0h5+XrUG98dzuNrsgEIi49CLkRQ9bCG6hZiQ3qjnn6VDqddbcOJSV7O/qTa1e+dukw/WYtRj5awl\n4pVY8jb60zS4rWcd8UosKRuTGPbY0wyxz2HCEGEWHjnuOdGsv3C+4FemnmJPuDIfgxwqV3TBdZ8F\nh3pvw3OfjGvhTgx77wj5o9ew8+ll4nObluXJJZlYXq37rdN5C4xnssXHO2xOR28hxdDQwJDOF6me\nEoHORoJMqaR+/ACedU9F0SBc1CRaGfb5sKkyHJ9vdRTPjSRu3g4AFl/3p7rWhqEjBdPH5R4ZjAw/\nLfb+s254MuOCmhGDx9LhcjOVWhuy5wYhtbHh8qIIfLZVsOj7ceIxBqyJ53DSWlHF1jQ8VBjEgF2J\njrUFx/BalMrIX6/T9FMbm7N0QvMfOhe/B4nBeFdf/y74K3vwe5Yu2v6KaKrMQltSp7ZFQXqjngFv\nxOG7MQ53mTVNTkbW13ai16fxbPrHcFqMenYXn2K5hxB/c3DrF4JyLDzIzHUz3ErGFp+fKPmwO93W\nmr9Rtm/fOWHp8cWvoZDIyBm4EbtpxTR3kDAjTyNaTE36f4L9+/49X7N41lRinVOF/m9rwWxQ1lh8\nki5RoKvjreE72f/WYOwLjTjkVLN1yVJ6rZ/JseWriYw7yRfnIvD/Io45XQXVnAnKX5zo9Wk8Vx9w\n+t0Qw5tROD9S1JJb7stA5qsit38LeoXQby6YEYD93l9Z46dC0WAQpbG6YdUczO/B5ZEKPJeksvKj\ncdSPH8A8l1zUQ9ZTr2sT2OT2b+FIkDXre33Jxc/9GWdXy94jOynvY0ndHjcsC6q4/HowDn0q0Gfn\nYu1fLR6fc7hgGGHaflhWCYGUTcNDMVhIBCJReBC7A5XiFkY7JowTDySIYpm/jP/APfjdDnAj8KNE\nIjnVThb3l9JFbz2S1vbHTTRReRcvioudCLeSMebVn1BPXoVCIsP7QDNTHa7x6nPbKRtoZFe9kqC1\ns/DZN00kT0R7BEN6FtJauWjO0POzeEYMncDPq9bg/IG5xLaj5Z1nRp2NkLyxo86BgwE/cO6lBMbZ\n1VLrL8z0s5RXRE354aS1eMvtSC7JZHZJqHh7Y2BnvOV22EsbKR4ixXFDGvrsXJZee4iWDgaiPYK5\nNM4N9ZD1dJ2fhnpDP2y3HxcGc3gQVQMrsagxCsvpr+4qDUqE16JU1FPsxdCC3PhObClMxXWPhv1v\nDUbf/wa5iT0IOWPAadYVfBY2o9r5Ah1X2NDZqRrbQuGjYleiY+mHCQx4I45uW2O5OkFJ1/lpYptw\nbcExAi2sqYlq4/a7L0sl880E9GoN1leNyL92oiImAtutHcT71B5wM/Nf17oKctWpn3xP6YRmgd+Q\nnkXZrEgqYiKQ9A2kqYOMiaOnY1D89Xx0E/5bl+iDWt0lhgMvSiSSB9v/8c+ki7aPD9ZXVaJefvt6\nhe5KIX4xJ5ldEsq6n4fQ/y3BA91ErFj/8hg0Y1czzq4W20Ij+cPXUTxXqCqbbIb85mdhfVXCtDdf\nRq5FLHSZloAm/JR5O8MNAe7LUrErbmbRsqfF21Q/Po9m7GqxH37DT0dMwSAe69dWdljukUGTs5Em\nnVxcwq/xU2FbKKV4riCbVD+ooMen5eLrNRUKfaecRr1yAE7bz4pbDRMf/HZbiJthGsz14wdQsK03\nUtdGCh+VIe/iRfeX08lo6sCVZ7tRPERK12cv4Tv1LBmzQzh3xYMWJxucu1bx+PIfkc5X0jm5Aknf\nQOpfrGGBKoTjH6zi0oREjBYK0RftlYvZjDw9HZ9903hQdRFoC2jMbtaiiwqhY2Ia1f5SnJPSzLLU\n18xcIRZK22NzDw/85pTivVCwr/r6lY/58q1lGM9kY3iygrwYu7suOt4V/htncKPRWNz6/RqCdUwY\nfzFd9Ob44If635lPXD9+AJfrnTk8+mMaXSSia6nv4akcTlor3s+uRKCtmphNPf4pPO0u9VG8xwst\nm6xX24z9ymPNl7n2bjd+8zzUvnyDjpltvfORPX9ldkkotaPqSG/Ukz96DaUxnmBpQfD78WLcsdSv\njox+3wCQ97Ilu4tP4XhJhzSiinq9Bc0RAWJ1uyImwsxCyXfm8VsslkwQ+8NLbr9cNxXZ7C4JNsaO\nDg34brhB2SOd8T8pmEQ29WkgNCyP3MQeYNAjPXoG780ypEfPEOGWz95QT9TxcuQJNRjPZOM0Mg+p\njQ3razsxYugE9GoNjuek5CWG8cKRZ6krcsDeuZ4k72P4bhS2SiZWnilYwuTUaupnAxTrlPR/q40G\nXNlDzow8DdoxYeiulonmF3O6RhBoYU3ndDuuFzmiGbv6Fgecv4L/uhlcIpHYSiQSe9PPwKMIErV7\nmi5aFKk1+729MaDD2XIuVyn5qUHFuZcSCPhlMsO7R0KhIDMNOjERgCZHmVjdrtI3iPxyhUQmFuii\nPYLF7YBJ6CDi5980xyCj3zcYFDIKdHU8FjGKfnZXSE7uj+vnVkw6Np2AXybz/YFNyDa0YIiqItDC\nGveAa2RFrhf/x9thuwn5eBalkTLcH8+hamCl+MEvj43A+YsT1D3oa2ZyaJqJb4bJUMH0/WYYHuhL\n9ZQIjGey0SyJwHGJLQ3etricruWHk32J9ghGNSmTqoGVdN4hp3SOsPy1utpAckkmyz0yKI0JRvPI\n53S0rBPcU9LtuLjOjzG2l9Fn55JckimQinpfJH/4OnL/lsBTqtPEFAzCNcPAjjoH3it+jPHrX6F+\nv0pIOVkUgdzNFfektv510oAQFA1t74fnklTW+KkoHC2Qj0a7ZdE0PBRdVAjRHsH8rOlOwJtqoj2C\n71kVHfivnMFdgWMSieQswkDdYzQa9yOkiw6VSCRq4JHW3zEajdmAKV10P3ebLnrTkrM4to2hpFdr\nsJTrWf/yGEYMHktW5Hpu7HQDL+GikBW2Gd/DU1E0GOhkeYOLB1SU6CUiAWReWRD+X8SJ+8TkolMk\nl2TisNVcR2yisd4OMqWSx/pFU/ZKI68XjmZP2g98dnEIByd/hM5WiqXGipyBG1FIZBRt9mFBzz0M\nnfAcFelu7KpXiqqzzT08sKgxCoNSKqN+/ABxxu6YmCZuVdqbHOpspUJrTGq+1/y9Qpv06Bmcf7iA\nzFeFam4aWlcLDAoJxjPZeHa9jnrlADRLhAFX2UOO62OFKCcV8eCXJ5lXFoTqx+fpmCmQVy7P98ei\nRkrGVS8U2TYoZTaiLnzYY0+LOXIKiYwjQdZceicA2+3HSSwczBafn2h008G6joLfm1zwsm+/Mtmb\nfQjl/jZ2nomKK9EKr3n52Yew3JeBPOUU9eMHYCizYm/2IbPElb8M438hVbXVNK5P61dgO9nbPU8X\nbW/a53HITCVHndaSDz5LpOgjS041Cb1Wk5JL9ePzzO5ziKJxOpZ7ZNDormdO1wgkrW3tzafDWPzE\n17z80H4KtglmDgFr4pF3cjF7jt+y4DX5oGWFbRY/zBn9vsFbbsex5avJmZEgyk+1UXWs8VOR9PUK\ncmYksMZPhXrIenbUObClMBWdjSB+l/TpQZ27DImlhfC4MWH4zjxOrbe5TN92+3HkKacYnFlHeWyE\nmE92LfL3r5sN4d2p7dOR0jmR1LnLsN1+nPLYCBwmVmG01uOUDQYfLd47ryKNKkQ+qoL9bw3m50UR\n2GRbIT16hu7vnKWhkwKZFqb7pmJfKExhl+f7021rLJeechCzyeaVBVGwMJLq7gosj7hxMOAHoj2C\nkdfIaHARPm6eh4WtlMjACw/C9/BUM685U35Zj0/LhXimdqGLDil5rBqVRNDSePxiT9ziCvtn8d/e\nB/+XY8WEdeIsVdfNXDro/cSvzFw0U2SGmfa0AB+Fb2fX1SAUhYIMUjN2NcklmZx/MQHNkgj8Yk7y\nsPVVthaG8I8+3+P/RRwyLdjvaDGzIb7TXtYE+x0tIl3VtC0w+YUN7x5JuJWMKn2DmOxhikqS9A3E\nZ9cM3lvxDE8Pe078f8Yz2biuSBUZc9Y/nGJ38SlcV6SytuAYg7O0Zu4pR4Ks6ZiYJgY2+MX+fiW9\n/sUabLcfx31ZKtbXDSCV0TExjdrNStx+lOPzQi6qZ36ltk9HZEolhoYG7I9fobqbjHq/ZvISw7iw\noicGhYQuWwrZWhhCrUq4QEnnXePShET07k086HqRrvPTOBWiwOfTHOojGtDqFLQY9RTOj0Q1N00s\nEFruEyixdv1byUTpWagmZZq1SN3/KVxsa/t0JNojGEnfQMHuamI46s+68O4bz+O+LJWChZFcerXH\n756Hu4bReHdf/ya4rwb4rK3TqNwl2DHd3C4DqAjVM1o9jHArmRgy0PMzIcig9IAX/oPyAfDdGEd6\no571tZ2Qa4UP46jsZzgWtJPRtlXYa6Der5lzu3qYUSMVtRJz/7GblsQZJ/zIGbiR4d0jyQrbzFP5\nD1P0kSUtRj2rLxwAEDPLAdHgcc62bwhYUYNljZH8hRa4rhCMB5NhGO5yAAAgAElEQVRLMmkaHirO\nZMqjHRjpGULtxHCiP3+dI0HWHP/2z/V45W6uSG1sxKIYtOafG/RI+gZiO0yDU2ox53f0oODtATjk\nVIszqNHeFs8lqSKNtOe7ZTg8W0Tz51B21pVGr2aRRTh0wnM4O9Wxe+Mg3tGcYpnmGHuzD6Eesp7R\nbln0/Gomjf6NwhYjPIjaieHUTgzHISVP3DuXzYpEsyTC/D1v3bLZbj+OLioE49kLjAh8CIf8BlST\nMsX72hYaaenwv+iiO+G+GuBd56dRUdk2YxXOb6uyls2KxH91Pe93+VaIDO5UTnlsBOdfTOCxiFFI\nW6Bgu4r0Rj3qyavwkGtZ/c5YfLYJFldO1sJ+r8c3L+IxJZ8es86zbPpas2Ke16JUZO3Th26qC3R/\nOZ3F1/3Zd1GoAlcNrETbYMlIzxBxtnZ+oojsZi3+X8TR6XgtVfoGPu4eyKVJTjg8W4TtfqE/7nix\nhSEx07E9U4DrceFJL1W5oIsKoamDBAeN8Clqz5UH0Gy6VT11O0cT3dUyJJ5ulMcK7ieXF0VQu0Lg\noJcsMFA4P5L8Z7xwX5aK98JUJHUNAj9+TBg5bzghtbFhZHYVfrEn0F0ppGWpG9KoQlRz08gbtlpk\n8FXPraf2rDN1wY2EWEKghTUjAh8ivVHP7kAlqrlpOB2xpMVOhiy3EIfN6ThsTidnaTfx/XX97Ljw\nf9sRktq/9yUPWFK604+92YcoCzVf2SknFeE7++Qtr/9P4b+Y6PJ/hvbssk6n20ab64pUShYYCLSw\nRqHQsct3v5jhpbtSSNarCfR46gILVCEE/DKZoRtfE0wDE2oo2Naby98LJIpLExJ51l0In3/UpoXi\nTV3Mnt81Jl/8+ZaIn/AgnnFsyxA3pHihHrJe3BP3+jSei2p35nSNoMVLEE7cMAqzS+5zq5DOV9Jx\n81kGzX6ByMXH+eCzRLrtrqR0kBz1hn5Yr3Wk6GELqnsZUEwqu61Kqvu0vFtu/7j77ey0AQsFHRMF\ns4Su89Oo2+OG+7JU0WbZotUbsXZiOM3eLjhsTqe8rxxaBCrrjlcfBQQlWrcFOWg2BdM53Q6/H+IY\nrR4GCFslk9f8SK8wgpbG0xDenYkH4ijY1pvK3X6cfG8V9pmltPTqCggXGb+YkyJ9Vu4lpMTaZ1vc\n9mU45hpwfzyHaI9gM/96gNovO5NcdOq2j/szuB+ii1r/dov+o/X2O2pAbof7YoCbikyAmOkN3BIG\naLu1A9Fjp2C901G8baSXUEUNOjGRjV0PYkjxwnvCeRS1Et45/Di/5njTWGuJ+7JUhuaM4qn8h/kg\nb5hYgW7KdjR7jpxiN/HnW/rP6VkMOTybp/IfpkBXJ0pR9747hHllQbhktWB1VSiQWeVaod7Qj0nn\np1CwMJL+b8WhmS3F4ycpx5avpk5vyYVmd5KT+9N1fhq+U05T6y1nUNSvTHvwMLbD2vriZufKpy0M\n4fcyxvQ5AtmEkmsYUrywvm4gLzGMwvmRaFXNuK4QVHbK/bnITwkVbI+jTXRKFar7VlcbkCmVlPex\nZIRTFnKFntIYT/JHr2GX7356fRpPv3fjqNI3QK2C5KJTWNQYKZioF+sDi3p8K5BcrhQiPXoGSd9A\ngWrajl7a57srqH583kxFJw5+N1d+XvqZeFFzX5YqMt7KYyNw3JB2Xxo+/JXoona4Wf8Bd9CA3An3\nxQA3NrUJBlz33f4qDuB0KJ/knRvEIk96o57i1wZQOzGcrLDNjLjwONKoQnYXnuCdmK+EiKHW/iyA\ns1U9tVMdxb3fjjoH3hy3w+w5un10a0Joe2ge+ZwtPj8Ro55IdrOWfu/GUdFLxrGF4RQPkeP7kIbL\ni4Stg++U0zTp5HgvTGXngo/Q64Q2U0zBIJZ7ZDDV4Rpdf6hH3sULma+KzDcTmOBynHkuuWiWRAj7\n25vSQ9obPdyJAHMz8l8KQBpViHJ/LlZX5XgtSsWq0ILaieFkVHYh531fFPscyEsMozTcklqVhNpn\nhOndtC9/4+RYJJn21HXvwFP5DwNC3G9VsJ6ProdjUyij29ZYOv1chmOqpdCG/MGOf44Zy+hvXxaK\nmVIZH3+7DpmvyiyG6uey7thl3j5J5cJcH0ZceFzgPZjOQSspSGT13VMu+j0rst2r6KKbcScNyG1x\nXwzwZs82NZnD5vQ2TvJNRS7d1TKiPYKx638d38NTWaAKwemCDuX+XIZOeE6cUUv1WsbZ1ZLdrGWL\nz0/iFb5qYCV7j+xEOyaM995exxo/Ffsrepkx2qTNOvODu+kYQk8/SdDSePb2+I7ZMTPRuknw3l+H\nfWYpqjdPsMt3P5YVEsGqeVtvrhc5Ujw3knFZz+N0xBJtgyVF4XVts056FuffdOfag64Mmv0CL554\nGt+Ncajmpv0uBfP3AglMNQSfT3OQ2thw4VMfvBemUrCtN10/zMRhczryURX0fL+UsiQf/GJPYFQI\nTDOHrxy49JQDskB/5K0ElJbABn5etYYzh/0ZMXgsqp1NPBx8ngMrB9LgpceoMFI0yo3Tb68SCCjb\nz/LRnvW4B1xDVilcMEb9/CJ6tcbMvLI0pxM661sPH8D9FyPSqEKM/j68oxGW4rUTw8XugtTGBlnF\nH1PX/Rb+QJHtXx1ddDv9B9xZA3Jb3BcDvJdzudnvRaNal8kG/S0srsrdfmT0+4aUQSsBeOmjLeS+\n7Y/06BmC3483a0/NjpnJgQaFyApLLsmk29ZYhr13hB4WVRRs681bnnswKNq2BfrsXLO+qiygu9nz\nO43Mw31ZKgqJjEVr1zB5bArJOzdw/m1XdheeoN+7cWS9msDlRRHkDNxI/ug1NAQ2sqLnZiLjTuK5\nUSFQbxdFiINctU2Pc1IaFb1kqCZl4p4qVLrlXQTGb/ukkPa4eXZvj/rxA8THG5uauLigD75TTiPz\nVeH9xK9IOzrTNDyU+qG9KB3eGetyHaVzIvFemIp2TBi2RVpUcwUxTMfENIb7ZSPNtyb09JPkPreK\nhu7OSI+e4afMnnR/Ppdeva8Q8I/LZL2awKCsseiiQjA0NDAx83nKzroKrEKDHt8ppwHQurSec6kM\n/4RrtDjcfla03SlsP41nslmgEkIaHDanc/7TXqhXDhBCDj+yvO1j/xTuvsj2r44u+k39B9ydBuS+\nGODnSzua/W7qgQJcDzKvqlRcVgoZ1MfjKP0ugDV+KlRBAuf8yekpRE2OEVNBHl/+I28snSY4mnbx\nwndjHCfHL8PDogpvuR05AzfyfW0wWjfzc7Q3+5D4883eZ/IuXhhSvFhf24m3np3GPBchz7t71zIx\nTinaIxiDHAbNfoH1tZ2wy7Qi3EpGbv8WDietZcI7++m2vky88MhTTqFeOYChI4WaQ+mEZhq8bdE8\nJzzXGj8VZbMiKdjW+67smaC1vXSlELmbK/VDe9FtS61gx6TW4H9SgdHOhoLHpDS4yKgKbRFYYg1G\nZL4qGlxklEbaUbnbj7yk/ryjOcWeoyH4D8rnepEjQUsF7bZ6pdC3/tBrF5erlLh9V0/w+/HYDtNQ\nEiv00LPCNmPtX4165QCqp0Sg/MWJ2onhWJhowgY91x50xelOUoTWVYhptWIiAV0f1YidRka0RzCe\nk/5U8OYtuJ+ii+6g/4A7a0Bui/tigMuazM9YwdtthIcmJ/O/eRwWZuLGCmvq8zuQXJKJs1U9ySWZ\n7Pz0YeQpp8gfvYahOaPYWhjC6bdXCTPKlUK8DzSjlNmwuYcHw7tHkt2sZZ5LrlniKMCQmDv7bOuu\nFCKNKmRzDw/R3D+kSwHSqEKSSzJJeH0lySWZqCevorqbjKkO16j3MuCzbxogDP5ZyisEf3ORwH6X\nAeHD6/qLhEvj3CieG0lIlwJqveVEDstitFsWySWZfPjSWryf+BXvhanookJEgs6dlunKX4SM75pB\nXbH+/gTGM9nY7xVigPOm+1HXvQOuv0jocLkZz30yymZFolQ3c/lJN6yeKEPaIrAHPT0reeNFweCy\naLMPHbLlZL2awOySUDofNCLRCVpt260dSPI+RsfHC9GOCaOpzEYU77g/noPvzOM4bkij/G0fHDan\nCzlprXBOSjPrXpig3tDP7D4A7klCRpvLD1bYlgrVrmmZv5nec/cw3p3Zw10aPvzp6KLf0H/AnTUg\nt8V9McANnfRmH9TOKW3Ck5uv7LZFWlQ/Ps/px/5J190t+G6MY4vPTwSdmMjJ91aJs2LFts70cynk\nwbgZHAvaiWZJBPKUU/T6NJ6m4aHc2OlGoIWw5FT9+LzZPvzm6v3NqJ4i0EWDlgqKMZPs1JQR3m1r\nLL4b45BGVBHwy2S6v5yOX8xJkksyxcDA77YP4vL3KpZdTrtFOplxwo+OmVouvRPA7kBhu9C+FZay\nMUkk6NzcNhKPcaY7mk3BAiFEKkO9cgDawcL/aHGywu5nNS02EpqUcjocu4zntkvIU06hVTVjO0yD\n64pUHH6ww3aYBst9GfivqxJ45FFVZDdrWe6Rwc+r1hDwj8s8lf8wLa30W2lUIU0dZGwakUDHFTZC\nQa61jqFeOQB5yil0USEUDheOs3huJIYUL5oG3xr4YFrOt4ehoQGjpQyDQoLu6Uq0Y8J449unb7nf\nn8Y96oMbjUYdYIouygG+MUUXmeKLEKKLzrXqPJbTFl10J/0H3EEDcifcFwNccl2G649t0UFNTm2f\nWpN5g3jfJj2HhyynRC8h6pNjqCcLBZ2eHcsY3j0S20Ip6Y16DAoJuf1bsC5rZH1tJ1TfCjLPcy8l\nUBmgYHUPwUcto983SCosuPGguZqt/exxc6HtWqSe/jYa6r2Evjy0WS73fyuOSxMSsS6T8JTqNN5P\n/CpSLKM9gtF1dcVn1ww6nW6h57gL4uNLvwug2duFer9mur+cjvToGYLeE1pDPT+LN3v+9m0hsZJ8\nE4xnsvGb01qLMejx3mPA5mIFcjdX5CmnuPRqD26ooGQIaGK7cWNAF2SB/tjmWaBeKdQIakfViSuF\nqw84IfNVEepWyJyuwsXwQIOCD9O/I2+jPx5T8oXC4sJIKodpCbeSIZ13jXO7eojsRKtSGYXzI6ny\ntRDbaJ5LUsXi6N1AsyRCMFocpkV71AVD3PU7qun+DO4lk+3PRhfdSf/R+rc7akBuh/tigPt7l3P+\n7bZioHVZ4x3vK71cQuzwGFZff5D+Nq3FJ6mMswd7cGFZL3b//UPCrWQkvryCvKT+JO/cQA+LUmZs\naKtfWD58ndkxM0X98aUJiaJwxQSfL9sKbxXPmSuW/FfX8/EzE7k0IRGfXTMonhvJay7pJJdkUhOl\nRbXzBZxyWvhmbRSWRwRr4bSPE4VebnoW8hoZpYPkQoV/7BQA3MfmCb3oFimaJYJnecbHwp7+vWe/\nEvfe7QU5fwRNjjLKP5FzbV0HQs4YkPrV0XV+Gq6/SFDUCue8ZXkD1g9cx6pUhqxBQmOtJfqltRhS\nvKjxN6JXazh0MlAMhcxt8mBO1wjmz/maXb77GZldhffCVJ7rlUbU5BikUYV477zKFB+hpuK1KBWv\nRanYXG9rJBcsjCRgTbxYIQdzFlt76KJC2iSykzJxvKTnUO9tosHHX4YRMBjv7uvfBPfFAJcgocv2\ndvGirXnVt4O+phZ9di6HN4WyeNZUwcrHoCdnRgISnYShG18DYIEqROx/h1vJyG0UUlejPYK5XuRI\n/KptZvrjaI9gM+mhPOWUOHvdXK02nsnGoJCxvrYT+aPXcO6lBIYuFDjo7lsteGpgGvUv1iBtMTLP\naw/VUyLo926c6PTStX8R3dYLApPknRuYeKGE5KJTXFznx/TII3ge1uGwOR2nQ/nkJfXnE80j5MxI\nYHCWVghBCA8yX2HcASYRC4BTajF1WksqKu04sHIgZNtTPSUCx++zBPJIrlBbcPjEHq9FqUh1ggll\n03J3ZK86YH1VytqCY2jGrsZn3zT6eBQzS3mFgoWRfD50MCOGTmCW8grKX5yY55LL9vUrhAuahYLv\nZj8iroI6p9uZcc69F6aSMyOBXTVt73d73/f2CS4WaW0JovIuXthuP85Iz5A7VuD/FP5HVb33OFfR\nkQ2rPzG7rTy4rTHa3qvLVFXNejUB2zMF5MxIYGR2FcMee5qOJ6R8OXElj/WLxpDiRfD7QvBd6Okn\nmefSVg3fPexTli6YxCeLPhNvszzihtsR89OxcMcG7oSLTyvMkkZPvidsFRznFLDYNQt3e6HvG24l\nw3FDGuPjf+L8wi6MzK7iac/jyNcJJJUddQ4s+TWafu/GoZqUyfdLH8ZyXwaaJRFcW9eBpQ98w4Ou\nAiPNpCJr7xRreaSNeXc7mCrduiuFKNLsMTbI6bTzAj5fX8Uhv5HLrwcjUyq5NrYHmk3BSJsNVMRE\n0GVLIfL6Fqy/P0HuTGsaAhuZ7j2I/m/F4be2meddj9JtayyK4Co+PPINew9uxf+LOJ7oeJJoj2CU\nMht8D09l+DfpwsA06CmbFUnGN0FmTi5bClOJKRjEri2Dbnv8dyL26K4Uot7QD7mbK5YVkts99E/h\nP01scl8EH3j3cjCqD7qIbSaAkdlVYoHpFoQHURhlh23EdTL6fUP02CloXa2wrGzh4NYv8D08FXme\nDc0dDKh2NnFw6xcsvu7fNkBAiNIdFcKw945wJEiwAKrXW5j5tBUsjBTthW6GKcQgaGk8C2K/4lR9\nVzQNLuRt9MeuVE95XzmOuQZGvXmIY4M92Jt9iBFDJ1Ab4AjTytnUcwPecjvW13Zicw8PaieGY1fU\nhKyuWSygSfoGYjyTTXJJJr4b48R6g3ZMGNbf353pYkVMBDV+4JJppKmDhI6JaYKzyilXMRIJBDGP\n27EaShYYcP9AweA1x9n0dRRdvryEsanZTKttecSNpofKwaCncrcfTiPzxIwwE6U02iOY+v0q6va4\n4Xy+CXnKKTHEIC+pv6hUK9jWmyat4rYFtZtRsDASnbURRa0Er0VCN6GmqwUdN5/lQP2GvxxEYN+h\ns7F/+Ky7uu/hA2/85wQf/KvRSdZsFhQIsG9Q26xtqjyLSM+ig8aA9qiLkLFtq8BxTgHeH6kJWhqP\nesh6vBemcmlCInWdLRmtHkad3tKsh9w51RqDQkJSykMA/KzpTsYJP7O9oPfCVJEscguaBW24145C\nxtnVstg1S1CXRdVh/f0JdNZGti5ZypEga7GvPvybdGqfqeVY0E6+qg5hSMx0Vn40juSSTCp7SdDO\nr8F4VghjyEvqz/V+QpRPtEcwqrltnOu7HdwAnX4uQ/VtPQ75DdS3vhSHWdDSwUDIGQNlsyKRu7ni\n8VUO1/s5CDz/9Cy2rI/CPVVLfkw3WrbbiYQjWWCrH3yrK06QSwmaJW2kHdN3mVJJRbobmW8mIE8R\n7msKMfDc1/ZeO2+1wXfK6Tvuu0HYe9dODMd7YSqquWniEr4ktpmT7626a8ru7+J/arJ/DUp1Vma5\nVAAOu9uWXe33kiYos6qQNwhtmaKHLSi94cChPD+6jtHg/0Wc2Ace9eYhNHtVhNhexvpq2ztTFF7H\nseWruTRBKH65b7Xg0oREFkx6zux5dO7tVhHtqul6tQbnrTYM/CGP4PfjCVoaT/WUCOb2ThbCCSol\nRB2bKc5sBbo6ZimvcCQkicXX/bGXNXI4aS1WT5The3gqLV5NgsDEoEe9oR/5w9fhcrr2T59TU/pJ\ni1sHYUmfnkX3NUXisbv+ImHr4Ujx/F4bK1TVq/2FNqD7slSq59bTNSGHpuXuWO7LYGR2lbhk9j08\nlcXX/UnyPiZKPYvnChr3eWVBfJ21h0avZnp9KnQATNbRzklp6NsxB223H6dgW+9b8tYAkVEoTzll\n1kY0QZJpb7Zq+KsQiC7Gu/r6d8F9McArquzFBFETMk74mYXTtXdbkdrYoM/OJfNNwT31zXE7WN/r\nSzSPfE7T4KvYFsLZEk/BQsgll6MzlxJqVcLsv7cJS8pjhVlnRVUXZpeE8vOqNYxWDyNv6k2ih/bJ\nKDfpw+vcZex9dwjNDkJNYP+ij5nqcI3g9+M591KCWJk30Wd99k1DKbNhnksuXy0Zgc++aRwL2ol6\nyHrBInlDPyyPuKF55HN6fhbP9X4O4h77j36I5SmnqJ4SYcZnN5lQgsD590+4huuKVDRLIqgc3IR9\na1PCaftZNJuC6aa8jt7fS+xqzFJewfBAXzRLIjg/OImj4c4Evx/PsstpSGvl6PvfINojmFN9pWQ0\ndWD14C9FPfsNP4HjX/pdAI7fm6fNKNLsb/sa9FVVVE+JEAttMl8VUhsbUTduDL5BckmmmRDlL8Nw\nl1//JrgvBnivjuViMcgEz8MG7M635YPpOrQNLkNDAzJfFRGvxNI0PJTP5z5OoIU1VfoGNJuCOf32\nKrosNogMtYymDoz+6HWzopjNdQPJJZnMUl5huUcGg2a/QNPgq7cU2n4Plf4yzr8oXGhCDs0kvVEv\nXngONCjw2TeNQVljifYIJn/4OtIb9RTo6nDIb2RiP2GpbZrlcqPWotmrItojGK9FqRgU8GuOt7gK\neEdz6vcFJgDhQeiiQnDckIZmSdsAMclLi+dGUjg/EveNgowUBFKJzXU9Ur86DA0NeCfJyF/tj9bV\niosTbPA/qcD/izgU5y7j94lwJVDsc+DrVz7mtcemIm2W4GwvMAqrp0TwqE0LPSyEfXv9fhUBKwSP\nvboih1uW1Hci6wBIW4wUjBT82fRqDZXj++AXc1LYj7fI8N0Yx4iMOyfS/FH8p83g90WRzaaTl7Hf\nwNnme8vwICyXXLstw6k9dhefYqRXGCGnBJnnYtcs0hv1hFvJxNZX/ug1xBQM4sz63iIxZNnlNOZ0\njRAHj0ypJOd9X/G+ReFtCqX2RaH68QPM2jzyLl68lLKfV8+Nx/3xHJZdTuPFuNmUDpJj8NEyu88h\nSps7sPtKIFlhm1lR1YWthSFU3LClqcwGj8NQPFwPLVL8Yk8gtbFB4uNF7jQllteleD4stK/a47eK\nf3dC4fxIGv0bcciw4obKgH/CNRJTNvBOyTCC7QtZq47Ec9IVmiMCKOtviX2BAYfN6YzMriIp8TGa\nHUAeWkVW2Gaq9A1Epr9AY60lAa9eQv1ZFyzP2tDgpceqVEaLg5GQQbkU3XDE7k1rjGeyxWKcqXBo\nQnJJplhoBMQQBRNqJ4aLy/PqKYL+2/TdlJ6qWRKB5vVX/nLRy8G+szG0/4t3dd+fDs/7tyiyyX//\nLv96SFuMpCSsYuT37VIz07OwkTvR1PrrzQNLamOD1MGeoNSpNC23QfZMNZKKalpOnWGBKoQZeRoM\nD/RlQO+L+OybhotrrRnra9Tuv6MpWU1MwSDq93eittGS/DCBw34w4AeiaWOLde/arp886wpN29sO\ns3i0FzcM1tQVOVA6J5KJmQFktYYxDO8eyayLV2gx6oVE1JxRXC5zxnWnJd5FWtTxLVjHlZPfyuTq\nlR9Ply8v0ehmR/eX05H0DUT/swsXP/Hk0oREsYD1RwZ39RSBkWb5M3TYZ4HD5lQsp0SgnuYqSFhH\n5lGqVCF9Rknuhw54dC+n4Zoj8gYrylYO4JNDYAxupkOmBa6P5xD03UTqihzwnSlEC+PRCb1OiktW\nC7ZbrlIT6kHFhAaqBlZyqPggI88I76nTyDzUKwcQ8I/LtBfkDomZjqzZgByhuOl4znwF5bA5nbyk\n/sitdagmpYmfF8MDfTmctJaIV2JR1N6rNtm/V7Dg3eBu44MdJRLJdolEckEikeRIJJKIexkfbFBI\nqDM0Ub/ffB8+0PGS+PPNJoyGhgZyPvQkZ+BGNGNXo55vjWZFJxQSGeWxEWwr78/lOCNPdDxJ/vB1\nVKqdzB6vGbua2SWhFNQrORa0k/r8DgSsiee1LvsJOjHRLC1DOr+t0Fa9zNvs/7iuSOX/nR1D54NG\ntP0bcP1Y2MOPVg9jU+6PDHgjDoVECEuQRhVim2bDseWrGbzmON6bZQR0uMrsklB8D0+lyclI2WMq\n5PXCauTaAAe082twOSNB9ePzbcfzO04u5bERYkHQcUMa3k/8SrMDlA1vpnZiONv+8RG+68pwebII\nXVQI6jd6UN/ZiOV1GQ4TqwR3mdBGfGcex/UXCT3fLUNn08YwC+urZkthKuoh6ylfYqTzDjmHk9ay\nJ+0Hji1fjfcTv7K24BgjPUPEOkr9fhW+M49jv8PcUOPgukTmrt5AXmKYkMk+rJqb4RdzEuuT7YIw\nNgtUXt+NcThsTqfR/ffto+8a/6Wuqp8C+41GYw+gDwJ5/p7FB3fqVMVPWjeOBZlnU32xYsRvHpTH\nLgXzyoJoMeoJ8LxKzsCNFOjqUKqbufi5P+oh6ylpUVKlb0Dq2mhG84z2CObg7lCqtdYEvx/PhSc/\nY+jIDB61aaGrsso8LSM9i9I5wofb+vsTZlbLAE1aBXY/q3ki4DTlwdbMKwuiafBVntWM5drgFhZf\n98dbbocuKoTMNxNYUdWFeS65XOun4NVOh8jt34J6yHrs86FTeiWFUcJ9tW4SKm7YYlmj59RDK1H+\n4iT4qof481vomJgmxBDZCOEE6pUD0NsY8Z1ymg4XbhD3wET0ag1Gfx/kKafo/s5ZOuTB38Yc48Kn\nPuQlhmGXaUXndDuuhUFNqAeeS1KRh1ahy1ByvtyVp7wixVSRn1etIfj9eAJ+mcxT+Q+LLTFDihd2\nF2uQ2thQeVQoFt6cB6eQyHjUpkUwd7xaJvrFgXm0kckn39Qd0SyJEGir4UG/6Wf/h2D8Lww+kEgk\nHYAHgSQAo9HYbDQaq7mH8cEuMh3vL3ma7GZzwUfHxDQz+mj7qjoIs3pGZRdGXHicXb77ifYI5vXC\n0ZTPamDmazvo924c2+ZFE3JoJp4bFbR4NZn9D++FqazouRnPbZcI+XgWuf1biPYIxkZ+a+b0gtiv\nxJ9LHupg9jefLyXURvmx2DWLzDcT+PZiH8ESefBV5NcV7H9rMKGnnyRlYxKqnS+wO1BJz8/iiRiV\nRVzYODSbgon2CCbh9ZXos3NxytWT/6wR74WpeD/xKwaFhBejOwsAACAASURBVLCj8Zy64s1TXpF3\nFbZXsK031WOCmN7pCL5faTH4aJH5qpCVXqd4tBfqDf0wnslG+YsT+y6mkvbuSrYejsR3ymm89kG9\nl4FzCb3x3XADh7NCEbQlU8n5FxOEQlmKF03DQ0kuyWTQ7BdwXSEc6zDnc0R3FpblhsWd0GfnYmho\n4PHxx4BbXWFnl4Te0VOtY6ZwzKb4pdqJ4VQNrESzKRjfDcKFQpZbeNvH/mn8h83gd7MH9wHKgS8k\nEkkf4BTwEr8dH9y+aXlbq5pWG5oZABadHDg0fyWBFtaUx0aY7ZXt89oOsbKHHM+b1K+aLE+kzRJ6\n/hRP18AKzpZY4NupnKVfjKc+WEeNvxTNI58zu2cohz0yCHV6kvJneuP9hKCNvtDszp7TyVTpG+jv\nNQeDg458n3Vme3CA19LH0+0BI9KjZ/DaUWi2j7RIy0GzrBdDc0ahXeWB9/bjRBOM/0kFl5OheIiU\nS/2+IejERDRjVzO69zC2dflYUJK1ErhUK1/grWf7ot1fg3SVMNtqNgWLiR7tkz3uBk1aBQ6b04nv\nMJOO6Wmo0qHlgb5I1RqanLvh7FQn2CR/aKC/iz/OX5zAkCAQX0Y8f4zTz/dGWqdFr9awtx1LzWff\nNAb0VXP2YA9cEVYndpfqkCqVBP9UwVSHa9z41Zp/HhhuFr+07cBAuvWtNXsd2jFhLPdYc8u5NkF6\n9Ax6wLHVg63aX0rPdDsIz+Ty/Ejivqniy2URKCcVwcN/6PTcGf8+Y/eucDdLdDnQD1hlNBr7AvXc\n5OT4Z+KD26eLSiUOrL02mBajHulNnofuqW3V7C5bbr1anxy/DPXkVUJbyUJOU5kNFw+oxP6rRY2U\nfu/GkfleX0YMHkunaTWoZrW1y1Z+NE7sT58cv0wUqCSXZJoJHfw+aRJnzvb9ZBCcTh8OPo9hcSeO\nLV+NZolQnb/WZM+XE1eyapQQdez+eA49P4tnl+9+Ru3+OwCqnS/guzGOH0b+k0vTJdgO04jdBJ1W\nTvWUCKq7CTuc9r7hv4eA14XWkdZNwsQLJZTHCj3xslmRNLno6TSthquDDZRGynBOSkNqZYnVVTm1\n/no2nw5Dv7QW9TThmh38fjyaJRGEnn4Se+d6tvj8hEwr5KBv0QgrgZJnAti/eiDRHsHEOmq4NCHR\n7HhUc9Mwnsk2s8NS1Olvmb3vFLQo6RuI98JUjn8bxMjsKozBN/iluptw7FH3bhaXGAx39fXvgrsZ\n4EVAkdFoNG10tiMM+L8UH9weFpVNFNQrUUhkPDtnr9nflB+36cRvHliEB9Fvz9/Fdph+aS2+M49z\n/sUEymZFMj3yCDkzEqiObMLuZzV6tYZmXw9eOvaTSEl1Tkrj4UCBHqqU2RDwy2QKdMJFxRRTDIKC\nrP3ysj19Vp+dS2qhD10XCSwvk6Rxi89PLC58jI+7BxLtEUx5bASN7nr8v4jDrnMt0R7BJA77HLlW\nwviMGUgqLNhdfErgyY8JQ1orxzUmH62b8IHKH77OLHX0t6C7Wkblbj9SY5bydfEA7Er1aJZE4Loi\nFcvrMozOjgSsqMF3XRnFcyMpm9KHPkOF8xCw7AYWTzejmpuGLioE1xWpuPYpo7rWBvcPFDzWL5ou\nX16i16fxZIVtRr1yAD2eusCNB7Usu5xGnaHJbODKAv1FSaexqUm8vehhi1uowJb7MkAqE/fa7c9/\neWwE1g9c56slI/B+4ldxP/9HLny/CSP/fUQXo9F4FSiUSCSm6SwKITn0nsUH2/k2U/6d8EaHWpsb\nDFYNrLxj1bg00o780WuEok7kEQ4G/CAMEIS0jCNB1gyd8ByaRz7n6oQeJJdksnfLOv45ZizFOqU4\nYAvqlaKlUs7AjUz3HoRq5wsMyhpr9nwBnm09+fb0WZlSifNWGzETuyImgh11DoLBQ7ywX9eOCeP0\n26vQjF2Nd3gRnV9ronO6HS99NV1wOP3BjksTEjnVBBj0FA4Ho8LIxQNtnnMD3ohjSMS5O85yJpTH\nRiDpG0jHyeVEJr2KNKoQm4J6uvYvQu7mikFhRJ+dS/kSI4N2ZON6somqYD1Fn/jiO/M4Vx9wwn5H\nC/XjBzD5sx+onRhOw3euqCZlCl5tD/mw53QyqhEafHbNwGit59yuHtj/bM2crhE85RUpup7KlEoo\nuYb3biFh5uKCPuJxdp2fxo1g91tfgEFP1cBKiudGUj0lAv+TAhPGdcNZnN9S4JDfiLyLF8VzI2ka\nHoqL65+n9LaHhLsjufw7EV3utoo+C/haIpFkAcHAYu5hfLABCSFPZ/FU/sMCF7ydz3XZrMg7ignc\nl6WKM4WJNplYrRKoi620UunRM6h2voDDFR0HGhT4p0wnd5qSL0sjeSJA2ABLh5aIS/NBWWO5+Ek4\nvjOPi+EDJmSf7nrb49BXVYnLaPXkVfSZ/iuJMeNQrxxAwUhn8hLDKH9Gy7yyIPF4S4a5cWZ9bzo/\nUMgu9VHRuWbSMcEPrsv3RmwKZYI2O6qQ0jmRHP9gFdM7HaHyhd+2Ce6YmEZejB2XXu0hRjcZz2RT\ndNQL3bXr+GwXBoTTyDzq9JaUhluizJRRMkRowTlc0XFjnIKXF29m44ujcNicjl2pnuSSTFoc4Opg\nA1GTY9BPMApBDy1SPJekUuNvRN7Fi+SSTIrC61CvHEDRcwFIHOzQZ+ei2RTMB3/7WjzOi5+EY/39\niVuil0xdCs8lqThuSCM5uT/JJZlcXNCHS68pKH7QmspITzyP1GG5L4PrZeY6hr+E/7Ai233BZHOw\n9zRWq4VZOr1Rz4LuYeIAlSmVZlLF9kyo2onhrHv/E9688jd2+QqWVYOyxmI7TENySSb93o3j9Nur\nGBH4EBfe88P3Ky0VgbacfG8VvoenkjJoJdO9BR1y8dxI9NYg00JTnwYotMayUoL37opbnFVvh4qY\nCJyT0kguyeSp/Ie5VOWC/GsnOsZeFir8nUOQe3lgKK9g38VUVDtfwKN7uXgRMXHNZ5eEMskpnfce\nelzckuiiQtBMkpA/fN3vpnjIu3hRs1qY8TrbV7Ox60FGjZwiLnHdjlaavZ7qKRHUqiT4fH1VDBQw\ntaAKFkbSpGrELtMK1wwtdZ0tBS91N1dq1tuKbc1uW2PFPfdo9TB2+e5nwBtxNHWQYFeqp8FFKhRO\npTJzPn94EMqPi25pnYEQb+S+LFUsNJoYh+2P23FDGlIbGwwNDfxo3P6XmWUdbD2M4T3ubLjZHgdO\nv/u7zyeRSIYhtJhlwDqj0fjBTX8fgrDy/f/tnXlYVGX/xj9nhkUWUQRlERBHAZEiFFEgSxMNXDOz\nXEozTQVLK5fKfEvt/Vn65lJqgBpqWqmZljumWJYCoiiRhIAOCggoAoosssyc3x8PMzCgZul7pb7c\n1zUXnJlz5ixznvM8z/d7f+9bpzi5XZblDyVJcgY2IALXMrBaluXPareZB0xEBL4B3qtVZL0p7gsu\nuruqkCEZIXisC2Pm21MMvKY0xcUGZZ71jQmsNsUzImIGlb3y9emWbzpvoGSUP/1SB3Pygwi6fhiG\nZGqCetgqBkUd5roKUqoqcFtQwcQXpujz2z2eTcbx10raLopFNVqoopY7awwaQ8P8d33YRMVR8Ux3\nhmSEsLn9IRZ0+p5qcwlXC9GD7s9JhMoqzn4hCDQ2rsWi0GRDVwPBx8m2vzBX5Yu2oJCseaKUM8/f\nVD/C+DNUudhiEaLm6k/2nPrZg0FtfYUCzRNdcBqVyZs76rgGvqe0tPruN8zyZVJn2JKxsgeDUopJ\nGL0EALN8GbfwGhyWxqL49RS/LBYCGeN/iSP3bGuCnXyFRLSZmJQGO/pQ86o520qt0BgLElCJi7Ar\nVi8KYHd2gkHRUIVdM46dMSQ36eCwNBaFubk+6m7wYFMo+fcHX5Ae2R3jfVb3zB/8Xs7B79K6qAaY\nIctyZ8AfoYtef9tl9ba5ZeOG+6QH7/ZYM7nFp1NQzrQi5+kWGJWLm+OmaNgLIGSA4nZ588dr4Wwr\ntSKxzJWP7JIJGjMBo5hE5qsT8W9Wx7VRbZ/MB32/1xef6G6e+qIFPadN5sjyVQwMGNw4uHcLzFcn\n8vZbYVSbK8jvpaXzvAvUuNphuugyrhaFpHWr1qea+nidIa+/MRmftyOj93p9DGBU1wQSu9Q9d7O2\nPopq6mXyhqqwzNPw1YqlvPLKGxjFNDDc8/eG+GTSo7rRcb1GHzHXBhVjFN0SyzyNSDMuikVpbc3e\nlJ9Iqapg/AfTeWTK70S5HGFIRgiuFoXsi+lGjW01FukmaMzgu3FLmDbhdWI2Rul3V7/BVfb3o6K1\nEabXNPwSsVp8Vvs7GbVzFiW38YYVZCCELVvGmhoKR9Zulz0nkPZfZeuvffacQFFS6u+NVKlBPpVC\n4YQAjMtlWu5IvieCDy3MHeUAtwl3tO7+5P+77f4kSQoA5smyHFy7PBtAluWP663TG5gpy/Kg2+1L\nkqQdwEpZlg/U9uClsiwvvpPjvC+46AAHPHeRtaOUQZ++jTaoWPg81GL3xcQ6tRdt4+l81iw33l7z\nnb5oIW96IB/NTCY3tIopy4sZfWQipupmVLXQ0vGteDp5XaX50zfwXD0FpyeyUdQ6zAQ7+pA9JxC/\nk+4U9oYnwyahXXUFi5C6fdXnxBu1czZo/HNVvpRMNaLyyetkPr6R4PVj9R5cqu2TsZqq5JHPAum8\nOZucC6UU7XYno+t6gsZMoPljJiTPDCfY0YdLUwOpsgLb0zXUqJXU5P+O/a8t0aSkMcx2FjYxN1ER\njU/GqJ0z7b6TyJlWg/RkIK4/FFKa1YKrHSTMdiToOQQlQe64/dwO1egkrGJySL7iSHwbDTVDK0kr\nrsbN7RKaDDVKNxWaDDWDVa8hDTLG5+MplDnJdJz/G1DO0vNxeJmYkVIVx6GyTky1vkCwow9rso4Q\nWRhIYheFMF8AZC8PjMKvUVRhrp+WtDpsKqSuIuv1zrW/r/OCWAOugc5p9vxgC70STZmzhFQt4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wDalPl63/91Yoi1axy+sreiVOwDm0iNT/tKVD2wLOpTqiLFOgeidO+JMFVeBqV8jZDAeMrinx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\nJjzE+H/7RUNoRcOc9AAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f61ca248290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"imgplot = plt.imshow(img, clim=(0.5,0.7))\n",
"plt.colorbar()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"img = Image.open('chameleon.png')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f61c9c8f4d0>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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vtHiO8vD3ABByHZqmvoTYcOFRuk/8jwDEIw/YsPdmP/fszSxXrio3CZ46Zqdu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\nr3MAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f61ca09d3d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(141)\n",
"plt.imshow(img)\n",
"img.thumbnail((64, 64), Image.ANTIALIAS)\n",
"plt.subplot(142)\n",
"plt.imshow(img)\n",
"plt.subplot(143)\n",
"plt.imshow(img, interpolation='nearest')\n",
"plt.subplot(144)\n",
"plt.imshow(img, interpolation='bicubic')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAMsAAACGCAYAAABzPX6BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACG5JREFUeJzt3V+MXGUZx/HvT1oSqQ2gbRHRjWjQUgwkZcWmaaRoRFol\nhKQXrX+aNCSbGjTqBdFogl5quDH4p01DCOGCcgNFTAryxyhErLA1BQpB0wJqa5O2tCkRjFp5vDjv\n6nTb7T6z+86ZGfr7JJvOnPO++5zTzq9zzs7Z5ygiMLPpvaPfG2A2LBwWsySHxSzJYTFLcljMkhwW\ns6RpwyLpTkkHJe2eYr0k3S5pj6TnJC3tWHedpD+Wdd+uueFmbcu8s9wFXHea9auAS8rXGLAJQNJZ\nwE/L+iXAOklLZrOxZv00bVgi4gngyGmG3ADcHY0dwHmSLgSuAvZExMsR8S/g3jLWbCjVOGe5CPhr\nx/N9ZdlUy82G0px+b8AESWM0h3HMmzfvysWLF/d5i+ztaufOnYcjYmG382qEZT/wgY7n7y/L5k6x\n/JQiYguwBWB0dDTGx8crbJrZyST9eSbzahyGPQisLz8VWwYci4gDwDPAJZIulnQ2sLaMNRtK076z\nSNoKrAQWSNoHfI/mXYOI2AxsB1YDe4A3gQ1l3XFJXwV+CZwF3BkRL/RgH8xaMW1YImLdNOsDuHmK\nddtpwmQ29PwJvlmSw2KW5LCYJTksZkkOi1mSw2KW5LCYJTksZkkOi1mSw2KW5LCYJTksZkkOi1mS\nw2KW5LCYJTksZkmpsEzXLE/SLZJ2la/dkv4j6d1l3auSni/r/Iv1NrQyv1Y80SzvMzTtjJ6R9GBE\nvDgxJiJuA24r468HvhkRnb3GromIw1W33KxlmXeWbpvlrQO21tg4s0GSCUu6WZ6kc2havd7XsTiA\nxyTtLL3BzIZS7SZ71wO/nXQItiIi9ktaBDwq6aXSEvYEnU32RkZGKm+W2exl3lmmaqJ3KmuZdAgW\nEfvLnweBbTSHdSeJiC0RMRoRowsXdt0s0KznMmFJNcuTdC5wNfDzjmXzJM2feAxcC5zy1hVmgy7T\nN+yUzfIkbSzrN5ehNwKPRMQbHdMvALZJmqh1T0Q8XHMHzNqipkfeYHGvY+slSTsjYrTbef4E3yzJ\nYTFLcljMkhwWsySHxSzJYTFLcljMkhwWsySHxSzJYTFLcljMkhwWsySHxSzJYTFLcljMkhwWs6Ra\nTfZWSjrW0Wjv1uxcs2FRpcle8WREfH6Gc80GXi+a7NWaazZQajbZWy7pOUkPSbqsy7lIGpM0Lmn8\n0KFDic0ya1etE/w/ACMRcTnwY+CBbr+B+4bZoKvSZC8iXo+Iv5fH24G5khZk5poNiypN9iS9V6U5\nmKSryvd9LTPXbFjUarK3BviKpOPAP4C10TQkO+XcHu2LWU+5yZ6dcdxkz6zHHBazJIfFLMlhMUty\nWMySHBazJIfFLMlhMUtyWMySHBazJIfFLMlhMUtyWMySHBazJIfFLKlW37AvlmYVz0t6StIVHete\nLct3SfIvqdjQqtU37BXg6og4KmkVsAX4RMf6ayLicMXtNmtdlb5hEfFURBwtT3fQNKYwe1up2Tds\nwk3AQx3PA3hM0k5JY91votlgmPYwrBuSrqEJy4qOxSsiYr+kRcCjkl6KiCdOMXcMGAMYGRmpuVlm\nVVTpGwYg6XLgDuCGiHhtYnlE7C9/HgS20RzWncRN9mzQ1eobNgLcD3w5Iv7UsXyepPkTj4Frgd21\nNt6sTbX6ht0KvAf4Wem1d7y0mrkA2FaWzQHuiYiHe7InZj3mvmF2xnHfMLMec1jMkhwWsySHxSzJ\nYTFLcljMkhwWsySHxSzJYTFLcljMkhwWsySHxSzJYTFLcljMkhwWsySHxSypVpM9Sbq9rH9O0tLs\nXLNhMW1YOprsrQKWAOskLZk0bBVwSfkaAzZ1MddsKFRpslee3x2NHcB5ki5MzjUbCrWa7E01ptsG\nfWYDq2qTvdnobLIH/FNSP1omLQD60ZO5X3X7Wbuf+/zRmUzKhCXTZG+qMXMTc4GmyR5NQ3Ekjc+k\n+8ZsnWl1+1m73/s8k3lVmuyV5+vLT8WWAcci4kByrtlQqNVkbzuwGtgDvAlsON3cnuyJWY+lzlki\nYjtNIDqXbe54HMDN2bkJW7ocX8uZVreftYdunweyI6XZIPLlLmZJfQvLbC6haaH2lPfI7GXdjnEf\nl3Rc0poadbO1Ja0s9/58QdJv2qgr6VxJv5D0bKm7oVLdOyUdnOojiBm9viKi9S+ak/29wIeAs4Fn\ngSWTxqymuYOYgGXA71usvRw4vzxeVaN2pm7HuF/RnOetaXGfzwNeBEbK80Ut1f0O8MPyeCFwBDi7\nQu1PAkuB3VOs7/r11a93ltlcQtPz2tGbe2RmL/35GnAfcLBCzW5qfwG4PyL+Av+7+VQbdQOYr+a+\nJO+iCcvx2RaO5u5yR04zpOvXV7/CMptLaNqo3WnyPTJ7VlfSRcCNlAtRK8rs80eA8yX9utz/c31L\ndX8CXAr8DXge+HpEvFWhdo1tO8HAXO4yiKa4R2Yv/Qj4VkS8VW4A1aY5wJXAp4F3Ar+TtCM67uTW\nI58FdgGfAj5Mc9/RJyPi9R7X7Vq/wjKbS2jaqN15j8xV0XGPzB7XHQXuLUFZAKyWdDwiHmih9j7g\ntYh4A3hD0hPAFcBswpKpuwH4QTQnEnskvQIsBp6eRd1a23aiGieQMzj5mgO8DFzM/0/8Lps05nOc\neAL2dIu1R2iuRlje5j5PGn8X9U7wM/t8KfB4GXsOzb0/P9ZC3U3A98vjC8oLdkGl/f4gU5/gd/36\n6nkwTrMjq2n+19oLfLcs2whsLI9F84tje2mOZUdbrH0HcJTm8GAXMN5G3Uljq4UlWxu4heYnYruB\nb7T0d/0+4JHyb7wb+FKluluBA8C/ad41b5rt68uf4Jsl+RN8sySHxSzJYTFLcljMkhwWsySHxSzJ\nYTFLcljMkv4LSp6choRC4KcAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f61ca2b98d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = plt.subplot2grid((2,2),(0,0))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAACwCAYAAAAMutWxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f61ca265510>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax2 = plt.subplot2grid((3,3),(1,0),colspan=2)\n",
"ax3 = plt.subplot2grid((3,3),(1,2),rowspan=2)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f61c9d83b90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax1 = plt.subplot2grid((3, 3), (0, 0), colspan=3)\n",
"ax2 = plt.subplot2grid((3, 3), (1, 0), colspan=2)\n",
"ax3 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)\n",
"ax4 = plt.subplot2grid((3, 3), (2, 0))\n",
"ax5 = plt.subplot2grid((3, 3), (2, 1))"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f61c930e050>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.gridspec as gridspec\n",
"\n",
"gs = gridspec.GridSpec(3,3)\n",
"ax = plt.subplot(gs[0,0])\n",
"ax2 = plt.subplot(gs[1,:-1])\n",
"ax3 = plt.subplot(gs[1,-1])"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Ai37CrsVbhnnqbZef3ctTawAflyTgY1QCf7y1ZWaFRDyZvf9E2mX9sjaQZ5fOw8D6SaZP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\nOPDNzBLx/5hUrfVqXyjHAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f61c91247d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gs = gridspec.GridSpec(3,3)\n",
"ax1 = plt.subplot(gs[0,:])\n",
"ax2 = plt.subplot(gs[1,:-1])\n",
"ax3 = plt.subplot(gs[1:,-1])\n",
"ax4 = plt.subplot(gs[2,0])\n",
"ax5 = plt.subplot(gs[2,1])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (py27)",
"language": "python",
"name": "py27"
},
"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 |
mfacorcoran/pycaldb | .ipynb_checkpoints/CHANDRA CALDB Update-checkpoint.ipynb | 1 | 15319 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How to update the Chandra CALDB at the HEASARC"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from astropy.io import fits as pyfits\n",
"from astropy.time import Time\n",
"from astropy.table import Table\n",
"import ftputil\n",
"import time\n",
"import tarfile\n",
"import os\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ChandraCaldbDir = \"/pub/arcftp/caldb\"\n",
"ChandraCaldbHost = \"cda.harvard.edu\"\n",
"CaldbWorkDir = \"/Volumes/USRA16/CaldbWorkDir\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"host = ftputil.FTPHost(ChandraCaldbHost, \"anonymous\", \"[email protected]\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#host.download(\"/pub/arcftp/caldb/caldb_4.7.2_main.tar.gz\",\"/Volumes/USRA16/CaldbWorkDir/caldb_4.7.2_main.tar.gz\",mode='b')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#host.download(\"/pub/arcftp/caldb/hrc_bkgrnd_4.7.0.tar.gz\",\"/Volumes/USRA16/ChandraCaldbWorkDir/hrc_bkgrnd_4.7.0.tar.gz\",mode='b')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['acis_bkgrnd_4.6.9.tar.gz',\n",
" 'caldb_4.7.2_main.tar.gz',\n",
" 'hrc_bkgrnd_4.7.0.tar.gz']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ChandraCaldbFiles = host.listdir(ChandraCaldbDir)\n",
"\n",
"host.close()\n",
"\n",
"ChandraCaldbTarFiles = [f for f in ChandraCaldbFiles if '.tar' in f ]\n",
"ChandraCaldbTarFiles "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Changing directory to /Volumes/USRA16/CaldbWorkDir\n",
"\n",
"File List:\n",
"['acis_bkgrnd_4.6.9.tar.gz', 'caldb_4.7.2_main.tar.gz', 'hrc_bkgrnd_4.7.0.tar.gz']\n",
"Getting file acis_bkgrnd_4.6.9.tar.gz\n",
"Untarring acis_bkgrnd_4.6.9.tar.gz\n",
"Getting file caldb_4.7.2_main.tar.gz\n",
"Untarring caldb_4.7.2_main.tar.gz\n"
]
}
],
"source": [
"print \"Changing directory to {0}\\n\".format(CaldbWorkDir)\n",
"os.chdir(CaldbWorkDir)\n",
"\n",
"print (\"File List:\")\n",
"print (ChandraCaldbTarFiles)\n",
"for f in ChandraCaldbTarFiles:\n",
" remotefile = ChandraCaldbDir+\"/\"+f\n",
" localfile = CaldbWorkDir+\"/\"+f\n",
" if os.path.exists(f):\n",
" print (\"File {0} already exists; deleting prior to download\".format(f))\n",
" os.remove(f)\n",
" print \"\\nGetting file \"+f\n",
" getfile = True\n",
" inum = 0 # download counter \n",
" itrymax = 3 # maximum number of download attempts\n",
" while (getfile and inum<itrymax):\n",
" try:\n",
" getfile=False\n",
" host.download(remotefile,localfile,mode='b')\n",
" except IOError:\n",
" print \"IOError on download of file {0}\".format(remotefile)\n",
" getfile = True\n",
" inum =+ 1\n",
" #\n",
" # once file downloaded, then untar it and delete the tar file\n",
" #\n",
" print \"Untarring {0}\".format(f)\n",
" tarf = tarfile.open(f)\n",
" try:\n",
" tarf.extractall()\n",
" except:\n",
" print \"ERROR UNTARRING {0}\".format(localfile)\n",
" inum = 0\n",
" print \"Deleting {0}\".format(f)\n",
" os.remove(f)\n",
" if inum >=itrymax:\n",
" print \"\\nCould not retrieve {0} after {1} tries; Returning\\n\".format({remotefile, inum})\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"MANIFEST_4.7.2_main.txt\n",
"README_caldb4.7.2.txt\n"
]
}
],
"source": [
"#\n",
"# don't forget the manifest and readme files\n",
"#\n",
"manfile = [f for f in ChandraCaldbFiles if 'MANIFEST' in f]\n",
"manfile = manfile[0]\n",
"readme = [f for f in ChandraCaldbFiles if 'README' in f]\n",
"readme = readme[0]\n",
"print manfile\n",
"print readme"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"maincaldb=[f for f in ChandraCaldbTarFiles if 'main' in f]\n",
"maincaldb = maincaldb[0]\n",
"tarf = tarfile.open(maincaldb)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tarf.extractall()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"os.chdir(CaldbWorkDir)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "IOError",
"evalue": "[Errno 28] No space left on device",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIOError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-9-eda44d64eb25>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0macisbkg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0macisbkg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mtarf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtarfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0macisbkg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtarf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextractall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/tarfile.pyc\u001b[0m in \u001b[0;36mextractall\u001b[0;34m(self, path, members)\u001b[0m\n\u001b[1;32m 2077\u001b[0m \u001b[0mtarinfo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarinfo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2078\u001b[0m \u001b[0mtarinfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0700\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2079\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarinfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2081\u001b[0m \u001b[0;31m# Reverse sort directories.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/tarfile.pyc\u001b[0m in \u001b[0;36mextract\u001b[0;34m(self, member, path)\u001b[0m\n\u001b[1;32m 2114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2115\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2116\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extract_member\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarinfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarinfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2117\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mEnvironmentError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2118\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrorlevel\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/tarfile.pyc\u001b[0m in \u001b[0;36m_extract_member\u001b[0;34m(self, tarinfo, targetpath)\u001b[0m\n\u001b[1;32m 2190\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2191\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtarinfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misreg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2192\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakefile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarinfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargetpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2193\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mtarinfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2194\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakedir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarinfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargetpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/tarfile.pyc\u001b[0m in \u001b[0;36mmakefile\u001b[0;34m(self, tarinfo, targetpath)\u001b[0m\n\u001b[1;32m 2231\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2232\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mbltn_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtargetpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"wb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2233\u001b[0;31m \u001b[0mcopyfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2234\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2235\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/tarfile.pyc\u001b[0m in \u001b[0;36mcopyfileobj\u001b[0;34m(src, dst, length)\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlength\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 266\u001b[0;31m \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopyfileobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 267\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/shutil.pyc\u001b[0m in \u001b[0;36mcopyfileobj\u001b[0;34m(fsrc, fdst, length)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mbuf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0mfdst\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_samefile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mIOError\u001b[0m: [Errno 28] No space left on device"
]
}
],
"source": [
"\n",
"acisbkg = [f for f in ChandraCaldbFiles if 'acis_bkgrnd' in f]\n",
"acisbkg = acisbkg[0]\n",
"tarf = tarfile.open(acisbkg)\n",
"tarf.extractall()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"hribkg = [f for f in ChandraCaldbFiles if 'hri_bkgrnd' in f]\n",
"hribkg = hribkg[0]\n",
"tarf = tarfile.open(hribkg)\n",
"tarf.extractall()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"os.chdir('/Users/corcoran')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/corcoran\r\n"
]
}
],
"source": [
"!pwd\n",
"\n"
]
},
{
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
Gleiwer/kaggle_house_prices | 2.3_Explore_Data.ipynb | 1 | 160320 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 2.3 Explore Data\n",
"### Outputs:\n",
"- Data Exploration Report"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import nltk\n",
"import pandas as pd\n",
"import math\n",
"%matplotlib inline\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import gridspec\n",
"\n",
"from sklearn import datasets, linear_model\n",
"import numpy as np\n",
"from numbers import Number\n",
"\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [],
"source": [
"train= pd.read_csv(\"../data/train.csv\")\n",
"train=train.set_index('Id')"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def hist_boxplot(column,figsize=(13,8)):\n",
" fig = plt.figure(figsize=figsize) \n",
" gs = gridspec.GridSpec(2, 1, height_ratios=[1,4])\n",
" ax0 = plt.subplot(gs[0])\n",
" ax0.grid(True)\n",
" ax0.boxplot(column.dropna(),vert=False)\n",
" ax1 = plt.subplot(gs[1])\n",
" ax1.grid(True)\n",
" ax1.hist(column.dropna())\n",
" print (column.describe())\n",
" print ('Null Values:',column.isnull().sum())\n",
" \n",
"def hist_and_info(column,figsize=(13,4)):\n",
" column.hist(figsize=figsize)\n",
" print (column.describe())\n",
" print ('Null Values:',column.isnull().sum())\n",
" \n",
"def value_counts_and_info(column,figsize=(13,4)):\n",
" column.value_counts().plot(kind='bar',figsize=figsize)\n",
" print (column.value_counts())\n",
" print ('Null Values:',column.isnull().sum())"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"20 536\n",
"60 299\n",
"50 144\n",
"120 87\n",
"30 69\n",
"160 63\n",
"70 60\n",
"80 58\n",
"90 52\n",
"190 30\n",
"85 20\n",
"75 16\n",
"45 12\n",
"180 10\n",
"40 4\n",
"Name: MSSubClass, dtype: int64\n",
"Null Values: 0\n"
]
},
{
"data": {
"image/png": 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+SZIkqXCGfkmSJKlwhn5JkiSpcP8f7Ud9wegFOn0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10eb19b00>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"value_counts_and_info(train['MSSubClass'])"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"RL 1151\n",
"RM 218\n",
"FV 65\n",
"RH 16\n",
"C (all) 10\n",
"Name: MSZoning, dtype: int64\n",
"Null Values: 0\n"
]
},
{
"data": {
"image/png": 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0adplAEA3VhwIWmv3JLln2D5YVV9IcnqSi5L8/NDt2iS3JLlyaL9u6H9rVW2rqtNaa/tG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\nAABAx/4/wf3Ks/dOangAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10df969e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"value_counts_and_info(train['MSZoning'])"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "KeyError",
"evalue": "0",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-136-cde979c62900>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#train['LotFrontage'].hist()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhist_boxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LotFrontage'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-134-899ec38d4142>\u001b[0m in \u001b[0;36mhist_boxplot\u001b[0;34m(column, figsize)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0max0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0max0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0max0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0max1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1817\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1818\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1819\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1820\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1821\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10dc96e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#train['LotFrontage'].hist()\n",
"hist_boxplot(train['LotFrontage'])"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"code_folding": [],
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"ename": "KeyError",
"evalue": "0",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-137-8b11b20002aa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhist_boxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LotArea'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-134-899ec38d4142>\u001b[0m in \u001b[0;36mhist_boxplot\u001b[0;34m(column, figsize)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0max0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0max0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0max0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0max1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1817\u001b[0m warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m 1818\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1819\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1820\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1821\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(self, x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_xticks, autorange)\u001b[0m\n\u001b[1;32m 3172\u001b[0m \u001b[0mbootstrap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'boxplot.bootstrap'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3173\u001b[0m bxpstats = cbook.boxplot_stats(x, whis=whis, bootstrap=bootstrap,\n\u001b[0;32m-> 3174\u001b[0;31m labels=labels, autorange=autorange)\n\u001b[0m\u001b[1;32m 3175\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mnotch\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3176\u001b[0m \u001b[0mnotch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'boxplot.notch'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/matplotlib/cbook.py\u001b[0m in \u001b[0;36mboxplot_stats\u001b[0;34m(X, whis, bootstrap, labels, autorange)\u001b[0m\n\u001b[1;32m 1996\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1997\u001b[0m \u001b[0;31m# convert X to a list of lists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1998\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_reshape_2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1999\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2000\u001b[0m \u001b[0mncols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/matplotlib/cbook.py\u001b[0m in \u001b[0;36m_reshape_2D\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m 2244\u001b[0m \u001b[0;31m# one item\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2245\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2246\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'shape'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2247\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2248\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 599\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 600\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 603\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m 2167\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2168\u001b[0m return self._engine.get_value(s, k,\n\u001b[0;32m-> 2169\u001b[0;31m tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m 2170\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2171\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3567)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3250)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4289)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8555)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:8499)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 0"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x10d752438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"hist_boxplot(train['LotArea'])"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Pave 1454\n",
"Grvl 6\n",
"Name: Street, dtype: int64\n",
"Null Values: 0\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10d8a6a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"value_counts_and_info(train['Street'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"## Se identificaron los valores NaN indica que no hay camino de entrada.\n",
"value_counts_and_info(train['Alley'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['LotShape'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['LandContour'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"#value_counts_and_info(train['Utilities'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['LotConfig'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['LandSlope'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['Neighborhood'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['Condition1'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['Condition2'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['BldgType'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['HouseStyle'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['OverallQual'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['OverallCond'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"hist_and_info(train['YearBuilt'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"hist_and_info(train['YearRemodAdd'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['RoofStyle'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['RoofMatl'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['Exterior1st'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['Exterior2nd'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"value_counts_and_info(train['MasVnrType'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
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RirSCf6W4/YBbI6K5oq5rgb2B+cARwM0UD5m9UlHmJGBnSY8CH2l1zszMzMx6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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10cf27898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"corrmat = train.iloc[:,:-1].corr()\n",
"f, ax = plt.subplots(figsize=(12, 9))\n",
"sns.heatmap(corrmat, vmax=.8, square=True);"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## 2.4 Verify Data Quality\n",
"### Outputs:\n",
"\n",
"- Data Quality Report"
]
}
],
"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": 2
}
| gpl-3.0 |
vallis/libstempo | demo/libstempo-toasim-demo.ipynb | 1 | 376559 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## libstempo tutorial: simulating residuals with toasim"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Michele Vallisneri, [email protected], 2014/10/31"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook demonstrates the `libstempo` module `toasim`, which allows the simple simulation of various kinds of noise."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING: AstropyDeprecationWarning: The private astropy._erfa module has been made into its own package, pyerfa, which is a dependency of astropy and can be imported directly using \"import erfa\" [astropy._erfa]\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import sys\n",
"\n",
"import numpy as N\n",
"import libstempo as T\n",
"import libstempo.plot as LP, libstempo.toasim as LT\n",
"\n",
"T.data = T.__path__[0] + '/data/' # example files"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Python version : 3.8.8\n",
"libstempo version: 2.3.5\n",
"Tempo2 version : 2020.7.1\n"
]
}
],
"source": [
"print(\"Python version :\",sys.version.split()[0])\n",
"print(\"libstempo version:\",T.__version__)\n",
"print(\"Tempo2 version :\",T.libstempo.tempo2version())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We open up a NANOGrav par/tim file combination with `libstempo`, and plot the residuals. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 390
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"psr = T.tempopulsar(parfile = T.data + 'B1953+29_NANOGrav_dfg+12.par',\n",
" timfile = T.data + 'B1953+29_NANOGrav_dfg+12.tim')\n",
"LP.plotres(psr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now remove the computed residuals from the TOAs, obtaining (in effect) a perfect realization of the deterministic timing model. The pulsar parameters will have changed somewhat, so `make_ideal` calls `fit()` on the pulsar object. "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 390
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"LT.make_ideal(psr)\n",
"LP.plotres(psr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now add a single line of noise at $10^{6.5}$ Hz, with an amplitude of 10 us. We also put back radiometer noise, with rms amplitude equal to 1x the nominal TOA errors.\n",
"\n",
"All the noise-generating commands take an optional argument `seed` that will reseed the numpy pseudorandom-number generator, so you are able to reproduce the same instance of noise. However, if you issue several noise-generating commands in sequence, you should use different seeds. "
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 390
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#LT.add_line(psr,f=10**6.5,A=1e-5)\n",
"LT.add_efac(psr,efac=1.0,seed=1234)\n",
"LP.plotres(psr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could also add EQUAD quadrature noise (with `add_equad`) or its coarse-grained version (with `add_jitter`), but instead we prefer some red noise of \"GW-like\" amplitude $10^{-12}$ and spectral slope $\\gamma = -3$."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 390
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"LT.add_rednoise(psr,1e-12,3)\n",
"LP.plotres(psr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or, we may add a GW background as simulated by the tempo2 GWbkgrd plugin (see the docstring below)."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 397
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"LT.add_gwb(psr,flow=1e-8,gwAmp=5e-12)\n",
"LP.plotres(psr)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function add_gwb in module libstempo.toasim:\n",
"\n",
"add_gwb(psr, dist=1, ngw=1000, seed=None, flow=1e-08, fhigh=1e-05, gwAmp=1e-20, alpha=-0.66, logspacing=True)\n",
" Add a stochastic background from inspiraling binaries, using the tempo2\n",
" code that underlies the GWbkgrd plugin.\n",
" \n",
" Here 'dist' is the pulsar distance [in kpc]; 'ngw' is the number of binaries,\n",
" 'seed' (a negative integer) reseeds the GWbkgrd pseudorandom-number-generator,\n",
" 'flow' and 'fhigh' [Hz] determine the background band, 'gwAmp' and 'alpha'\n",
" determine its amplitude and exponent, and setting 'logspacing' to False\n",
" will use linear spacing for the individual sources.\n",
" \n",
" It is also possible to create a background object with\n",
" \n",
" gwb = GWB(ngw,seed,flow,fhigh,gwAmp,alpha,logspacing)\n",
" \n",
" then call the method gwb.add_gwb(pulsar[i],dist) repeatedly to get a\n",
" consistent background for multiple pulsars.\n",
" \n",
" Returns the GWB object\n",
"\n"
]
}
],
"source": [
"help(LT.add_gwb)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 397
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"LT.createGWB([psr],Amp=5e-15,gam=13./3.)\n",
"LP.plotres(psr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Refitting will remove some of the power."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 390
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"psr.fit()\n",
"LP.plotres(psr)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"All done! We can save the resulting par and tim file, and analyze them with a favorite pipeline."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"psr.savepar('B1953+29-simulate.par')\n",
"psr.savetim('B1953+29-simulate.tim')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that currently the tim file that is output by tempo2 has a spurious \"`MODE 1`\" line that tempo2 does not like upon reloading. To erase it, you can do"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"T.purgetim('B1953+29-simulate.tim')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And if we reload the files we get pack the same thing..."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 390
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"psr2 = T.tempopulsar(parfile = 'B1953+29-simulate.par',\n",
" timfile = 'B1953+29-simulate.tim')\n",
"LP.plotres(psr2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's also possible to obtain a perfect realization of the timing model described in a par file without a tim file, by specifying a new set of observation times (in MJD) and errors (in us). The observation frequency, observatory, and flags can also be specified (see the docstring below)."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 393
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"psr = LT.fakepulsar(parfile=T.data+'B1953+29_NANOGrav_dfg+12.par',\n",
" obstimes=N.arange(53000,54800,30)+N.random.randn(60), # observe every 30+-1 days\n",
" toaerr=0.1)\n",
"\n",
"LT.add_efac(psr,efac=1.0,seed=1234)\n",
"LP.plotres(psr)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Help on function fakepulsar in module libstempo.toasim:\n",
"\n",
"fakepulsar(parfile, obstimes, toaerr, freq=1440.0, observatory='AXIS', flags='', iters=3)\n",
" Returns a libstempo tempopulsar object corresponding to a noiseless set\n",
" of observations for the pulsar specified in 'parfile', with observations\n",
" happening at times (MJD) given in the array (or list) 'obstimes', with\n",
" measurement errors given by toaerr (us).\n",
" \n",
" A new timfile can then be saved with pulsar.savetim(). Re the other parameters:\n",
" - 'toaerr' needs to be either a common error, or a list of errors\n",
" of the same length of 'obstimes';\n",
" - 'freq' can be either a common observation frequency in MHz, or a list;\n",
" it defaults to 1440;\n",
" - 'observatory' can be either a common observatory name, or a list;\n",
" it defaults to the IPTA MDC 'AXIS';\n",
" - 'flags' can be a string (such as '-sys EFF.EBPP.1360') or a list of strings;\n",
" it defaults to an empty string;\n",
" - 'iters' is the number of iterative removals of computed residuals from TOAs\n",
" (which is how the fake pulsar is made...)\n",
"\n"
]
}
],
"source": [
"help(LT.fakepulsar)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rather than generating fake TOAs you might want to calculate a pulsar's phase at a particular set of times. Using the `tempopulsar` object you can input an arbitrary set of observation times and use the residuals to get the pulsar's relative phase. For example:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# create a set of times (in MJD)\n",
"obstimes = N.arange(53000, 54800, 10, dtype=N.float128)\n",
"toaerr = 1e-3 # set the (probably arbitrary) errors in the times (us)\n",
"observatory = \"ao\" # the observatory\n",
"obsfreq = 1440.0 # the observation frequency (MHz)\n",
"\n",
"psr = T.tempopulsar(\n",
" parfile=\"B1953+29-simulate.par\",\n",
" toas=obstimes,\n",
" toaerrs=toaerr,\n",
" observatory=observatory,\n",
" obsfreq=obsfreq,\n",
" dofit=False,\n",
")\n",
"\n",
"# get the phases in cycles (mod 1) referenced to the initial observation time\n",
"phases = psr.phaseresiduals(removemean=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The observation times can be input as an array of astropy Time objects. The TOA error values, observatory values, and observation frequencies, can also be arrays of the same length as array of observation times.\n",
"\n",
"If you want to extract phases referenced to a particular epoch, observatory and frequency, you can use the `refphs` argument to the `residuals` (or `phaseresiduals`) method of the `tempopulsar` object. For example, to reference the phase to an epoch of 52973 at the solar system barycentre you could use:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"phaseref = psr.phaseresiduals(removemean=\"refphs\", epoch=52973.0, site=\"@\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: this can also be set by using a parameter file containing the line `REFPHS TZR` and having that values `TZRMJD`, `TZRSITE` and `TZRFREQ` set."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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| mit |
ALEXKIRNAS/DataScience | CS231n/assignment2/ConvolutionalNetworks.ipynb | 1 | 376543 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Convolutional Networks\n",
"So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
"\n",
"First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"run the following from the cs231n directory and try again:\n",
"python setup.py build_ext --inplace\n",
"You may also need to restart your iPython kernel\n"
]
}
],
"source": [
"# As usual, a bit of setup\n",
"from __future__ import print_function\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from cs231n.classifiers.cnn import *\n",
"from cs231n.data_utils import get_CIFAR10_data\n",
"from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
"from cs231n.layers import *\n",
"from cs231n.fast_layers import *\n",
"from cs231n.solver import Solver\n",
"\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
"plt.rcParams['image.interpolation'] = 'nearest'\n",
"plt.rcParams['image.cmap'] = 'gray'\n",
"\n",
"# for auto-reloading external modules\n",
"# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"\n",
"def rel_error(x, y):\n",
" \"\"\" returns relative error \"\"\"\n",
" return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"X_train: (49000, 3, 32, 32)\n",
"y_train: (49000,)\n",
"X_val: (1000, 3, 32, 32)\n",
"y_val: (1000,)\n",
"X_test: (1000, 3, 32, 32)\n",
"y_test: (1000,)\n"
]
}
],
"source": [
"# Load the (preprocessed) CIFAR10 data.\n",
"\n",
"data = get_CIFAR10_data()\n",
"for k, v in data.items():\n",
" print('%s: ' % k, v.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Convolution: Naive forward pass\n",
"The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
"\n",
"You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
"\n",
"You can test your implementation by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing conv_forward_naive\n",
"difference: 2.21214764175e-08\n"
]
}
],
"source": [
"x_shape = (2, 3, 4, 4)\n",
"w_shape = (3, 3, 4, 4)\n",
"x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
"w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
"b = np.linspace(-0.1, 0.2, num=3)\n",
"\n",
"conv_param = {'stride': 2, 'pad': 1}\n",
"out, _ = conv_forward_naive(x, w, b, conv_param)\n",
"correct_out = np.array([[[[-0.08759809, -0.10987781],\n",
" [-0.18387192, -0.2109216 ]],\n",
" [[ 0.21027089, 0.21661097],\n",
" [ 0.22847626, 0.23004637]],\n",
" [[ 0.50813986, 0.54309974],\n",
" [ 0.64082444, 0.67101435]]],\n",
" [[[-0.98053589, -1.03143541],\n",
" [-1.19128892, -1.24695841]],\n",
" [[ 0.69108355, 0.66880383],\n",
" [ 0.59480972, 0.56776003]],\n",
" [[ 2.36270298, 2.36904306],\n",
" [ 2.38090835, 2.38247847]]]])\n",
"\n",
"# Compare your output to ours; difference should be around 2e-8\n",
"print('Testing conv_forward_naive')\n",
"print('difference: ', rel_error(out, correct_out))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Aside: Image processing via convolutions\n",
"\n",
"As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
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VgIXcz4Gyks9gbgxqyYH3tB7CsqZpTq0Bado5KfXhVDywtHxpeno8b8qI5YCP\n/EzT1P0zZWDmrul007UiN5akr6fG2eOWCwHAemEDMtyzVptIatlOSU+nP0I7pgv51GK9MjnMqsKQ\nrdHRs2aVQjUYwiZMTXgAYLUgEzGquoKPbWlI4jURPC9HdWJmhC3IwAAxAlAL7WTgva5X7jBCYOW8\nC+whe4NwbjQAxbGy1+UOf3cxPUsGiCyaT/rIOXGul/wjQcoAYEEEyClYPXkHy74GM8G5APKtCbF5\nyYT6tW3wK2NvYC4ACZYGYE2BUjr+UraxtCWZs3LT++cReV6YJ/1+zjk9l34K2lJrNQVAkkZJcZQA\njZ6D6WKv51eJ+UiVYs6BXeel42np+9k1Qt3XzzdN0/+tncyF7ZM8dD56fZK1Q06I6rSt8onVdZOt\nVvHRuyhM2BSoT8tXUtbrxnzOUMmdjiy1h7ybC8+gRc9PbVBuyiDlfp9iSnNl14AtxJYct1kObKZz\nOMcolXRxqW5T7+j7UwAsPcVZAuUp2EvLpcd8rhwlo/hR5YIAsBinCkDww1rvd5BbxNa9008md/79\nXFlwtSLU4720BQEgnjgkofMAhK3JFUuYxu8LWNRH7nVd0nxcDLNhK1HCMQhnH0FeovQ30NY1sxxm\nCKcEiSwYLm5ZBtATgEx+koWgqlFJOUSQNZxeBMS3KH+6sZUJBtdvUaY95JzkP/wjAgiDH1EIb7YK\nEpk5fusxgLSuC3HgDBMIJp5ODWDQuRCS42lLbqKvC/dQAl/rFr4p4FEqi/67xJzlwiakZdW/b7J9\nM8y98SGSEluWu58ybNrqHc3HZH1Jx33OgV2el/f19U0VTc43K1eWdNsw5zuWGiO6bfQhCPFnWywW\nvULWjsbp1u7TkNz4KAGkKSMjBcrr8jkPMEoBnM4jB7Z1HUplTGWKcChdT8G5fr7EFGqGdJ1o9jtl\nUdPnSlJqHy16jOv3UuCVyzd1f8itd2lfl9aBxyVPf1YhAIVQuaAIDZle2WulEwKq0srCO2XVpwqi\nB1IbtGFp4gwdsKqYcgu9Dy/Kg+GaWwVsjHEsGmMCI6SfSeunn69stTI4+vaDtKm0iUFV6a26/o0h\nQn41DNh+MVbMXT8RYiBa9hSBEkMXQ08O+VSTtA9ziI1GDHDnhn4x49OulSEAg/NtHw0bAWRylVph\n6McQM8BVBJHeoDWMlhitIxgitDEmGjODycJWTx+AaUmtzCnwlb6nf9dzpMQYnKdMmy5EkrZ2Rn9U\np9YcWJzN0kdxAAAgAElEQVRaA3LgMG2r3HZSyjLk7mulo321UkCZ86/ScyIFVfoZDZo0SMwpGV3/\nVNnoT1LJ+5oB06yXfJdzZS16ylIC+utA7RTwWQd0Nkkz9/wmZICkVRq3697fZA5q4y035qbIC/2V\ni1J9c2Nxavt0nT9jSfQzOTeCVFIWK2V5gc0M1ZzRtCkg30QuBACTChGFD2hLDC+51/+k6U4qnTbq\n05ALmTE7GvwoI129ECKzTZrzF8CaSZI+X5pUemtBW7jybjixOH7HRjBFFLYfGQSi4XQi92EpDCDf\nbfSBqtP+Wpaq8GzsB90ezOFEKMjAVjbkw0M4gPG+ulpEI45e+shoNBbEwaW/Zb+Sh4wL7WwdFpYh\n4jszj7ZleiYinuJkBsgYkCEYZ6Pvl0fXcTjkYC2YHw0cPE7Jga4c+M79rtMQSRfRTRaQ3POlsbmp\nVf4oC9dU6IFcu+QW67QuaX1ygDRlytYtxLlAsDnQlUsrLb+AotJ3CXOAToOlFOymLJFOP2UV5PNV\ncoL4cVr8b1WmjG39DDAGuenzUwbIJiAvNxamyjL13uNo3xKQyrE3JaBRIi9KotNO2e5S+XKGw3nX\nIv13bm3Y1FhY56c39VzqIvKocnEAWOw3ay0qQ+hMtBxJngEqhGChemA4HgbeOufdPkQEYUg30wTD\nYPZA75APyB6ifLy59W6YQBQBklud5BVRAkKA0bev47UO0RFcWRZ1NXz7zAswYCD9diPHz/dQCgr7\nKPFDOuxJYVBpiMGfJgxAA04YPg1yRlaUICmSuqw6MgpjYABYW43iNdW1BfnwwWzDod8tjYGs9ww2\nHcAGVWSrvAMsqQMJCADUoBr5VDgXPljOLAqSYUyNyjFcZ2EsA0uC5QowC9TNBXACw8BkaOWYWnU6\nVMJ55DyW/9RCPKVAzmss5bYo1zkUT1nopbLnGJEcO5A+r9eYUllyLI328Sm9q8sg/Z22wdj4K7MJ\nohA1+EwVlXyQXOZ0DuzLO6lz/tMWzbzk5kVpLKSGTGnclJzj15VnE3CYplv6exNAtylw021Vmq+5\ncufGbDrvSuVL550ug/7aRcoAloBtKS9JT99P0y6109SHxHOHWnQ5HpdRcmEA2IhVofE93UGrLMBq\nerptxoMgl3tmwrDBECVefNJE0Q1bZxQ+rz2kAf3euAx6YJasIIMARAkh9AYzg/2w+I3rnpmg2Tk7\ntshDO5QnTknSiVKSvi5mvFXZLwBYHfje+xBaolCefuHQz4vVxX6oY/yuZVVVvX8eM8Nagu+D81o0\njemDTcKGYwd1Q3Cdh6nqC+Hvkm6zlyy9kuWbSkk5pM6r697PKa8cm7ROmeTATO6TPalf25T1vCkI\nnVpEddlzoCPd2ss5/+bSzvVf2h+5ttXzJy2DNopE0m9i5oBlrmwa9MmpYmHAqqpa+SLD0xBd17Td\n153k02tw6quXyyO9njupOzWOUsWflrs0bqcMgSkwsQkgSAGollwZcsZECuZy76RlLRmPJfBcMkpK\ndSoZclP5A6t9qtPRulLmkk4/vfao8vQ1DQCQRYjRRPDEsDGaOZFFcMqP1kV8BlCwIgu29PcKVSPR\n6oQRxkj3jUSlinYmALPSmSaenkutY1sYlOtAC4C49TdedAklCzoHwHILQfipF+TcQjw1mdZdy9Un\ntT704rliVRkO37Hs/d385KTsB36SvV5gVxZZDh9iZ3ZxbIXPQTniwKjFD8Cbx0QtP25JLbJHAR76\n+ZStyY2FnPWrF0T9TgoUcspjCpitG0up6MVP0ln3OZVceaYs7HWSA7VToE6DsHXAOQfGSnkD047P\nKdjNKVRgVSHp/C8CA6ZPqG3qQzgFtlKZ2urOtU3a5yXDWtIuSc6YSa+n6aVlyKV5HimByNIcPQ8Q\nPC9oXGfITREIKSCb6vMpsKuf2WSdelS5EACMKX5uhQAQwwmhQQRwhEiMGLw0OcnAY/+i/r3+GfRW\nnHNCwarFKCanGZuhjYOvkJ7woUMsmAdwJ9kFp/DhA8WpE+6ICSJasQIaCYgn1j5z/7ukJ8rXZww+\nypzek5hWOStaHG83HVQ5pqMkuYWPiEAJKB2UZojXRpEMlRKtKrkUFHD/dPgmJOARouXrfrOW0LUe\nAAXWtArhMgCHzgcwRmSzIPZpSc45XGRq0Sg9U3I6TZVTjpHR43ZqvKwzNFJwnssrvZaOlRRsTH1g\nO6f41m2tbGIsybNpKIrcuxp8aiYvfTZnaOXaXAPn3P0USKQASh8cSNcAqYu06yZz/UlJiZ3a5L1N\njOBc24uk4D43N9N+mALPaflKin4dmCjd3wSs5NJI310H+DYFSDlZB2zOM/Zybb3OSD0PWNTpTAH1\n88qFAGDgffS7d4aDEvSR7VKLDGLUcxFDBHDbf1NwYLOSxiHAtR6Q+GKjaKDhh4RcMEQg8YciD2YP\n73nIV3eQ6tShQ3yvLKZOWeQWT4pVp/5Z6p3kgbzVP65mmQHTIunIAhz+zoQIUAFJdUTm1TxWJ2G6\nVZP+PgaUPrBgUv/4YXGdHhEFtCwfCO/TG/ukBSUn8cdU3gQYCxCEufRwDFhrUIERDqUSyFyM7RZ9\n4CInevyk16eeT5/bZKEvAYBU1lmLJXCVK2tuzMjip98pfc/uUeU8Bkn6npZN2iuVqfAHmyrXXLly\nTIq8rwGhBoAanOXq9zRE+v88oEQ/k1PoU/02lU8u5MKUT5FOJzcvcuXO5Ts1ljZhx3KgqvTspvO+\nlNa6Z88D7krppWt/mla6HpVA3VQfyLVcn79VuRAALAAOC5iwFeQAEHdRiQ4NWdka8ErxMEBmHhvI\nRTbLA+oEXr8AxU8G+YSB6aPIM4PIwDkf4nZhrLwpCZwaHohcDfn+I9XCoHkvgC+WiTAOLhqDoeqy\nkDchJBoAtgaOAKMYvpFlYladr50bvnnXD8xKB3ENz/qu7U9KkaB5Nr0VaPoj9UPMJUnTcxeZPmXZ\n+9X4KqnjYu+zIT8pHDYAUfiCAAAykbGyBuQEBPkeUIeTlgxGjORvGDx8HgDM48/aSL6h/cJ3FIgY\nHL6tAFcBhgzskmEMYMnAoVuZnE9TNi1LiSmZer+06Kyz2ksL4zqGoWSllt6X8k8xE7oMpUU3JyVn\n2/SavrfuQEDpWlq/qTJtmkfaHqWTYFOHBvR7KcMujvfM4xAWT1P6r58kQEeP8xKAkr9zRnDpuZzC\nHoy80L5pvDX9LLAac24TcKLf14ZYCTzl3kvz0L9veqqvVNZ1a0zaVnqclYy0RzF8ztMeKXv6KMAy\nPXX+OPTEhQBg1sxgyISPcMsWk8SLkg4mEqQG0ACkAukT/LS8RwRDChAAkD1M70N4gt7BDv3NQIxF\nBT8cUdQD2MUPR+uOEuaI0DNkFE/fwfenCAkZxMwSsVQ6kQCuenaOfQhayrIlycPWaBgww+lGlSiA\nJIBjXFT1olUlDqyhfcNWnrGxDv12Jsffwz+K35YM74byE+VPkk5Z7/oZTu4REZgGRlFArjw4cmSl\ncaiB3OkaeTbcI1hrwrc7+3fCx9rJhM9UXQQAVlVVz0SI6EVEt1WprUuimQ5po9IBjRwoGBkCCsRt\nolxyC7JILr5Pqgx1nho8lEBXWi4BGWm90jAMJVCaO6KugUsOZOYUYWmc6nslvy19LU1Pl0vWOql3\nKqVyaT+rTcD1k5K2bUfjfx2bkyt7+k6un3O+eiJyvW3b0d8A+tN9UyzKecFH6XBBDvjk0kgPsaS/\n5+Zhek/yk7bP+X9OlaNkHKV/l9LU7ZSC01wacnI3V9/cOlUChbkyPm65EACMaA5j7KihAoAZ+zkQ\nh21GHWxBGm8ACX7FjScwYIChRe/T1U9E3bnR+4iEVRkph9PVQSqnJdmD1fcjhSESwMArcd0B+WZk\nv20az1R6FfbCex8/65MEPwWFkAxIkDzGCoQ5QkYGVnzniCIjRz0G1JYdoP0SBjAW2sSAYgUH8CZl\n6HPpt0T1F5Y0WxbwEyEE4AjATvy4+m1kFQoELAwfjdqjZ+cSCyedYOGnAHWCNXFhqSgUAcOBj6ct\n+ZOvg6W6Ceiaoso147FpWYDzB1DdhC1bZ31q4LVJ/inwBsaKSNKZYo1K4CbXZqlrQM4HS/9eAg2p\nP1sJiOXql15PQ0zkgFhqsOTySAH/05Spcb8JUNz0eQ2+9BqSA9c54KvzyPV9CkhS4yIFOZuko8uo\n30kNnhJo29SAKrXZpjLVXqVntaGlx7CshSlYG+uxzVhHLVMAXufxjmHAyDZgIpiq7oNoGvkYdw8e\nwilIMiZ+YDs2EhygLU/ygE8HrHQiAYgxpfrOiFtvHKKhe15d7AagNO5oQxU8dwCCr1h4ycP7cedZ\nGrb3+gHXd7IMLMDFUAw+8mzsHdhzD9bCc6EO8O1okgHB0dx7H+JjWQvnHYiG05R92ckPW499XTkk\nG//WMcekLZ13qONnjHrgi7y/DqCZPzXwVV8NJz1rIAZ+JRiwFxA2gMOQiv580+pCvKn1aUyIcWbI\nxfYHUBHIBcbxIogwKqPtZCozXyUglTJJKUtT2m6Te0TjLZ/SNlluMZpa1HVZ1jnF52RqcdRzQs+7\nUrwgnc4U65Q+ny7EqbJLy5qT1KJPGZr02Vz7594pzYkSI6bT1mPrUePMvR2S84tc116lvzXw0JKm\nVwLMuS2oFADJz1K+IyJA5VcC2bk6pWAuV6cSg52mM1Xu9N11ICWXflrfXNuma5zoPZnD+vn0UE6J\nEc/VexMAlSvfpvXdVC4EADNVpG4p/M7MMEgcoYkA9iBjAT3ohB2Ci75bdhyuQp41JrIvotQjUib1\nGSRQBCJ6wAqzVmEEJMAhthTCR6w93NhqweB/5F2nOlNoMcVI+fFg9szwFACPiYcRmBmIYTnYM7oe\nxw2LpWu7YQK7wBg6161YBICHMRWYNb3dQYshZf0iKGFrZuDomCvjj2Jk/aFVMlT26NRpt+rDgabv\nD8g/ioCZCeqEBhBPS/b5sQ45srp4DYpX+8ERQBS2WyOgZQbIeGTIyqciKdOVs4KBsrN+blFPJWXB\nUrZDypD6/6SKOgU9kn9OWaaMSwmk5RRCugDr+uVARa6cAFYA5VQb6TKUxlbKLkwxYDnZhKHQeab1\nS9+bUujSTqUTlyJ6S/oigC8RAYolyfn0TT2bq1sOsKTPlra/UgYmVdiltkwNqnWgcgpM5uZdmkeJ\nxUnBT0lyAFbPQ70ln75T+juVXLzINK+cwZNjqTdp25yU2uEdBcBk71wWWAFUIqEzg8N0Oric82pS\nrg72sSLQi1FkWVBFEBGfBwNm2N/v0+LA0si1ytr43UOKJ/goMDg0MGYSqNVzZPD0YIT27UHMI9Y/\n1iSAgjqUgRmeEVg370BoYIxBXddo2xZd26E2dQQWNrJI4VQnhxgeQ35kQaYBfKfKMxZjDKwJk8A5\nj6bZBRGhXT5MFguCHp/Zwc16MXCwJrKbiKybl75T/7jDAJY9ZPtxnYi/m1aGwHhMEAUGzI3eAwLA\ny/tcPGlJfb/Wga/SgptKOjdyW15aeaSKOi3LaI4kz+Ss21x6OUkX7pxSLDET2mIGxlsVJQVeUmzp\nGqKBoPjpTbGI0kZTklOEmyiJTbeDdVtJ/XVfSHlzfXmRwJdIyXdPxmuuzCUAX5oj6TjYNDhzCdBI\nOunvJQZMf8kgBUu677z3cY0eHz7SH1InIlRV1Y/XXNk2bYepcan9DnNrQknSvFOH/SkQW2Ljz+sq\nIVJyASgxkm9VLgQAa2gmBFSvlLXIQDCKX/GRHCGzMzznQygDmDhgjYEhhZJJwA0rXzC3MhBZfI3i\nKUZjDdiJo/fgowWyAewwgyiAih4AVDWqZgdnZ2eodoDl4hSWAHYetiKwG7byevCJNhaihvMGO/Nd\n1M0uzs4WsMbg+Ogy9vcPcfPmTYBa7O3u4O7t11FVQNs9hLNAJQyhc2DfobOylTtM4MoYuI565i1I\n3Q92YwzYRX8s9pjVFdgzFsslYPaGtgodNoo/1g9U7/p+k8XPe4/KNNBgNJTJhV5RISXY7ADwMOxD\nuzHDsx9NymERG7ZanIsnXhWbxszwaAEKwXXDFrSH9U0EtxU618XYahcDgE1Z72MwmWe2REpsiG7D\nnOWc/p7mHb42MGYFchau3JdwJ7PZDKenp32kdWstuq4blU2XTxRqXdeo67o3OuTf/v4+vPe4f/8+\nAKDrOpydnaHrOiyXy9EimpY3daDWhzlK7SwAT+onztjnkdTY1HVPpQQy9bwqPZvb0kyZMBFd9/T5\ndYr3SUlqlACrCn6KuTnPSc4cUJsCE6U8pw6byLXcd31zIEbG8HK5xHK5xJ07d/rgtG3b9mO/qirs\n7Oz042x3dxd1XaNpmtHcqapqRH7oukp5Stvp+hm5Jp+3Ou/hjdKzpXVuHXuXtvd5ZGo+p+vgJv6z\n6+RCADAtU0icgB6cRfIFJvo4QRay0TNjy0g716aL3qjz/Jg18Y6jP1PCREDAFyAckqlmaHmJyjaw\nsz0YV2F3rwajAbxDy2cgsjA2KAtbicO/w3x2GcZYXH3mOewdHKLtPG68/DKWyyUuHV8BkcWrr76K\nd3/Ke7A783jzzTt48+513Lz5cVSnD+A80C5PApCjJTwRTASGZNRi6gwgbaXbnRF9oAysMajrqp/w\nzjnU1Q5cPIwQ2i2mMNqZje1phu3UwOpxCBNiAKcWJY4x1gLziBFokiMJhgCw79MKZwEEZEvGBqCw\nbWvBw/cz48lXnxxCYGZYjt+7ZAa8DYclGBuzbW+nTC1cJXCUvrtukRoB7nVzInOtxBal9wU4AcB8\nPodzDjs7O/DeY7FYjN7VvlrGGDRNA2stbty4gaZp4L3H8fEx5vM5Dg4O0HUd3nzzTRARdnZ2cOfO\nHdy/fx937tzB6ekpzs7OsFwuR3UQ0dtspfrpxV4rq67rRlt5m0jOoi6BaA34cmArFQ2sckAhBd7r\nyjw1bi6CrBvXqX+Qni+5MZoDXKW2lN9Lhzhy91JDtOsGVwz9t4z5NO6igP3lconFYoGHDx8GQxwD\nGDAmhBba2QmkxMnJST/WmqbBYrEAEWF/fx+z2ayfW9rPLzfnc0aJlFvupYZO2oap0ZyKZq1y65aW\nTdio0hpYAug5QJnOUf3e4wBfwAUBYGkjlqwZ74GqqQe6NWKAUUcD/elJ3fnAeDIO94ZtF2PCNwKD\nf5TuuMCsMXtAKSxDVfDTMgbGhvdn830czxp4EEzV4PiowrUrl/CRj3wIpw9PsDc/gDEEtzgDM+Pg\n4ADOOVy7dg1da/Hulz4F7373u7F3eADPwEHjYEyFpmlwenqGz7nxHHZ3d/FwscTJyQl++f/7ZVy7\n+izaboHXX7uNBw/u4v6Duzhz98DGAajQuSWYOzAcqqoGYEcHGUKbx3aBh7GBDeucD4ySmcHAwzPA\nmMFYM1Ii4qgfJlFoD/kiAJj7bdCwAI16GnKiEcJayRYkOYB9OJTACFuSZDAcdoj9QjLppZ89HDvA\nBHcuDxd90AY/jvAvVpojSEQF5zq4dhxb7GnJ1JyQvzWA0s+k6cjilioVfV/PtdIWXaqQUgNHvy9p\nGmMwn89R1zW6rsP+/n4PnhaLBW7duoWdnZ3RAl3XYfwdHR2hqiocHBzg8z//8/stlPl8DiJC0zQ4\nOzuDcw6z2QzMjNdffx03b97E5cuX0XUdXnnlFZycnODs7AxnZ2cjZ15RiBqAlto8VTC6zdPvxqV9\nmNsWWwewdRnS9UskZVdSkKX7V/d/WsecctXPCcvytCUHnkpMWA4YT9U/BRZ6XuTKUQoPIZL6QOn2\nb9sWi8UCi8Wif17AlYxrKZseU2dnZz3T1bZtP2+E8ZrNZtjZ2QEzY7lcYj6fj7bkF4tF/33Pk5OT\n/hufdV1jPp/DWjvapkz1pRaZAykrKfMht907xUqVjKDcu+c1BnL5lsq2jjkb45G3PicuJABLr/eT\nwViwJxBsz3T1Cp+G8BRjQma9tSPX2rZF0zRYLrt+0R1AxrCV2W9lkMfe/jxsqdQVrl27hqae46WX\nPgW3795FPdtBVTV44YXn8eL1d+G1V1/BlUvHODs7w7wK2zFt2+Lo6AhXr17F2aLFZ37mZ6KeNYAN\ng9svH6Kua3gPuMP9YImbGkftGe7fNzj4gvei7Txu376Nz3iZcP/+fbx5/y5u376NN964iTt33sRy\neYZlewpjfPSti6f9EkXsmVE1M4AIXfSj8hIXq98m3YGP3+ochTBDBHExkCxYLLi+hQEEVqvvAw4I\nmtT94TkHcQEzxPBUwxBGvoFAjHNGw0eEIzE6MBfsUZkQ1EQOV/Tf3JTwGRS2lk3MIxfX7GlJTpnm\nlIbcT/9O5xAwHv9T4E2u5/LMLcgyZ2RbcTab4eDgADs7O7h8+TLatsVsNoO1FteuXcNyucTrr7/e\nM15E1LNl8/kcx8fH8N7j+eefxwsvvDDyuUqBheTpnMPeXtgmPzs7w+7uLk5PT/Hw4UPcu3cPJycn\nuHPnDrqu67dAUwCatqMwX9q3UNquFC0+bXfdXlOxuXLp5ECzTicHFHNO4Gm/l5zQNXgWRfu4LP7H\nKTlWSiTnBA6UGZD03VRy7b+OqUnfE2AkxoAYBPLVAQFWDx48ADOv+HYBgz/j/v4+nnnmGVRVNeof\nDQ5l3D58+LBnw6y1ePDgAdq27T+6PpvNcPXqVezs7KBpGlRV1f9MRaetfStlPOtxX2KScm1XMgT0\n/dI9fX+q/fW1nEFZKkPJ6DwvEMzJxdE0UaYWMcIQK0cAgDBY3ntYE09tmVVfAWD6czqyGAYauOoD\nuQlOoHharq6rHuUv2jPUtQVzg5deegn7+/t4/tq7cPWZZ/HZn/3ZODk5wWy+h4PdPdQvv4Q7N17E\nTlOhris05DGbzXB2doajoyMsl0vMZjX29ncAa0J4C2IsaA5HBLIGpgrO4t4D+8Zgp9pHhwN03uH5\n69fQnnmADRadw61bt/Daxz6OD33kw7hz9xM4O3uIm5/4GNp2gbYNlpe2XsTyEn+CdFtq2G6RSTQs\n/MJi6e0jVgo5TEwggCuhu9GziaHtow9Y7FeGgWFhGjyIHJioD58RJpBHXc8iMARAMSaY78Dew7OH\nqeqYfjhJGcaRDIjQxwTAGA+YDoYaEC4OA1YCWelClyqNTRYUeW6dCNAoKbx0MZPF+9KlS7hy5Qou\nX76MK1eu9ArBWouDgwPMZjO88MIL/bgRv5Su63BwcNBb5IeHh5jNZlkwo6WqKly/fr03ptq2xbPP\nPttv29y5cwe3b9/Gr/7qr+L+/fv9FqUAt5TJ0wpI++noRVjPE82G6XbSc0i3f4nR0pIC3hR463TT\nd9JrUhYNeHNAWp6fAmlPQ9Ixra9r2USR69/TsVyaS6U80j5KmSsZj8vlEvfu3eu3r+U9730PhMSP\nS9Zb/Vm0uq4xm816xurSpUsjoJ3Od+m/wR3D90aHzA8xRO7du4ezszPMZjPs7u5CM9GpCOAbERQo\nB3Oe2qpN2zQ1TFLgtWmfltKfSmuFdEneS/N8x/iAORaKnhE+2+MBDqcTGXKikFAZ06tGw4E76T+b\nYys1EYa0dQBScaDXC48zBsTB/YgYgCewDZ+mgXcwRLCGcBYT9t6hthX29vaw72Z45toz2N3dxY0b\nN/Dss8/i0vEhmqbBbGcXni/DNjNUFAbnlaM9LJfLsMe/XKCuaxxdugRmxs7uLoypwnZbBxA1IAJm\n7CJgiFsfMeyGtxXIWDREqClMrp0qOPLPHbA7O8K1y3t44aWr+MTNW/joR1/F8fElfOITt3Dn7uto\n2+DYH1i/CkQWy2ULIoa10mY6erIs2oGhGr63SDGSA8GSBVxgpJwNW8EOcTvQh088sVdKlEI0ejYU\nDjpS/BYnBw4MBFDsH08++IKpKP3GAi5uTRoGDDyI44JIYRwRKB7EQNgWVlsTFLezK2Pgly0aU6M1\nEvLi6UpuK0l+ptfShUDqmIKBR6HMS4xTulCJFT2bzXD58mUcHh7i5ZdfxsHBAfb29nonYO1cLCyF\nfPYmx7bo7bucxayVTlWFeZla5aIAhWV473vfi7t37+KNN97o/33oQx8a+YrpNFLQk7bPlHVeup8D\nwTlApp/TJxb133VdF1n9ElBOmbNUiTGzMkIfj7J5HDLFaKx7fhNlDqwG4k2lxAbL7oiAHvFPlG1y\n2TaU7cK6rkf+V5rhlTbX+cjvYsiU2iAFlvv7+9l5JMb2YrHA3bt3sVgscHJygpOTk95lQIwimfMC\nCIWhS9NMxxtQDpiauybPbyqbGJBS1ynRbVIyTvSz7zgfsH4ykGwirU6qnH+FoPtSY+UaftzAq5PQ\nOYfaWjgVpqGJkfqJgcO9fVy+fBkvPHMJly9fxvXr13F8fIymadDMZmBjYWzYW3d+WOBms6afQBbD\nojYEiNTIHwCo3zIMlZZtP46hJAjsOnjXhnTjKVFDDNtYzGwFS8Cl+T6eOTrCRz7yUdjWw/MCDx48\nwOnpKepmFwvXwbgOtpJJGhipUh+lC3c/CYHhVGjCIvUbxJShbmOXCztFQwNE0BnzyfrS2CEJYcDg\nIB5gfT9z8EsjhPAaAAKQYxfvhft1bc61ALxdMmUlrhMZ89pXaROZSlvYHUlbtiCMMb31fuPGDVy6\ndKnfUj8+Ph6dXtQOv5Km3jpJ/8n1dG6ni3wKltJnpLxShsuXL2N/fx/Hx8c4OjrCfD7H7du38eDB\nAyyXy+zJtSkQpfPUIFU/t4llnuY1dTpN56ffy4GLEpDX8zc3t6UMOYV/kWSdstQ/UyltJeXAa+l9\n3e8CtMRfS8CXMQZ7e3v9lrzeOkwPCqTlyvXVunqJCLMsoCFll6R/mbnfFl0sFjg7O8OtW7ewu7vb\n+3BqIFYCvVPl2WTtSuteen9TMK3ruakBWjJsNwV755ELAcBGn87hyLAkDUtE/TUtucGbO40S0h0v\n9t57SIwqZoZVLI9WDsyM+azB/u4u5rMdvPjii7h6+QpuPHsJh4eH2N3dxWwWtsIQ4620XgK9yhYd\nAOsT954AACAASURBVPawJgCkrg8cG4BAIHci4CMCiMDOBfZGwjYwgzh85qjtPCpr0S1P0VQ1vOsA\n72EtA9yiMjVAFrPjfbiOcbS3iytHx/j0l1/Cb7z2EfzzX/oVfORjr+H09AyNqWFsh65bovNttFgi\nFk4kR8+Ov8sZJyanjt1CT4/j1QwAaThEEeF3SD/+boyB88M36gYrJPq8qPKyD181AEVrBoCxNYxJ\nlXQHG9OoKoZ3gOfFhVA2YlSUGIwpq1/eL12b2uoqSbooEYUt6/l8jvl8jsuXL+Pll1/G5cuX+0Vb\nTmOlwCvNT5c1V7eUBSqBLLmmw1ro50XZiNPyfD7H/v4+rl27BuccXnvtNdy9excPHz7Ecrnst2c0\nA1iSFBSVQGIK1jbdPklFxn/KBKYKSeet89DuAhqo6/aS7WABC7+ZJQXo6T3dL9ImqeRYJ2AcVkK2\n+SQMirU2GOZN02/vaf+p0rhKAUZqrMjP3JxOx4A+haxZYam3jCMp48nJCR48eNCfKJ7P59jd3cXu\n7i729/f78BUl5jdHiKRtv8l6kwNc6e+la7lnUhCWA2U5f8fUdeBxyoUAYEPFhlOJ4yAJwo7kUPHw\n+7oFrGdlIlAY/eTwyR/vGWwYxlrUdTglcnRwAHiH5559Fi+//DIuHR7hyqXLqBsbTp7M54Gutxa+\na0GIAIBjCIQIRiRoLCH4ckk/+wi8LMIpS3ES997DRUAExAWAg1KpqQKWJ7DLE2DpQghRrsHWw3cd\nbLMDkAfZXRjDsBWhaQ6xv7+D5585xud+6qfg1z/6Gj766sfxGx/+MG6/eQf37t0Fuai0LElDFfpK\nt6v6yaq/ClbMinWnEhEA1vvxea/AWEhT0+B9EVkvPAOtL9up3ncKVMaFQ0JzeA9jYn39xfgYt8gU\n4/JW0pq6JpJjdMSabpoGOzs7OD4+7n28nn32Wezt7WFnZycbXFnS1GAhZ+mXGI2VLyhkxphsNeYc\n3WUeafbOWovd3V28733vwwsvvICbN2/i5s2buH37Nm7evDk6NflWrfup+VBau3LGZPpuesqypBhT\nxa0VcAqw0+3gizQnUpla9zcBtekcy7Vp6T15vm1b3Lt3r996lO3b/f39futdthxTB3adTo7VLNWp\nJxEy91OjQ5c5NUoA9GUTNnt3NwTePjk5wcOHD7FYLLBcLkcnlsP6urpbUBrPJUYxZ9Tnrpf6Mjc3\n9RpSMvbkb92G2u9Tt1FapsfFhl08ACYNxKsNmgNgOZCwLp9S48kAdMYAESzN53Ncv34dL734Ao6O\njnB8cIjjw6NAK1cWtqqxbB2sjU3ZLeAcwVbBibzj8faN/C5W+rgADPjwXUnxFZNdydTZkrszuOUp\njFuEk6DE6NjDRBq8thVQVRHA1fDeoWpqkN2BXT5Ec+kIMBUuXbqCqqrwax/5EIjEeXmBzi3h3GpQ\nuvUATNrWRDCdsjjjY/wlACbhJkjlWatvhfaTnFYt8yxQIT9a6ML9GJQ1fgQ8gLCLAcA2ocwf1Vdi\nSqnn3tvd3QWA/tj6wcEB3vve9+Lw8LBngAWQyaIsCl7qIY7G8ruuY8rQpEpQWC25plkZfdy967rR\n0f60rjL39CeWxH/qhRdewHPPPYezszM8fPgQd+/exc/+7M/i9u3bODk56bdlZJtJM+vrgG1uvUnv\nazCZU7DDIZi8Q3NJ4aR5auWYAl8R3T8XCYBpxkl+bgKUtJSAQWl+pNekrcRXarFYoG3bfowC6AME\nS6wteU+26zVLq30icydytREx3iVa/XRYWjfpPzFK5JqkL2NZxp9EyxcAJkGOhc07OTnBK6+80pdV\nmGRhziTQaw6wpO2vx/wmhx5SyRmmufrrZ1K9IPnkDttI+8rzOePnccyJCwHAAOkQiWFgoYKrT4Cv\n/MLATn1WRWl2z6tbOgQftbzDsuvQNA3edfUZNNbgaH8P1y4f4cYLL+LypauY7+3Cx/wCmKlhmhrM\nDuw6WGZwvQv2XfiW4nKJijhE5ncMcg6VtfBLjwoGpmMwhRAJVVWh7Ww4f+AZFROMc6iYga4LwNSH\nbUlrLPzZfcB7dK6FYYANo+VT7JoGrnVwtYFBFQJ2uA4VAeAOlhhoDIgZlw5r7DYzHL7nBm7cuIZX\nXr+FV2/excOHp1g8eIAzf4KTh/exWJzCtacgZni/OvB138gnLokdhu1GPTGoB6s94+HDB9W99/BE\n8DyOWZUuOGOFtQpkuV+YlLLhGiGAqx4zMdwIEYzxYAZc9Ke76LJuS0gvMnrh2dRy09a4nGg8ODjA\n7u4uDg4O8Pzzz2M+n2M2m/VgSBRKanETUQ9aUkUvbJVe+PTilm6XiJLTSkneFWWSc6LWhxJSJQYM\nYHY+n/fH8N/znvfg1q1buHv3Lk5OTvDmm29isQj+kxLg9VGZrxIbkP6ds8I3AdCblKsERqQf17F+\nT0sE3GvwApSZFLlXGvubKFPNBot0XYf79+/3p8YvXbrUO9fLnMidii3lmYY5yZUzrY8YJfoZ7aOl\n5wewOl6m+lmAooCt+Xw+Apxt265Ev9dzvF/faZXdS1nxqTqvYwFLot97FMYqa8gn996qXBgANiW5\nwdo3qPqodc/EWIVa9XuFjjQMwDMsOVRssOs93vd5n4e9vV3sH+2haRrs7u4AhlFXNUzEiZY4xqhi\n1GRBnQdHpsUtzgIA8ADBwDsH13WwJJ81ciD2gAmnCNkDhik40SOUm9sWLIDDeXCM6k5ttKDgAefh\n2YE8oaoNXLuE6zrUbgH4GbjzI0uRmfsPh9dNE04degbmO/Ek2zO4/+AUd+7cweu3Pg524aSMhwWR\nByUR5aV/Mr022adjBSTWpfQ10G8TkoJ3gqVH+dmJ/Hno/swz2oK8iIpmStIyb6JcNHidEklLLOim\naXDp0iXcuHEDh4eH2NnZ6f0exarPsSlaYaXsjYCu1OFdHJbTOqRl01uD2prX4SJEOWlgpq1+PSd0\nfSX/a9euYXd3F8fHxzg5OcHHP/5x3L59e/SZo/OcLN203VeNjHL/5ny3NinHlNJNWZiLJiXFuikY\ne9S8ZOwsl0vcvXu3B15ywnc+n/fbjbmTvHpM6jmS8+OaAui5esnzmkWT67l1Oh03ubEsQFyeF98v\nmWfCHsn1HNBK67WuPlN1nKpPaayWrufWpZRNLf3+OOXCALAUberqTil671cXqoDJON3FHC08/cLc\nMsgy5k2NZ65cw2d91mfg0595F3YP9rF7eABPBrPdORrDOF0s+1hZtqpgYNEtF6h3GnDXBkDlHAgM\n37WoDOBdB0IN71xwFIeHdy0sLAwxnPMhsjx3qGYz+Pg+GWBxeorZbB7p6rB917UtOtcCzNH9PHzj\n0i2XINvAuw6WCMQeBBci2RNF6seEwB6i7OLx/7qusWccuh0DAwviCnfvEqyZYWe2h/v378M5QmVC\nVH2h0XMWTtrW6xZxbQXpU6HOdaMxUdoiYB5PwsDYjA91hLRXHZG1o3tQ0BfP4p9iuqbAVGptbsKC\n6cVInIfn8zk+93M/F8888wyOj4/7+Fxi5WtfIVms5dq4bceASQOp1GlXU//6mL7cS/27UmClmVM5\naq+dhqdOTmvWTw4RSDlu3brVswF1XRe/G5du82wq6ZzR/SZ9U/L3WseKnQf8pX9fFANlk7VkirVI\npaTA9fiR5yRI6mKxwOnpKR48eBDWzb29PriwBl+5/HPX9dZm7pTiFPjIgfSU6dVb9GmddV7pmNHz\nSK/3sqUq6YrPmzak1rlGlMDxFNN1Hsmtezotmc+5uZPL++0c+xcCgOU6IEWfJcVhTNXfD/GEbAhi\nilzDrQZF3NnZwf68xrueu4ZPffE6nr12FUeXD1FVDTwYdWNBntH5JarKIMTJIoAYcB2aWY12uQS3\nS9REqBAUAfkW8AzfdSDmPuYWiEKjsw+xr7wDw6A2IZ3KxHJ3wdKqTVB0bnkCggd3Z2AXQiegNuAu\nKAH2HYwD4HyIcu+WgFv0ccMiggOMgbEVyBjIPKmqCjtguBmhIoPl0uDwcB8LB5ydnGJv9xAWhOXi\ntKi40oXvfBMn5ys2AHHSDmIYmJU0zEJpAhtj4veMxmmz/N4rmXMU+W0WbRnra7nfc6J9BvXP9F2t\n2EXEn0MCqb744ot9mBWxeEWZpFstWpHo+F4pUyX9ohVEykhqsCbzWwdTli0bYdJ0wElgzKal4Exb\nvQBWtrOkbrL1It+v3N/f709Ieu/x8OHDjfpB6jfVf1MKN5V0PTwP4EvBeVqei8p8vRVJ21LGfBqU\nNmc4ylgWALZcLnFwcNBvwcuhIH1aNG1DzVBtAnz077m1oARiVnyFVRpTrFQaRDU3jtI5ImygnpPS\npjln9ly5S9dyz6Tr1Lr0HnVMl+Zm6d5bkQsFwPTvrBRmbgDpeyL6uK0syCNlg7FiE0t+NqtwfHiA\n5599BlcvXYKZN6iqBswEEz9L07Zt2C40hLqaBQbKGLi2Q1UZLE5bLJ1H09jAQoEBdrAm1IW9Q20s\n3LINg5MAsIfrWtSmAXkPMg5kasCFyd5UBr5zYN+haxeoLcCuA7gD0U6MXBVYtVBPRteGYJKuXQJ2\nidYNwRqd96iaBmxs8JMCAuNGBEuMmfFADVy5fIiHroGPecxmM7x55xN48+4dnJ61fbvmrM2BvTjH\nYGUL9J8hkoEQJzVWAdiIMlbBYsHcvyHJGbIx2dVtCwkDclEBGLC6LbWppAylsFQpGAIGy1ws3OvX\nr+PKlSt4/vnn+3hess2gF2Hxx0qBmAApASm5T+bINgYR9QyViAA3XXbtvC/MkwZcqVO8Zr9ku3Cx\nWPT1l/ZgHpxw0zZvmqZnyufzOZgZDx48wNHREW7fvo0333wTb7zxRh9NX97Vylazf7mtJp1v2n+6\n31MlXGJ60jhraX4pY5KKvi9lXvfdwyclqZ4o6YUccM1tLwmDI++krJd+5v79+/0Bj729Pezt7fVs\nl9621mujnnfAEPg09UXUrLNmkbVxouuqy6o/DSRzLid6jkh7aPcB8evSbdc0zQrgOjs7G7XRbDbr\nv2spRo5zbhT/TxindX01pS90O+beL80hfU+zXpLfJuzbuufeilwIAGY47pDRcCpKx2zy3ke6Ilga\nnsI/BlD5AEEAWdSHBbw2BlVlYuczFosWvnMgH3yxDnfmeOnFZ/DSjRu4evkKjg4OUVcNiKrekZ99\nF/29QmT+BhbGe3TLJc44dE5TWcysQetadG3YOvMEOB/jX7Wn4buDJGEu9mCrKviEeaA7W4BnDK4J\nrmOQd6irCswzLE8ehpMptsbyNHzPy8CCqxbMDYyp4LoTGOvBbQV4wC0WmJkKYA+qwoeLfecAsvDe\nwLehnJWlED2eCLaeYQ4Lc7ZE7Rl0DBzsH+F4L5zouX/lKj7+iY/hYzFiuLEcYo6Rx+lCAsFGJeF8\nCFMPQDNb3AOkcL0XCp8qMorVEN8+zwyI0y1HipvUAkbDOLGVsHPB92sE4qEYr3hZFrEwuSVUxYWY\nEllJQViqgEpWtyzyWinIz1QBNU2D5557DtevX++DlOa++5bmL/M0/VacLPy6jNovS95LgZYGLOlC\nqE90aVYsdT7W5ZWTk6IMtLIRBk3KJmnoAJZEhIODg54FlO2nk5OT3hm/Z1uRV3hSh018xzTAyvl3\npUaPrrfuG513jm2RtKaA/roDH09TUpZn3bNaxHDQ95h5dKpR+ylKPK9cIFU9vnNAQwP+8Xo4uEKk\nB1Vyc13Knd4v7QbkAI6e/6Vxld5Px7Z+rqqqvuxyTxtsuTqkQEh+bgpw1o3ZtK1KY2MTRq70zMYE\nw4RcCG0jYCro3RDYk72AsrDNeB4Z4kQNjrnMDi1b7M7muHp8hKa2OD48wvPPHOLy8RH2d+cgCoDC\nUhxkqt1lcJ2enmJ3dzccX3cO3C3ROkJlQ9gJZgcTB53zHZgJTV2j61rYqsKy7WBBcL4FeR/Qpwss\nl0VgvwwAuHDysbfkF0tYUSzGwFgD58QPTI4eerBbgrhF156iJoIzhMo2MGRxdnqGxhNabmHg0bUh\nmKutLLxnmKrCzm4F5zxOncfV2mAHO2iqCq9VFvdOTrCzsxuiPXdnIX+DIQit8zGkBK18EF0mdLr4\nlCS1VrX0aax5NzdBVq6xQR+FnwyMuVh+YBpEiDJOtwI2ldTiZuaV4+S7u7u4cuUKDg8Pe/CllXvK\nFGirV/wj5V8uWKOcesydfJQyAQODnYIV/VzbtivjRMomikAzfhLKAsAoHIDehtJgUBhyUZ5y4lOu\n7+zs4NVXX+0/bqwZxdJW1FuR3JzYVBHkgN+6eaLn7EWaE8AqONE/0/Eq93Qd5J4G3P8/d28aa1l2\n3ff99pnv/OZ69aq6q7vZTTabTVEmKYlgrAgQFRmJJdiSFQSxYCcIjMQBEn9xAgQwkMBIgCAD8imI\nEAUJnCARJEAKrNhWJEEhRZqSOIlsDj2wu5rdXXO96c73zHvnw7nrvH1P3feqmmyyS9qFwnvv3jOf\nvff6r//6r7VlXylAK4xSlmV1ZqwNwG1w0Xyu0m+EdbX7lPRJ21Gwr2lda4JgeS82qy1jqwn+m+eS\nY9ngUcaC3daxaTLG7bIwURTVmsjmee1nvO7+HtavLgJO57FZ9j0397no7/Pauu3eq/HwWACwdQNF\nsv9QqlqMRy2z9ViCNUfQ/Jn3rbWu0fjyyFZJgmqR7rxIiRczBjub7G716XW6BJ5fDzyjFFmyWB77\nbHAqpXAVlKbk9PiQnZ2dOiuxyFJKZQjDav0sw1mKbhXi86HUpEWC5wfVwtttH+NCMosrg6Q8VJae\nvVjHIY+TKnS4NESKs85clgbP9TBLsXpZ6ApA6hLf0RTxFMcUKFUVYjXFsgZNaQh8j7LIMFpjHAeK\nkhJNkWb4jofj+URBQBGnaE+TRgbf0/i+T7+3sSw0mAOasszRAmA401RJdXx78MHFk77tZUmfdywQ\napRakp3qbBvr/cpxm+dq/n4GQByUqzBGIcs8KfX4LD4MD3p36wz7eV63fQzZrjkx2ZNpu91md3d3\npXik1KxbF660J24xUnKeNE0JguAB8Cjv0gbj9tgVo2cDKduAyL62kblogrdZLgmNCPiS/tY0ZHJv\ndohVwjVa6xp8JUlS10CSbW0Wo8kgNFvTqJ4X6nuY934RA2D/bhtfee7NbZpMy0X96kfdLjKoD+v7\nF323zv7Y65aKFlDAt80gyT52uNkYU+l3l2E4eyF7AXdnUo0HgZHNjK1rcl32MWzdY3Ocn6fblX3t\ncKRc2zrmTKmzGn8yL4htkiSEPM9rVtjWgTXZ6XXtIjbqvHf/KI76u23vxqn5QdtjAcBKs6T/lVOt\nZmgMSjs1AwbVT9e6X2MMBijLM0ZgnV7hbGLW+J4h9Bz6LZdLmx02Wy79dovIc/EcKMoMnWt8vQyD\nuW4NJEqqgRgGPhjN8PSEQbeD60BWJKR5hiojlOeiqZYg0mVl2Mq8GiBREJCVJZ4TUOYpptQoU1Z6\n/jwjS9NK5JsW+H5YXYORgWhwqOpwectBrbWu6ospF89TFGWKqzSmSCnSBYFb4qIrNjHw0I4Lrlvx\nZW5EEFQh1sJUayd6auntakPg+fiRQacZgZuzO+iA8SCOmU6nOI6sPOme0d/WCzvPINrvRdqjCIcr\nUL76mYHlUkJnfcI+d9Mjap67NM6yz4FWhodfxY+u2WE4G7A0QYe0i4xRkyFoAjrHqTJhZVmh5tIl\nQA2KmueXiTvPc+I4rkGKVAOXrCk5VpOBs5k025GSsVyW5YpGa10oVb6TY5zHjohxCsOwNprnGYXm\ns5ZnJN8Jy7Gzs1Nfpw0w5d6aDIdca1OP0nxv695ns383wdLDWvN8F4Gx95K5ey/bRc7cw9iVdYzJ\nOvAl7KuAkab2Tt6fNHusSD/UWpMkCVEUrYxdAXGwXpvX7DvNY9v3ajsN0taFvpv3ajs+Yi/tMKL8\ntBkw+xjN525rNuWctr5s3bN/FLtg7/eoDvy638/b/rx9f5QOx2MBwKRAqrG9XV0+8LKdJetlZB+o\nwnlWVtXqgzfoJS3rOC69VsD+9oAn9rY52Nlka9AnbPVRnotyHAqj0csyEcYY0qQ8o2n9iNlsVlOu\nURQRL2Y4ainiVU71dxCuhC5kILtelQXpBQFGA8tBrHz/bMAUBRQFRRIvMyd9sjxfYRaCMMQUS6q5\nXHYUUy1AvYQSaJ0TeQ66SCEtKfySaKtL7iicwEMpD4yp7lUbAj8EvWBJ8dXPVpsqPNUJNbkuiRyN\nUmfVwyV7UakHB9M6I9H8Xtp5GhV7W6WqgrXyHOzWnKweNjhXPXy5h+U1r72CH31bV1wUVsMJsKrr\naO4LZ6LspmMi7NbOzg67u7tsb2/XZSb6/f6KZkVE7zI5y7NuskZhGNb6GfGQkySpwYuMCwlVCEMm\njJTco2+NCTGCeZ7Xong7xCn3IuzWeX3I1rqNRqN6fTsJJ9kVy2XbdUBKAGanU9UHHA6HdWacSBTW\nMUvyji4CNk1AdJ6hks9sEHeegWuOhfMcnzNm/SyjTY7zuIjwpa0b503jexHjeJ6xFQA0n89rMO84\nTh2ab4rpm+ewRfmiEROWSK4pjuOV8GUTFMnv9moStrZKrlHGVPMZNO/d3l+egZ2dLIkFtj5NHIo0\nTVf6kZxTGC8Jy9vnlM+FVbe1ng/TEl7U3y9iks87VvMYF227DpA3j9V09t4LJ+WxAGAYs5KRV3WW\nZUjIGmyF5QGo5X9dK6orFqP6lwIeGAfHSLwbrmy2uLLTZqcf0IpccBVFPscpHRzPBxwc0a4oUI5H\nWpQUWYmbzvF9F8qCxWRMEHgY38cpFfmiJAwDcEAvZkT9AXlR4noG19Vk2hA6Lp7y0EW1bmNcuDi6\nxFcuvieMjAdZiZtnVeV6X6HKHKWqDpjlOa7fptAFBo3rehgnIC8SjHEJ3IBcgyk0petgHBfSgqgb\ngMkJQx/CCB1s4KBxdImjy2rJIW1wywJMiYMmUGW1DJLR+Erj6ZzId9jstog3e2QkzOMFeZyh9DK9\nGo02CqMclNY4ykNjKJfvBsArH/TkC9fg6QpMix7QmNVQpFJVwoAxhrNXrsAKy9aTlKm6jz2YnDWT\ntb/MkNS6YiyVo0nxMI/NsFhfLXodw2e35kRne8v2MXzfr4GXLJ3ium6trbINljxjMRx5ntcesuhO\nJOwCkKbpihMi4Q37OmzGwL5W+75kopOFje33DKt1xOzrE8fHNkByP6LjEQMioSJ5rhI2amaGNYG+\nnfkoRjcIgnMz0dYZkibDIp/Z78nef93xzusb5zlAF33edIbWAfz3qzVZFnjwOm2maB0YsbeV/eVz\nm8mUPiGOSjP0bu+7LonDdiZs4L1uJYdma4IVAf7rxrud/bhOf7XuePb5pdmrOsiYE6ekyZrJMddl\nNgpIs52YJitn39ejtOY55JrO2/a87c4737pxZG/fHCvvBfCS9phYmmz5s2JkqglWFs4+EwxWYKzx\nEO2HqiqDbHRVUsBFASW+p9je7HP50h6DTkgYeFVFel3g+h4oVVc11kaRlxq9PI4fhJRao1yHLKsA\nShT4FFlOmSeEqsqKxPNxKCnLgjLLKZWPUi5pnFXhOq+6dFNqClPihQGKSj+GLsGvhPlFadAmYxEn\nRI4ijKKqiKs1gFzXpapvUWVVep5HlhVkRYnj+CQl+MpQ5imd7g7aOPhBG8IOuRehPHCp9lc4lKUD\nhaEkwdUZJo+BnLLQUBZQZLg6Q2VzTJ6SxzGzybQKtTqqWiLpETulUZyxTPbv5zQZGFprXOfss/p4\nLPVmMtnAspBttXyVsKp2tzGNX86uZ31m0PvR7AlP7l/Ai11p2zYGdls34dri/SAI6PV6daajXcOo\nBsZLwCTnFwNjH0eMlq0JEUAmx6tZYEtPZk948rcNnuwaXnLcLKvmieb55X5tw2cb4XXPwfbi1xXP\ntJ+BDeKaz9m+B2HCmmD3ovb9ABv7vs5jus4Da83fH+VcjwP4Alb6vd3OA6zrnon9DCTMJu9VwJQA\ndLtf2IyYACL7/KKDhLOq8dK37G2lL9vZlU3W0Q5vymfrogR2SNNmp5t9v8n8CaCSMdo8pzQ78iLX\na4/BdWyRzdjLfGGz6eva9wNofpA5ep0jdBHT/F6f326PBQBzVEWDGkqUAuUYqtpQ4CwXR3Ydl9JU\n5nUFcVvCIMWy0zkOgesSeCEt36Hd9uh2I1RZQOmiSgdlymWh1AyNQ5YX6GU4UxcleVkQBi1CqgW5\nHcclcxIocmbzCWWWo8gJ2iF5GmNyH9cPcdGURUZ30GOW5EStLkUSQ6kptcZVhjRL8d1qCaQ0q+qq\nmFLRbreYTcd0Q5fpdEaZuWhdDQRdVFqxIknxwpCsyHCUi+t65FKMVUGuHUp8AkAVOUme0/JDyixB\naY0bhJRGUypVMYy4aFfhuT208iiZofMYXzmUZYGroBMoKB3o+gxD2Bq0OBqFLLKUwqTL7MyzWmQP\nh1WP3mxBM2tUWko5GKOtQaUojamWWFLLZZ6WajVp8ruzRHTKWVLKThXmfhwAGKwaV5lEm57xoxpT\n13Vrtsd1XaIootPprEzOMnHaKeWwGnq0jyMTrNTaEtZLWLA8z+vtxLDJddthFTlHE3DJxC0MWhzH\n9b0IYyXhIDmPPA/bUDVDaKJNs8OnzWZrviSTTZ6TGOAgCOqaUPP5vM6GtD3qH0ZfaoKuR3Z+1oAy\n+zjye3O7x8UpWceESB9ptocxX/JZkiQkSVK/N9/32djYWAH5TdbLBhMyNm0Bu4wXG4DJNVbOcrbC\n/DYBmM3yrQub2gyrrcUUB8e+Lvu+bafFduzs8Ww7GzY4kWPZWcG2k2b3RTt0KddlJ+Rc9F7k80dl\nvC5qTaC17twPO++66zvvs++nPRYAzGW2vPklzY8BdVZ3yFEOyji47uqac8aYFVtvqIxpFLQY9Dpc\n3rnCtYOrbPZ6uA7o+TE+BZGrMHkGjkte5GhUFTpblvX0w5BAhRhdUbteWbJISlyj0XlOulhQZ606\nhgAAIABJREFUJDFOMeHkNCbyQLkurfY2nq8xKmM+B8fvVmLkLCf0A+L5lH6/DSZFL1JAEamqvIPf\naaHcgP7GNsO7N2iHPnkc47VaaBRuq0WonIrRyXJc30WXVWgGVRnG8Swh8kNKJ6rKRMQJZPdp6xjH\n05APKJMQ5VahJuV41eNTDkZDUhhK7RAEHdLZUmi/9NZCD7baPs88uYPnag5PxyQnMZRFnaVqjN0x\npQL/koZehgpLCw6p5fuSVhnOamFue3DW/cBZNTjGGErl1DXHSq2peC+Hqv6skbPU2yul6vMX2lqk\ndrl01XtJL/+gbR2L0xz454VkpNUZtEqxubnJ7u4u+/v7dWV7ATViEKTYot1sMALUbJZ8J4zPYrGo\nwZiUthCdlei35DjCLCml6uypNE3r+5BwZhAE+L7PeDwGKjAURRFZltX1mOQ+mwyPaFHkuUjBSNd1\nybKMdrtdT8xRFK0k89jPVzLi5FnZyQHb29uEYVhXRr916xb379+vn806b1uOIdusYy+bTEez2QCv\nyYitMzQP69dieJufrTvm+9XW6W7WgcOmYT/PgLquy/b2NlmWMZ/PSZKk1j4BK0sLybtsgheb6WmG\n7GwdlfR/oA4nCjiB1VpuojO0PxOwI3/neU6e5yRJspKkYusypWVZtuKcyHntMGu/3ycMQ4B6LM7n\n85rxEudLxtRisVhxRmzG0PM8kiRhNpshK0hsbW09AJTlvs4L/a377GFOjf39uu2a+zftzLq2bvz+\npQJgjhKzbP/LVhC84zgYJ1h5GI6jqoW0l00ebmk0pY7QOsdoTeCEtKMArRJMMsfoDMfRlHlKvlwr\nUbkeRrmYZdhSY8A4FEVZFXwN+1UWYg6tKCLVOdk8psgnFCbBc0N0ruht+qSxodsZYCgwRgMO88mU\nNFsQBg5RGJDPZhRliVEercBnkaS4voYypdMZkC0m5HmB365AoFlUpTE83ydLU7wgwm9F+KXBmByt\nC6J2h2QRkxWa6ekJXZUTz04ws0PM4S3aT5aobQPBgKjdRQUuyqlAp8LQ6bQotAd5jqsCVD4niacU\nRYrOc3Sh8Z2SJ65cZlYGzJKYYbkgz8qVQp3wQKD47B01QpAIacbqhL/O82syGRVYtwfT8oBWnxA2\npPopa0Iut7eWprJZmUcNH/0wW3MSse9D/m6CL+CBUIJtoGxBsBigIAjqpXWaYUQJM9ggToxDHMe1\nYRIwJd5vlmV1dXhjDJ1OB2PMA8UZZXK3769pZOxlXvL8bBUGuVYxDHJPsr0ASltsPJvNUEqxWCyY\nTCbLxed3GAwGaK1rsGhPykEQ1M/DzowzxtTJBGEYsr29DUAcxxwfHz+QQXcegFnX15panovaOtC0\nzsg0jcc6wNVssp0dLnu/W/M67GdsC83PM5D2s1BK1U5Cr9erQY0NVOSngG47a1CSPuS8ErqUvmeH\nNcWpCcNwxdmxQZzdl5ussz3uRas5mUxWnoGAwKZswP5d7kecIXFipMadzAUylux3b8+VtkMGZ6ya\n7XTI3CJ6ymbSgH1MeTfvBjy9m9bsE81xsu68TeBl9533yiF5LAAYTlrV63JUpeUxZlnuyarh5YJa\nhiqB2sLrpdjaUW5VFR0Hv/CIJ6cMlWK736bXCcDt4uYJeT7HNSURPmUWo9wAN2zT6nRIcr0UkBf4\nbkCpPCghTg3F5G087aCzGN8pSbMJbj5jPp7gq4JWEOO5OYvRDn4UwSTD67RwQoeJLiDL8LTGpDmL\nGHxVkqfJ8vwu6AKlR2hclN/C7wa0nQIn6lAWKdok+J4DpSEIPCgMuAa8EOVFlIsJba9NVi7wdMHJ\n8D6LdALplJNsSHvwEVp+QR7fIGpfobfj09qKQBt8J0M5AQ4OrgkpHUj8BUGuUH6EU2hKY3ADQzdy\nSAvFwaVN7tzqkk2PWPhVJmFR5FVigDHgLAEDCtcYXKgKzioru1WilVp+1Op5Vvu3wlEK3fT0MShz\ntiCyQ8WIlsswtdLV37UuDI0ubXRWLrvbUluoFZRu9fN9bmesn7MyOdug67yCrM3JRf6ezWYrbI3j\nOGRZtpLRJecRD9k2PHYRSfGqBTSJoZFyFHIPco0S5hAvG1ZFzDLOm5O/aKqkCKb9vS3qt7UsNmsg\ngKosS8bjMVmW1ZmKAgxlGRXHcWi1Wtac4668AzFcdsq+LFEk1zWfz7l+/fqKCFmaGPLvR1O1Dlg1\n3++67e12Hoi6yJg8LuyXtHWMoc34CJC/KCzZBGq+79Nut2uNUxzHDxhb2UfYJgEs8r1owKRP2uFz\nYcHkfQnrJMeX7ezzyPltIG8DTdlXGF5ZNshmRW2nzXZS7AxL2VdrXbOASina7TaDwaCu67VYLGqG\n0J53ZGw1kwBsoJfnOfP5nFarVQPEi4CW/a4epTUBXJMBXed8nMd4Per1/KViwJRT1OsZOpZ3Znck\nlF6K7Feb6y2XL8JUwnJKlF4QuCFOOWd09226Jsff3MajqOpelRmLLKXb7oHSmDxmdDynwKXQ4KgY\njUuagfJCeoMNot4mJktB5ZTxlJbOUGFIFEWU6QzXdTk9PaW34RCFIVov0EVApguUChjPZ+wMOpyO\nhgw2tykL0CiU51cL+voui3mG77WI+iH4PpPxKWGyIAw9lAbKpd7FC9GhhxOElFRL8GjXQ5kUXeYo\nNMkiZnT3NjFjFoVHMJtxRR3D3ZTW4B3UaI/wyWegf5mkPSB0zupgGe0SqhZB2ycoczKvMn4KTV4U\nBMpjhxYffP5FUuVy994NFovFcnKUWkzC/jXqMslPu0Nbv56xVfqByURChLZHtnLsesJc/l5qHFWx\npJnO1wz6iuWUC9Bao02BMY9P2r19zRdNHM19mpOQtCRJODk5qZksmfzF4xbjYGcR2hmPMoFLWr7s\na5eWkDCkTOpAnV0pRgWowVVRFLVHLUZGSl9IqNAWM4uGy2Zo5LnYk7vNBAjYPDw8ZD6fY4yh3+8D\nMBwOieOYxWLB1tYW7XZ7hUmQZhfmlOuxgZbneRwcHLC1tcVwOHygMritqbuoNft0EwisC22uGxPr\nzmMf22ZUz2s2i/I4tPOenX0f61hHG5QJiJOweVEU9eomYRjWQKMJvGwxvTgHdh8Uhla2lxCmfGeX\nnhCHxNZc2tdqOycCeuyEAK11HYqX+xQHyb53OzRv/yyKog7JCzslGs7t7W12dnZqVnexWHD//n3u\n37/PZDKp+409R9iAV1hA+3MpI9Ocw+SemoCuObc337F9j837Ow+ArWOCzztmc7/mdVyUVPBu2mMC\nwMoagEnHcz0XpQRzLWmSdd4+oE0JxsHBxTGKwPPoRBFd39Bxc/T0iIIcHQUEvsKhqn2V5zllHmOU\nS4GLF7ZwFQTRJto4tFo+nh/h+i1yVRLnc3qeAzojnw3RStHpdJhmcxaLBa0oIFuccJplbHoBKA8d\n9Gi1elThMacCX6Wh3e2DE7OYLzAKup0Os2lBuxsRz+YE3S69nV3uf+9tLu3vkGYFrSjAxUGXGuN5\nEAWY0oDvocIQr0hpddpMFyO2dna59ear5KXhzsywuZNz+o0/xEsOMU7CRz/4DIu3rvLUT/0Ceus5\nim6E40WwLDiLCak4JYXjFuSOj85isqIk9F0GgWKzE4LjoosMU1ZCZT+QxZgrxknZMUbAGP+Bd6is\nAVPqaskmx7Fo8+U/Ux0AI4VyUaxUZxWPEjG8ZbWcExB4auV41fbWpFcqTJGjTYbhjFV7P9tF4Ms2\nMusmETE4tvcsoQthw9Sy/64DaXJOMRr231EUrRgImYzsMIsYFqWqkJ8YtI2NjXpdRniwZpkUcAVW\nGC8R8UdRVBtNAXw2+BIjJMcXsBSGIf1+n3v37jEcDuu5Zjqdkuc5W1tb7O7usrm5yZNPPsmlS5dW\nsu7srC/Rf9nMl4Sk2u02rVbrAUF+szUNQfMz+dt+lzbgtY/5qGHzZtLBRUBdzt8UZL+f7bxraI6N\n855FM7QrwGixWBDHcc3QyHHsmne2VkuAm4TZ5X3Y2ZPSv+ykFGFkz3vuNkBYB6Rk++Y1JUlSAzEb\nhJ3X5LoF3E0mEzqdDlEUcenSJZ5++mm2trbQWterPWxvb9cyABnnxlTaSbnX80KxEvK35Q3y/bp+\nZ69U8W5b83nabZ3Wsvn9eQxz87N1TOz30x4PAKaqNQWVanovIqIWtGrvsxwkRuP7LrowOMYQOB7b\nnQGDbo9Bq0MvbBEqlyyZ4ZsQbTxc3yfLE3Tp4Dki+q/0G0op4iSi3d8k9Dt4UQ8/bBG4Ma5ySMb3\nSOKUdDIlaEUkZWUwTJFxdHiPva0++Jp2MseYMRv7e4ymE3r9DfIioygSPD/EuAGuZwhCg+spTo9P\n6G9doshS0vkcHGj1++w/+0FMviDyXdIkJmx3KOIMr9VGF1X5i7Io8cMAR7douw66yGl5iv72ZYZ3\nJ+jM4Y8+/zneeP0lIgP9CF5//XX+zb/5i9x79U9o796g+8yncPublG6EdlySRYLScwLPpSxyPAWF\n45DGCenwhK6veG6vT+vTH+frW4obN24wmZyyiOc4jgY00r8rIL0MDVA8OPDUmRjTdTVan3l1ji3S\nr3oGriX8U5wNIr3MhqxE+cvtVvRlZwVX62ZEZ2hQqsRxc5R6//Uu60Io60IM9nfSZKIXRku0Hnt7\ne4RhWIdPxABJlpZ4tHCWWi/hvzAM658isJdtBGiICF+u5/j4GK01vV6vDhm2Wi263S5pWi27Jeya\naFIkpCc6siiKahbOcRy63S5RFNV/y087tCJGyHVder0enU6H4XBYf3/v3j3eeecdvvrVr3L//n2M\nMbRaLba3t3nmmWf41Kc+xU/+5E/yyU9+ciVzNI7jleQBeV6SAWlMlUX34Q9/mDAMOTk5YTqdPpDU\nsO59XcRs2o6pve+jGCgZV+sM03sVRvlRNfvZ2+08pmSdAbaLogoAUUqRJAmLxaLWRdp9ClgpWCoA\nv9vtrtgrASFSgFicBgHl0jcFzMtYabfbdQjTBtjNkJodfpd7FLY2y7I6E1muU85lhzvl+mywMZvN\nACoyYTrl85//PKenpxwcHHBwcMClS5cYDAZ0Oh3G43EtzheWW+YPuX8Ze51Oh263i1KKo6OjlXPK\nO1gHtOz5bd04aW77sHFwZkseXDXFbs2F2c87vjCIf2kYsApILpU65kzvAqsswNpJBBfH9fAch0D5\nhI5Hx+ux0dqi02oT+C6+U9HHIQWuBwpNmlYLZadaU+BS6oS0hHa3R28rIC7moAK0icnSjMl0RJwt\naLsuTmtAZ1MzGd5hOhnj6BSdLxiNRhitCDsGfe8+3e0W08MjiqiHcjWeLogCH+U6OJ0u6Sym3e6g\nTF4tA9OJmA9P2Oi3WKQxJDE66FA6EY7rEXZ6pLMFfrtXhddcH8dxwRjyvMD4AZ4b4HdTsjxj++Ap\nRskN7t885Q+/8ipzp0WWdgn9KV+8d4/f++av869/+nn+4d//O/j7HyYrcnRngN/q4vk+Js5ZTKe0\nI49SZ5RpjIdhMR2ySE7IXZ+89Llx8w2msynzeIRyNFDwQNdSCtA42MV2RXDv1dq/ShBWrYoA1IV4\npYecCceknZ3HdZeerSpxtME4Sz2hAYNeybismrNMAlDLxI8cVFH9f59bc+DbOqd1kxOsGlalVC2w\nlclYVnCwWSM5tg2+7HAjnIUU7O/iOF4xFhKWHI/HxHFcZ1EJOBkMBjWAMsaslIGwvXkxinLttrEQ\nAyNMnuy3LswirJxMqq1WizRNGQwGBEHA6ekph4eHAPT7fU5OThiNRty6dYvvfe973L17l+eee64G\nfJLh1qxRJmFGyQyTbZIkORd8rXvXj9JkzKyr2XTRsc/rJ+exc/Z2jwv7BeuZiObvdpMx1AwricPg\nOE69VJBdLkJ+yv4CKOQ9C5NztiLI2XOW0J4AFNEbyniEs7CgsJr2O5LwvV1TTPq6hDjlGsX56HQ6\ntNvtOtFk3cLw9nwi/UdAnz1Oh8Mhr732Grdv3+ZjH/sYzz//PM8++yzPPPMM+/v7XL58mcPDQ0aj\n0QrrZYdsxSmT5yBjPk3TOpT7MNbWfq7nOQznsYjNvtJs9lxq9xX5bt05mp+Jw/qDtscCgCnHowox\n6mXFclMvsnxmbBW6Vik5gGSoCEUfsNXb4krUoR16hJ7CQ+PkhkLPwNe4xiFfVMVePTeizGFR5stB\nGKD9DsqJIK3WCYyTEj90cZRhcOUpthW4OseJr5CMj1Btj1nyMnq64LtvvAV+h/tHtzi4sk9hApTX\nYzwt2d17ghzweh2yMqDT6zO+c0qvHUG+QGPobe4wObxNd2OLeQZBq83R9VfZvbyH0+tg2j1y5REM\ndjC5oSCvAEeW4HgOvu9h/BZoTbSzh9du8XR/myeeS/ji7d9h/yf+Bj/zy3+bf+vf+Rv85//db3P7\nX/yvvPXWV/jfvvAmJ6f/I//u332Tj3z8F3EPPkGCx1Y+Y5rFZFkKZQWQSuVTGEXQ22R49CqRyXh+\n+4D0wz/O733xDxgxRbkOUQqlfxbGqzqxqlimZZdz3TMqvTR5hYPq0CWo8mzirz0X46GNXmFCtTrT\n/2hdgTfHGIwy9XbGGBwe9JyqL1UVyTQK1y0wzoJHtIc/9NYMOa7TJzxsn16v90C1dxtE2KE1OYfN\nitnC3yzLahZMPFzZXwCIgA9jDMfHx3UIQq5pMBgAlecu1yqCfwFmtp5MjJUYnOl0Wod2RDDf9PDl\nXuBMYCzn63Q6vPDCC7zzzju88MILvPDCC/zsz/4sv/Zrv8brr7/OeDzmrbfeIs9zfvEXf5FnnnmG\nzc3N2mAIUG06hlEUMZ9XUoTd3V2eeOIJhsNh/Sxk20dp67L6moalaXTWpfjb29sasfPaOm/fFpS/\n382+hofdB6zX88g9CusaRdEDFe9trRacaf9sQCYaMDtEK4DGZoKAGogBNQgTxleAvfwtLJjd5Fh2\n8oc4JsLESNhPWDe5pnVhtzAMHyjpcXR0xJ07dxiNRsxms9phOT4+xnEc5vM54/GYp59+unbCBLg1\nBfh2uHY6ndbPZTgcUpYl7Xa7Ho8XJUvAqkShOQf+ICFAWxMo53y3jtBfGgbsB2m+CtEa2m6bSEX0\n2wMi38N1/UoThkbrEFNmJFkMVOzaIl2QJiWLPMYUHaLOgDBwybME095AuSFO2KX027S6XbwoxHed\nCoAFCqdMcecR2WDAreNb9Loehyf38ZyQ4ckhs9mU0+GEg6c/ytFRQafbZ3OnRxAGdFoRTuBQ5Ale\np4uTp8RJQtDuEucF7W6PPEvob3WIFwlKQzaL6W/vYJIZjhvieFUF/wqu+Bit0YsEVxmUKQk7bcJe\nFzZy/sF/9Hc5/LXf5sZwwX/9v3+Z0cktVCuiv7PP0a1TvvLSXZ579st0o0tcw8HsP8UxfRxdGRpH\nGXSeUqYJxXzE5OSQluqRTm8Th7f46NWPEP7Vn+Kff/lrHCUzymBSh/aqJqFkg3Lsid6AsgqsquUS\nVCw3r5cZWuqJ0Cin6QlblPWSPVM0hJdaV0yhfUXGoLXEJ6v4t+NqHKOrQsB/gZtMbAJsZJkhATl2\niENCFjbDZdfgsjP/bIPV6XRWFu3WWtPv92svfDqd1p6853mMRiOSJGFjY6MGc3JtdqkJCeOtYxl8\n3ydJkpphk/uzmSEbGNnGUQwUwIsvvshiseDKlSv0ej0cx+Hg4IDJZILjOIxGIw4PD/nWt75VJwkM\nBoOaBbH1deLpSxKKXOtTTz1Fmqa89tprdbkAu60DUrauxJ7kHxZiuygMcx6DcJHBaYa63yvB8XvZ\nHjX8Kk30TPL+5B5FOyXgRfp6k2W267Y1s4PtAqzrNIk2+yxOkFKqdmbk+ALsJQxqgz67OLGcS8bN\nYrGoQ/pS5kL2F42bXSai1WrVOkaRJ8RxzOHhId1ul49//OO8+OKL5HnO/fv3SZKkiu6YSsMZhiGb\nm5sopRiPxw+Mv1arVY9LySjd2NggyzLu3bvHbDbj8uXLNRiV93Nee7fg//t1FpqAVY7THCfvFfsF\nfwkAmKNdNtoDNqMBe70ttvubwDIUlaUUeUqaJbgKPGXIi5QsUxS5IYq6lCjyNKbUir3tJ8jwyJWL\n1+7T3bmEG0b4vS5ekeAqBdowHU9YjCfEh2PeeuMdju4fcTq8yyyJcZ028+kJxhh291MGGzu0egGe\nFxH5Je1OwGR4TLfTJs0ylBuwSNMqVh50yRcxaTIjagVo5aBMBxWFhCaH6ZhyNmM8HFK2+uwdPIHG\nQ+FjcGExp3QU+Aqj3EqEHoU8+/Qu//N/9R/zz79+hz/85m3+6Mu/x4c2N7k9nrOxuUuSTPjat97i\nqcGfsRsZdjY80v5HcVIodE5R5JRxTDafcffmK5QLzcnJkI0g5m72Fs+2PP7K1WcZT/8Kn/3Otzgt\nhyKZB8B1q0zDakLMl8Vf4WIAVgE0bcBRDo4LSi0nKSuU6GCVZliuBWqHHrSpljCqQqNnzWhdFaet\ngaKD0lRZpOrxyYL8flsURQwGA7rdLu12u56YgRXv3dau2KJjAUk2uyT1gqR2khgje6JN05TJZMJw\nOFwxDIvFAs/z6Pf7lGXJ5uZmDaCEebAzyMR4iRGRa1pXF0wyueT4cDaZ2uFC8cyvXbvG9vY2t27d\n4mtf+xqf/exnee211zCmEhX3+32MMXznO9+pwWur1SIMw9rYikGTsM10Oq01pKPRiL29PT784Q9j\njOEb3/jGAyGXdxNGbDYbHMGDxsv+zmaR7e3WhSbtJu+jGSJ7P9s6Zmtduwiw2okIdnhuXajOPpYx\n5gGwZQN8YUF936/DbNKPRVLT1BhJ35XkEwnZCeNrX7ec3wY74hDYrJmEzOVe5HrkOdissxwnCIJa\nH2qvErGzs0NRFIzH4xXHSsatMOE24LSPL2BWnLDNzU1msxmHh4ckSbLyTOzn0mTxbbbqUVgv20Gy\n2zpg9ijHbvanplj/B2mPBQCr2RAuLsRmtzrspKqO7KEIloLswA8rcX2kWMzTZUfPMU5OUWSUpcFz\nQ05OjnB9j42tHVr9HY4nC7qDbVrdDfp7l3HCFjlgygKTLUiynPnomGx8zOGNN4nvDZkXIUcTOJ04\nxImm3YZ0Macd+AySnOloyNblKzi65OT4Pmmas7F9ibxICFsRGZqo20NTQJqjKIgiBekCjCI3JYEx\nKKPIk4QkTdje2yPDAXJUmaGMi9Ia3KXWiYpBQLlkLrR0ziDI+NWf2OOnnwjYmf9V/uVnv06/5fOv\n/cxn+J3f+k02Tzq8+vpNBqHmWd9j/4VNMp0xG0/wHJfA81HKsNGJuH7re1zqbdCODB1vi2x2jN/e\n4ic+/CEm2vAH37iN668yLWfNNgJgTFmH/NQSdJ2BgLOsnqo/lDjOWWgRwLEyGasarEvR8fJvEdQb\n1QituGCMXoK+ajkjAG1SeAwA2DrW4mFjwp5gRPjbbrdrPVUQBKRpWjNIArjsQqrCZIloGM7Wjex2\nu/WEGcdxrckqy5LpdMorr7zC8fExo9GIO3fucO/evTqLSlgo0atI8dPZbEYURXS73Tp8YoypmYFm\n6EUmeflMWA0xOhK+tMNIsp8NKLrdLs8//zwf+tCH+OVf/mV+/dd/nc9//vMkScLVq1c5OTnhu9/9\nLo7jcHp6yjPPPMMnP/nJFSbFdd1aO3Pnzp0aABZFwWw2Y3Nzk09/+tOMx2Nu3LhR63rsid4OM67z\nvpt9oclcrQs/2n3FDputC202r8M+tl0H7XEIQX4/LJwdRpQ+LeBIQIO9YLYNcmRMZFnGeDyuAQ2c\nlUkRZkv6sF3IV4CKZAEOh8PaqbGF9MKkSoZgq9WqWSqp0yfXJNduX4cNIuM4rtlnqMbu1tZWfe8S\nFpQm93TlyhX29/drCcJ3v/tdXnnlFba2turSKkopvvSlL9UM2KVLl9jf32c2m9XLdYkTZrPoZVly\n48YNfN9nc3MT3/e5ceMGrVaLjY2NuszM99NsZ/uiuVKkF+uavbTTOvBuM5Hrjv2DtMcCgFU3pOBd\n3pjjOGhl8COXdruF5znM0wVKQ65y4viE4el9XBRaQ46mKBKMUaTJjK3NHaL+Bq4XMpqnuIMdSq+F\nCdrMkhSV5+gyIx0V6OmYPElRRczo7g2uf+clppOM0/GUud7g/nzIdOyyURQEBDhKUWoXRUjotmlH\nPU6Oh4RBm+nokKjbY57M6W7toIzGURkUC7JkRpEU6AxKQtx+GzCU2uD4EZ3tDrRbBCaHLKHMMigX\nUGrcbo8yzdF5VRDVdV08sool0wrlKZ486PNf/r2/xf+xcYn/9w80b11/GTfo4vSeor19hVavx603\nX+Xy7jOoaJNe4BEnGdM44fh0CLMpSbLge5P7fGB/i8IoegcttNJ0IsMnPvI8N0/v8cbdby4BVjPU\n8bCJXEAYKOUs/5/1kXfb+evt1+xnjzWlFMrROG6Jct5/APb9DHIxluLVir4kTdPawxadkhhluwgr\nUIdF5PxyHKnjJR56HMcrS/OMRiNu3rzJ6ekp4/GY6XRap/dnWUYYhrWhEKag1WqtsG+yTbPGly10\ntyd12dZehUHqjkmTMIftFdvgTEImv/RLv0QURXzxi1/k7bffRusqe3NjY4PNzU2Koqir5wswSdOU\n09PT+l5lTcEoimq2xHEcnn/+eQDeeeed2nDL9djA5iKQ02QHbBbsUYTDdpPn0Ax/ynXZzWY23u92\nHrP1sG1t8C4AzF4ayGZ/bZZY+rv0OwFjshyW/JfMYAFxEgKU5a0kzNfpdDg9PWWxWNQgSJJI5L/W\nVYkLewxIP2+1WjUAM8YwmUzq/iTnlvcnTHWr1aLX660UXxVHRUCf3L/oLYMgYHd3l16vV4cMr127\nRq/XYzKZkGUZN2/epN/v89GPfrR+XnYIV4CtZF3b9cJEdiCZzrbUQd7dee+22QfWsWbrjrFuTFwU\ntpfv10kAmtv9IO2xAGDVw3Iw2qDcShD9wFo2S2OO52OMwildfB3QbXdouSFKZygdk4ym5j3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z6fc3JywnA4XFmirNfr8eKLL9YlYoQZFmBoC/SFJZYSGvKsRbcmzK8UX7azTqUmmawfKfclDL0N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\nN2i32xwfH5u4N3HfnZycUCqVaLfbRkJDmC6Ji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Z/PEwgE+Mu//EsSfX20LZNSqYDH7yOb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\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Op43RsQPEQkGuXZ4kGI4wMTHB3NwcVosu3k/E4wQCAXpiERUA+sADD9DV4Cvf+Cr1eheb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dx1F6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\nJ8b/B3O1kmksldKUAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1145fcf28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from scipy.misc import imread, imresize\n",
"\n",
"kitten, puppy = imread('kitten.jpg'), imread('puppy.jpg')\n",
"# kitten is wide, and puppy is already square\n",
"d = kitten.shape[1] - kitten.shape[0]\n",
"kitten_cropped = kitten[:, d//2:-d//2, :]\n",
"\n",
"img_size = 200 # Make this smaller if it runs too slow\n",
"x = np.zeros((2, 3, img_size, img_size))\n",
"x[0, :, :, :] = imresize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
"x[1, :, :, :] = imresize(kitten_cropped, (img_size, img_size)).transpose((2, 0, 1))\n",
"\n",
"# Set up a convolutional weights holding 2 filters, each 3x3\n",
"w = np.zeros((2, 3, 3, 3))\n",
"\n",
"# The first filter converts the image to grayscale.\n",
"# Set up the red, green, and blue channels of the filter.\n",
"w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
"w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
"w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
"\n",
"# Second filter detects horizontal edges in the blue channel.\n",
"w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
"\n",
"# Vector of biases. We don't need any bias for the grayscale\n",
"# filter, but for the edge detection filter we want to add 128\n",
"# to each output so that nothing is negative.\n",
"b = np.array([0, 128])\n",
"\n",
"# Compute the result of convolving each input in x with each filter in w,\n",
"# offsetting by b, and storing the results in out.\n",
"out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
"\n",
"def imshow_noax(img, normalize=True):\n",
" \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
" if normalize:\n",
" img_max, img_min = np.max(img), np.min(img)\n",
" img = 255.0 * (img - img_min) / (img_max - img_min)\n",
" plt.imshow(img.astype('uint8'))\n",
" plt.gca().axis('off')\n",
"\n",
"# Show the original images and the results of the conv operation\n",
"plt.subplot(2, 3, 1)\n",
"imshow_noax(puppy, normalize=False)\n",
"plt.title('Original image')\n",
"plt.subplot(2, 3, 2)\n",
"imshow_noax(out[0, 0])\n",
"plt.title('Grayscale')\n",
"plt.subplot(2, 3, 3)\n",
"imshow_noax(out[0, 1])\n",
"plt.title('Edges')\n",
"plt.subplot(2, 3, 4)\n",
"imshow_noax(kitten_cropped, normalize=False)\n",
"plt.subplot(2, 3, 5)\n",
"imshow_noax(out[1, 0])\n",
"plt.subplot(2, 3, 6)\n",
"imshow_noax(out[1, 1])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Convolution: Naive backward pass\n",
"Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
"\n",
"When you are done, run the following to check your backward pass with a numeric gradient check."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing conv_backward_naive function\n",
"dx error: 1.15976707193e-08\n",
"dw error: 2.24712817884e-10\n",
"db error: 3.3726400665e-11\n"
]
}
],
"source": [
"np.random.seed(231)\n",
"x = np.random.randn(4, 3, 5, 5)\n",
"w = np.random.randn(2, 3, 3, 3)\n",
"b = np.random.randn(2,)\n",
"dout = np.random.randn(4, 2, 5, 5)\n",
"conv_param = {'stride': 1, 'pad': 1}\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
"dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
"db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
"\n",
"out, cache = conv_forward_naive(x, w, b, conv_param)\n",
"dx, dw, db = conv_backward_naive(dout, cache)\n",
"\n",
"# Your errors should be around 1e-8'\n",
"print('Testing conv_backward_naive function')\n",
"print('dx error: ', rel_error(dx, dx_num))\n",
"print('dw error: ', rel_error(dw, dw_num))\n",
"print('db error: ', rel_error(db, db_num))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Max pooling: Naive forward\n",
"Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
"\n",
"Check your implementation by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing max_pool_forward_naive function:\n",
"difference: 4.16666651573e-08\n"
]
}
],
"source": [
"x_shape = (2, 3, 4, 4)\n",
"x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
"pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
"\n",
"out, _ = max_pool_forward_naive(x, pool_param)\n",
"\n",
"correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
" [-0.20421053, -0.18947368]],\n",
" [[-0.14526316, -0.13052632],\n",
" [-0.08631579, -0.07157895]],\n",
" [[-0.02736842, -0.01263158],\n",
" [ 0.03157895, 0.04631579]]],\n",
" [[[ 0.09052632, 0.10526316],\n",
" [ 0.14947368, 0.16421053]],\n",
" [[ 0.20842105, 0.22315789],\n",
" [ 0.26736842, 0.28210526]],\n",
" [[ 0.32631579, 0.34105263],\n",
" [ 0.38526316, 0.4 ]]]])\n",
"\n",
"# Compare your output with ours. Difference should be around 1e-8.\n",
"print('Testing max_pool_forward_naive function:')\n",
"print('difference: ', rel_error(out, correct_out))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Max pooling: Naive backward\n",
"Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
"\n",
"Check your implementation with numeric gradient checking by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing max_pool_backward_naive function:\n",
"dx error: 3.27562514223e-12\n"
]
}
],
"source": [
"np.random.seed(231)\n",
"x = np.random.randn(3, 2, 8, 8)\n",
"dout = np.random.randn(3, 2, 4, 4)\n",
"pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
"\n",
"out, cache = max_pool_forward_naive(x, pool_param)\n",
"dx = max_pool_backward_naive(dout, cache)\n",
"\n",
"# Your error should be around 1e-12\n",
"print('Testing max_pool_backward_naive function:')\n",
"print('dx error: ', rel_error(dx, dx_num))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Fast layers\n",
"Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
"\n",
"The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n",
"\n",
"```bash\n",
"python setup.py build_ext --inplace\n",
"```\n",
"\n",
"The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
"\n",
"**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
"\n",
"You can compare the performance of the naive and fast versions of these layers by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing conv_forward_fast:\n",
"Naive: 4.566963s\n",
"Fast: 0.010755s\n",
"Speedup: 424.633806x\n",
"Difference: 4.92640785149e-11\n",
"\n",
"Testing conv_backward_fast:\n",
"Naive: 8.213609s\n",
"Fast: 0.010147s\n",
"Speedup: 809.454253x\n",
"dx difference: 1.94976477535e-11\n",
"dw difference: 8.92933135569e-13\n",
"db difference: 0.0\n"
]
}
],
"source": [
"from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
"from time import time\n",
"np.random.seed(231)\n",
"x = np.random.randn(100, 3, 31, 31)\n",
"w = np.random.randn(25, 3, 3, 3)\n",
"b = np.random.randn(25,)\n",
"dout = np.random.randn(100, 25, 16, 16)\n",
"conv_param = {'stride': 2, 'pad': 1}\n",
"\n",
"t0 = time()\n",
"out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
"t1 = time()\n",
"out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
"t2 = time()\n",
"\n",
"print('Testing conv_forward_fast:')\n",
"print('Naive: %fs' % (t1 - t0))\n",
"print('Fast: %fs' % (t2 - t1))\n",
"print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
"print('Difference: ', rel_error(out_naive, out_fast))\n",
"\n",
"t0 = time()\n",
"dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
"t1 = time()\n",
"dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
"t2 = time()\n",
"\n",
"print('\\nTesting conv_backward_fast:')\n",
"print('Naive: %fs' % (t1 - t0))\n",
"print('Fast: %fs' % (t2 - t1))\n",
"print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
"print('dx difference: ', rel_error(dx_naive, dx_fast))\n",
"print('dw difference: ', rel_error(dw_naive, dw_fast))\n",
"print('db difference: ', rel_error(db_naive, db_fast))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing pool_forward_fast:\n",
"Naive: 0.009576s\n",
"fast: 0.003754s\n",
"speedup: 2.551032x\n",
"difference: 0.0\n",
"\n",
"Testing pool_backward_fast:\n",
"Naive: 0.020518s\n",
"speedup: 1.685283x\n",
"dx difference: 0.0\n"
]
}
],
"source": [
"from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
"np.random.seed(231)\n",
"x = np.random.randn(100, 3, 32, 32)\n",
"dout = np.random.randn(100, 3, 16, 16)\n",
"pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
"\n",
"t0 = time()\n",
"out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
"t1 = time()\n",
"out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
"t2 = time()\n",
"\n",
"print('Testing pool_forward_fast:')\n",
"print('Naive: %fs' % (t1 - t0))\n",
"print('fast: %fs' % (t2 - t1))\n",
"print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
"print('difference: ', rel_error(out_naive, out_fast))\n",
"\n",
"t0 = time()\n",
"dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
"t1 = time()\n",
"dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
"t2 = time()\n",
"\n",
"print('\\nTesting pool_backward_fast:')\n",
"print('Naive: %fs' % (t1 - t0))\n",
"print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
"print('dx difference: ', rel_error(dx_naive, dx_fast))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Convolutional \"sandwich\" layers\n",
"Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing conv_relu_pool\n",
"dx error: 7.39978728349e-09\n",
"dw error: 9.51792341939e-09\n",
"db error: 3.72167075082e-10\n"
]
}
],
"source": [
"from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
"np.random.seed(231)\n",
"x = np.random.randn(2, 3, 16, 16)\n",
"w = np.random.randn(3, 3, 3, 3)\n",
"b = np.random.randn(3,)\n",
"dout = np.random.randn(2, 3, 8, 8)\n",
"conv_param = {'stride': 1, 'pad': 1}\n",
"pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
"\n",
"out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
"dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
"dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
"db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
"\n",
"print('Testing conv_relu_pool')\n",
"print('dx error: ', rel_error(dx_num, dx))\n",
"print('dw error: ', rel_error(dw_num, dw))\n",
"print('db error: ', rel_error(db_num, db))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Testing conv_relu:\n",
"dx error: 3.10589677572e-09\n",
"dw error: 3.82831406176e-10\n",
"db error: 2.94490346032e-10\n"
]
}
],
"source": [
"from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
"np.random.seed(231)\n",
"x = np.random.randn(2, 3, 8, 8)\n",
"w = np.random.randn(3, 3, 3, 3)\n",
"b = np.random.randn(3,)\n",
"dout = np.random.randn(2, 3, 8, 8)\n",
"conv_param = {'stride': 1, 'pad': 1}\n",
"\n",
"out, cache = conv_relu_forward(x, w, b, conv_param)\n",
"dx, dw, db = conv_relu_backward(dout, cache)\n",
"\n",
"dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
"dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
"db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
"\n",
"print('Testing conv_relu:')\n",
"print('dx error: ', rel_error(dx_num, dx))\n",
"print('dw error: ', rel_error(dw_num, dw))\n",
"print('db error: ', rel_error(db_num, db))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Three-layer ConvNet\n",
"Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
"\n",
"Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Run the following cells to help you debug:"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Sanity check loss\n",
"After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization this should go up."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Initial loss (no regularization): 2.30258422991\n",
"Initial loss (with regularization): 2.50845554372\n"
]
}
],
"source": [
"model = ThreeLayerConvNet()\n",
"\n",
"N = 50\n",
"X = np.random.randn(N, 3, 32, 32)\n",
"y = np.random.randint(10, size=N)\n",
"\n",
"loss, grads = model.loss(X, y)\n",
"print('Initial loss (no regularization): ', loss)\n",
"\n",
"model.reg = 0.5\n",
"loss, grads = model.loss(X, y)\n",
"print('Initial loss (with regularization): ', loss)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Gradient check\n",
"After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to 1e-2."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"W1 max relative error: 1.380104e-04\n",
"W2 max relative error: 1.822723e-02\n",
"W3 max relative error: 3.064049e-04\n",
"b1 max relative error: 3.477652e-05\n",
"b2 max relative error: 2.516375e-03\n",
"b3 max relative error: 7.945660e-10\n"
]
}
],
"source": [
"num_inputs = 2\n",
"input_dim = (3, 16, 16)\n",
"reg = 0.0\n",
"num_classes = 10\n",
"np.random.seed(231)\n",
"X = np.random.randn(num_inputs, *input_dim)\n",
"y = np.random.randint(num_classes, size=num_inputs)\n",
"\n",
"model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
" input_dim=input_dim, hidden_dim=7,\n",
" dtype=np.float64)\n",
"loss, grads = model.loss(X, y)\n",
"for param_name in sorted(grads):\n",
" f = lambda _: model.loss(X, y)[0]\n",
" param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
" e = rel_error(param_grad_num, grads[param_name])\n",
" print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Overfit small data\n",
"A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(Iteration 1 / 30) loss: 2.414060\n",
"(Epoch 0 / 15) train acc: 0.190000; val_acc: 0.128000\n",
"(Iteration 2 / 30) loss: 2.609504\n",
"(Epoch 1 / 15) train acc: 0.230000; val_acc: 0.094000\n",
"(Iteration 3 / 30) loss: 2.113380\n",
"(Iteration 4 / 30) loss: 1.971811\n",
"(Epoch 2 / 15) train acc: 0.310000; val_acc: 0.098000\n",
"(Iteration 5 / 30) loss: 1.676728\n",
"(Iteration 6 / 30) loss: 1.801782\n",
"(Epoch 3 / 15) train acc: 0.570000; val_acc: 0.191000\n",
"(Iteration 7 / 30) loss: 1.652683\n",
"(Iteration 8 / 30) loss: 1.598651\n",
"(Epoch 4 / 15) train acc: 0.570000; val_acc: 0.194000\n",
"(Iteration 9 / 30) loss: 1.070849\n",
"(Iteration 10 / 30) loss: 1.408982\n",
"(Epoch 5 / 15) train acc: 0.740000; val_acc: 0.188000\n",
"(Iteration 11 / 30) loss: 0.816042\n",
"(Iteration 12 / 30) loss: 0.807953\n",
"(Epoch 6 / 15) train acc: 0.820000; val_acc: 0.256000\n",
"(Iteration 13 / 30) loss: 0.971160\n",
"(Iteration 14 / 30) loss: 0.568949\n",
"(Epoch 7 / 15) train acc: 0.860000; val_acc: 0.236000\n",
"(Iteration 15 / 30) loss: 0.394380\n",
"(Iteration 16 / 30) loss: 0.401405\n",
"(Epoch 8 / 15) train acc: 0.910000; val_acc: 0.194000\n",
"(Iteration 17 / 30) loss: 0.723469\n",
"(Iteration 18 / 30) loss: 0.258560\n",
"(Epoch 9 / 15) train acc: 0.910000; val_acc: 0.169000\n",
"(Iteration 19 / 30) loss: 0.242372\n",
"(Iteration 20 / 30) loss: 0.235556\n",
"(Epoch 10 / 15) train acc: 0.940000; val_acc: 0.199000\n",
"(Iteration 21 / 30) loss: 0.212628\n",
"(Iteration 22 / 30) loss: 0.126721\n",
"(Epoch 11 / 15) train acc: 0.940000; val_acc: 0.213000\n",
"(Iteration 23 / 30) loss: 0.113127\n",
"(Iteration 24 / 30) loss: 0.270010\n",
"(Epoch 12 / 15) train acc: 0.970000; val_acc: 0.204000\n",
"(Iteration 25 / 30) loss: 0.067624\n",
"(Iteration 26 / 30) loss: 0.081088\n",
"(Epoch 13 / 15) train acc: 1.000000; val_acc: 0.207000\n",
"(Iteration 27 / 30) loss: 0.029606\n",
"(Iteration 28 / 30) loss: 0.051089\n",
"(Epoch 14 / 15) train acc: 1.000000; val_acc: 0.209000\n",
"(Iteration 29 / 30) loss: 0.027549\n",
"(Iteration 30 / 30) loss: 0.025578\n",
"(Epoch 15 / 15) train acc: 1.000000; val_acc: 0.209000\n"
]
}
],
"source": [
"np.random.seed(231)\n",
"\n",
"num_train = 100\n",
"small_data = {\n",
" 'X_train': data['X_train'][:num_train],\n",
" 'y_train': data['y_train'][:num_train],\n",
" 'X_val': data['X_val'],\n",
" 'y_val': data['y_val'],\n",
"}\n",
"\n",
"model = ThreeLayerConvNet(weight_scale=1e-2)\n",
"\n",
"solver = Solver(model, small_data,\n",
" num_epochs=15, batch_size=50,\n",
" update_rule='adam',\n",
" optim_config={\n",
" 'learning_rate': 1e-3,\n",
" },\n",
" verbose=True, print_every=1)\n",
"solver.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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AUoFABiArJStIsY0TgFQgkAHISskKUmzjBCAVCGQAslKyghTbOAFIBQIZgKyUrCA1Z1qR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\n7Myuh1bEAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x116fbeb38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(2, 1, 1)\n",
"plt.plot(solver.loss_history, 'o')\n",
"plt.xlabel('iteration')\n",
"plt.ylabel('loss')\n",
"\n",
"plt.subplot(2, 1, 2)\n",
"plt.plot(solver.train_acc_history, '-o')\n",
"plt.plot(solver.val_acc_history, '-o')\n",
"plt.legend(['train', 'val'], loc='upper left')\n",
"plt.xlabel('epoch')\n",
"plt.ylabel('accuracy')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Train the net\n",
"By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(Iteration 1 / 980) loss: 2.304740\n",
"(Epoch 0 / 1) train acc: 0.103000; val_acc: 0.107000\n",
"(Iteration 21 / 980) loss: 2.132736\n",
"(Iteration 41 / 980) loss: 1.890223\n",
"(Iteration 61 / 980) loss: 1.790478\n",
"(Iteration 81 / 980) loss: 1.745026\n",
"(Iteration 101 / 980) loss: 1.892161\n",
"(Iteration 121 / 980) loss: 1.890149\n",
"(Iteration 141 / 980) loss: 1.964029\n",
"(Iteration 161 / 980) loss: 1.741861\n",
"(Iteration 181 / 980) loss: 1.886438\n",
"(Iteration 201 / 980) loss: 1.958140\n",
"(Iteration 221 / 980) loss: 1.789366\n",
"(Iteration 241 / 980) loss: 1.693459\n",
"(Iteration 261 / 980) loss: 1.507454\n",
"(Iteration 281 / 980) loss: 1.622408\n",
"(Iteration 301 / 980) loss: 1.845511\n",
"(Iteration 321 / 980) loss: 1.737180\n",
"(Iteration 341 / 980) loss: 1.680309\n",
"(Iteration 361 / 980) loss: 1.749498\n",
"(Iteration 381 / 980) loss: 1.420701\n",
"(Iteration 401 / 980) loss: 1.685015\n",
"(Iteration 421 / 980) loss: 1.733135\n",
"(Iteration 441 / 980) loss: 1.591582\n",
"(Iteration 461 / 980) loss: 1.700545\n",
"(Iteration 481 / 980) loss: 1.492935\n",
"(Iteration 501 / 980) loss: 1.404087\n",
"(Iteration 521 / 980) loss: 1.712702\n",
"(Iteration 541 / 980) loss: 1.459696\n",
"(Iteration 561 / 980) loss: 1.607686\n",
"(Iteration 581 / 980) loss: 1.232315\n",
"(Iteration 601 / 980) loss: 1.641778\n",
"(Iteration 621 / 980) loss: 1.560701\n",
"(Iteration 641 / 980) loss: 1.642898\n",
"(Iteration 661 / 980) loss: 1.651685\n",
"(Iteration 681 / 980) loss: 1.799905\n",
"(Iteration 701 / 980) loss: 1.447530\n",
"(Iteration 721 / 980) loss: 1.538806\n",
"(Iteration 741 / 980) loss: 1.893407\n",
"(Iteration 761 / 980) loss: 1.408911\n",
"(Iteration 781 / 980) loss: 2.160169\n",
"(Iteration 801 / 980) loss: 1.850402\n",
"(Iteration 821 / 980) loss: 1.542580\n",
"(Iteration 841 / 980) loss: 1.441510\n",
"(Iteration 861 / 980) loss: 1.779246\n",
"(Iteration 881 / 980) loss: 1.656049\n",
"(Iteration 901 / 980) loss: 1.455208\n",
"(Iteration 921 / 980) loss: 1.820103\n",
"(Iteration 941 / 980) loss: 1.526195\n",
"(Iteration 961 / 980) loss: 1.639982\n",
"(Epoch 1 / 1) train acc: 0.484000; val_acc: 0.485000\n"
]
}
],
"source": [
"model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
"\n",
"solver = Solver(model, data,\n",
" num_epochs=1, batch_size=50,\n",
" update_rule='adam',\n",
" optim_config={\n",
" 'learning_rate': 1e-3,\n",
" },\n",
" verbose=True, print_every=20)\n",
"solver.train()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Visualize Filters\n",
"You can visualize the first-layer convolutional filters from the trained network by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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AD5xV91NE5P9RURMRp6ioiYhTVNRExCkqaiLilJZ2P5/36FKtexy11p6zN8oN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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x112cc0f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from cs231n.vis_utils import visualize_grid\n",
"\n",
"grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
"plt.imshow(grid.astype('uint8'))\n",
"plt.axis('off')\n",
"plt.gcf().set_size_inches(5, 5)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Spatial Batch Normalization\n",
"We already saw that batch normalization is a very useful technique for training deep fully-connected networks. Batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
"\n",
"Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
"\n",
"If the feature map was produced using convolutions, then we expect the statistics of each feature channel to be relatively consistent both between different imagesand different locations within the same image. Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over both the minibatch dimension `N` and the spatial dimensions `H` and `W`."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Spatial batch normalization: forward\n",
"\n",
"In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before spatial batch normalization:\n",
" Shape: (2, 3, 4, 5)\n",
" Means: [ 9.33463814 8.90909116 9.11056338]\n",
" Stds: [ 3.61447857 3.19347686 3.5168142 ]\n",
"After spatial batch normalization:\n",
" Shape: (2, 3, 4, 5)\n",
" Means: [ 6.18949336e-16 5.99520433e-16 -1.22124533e-16]\n",
" Stds: [ 0.99999962 0.99999951 0.9999996 ]\n",
"After spatial batch normalization (nontrivial gamma, beta):\n",
" Shape: (2, 3, 4, 5)\n",
" Means: [ 6. 7. 8.]\n",
" Stds: [ 2.99999885 3.99999804 4.99999798]\n"
]
}
],
"source": [
"np.random.seed(231)\n",
"# Check the training-time forward pass by checking means and variances\n",
"# of features both before and after spatial batch normalization\n",
"\n",
"N, C, H, W = 2, 3, 4, 5\n",
"x = 4 * np.random.randn(N, C, H, W) + 10\n",
"\n",
"print('Before spatial batch normalization:')\n",
"print(' Shape: ', x.shape)\n",
"print(' Means: ', x.mean(axis=(0, 2, 3)))\n",
"print(' Stds: ', x.std(axis=(0, 2, 3)))\n",
"\n",
"# Means should be close to zero and stds close to one\n",
"gamma, beta = np.ones(C), np.zeros(C)\n",
"bn_param = {'mode': 'train'}\n",
"out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
"print('After spatial batch normalization:')\n",
"print(' Shape: ', out.shape)\n",
"print(' Means: ', out.mean(axis=(0, 2, 3)))\n",
"print(' Stds: ', out.std(axis=(0, 2, 3)))\n",
"\n",
"# Means should be close to beta and stds close to gamma\n",
"gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
"out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
"print('After spatial batch normalization (nontrivial gamma, beta):')\n",
"print(' Shape: ', out.shape)\n",
"print(' Means: ', out.mean(axis=(0, 2, 3)))\n",
"print(' Stds: ', out.std(axis=(0, 2, 3)))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"After spatial batch normalization (test-time):\n",
" means: [-0.08034406 0.07562881 0.05716371 0.04378383]\n",
" stds: [ 0.96718744 1.0299714 1.02887624 1.00585577]\n"
]
}
],
"source": [
"np.random.seed(231)\n",
"# Check the test-time forward pass by running the training-time\n",
"# forward pass many times to warm up the running averages, and then\n",
"# checking the means and variances of activations after a test-time\n",
"# forward pass.\n",
"N, C, H, W = 10, 4, 11, 12\n",
"\n",
"bn_param = {'mode': 'train'}\n",
"gamma = np.ones(C)\n",
"beta = np.zeros(C)\n",
"for t in range(50):\n",
" x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
" spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
"bn_param['mode'] = 'test'\n",
"x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
"a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
"\n",
"# Means should be close to zero and stds close to one, but will be\n",
"# noisier than training-time forward passes.\n",
"print('After spatial batch normalization (test-time):')\n",
"print(' means: ', a_norm.mean(axis=(0, 2, 3)))\n",
"print(' stds: ', a_norm.std(axis=(0, 2, 3)))"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Spatial batch normalization: backward\n",
"In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dx error: 2.78664819776e-07\n",
"dgamma error: 7.09748171136e-12\n",
"dbeta error: 3.27560872528e-12\n"
]
}
],
"source": [
"np.random.seed(231)\n",
"N, C, H, W = 2, 3, 4, 5\n",
"x = 5 * np.random.randn(N, C, H, W) + 12\n",
"gamma = np.random.randn(C)\n",
"beta = np.random.randn(C)\n",
"dout = np.random.randn(N, C, H, W)\n",
"\n",
"bn_param = {'mode': 'train'}\n",
"fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
"fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
"fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
"\n",
"dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
"da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
"db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
"\n",
"_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
"dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
"print('dx error: ', rel_error(dx_num, dx))\n",
"print('dgamma error: ', rel_error(da_num, dgamma))\n",
"print('dbeta error: ', rel_error(db_num, dbeta))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Extra Credit Description\n",
"If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
]
}
],
"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": 0
}
| mit |
charmoniumQ/Science-Fair-dump | util.ipynb | 1 | 13137 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
"import numpy as np\n",
"import collections as c\n",
"from scipy.stats import norm\n",
"from matplotlib import animation\n",
"from tempfile import NamedTemporaryFile\n",
"from IPython.display import HTML\n",
"import math\n",
"import matplotlib.mlab as mlab\n",
"\n",
"def parametric(x_func, y_func, t_values, grid):\n",
" fig = plt.figure()\n",
" scat = plt.scatter(x, y, c=c, s=100)\n",
" ax = plt.axes(*zip(grid[0], grid[1]))\n",
" path = ax.scatter([], [])\n",
" def init():\n",
" path.set_offsets([[]])\n",
" return path,\n",
" def animate(i):\n",
" t = t_values[i]\n",
" x = x_func(t)\n",
" y = y_func(t)\n",
" scat.set_array(data[i])\n",
" return scat,\n",
" return animation.FuncAnimation(fig, animate, init_func=init,\n",
" frames=len(t_values), interval=20, blit=True, fargs=(scat,))\n",
"\n",
"def display_animation(anim):\n",
" plt.close(anim._fig)\n",
" return HTML(anim_to_html(anim))\n",
" \n",
"def square(x):\n",
" return x**2\n",
"\n",
"def probability(x, mean, stddev):\n",
" return mlab.normpdf(x,mean,stddev)\n",
" \n",
"\n",
"\n",
"times = np.arange(0, 10, .4) # time goes from 0 sec to 10 sec in increments of 0.1\n",
"start = vector(4, 1) # the target starts at 8 meters to the right, and 5 meters up from the origin\n",
"velocity = vector(-0.1, 0.7) # the target moves 0.2 meters to the left and 0.5 meters up in 1 sec\n",
"def target_position(time): return start + velocity * time\n",
"def x(t): return getX(target_position(t))\n",
"def y(t): return getY(target_position(t)) # TODO: make this more elegant\n",
"\n",
" mean = distance.euclidean(own_position, target) # this is where I want the data centered\n",
" data = normal(mean, standard_deviation, sample_size) # data is a normal/standard distribution\n",
" data = data.round(int(-log10(interval))) # round to the nearest interval, defined above\n",
" # Next, count up each thing so that {distance_1: number of light rays for distance 1, distance_2: number of light rays for distance 2}\n",
" return Counter(data)"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Next, I should try to triangulate position for a single target, using simple triangles and circles. First, I need a peak extractor. A peak is where one value is higher than those adjacent to it. Each peak is part of a larger event, where the data is strictly increasing to the left, and strictly decreasing to the right. Each peak can be weighted by the length of the event it is a part of. From this we can produce a bar chart with distance on the x, and how long the event is on the y. We will only bother plotting an x and its associated y if it is a peak. To get a single number, we generate a weighted average. Since the data was generated while looking at a point, that was 5.6 meters away, if this works it should detect a peak in the returning light rays at 5.6.\n",
"\n",
"def get_peaks(data):\n",
" data = sort(data)\n",
" # data[x][0] is the distance, data[x][1] is the weight, or amplitude\n",
" peaks = {}\n",
" event_length = 0\n",
" current_peak = None\n",
" prior_state = 'increasing'\n",
" for distance in range(len(data) - 1):\n",
" if data[distance][1] <= data[distance + 1][1]: # incerasing\n",
" if prior_state == 'increasing':\n",
" # continuation, status quo\n",
" event_length += 1 # increment by one\n",
" if prior_state == 'decreasing':\n",
" # used to be decreasing, now increasing\n",
" # start of a new event, so save old one and reset\n",
" peaks[current_peak] = event_length # save old one\n",
" event_length = 0 # reset\n",
" # now set prior state to current state, for next time\n",
" prior_state = 'increasing'\n",
" if data[distance][1] >= data[distance + 1][1]:\n",
" if prior_state == 'decreasing':\n",
" # continuation, status quo\n",
" event_length += 1\n",
" if prior_state == 'increasing':\n",
" # used to be increasing, now decreasing\n",
" # This must be a peak\n",
" current_peak = data[distance][0]\n",
" # now set prior state to current state, for next time\n",
" prior_state = 'decreasing'\n",
" return peaks\n",
"\n",
"peaks = get_peaks(test_data)\n",
"bar_chart1(peaks, width=interval * 4)\n",
"print ('Average: {average_peak!s}'.format(average_peak=weighted_average(peaks)))\n",
"\n",
"To make the classical algorithm work for multiple targets, I would need a way to decompose the sum of several Gaussian functions into their substituents. Imagine being able to see the green bell curve (the data coming back from the radar), and be able to split it into its substituent parts (the blue and yellow curve). This would be like what the Fast Fourier Transform does, except operating on Gaussian bell-curves instead of Sine waves\n",
"\n",
"t = np.arange(0., 5., 0.1)\n",
"bell = lambda mean, stddev, t: exp(-(t - mean)**2 / (2*stddev)**2) / sqrt(2 * pi * mean**2) + 0.05\n",
"a = bell(2, 0.5, t)\n",
"b = bell(4, 0.5, t)\n",
"c = (a + b)\n",
"plt.plot(t, a, 'b', t, b, 'y', t, c, 'g--')"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Global variables: interval, grid, "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import things for graphing..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#%pylab inline\n",
"from operator import itemgetter\n",
"#from JSAnimation import IPython_display\n",
"from matplotlib import animation\n",
"from collections import Counter, OrderedDict\n",
"from scipy.spatial import distance\n",
"import matplotlib.patches as patches"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
"For more information, type 'help(pylab)'.\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Make some simple geometric functions for readability..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"getX = lambda point: point[0]\n",
"getY = lambda point: point[1]\n",
"#sort = lambda dictionary: sorted(dictionary.items(), key=itemgetter(0))\n",
"in_rectangle = lambda point, grid: getX(grid[0]) < getX(point) < getX(grid[1]) and \\\n",
" getY(grid[0]) < getY(point) < getY(grid[1])\n",
"grid_array = lambda: zeros(shape=((getX(grid[1]) - getX(grid[0])) / res, (getY(grid[1]) - getY(grid[0])) / res), dtype=float64)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And some data processing stuff as well..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"normalize = lambda data: [float(datum) / sum(data) for datum in data]\n",
"weighted_average = lambda data: average(distances, weights=data)\n",
"nearest = lambda value: min(int(value / interval), int(max_distance / interval) - 1)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def time_point(x_func, y_func, t_values):\n",
" fig = plt.figure()\n",
" ax = plt.axes(xlim=zip(*grid)[0], ylim=zip(*grid)[1])\n",
" path = ax.scatter([], [])\n",
" def init():\n",
" path.set_offsets([[]])\n",
" return path,\n",
" def animate(i):\n",
" t = t_values[i]\n",
" x = x_func(t)\n",
" y = y_func(t)\n",
" path.set_offsets([[x, y]])\n",
" return path,\n",
" return animation.FuncAnimation(fig, animate, init_func=init, frames=len(t_values), interval=20, blit=True)\n",
"\n",
"def bar_chart1(data):\n",
" plt.figure()\n",
" plt.title('Signal strength by distance')\n",
" plt.xlabel('distance (in meters)'.format(interval))\n",
" plt.ylabel('signal strength (normalized to 1)')\n",
" plt.xlim(0, max_distance)\n",
" plt.bar(distances, data, width=interval, linewidth=0)\n",
" plt.show()\n",
"\n",
"def visualize_circles(circles, points, ccolors, pcolors):\n",
" plt.figure()\n",
" ax = plt.axes(xlim=zip(*grid)[0], ylim=zip(*grid)[1], aspect='equal')\n",
" for (center, radius), color in zip(circles, ccolors):\n",
" ax.add_patch(patches.Circle(center, radius, fill=False, ls='solid',\n",
" lw=1.0, edgecolor=color))\n",
" for point, color in zip(points, pcolors):\n",
" ax.add_patch(patches.Circle(point, 0.1, fill=False, ls='solid',\n",
" lw=4.0, edgecolor=color))\n",
" plt.show()\n",
"\n",
"def heatmap1(data):\n",
" plt.figure(figsize=(10, 2))\n",
" ax1 = plt.subplot(211)\n",
" plt.title('Signal strength by distance')\n",
" plt.xlim(0, max_distance)\n",
" plt.bar(distances, data, width=interval, linewidth=0)\n",
" \n",
" height = 10\n",
" ax2 = plt.subplot(212, sharex=ax1)\n",
" d = [data] * height\n",
" ax2.imshow(d, interpolation='none', extent=[0, max_distance, 0, height])\n",
" plt.show()\n",
"\n",
"def heatmap2(f, title):\n",
" Z = grid_array()\n",
" for x in range(len(Z)):\n",
" for y in range(len(Z[0])):\n",
" Z[x][y] = f(x * res, y * res)\n",
" plt.figure()\n",
" ax = plt.axes(aspect='equal')\n",
" ax.imshow(Z.T, origin='lower', interpolation='none',\n",
" extent=[grid[0][0], grid[1][0], grid[0][1], grid[1][1]])\n",
" plt.title(title)\n",
" plt.show()\n",
"\n",
"def heatmap3(a, b, titlea, titleb, targets):\n",
" Za = grid_array()\n",
" Zb = grid_array()\n",
" for x in range(len(Za)):\n",
" for y in range(len(Za[0])):\n",
" Za[x][y] = a(x * res, y * res)\n",
" Zb[x][y] = b(x * res, y * res)\n",
" plt.figure(1, figsize=(9, 9))\n",
" \n",
" axa = plt.subplot(121, aspect='equal')\n",
" axa.imshow(Za.T, origin='lower', interpolation='none', #cmap='Greys',\n",
" extent=[grid[0][0], grid[1][0], grid[0][1], grid[1][1]])\n",
" axa.set_xticks(numpy.arange(grid[0][0], grid[1][0], 1))\n",
" axa.set_yticks(numpy.arange(grid[0][1], grid[1][1], 1))\n",
" axa.plot(targets.T[0], targets.T[1], 'gx', markersize=25.0)\n",
" plt.grid()\n",
" plt.title(titlea)\n",
" \n",
" axb = plt.subplot(122, aspect='equal')\n",
" axb.imshow(Zb.T, origin='lower', interpolation='none', #cmap='Greys',\n",
" extent=[grid[0][0], grid[1][0], grid[0][1], grid[1][1]])\n",
" axb.set_xticks(numpy.arange(grid[0][0], grid[1][0], 1))\n",
" axb.set_yticks(numpy.arange(grid[0][1], grid[1][1], 1))\n",
" axb.plot(targets.T[0], targets.T[1], 'wx', markersize=25.0)\n",
" plt.grid()\n",
" plt.title(titleb)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
}
],
"metadata": {}
}
]
} | gpl-3.0 |
niketanpansare/systemml-book | Chapter2_Preliminaries.ipynb | 1 | 31447 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preliminaries\n",
"\n",
"This chapter is in extremely condensed form and lacks lot of explanatory information for the sake of brevity. If you are new to the field of machine learning, I recommend that you first read [Murphy (2012)](https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/) (comprehensive reference) or [Abu-Mostafa et al. (2012)](https://www.amazon.com/Learning-Data-Yaser-S-Abu-Mostafa/dp/1600490069/) (cheap and very accessible) or [Bishop (2006)](https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738/) (first book that I read on Machine Learning) or [Hastie et al. (2003)](http://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf). There are also lot of free introductory lectures by professors like [Andrew Ng](https://www.coursera.org/learn/machine-learning), [Yaser Abu-Mostafa](http://work.caltech.edu/telecourse.html), William Press etc that you can watch. I will assume that you have basic understanding of probability, statistics and linear algebra. Also, I admit that I have \"oversimplified\" few sections by providing you with high-level intuition, examples and code (in R or Mathematica) instead of mathematical rigor (i.e. proofs).\n",
"\n",
"## Overview of Probability and Statistics\n",
"\n",
"Probability $p$ is a non-negative real number between 0 and 1 (in particular a measure of expectation of an event) that obeys the law of an axiomatic system and can have one of following interpretation<sup>1</sup>:\n",
"1. **Classical interpretation**: We have knowledge of all possible outcomes of an experiment and believe that all outcomes have equal probability. For example: the probability that coin lands head is \n",
"$$p(head) = \\dfrac{\\text{number of possible outcomes corresponding to head}}{\\text{total number of outcomes}} = \\dfrac{1}{2}$$\n",
"2. **Frequency/Objective interpretation**: The probability is a *relative frequency* of occurrence, in the limit as the number of trials approaches infinity, of an experiment's outcome. For example: As number of coin tosses $n \\rightarrow \\infty$, \n",
"$$ p(head) = \\frac{\\text{number of heads}}{n} $$ \n",
"\n",
" An obvious counter-example to this interpretation: probability of survival after surgery for patient X cannot be found by doing surgery $n$ times on that patient and counting how many times he/she survives. Instead, the doctor uses his experience (i.e. combination of outcomes of similar surgeries in the past and his state of mind) to estimate the probability of survival, i.e. the doctor specifies his degree of belief.\n",
"\n",
"3. **Subjective interpretation**: The probability is a measure of the *degree of belief*, rather than actual frequency. An objection to this interpretation will be that different people have different viewpoints of the world and hence different degree of beliefs. Also, most people are bad at reasoning with probabilities (as you will find out in [the later section](#Monte-Hall-Problem)). So, we use bayesian probability theory, that specifies a methodology to update our degree of belief in a given hypothesis in light of new evidence, to distinguish between good reasoning and bad reasoning. \n",
"\n",
"Rather than worrying about which interpretation of probability is correct, you should ask yourself which interpretation is applicable for your problem.\n",
"\n",
"Here is a high-level intuition of probability using the **hidden variable theory**: The deterministic point of view<sup>2</sup> says that if we knew the exact weight/shape of coin, environmental factors (wind direction/strength, humidity), surface friction, density of coin, exact hand movements (angle of release, force during release), effect due to rotation of the earth and many many other factors, then we can predict the exact outcome of the coin. Since it is not feasible to calculate precisely many of these factors (i.e hidden variables), we cannot predict the exact outcome of the coin. To model these variables, so that we can get very close to predicting the outcome and in some way make the experiment useful, we use the concept of probability. This is precisely why probability is so useful in Machine Learning.\n",
"\n",
"\n",
"### Axioms of probability\n",
"\n",
"For any event A in sample space (i.e. set of all possible outcomes) $\\Omega$, \n",
"\n",
"1. $p(A) \\geqslant 0$\n",
"2. $p(\\Omega) = 1$\n",
"3. Let $A_1, A_2, ..., A_k$ be pairwise mutually exclusive event (ME), then $p(A_1 \\cup A_2 \\cup ... \\cup A_k) = \\sum_{i=1}^k p(A_i)$\n",
"\n",
"### Laws of probability\n",
"\n",
"To prove the following laws, either draw Venn diagram or derive them using above axioms. For now, we will just use them without any proof.\n",
"\n",
"1. **Sum rule**: $p(A_1 \\cup A_2) = p(A_1) + p(A_2) - p(A_1 \\cap A_2)$\n",
" * Extension to 3 events: $p(A_1 \\cup A_2 \\cup A_3) = p(A_1) + p(A_2) + p(A_3) - p(A_1 \\cap A_2) - p(A_1 \\cap A_3) - p(A_2 \\cap A_3) + p(A_1 \\cap A_2 \\cap A_3)$\n",
" * General case: $p(\\cup_{i=1}^n A_i) = \\displaystyle\\sum_{k=1}^{n} \\left\\{ (-1)^{k-1} \\sum_{I \\subset \\{1, 2, ..., n\\}, |I| = k} p(\\cap_{i \\in I} A_i) \\right\\}$.\n",
" \n",
" For example: the second term (i.e. $k = 2$) is \"$-\\displaystyle\\sum_{i, j : i < j} p(A_i \\cap A_j)$\" and the third term is $\\displaystyle\\sum_{i, j, k : i < j < k} p(A_i \\cap A_j \\cap A_j)$.\n",
"\n",
"2. **Multiplicative rule**: $p(A_1 \\cap A_2) = p(A_1) \\; p(A_2 \\; | \\; A_1) = p(A_2) \\; p(A_1 \\; | \\; A_2)$\n",
" * General case: $p(A_1 \\cap A_2 \\cap ... \\cap A_k) = p(A_1) \\; p(A_2 \\; | \\; A_1) \\; p(A_3 \\; | \\; A_2 \\cap A_1) \\; ... \\; p(A_k \\; | \\; A_{k-1} \\cap ... \\cap A_1)$.\n",
"\n",
"3. **Mutually exclusive** (events A, B): $\\;\\; p(A \\cap B) = 0$\n",
"\n",
"4. **Independent** (events A, B): $\\qquad \\; p(A \\cap B) = p(A) p(B)$\n",
"\n",
"Other useful laws:\n",
"\n",
"1. **Law of Exhaustion**: Assuming that the sample space consists of mutually exclusive events $H_i$, then $\\displaystyle\\sum_{i} p(H_i) = 1$.\n",
"\n",
"2. **Law of Total probability**: If we add an another event $E$ in the sample space described in point 1, then\n",
"\n",
"$$p(E) = p(E \\cap H_1) + p(E \\cap H_2) + p(E \\cap H_3) + p(E \\cap H_4) + p(E \\cap H_5)$$\n",
"$$p(E) = \\displaystyle\\sum_{i} p(E \\; | \\; H_i) p(H_i)$$\n",
"\n",
"An easy way to understand the above two laws is by drawing the Venn diagram of the sample space. First, we draw a sample space (as a square), then divide it into mutually exclusive events $H_1, H_2, H_3, H_4, H_5$. Finally, we draw an event E (as an ellipse).\n",
" \n",
"<img src=\"images/probability_bayes.png\" alt=\"Venn diagram explaining taw of total probability\" style=\"width: 250px;\"/>\n",
" \n",
"### Bayes' theorem\n",
"\n",
"Let's derive Bayes' theorem using above laws,\n",
"$$\n",
"\\begin{align*}\n",
"p(H_i | E) &= \\dfrac{p(H_i \\cap E)}{p(E)} & \\text{ ... Multiplicative rule} \\\\\n",
"&= \\dfrac{p(H_i \\cap E)}{\\displaystyle\\sum_{i} p(E | H_i) p(H_i)} & \\text{ ... Law of total probability} \\\\\n",
"&= \\dfrac{p(H_i) p(E | H_i)}{\\displaystyle\\sum_{i} p(E | H_i) p(H_i)} & \\text{ ... Multiplicative rule} \\\\\n",
"p(H_i | E) &\\propto p(H_i) \\times p(E | H_i) & \\text{ ... since denominator can be treated as normalizing constant} \\\\\n",
"\\text{i.e., posterior } &\\propto \\text{prior } \\times \\text{ likelihood} & \\text{ ... hypothesis or model parameters: } H_i \\text{ and evidence or observed data: } E \\\\\n",
"\\end{align*}\n",
"$$\n",
"\n",
"### Monte Hall Problem\n",
"\n",
"Few years back, I gave a lecture at Jacob Sir's classes, where I described Monte Hall problem, which suprisingly none of my students had heard about. This was fortunate because this meant everyone would have to think about it on their own, rather than provide a prepared answer (thought-through by someone else). So, here is the problem:\n",
"\n",
"There are three doors D1, D2 and D3; behind two of them there is a goat and behind the other is a car. The objective of the game is to win a car. So, you have to guess which door to chose ... Does it make difference whether you chose D1, D2 or D3 ? ... The consensus was NO, because the Prob(win)=0.33 for each of the door. \n",
"\n",
"To make the problem interesting the game show host (who know which door has car and which doors has goats), opens one of the remaining door that has goat. He now asks you based on this information whether you would like to switch your earlier choice. For example, earlier you chose D1 and the game show host opens D2 and shows that it has goat. Now you can either stick to D1 or switch to D3. The real question we are interested in is:\n",
"Does switching the door make more sense or staying with the same door ? or It does not matter whether you chose D1 or D3.\n",
"\n",
"Interesting but incorrect answers that were given by my students:\n",
"1. It does not matter whether you chose D1 or D3, because Prob(win) for each door is now 0.5\n",
"2. Staying with the door makes more sense, since we have increased the Prob(win) from 0.3 to 0.5\n",
"3. We really don't have plausible reason to switch, hence stay with the same door; especially since the game show host may want to trick us.\n",
"\n",
"The correct answer is switching doubles the Prob(win), hence it makes more sense. Let's solve this problem using Bayes' theorem.\n",
"\n",
"Let's use following notation for Monte Hall problem:\n",
"\n",
"C1 $\\Rightarrow$ car is behind door 1\n",
"\n",
"D2 $\\Rightarrow$ game show host opened door 2\n",
"\n",
"S3 $\\Rightarrow$ before game show host revealing any doors, you selected door 3\n",
"\n",
"Let's assume you pick door 2 and game show host opens door 3. Note, the choice of our doors is arbitrary and hence there is no loss of generality.\n",
"\n",
"Before game show reveals the door, car is equally likely to be behind any of the doors:\n",
"$$\n",
"p(C1) = p(C2) = p(C3) = \\frac{1}{3}\n",
"$$\n",
"\n",
"This does not change if you select any arbitrary door with no information from the game show host:\n",
"$$\n",
"p(C1 \\; | \\; S1) = p(C1 \\; | \\; S2) = p(C1 \\; | \\; S3) = p(C2 \\; | \\; S1) = p(C2 \\; | \\; S2) = ... = \\frac{1}{3} \\qquad (prior \\; knowledge)\n",
"$$\n",
"\n",
"Let's evaluate few cases:\n",
"1. Car is behind door 1 and you picked door 2. Therefore, the game show host is forced to open door 3 $\\Rightarrow p(D3 \\; | \\; C1 \\cap S2) = 1$.\n",
"2. Car is behind door 2 and you picked door 2. Assuming the game show host has no preference for door 1 or door 3, he can randomly open any of the two remaining doors $\\Rightarrow p(D3 \\; | \\; C2 \\cap S2) = \\frac{1}{2}$.\n",
"3. Car is behind door 3 and you picked door 2. Since the car is behind door 3, the game show host cannot open door 3 $\\Rightarrow p(D3 \\; | \\; C3 \\cap S2) = 0$.\n",
"\n",
"\n",
"Using above information, examine the probability of car behind each of the door using this information<sup>3</sup>:\n",
"$$\n",
"\\begin{align*}\n",
"p(C1 \\; | \\; D3 \\cap S2) & \\propto p(D3 \\; | \\; C1 \\cap S2) p(C1 \\; | \\; S2) = \\frac{2}{6} \\\\\n",
"p(C2 \\; | \\; D3 \\cap S2) & \\propto p(D3 \\; | \\; C2 \\cap S2) p(C2 \\; | \\; S2) = \\frac{1}{6} \\\\\n",
"p(C3 \\; | \\; D3 \\cap S2) & \\propto p(D3 \\; | \\; C3 \\cap S2) p(C3 \\; | \\; S2) = 0\n",
"\\end{align*}\n",
"$$\n",
"\n",
"This means if you have selected door 2, its better to switch your choice to door 1 because considering the structure of the game, the car is twice as likely to be in the door 1 (i.e. the door you didn't select) than in door 2 (i.e. door you did select). This problem is a good exercise to see how probabilities are modified by the data. \n",
"\n",
"### Moments\n",
"\n",
"The $k^{th}$ moment of a random variable $X$ with probability distribution function (pdf) $f(x)$ about a value c is:\n",
"$$\n",
"\\begin{align*}\n",
"\\mathbb{E}[(X - c)^k] = \\int\\limits_{- \\infty}^{\\infty} (x - c)^k f(x) dx\n",
"\\end{align*}\n",
"$$\n",
"\n",
"There are three types of moments:\n",
"1. Raw moment: $c = 0$\n",
"2. Central moment: $c = $ mean $= \\mu$\n",
"3. Standardized moment: $\\mathbb{E}\\left[ \\left(\\dfrac{X-\\mu}{\\sigma}\\right)^k \\right] = \\dfrac{\\text{Central moment}}{\\sigma^k}$\n",
"\n",
"There are four important statistic that can be used to describe at a high level how the pdf of a distribution looks like:\n",
"1. Mean ($\\mu = \\mathbb{E}[X]$): It is the first raw moment<sup>4</sup> and specifies where the distribution might be centered<sup>5</sup>.\n",
"\n",
"2. Variance ($\\sigma^2 = \\mathbb{E}[(X - \\mu)^2]$): It is the second central moment and specifies how wide the distribution is. \n",
"\n",
"3. Skewness: It is the third standardized moment and specifies how asymmetric a distribution is (as show in below figure).<sup>6</sup>\n",
"\n",
" <img src=\"images/skewness.png\" alt=\"Skewness\" style=\"width: 400px;\"/>\n",
"\n",
"4. Kurtosis: It is the fourth standardized moment and specifies whether you distribution looks a pointy hat<sup>7</sup> or like square hat. \n",
" \n",
" <img src=\"images/kurtosis.jpg\" alt=\"Skewness\" style=\"width: 300px;\"/>\n",
" \n",
"The below code shows how to compute the above mentioned statistics on a given data. The histogram of the data is plotted for your understanding of above concepts."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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IyKF19uLgUuRuuMFn1mQ67zz44Q998dQ//IMfO/lkn6HT0ABz5/oCq3HjfIXt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\nKjrutMW6AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xf73f898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean: 4.01695797458, Variance: 7.86913862271, 3rd Moment: 29.7633605389, 4th Moment: 350.379427229, Skewness: 1.34831439549, Kurtosis: 2.65827702702\n"
]
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import systemml as sml\n",
"import numpy as np\n",
"import math\n",
"# Generate random data from gamma distribution\n",
"data = sml.matrix(np.random.gamma(2, scale=2, size=(10000, 1)))\n",
"# Plot the generated data \n",
"y, binEdges=np.histogram(data,bins=200)\n",
"bincenters = 0.5*(binEdges[1:]+binEdges[:-1])\n",
"plt.plot(bincenters, y, '-')\n",
"plt.show()\n",
"# Compute mean, variance, 3rd and 4th moment as well as skew and kurtosis using SystemML\n",
"m1 = data.mean().toNumPyArray()\n",
"m2 = data.var().toNumPyArray()\n",
"m3 = data.moment(3).toNumPyArray()\n",
"m4 = data.moment(4).toNumPyArray()\n",
"std_dev = math.sqrt(m2)\n",
"sk = m3/(std_dev**3)\n",
"kt= m4/(std_dev**4) - 3\n",
"print(\"Mean: \" + str(m1) + \", Variance: \" + str(m2) + \", 3rd Moment: \" + str(m3) + \", 4th Moment: \" + str(m4) \n",
" + \", Skewness: \" + str(sk) + \", Kurtosis: \" + str(kt))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"\n",
"Footnotes:\n",
"\n",
"<sup>1</sup> I am omitting propensity interpretation of probability by Karl Popper, which is used in Philosophy to make sense of single-case probabilities (eg: outcome of a particular coin toss), but almost always ignored in Statistics and related branches.\n",
"\n",
"<sup>2</sup> A stochastic point of view, on other hand, says that there is inherent quantum indeterminacy in every physical system (search wikipedia for Hiesenberg's uncertainty principle if you want to know more), which means you cannot precisely determine the state of a physical system.\n",
"\n",
"<sup>3</sup> In this case, the hypothesis is $H = \\{ C_1, C_2, C_3\\}$ and evidence is $E = \\{ D_3 \\}$.\n",
"\n",
"<sup>4</sup> Note, first central and standardized moments evaluate to zero (i.e. $\\mathbb{E}[X - \\mu] = \\mathbb{E}[\\left( \\dfrac{X - \\mu}{\\sigma} \\right)]$).\n",
"\n",
"<sup>5</sup> The peak of the distribution is at *mode* (i.e. most frequent value) and the median is the middle value separating the greater and lesser halves of a data set.\n",
"\n",
"<sup>6</sup> Note, $\\sigma = \\sqrt{variance} =$ standard deviation and covariance of two random variable $X, Y = cov(X, Y) = \\mathbb{E}[(X -\\mu_X)(Y-\\mu_Y)]$.\n",
"\n",
"<sup>7</sup> Mnemonic: \"P\" for pointy hat as well as positive kurtosis.\n",
"\n",
"[Previous Chapter](http://nbviewer.jupyter.org/github/niketanpansare/systemml-book/blob/master/Chapter1_Introduction.ipynb) $\\qquad \\qquad \\qquad \\qquad $ [Main Page](https://niketanpansare.github.io/systemml-book/) $\\qquad \\qquad \\qquad \\qquad $ Next Chapter"
]
},
{
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
TESScience/httm | test/notebooks/tutorial.ipynb | 1 | 41664 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tutorial"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial demonstrates basic usage of `httm`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Getting Started"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importing `matplotlib`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To start, we will import `matplotlib` and increase the figure size so we can reasonably see artifacts in various FITS images we are going to be looking at."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'png'"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import matplotlib\n",
"matplotlib.rcParams['figure.figsize'] = (8, 8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Viewing a RAW FITS File"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Assume you have a file: \n",
" \n",
" fits_data/raw_fits/single_ccd.fits\n",
"\n",
"...containing an unmodified FITS full frame image."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get started, open this file and extract a `httm.data_structures.raw_converter.SingleCCDRawConverter` object."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is done by calling `httm.fits_utilities.raw_fits.raw_converter_from_fits`."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import httm"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from httm.fits_utilities.raw_fits import raw_converter_from_fits\n",
"\n",
"raw_data = raw_converter_from_fits('fits_data/raw_fits/single_ccd.fits')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each raw image contains the data for a single CCD. It contains 4 slices if it was taken by the instrument, and either 1 or 4 if it was created synthetically."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Below, we visualize the first slice of the image."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAN8AAAKTCAYAAACZ/N0SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAGIBJREFUeJzt3H20XXV95/H3B8KDoiEUSGJHrLQURMtDBAQqIDWViA+0\nsxyith1rpWvJqrXUrmqWM85A1a4puGSsAo5LcHzELqDVakGiUKcUAakhWhyeZtkoU5lEIkzCIJCE\n/OaPvW88niQ3997ck++9yfu11l035+zf2b+9k/s++5y9T25aa0ja9faq3gBpT2V8UhHjk4oYn1TE\n+KQixicVMT6pyJzqDZguSQ4GlgDfB56s3RrtYfYHng8sb639eKIP2m3iowvvc9UboT3abwNXT3Tw\n7hTf97tvVwFH7eKplwEX7+I5q+bdk/Z1ovPeD5wHW34GJ2Z3iq9/qXkUsGgXT31gwZxV8+5J+zrp\neSf1dscTLlIR45OKGJ9UxPimxbl70Lx70r6Odt7sLv+fL8mLgRVwKzVvzLXnWgmcBnBCa+2uiT7K\nI59UxPikIsYnFTE+qYjxSUWMTypifFIR45OKGJ9UxPikIpOKL8m7k9yZZH2SNUm+kOTIoTH7Jbk8\nydokjyW5Lsn8oTGHJbk+yeNJVie5JMleQ2POTLIiyZNJHkjyu1PfTWnmmeyR73TgI8DJwK8D+wBf\nTfKMgTEfAl4NvA44A/h54K/HFvaR3UD3H3lPAX4XeDPw3oExzwf+DrgZOA74S+DKJK+Y5PZKM9ZO\nfbA6ySHAj4AzWmu3JpkLPAy8obX2hX7MUcC9wCmttTuTnA18CXhOa21tP+atwF8Ah7bWNiW5GDi7\ntXbswFyfBw5srb1qO9viB6tVpOaD1fOABjzS3z6B7oh289iA1tr9wIPAqf1dpwB3j4XXW073//Vf\nNDDmpqG5lg+sQ5r1phxfktC9xLy1tXZPf/dCYENrbf3Q8DX9srExa7axnAmMmZtkv6luszST7Mwv\nULoCeCH98XYHQneE3JHxxmQCY6RZY0rxJbkMeBVwemvtoYFFq4F9k8wdOvrN56dHstXASUOrXDCw\nbOz7gqEx84H1rbUN42/dMrpXsIPOBZaO/zBpQq4Brh26b92U1jTp+PrwfgN4WWvtwaHFK4BNwGJg\n7ITLkcDzgNv6MbcD/yHJIQPv+86i24N7B8acPbTus/r7d+BiPOGi0VnK1k/kW064TMqk4ktyBfBG\n4Bzg8SRjR6d1rbUnW2vrk1wFXJrkUeAx4MPAN1pr/9SP/SpwD/CZJMuA5wDvAy5rrW3sx/w34A/7\ns56foIv539EdbaXdwmRPuJwPzAX+B/DQwNfgU8E76K7RXTcw7nVjC1trm4HXAE/THQ0/DXwSuHBg\nzPfprhX+OvDtfp3ntdaGz4BKs5a/QEnaaf4CJWlWMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicV\nMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6p\niPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJ\nRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxP\nKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8\nUhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHj\nk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oY\nn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE\n+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6pyKTjS3J6ki8l+WGSzUnOGVr+3/v7\nB79uGBpzUJLPJVmX5NEkVyY5YGjMsUluSfJEkh8keefUdlGamaZy5DsA+DbwNqBtZ8xXgAXAwv7r\njUPLrwaOBhYDrwbOAD42tjDJs4HlwCrgxcA7gYuS/P4UtleakeZM9gGttRuBGwGSZDvDnmqtPbyt\nBUleACwBTmitrezveztwfZI/ba2tBn4H2Ac4r7W2Cbg3ySLgT4ArJ7vN0kw0qvd8ZyZZk+S+JFck\n+bmBZacCj46F17uJ7ih6cn/7FOCWPrwxy4Gjkhw4om2WdqlRxPcV4E3Ay4F3AS8Dbhg4Si4EfjT4\ngNba08Aj/bKxMWuG1rtmYJk06036ZeeOtNauGbj5P5PcDXwPOBP4+jgPDdt/Dzm2nB2MkWaNaY9v\nWGttVZK1wBF08a0G5g+OSbI3cFC/jP77gqFVjT1m+Ig4ZBkw/Mr0XGDpJLdc2pZrgGuH7ls3pTWN\nPL4kzwUOBv5Pf9ftwLwkiwbe9y2mO7LdOTDm/Un27l+SApwF3N9a28GeXgwsmr4dkH7GUrZ+Il8J\nnDbpNU3lOt8BSY5Lcnx/1y/2tw/rl12S5OQkv5BkMfBF4AG6Eya01u7r//zxJCcleSnwEeDz/ZlO\n6C5FbAA+keSFSV4P/BHwwUnvoTRDTeXIdyLdy8fWf40F8SngD4Bj6U64zAMeogvtP7fWNg6s47eA\ny+jOcm4GrgMuGFvYWlufZEk/5lvAWuCi1tpVU9heaUaaynW+f2D8I+YrJ7CO/0t3LW+8MXfTnSmV\ndkt+tlMqYnxSEeOTihifVMT4pCLGJxUxPqmI8UlFjE8qYnxSEeOTihifVMT4pCLGJxUxPqmI8UlF\njE8qYnxSEeOTihifVMT4pCLGJxUxPqmI8UlFjE8qYnxSEeOTihifVMT4pCLGJxUxPqmI8UlFjE8q\nYnxSEeOTihifVMT4pCLGJxUxPqmI8UlFjE8qYnxSEeOTihifVMT4pCLGJxUxPqmI8UlFjE8qYnxS\nEeOTihifVMT4pCLGJxUxPqmI8UlFjE8qYnxSEeOTihifVMT4pCLGJxUxPqmI8UlFjE8qYnxSEeOT\nihifVMT4pCLGJxUxPqmI8UlFjE8qYnxSEeOTihifVMT4pCLGJxUxPqmI8UlFjE8qYnxSEeOTihif\nVMT4pCLGJxUxPqmI8UlFjE8qYnxSEeOTihifVMT4pCLGJxUxPqmI8UlFjE8qYnxSEeOTihifVMT4\npCLGJxUxPqmI8UlFjE8qYnxSEeOTihifVMT4pCLGJxUxPqmI8UlFjE8qYnxSEeOTihifVMT4pCLG\nJxUxPqmI8UlFjE8qYnxSEeOTihifVMT4pCLGJxUxPqmI8UlFjE8qYnxSEeOTihifVMT4pCLGJxUx\nPqmI8UlFjE8qYnxSkUnHl+T0JF9K8sMkm5Ocs40x703yUJKfJPlakiOGlh+U5HNJ1iV5NMmVSQ4Y\nGnNskluSPJHkB0neOfndk2auqRz5DgC+DbwNaMMLkywD/hB4K/AS4HFgeZJ9B4ZdDRwNLAZeDZwB\nfGxgHc8GlgOrgBcD7wQuSvL7U9heaUaaM9kHtNZuBG4ESJJtDLkAeF9r7cv9mDcBa4DfBK5JcjSw\nBDihtbayH/N24Pokf9paWw38DrAPcF5rbRNwb5JFwJ8AV052m6WZaFrf8yU5HFgI3Dx2X2ttPfBN\n4NT+rlOAR8fC691EdxQ9eWDMLX14Y5YDRyU5cDq3Waoy3SdcFtJFtGbo/jX9srExPxpc2Fp7Gnhk\naMy21sHAGGlW21VnO8M23h9OcszYS9wdrUeaFSb9nm8HVtNFsoCfPXLNB1YOjJk/+KAkewMH9cvG\nxiwYWvfYY4aPiEOWAcOvTM8Flu5o26UJuAa4dui+dVNa07TG11pblWQ13VnMfwZIMpfuvdzl/bDb\ngXlJFg2871tMF+2dA2Pen2Tv/iUpwFnA/a21HezpxcCi6dkhaStL2fqJfCVw2qTXNJXrfAckOS7J\n8f1dv9jfPqy//SHgPUlem+QY4NPAvwJ/C9Bau4/u5MnHk5yU5KXAR4DP92c6obsUsQH4RJIXJnk9\n8EfABye9h9IMNZUj34nA1+neezV+GsSngLe01i5J8ky663bzgH8Ezm6tbRhYx28Bl9Gd5dwMXEd3\niQLozpAmWdKP+RawFriotXbVFLZXmpHS2u5x/iLJi4EVcCu+7NSuteVl5wmttbsm+ig/2ykVMT6p\niPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJ\nRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxP\nKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8\nUhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHj\nk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oY\nn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE\n+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQi\nxicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicVMT6piPFJRYxPKmJ8UhHjk4oYn1TE+KQixicV\nMT6piPFJRaY9viQXJtk89HXPwPL9klyeZG2Sx5Jcl2T+0DoOS3J9kseTrE5ySRKfKLRbmTOi9X4X\nWAykv71pYNmHgLOB1wHrgcuBvwZOB+gjuwF4CDgF+HngM8AG4D0j2l5plxtVfJtaaw8P35lkLvAW\n4A2ttX/o7/s94N4kL2mt3QksAV4A/FprbS1wd5L/BPxFkotaa5uG1yvNRqN6KffLSX6Y5HtJPpvk\nsP7+E+iCv3lsYGvtfuBB4NT+rlOAu/vwxiwHDgReNKLtlXa5UcR3B/BmuiPY+cDhwC1JDgAWAhta\na+uHHrOmX0b/fc02ljMwRpr1pv1lZ2tt+cDN7ya5E/gBsBR4cjsPC9AmsvodD1lGd5AcdG4/vbSz\nrgGuHbpv3ZTWNKr3fFu01tYleQA4ArgJ2DfJ3KGj33x+enRbDZw0tJoF/ffhI+I2XAws2plNlsax\nlK2fyFcCp016TSM/fZ/kWcAv0Z29XEF35nPxwPIjgecBt/V33Q4ck+SQgdWcRff0cg/SbmLaj3xJ\nPgB8me6l5r8B/owuuL9qra1PchVwaZJHgceADwPfaK39U7+Kr9JF9pkky4DnAO8DLmutbZzu7ZWq\njOJl53OBq4GDgYeBW4FTWms/7pe/A3gauA7YD7gReNvYg1trm5O8Bvgo3dHwceCTwIUj2FapzChO\nuLxxB8ufAt7ef21vzP8GXjPNmybNKH5kSypifFIR45OKGJ9UxPikIsYnFTE+qYjxSUWMTypifFIR\n45OKGJ9UxPikIsYnFTE+qYjxSUWMTypifFIR45OKGJ9UxPikIsYnFTE+qYjxSUWMTypifFIR45OK\nGJ9UxPikIsYnFTE+qYjxSUWMTypifFIR45OKGJ9UxPikIsaniTt+TvUW7FaMTxM0h7/9m9cA+wBG\nOB2MTxMQYB+ezWOw9z50Ae5TvE2zn/FpAubAs8K8hY/w0YffDM9M9QbtFnz9oInZCIc+Yy0Ht0dg\nQ/XG7B6MTxOwEZ4Ka1Yt5IFVR8GmDcCm6o2a9XzZqQnawJzNG3nP4j/D8KaH8WnC5mwyuulkfJqw\nP175X/GoN32MTxN20xtfgT8y08e/SU2SPzLTxb9JqYjxSUWMTypifFIR45OKGJ9UxPikIsYnFTE+\nqYjxSUWMTypifFIR45OKGJ9UxPikIsYnFTE+qYjxSUWMTypifFIR45OKGJ9UxPikIsYnFTE+qYjx\nSUWMTypifFIR45OKGJ9UxPikIsYnFTE+qYjxSUWMTypifFIR45OKGJ9UxPikIsYnFTE+qYjxSUWM\nTypifFIR45OKGJ9UxPikIsYnFTE+qYjxSUWMTypifFIR45OKGJ9UxPikIsYnFTE+qYjxSUWMTypi\nfFIR45OKGJ9UxPikIsYnFTE+qYjxSUWMTypifFIR45OKGJ9UxPikIsYnFTE+qYjxSUWMTypifFIR\n45OKGJ9UxPikIsYnFTE+qYjxSUWMTypifFIR45OKGJ9UxPikIsYnFTE+qYjxTYtr9qB596R9He28\nMzq+JG9LsirJE0nuSHJS9TZt27V70Lx70r6Odt4ZG1+S1wMfBC4EFgHfAZYnOaR0w6RpMmPjA94B\nfKy19unW2n3A+cBPgLfUbpY0PWZkfEn2AU4Abh67r7XWgJuAU6u2S5pOc6o3YDsOAfYG1gzdvwY4\najuP2b/7dv/INmr71gEr95B596R9nei8W37m9p/MmmdqfNsToG1n2fO7b+ftok0ZdtoeNO+etK+T\nmvf5wG0THTxT41sLPA0sGLp/PlsfDccsB34b+D7w5Mi2TNra/nThLZ/Mg9K9lZp5ktwBfLO1dkF/\nO8CDwIdbax8o3ThpGszUIx/ApcCnkqwA7qQ7+/lM4JOVGyVNlxkbX2vtmv6a3nvpXn5+G1jSWnu4\ndsuk6TFjX3ZKu7sZeZ1P2hMYn1Rkt4hvuj+AneT0JF9K8sMkm5Ocs40x703yUJKfJPlakiOGlh+U\n5HNJ1iV5NMmVSQ4YZ853J7kzyfoka5J8IcmRQ2P2S3J5krVJHktyXZL5Q2MOS3J9kseTrE5ySZLt\n/jsnOT/Jd/rtXJfktiSvHOWc4+z/5iSXjnh/L+znGfy6Z1fvLwCttVn9Bbye7rrem4AXAB8DHgEO\n2Yl1vpLuRM9v0l1vPGdo+bJ+jtcCvwJ8EfgesO/AmK8AdwEnAr8KPAB8dpw5bwD+PXA0cAzwd3TX\nLJ8xMOaj/X0vo/uw+W3APw4s3wu4m+560zHAEuBHwPvHmffV/f4e0X+9H3gKOHpUc25jG04C/oXu\noySXjnh/LwT+GTiU7rrxfODnRjnndrelOp5piO8O4C8Hbgf4V+Bd07T+zduI7yHgHQO35wJPAEv7\n20f3j1s0MGYJsAlYOMF5D+nXcdrAHE8B/3ZgzFH9mJf0t88GNjLwxAO8FXgUmDOJff4x8Hu7Yk7g\nWXSfz3o58PWx+EY1dx/fXdtZtsv+jltrs/tlZ8UHsJMcDiwcmnM98M2BOU8BHm2tDX4o8Ca6j8ad\nPMGp5vXjH+lvn0B3aWhw3vvpPngwOO/drbW1A+tZDhwIvGgC+7ZXkjfQXU+9fVfMCVwOfLm19vdD\n9584wrl/uX9L8b0kn01yWH//rtjfLWZ1fIz/AeyFI5pzIV0U4825kO6lyBattafpQtrhdvWf5vkQ\ncGtrbez9yEJgQx/6ePNua7tgnHmT/EqSx+ie9a+ge+a/b5Rz9vO+ATgeePc2Fi8Y0dx3AG+meyVy\nPnA4cEv/fnyk+ztsxl5k30njfQC7cs6JbtcVwAuZ2Cd6J7rO8cbcBxxHd7R9HfDpJGeMcs4kz6V7\ngnlFa23jBNY1LXO31gY/f/ndJHcCPwCWsv3PBE/H3/FWZvuRbyofwN5Zq+n+Mcabc3V/e4skewMH\n7Wi7klwGvAo4s7X20NC8+yaZu4N5h7dr7PZ2522tbWqt/Utr7a7W2n+k+60BF4xyTrqXeIcCK5Js\nTLKR7iTHBUk29I/db0Rzb9FaW0d3MuwIRru/W5nV8fXPmCuAxWP39S/ZFjOJ/9oxyTlX0f0DDM45\nl+693NictwPzkiwaeOhiumi/ub119+H9BvBrrbUHhxavoDthMzjvkcDzhuY9Jj/7qzbOovtPafcw\ncXsB+414zpvozhYeT3fUPQ74FvDZgT9vHNHcWyR5FvBLdCfRduXf8W5xtnMp3ZnGwUsNPwYO3Yl1\nHkD3A3A83ZmuP+5vH9Yvf1c/x2vpfoC+CPwvfvZSww10P0AnAS+lO6P3mXHmvILujNnpdM+kY1/7\nD41ZBZxJd+T4BlufBv8O3WWOY+ne16wB3jfOvH9O9/L2F+gum/wXuh/Al49qznG2ZcvZzhHu7weA\nM/r9/VXga/1jDt7l+1sdzzQF+Ad012aeoHtmOnEn1/eyPrqnh74+MTDmIrpny5/Qne06Ymgd8+ie\nxdf1UX0ceOY4c25rvqeBNw2M2Q/4CN3L7cfofrXW/KH1HEZ3jfD/9T8UFwN7jTPvlXTX2J6gO6J/\ndSy8Uc05zrb8/VB8o9jfz9NdinqC7izm1cDhFfvrB6ulIrP6PZ80mxmfVMT4pCLGJxUxPqmI8UlF\njE8qYnxSEeOTihifVMT4pCL/H+Y0g8RtM/9YAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10eeb4e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"matplotlib.pyplot.imshow(raw_data.slices[0].pixels)\n",
"matplotlib.pyplot.gca().invert_yaxis()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Viewing an Electron Flux FITS Image"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from httm.fits_utilities.electron_flux_fits import electron_flux_converter_from_fits\n",
"\n",
"electron_flux_data = electron_flux_converter_from_fits('fits_data/electron_flux_fits/small_simulated_data.fits')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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e9UHMYtrn6tWbPO+ep3jRi57ghS96nBe96AkefuxePvf5F7Dz+fs43jnkybP9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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f644a58>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"matplotlib.pyplot.imshow(electron_flux_data.slices[0].pixels)\n",
"matplotlib.pyplot.gca().invert_yaxis()"
]
}
],
"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": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
dennybritz/reinforcement-learning | DQN/Deep Q Learning.ipynb | 1 | 20968 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import gym\n",
"from gym.wrappers import Monitor\n",
"import itertools\n",
"import numpy as np\n",
"import os\n",
"import random\n",
"import sys\n",
"import tensorflow as tf\n",
"\n",
"if \"../\" not in sys.path:\n",
" sys.path.append(\"../\")\n",
"\n",
"from lib import plotting\n",
"from collections import deque, namedtuple"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"env = gym.envs.make(\"Breakout-v0\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Atari Actions: 0 (noop), 1 (fire), 2 (left) and 3 (right) are valid actions\n",
"VALID_ACTIONS = [0, 1, 2, 3]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class StateProcessor():\n",
" \"\"\"\n",
" Processes a raw Atari images. Resizes it and converts it to grayscale.\n",
" \"\"\"\n",
" def __init__(self):\n",
" # Build the Tensorflow graph\n",
" with tf.variable_scope(\"state_processor\"):\n",
" self.input_state = tf.placeholder(shape=[210, 160, 3], dtype=tf.uint8)\n",
" self.output = tf.image.rgb_to_grayscale(self.input_state)\n",
" self.output = tf.image.crop_to_bounding_box(self.output, 34, 0, 160, 160)\n",
" self.output = tf.image.resize_images(\n",
" self.output, [84, 84], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)\n",
" self.output = tf.squeeze(self.output)\n",
"\n",
" def process(self, sess, state):\n",
" \"\"\"\n",
" Args:\n",
" sess: A Tensorflow session object\n",
" state: A [210, 160, 3] Atari RGB State\n",
"\n",
" Returns:\n",
" A processed [84, 84] state representing grayscale values.\n",
" \"\"\"\n",
" return sess.run(self.output, { self.input_state: state })"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"class Estimator():\n",
" \"\"\"Q-Value Estimator neural network.\n",
"\n",
" This network is used for both the Q-Network and the Target Network.\n",
" \"\"\"\n",
"\n",
" def __init__(self, scope=\"estimator\", summaries_dir=None):\n",
" self.scope = scope\n",
" # Writes Tensorboard summaries to disk\n",
" self.summary_writer = None\n",
" with tf.variable_scope(scope):\n",
" # Build the graph\n",
" self._build_model()\n",
" if summaries_dir:\n",
" summary_dir = os.path.join(summaries_dir, \"summaries_{}\".format(scope))\n",
" if not os.path.exists(summary_dir):\n",
" os.makedirs(summary_dir)\n",
" self.summary_writer = tf.summary.FileWriter(summary_dir)\n",
"\n",
" def _build_model(self):\n",
" \"\"\"\n",
" Builds the Tensorflow graph.\n",
" \"\"\"\n",
"\n",
" # Placeholders for our input\n",
" # Our input are 4 grayscale frames of shape 84, 84 each\n",
" self.X_pl = tf.placeholder(shape=[None, 84, 84, 4], dtype=tf.uint8, name=\"X\")\n",
" # The TD target value\n",
" self.y_pl = tf.placeholder(shape=[None], dtype=tf.float32, name=\"y\")\n",
" # Integer id of which action was selected\n",
" self.actions_pl = tf.placeholder(shape=[None], dtype=tf.int32, name=\"actions\")\n",
"\n",
" X = tf.to_float(self.X_pl) / 255.0\n",
" batch_size = tf.shape(self.X_pl)[0]\n",
"\n",
" # Three convolutional layers\n",
" conv1 = tf.contrib.layers.conv2d(\n",
" X, 32, 8, 4, activation_fn=tf.nn.relu)\n",
" conv2 = tf.contrib.layers.conv2d(\n",
" conv1, 64, 4, 2, activation_fn=tf.nn.relu)\n",
" conv3 = tf.contrib.layers.conv2d(\n",
" conv2, 64, 3, 1, activation_fn=tf.nn.relu)\n",
"\n",
" # Fully connected layers\n",
" flattened = tf.contrib.layers.flatten(conv3)\n",
" fc1 = tf.contrib.layers.fully_connected(flattened, 512)\n",
" self.predictions = tf.contrib.layers.fully_connected(fc1, len(VALID_ACTIONS))\n",
"\n",
" # Get the predictions for the chosen actions only\n",
" gather_indices = tf.range(batch_size) * tf.shape(self.predictions)[1] + self.actions_pl\n",
" self.action_predictions = tf.gather(tf.reshape(self.predictions, [-1]), gather_indices)\n",
"\n",
" # Calculate the loss\n",
" self.losses = tf.squared_difference(self.y_pl, self.action_predictions)\n",
" self.loss = tf.reduce_mean(self.losses)\n",
"\n",
" # Optimizer Parameters from original paper\n",
" self.optimizer = tf.train.RMSPropOptimizer(0.00025, 0.99, 0.0, 1e-6)\n",
" self.train_op = self.optimizer.minimize(self.loss, global_step=tf.contrib.framework.get_global_step())\n",
"\n",
" # Summaries for Tensorboard\n",
" self.summaries = tf.summary.merge([\n",
" tf.summary.scalar(\"loss\", self.loss),\n",
" tf.summary.histogram(\"loss_hist\", self.losses),\n",
" tf.summary.histogram(\"q_values_hist\", self.predictions),\n",
" tf.summary.scalar(\"max_q_value\", tf.reduce_max(self.predictions))\n",
" ])\n",
"\n",
"\n",
" def predict(self, sess, s):\n",
" \"\"\"\n",
" Predicts action values.\n",
"\n",
" Args:\n",
" sess: Tensorflow session\n",
" s: State input of shape [batch_size, 4, 84, 84, 1]\n",
"\n",
" Returns:\n",
" Tensor of shape [batch_size, NUM_VALID_ACTIONS] containing the estimated \n",
" action values.\n",
" \"\"\"\n",
" return sess.run(self.predictions, { self.X_pl: s })\n",
"\n",
" def update(self, sess, s, a, y):\n",
" \"\"\"\n",
" Updates the estimator towards the given targets.\n",
"\n",
" Args:\n",
" sess: Tensorflow session object\n",
" s: State input of shape [batch_size, 4, 84, 84, 1]\n",
" a: Chosen actions of shape [batch_size]\n",
" y: Targets of shape [batch_size]\n",
"\n",
" Returns:\n",
" The calculated loss on the batch.\n",
" \"\"\"\n",
" feed_dict = { self.X_pl: s, self.y_pl: y, self.actions_pl: a }\n",
" summaries, global_step, _, loss = sess.run(\n",
" [self.summaries, tf.contrib.framework.get_global_step(), self.train_op, self.loss],\n",
" feed_dict)\n",
" if self.summary_writer:\n",
" self.summary_writer.add_summary(summaries, global_step)\n",
" return loss"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# For Testing....\n",
"\n",
"tf.reset_default_graph()\n",
"global_step = tf.Variable(0, name=\"global_step\", trainable=False)\n",
"\n",
"e = Estimator(scope=\"test\")\n",
"sp = StateProcessor()\n",
"\n",
"with tf.Session() as sess:\n",
" sess.run(tf.global_variables_initializer())\n",
" \n",
" # Example observation batch\n",
" observation = env.reset()\n",
" \n",
" observation_p = sp.process(sess, observation)\n",
" observation = np.stack([observation_p] * 4, axis=2)\n",
" observations = np.array([observation] * 2)\n",
" \n",
" # Test Prediction\n",
" print(e.predict(sess, observations))\n",
"\n",
" # Test training step\n",
" y = np.array([10.0, 10.0])\n",
" a = np.array([1, 3])\n",
" print(e.update(sess, observations, a, y))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def copy_model_parameters(sess, estimator1, estimator2):\n",
" \"\"\"\n",
" Copies the model parameters of one estimator to another.\n",
"\n",
" Args:\n",
" sess: Tensorflow session instance\n",
" estimator1: Estimator to copy the paramters from\n",
" estimator2: Estimator to copy the parameters to\n",
" \"\"\"\n",
" e1_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator1.scope)]\n",
" e1_params = sorted(e1_params, key=lambda v: v.name)\n",
" e2_params = [t for t in tf.trainable_variables() if t.name.startswith(estimator2.scope)]\n",
" e2_params = sorted(e2_params, key=lambda v: v.name)\n",
"\n",
" update_ops = []\n",
" for e1_v, e2_v in zip(e1_params, e2_params):\n",
" op = e2_v.assign(e1_v)\n",
" update_ops.append(op)\n",
"\n",
" sess.run(update_ops)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def make_epsilon_greedy_policy(estimator, nA):\n",
" \"\"\"\n",
" Creates an epsilon-greedy policy based on a given Q-function approximator and epsilon.\n",
"\n",
" Args:\n",
" estimator: An estimator that returns q values for a given state\n",
" nA: Number of actions in the environment.\n",
"\n",
" Returns:\n",
" A function that takes the (sess, observation, epsilon) as an argument and returns\n",
" the probabilities for each action in the form of a numpy array of length nA.\n",
"\n",
" \"\"\"\n",
" def policy_fn(sess, observation, epsilon):\n",
" A = np.ones(nA, dtype=float) * epsilon / nA\n",
" q_values = estimator.predict(sess, np.expand_dims(observation, 0))[0]\n",
" best_action = np.argmax(q_values)\n",
" A[best_action] += (1.0 - epsilon)\n",
" return A\n",
" return policy_fn"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def deep_q_learning(sess,\n",
" env,\n",
" q_estimator,\n",
" target_estimator,\n",
" state_processor,\n",
" num_episodes,\n",
" experiment_dir,\n",
" replay_memory_size=500000,\n",
" replay_memory_init_size=50000,\n",
" update_target_estimator_every=10000,\n",
" discount_factor=0.99,\n",
" epsilon_start=1.0,\n",
" epsilon_end=0.1,\n",
" epsilon_decay_steps=500000,\n",
" batch_size=32,\n",
" record_video_every=50):\n",
" \"\"\"\n",
" Q-Learning algorithm for off-policy TD control using Function Approximation.\n",
" Finds the optimal greedy policy while following an epsilon-greedy policy.\n",
"\n",
" Args:\n",
" sess: Tensorflow Session object\n",
" env: OpenAI environment\n",
" q_estimator: Estimator object used for the q values\n",
" target_estimator: Estimator object used for the targets\n",
" state_processor: A StateProcessor object\n",
" num_episodes: Number of episodes to run for\n",
" experiment_dir: Directory to save Tensorflow summaries in\n",
" replay_memory_size: Size of the replay memory\n",
" replay_memory_init_size: Number of random experiences to sampel when initializing \n",
" the reply memory.\n",
" update_target_estimator_every: Copy parameters from the Q estimator to the \n",
" target estimator every N steps\n",
" discount_factor: Gamma discount factor\n",
" epsilon_start: Chance to sample a random action when taking an action.\n",
" Epsilon is decayed over time and this is the start value\n",
" epsilon_end: The final minimum value of epsilon after decaying is done\n",
" epsilon_decay_steps: Number of steps to decay epsilon over\n",
" batch_size: Size of batches to sample from the replay memory\n",
" record_video_every: Record a video every N episodes\n",
"\n",
" Returns:\n",
" An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\n",
" \"\"\"\n",
"\n",
" Transition = namedtuple(\"Transition\", [\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n",
"\n",
" # The replay memory\n",
" replay_memory = []\n",
"\n",
" # Keeps track of useful statistics\n",
" stats = plotting.EpisodeStats(\n",
" episode_lengths=np.zeros(num_episodes),\n",
" episode_rewards=np.zeros(num_episodes))\n",
"\n",
" # Create directories for checkpoints and summaries\n",
" checkpoint_dir = os.path.join(experiment_dir, \"checkpoints\")\n",
" checkpoint_path = os.path.join(checkpoint_dir, \"model\")\n",
" monitor_path = os.path.join(experiment_dir, \"monitor\")\n",
"\n",
" if not os.path.exists(checkpoint_dir):\n",
" os.makedirs(checkpoint_dir)\n",
" if not os.path.exists(monitor_path):\n",
" os.makedirs(monitor_path)\n",
"\n",
" saver = tf.train.Saver()\n",
" # Load a previous checkpoint if we find one\n",
" latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)\n",
" if latest_checkpoint:\n",
" print(\"Loading model checkpoint {}...\\n\".format(latest_checkpoint))\n",
" saver.restore(sess, latest_checkpoint)\n",
" \n",
" # Get the current time step\n",
" total_t = sess.run(tf.contrib.framework.get_global_step())\n",
"\n",
" # The epsilon decay schedule\n",
" epsilons = np.linspace(epsilon_start, epsilon_end, epsilon_decay_steps)\n",
"\n",
" # The policy we're following\n",
" policy = make_epsilon_greedy_policy(\n",
" q_estimator,\n",
" len(VALID_ACTIONS))\n",
"\n",
" # Populate the replay memory with initial experience\n",
" print(\"Populating replay memory...\")\n",
" state = env.reset()\n",
" state = state_processor.process(sess, state)\n",
" state = np.stack([state] * 4, axis=2)\n",
" for i in range(replay_memory_init_size):\n",
" # TODO: Populate replay memory!\n",
" pass\n",
"\n",
" # Record videos\n",
" env= Monitor(env,\n",
" directory=monitor_path,\n",
" resume=True,\n",
" video_callable=lambda count: count % record_video_every == 0)\n",
"\n",
" for i_episode in range(num_episodes):\n",
"\n",
" # Save the current checkpoint\n",
" saver.save(tf.get_default_session(), checkpoint_path)\n",
"\n",
" # Reset the environment\n",
" state = env.reset()\n",
" state = state_processor.process(sess, state)\n",
" state = np.stack([state] * 4, axis=2)\n",
" loss = None\n",
"\n",
" # One step in the environment\n",
" for t in itertools.count():\n",
"\n",
" # Epsilon for this time step\n",
" epsilon = epsilons[min(total_t, epsilon_decay_steps-1)]\n",
"\n",
" # Add epsilon to Tensorboard\n",
" episode_summary = tf.Summary()\n",
" episode_summary.value.add(simple_value=epsilon, tag=\"epsilon\")\n",
" q_estimator.summary_writer.add_summary(episode_summary, total_t)\n",
"\n",
" # TODO: Maybe update the target estimator\n",
" if total_t % update_target_estimator_every == 0:\n",
" pass\n",
"\n",
" # Print out which step we're on, useful for debugging.\n",
" print(\"\\rStep {} ({}) @ Episode {}/{}, loss: {}\".format(\n",
" t, total_t, i_episode + 1, num_episodes, loss), end=\"\")\n",
" sys.stdout.flush()\n",
"\n",
" # Take a step in the environment\n",
" # TODO: Implement!\n",
"\n",
" # If our replay memory is full, pop the first element\n",
" if len(replay_memory) == replay_memory_size:\n",
" replay_memory.pop(0)\n",
"\n",
" # TODO: Save transition to replay memory\n",
"\n",
" # Update statistics\n",
" stats.episode_rewards[i_episode] += reward\n",
" stats.episode_lengths[i_episode] = t\n",
"\n",
" # TODO: Sample a minibatch from the replay memory\n",
" # TODO: Calculate q values and targets\n",
" # TODO Perform gradient descent update\n",
"\n",
" if done:\n",
" break\n",
"\n",
" state = next_state\n",
" total_t += 1\n",
"\n",
" # Add summaries to tensorboard\n",
" episode_summary = tf.Summary()\n",
" episode_summary.value.add(simple_value=stats.episode_rewards[i_episode], node_name=\"episode_reward\", tag=\"episode_reward\")\n",
" episode_summary.value.add(simple_value=stats.episode_lengths[i_episode], node_name=\"episode_length\", tag=\"episode_length\")\n",
" q_estimator.summary_writer.add_summary(episode_summary, total_t)\n",
" q_estimator.summary_writer.flush()\n",
"\n",
" yield total_t, plotting.EpisodeStats(\n",
" episode_lengths=stats.episode_lengths[:i_episode+1],\n",
" episode_rewards=stats.episode_rewards[:i_episode+1])\n",
"\n",
" env.monitor.close()\n",
" return stats"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"\n",
"# Where we save our checkpoints and graphs\n",
"experiment_dir = os.path.abspath(\"./experiments/{}\".format(env.spec.id))\n",
"\n",
"# Create a glboal step variable\n",
"global_step = tf.Variable(0, name='global_step', trainable=False)\n",
" \n",
"# Create estimators\n",
"q_estimator = Estimator(scope=\"q\", summaries_dir=experiment_dir)\n",
"target_estimator = Estimator(scope=\"target_q\")\n",
"\n",
"# State processor\n",
"state_processor = StateProcessor()\n",
"\n",
"# Run it!\n",
"with tf.Session() as sess:\n",
" sess.run(tf.initialize_all_variables())\n",
" for t, stats in deep_q_learning(sess,\n",
" env,\n",
" q_estimator=q_estimator,\n",
" target_estimator=target_estimator,\n",
" state_processor=state_processor,\n",
" experiment_dir=experiment_dir,\n",
" num_episodes=10000,\n",
" replay_memory_size=500000,\n",
" replay_memory_init_size=50000,\n",
" update_target_estimator_every=10000,\n",
" epsilon_start=1.0,\n",
" epsilon_end=0.1,\n",
" epsilon_decay_steps=500000,\n",
" discount_factor=0.99,\n",
" batch_size=32):\n",
"\n",
" print(\"\\nEpisode Reward: {}\".format(stats.episode_rewards[-1]))"
]
}
],
"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 |
hich28/mytesttxx | tests/python/formulas.ipynb | 1 | 27429 | {
"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": "3.5.3rc1"
},
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Handling LTL and PSL formulas\n",
"============================="
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import spot"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For interactive use, formulas can be entered as text strings and passed to the `spot.formula` constructor."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f = spot.formula('p1 U p2 R (p3 & !p4)')\n",
"f"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$p_{1} \\mathbin{\\mathsf{U}} (p_{2} \\mathbin{\\mathsf{R}} (p_{3} \\land \\lnot p_{4}))$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"p1 U (p2 R (p3 & !p4))"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"g = spot.formula('{a;b*;c[+]}<>->GFb'); g"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$\\{a \\mathbin{\\mathsf{;}} b^{\\star} \\mathbin{\\mathsf{;}} c^+\\}\\mathrel{\\Diamond\\kern-1.7pt\\raise.4pt\\hbox{$\\mathord{\\rightarrow}$}} \\mathsf{G} \\mathsf{F} b$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"{a;b[*];c[+]}<>-> GFb"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default the parser recognizes an infix syntax, but when this fails, it tries to read the formula with the [LBT](http://www.tcs.hut.fi/Software/maria/tools/lbt/) syntax:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"h = spot.formula('& | a b c'); h"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$c \\land (a \\lor b)$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"c & (a | b)"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, a formula object is presented using mathjax as above.\n",
"When a formula is converted to string you get Spot's syntax by default:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"str(f)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"'p1 U (p2 R (p3 & !p4))'"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you prefer to print the string in another syntax, you may use the `to_str()` method, with an argument that indicates the output format to use. The `latex` format assumes that you will the define macros such as `\\U`, `\\R` to render all operators as you wish. On the otherhand, the `sclatex` (with `sc` for self-contained) format hard-codes the rendering of each of those operators: this is typically the output that is used to render formulas using MathJax in a notebook. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for i in ['spot', 'spin', 'lbt', 'wring', 'utf8', 'latex', 'sclatex']:\n",
" print(\"%-10s%s\" % (i, f.to_str(i)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"spot p1 U (p2 R (p3 & !p4))\n",
"spin p1 U (p2 V (p3 && !p4))\n",
"lbt U p1 V p2 & p3 ! p4\n",
"wring (p1=1) U ((p2=1) R ((p3=1) * (p4=0)))\n",
"utf8 p1 U (p2 R (p3\u2227\u00acp4))\n",
"latex p_{1} \\U (p_{2} \\R (p_{3} \\land \\lnot p_{4}))\n",
"sclatex p_{1} \\mathbin{\\mathsf{U}} (p_{2} \\mathbin{\\mathsf{R}} (p_{3} \\land \\lnot p_{4}))\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Formulas output via `format()` can also use some convenient shorthand to select the syntax:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(\"\"\"\\\n",
"Spin: {0:s}\n",
"Spin+parentheses: {0:sp}\n",
"Spot (default): {0}\n",
"Spot+shell quotes: {0:q}\n",
"LBT, right aligned: {0:l:~>40}\n",
"LBT, no M/W/R: {0:[MWR]l}\"\"\".format(f))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Spin: p1 U (p2 V (p3 && !p4))\n",
"Spin+parentheses: (p1) U ((p2) V ((p3) && (!(p4))))\n",
"Spot (default): p1 U (p2 R (p3 & !p4))\n",
"Spot+shell quotes: 'p1 U (p2 R (p3 & !p4))'\n",
"LBT, right aligned: ~~~~~~~~~~~~~~~~~~~~~U p1 V p2 & p3 ! p4\n",
"LBT, no M/W/R: U p1 U & p3 ! p4 | & & p2 p3 ! p4 G & p3 ! p4\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The specifiers that can be used with `format` are documented as follows:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"help(spot.formula.__format__)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Help on function __format__ in module spot:\n",
"\n",
"__format__(self, spec)\n",
" Format the formula according to `spec`.\n",
" \n",
" Parameters\n",
" ----------\n",
" spec : str, optional\n",
" a list of letters that specify how the formula\n",
" should be formatted.\n",
" \n",
" Supported specifiers\n",
" --------------------\n",
" \n",
" - 'f': use Spot's syntax (default)\n",
" - '8': use Spot's syntax in UTF-8 mode\n",
" - 's': use Spin's syntax\n",
" - 'l': use LBT's syntax\n",
" - 'w': use Wring's syntax\n",
" - 'x': use LaTeX output\n",
" - 'X': use self-contained LaTeX output\n",
" \n",
" Add some of those letters for additional options:\n",
" \n",
" - 'p': use full parentheses\n",
" - 'c': escape the formula for CSV output (this will\n",
" enclose the formula in double quotes, and escape\n",
" any included double quotes)\n",
" - 'h': escape the formula for HTML output\n",
" - 'd': escape double quotes and backslash,\n",
" for use in C-strings (the outermost double\n",
" quotes are *not* added)\n",
" - 'q': quote and escape for shell output, using single\n",
" quotes or double quotes depending on the contents.\n",
" - '[...]': rewrite away all the operators specified in brackets,\n",
" using spot.unabbreviate().\n",
" \n",
" - ':spec': pass the remaining specification to the\n",
" formating function for strings.\n",
"\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A `spot.formula` object has a number of built-in predicates whose value have been computed when the formula was constructed. For instance you can check whether a formula is in negative normal form using `is_in_nenoform()`, and you can make sure it is an LTL formula (i.e. not a PSL formula) using `is_ltl_formula()`:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f.is_in_nenoform() and f.is_ltl_formula()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"True"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"g.is_ltl_formula()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"False"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similarly, `is_syntactic_stutter_invariant()` tells wether the structure of the formula guarranties it to be stutter invariant. For LTL formula, this means the `X` operator should not be used. For PSL formula, this function capture all formulas built using the [siPSL grammar](http://www.daxc.de/eth/paper/09atva.pdf)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f.is_syntactic_stutter_invariant()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"True"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"spot.formula('{a[*];b}<>->c').is_syntactic_stutter_invariant()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"False"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"spot.formula('{a[+];b[*]}<>->d').is_syntactic_stutter_invariant()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"True"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`spot.relabel` renames the atomic propositions that occur in a formula, using either letters, or numbered propositions:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gf = spot.formula('(GF_foo_) && \"a > b\" && \"proc[2]@init\"'); gf"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$``\\mathit{a > b}\\textrm{''} \\land ``\\mathit{proc[2]@init}\\textrm{''} \\land \\mathsf{G} \\mathsf{F} \\mathit{\\_foo\\_}$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"\"a > b\" & \"proc[2]@init\" & GF_foo_"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"spot.relabel(gf, spot.Abc)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$a \\land b \\land \\mathsf{G} \\mathsf{F} c$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"a & b & GFc"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"spot.relabel(gf, spot.Pnn)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$p_{0} \\land p_{1} \\land \\mathsf{G} \\mathsf{F} p_{2}$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"p0 & p1 & GFp2"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The AST of any formula can be displayed with `show_ast()`. Despite the name, this is not a tree but a DAG, because identical subtrees are merged. Binary operators have their left and right operands denoted with `L` and `R`, while non-commutative n-ary operators have their operands numbered."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(g); g.show_ast()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"{a;b[*];c[+]}<>-> GFb\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"svg": [
"<svg height=\"260pt\" viewBox=\"0.00 0.00 269.00 260.00\" width=\"269pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
"<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 256)\">\n",
"<title>G</title>\n",
"<polygon fill=\"white\" points=\"-4,4 -4,-256 265,-256 265,4 -4,4\" stroke=\"none\"/>\n",
"<!-- 0 -->\n",
"<g class=\"node\" id=\"node1\"><title>0</title>\n",
"<ellipse cx=\"106\" cy=\"-234\" fill=\"none\" rx=\"40.8928\" ry=\"18\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"106\" y=\"-230.3\">EConcat</text>\n",
"</g>\n",
"<!-- 1 -->\n",
"<g class=\"node\" id=\"node2\"><title>1</title>\n",
"<ellipse cx=\"155\" cy=\"-162\" fill=\"none\" rx=\"35.9954\" ry=\"18\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"155\" y=\"-158.3\">Concat</text>\n",
"</g>\n",
"<!-- 0->1 -->\n",
"<g class=\"edge\" id=\"edge6\"><title>0->1</title>\n",
"<path d=\"M117.612,-216.411C123.593,-207.868 131.005,-197.278 137.649,-187.787\" fill=\"none\" stroke=\"black\"/>\n",
"<polygon fill=\"black\" points=\"140.604,-189.669 143.471,-179.47 134.869,-185.655 140.604,-189.669\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"113.112\" y=\"-205.211\">L</text>\n",
"</g>\n",
"<!-- 7 -->\n",
"<g class=\"node\" id=\"node8\"><title>7</title>\n",
"<ellipse cx=\"58\" cy=\"-162\" fill=\"none\" rx=\"27\" ry=\"18\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"58\" y=\"-158.3\">G</text>\n",
"</g>\n",
"<!-- 0->7 -->\n",
"<g class=\"edge\" id=\"edge9\"><title>0->7</title>\n",
"<path d=\"M94.6247,-216.411C88.6815,-207.744 81.2945,-196.971 74.7146,-187.375\" fill=\"none\" stroke=\"black\"/>\n",
"<polygon fill=\"black\" points=\"77.5052,-185.256 68.9633,-178.988 71.732,-189.215 77.5052,-185.256\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"89.6247\" y=\"-205.211\">R</text>\n",
"</g>\n",
"<!-- 2 -->\n",
"<g class=\"node\" id=\"node3\"><title>2</title>\n",
"<polygon fill=\"none\" points=\"261,-36 207,-36 207,-0 261,-0 261,-36\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"234\" y=\"-14.3\">a</text>\n",
"</g>\n",
"<!-- 1->2 -->\n",
"<g class=\"edge\" id=\"edge1\"><title>1->2</title>\n",
"<path d=\"M173.171,-146.485C184.356,-136.683 198.19,-122.858 207,-108 218.359,-88.8432 225.316,-64.4933 229.316,-46.1073\" fill=\"none\" stroke=\"black\"/>\n",
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"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"169.671\" y=\"-135.285\">1</text>\n",
"</g>\n",
"<!-- 3 -->\n",
"<g class=\"node\" id=\"node4\"><title>3</title>\n",
"<ellipse cx=\"99\" cy=\"-90\" fill=\"none\" rx=\"27\" ry=\"18\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"99\" y=\"-86.3\">Star</text>\n",
"</g>\n",
"<!-- 1->3 -->\n",
"<g class=\"edge\" id=\"edge3\"><title>1->3</title>\n",
"<path d=\"M142.293,-145.116C135.032,-136.04 125.792,-124.49 117.715,-114.393\" fill=\"none\" stroke=\"black\"/>\n",
"<polygon fill=\"black\" points=\"120.252,-111.962 111.272,-106.34 114.786,-116.335 120.252,-111.962\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"138.793\" y=\"-133.916\">2</text>\n",
"</g>\n",
"<!-- 5 -->\n",
"<g class=\"node\" id=\"node6\"><title>5</title>\n",
"<ellipse cx=\"171\" cy=\"-90\" fill=\"none\" rx=\"27\" ry=\"18\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"171\" y=\"-86.3\">Star</text>\n",
"</g>\n",
"<!-- 1->5 -->\n",
"<g class=\"edge\" id=\"edge5\"><title>1->5</title>\n",
"<path d=\"M158.873,-144.055C160.655,-136.261 162.812,-126.822 164.811,-118.079\" fill=\"none\" stroke=\"black\"/>\n",
"<polygon fill=\"black\" points=\"168.235,-118.804 167.051,-108.275 161.411,-117.244 168.235,-118.804\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"155.373\" y=\"-132.855\">3</text>\n",
"</g>\n",
"<!-- 4 -->\n",
"<g class=\"node\" id=\"node5\"><title>4</title>\n",
"<polygon fill=\"none\" points=\"81,-36 27,-36 27,-0 81,-0 81,-36\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"54\" y=\"-14.3\">b</text>\n",
"</g>\n",
"<!-- 3->4 -->\n",
"<g class=\"edge\" id=\"edge2\"><title>3->4</title>\n",
"<path d=\"M88.7888,-73.1159C83.4437,-64.8013 76.7639,-54.4105 70.6903,-44.9627\" fill=\"none\" stroke=\"black\"/>\n",
"<polygon fill=\"black\" points=\"73.4681,-42.8113 65.1164,-36.2921 67.5799,-46.5966 73.4681,-42.8113\" stroke=\"black\"/>\n",
"</g>\n",
"<!-- 6 -->\n",
"<g class=\"node\" id=\"node7\"><title>6</title>\n",
"<polygon fill=\"none\" points=\"189,-36 135,-36 135,-0 189,-0 189,-36\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"162\" y=\"-14.3\">c</text>\n",
"</g>\n",
"<!-- 5->6 -->\n",
"<g class=\"edge\" id=\"edge4\"><title>5->6</title>\n",
"<path d=\"M168.821,-72.055C167.83,-64.3456 166.632,-55.0269 165.518,-46.3642\" fill=\"none\" stroke=\"black\"/>\n",
"<polygon fill=\"black\" points=\"168.968,-45.7473 164.221,-36.2753 162.025,-46.64 168.968,-45.7473\" stroke=\"black\"/>\n",
"</g>\n",
"<!-- 8 -->\n",
"<g class=\"node\" id=\"node9\"><title>8</title>\n",
"<ellipse cx=\"27\" cy=\"-90\" fill=\"none\" rx=\"27\" ry=\"18\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"27\" y=\"-86.3\">F</text>\n",
"</g>\n",
"<!-- 7->8 -->\n",
"<g class=\"edge\" id=\"edge8\"><title>7->8</title>\n",
"<path d=\"M50.6534,-144.411C46.9858,-136.129 42.4667,-125.925 38.3646,-116.662\" fill=\"none\" stroke=\"black\"/>\n",
"<polygon fill=\"black\" points=\"41.5434,-115.196 34.2938,-107.47 35.1429,-118.031 41.5434,-115.196\" stroke=\"black\"/>\n",
"</g>\n",
"<!-- 8->4 -->\n",
"<g class=\"edge\" id=\"edge7\"><title>8->4</title>\n",
"<path d=\"M33.3986,-72.411C36.4351,-64.5386 40.1417,-54.9289 43.5695,-46.0421\" fill=\"none\" stroke=\"black\"/>\n",
"<polygon fill=\"black\" points=\"46.9373,-47.0364 47.2705,-36.4468 40.4062,-44.5172 46.9373,-47.0364\" stroke=\"black\"/>\n",
"</g>\n",
"</g>\n",
"</svg>"
],
"text": [
"<IPython.core.display.SVG object>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Any formula can also be classified in the temporal hierarchy of Manna & Pnueli"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"g.show_mp_hierarchy()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"svg": [
"<svg height=\"210\" version=\"1.1\" width=\"220\" xmlns=\"http://www.w3.org/2000/svg\">\n",
"<polygon fill=\"cyan\" opacity=\".2\" points=\"20,0 200,120 200,210 20,210\"/>\n",
"<polygon fill=\"cyan\" opacity=\".2\" points=\"20,120 155,210 20,210\"/>\n",
"<polygon fill=\"magenta\" opacity=\".15\" points=\"200,0 20,120 20,210 200,210\"/>\n",
"<polygon fill=\"magenta\" opacity=\".15\" points=\"200,120 65,210 200,210\"/>\n",
"<g transform=\"translate(40,80)\">\n",
" <line stroke=\"red\" stroke-width=\"5\" x1=\"-10\" x2=\"10\" y1=\"-10\" y2=\"10\"/>\n",
" <line stroke=\"red\" stroke-width=\"5\" x1=\"-10\" x2=\"10\" y1=\"10\" y2=\"-10\"/>\n",
" </g>\n",
"<g font-size=\"14\" text-anchor=\"middle\">\n",
"<text x=\"110\" y=\"20\">Reactivity</text>\n",
"<text x=\"60\" y=\"65\">Recurrence</text>\n",
"<text x=\"160\" y=\"65\">Persistence</text>\n",
"<text x=\"110\" y=\"125\">Obligation</text>\n",
"<text x=\"60\" y=\"185\">Safety</text>\n",
"<text x=\"160\" y=\"185\">Guarantee</text>\n",
"</g>\n",
"<g font-size=\"14\">\n",
"<text fill=\"gray\" text-anchor=\"begin\" transform=\"rotate(-90,18,210)\" x=\"18\" y=\"210\">Monitor</text>\n",
"<text fill=\"gray\" text-anchor=\"end\" transform=\"rotate(-90,18,0)\" x=\"18\" y=\"0\">Deterministic B\u00fcchi</text>\n",
"<text fill=\"gray\" text-anchor=\"begin\" transform=\"rotate(-90,214,210)\" x=\"214\" y=\"210\">Terminal B\u00fcchi</text>\n",
"<text fill=\"gray\" text-anchor=\"end\" transform=\"rotate(-90,214,0)\" x=\"214\" y=\"0\">Weak B\u00fcchi</text>\n",
"</g>\n",
"</svg>"
],
"text": [
"<IPython.core.display.SVG object>"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"spot.mp_class(g, 'v')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 24,
"text": [
"'recurrence'"
]
}
],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f = spot.formula('F(a & X(!a & b))'); f"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$\\mathsf{F} (a \\land \\mathsf{X} (\\lnot a \\land b))$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"F(a & X(!a & b))"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Etessami's rule for removing X (valid only in stutter-invariant formulas)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"spot.remove_x(f)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$\\mathsf{F} (a \\land ((a \\land (a \\mathbin{\\mathsf{U}} (\\lnot a \\land b)) \\land ((\\lnot b \\mathbin{\\mathsf{U}} \\lnot a) \\lor (b \\mathbin{\\mathsf{U}} \\lnot a))) \\lor (\\lnot a \\land (\\lnot a \\mathbin{\\mathsf{U}} (a \\land \\lnot a \\land b)) \\land ((\\lnot b \\mathbin{\\mathsf{U}} a) \\lor (b \\mathbin{\\mathsf{U}} a))) \\lor (b \\land (b \\mathbin{\\mathsf{U}} (\\lnot a \\land b \\land \\lnot b)) \\land ((\\lnot a \\mathbin{\\mathsf{U}} \\lnot b) \\lor (a \\mathbin{\\mathsf{U}} \\lnot b))) \\lor (\\lnot b \\land (\\lnot b \\mathbin{\\mathsf{U}} (\\lnot a \\land b)) \\land ((\\lnot a \\mathbin{\\mathsf{U}} b) \\lor (a \\mathbin{\\mathsf{U}} b))) \\lor (\\lnot a \\land b \\land (\\mathsf{G} \\lnot a \\lor \\mathsf{G} a) \\land (\\mathsf{G} \\lnot b \\lor \\mathsf{G} b))))$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"F(a & ((a & (a U (!a & b)) & ((!b U !a) | (b U !a))) | (!a & (!a U (a & !a & b)) & ((!b U a) | (b U a))) | (b & (b U (!a & b & !b)) & ((!a U !b) | (a U !b))) | (!b & (!b U (!a & b)) & ((!a U b) | (a U b))) | (!a & b & (G!a | Ga) & (G!b | Gb))))"
]
}
],
"prompt_number": 19
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Removing abbreviated operators"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f = spot.formula(\"G(a xor b) -> F(a <-> b)\")\n",
"spot.unabbreviate(f, \"GF^\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$(\\bot \\mathbin{\\mathsf{R}} \\lnot (a \\leftrightarrow b)) \\rightarrow (\\top \\mathbin{\\mathsf{U}} (a \\leftrightarrow b))$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"(0 R !(a <-> b)) -> (1 U (a <-> b))"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"spot.unabbreviate(f, \"GF^ei\")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"latex": [
"$(\\top \\mathbin{\\mathsf{U}} ((a \\land b) \\lor (\\lnot a \\land \\lnot b))) \\lor \\lnot (\\bot \\mathbin{\\mathsf{R}} ((\\lnot a \\land b) \\lor (a \\land \\lnot b)))$"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"(1 U ((a & b) | (!a & !b))) | !(0 R ((!a & b) | (a & !b)))"
]
}
],
"prompt_number": 21
}
],
"metadata": {}
}
]
}
| gpl-3.0 |
twosigma/beakerx | doc/python/AutoTranslation.ipynb | 2 | 6297 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Autotranslation: Python to JavaScript and D3\n",
"\n",
"Generate a random graph with Python, then visualize it with a [D3](http://d3js.org/) interactive, force-directed graph.\n",
"\n",
"The first cell imports the BeakerX package and initializes the runtime. \n",
"\n",
"Then we generates the graph (one made of nodes and edges, like a social network graph)\n",
"and store it in the BeakerX object.\n",
"\n",
"Then we load D3 and set its styles.\n",
"\n",
"Finally, a JavaScript cell gets the data from the BeakerX object and renders it with D3.\n",
"\n",
"This final cell was\n",
"copied almost verbatim from the [D3 documentation](http://bl.ocks.org/mbostock/4062045). Other D3 examples\n",
"should be similarly easy to get working in BeakerX."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from beakerx.object import beakerx"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from random import randrange\n",
"import math\n",
"\n",
"nnodes = 100\n",
"\n",
"nodes = []\n",
"links = []\n",
"\n",
"for i in range(0, nnodes):\n",
" nodes.append({\"name\": str(i), \"group\": int(i*7/nnodes)})\n",
"\n",
"for i in range(0, int(nnodes*1.15)):\n",
" source = i % nnodes\n",
" target = int(math.log(1 + randrange(nnodes), 1.3))\n",
" value = 10.0 / (1 + abs(source - target))\n",
" links.append({\"source\": source, \"target\": target, \"value\": value * value})\n",
"\n",
"beakerx.graph = {\"nodes\":nodes, \"links\":links}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"%%javascript\n",
"require.config({\n",
" paths: {\n",
" d3: '//cdnjs.cloudflare.com/ajax/libs/d3/4.9.1/d3.min'\n",
" }});"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%html\n",
"<style>\n",
".node {\n",
" stroke: #fff;\n",
" stroke-width: 1.5px;\n",
"}\n",
"\n",
".link {\n",
" stroke: #999;\n",
" stroke-opacity: .6;\n",
"}\n",
"</style>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%javascript\n",
"\n",
"beakerx.displayHTML(this, '<div id=\"fdg\"></div>');\n",
"\n",
"var graph = beakerx.graph\n",
"\n",
"var d3 = require(['d3'], function (d3) {\n",
" \n",
" var width = 600,\n",
" height = 500;\n",
"\n",
" var color = d3.scaleOrdinal(d3.schemeCategory20);\n",
"\n",
" var simulation = d3.forceSimulation()\n",
" .force(\"link\", d3.forceLink().distance(30))\n",
" .force(\"charge\", d3.forceManyBody().strength(-200))\n",
" .force(\"center\", d3.forceCenter(width / 2, height / 2))\n",
" .force(\"y\", d3.forceY(width / 2).strength(0.3))\n",
" .force(\"x\", d3.forceX(height / 2).strength(0.3));\n",
"\n",
" var svg = d3.select(\"#fdg\")\n",
" .append(\"svg\")\n",
" .attr(\"width\", width)\n",
" .attr(\"height\", height)\n",
" .attr(\"transform\", \"translate(\"+[100, 0]+\")\");\n",
"\n",
" simulation\n",
" .nodes(graph.nodes)\n",
" .force(\"link\")\n",
" .links(graph.links);\n",
"\n",
" var link = svg.selectAll(\".link\")\n",
" .data(graph.links)\n",
" .enter().append(\"line\")\n",
" .attr(\"class\", \"link\")\n",
" .style(\"stroke-width\", function(d) { return Math.sqrt(d.value); });\n",
"\n",
" var node = svg.selectAll(\".node\")\n",
" .data(graph.nodes)\n",
" .enter().append(\"circle\")\n",
" .attr(\"class\", \"node\")\n",
" .attr(\"r\", 10)\n",
" .style(\"fill\", function(d) { return color(d.group); });\n",
"\n",
" node.append(\"title\")\n",
" .text(function(d) { return d.name; });\n",
"\n",
" simulation.on(\"tick\", function() {\n",
" link.attr(\"x1\", function(d) { return d.source.x; })\n",
" .attr(\"y1\", function(d) { return d.source.y; })\n",
" .attr(\"x2\", function(d) { return d.target.x; })\n",
" .attr(\"y2\", function(d) { return d.target.y; });\n",
"\n",
" node.attr(\"cx\", function(d) { return d.x; })\n",
" .attr(\"cy\", function(d) { return d.y; });\n",
" });\n",
" \n",
" node.call(d3.drag()\n",
" .on(\"start\", dragstarted)\n",
" .on(\"drag\", dragged)\n",
" .on(\"end\", dragended)\n",
" );\n",
" \n",
" function dragstarted(d) {\n",
" if (!d3.event.active) simulation.alphaTarget(0.3).restart();\n",
" d.fx = d.x;\n",
" d.fy = d.y;\n",
" }\n",
"\n",
" function dragged(d) {\n",
" d.fx = d3.event.x;\n",
" d.fy = d3.event.y;\n",
" }\n",
"\n",
" function dragended(d) {\n",
" if (!d3.event.active) simulation.alphaTarget(0);\n",
" d.fx = null;\n",
" d.fy = null;\n",
" }\n",
"});"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"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": "3.5.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
pgroth/independence-indicators | AuthorCrawl/LinkedIn.ipynb | 1 | 181380 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%pylab inline\n",
"%load_ext autoreload\n",
"%autoreload 2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"WARNING: pylab import has clobbered these variables: ['exp']\n",
"`%pylab --no-import-all` prevents importing * from pylab and numpy\n"
]
}
],
"prompt_number": 30
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import requests\n",
"from BeautifulSoup import BeautifulSoup"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 31
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"FIRST = 'Paul'\n",
"LAST = 'Groth'\n",
"INSTITUTION = 'Vrije Universiteit'\n",
"LOCATION = 'Amsterdam'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from selenium import webdriver\n",
"from selenium.common.exceptions import *\n",
"browser = webdriver.Firefox()\n",
"browser.get('https://www.linkedin.com/uas/login-submit')\n",
"browser.find_element_by_name('session_key').send_keys('[email protected]')\n",
"browser.find_element_by_name('session_password').send_keys('')\n",
"browser.find_element_by_id('signin').click()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 33
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"browser.find_element_by_id('control_gen_2').click()\n",
"browser.find_element_by_class_name('people').click()\n",
"keywords = browser.find_element_by_name('keywords')\n",
"keywords.clear()\n",
"keywords.send_keys(' '.join([FIRST,LAST,INSTITUTION,LOCATION]))\n",
"browser.find_element_by_name('search').click()\n",
"results = browser.find_elements_by_css_selector('#results > li')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for person in results:\n",
" #Do a match check\n",
" #if match:\n",
" person.find_element_by_css_selector('.title').click()\n",
" break"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"browser.find_element_by_css_selector('.show-more-info > a').click()\n",
"twitter = browser.find_element_by_css_selector('.twitter-presence > td')\n",
"username = twitter.text"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 50
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"twitter = requests.get('http://www.twitter.com/'+username)\n",
"print twitter.text"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
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" <ul class=\"nav js-global-actions\"> <li class=\"home\" data-global-action=\"t1home\"> <a class=\"nav-logo-link\" href=\"/\" data-nav=\"front\"> <span class=\"Icon Icon--bird\"><span class=\"visuallyhidden\">Twitter</span></span> </a> </li> </ul> <div class=\"pull-right\"> <ul class=\"nav secondary-nav language-dropdown\"> <li class=\"dropdown js-language-dropdown\"> <a href=\"#supported_languages\" class=\"dropdown-toggle js-dropdown-toggle\"> <small>Sprache:</small> <span class=\"js-current-language\">Deutsch</span> <b class=\"caret\"></b> </a> <div class=\"dropdown-menu\"> <div class=\"dropdown-caret right\"> <span class=\"caret-outer\"> </span> <span class=\"caret-inner\"></span> </div> <ul id=\"supported_languages\"> <li><a href=\"?lang=id\" data-lang-code=\"id\" title=\"Indonesisch\" class=\"js-language-link js-tooltip\">Bahasa Indonesia</a></li> <li><a href=\"?lang=msa\" data-lang-code=\"msa\" title=\"Malaiisch\" class=\"js-language-link js-tooltip\">Bahasa Melayu</a></li> <li><a href=\"?lang=da\" data-lang-code=\"da\" title=\"D\u00e4nisch\" class=\"js-language-link js-tooltip\">Dansk</a></li> <li><a href=\"?lang=en\" data-lang-code=\"en\" title=\"Englisch\" class=\"js-language-link js-tooltip\">English</a></li> <li><a href=\"?lang=en-gb\" data-lang-code=\"en-gb\" title=\"English UK\" class=\"js-language-link js-tooltip\">EnglishUK</a></li> <li><a href=\"?lang=es\" data-lang-code=\"es\" title=\"Spanisch\" class=\"js-language-link js-tooltip\">Espa\u00f1ol</a></li> <li><a href=\"?lang=eu\" data-lang-code=\"eu\" title=\"Baskisch\" class=\"js-language-link js-tooltip\">Euskara</a></li> <li><a href=\"?lang=fil\" data-lang-code=\"fil\" title=\"Philippinisch\" class=\"js-language-link js-tooltip\">Filipino</a></li> <li><a href=\"?lang=gl\" data-lang-code=\"gl\" title=\"Galizisch\" class=\"js-language-link js-tooltip\">Galego</a></li> <li><a href=\"?lang=it\" data-lang-code=\"it\" title=\"Italienisch\" class=\"js-language-link js-tooltip\">Italiano</a></li> <li><a href=\"?lang=xx-lc\" data-lang-code=\"xx-lc\" title=\"Lolcat\" class=\"js-language-link js-tooltip\">LOLCATZ</a></li> <li><a href=\"?lang=hu\" data-lang-code=\"hu\" title=\"Ungarisch\" class=\"js-language-link js-tooltip\">Magyar</a></li> <li><a href=\"?lang=nl\" data-lang-code=\"nl\" title=\"Niederl\u00e4ndisch\" class=\"js-language-link js-tooltip\">Nederlands</a></li> <li><a href=\"?lang=no\" data-lang-code=\"no\" title=\"Norwegisch\" class=\"js-language-link js-tooltip\">Norsk</a></li> <li><a href=\"?lang=pl\" data-lang-code=\"pl\" title=\"Polnisch\" class=\"js-language-link js-tooltip\">Polski</a></li> <li><a href=\"?lang=pt\" data-lang-code=\"pt\" title=\"Portugiesisch\" class=\"js-language-link js-tooltip\">Portugu\u00eas</a></li> <li><a href=\"?lang=fi\" data-lang-code=\"fi\" title=\"Finnisch\" class=\"js-language-link js-tooltip\">Suomi</a></li> <li><a href=\"?lang=sv\" data-lang-code=\"sv\" title=\"Schwedisch\" class=\"js-language-link js-tooltip\">Svenska</a></li> <li><a href=\"?lang=tr\" data-lang-code=\"tr\" title=\"T\u00fcrkisch\" class=\"js-language-link js-tooltip\">T\u00fcrk\u00e7e</a></li> <li><a href=\"?lang=ca\" data-lang-code=\"ca\" title=\"Katalanisch\" class=\"js-language-link js-tooltip\">catal\u00e0</a></li> <li><a href=\"?lang=fr\" data-lang-code=\"fr\" title=\"Franz\u00f6sisch\" class=\"js-language-link js-tooltip\">fran\u00e7ais</a></li> <li><a href=\"?lang=ro\" data-lang-code=\"ro\" title=\"Rum\u00e4nisch\" class=\"js-language-link js-tooltip\">rom\u00e2n\u0103</a></li> <li><a href=\"?lang=cs\" data-lang-code=\"cs\" title=\"Tschechisch\" class=\"js-language-link js-tooltip\">\u010ce\u0161tina</a></li> <li><a href=\"?lang=el\" data-lang-code=\"el\" title=\"Griechisch\" class=\"js-language-link js-tooltip\">\u0395\u03bb\u03bb\u03b7\u03bd\u03b9\u03ba\u03ac</a></li> <li><a href=\"?lang=ru\" data-lang-code=\"ru\" title=\"Russisch\" class=\"js-language-link js-tooltip\">\u0420\u0443\u0441\u0441\u043a\u0438\u0439</a></li> <li><a href=\"?lang=uk\" data-lang-code=\"uk\" title=\"Ukrainisch\" class=\"js-language-link js-tooltip\">\u0423\u043a\u0440\u0430\u0457\u043d\u0441\u044c\u043a\u0430 \u043c\u043e\u0432\u0430</a></li> <li><a href=\"?lang=he\" data-lang-code=\"he\" title=\"Hebr\u00e4isch\" class=\"js-language-link js-tooltip\">\u05e2\u05b4\u05d1\u05b0\u05e8\u05b4\u05d9\u05ea</a></li> <li><a href=\"?lang=ur\" data-lang-code=\"ur\" title=\"Urdu\" class=\"js-language-link js-tooltip\">\u0627\u0631\u062f\u0648</a></li> <li><a href=\"?lang=ar\" data-lang-code=\"ar\" title=\"Arabisch\" class=\"js-language-link js-tooltip\">\u0627\u0644\u0639\u0631\u0628\u064a\u0629</a></li> <li><a href=\"?lang=fa\" data-lang-code=\"fa\" title=\"Farsi (Persisch)\" class=\"js-language-link js-tooltip\">\u0641\u0627\u0631\u0633\u06cc</a></li> <li><a href=\"?lang=hi\" data-lang-code=\"hi\" title=\"Hindi\" class=\"js-language-link js-tooltip\">\u0939\u093f\u0928\u094d\u0926\u0940</a></li> <li><a href=\"?lang=th\" data-lang-code=\"th\" title=\"Thail\u00e4ndisch\" class=\"js-language-link js-tooltip\">\u0e20\u0e32\u0e29\u0e32\u0e44\u0e17\u0e22</a></li> <li><a href=\"?lang=ja\" data-lang-code=\"ja\" title=\"Japanisch\" class=\"js-language-link js-tooltip\">\u65e5\u672c\u8a9e</a></li> <li><a href=\"?lang=zh-cn\" data-lang-code=\"zh-cn\" title=\"Vereinfachtes Chinesisch\" class=\"js-language-link js-tooltip\">\u7b80\u4f53\u4e2d\u6587</a></li> <li><a href=\"?lang=zh-tw\" data-lang-code=\"zh-tw\" title=\"Traditionelles Chinesisch\" class=\"js-language-link js-tooltip\">\u7e41\u9ad4\u4e2d\u6587</a></li> <li><a href=\"?lang=ko\" data-lang-code=\"ko\" title=\"Koreanisch\" class=\"js-language-link js-tooltip\">\ud55c\uad6d\uc5b4</a></li> </ul> </div> <div class=\"js-front-language\"> <form action=\"/sessions/change_locale\" class=\"language\" method=\"POST\"> <input type=\"hidden\" name=\"lang\"> <input type=\"hidden\" name=\"redirect\"> <input type=\"hidden\" name=\"authenticity_token\" value=\"4ab8da53707e6fcf00df4b650c6d70043d090934\"> </form> </div> </li> </ul> </div>\n",
"\n",
" </div>\n",
" </div>\n",
" </div>\n",
"\n",
"</div>\n",
"\n",
" <div id=\"page-outer\">\n",
" <div id=\"page-container\" class=\"AppContent wrapper-front white\">\n",
" \n",
"\n",
"\n",
"\n",
"\n",
" <div class=\"BannersContainer\">\n",
" <div class=\"Banner eu-cookie-notice\">\n",
" <style>\n",
" \n",
" .front-page .eu-cookie-notice {\n",
" top: 56px;\n",
" width: 837px;\n",
" position: relative;\n",
" z-index: 2;\n",
" margin: 0 auto;\n",
" }\n",
" .front-page .front-card {\n",
" margin-top: -100px;\n",
" }\n",
" .eu-cookie-notice button {\n",
" top: 5px;\n",
" border: 0;\n",
" position: absolute;\n",
" top: 0;\n",
" right: 0;\n",
" padding: 11px 12px;\n",
" cursor: pointer;\n",
" -webkit-box-shadow: none;\n",
" box-shadow: none;\n",
" background: transparent;\n",
" }\n",
" </style>\n",
" <div class=\"flex-module\">\n",
" <div class=\"first-banner-row\">\n",
" <span class=\"title\">Um Dir Twitter zur Verf\u00fcgung zu stellen, benutzen wir und unsere Partner Cookies auf unserer und anderen Websites. Cookies helfen dabei, Inhalte von Twitter pers\u00f6nlich abzustimmen, Twitter Annoncen individuell anzupassen, ihre Performance zu messen und Twitter f\u00fcr Dich besser, schneller und sicherer zu machen. Durch die Nutzung unserer Services erkl\u00e4rst Du Dich mit unserer <a href=\"https://support.twitter.com/articles/20170514\">Nutzung von Cookies</a> einverstanden.</span>\n",
" <button type=\"button\"><span class=\"icon close-medium\"><span class=\"visuallyhidden\">Schlie\u00dfen</span></span></button>\n",
" </div>\n",
" </div>\n",
"</div>\n",
" \n",
" \n",
" </div>\n",
"\n",
" <div class=\"front-container \" id=\"front-container\">\n",
"\n",
" <noscript>\n",
" <div class=\"front-warning\">\n",
" <h3>Twitter.com benutzt sehr viel JavaScript</h3>\n",
" <p>Falls Du diese Option nicht in den Browsereinstellungen aktivieren kannst, k\u00f6nnte Dir die <a href=\"http://m.twitter.com\">Mobile Seite</a> weiterhelfen.</p>\n",
" </div>\n",
"</noscript>\n",
"\n",
"<div class=\"front-warning\" id=\"front-no-cookies-warn\">\n",
" <h3>Twitter.com macht intensiven Gebrauch von Browser Cookies</h3>\n",
" <p>Bitte aktiviere Cookies in den Einstellungen Deines Browsers, bevor Du Dich einloggst.</p>\n",
"</div>\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" \n",
"<div class=\"front-card\">\n",
" <div class=\"front-welcome \">\n",
" <div class=\"phone-image\"></div>\n",
" <div class=\"callout-copy\">\n",
" <h1>Willkommen bei Twitter!</h1>\n",
" <p>Beginne ein Gespr\u00e4ch, entdecke Deine Interessen und blicke durch.</p>\n",
" </div>\n",
" <div class=\"store-links \">\n",
" <a href=\"http://itunes.apple.com/de/app/twitter/id333903271?mt=8\" class=\"store-button app-store\" target=\"_blank\">Im App Store herunterladen</a>\n",
" <a href=\"https://play.google.com/store/apps/details?id=com.twitter.android&referrer=utm_source%3Dsignin%26utm_medium%3Dtwitterdotcom%26utm_content%3Dstratos%26utm_campaign%3Dloggedout\" class=\"store-button google-play\" target=\"_blank\">Android App auf Google Play</a>\n",
" </div>\n",
" <p class=\"devices-link\"><a href=\"https://about.twitter.com/products\">Weitere Ger\u00e4te anzeigen</a></p>\n",
" </div>\n",
"\n",
" <div class=\"front-signin js-front-signin\">\n",
" <form action=\"https://twitter.com/sessions\" class=\"signin\" method=\"post\">\n",
" <div class=\"username\">\n",
" <input type=\"text\" id=\"signin-email\" class=\"text-input email-input\" name=\"session[username_or_email]\" autocomplete=\"on\" placeholder=\"Benutzername oder E-Mail\">\n",
" </div>\n",
"\n",
" <table class=\"flex-table password-signin\">\n",
" <tbody>\n",
" <tr>\n",
" <td class=\"flex-table-primary\">\n",
" <div class=\"password flex-table-form\">\n",
" <input type=\"password\" id=\"signin-password\" class=\"text-input flex-table-input\" name=\"session[password]\" placeholder=\"Passwort\">\n",
" </div>\n",
" </td>\n",
" <td class=\"flex-table-secondary\">\n",
" <button type=\"submit\" class=\"submit btn primary-btn flex-table-btn js-submit\">\n",
" Anmelden\n",
" </button>\n",
" </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
"\n",
" <div class=\"remember-forgot\">\n",
" <label class=\"remember\">\n",
" <input type=\"checkbox\" value=\"1\" name=\"remember_me\" checked=\"checked\">\n",
" <span>Angemeldet bleiben</span>\n",
" </label>\n",
" <span class=\"separator\">·</span>\n",
" <a class=\"forgot\" href=\"/account/resend_password\">Passwort vergessen?</a>\n",
" </div>\n",
"\n",
" <input type=\"hidden\" name=\"return_to_ssl\" value=\"true\">\n",
"\n",
" <input type=\"hidden\" name=\"scribe_log\">\n",
" <input type=\"hidden\" name=\"redirect_after_login\" value=\"/\">\n",
" <input type=\"hidden\" value=\"4ab8da53707e6fcf00df4b650c6d70043d090934\" name=\"authenticity_token\">\n",
" </form>\n",
"</div>\n",
" <div class=\"front-signup js-front-signup \">\n",
" <h2><strong>Neu bei Twitter?</strong> Registriere Dich!</h2>\n",
"\n",
" <form action=\"https://twitter.com/signup\" class=\"signup\" method=\"post\" id=\"frontpage-signup-form\">\n",
"\n",
" <div class=\"field\">\n",
" <input type=\"text\" class=\"text-input\" autocomplete=\"off\" name=\"user[name]\" maxlength=\"20\" placeholder=\"Vollst\u00e4ndiger Name\">\n",
" </div>\n",
" <div class=\"field\">\n",
" <input type=\"text\" class=\"text-input email-input\" autocomplete=\"off\" name=\"user[email]\" placeholder=\"E-Mail\">\n",
" </div>\n",
" <div class=\"field\">\n",
" <input type=\"password\" class=\"text-input\" name=\"user[user_password]\" placeholder=\"Passwort\">\n",
" </div>\n",
"\n",
"\n",
" <input type=\"hidden\" value=\"\" name=\"context\">\n",
" <input type=\"hidden\" value=\"4ab8da53707e6fcf00df4b650c6d70043d090934\" name=\"authenticity_token\">\n",
" <button type=\"submit\" class=\"btn signup-btn u-pullRight\">\n",
" Registriere Dich bei Twitter!\n",
" </button>\n",
" </form>\n",
"</div>\n",
"\n",
"</div>\n",
"\n",
"\n",
"\n",
" <div class=\"footer inline-list\">\n",
" <ul>\n",
" <li><a href=\"/about\">\u00dcber uns</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"//support.twitter.com\">Hilfe</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"https://blog.twitter.com/deutschland\">Blog</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"http://status.twitter.com\">Status</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"/jobs\">Jobs</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"/tos\">Bedingungen</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"/privacy\">Privatsph\u00e4re</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"//support.twitter.com/articles/20170514\">Cookies</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"//support.twitter.com/articles/20170451\">Werbung Info</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"//about.twitter.com/press/brand-assets\">Marke</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"//ads.twitter.com/start?ref=gl-tw-tw-twitter-advertise\">Werben</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"https://business.twitter.com\">Unternehmen</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"//media.twitter.com\">Medien</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"//dev.twitter.com\">Entwickler</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><a href=\"/i/directory/profiles\">Adressbuch</a><span class=\"dot divider\"> ·</span></li>\n",
" <li><span class=\"copyright\">© 2014 Twitter</span></li>\n",
" </ul>\n",
"</div>\n",
"\n",
"\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n",
" \n",
" </div>\n",
" <div class=\"alert-messages hidden\" id=\"message-drawer\">\n",
" <div class=\"message \">\n",
" <div class=\"message-inside\">\n",
" <span class=\"message-text\"></span>\n",
" <a role=\"button\" class=\"Icon Icon--close Icon--medium dismiss\" href=\"#\">\n",
" <span class=\"visuallyhidden\">Verwerfen</span>\n",
" </a>\n",
" </div>\n",
"</div>\n",
"</div>\n",
"\n",
" \n",
"\n",
"<div class=\"gallery-overlay\"></div>\n",
"<div class=\"Gallery Gallery--new\">\n",
" <div class=\"Gallery-closeTarget\"></div>\n",
" <div class=\"Gallery-content\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--large\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
" <div class=\"Gallery-media\"></div>\n",
" <div class=\"GalleryNav GalleryNav--prev\">\n",
" <span class=\"GalleryNav-handle GalleryNav-handle--prev\">\n",
" <span class=\"Icon Icon--caretLeft Icon--large\">\n",
" <span class=\"u-isHiddenVisually\">\n",
" Vorherige\n",
" </span>\n",
" </span>\n",
" </span>\n",
" </div>\n",
" <div class=\"GalleryNav GalleryNav--next\">\n",
" <span class=\"GalleryNav-handle GalleryNav-handle--next\">\n",
" <span class=\"Icon Icon--caretRight Icon--large\">\n",
" <span class=\"u-isHiddenVisually\">\n",
" Weiter\n",
" </span>\n",
" </span>\n",
" </span>\n",
" </div>\n",
" <div class=\"GalleryTweet GalleryTweet--new\"></div>\n",
" </div>\n",
"</div>\n",
"\n",
"\n",
"\n",
"<div class=\"modal-overlay\"></div>\n",
"\n",
"\n",
"\n",
"\n",
"<div id=\"goto-user-dialog\" class=\"modal-container\">\n",
" <div class=\"modal modal-small draggable\">\n",
" <div class=\"modal-content\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--medium\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
"\n",
" <div class=\"modal-header\">\n",
" <h3 class=\"modal-title\">Vollst\u00e4ndiges Profil ansehen</h3>\n",
" </div>\n",
"\n",
" <div class=\"modal-body\">\n",
" <div class=\"modal-inner\">\n",
" <form class=\"goto-user-form\">\n",
" <input class=\"input-block username-input\" type=\"text\" placeholder=\"Gib einen Namen ein, um zu einem Profil zu springen\" aria-label=\"Nutzer\">\n",
" \n",
"\n",
"\n",
"<div role=\"listbox\" aria-hidden=\"true\" class=\"dropdown-menu typeahead\">\n",
" <div aria-hidden=\"true\" class=\"dropdown-caret\">\n",
" <div class=\"caret-outer\"></div>\n",
" <div class=\"caret-inner\"></div>\n",
" </div>\n",
" <div role=\"presentation\" class=\"dropdown-inner js-typeahead-results\">\n",
" <div role=\"presentation\" class=\"typeahead-saved-searches\">\n",
" <h3 id=\"saved-searches-heading\" class=\"typeahead-category-title saved-searches-title\">Gespeicherte Suchanfragen</h3>\n",
" <ul role=\"presentation\" class=\"typeahead-items saved-searches-list\">\n",
" \n",
" <li role=\"presentation\" class=\"typeahead-item typeahead-saved-search-item\">\n",
" <span class=\"icon close\" aria-hidden=\"true\"><span class=\"visuallyhidden\">Entfernen</span></span>\n",
" <a role=\"option\" aria-describedby=\"saved-searches-heading\" class=\"js-nav\" href=\"\" data-search-query=\"\" data-query-source=\"\" data-ds=\"saved_search\" tabindex=\"-1\"></a>\n",
" </li>\n",
" </ul>\n",
"</div>\n",
"\n",
" <ul role=\"presentation\" class=\"typeahead-items typeahead-topics\">\n",
" \n",
" <li role=\"presentation\" class=\"typeahead-item typeahead-topic-item\">\n",
" <a role=\"option\" class=\"js-nav\" href=\"\" data-search-query=\"\" data-query-source=\"typeahead_click\" data-ds=\"topics\" tabindex=\"-1\">\n",
" </a>\n",
" </li>\n",
"</ul>\n",
"\n",
"\n",
" \n",
"\n",
"\n",
"<ul role=\"presentation\" class=\"typeahead-items typeahead-accounts js-typeahead-accounts\">\n",
" \n",
" <li role=\"presentation\" data-user-id=\"\" data-user-screenname=\"\" data-remote=\"true\" data-score=\"\" class=\"typeahead-item typeahead-account-item js-selectable\">\n",
" \n",
" <a role=\"option\" class=\"js-nav\" data-query-source=\"typeahead_click\" data-search-query=\"\" data-ds=\"account\">\n",
" <img class=\"avatar size24\" alt=\"\">\n",
" <span class=\"typeahead-user-item-info\">\n",
" <span class=\"fullname\"></span>\n",
" <span class=\"js-verified hidden\"><span class=\"Icon Icon--verified Icon--small\"><span class=\"u-isHiddenVisually\">Verifizierter Account</span></span></span>\n",
" <span class=\"username\"><s>@</s><b></b></span>\n",
" </span>\n",
" </a>\n",
" </li>\n",
" <li role=\"presentation\" class=\"js-selectable typeahead-accounts-shortcut js-shortcut\"><a role=\"option\" class=\"js-nav\" href=\"\" data-search-query=\"\" data-query-source=\"typeahead_click\" data-shortcut=\"true\" data-ds=\"account_search\"></a></li>\n",
"</ul>\n",
"\n",
" <ul role=\"presentation\" class=\"typeahead-items typeahead-trend-locations-list\">\n",
" \n",
" <li role=\"presentation\" class=\"typeahead-item typeahead-trend-locations-item\"><a role=\"option\" class=\"js-nav\" href=\"\" data-ds=\"trend_location\" data-search-query=\"\" tabindex=\"-1\"></a></li>\n",
"</ul>\n",
" <ul role=\"presentation\" class=\"typeahead-items typeahead-context-list\">\n",
" \n",
" <li role=\"presentation\" class=\"typeahead-item typeahead-context-item\"><a role=\"option\" class=\"js-nav\" href=\"\" data-ds=\"context_helper\" data-search-query=\"\" tabindex=\"-1\"></a></li>\n",
"</ul>\n",
" </div>\n",
"</div>\n",
"\n",
" </form>\n",
" </div>\n",
" </div>\n",
"\n",
" </div>\n",
" </div>\n",
"</div>\n",
"\n",
"\n",
" <div id=\"retweet-tweet-dialog\" class=\"modal-container\">\n",
" <div class=\"close-modal-background-target\"></div>\n",
" <div class=\"modal draggable\">\n",
" <div class=\"modal-content\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--medium\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
"\n",
" <div class=\"modal-header\">\n",
" <h3 class=\"modal-title\">Retweet an Deine Follower senden?</h3>\n",
" </div>\n",
"\n",
" <div class=\"tweet-loading\">\n",
" <div class=\"spinner-bigger\"></div>\n",
"</div>\n",
"\n",
" <div class=\"modal-body modal-tweet\"></div>\n",
"\n",
" <div class=\"modal-footer\">\n",
" <button class=\"btn cancel-action js-close\">Abbrechen</button>\n",
" <button class=\"btn primary-btn retweet-action\">Retweeten</button>\n",
" </div>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"\n",
" <div id=\"delete-tweet-dialog\" class=\"modal-container\">\n",
" <div class=\"close-modal-background-target\"></div>\n",
" <div class=\"modal draggable\">\n",
" <div class=\"modal-content\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--medium\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
"\n",
" <div class=\"modal-header\">\n",
" <h3 class=\"modal-title\">Bist Du sicher, dass Du diesen Tweet l\u00f6schen m\u00f6chtest?</h3>\n",
" </div>\n",
"\n",
" <div class=\"tweet-loading\">\n",
" <div class=\"spinner-bigger\"></div>\n",
"</div>\n",
"\n",
" <div class=\"modal-body modal-tweet\"></div>\n",
"\n",
" <div class=\"modal-footer\">\n",
" <button class=\"btn cancel-action js-close\">Abbrechen</button>\n",
" <button class=\"btn primary-btn delete-action\">L\u00f6schen</button>\n",
" </div>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"\n",
"\n",
"<div id=\"block-user-dialog\" class=\"modal-container\">\n",
" <div class=\"close-modal-background-target\"></div>\n",
" <div class=\"modal draggable\">\n",
" <div class=\"modal-content\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--medium\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
"\n",
" <div class=\"modal-header\">\n",
" <h3 class=\"modal-title\">Blockieren</h3>\n",
" </div>\n",
"\n",
" <div class=\"tweet-loading\">\n",
" <div class=\"spinner-bigger\"></div>\n",
"</div>\n",
"\n",
" <div class=\"modal-body modal-tweet\"></div>\n",
"\n",
" <div class=\"modal-footer\">\n",
" <button class=\"btn cancel-action js-close\">Abbrechen</button>\n",
" <button class=\"btn primary-btn block-action\">Blockieren</button>\n",
" </div>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"\n",
"\n",
"\n",
" \n",
" \n",
"\n",
" <div id=\"geo-disabled-dropdown\">\n",
" <div class=\"dropdown-menu\" tabindex=\"-1\">\n",
" <div class=\"dropdown-caret\">\n",
" <span class=\"caret-outer\"></span>\n",
" <span class=\"caret-inner\"></span>\n",
" </div>\n",
" <ul>\n",
" <li class=\"geo-not-enabled-yet\">\n",
" <h2>F\u00fcge einen Standort zu Deinen Tweets hinzu</h2>\n",
" <p>\n",
" Twitter speichert Deine Standortangaben. \n",
" Du kannst die Standortangabe vor jedem Tweet ein- oder ausschalten und Du hast jederzeit die M\u00f6glichkeit, Standortangaben nachtr\u00e4glich zu l\u00f6schen.\n",
" <a href=\"http://support.twitter.com/forums/26810/entries/78525\" target=\"_blank\">Mehr erfahren</a>\n",
" </p>\n",
" <div>\n",
" <button type=\"button\" class=\"geo-turn-on btn primary-btn\">Standortangabe einschalten</button>\n",
" <button type=\"button\" class=\"geo-not-now btn-link\">Nicht jetzt</button>\n",
" </div>\n",
" </li>\n",
" </ul>\n",
"</div>\n",
" </div>\n",
"\n",
" <div id=\"geo-enabled-dropdown\">\n",
" <div class=\"dropdown-menu\" tabindex=\"-1\">\n",
" <div class=\"dropdown-caret\">\n",
" <span class=\"caret-outer\"></span>\n",
" <span class=\"caret-inner\"></span>\n",
" </div>\n",
" <ul>\n",
" <li class=\"geo-query-location\">\n",
" <input type=\"text\" autocomplete=\"off\" placeholder=\"Suche nach Stadtteil oder Stadt\">\n",
" <span class=\"icon generic-search\"></span>\n",
" </li>\n",
" <li class=\"geo-dropdown-status\"></li>\n",
" <li class=\"dropdown-link geo-turn-off-item geo-focusable\">\n",
" <span class=\"icon close\"></span>Standort ausschalten\n",
" </li>\n",
" </ul>\n",
"</div>\n",
" </div>\n",
"\n",
"\n",
" <div id=\"profile_popup\" class=\"modal-container ProfilePopupContainer\">\n",
" <div class=\"close-modal-background-target\"></div>\n",
" <div class=\"modal modal-small draggable\">\n",
" <div class=\"modal-content clearfix\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--medium\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
" <div class=\"modal-header\">\n",
" <h3 class=\"modal-title\">Kurzprofil</h3>\n",
" </div>\n",
"\n",
" <div class=\"modal-body profile-modal\">\n",
"\n",
" </div>\n",
"\n",
" <div class=\"loading\">\n",
" <span class=\"spinner-bigger\"></span>\n",
" </div>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"\n",
" <div id=\"list-membership-dialog\" class=\"modal-container\">\n",
" <div class=\"close-modal-background-target\"></div>\n",
" <div class=\"modal modal-small draggable\">\n",
" <div class=\"modal-content\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--medium\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
" <div class=\"modal-header\">\n",
" <h3 class=\"modal-title\">Deine Listen</h3>\n",
" </div>\n",
" <div class=\"modal-body\">\n",
" <div class=\"list-membership-content\"></div>\n",
" <span class=\"spinner lists-spinner\" title=\"L\u00e4dt…\"></span>\n",
" </div>\n",
" </div>\n",
" </div>\n",
"</div>\n",
" <div id=\"list-operations-dialog\" class=\"modal-container\">\n",
" <div class=\"close-modal-background-target\"></div>\n",
" <div class=\"modal modal-medium draggable\">\n",
" <div class=\"modal-content\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--medium\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
" <div class=\"modal-header\">\n",
" <h3 class=\"modal-title\">Eine neue Liste anlegen</h3>\n",
" </div>\n",
" <div class=\"modal-body\">\n",
" \n",
"<div class=\"list-editor\">\n",
" <div class=\"field\">\n",
" <label for=\"list-name\">Name der Liste</label>\n",
" <input id=\"list-name\" type=\"text\" class=\"text\" name=\"name\" value=\"\" />\n",
" </div>\n",
" <hr/>\n",
"\n",
" <div class=\"field\">\n",
" <label for=\"list-description\">Beschreibung</label>\n",
" <textarea id=\"list-description\" name=\"description\"></textarea>\n",
" <span class=\"help-text\">Weniger als 100 Zeichen, optional</span>\n",
" </div>\n",
" <hr/>\n",
"\n",
" <fieldset class=\"field\">\n",
" <legend>Liste: Privatsph\u00e4re</legend>\n",
" <div class=\"options\">\n",
" <label for=\"list-public-radio\">\n",
" <input class=\"radio\" type=\"radio\" name=\"mode\" id=\"list-public-radio\" value=\"public\" checked=\"checked\" />\n",
" <b>\u00d6ffentlich</b> · Jeder kann dieser Liste folgen\n",
" </label>\n",
" <label for=\"list-private-radio\">\n",
" <input class=\"radio\" type=\"radio\" name=\"mode\" id=\"list-private-radio\" value=\"private\" />\n",
" <b>Privat</b> · Nur Du hast Zugriff auf diese Liste\n",
" </label>\n",
" </div>\n",
" </fieldset>\n",
" <hr/>\n",
"\n",
" <div class=\"list-editor-save\">\n",
" <button type=\"button\" class=\"btn btn-primary update-list-button\" data-list-id=\"\">Liste speichern</button>\n",
" </div>\n",
"\n",
"</div>\n",
" </div>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"\n",
" <div id=\"activity-popup-dialog\" class=\"modal-container\">\n",
" <div class=\"close-modal-background-target\"></div>\n",
" <div class=\"modal draggable\">\n",
" <div class=\"modal-content clearfix\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--medium\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
"\n",
" <div class=\"modal-header\">\n",
" <h3 class=\"modal-title\"></h3>\n",
" </div>\n",
"\n",
" <div class=\"modal-body\">\n",
" <div class=\"tweet-loading\">\n",
" <div class=\"spinner-bigger\"></div>\n",
"</div>\n",
"\n",
" <div class=\"activity-tweet modal-tweet clearfix\"></div>\n",
" <div class=\"loading\">\n",
" <span class=\"spinner-bigger\"></span>\n",
" </div>\n",
" <div class=\"activity-content clearfix\"></div>\n",
" </div>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" <div id=\"embed-tweet-dialog\" class=\"modal-container\">\n",
" <div class=\"close-modal-background-target\"></div>\n",
" <div class=\"modal modal-medium draggable\">\n",
" <div class=\"modal-content\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--medium\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
" <div class=\"modal-header\">\n",
" <h3 class=\"modal-title\">Diesen Tweet integrieren</h3>\n",
" </div>\n",
" <div class=\"modal-body\">\n",
" <div class=\"embed-code-container\">\n",
" <p>F\u00fcge diesen Tweet zu Deiner Website hinzu, indem Du den untenstehenden Code einf\u00fcgst. <a href=\"//dev.twitter.com/docs/embedded-tweets\">Erfahre mehr</a></p>\n",
" <form>\n",
"\n",
" <div class=\"embed-destination-wrapper\">\n",
" <div class=\"embed-overlay embed-overlay-spinner\"><div class=\"embed-overlay-content\"></div></div>\n",
" <div class=\"embed-overlay embed-overlay-error\">\n",
" <p class=\"embed-overlay-content\">Hmm, es gab ein Problem, den Server zu erreichen. <button type=\"button\" class=\"btn-link retry-embed\">Erneut versuchen?</button></p>\n",
" </div>\n",
" <textarea class=\"embed-destination js-initial-focus\"></textarea>\n",
" <div class=\"embed-options\">\n",
" <div class=\"embed-include-parent-tweet\">\n",
" <label for=\"include-parent-tweet\">\n",
" <input type=\"checkbox\" id=\"include-parent-tweet\" class=\"include-parent-tweet\" checked>\n",
" Vorl\u00e4ufigen Tweet einf\u00fcgen\n",
" </label>\n",
" </div>\n",
" <div class=\"embed-include-card\">\n",
" <label for=\"include-card\">\n",
" <input type=\"checkbox\" id=\"include-card\" class=\"include-card\" checked>\n",
" Medien beif\u00fcgen\n",
" </label>\n",
" </div>\n",
" </div>\n",
" </div>\n",
" </form>\n",
" <div class=\"embed-preview\">\n",
" <h3>Vorschau</h3>\n",
" </div>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" \n",
" <div id=\"signin-or-signup-dialog\">\n",
" <div id=\"signin-or-signup\" class=\"modal-container\">\n",
" <div class=\"close-modal-background-target\"></div>\n",
" <div class=\"modal modal-medium draggable\">\n",
" <div class=\"modal-content\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--medium\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
" <div class=\"modal-header\">\n",
" <h3 class=\"modal-title modal-long-title signup-only\">Registriere Dich bei Twitter und folge @<span></span></h3>\n",
" <h3 class=\"modal-title not-signup-only\">Bei Twitter anmelden</h3>\n",
" </div>\n",
" <div class=\"modal-body signup-only\">\n",
" <form action=\"https://twitter.com/signup\" class=\"clearfix signup\" method=\"post\">\n",
" <div class=\"field name\">\n",
" <input type=\"text\" autocomplete=\"off\" name=\"user[name]\" maxlength=\"20\" class=\"js-initial-focus\" placeholder=\"Vollst\u00e4ndiger Name\">\n",
" </div>\n",
" <div class=\"field email\">\n",
" <input class=\"email-input\" type=\"text\" autocomplete=\"off\" name=\"user[email]\" placeholder=\"E-Mail\">\n",
" </div>\n",
" <div class=\"field password\">\n",
" <input type=\"password\" name=\"user[user_password]\" placeholder=\"Passwort\">\n",
" </div>\n",
" <input type=\"hidden\" value=\"\" name=\"context\">\n",
" <input type=\"hidden\" value=\"4ab8da53707e6fcf00df4b650c6d70043d090934\" name=\"authenticity_token\"/>\n",
" <input name=\"follows\" type=\"hidden\" value=\"\">\n",
" <input type=\"submit\" class=\"btn signup-btn js-submit js-signup-btn\" value=\"Registrieren\">\n",
"</form>\n",
"\n",
" </div>\n",
" <div class=\"modal-body not-signup-only\">\n",
" <form action=\"https://twitter.com/sessions\" class=\"signin\" method=\"post\">\n",
" <fieldset>\n",
"\n",
" <legend class=\"visuallyhidden\">Anmelden</legend>\n",
"\n",
" <div class=\"clearfix field\">\n",
" <input class=\"js-username-field email-input js-initial-focus\" type=\"text\" name=\"session[username_or_email]\" autocomplete=\"on\" value=\"\" placeholder=\"Benutzername oder E-Mail\">\n",
" </div>\n",
"\n",
" <div class=\"clearfix field\">\n",
" <input class=\"js-password-field\" type=\"password\" name=\"session[password]\" placeholder=\"Passwort\">\n",
" </div>\n",
"\n",
" <input type=\"hidden\" value=\"4ab8da53707e6fcf00df4b650c6d70043d090934\" name=\"authenticity_token\"/>\n",
"\n",
"</fieldset>\n",
"\n",
" <div class=\"clearfix\">\n",
"\n",
" <input type=\"hidden\" name=\"scribe_log\">\n",
" <input type=\"hidden\" name=\"redirect_after_login\" value=\"/\">\n",
" <input type=\"hidden\" value=\"4ab8da53707e6fcf00df4b650c6d70043d090934\" name=\"authenticity_token\"/>\n",
" <button type=\"submit\" class=\"submit btn primary-btn\">Anmelden</button>\n",
"\n",
" <div class=\"subchck\">\n",
" <label class=\"remember\">\n",
" <input type=\"checkbox\" value=\"1\" name=\"remember_me\" checked=\"checked\">\n",
" Angemeldet bleiben\n",
" <span class=\"separator\">\u00b7</span>\n",
" <a class=\"forgot\" href=\"/account/resend_password\">Passwort vergessen?</a>\n",
" </label>\n",
" </div>\n",
"</div>\n",
"\n",
" <div class=\"divider\"></div>\n",
" <p>\n",
" <a class=\"forgot\" href=\"/account/resend_password\">Passwort vergessen?</a><br />\n",
" <a class=\"mobile has-sms\" href=\"/account/complete\">Benutzt Du Twitter bereits via SMS?</a>\n",
" </p>\n",
"</form>\n",
"\n",
" <div class=\"signup\">\n",
" <h2>Noch nicht bei Twitter? Melde Dich an, wirf einen Blick auf Dinge, die Dich interessieren und bleibe stets auf dem Laufenden.</h2>\n",
" <form action=\"https://twitter.com/signup\" class=\"signup\" method=\"get\">\n",
" <button class=\"btn promotional signup-btn\" type=\"submit\">Registriere Dich »</button>\n",
"</form>\n",
"\n",
" </div>\n",
" </div>\n",
" </div>\n",
" </div>\n",
" </div>\n",
" </div>\n",
"\n",
" <div id=\"sms-codes-dialog\" class=\"modal-container\">\n",
" <div class=\"close-modal-background-target\"></div>\n",
" <div class=\"modal modal-medium draggable\">\n",
" <div class=\"modal-content\">\n",
" <button type=\"button\" class=\"modal-btn modal-close js-close\">\n",
" <span class=\"Icon Icon--close Icon--medium\">\n",
" <span class=\"visuallyhidden\">Schlie\u00dfen</span>\n",
" </span>\n",
"</button>\n",
"\n",
" <div class=\"modal-header\">\n",
" <h3 class=\"modal-title\">Zweiwege-Kurz-Codes (zum Senden und Empfangen)</h3>\n",
" </div>\n",
" <div class=\"modal-body\">\n",
" \n",
"<table id=\"sms_codes\" cellpadding=\"0\" cellspacing=\"0\">\n",
" <thead>\n",
" <tr>\n",
" <th>Land</th>\n",
" <th>Code</th>\n",
" <th>F\u00fcr Kunden von</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>Vereinigte Staaten</td>\n",
" <td>40404</td>\n",
" <td>(beliebig)</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Kanada</td>\n",
" <td>21212</td>\n",
" <td>(beliebig)</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Vereinigtes K\u00f6nigreich</td>\n",
" <td>86444</td>\n",
" <td>Vodafone, Orange, 3, O2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Brasilien</td>\n",
" <td>40404</td>\n",
" <td>Nextel, TIM</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Haiti</td>\n",
" <td>40404</td>\n",
" <td>Digicel, Voila</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Irland</td>\n",
" <td>51210</td>\n",
" <td>Vodafone, O2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Indien</td>\n",
" <td>53000</td>\n",
" <td>Bharti Airtel, Videocon, Reliance</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Indonesien</td>\n",
" <td>89887</td>\n",
" <td>AXIS, 3, Telkomsel, Indosat, XL Axiata</td>\n",
" </tr>\n",
" <tr>\n",
" <td rowspan=\"2\">Italien</td>\n",
" <td>4880804</td>\n",
" <td>Wind</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3424486444</td>\n",
" <td>Vodafone</td>\n",
" </tr>\n",
" </tbody>\n",
" <tfoot>\n",
" <tr>\n",
" <td colspan=\"3\">\n",
" » <a class=\"js-initial-focus\" target=\"_blank\" href=\"http://support.twitter.com/articles/14226-how-to-find-your-twitter-short-code-or-long-code\">Zeige SMS-Kurzwahlen f\u00fcr andere L\u00e4nder</a>\n",
" </td>\n",
" </tr>\n",
" </tfoot>\n",
"</table>\n",
" </div>\n",
" </div>\n",
" </div>\n",
"</div>\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"<div id=\"create-custom-timeline-dialog\" class=\"modal-container\"></div>\n",
"<div id=\"edit-custom-timeline-dialog\" class=\"modal-container\"></div>\n",
"<div id=\"curate-dialog\" class=\"modal-container\"></div>\n",
"\n",
"\n",
" <div class=\"hidden\">\n",
" <iframe aria-hidden=\"true\" class=\"tweet-post-iframe\" name=\"tweet-post-iframe\"></iframe>\n",
"\n",
"</div>\n",
" \n",
" <div id=\"spoonbill-outer\"></div>\n",
" </body>\n",
"</html>\n",
" <input type=\"hidden\" id=\"init-data\" class=\"json-data\" value=\"{"profileHoversEnabled":false,"noNewDedup":false,"permalinkOverlayEnabled":false,"baseFoucClass":"swift-loading","bodyFoucClassNames":"swift-loading","macawSwift":true,"assetsBasePath":"https:\\/\\/abs.twimg.com\\/a\\/1396889640\\/","assetVersionKey":"c72489","environment":"production","formAuthenticityToken":"4ab8da53707e6fcf00df4b650c6d70043d090934","loggedIn":false,"screenName":null,"fullName":null,"userId":null,"scribeBufferSize":3,"pageName":"front","sectionName":"front","scribeParameters":{},"internalReferer":null,"geoEnabled":false,"typeaheadData":{"accounts":{"localQueriesEnabled":false,"remoteQueriesEnabled":false,"enabled":false,"limit":6},"trendLocations":{"enabled":false},"savedSearches":{"enabled":false,"items":[]},"dmAccounts":{"enabled":false,"localQueriesEnabled":false,"onlyDMable":true,"remoteQueriesEnabled":false},"topics":{"enabled":false,"localQueriesEnabled":false,"prefetchLimit":500,"remoteQueriesEnabled":false,"limit":4},"concierge":{"enabled":false,"localQueriesEnabled":true,"remoteQueriesEnabled":false,"prefetchLimit":500,"limit":3},"recentSearches":{"enabled":false},"contextHelpers":{"enabled":false,"page_name":"front","section_name":"front","screen_name":null},"hashtags":{"enabled":false,"localQueriesEnabled":false,"prefetchLimit":500,"remoteQueriesEnabled":false},"showSearchAccountSocialContext":false,"showTypeaheadTopicSocialContext":false,"showDebugInfo":false,"useThrottle":true,"accountsOnTop":false,"remoteDebounceInterval":300,"remoteThrottleInterval":300,"reverseBoldingEnabled":false,"tweetContextEnabled":false,"fullNameMatchingInCompose":true,"topicsWithFiltersEnabled":false},"pushStatePageLimit":500000,"routes":{"profile":"\\/"},"pushState":true,"viewContainer":"#page-container","asyncSocialProof":true,"dragAndDropPhotoUpload":true,"href":"\\/","searchPathWithQuery":"\\/search?q=query&src=typd","timelineCardsGallery":true,"mediaGrid":true,"deciders":{"pushState":true,"disable_profile_popup":false,"hqImageUploads":false,"mqImageUploads":false,"dynamicLoadMediaForward":true,"scribeActionQueue":false,"scribeReducedActionQueue":true,"modal_tweet_from_server_enabled":true,"custom_timeline_curation":false},"experiments":{"reply140":false},"permalinkCardsGallery":false,"toasts_dm":false,"toasts_spoonbill":false,"toasts_timeline":false,"toasts_dm_poll_scale":60,"uploadDomain":"upload.twitter.com","lifelineAlertEnabled":false,"freezeDashboard":false,"swift_dm_create":false,"enableActivity":true,"initialState":{"title":"Twitter","section":null,"module":"app\\/pages\\/frontpage","cache_ttl":300,"body_class_names":"highline logged-out western de mobile-callout front-page","doc_class_names":null,"route_name":"","page_container_class_names":"AppContent wrapper-front white","ttft_navigation":false}}\">\n",
"\n",
" \n",
"\n",
" <input type=\"hidden\" class=\"swift-boot-module\" value=\"app/pages/frontpage\">\n",
" <input type=\"hidden\" id=\"swift-module-path\" value=\"https://abs.twimg.com/c/swift/de\">\n",
"\n",
" \n",
" <script src=\"https://abs.twimg.com/c/swift/de/init.023a84fd6710bf050d22965e17923e590c03307a.js\" async></script>\n",
"\n",
"\n"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"experiences = browser.find_elements_by_css_selector('#background-experience > div')\n",
"print \"Experiences\"\n",
"for exp in experiences:\n",
" occupation = exp.find_element_by_css_selector('h4').text\n",
" loc = exp.find_element_by_css_selector('h5:not([class])')\n",
" \n",
" print \"Location:\", loc.text\n",
" print \"Occupation:\",occupation\n",
"print\n",
" \n",
"educations = browser.find_elements_by_css_selector('#background-education > div')\n",
"print \"Education\"\n",
"for edu in educations:\n",
" header = edu.find_element_by_css_selector('header')\n",
" location = header.find_element_by_css_selector('h4').text\n",
" type_edu = header.find_element_by_css_selector('h5').text\n",
" \n",
" print \"Location:\", location\n",
" print \"Type:\",type_edu\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Experiences\n",
"Location: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Vrije Universiteit Amsterdam\n",
"Occupation: professor\n",
"Location: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Rathenau Instituut (Royal Netherlands Academy of Arts and Sciences)\n",
"Occupation: Head of department / research director\n",
"Location: "
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"University of Amsterdam\n",
"Occupation: Professor of communication studies\n",
"\n",
"Education\n",
"Location:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" University of Amsterdam\n",
"Type: PhD, Information Science\n",
"Location:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" University of Amsterdam\n",
"Type: MSc (with honors), Philosophy\n",
"Location:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Utrecht University\n",
"Type: BSc, Mathematics\n",
"Location:"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
" Technical University Eindhoven\n",
"Type: Propedeuse, Mechanical Engineering\n"
]
}
],
"prompt_number": 29
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Fill in your details here to be posted to the login form.\n",
"payload = {\n",
" 'isJsEnabled':'false',\n",
" 'source_app':'',\n",
" 'tryCount':'',\n",
" 'session_key':'[email protected]',\n",
" 'session_password':'',\n",
" 'signin':'Sign In',\n",
" 'session_redirect':'',\n",
" 'trk':'hb_signin',\n",
" 'loginCsrfParam':'d4e7deab-11b3-4a49-8721-17f3d2c572f2',\n",
" 'csrfToken':'ajax:7023386653850872419',\n",
" 'sourceAlias':'0_7r5yezRXCiA_H0CRD8sf6DhOjTKUNps5xGTqeX8EEoi',\n",
" 'client_ts':'1396950900277',\n",
" 'client_r':'[email protected]:833671750:179666681:716022599',\n",
" 'client_output':'-660310111',\n",
" 'client_n':'833671750:179666681:716022599',\n",
" 'client_v':'1.0.1'\n",
"}\n",
"\n",
"# Use 'with' to ensure the session context is closed after use.\n",
"with requests.Session() as s:\n",
" login = s.post('https://www.linkedin.com/uas/login-submit', data=payload)\n",
" search = s.get('https://www.linkedin.com/vsearch/p?orig=SEO_SN&firstName=xiaoli&lastName=gou&trk=SEO_SN')\n",
" soup = BeautifulSoup(search.text)\n",
" [s.extract() for s in soup('script')]\n",
" [s.extract() for s in soup('#srp_main')]\n",
" print soup"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<!DOCTYPE html>\n",
"<!--[if lt IE 7]> <html lang=\"en\" class=\"ie ie6 lte9 lte8 lte7 os-win\"> <![endif]-->\n",
"<!--[if IE 7]> <html lang=\"en\" class=\"ie ie7 lte9 lte8 lte7 os-win\"> <![endif]-->\n",
"<!--[if IE 8]> <html lang=\"en\" class=\"ie ie8 lte9 lte8 os-win\"> <![endif]-->\n",
"<!--[if IE 9]> <html lang=\"en\" class=\"ie ie9 lte9 os-win\"> <![endif]-->\n",
"<!--[if gt IE 9]> <html lang=\"en\" class=\"os-win\"> <![endif]-->\n",
"<!--[if !IE]><!--> <html lang=\"en\" class=\"os-win\"> <!--<![endif]-->\n",
"<head>\n",
"<meta name=\"lnkd-track-json-lib\" content=\"https://static.licdn.com/scds/concat/common/js?h=2jds9coeh4w78ed9wblscv68v-ebbt2vixcc5qz0otts5io08xv\" />\n",
"<meta name=\"lnkd-track-lib\" content=\"https://static.licdn.com/scds/concat/common/js?h=ebbt2vixcc5qz0otts5io08xv\" />\n",
"<meta name=\"treeID\" content=\"0JErSMoCYxOw0oXHmisAAA==\" />\n",
"<meta name=\"appName\" content=\"voltron\" />\n",
"<meta name=\"lnkd-track-error\" content=\"/lite/ua/error?csrfToken=ajax%3A2677160452502137291\" />\n",
"\n",
"<meta http-equiv=\"content-type\" content=\"text/html; charset=utf-8\" />\n",
"<meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge\" />\n",
"<meta name=\"pageImpressionID\" content=\"863f11c4-b810-4c6e-bed4-0cd6b96ff00c\" />\n",
"<meta name=\"pageKey\" content=\"voltron_people_search_internal_jsp\" />\n",
"<meta name=\"analyticsURL\" content=\"/analytics/noauthtracker\" />\n",
"<link rel=\"openid.server\" href=\"\" />\n",
"<link rel=\"apple-touch-icon-precomposed\" href=\"https://static.licdn.com/scds/common/u/img/icon/apple-touch-icon.png\" />\n",
"<!--[if lte IE 8]>\n",
" <link rel=\"shortcut icon\" href=\"https://static.licdn.com/scds/common/u/images/logos/favicons/v1/16x16/favicon.ico\">\n",
"<![endif]-->\n",
"<!--[if IE 9]>\n",
" <link rel=\"shortcut icon\" href=\"https://static.licdn.com/scds/common/u/images/logos/favicons/v1/favicon.ico\">\n",
"<![endif]-->\n",
"<link rel=\"icon\" href=\"https://static.licdn.com/scds/common/u/images/logos/favicons/v1/favicon.ico\" />\n",
"<meta name=\"msapplication-TileImage\" content=\"https://static.licdn.com/scds/common/u/images/logos/linkedin/logo-in-win8-tile-144_v1.png\" />\n",
"<meta name=\"msapplication-TileColor\" content=\"#0077B5\" />\n",
"<meta name=\"application-name\" content=\"LinkedIn\" />\n",
"<link rel=\"stylesheet\" type=\"text/css\" href=\"https://static.licdn.com/scds/concat/common/css?h=3bifs78lai5i0ndyj1ew7316e-c8kkvmvykvq2ncgxoqb13d2by-3okvx341grol1nm5116saqq26-2it1to3q1pt5evainys9ta07p-4uu2pkz5u0jch61r2nhpyyrn8-7poavrvxlvh0irzkbnoyoginp-4om4nn3a2z730xs82d78xj3be-dd3oc1fccygvv3uaj5lnt442j-ct4kfyj4tquup0bvqhttvymms-aacpb35j20e026x7dvhr3g6dx-9zbbsrdszts09by60it4vuo3q-8ti9u6z5f55pestwbmte40d9-d8hkit8s01fcga0p1mt4hsfdw-3pwwsn1udmwoy3iort8vfmygt-6ramlbadr9lh7v5r7vuc6t4ld-1uqesw6kopz7vp6kkmwyqp32d\" />\n",
"\n",
"\n",
"\n",
"\n",
"<meta name=\"remote-nav-init-marker\" content=\"true\" />\n",
"<link rel=\"stylesheet\" type=\"text/css\" href=\"https://static.licdn.com/scds/concat/common/css?h=aacpb35j20e026x7dvhr3g6dx-avbjbi1inu4u89givztep9q2u\" />\n",
"\n",
"\n",
"<title>Search | LinkedIn</title>\n",
"<link rel=\"stylesheet\" type=\"text/css\" href=\"https://static.licdn.com/scds/concat/common/css?h=eefvysj04e847p9ekg2f3z8g4-2wumwit1vypojnesb9wct6wnn-1hf698ru41sqw318wl97d377o-ep1xxywwdww9etk7kla3p6n34\" />\n",
"\n",
"<link rel=\"stylesheet\" type=\"text/css\" href=\"https://static.licdn.com/scds/concat/common/css?h=7nkyrgi6spbknqf0ji997dxlt-chieycw7hfzztmj9tnqc8di21-b1x464t79mazkfmobn10j7ha3-bvojuz3ersl6hvkmx2xyhmpju-wrbcojvu29w0k4vfiycsq92n-cpvxowd3pnaxv3pupn4xfohty-bly8qs2qbi1e0xkkhadklk53y-873zyp4u00ui4pxufyu2ntt5e-3jeldink1kknem59xippfn49r-62t72qmpo589qo2eix1dhfz0s-50of46vdlrvgezrdx8rk5d7ng-1sy9o9tjefwjd6ig1170lpa8j-76wknejo919k9gx33gnxmmpq3-2shz1vwqoq3u54aigfom24ney-87xhud1gl1789wkhsce02wout-bgt4fxvlqtjpamv1nqa7k510b-12f161c4px2kpwetngl23tqzd-3684f4mczwlgc8plkne7rv0cx-1dtfluhcgle1k0evu33w7naps-98c7s01i4e1xllxhze87ehcml-ef6mo6oe900n4re189331bei0-50vrqk5idst99q22c9uu9f2ob-bpod274f0q3d8nscreey6go7w-30vaj51m0140n60qdlfvpa9u7-96y609bt7axngk5b2xl2s8n5z-b1nvu6r7jblcwiolcgxzm9tp8-8tme67sacsxobn6meorgidw7r-eorog5xxxbfjlkivn6efwwjn8-ca2lplulcxtqxq1rmc7u4y9ag-8re2trni9jo32jxdueg45in9q-138sla37exzpoyfktq2bt0sy0-87u7duja6spj0pj1ma5v32r0c-2iu8tj1zv1cmzvz3k9n9q3k32-azni23nwzzjea4g8fh3abjlvv\" />\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"</head>\n",
"<body dir=\"ltr\" class=\"member v2 voltron-page chrome-v5-retract-nav-enabled chrome-v5 chrome-v5-responsive sticky-bg\" id=\"pagekey-voltron_people_search_internal_jsp\">\n",
"<input id=\"inSlowConfig\" type=\"hidden\" value=\"false\" />\n",
"\n",
"<div id=\"a11y-menu\" class=\"a11y-skip-nav-container\">\n",
"<div class=\"a11y-skip-nav a11y-hidden\">\n",
"<a href=\"#a11y-content\" id=\"a11y-skip-nav-link\">Skip to main content</a>\n",
"</div>\n",
"\n",
"\n",
"</div>\n",
"<div id=\"header\" class=\"global-header responsive-header nav-v5-2-header responsive-1 remote-nav\" role=\"banner\">\n",
"<div id=\"top-header\">\n",
"<div class=\"wrapper\">\n",
"<div class=\"header-section first-child\">\n",
"<h2 class=\"logo-container\" tabindex=\"0\">\n",
"<a href=\"http://www.linkedin.com/?trk=nav_logo\" class=\"logo\" id=\"in-logo\">\n",
" LinkedIn\n",
" </a>\n",
"</h2>\n",
"<form id=\"global-search\" role=\"search\" action=\"/vsearch/f\" method=\"get\" accept-charset=\"UTF-8\" name=\"commonSearch\" class=\"global-search voltron voltron-vertical-selector\">\n",
"<fieldset>\n",
"<legend>Find People, Jobs, Companies, and More</legend>\n",
"<div class=\"search-scope\">\n",
"<label for=\"main-search-category\">Search for:</label>\n",
"<select name=\"type\" id=\"main-search-category\" class=\"search-category\">\n",
"<option class=\"all\" data-li-advanced-link=\"/vsearch/f?adv=true&trk=federated_advs\" data-li-styled-dropdown-class=\"all\" data-li-search-action=\"/vsearch/f\" data-li-ghost-text=\"Search for people, jobs, companies, and more...\" data-li-trk-code=\"vsrp_all_vertical_selector_item\" title=\"Search for people, jobs, companies, and more...\" value=\"all\">All</option>\n",
"<option class=\"people\" data-li-advanced-link=\"/vsearch/p?adv=true&trk=advsrch\" data-li-styled-dropdown-class=\"people\" data-li-search-action=\"/vsearch/p\" data-li-ghost-text=\"Search people...\" data-li-trk-code=\"vsrp_people_vertical_selector_item\" title=\"Keyword, name, company or title\" value=\"people\" selected=\"selected\">People</option>\n",
"<option class=\"jobs\" data-li-advanced-link=\"/vsearch/j?adv=true&trk=hb_advjs\" data-li-styled-dropdown-class=\"jobs\" data-li-search-action=\"/vsearch/j\" data-li-ghost-text=\"Search jobs...\" data-li-trk-code=\"vsrp_jobs_vertical_selector_item\" title=\"Keyword, company or job title\" value=\"jobs\">Jobs</option>\n",
"<option class=\"companies\" data-li-styled-dropdown-class=\"companies\" data-li-search-action=\"/vsearch/c\" data-li-ghost-text=\"Search companies...\" title=\"Keyword\" data-li-trk-code=\"vsrp_companies_vertical_selector_item\" value=\"companies\">Companies</option>\n",
"<option class=\"groups\" data-li-styled-dropdown-class=\"groups\" data-li-search-action=\"/vsearch/g\" data-li-ghost-text=\"Search groups...\" data-li-trk-code=\"vsrp_groups_vertical_selector_item\" title=\"Keyword\" value=\"groups\">Groups</option>\n",
"<option class=\"edu\" data-li-styled-dropdown-class=\"edu\" data-li-search-action=\"/vsearch/e\" data-li-ghost-text=\"Search universities...\" data-li-trk-code=\"vsrp_edu_vertical_selector_item\" title=\"Keyword\" value=\"edu\">Universities</option>\n",
"<option class=\"content\" data-li-styled-dropdown-class=\"content\" data-li-search-action=\"/vsearch/ic\" data-li-ghost-text=\"Search articles...\" data-li-trk-code=\"vsrp_content_vertical_selector_item\" title=\"Keyword\" value=\"content\">Articles</option>\n",
"<option class=\"inbox\" data-li-styled-dropdown-class=\"inbox\" data-li-search-action=\"/inbox/messages/search\" data-li-ghost-text=\"Search inbox...\" data-li-trk-code=\"vsrp_inbox_vertical_selector_item\" title=\"Keyword\" value=\"inbox\">Inbox</option>\n",
"</select>\n",
"</div>\n",
"\n",
"<div class=\"search-box-container\" id=\"search-box-container\">\n",
"<span id=\"search-autocomplete-container\" title=\"Tip: You can also search by keyword, company, school...\" class=\"http://www.linkedin.com/typeahead\">\n",
"<label for=\"main-search-box\" class=\"ghost\">Search people...</label>\n",
"\n",
"<input name=\"keywords\" id=\"main-search-box\" class=\"search-term typeahead-instant\" type=\"text\" value=\"\" autocomplete=\"off\" />\n",
"<span id=\"typeahead-loader\"></span>\n",
"<button id=\"clear-main-search\" type=\"button\">\n",
"<span class=\"description\">Clear</span>\n",
"</button>\n",
"<span id=\"search-typeahead-container\"></span>\n",
"</span>\n",
"<input name=\"orig\" type=\"hidden\" value=\"GLHD\" />\n",
"<input name=\"rsid\" type=\"hidden\" />\n",
"<input name=\"pageKey\" type=\"hidden\" value=\"voltron_people_search_internal_jsp\" />\n",
"<input name=\"trkInfo\" id=\"main-search-trkInfo\" type=\"hidden\" value=\"\" />\n",
"</div>\n",
"<button name=\"search\" value=\"Search\" class=\"search-button\" type=\"submit\">\n",
"<span>Search</span>\n",
"</button>\n",
"</fieldset>\n",
"<div class=\"advanced-search-outer\">\n",
"<div class=\"advanced-search-inner\">\n",
"<a href=\"/vsearch/p?adv=true&trk=advsrch\" class=\"advanced-search\" id=\"advanced-search\">Advanced\n",
" </a>\n",
"</div>\n",
"</div>\n",
"</form>\n",
"\n",
"</div>\n",
"<div class=\"header-section last-child\">\n",
"<ul class=\"nav utilities\" role=\"navigation\">\n",
"<li class=\"nav-item activity-tab\" data-li-activity-type=\"messages\">\n",
"<a href=\"http://www.linkedin.com/inbox/messages/received?trk=nav_utilities_inbox\" class=\"activity-toggle inbox-alert\">\n",
" Inbox\n",
" </a>\n",
"<div class=\"activity-container\" id=\"inbox\">\n",
"<div class=\"activity-drop activity-drop-loading\">\n",
"<div class=\"activity-drop-body\"></div>\n",
"</div>\n",
"</div>\n",
"</li>\n",
"<li class=\"nav-item activity-tab\" data-li-new-count=\"0\" data-li-action-type-click=\"ntf_click_notifications_icon\" data-li-action-type-pagination=\"ntf_scroll\" data-li-activity-type=\"notifications\">\n",
"<a href=\"#notifications\" class=\"activity-toggle notifications-alert\">\n",
" Notification\n",
" </a>\n",
"<div class=\"activity-container\" id=\"notifications\">\n",
"<div class=\"activity-drop activity-drop-loading\">\n",
"<div class=\"activity-drop-body\"></div>\n",
"</div>\n",
"</div>\n",
"</li>\n",
"<li class=\"nav-item activity-tab\" data-li-activity-type=\"addconnections\">\n",
"<a id=\"dropdowntest\" href=\"/fetch/importAndInviteEntry?trk=nav_utilities_add_connx\" class=\"activity-toggle add-connections-btn\">\n",
" Add Connections\n",
" </a>\n",
"<div class=\"activity-container\" id=\"addconnections\">\n",
"<div class=\"activity-drop activity-drop-loading\">\n",
"<div class=\"activity-drop-body\">\n",
"<section class=\"invite-securely-via-email-lix\">\n",
"<div class=\"invite-securely-via-email-lix-div-fallback error-message\">\n",
"<div class=\"activity-drop-header\">\n",
"<h3>Add Connections<span class=\"sub-nav-header-arrow\" role=\"presentation\"></span></h3>\n",
"</div>\n",
"<h4>Invite your contacts</h4>\n",
"<p>Quickly find people you may know by searching your email contacts:</p>\n",
"<a href=\"/secure/importAndInvite?trk=nav_utilities_add_connx\" class=\"modal-overlay-link\"></a>\n",
"<ul class=\"providers\">\n",
"<li id=\"gmail-li\" class=\"gmail\"><a href=\"/secure/importAndInvite?trk=nav_utilities_add_connx\">Gmail</a></li>\n",
"<li class=\"yahoo\"><a href=\"/secure/importAndInvite?trk=nav_utilities_add_connx\">AOL</a></li>\n",
"<li class=\"hotmail\"><a href=\"/secure/importAndInvite?trk=nav_utilities_add_connx\">Hotmail</a></li>\n",
"<li class=\"other\"><a href=\"/secure/importAndInvite?trk=nav_utilities_add_connx\">Other</a></li>\n",
"</ul>\n",
"</div>\n",
"</section>\n",
"</div>\n",
"</div>\n",
"</div>\n",
"</li>\n",
"<li class=\"nav-item account-settings-tab\">\n",
"<a href=\"http://www.linkedin.com/profile/view?id=334679802&trk=nav_responsive_tab_profile_pic\" class=\"account-toggle\">\n",
"<img src=\"https://static.licdn.com/scds/common/u/images/themes/katy/ghosts/person/ghost_person_30x30_v1.png\" alt=\"Luka Stout\" class=\"nav-profile-photo\" height=\"20\" width=\"20\" />\n",
"</a>\n",
"<div class=\"account-sub-nav\" id=\"account-sub-nav\">\n",
"<div class=\"account-sub-nav-options\">\n",
"<div class=\"account-sub-nav-header\">\n",
"<h3>Account & Settings<span class=\"sub-nav-header-arrow\" role=\"presentation\"></span></h3>\n",
"</div>\n",
"<div class=\"account-sub-nav-body\">\n",
"<ul class=\"account-settings\">\n",
"<li class=\"self\">\n",
"<div class=\"account-settings-link\">\n",
"<span class=\"act-set-row\">\n",
"<span class=\"act-set-icon\">\n",
"<a href=\"http://www.linkedin.com/profile/view?id=334679802&trk=nav_responsive_tab_profile_pic\">\n",
"<span class=\"act-set-icon-image\" role=\"presentation\">\n",
"<img src=\"https://static.licdn.com/scds/common/u/images/themes/katy/ghosts/person/ghost_person_30x30_v1.png\" alt=\"Luka Stout\" class=\"profile-photo\" height=\"20\" width=\"20\" />\n",
"</span>\n",
"</a>\n",
"</span>\n",
"<span class=\"act-set-name\">\n",
"<a href=\"http://www.linkedin.com/profile/view?id=334679802&trk=nav_responsive_tab_profile_pic\" class=\"act-set-name-split-link\">\n",
"Luka Stout </a>\n",
"</span>\n",
"<span class=\"act-set-action\">\n",
"<a href=\"http://www.linkedin.com/uas/logout?session_full_logout=&csrfToken=ajax%3A2677160452502137291&trk=nav_account_sub_nav_signout\" class=\"account-submenu-split-link\">\n",
" Sign Out\n",
" </a>\n",
"</span>\n",
"</span>\n",
"</div>\n",
"</li>\n",
"<li class=\"account-type\">\n",
"<a href=\"http://www.linkedin.com/mnyfe/subscriptionv2?displayProducts=&trk=nav_account_sub_nav_upgrade\" class=\"account-settings-link\" target=\"_self\">\n",
"<span class=\"act-set-row\">\n",
"<span class=\"act-set-icon\"><span class=\"act-set-icon-image\" role=\"presentation\"></span></span>\n",
"<span class=\"act-set-name\">\n",
" Account: Basic\n",
" </span>\n",
"<span class=\"act-set-action\">\n",
" Upgrade\n",
" </span>\n",
"</span>\n",
"</a>\n",
"</li>\n",
"<li class=\"job-posting\">\n",
"<a href=\"http://www.linkedin.com/job/consumer/manageConsumer?trk=nav_account_sub_nav_job_manage\" class=\"account-settings-link\">\n",
"<span class=\"act-set-row\">\n",
"<span class=\"act-set-icon\"><span class=\"act-set-icon-image\" role=\"presentation\"></span></span>\n",
"<span class=\"act-set-name\">\n",
" Job Posting\n",
" </span>\n",
"<span class=\"act-set-action\">\n",
" Manage\n",
" </span>\n",
"</span>\n",
"</a>\n",
"</li>\n",
"<li class=\"language-settings\">\n",
"<a href=\"/settings/?tab=account&modal=nsettings-select-language&trk=nav_account_sub_nav_language\" class=\"account-settings-link\">\n",
"<span class=\"act-set-row\">\n",
"<span class=\"act-set-icon\"><span class=\"act-set-icon-image\" role=\"presentation\"></span></span>\n",
"<span class=\"act-set-name\">\n",
" Language\n",
" </span>\n",
"<span class=\"act-set-action\">\n",
" Change\n",
" </span>\n",
"</span>\n",
"</a>\n",
"</li>\n",
"<li class=\"privacy-settings\">\n",
"<a href=\"/secure/settings?trk=nav_account_sub_nav_settings\" class=\"account-settings-link\">\n",
"<span class=\"act-set-row\">\n",
"<span class=\"act-set-icon\"><span class=\"act-set-icon-image\" role=\"presentation\"></span></span>\n",
"<span class=\"act-set-name\">\n",
" Privacy & Settings\n",
" </span>\n",
"<span class=\"act-set-action\">\n",
" Review\n",
" </span>\n",
"</span>\n",
"</a>\n",
"</li>\n",
"<li class=\"help-center\">\n",
"<span class=\"qh-icon\"></span>\n",
"<span class=\"account-settings-link\">\n",
"<a href=\"https://help.linkedin.com/app/home/loc/hd/trk/voltron_people_search_internal_jsp/\" target=\"_blank\" rel=\"nofollow\" class=\"act-set-name\">Help Center</a>\n",
"<a href=\"https://help.linkedin.com/app/home/loc/hd/trk/voltron_people_search_internal_jsp/\" target=\"_blank\" rel=\"nofollow\" class=\"act-set-action\">Get Help</a>\n",
"<div id=\"qh-tourlist-loader\" class=\"loading\"></div>\n",
"<ul class=\"qh-page-tours\"></ul>\n",
"</span>\n",
"\n",
"</li>\n",
"</ul>\n",
"</div>\n",
"</div>\n",
"</div>\n",
"</li>\n",
"</ul>\n",
"\n",
"</div>\n",
"</div>\n",
"</div>\n",
"<div class=\"responsive-nav\" id=\"responsive-nav-scrollable\">\n",
"<div class=\"wrapper\">\n",
"<ul class=\"nav main-nav\" role=\"navigation\">\n",
"<li class=\"nav-item\">\n",
"<a href=\"http://www.linkedin.com/home?trk=nav_responsive_tab_home\" class=\"nav-link\">\n",
" Home\n",
" </a>\n",
"</li>\n",
"<li class=\"nav-item\">\n",
"<a href=\"http://www.linkedin.com/profile/view?id=334679802&trk=nav_responsive_tab_profile\" class=\"nav-link\">\n",
" Profile\n",
" </a>\n",
"<ul class=\"sub-nav\" id=\"profile-sub-nav\">\n",
"<li>\n",
"<a href=\"http://www.linkedin.com/profile/edit?trk=nav_responsive_sub_nav_edit_profile\">\n",
" Edit Profile\n",
" </a>\n",
"</li>\n",
"<li>\n",
"<a href=\"http://www.linkedin.com/wvmx/profile?trk=nav_responsive_sub_nav_wvmp\">\n",
" Who's Viewed Your Profile\n",
" </a>\n",
"</li>\n",
"</ul>\n",
"</li>\n",
"<li class=\"nav-item\">\n",
"<a href=\"http://www.linkedin.com/connections?type=combined&trk=nav_responsive_tab_network\" class=\"nav-link\">\n",
" Network\n",
" </a>\n",
"<ul class=\"sub-nav\">\n",
"<li>\n",
"<a href=\"http://www.linkedin.com/connections?type=combined&trk=nav_responsive_sub_nav_network\">\n",
" Contacts\n",
" </a>\n",
"</li>\n",
"<li>\n",
"<a href=\"/fetch/importAndInviteEntry?trk=nav_responsive_sub_nav_add_connections\">\n",
" Add Connections\n",
" </a>\n",
"</li>\n",
"<li>\n",
"<a href=\"http://www.linkedin.com/edu/alumni?trk=nav_responsive_sub_nav_find_alumni\">\n",
" Find Alumni\n",
" </a>\n",
"</li>\n",
"</ul>\n",
"</li>\n",
"<li class=\"nav-item\">\n",
"<a href=\"http://www.linkedin.com/jobs?displayHome=&trk=nav_responsive_sub_nav_jobs\" class=\"nav-link\">\n",
" Jobs\n",
" </a>\n",
"</li>\n",
"<li class=\"nav-item\">\n",
"<button id=\"nav-link-interests\" class=\"nav-link no-link\">\n",
" Interests\n",
" </button>\n",
"<ul class=\"sub-nav\" id=\"interests-sub-nav\">\n",
"<li>\n",
"<a href=\"http://www.linkedin.com/company/home?trk=nav_responsive_sub_nav_companies\">\n",
" Companies\n",
" </a>\n",
"</li>\n",
"<li>\n",
"<a href=\"http://www.linkedin.com/myGroups?trk=nav_responsive_sub_nav_groups\">\n",
" Groups\n",
" </a>\n",
"</li>\n",
"<li>\n",
"<a href=\"http://www.linkedin.com/today/?trk=nav_responsive_sub_nav_pulse\">\n",
" Pulse\n",
" </a>\n",
"</li>\n",
"<li>\n",
"<a href=\"http://www.linkedin.com/edu/?trk=nav_responsive_sub_nav_edu\">\n",
" Education\n",
" </a>\n",
"</li>\n",
"</ul>\n",
"</li>\n",
"</ul>\n",
"\n",
"<ul class=\"nav premium-nav\" role=\"navigation\">\n",
"<li class=\"nav-item\">\n",
"<button class=\"nav-link no-link\">\n",
" \n",
" \n",
" Business Services\n",
" \n",
" \n",
" </button>\n",
"<ul class=\"sub-nav\" id=\"business-sub-nav\">\n",
"<li>\n",
"<a href=\"http://www.linkedin.com/jobs/post?editAttributes=&trk=nav_responsive_sub_nav_post_job\">\n",
" Post a Job\n",
" </a>\n",
"</li>\n",
"<li>\n",
"<a href=\"http://www.linkedin.com/mnyfe/subscriptionv2?displayProducts=&family=talent&trk=li-nav\" target=\"_blank\">Talent Solutions</a>\n",
"</li><li>\n",
"</li><li>\n",
"<a href=\"/ads/start?utm_source=li&utm_medium=el&utm_campaign=hb_tab_ads&src=en-all-el-li-hb_tab_ads&trk=nav_responsive_sub_nav_advertise\" target=\"_blank\">\n",
" Advertise\n",
" </a>\n",
"</li>\n",
"</ul>\n",
"</li>\n",
"<li class=\"nav-item\">\n",
"<a href=\"http://www.linkedin.com/mnyfe/subscriptionv2?displayProducts=&trk=nav_responsive_sub_nav_upgrade\" id=\"\" class=\"nav-link\" data-li-phref=\"\" target=\"_self\">\n",
" Upgrade\n",
" </a>\n",
"</li>\n",
"</ul>\n",
"\n",
"</div>\n",
"</div>\n",
"\n",
"<div class=\"a11y-content\">\n",
"<a name=\"a11y-content\" tabindex=\"0\" id=\"a11y-content-link\">Main content starts below.</a>\n",
"</div>\n",
"</div>\n",
"\n",
"\n",
"<div id=\"body\" class=\"\" role=\"main\">\n",
"<div class=\"wrapper hp-nus-wrapper\">\n",
"<div id=\"global-error\">\n",
"</div>\n",
"<div id=\"srp_main_\">\n",
"<code id=\"voltron_srp_main-content\" style=\"display:none;\"><!--{\"content\":{\"WhoSharedDialogJS\":\"https://static.licdn.com/scds/concat/common/js?h=3m0wwwerqvp8618uhx52in5b\\u002d7tscxcclxq1zcpxgkn2ul1fe7\\u002df2ve2m4snne5xyn5408bsek5n\\u002dcz35wdvsh3whk61r5ab6knzup\\u002d85t6m5rnn1zncu7j06kyx3y8p\\u002dd9ph32rbhmv9lhj13no8z4vdc\",\"FeedbackDialogCSS\":\"https://static.licdn.com/scds/concat/common/css?h=&v=build\\u002d2000_8_34028\\u002dprod&f=scss%2Fmodules%2Ffeedback_en_US\",\"EndorseDialogCSS\":\"https://static.licdn.com/scds/concat/common/css?h=1e66kchoas89lqt97f1urrgat\",\"dialog__text_plain__dialog_submit\":\"Submit\",\"dialog__text_plain__dialog_error_generic\":\"We're sorry. Something unexpected happened and your request could not be completed. Please try again.\",\"lix_instant_connect\":\"pymk\",\"lix_server_messages\":\"control\",\"lix_header_lowercase\":\"control\",\"ComposeDialogCSS\":\"https://static.licdn.com/scds/concat/common/css?h=154kxlhs4z8rrtcvqfbage7t\",\"lix_typeahead2_facets\":\"enabled\",\"page\":{\"advJobsSearchUrl\":\"/vsearch/ajj\",\"reference_search_link\":\"http://www.linkedin.com/rs\",\"i18n_pagination_error\":\"Oops, we couldn't move you off this page. Please refresh the page and try again.\",\"profile_organizer_i18n\":\"Profile Organizer\",\"saved_search_max_jobs_i18n\":\"You have reached your current limit of saved jobs searches.\",\"i18n_facet_expand_error\":\"Hmmm, something went wrong while loading these filters. Please refresh the page and try again.\",\"reference_search_i18n\":\"Reference Search\",\"feedback\":\"Feedback\",\"i18n_results_text\":\"results\",\"voltron_unified_search_json\":{\"search\":{\"i18n_some_suggestions\":\"Some suggestions:\",\"find_key_people_i18n\":\"Find key people in half the time with Premium Filters\",\"lix_newFacetUpsell\":\"control\",\"i18n_join_to_view_all_members\":\"Join to view all members &#187\",\"advs_groups_unavailable_i18n\":\"Advanced Search is not available for groups\",\"i18n_similar_job\":\"Similar\",\"i18n_sorry_no_sim_influencer_content_results\":\"Sorry, there are no similar articles at the moment.\",\"i18n_survey_info\":\"Your feedback will help us understand how we can make LinkedIn search better for you.\",\"i18n_saved_person_badge\":\"[Saved]\",\"i18n_open_in_recruiter\":\"Open In Recruiter\",\"i18n_saved_job_badge\":\"[Saved]\",\"i18n_connect\":\"Connect\",\"i18n_related_article\":\"Related\",\"i18n_find_members\":\"Find Members\",\"lix_group\":\"control\",\"i18n_similar_company\":\"Similar\",\"i18n_members_count\":\"Members Count\",\"search_like_pro_i18n\":\"Search like a Pro\",\"people_i18n\":\"People\",\"i18n_activity\":\"Activity\",\"lix_wrap_facets\":\"control\",\"advancedSearchForm\":{\"reset_i18n\":\"Reset\",\"formMethod\":\"POST\",\"formName\":\"peopleSearchForm\",\"searchFields\":[{\"name\":\"keywords\",\"labelName\":\"Keywords\"},{\"name\":\"firstName\",\"value\":\"xiaoli\",\"labelName\":\"First Name\"},{\"name\":\"lastName\",\"value\":\"gou\",\"labelName\":\"Last Name\"},{\"name\":\"title\",\"labelName\":\"Title\"},{\"isHidden\":true,\"name\":\"titleScope\",\"options\":[{\"value\":\"CP\",\"displayValue\":\"Current or past\"},{\"value\":\"C\",\"displayValue\":\"Current\"},{\"value\":\"P\",\"displayValue\":\"Past\"},{\"value\":\"PNC\",\"displayValue\":\"Past not current\"}]},{\"name\":\"company\",\"labelName\":\"Company\"},{\"isHidden\":true,\"name\":\"companyScope\",\"options\":[{\"value\":\"CP\",\"displayValue\":\"Current or past\"},{\"value\":\"C\",\"displayValue\":\"Current\"},{\"value\":\"P\",\"displayValue\":\"Past\"},{\"value\":\"PNC\",\"displayValue\":\"Past not current\"}]},{\"name\":\"school\",\"labelName\":\"School\"},{\"name\":\"locationType\",\"labelName\":\"Location\",\"options\":[{\"value\":\"Y\",\"displayValue\":\"Anywhere\"},{\"value\":\"I\",\"displayValue\":\"Located in or near:\"}]},{\"isHidden\":true,\"name\":\"countryCode\",\"labelName\":\"Country\",\"options\":[{\"value\":\"us\",\"displayValue\":\"United States\"},{\"value\":\"af\",\"displayValue\":\"Afghanistan\"},{\"value\":\"ax\",\"displayValue\":\"Aland Islands\"},{\"value\":\"al\",\"displayValue\":\"Albania\"},{\"value\":\"dz\",\"displayValue\":\"Algeria\"},{\"value\":\"as\",\"displayValue\":\"American Samoa\"},{\"value\":\"ad\",\"displayValue\":\"Andorra\"},{\"value\":\"ao\",\"displayValue\":\"Angola\"},{\"value\":\"ai\",\"displayValue\":\"Anguilla\"},{\"value\":\"aq\",\"displayValue\":\"Antarctica\"},{\"value\":\"ag\",\"displayValue\":\"Antigua and Barbuda\"},{\"value\":\"ar\",\"displayValue\":\"Argentina\"},{\"value\":\"am\",\"displayValue\":\"Armenia\"},{\"value\":\"aw\",\"displayValue\":\"Aruba\"},{\"value\":\"au\",\"displayValue\":\"Australia\"},{\"value\":\"at\",\"displayValue\":\"Austria\"},{\"value\":\"az\",\"displayValue\":\"Azerbaijan\"},{\"value\":\"bs\",\"displayValue\":\"Bahamas\"},{\"value\":\"bh\",\"displayValue\":\"Bahrain\"},{\"value\":\"bd\",\"displayValue\":\"Bangladesh\"},{\"value\":\"bb\",\"displayValue\":\"Barbados\"},{\"value\":\"by\",\"displayValue\":\"Belarus\"},{\"value\":\"be\",\"displayValue\":\"Belgium\"},{\"value\":\"bz\",\"displayValue\":\"Belize\"},{\"value\":\"bj\",\"displayValue\":\"Benin\"},{\"value\":\"bm\",\"displayValue\":\"Bermuda\"},{\"value\":\"bt\",\"displayValue\":\"Bhutan\"},{\"value\":\"bo\",\"displayValue\":\"Bolivia\"},{\"value\":\"ba\",\"displayValue\":\"Bosnia and Herzegovina\"},{\"value\":\"bw\",\"displayValue\":\"Botswana\"},{\"value\":\"br\",\"displayValue\":\"Brazil\"},{\"value\":\"io\",\"displayValue\":\"British Indian Ocean Territory\"},{\"value\":\"bn\",\"displayValue\":\"Brunei Darussalam\"},{\"value\":\"bg\",\"displayValue\":\"Bulgaria\"},{\"value\":\"bf\",\"displayValue\":\"Burkina Faso\"},{\"value\":\"bi\",\"displayValue\":\"Burundi\"},{\"value\":\"kh\",\"displayValue\":\"Cambodia\"},{\"value\":\"cm\",\"displayValue\":\"Cameroon\"},{\"value\":\"ca\",\"displayValue\":\"Canada\"},{\"value\":\"cv\",\"displayValue\":\"Cape Verde\"},{\"value\":\"cb\",\"displayValue\":\"Caribbean Nations\"},{\"value\":\"ky\",\"displayValue\":\"Cayman Islands\"},{\"value\":\"cf\",\"displayValue\":\"Central African Republic\"},{\"value\":\"td\",\"displayValue\":\"Chad\"},{\"value\":\"cl\",\"displayValue\":\"Chile\"},{\"value\":\"cn\",\"displayValue\":\"China\"},{\"value\":\"cx\",\"displayValue\":\"Christmas Island\"},{\"value\":\"cc\",\"displayValue\":\"Cocos (Keeling) Islands\"},{\"value\":\"co\",\"displayValue\":\"Colombia\"},{\"value\":\"km\",\"displayValue\":\"Comoros\"},{\"value\":\"cg\",\"displayValue\":\"Congo\"},{\"value\":\"ck\",\"displayValue\":\"Cook Islands\"},{\"value\":\"cr\",\"displayValue\":\"Costa Rica\"},{\"value\":\"ci\",\"displayValue\":\"Cote D'Ivoire (Ivory Coast)\"},{\"value\":\"hr\",\"displayValue\":\"Croatia\"},{\"value\":\"cu\",\"displayValue\":\"Cuba\"},{\"value\":\"cy\",\"displayValue\":\"Cyprus\"},{\"value\":\"cz\",\"displayValue\":\"Czech Republic\"},{\"value\":\"cd\",\"displayValue\":\"Democratic Republic of the Congo\"},{\"value\":\"dk\",\"displayValue\":\"Denmark\"},{\"value\":\"dj\",\"displayValue\":\"Djibouti\"},{\"value\":\"dm\",\"displayValue\":\"Dominica\"},{\"value\":\"do\",\"displayValue\":\"Dominican Republic\"},{\"value\":\"tp\",\"displayValue\":\"East Timor\"},{\"value\":\"ec\",\"displayValue\":\"Ecuador\"},{\"value\":\"eg\",\"displayValue\":\"Egypt\"},{\"value\":\"sv\",\"displayValue\":\"El Salvador\"},{\"value\":\"gq\",\"displayValue\":\"Equatorial Guinea\"},{\"value\":\"er\",\"displayValue\":\"Eritrea\"},{\"value\":\"ee\",\"displayValue\":\"Estonia\"},{\"value\":\"et\",\"displayValue\":\"Ethiopia\"},{\"value\":\"fk\",\"displayValue\":\"Falkland Islands (Malvinas)\"},{\"value\":\"fo\",\"displayValue\":\"Faroe Islands\"},{\"value\":\"fm\",\"displayValue\":\"Federated States of Micronesia\"},{\"value\":\"fj\",\"displayValue\":\"Fiji\"},{\"value\":\"fi\",\"displayValue\":\"Finland\"},{\"value\":\"fr\",\"displayValue\":\"France\"},{\"value\":\"gf\",\"displayValue\":\"French Guiana\"},{\"value\":\"pf\",\"displayValue\":\"French Polynesia\"},{\"value\":\"tf\",\"displayValue\":\"French Southern Territories\"},{\"value\":\"ga\",\"displayValue\":\"Gabon\"},{\"value\":\"gm\",\"displayValue\":\"Gambia\"},{\"value\":\"ge\",\"displayValue\":\"Georgia\"},{\"value\":\"de\",\"displayValue\":\"Germany\"},{\"value\":\"gh\",\"displayValue\":\"Ghana\"},{\"value\":\"gi\",\"displayValue\":\"Gibraltar\"},{\"value\":\"gr\",\"displayValue\":\"Greece\"},{\"value\":\"gl\",\"displayValue\":\"Greenland\"},{\"value\":\"gd\",\"displayValue\":\"Grenada\"},{\"value\":\"gp\",\"displayValue\":\"Guadeloupe\"},{\"value\":\"gu\",\"displayValue\":\"Guam\"},{\"value\":\"gt\",\"displayValue\":\"Guatemala\"},{\"value\":\"gg\",\"displayValue\":\"Guernsey\"},{\"value\":\"gn\",\"displayValue\":\"Guinea\"},{\"value\":\"gw\",\"displayValue\":\"Guinea\\u002dBissau\"},{\"value\":\"gy\",\"displayValue\":\"Guyana\"},{\"value\":\"ht\",\"displayValue\":\"Haiti\"},{\"value\":\"hn\",\"displayValue\":\"Honduras\"},{\"value\":\"hk\",\"displayValue\":\"Hong Kong\"},{\"value\":\"hu\",\"displayValue\":\"Hungary\"},{\"value\":\"is\",\"displayValue\":\"Iceland\"},{\"value\":\"in\",\"displayValue\":\"India\"},{\"value\":\"id\",\"displayValue\":\"Indonesia\"},{\"value\":\"ir\",\"displayValue\":\"Iran\"},{\"value\":\"iq\",\"displayValue\":\"Iraq\"},{\"value\":\"ie\",\"displayValue\":\"Ireland\"},{\"value\":\"im\",\"displayValue\":\"Isle of Man\"},{\"value\":\"il\",\"displayValue\":\"Israel\"},{\"value\":\"it\",\"displayValue\":\"Italy\"},{\"value\":\"jm\",\"displayValue\":\"Jamaica\"},{\"value\":\"jp\",\"displayValue\":\"Japan\"},{\"value\":\"je\",\"displayValue\":\"Jersey\"},{\"value\":\"jo\",\"displayValue\":\"Jordan\"},{\"value\":\"kz\",\"displayValue\":\"Kazakhstan\"},{\"value\":\"ke\",\"displayValue\":\"Kenya\"},{\"value\":\"ki\",\"displayValue\":\"Kiribati\"},{\"value\":\"kr\",\"displayValue\":\"Korea\"},{\"value\":\"kp\",\"displayValue\":\"Korea 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Islands\"},{\"value\":\"mq\",\"displayValue\":\"Martinique\"},{\"value\":\"mr\",\"displayValue\":\"Mauritania\"},{\"value\":\"mu\",\"displayValue\":\"Mauritius\"},{\"value\":\"yt\",\"displayValue\":\"Mayotte\"},{\"value\":\"mx\",\"displayValue\":\"Mexico\"},{\"value\":\"md\",\"displayValue\":\"Moldova\"},{\"value\":\"mc\",\"displayValue\":\"Monaco\"},{\"value\":\"mn\",\"displayValue\":\"Mongolia\"},{\"value\":\"me\",\"displayValue\":\"Montenegro\"},{\"value\":\"ms\",\"displayValue\":\"Montserrat\"},{\"value\":\"ma\",\"displayValue\":\"Morocco\"},{\"value\":\"mz\",\"displayValue\":\"Mozambique\"},{\"value\":\"mm\",\"displayValue\":\"Myanmar\"},{\"value\":\"na\",\"displayValue\":\"Namibia\"},{\"value\":\"nr\",\"displayValue\":\"Nauru\"},{\"value\":\"np\",\"displayValue\":\"Nepal\"},{\"value\":\"nl\",\"displayValue\":\"Netherlands\"},{\"value\":\"an\",\"displayValue\":\"Netherlands Antilles\"},{\"value\":\"nc\",\"displayValue\":\"New Caledonia\"},{\"value\":\"nz\",\"displayValue\":\"New Zealand\"},{\"value\":\"ni\",\"displayValue\":\"Nicaragua\"},{\"value\":\"ne\",\"displayValue\":\"Niger\"},{\"value\":\"ng\",\"displayValue\":\"Nigeria\"},{\"value\":\"nu\",\"displayValue\":\"Niue\"},{\"value\":\"nf\",\"displayValue\":\"Norfolk Island\"},{\"value\":\"mp\",\"displayValue\":\"Northern Mariana Islands\"},{\"value\":\"no\",\"displayValue\":\"Norway\"},{\"value\":\"om\",\"displayValue\":\"Oman\"},{\"value\":\"pk\",\"displayValue\":\"Pakistan\"},{\"value\":\"pw\",\"displayValue\":\"Palau\"},{\"value\":\"ps\",\"displayValue\":\"Palestinian Territory\"},{\"value\":\"pa\",\"displayValue\":\"Panama\"},{\"value\":\"pg\",\"displayValue\":\"Papua New 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Marino\"},{\"value\":\"st\",\"displayValue\":\"Sao Tome and Principe\"},{\"value\":\"sa\",\"displayValue\":\"Saudi Arabia\"},{\"value\":\"sn\",\"displayValue\":\"Senegal\"},{\"value\":\"rs\",\"displayValue\":\"Serbia\"},{\"value\":\"sc\",\"displayValue\":\"Seychelles\"},{\"value\":\"sl\",\"displayValue\":\"Sierra Leone\"},{\"value\":\"sg\",\"displayValue\":\"Singapore\"},{\"value\":\"sk\",\"displayValue\":\"Slovak Republic\"},{\"value\":\"si\",\"displayValue\":\"Slovenia\"},{\"value\":\"sb\",\"displayValue\":\"Solomon Islands\"},{\"value\":\"so\",\"displayValue\":\"Somalia\"},{\"value\":\"za\",\"displayValue\":\"South Africa\"},{\"value\":\"es\",\"displayValue\":\"Spain\"},{\"value\":\"lk\",\"displayValue\":\"Sri Lanka\"},{\"value\":\"sd\",\"displayValue\":\"Sudan\"},{\"value\":\"sr\",\"displayValue\":\"Suriname\"},{\"value\":\"sj\",\"displayValue\":\"Svalbard and Jan Mayen\"},{\"value\":\"sz\",\"displayValue\":\"Swaziland\"},{\"value\":\"se\",\"displayValue\":\"Sweden\"},{\"value\":\"ch\",\"displayValue\":\"Switzerland\"},{\"value\":\"sy\",\"displayValue\":\"Syria\"},{\"value\":\"tw\",\"displayValue\":\"Taiwan\"},{\"value\":\"tj\",\"displayValue\":\"Tajikistan\"},{\"value\":\"tz\",\"displayValue\":\"Tanzania\"},{\"value\":\"th\",\"displayValue\":\"Thailand\"},{\"value\":\"tl\",\"displayValue\":\"Timor\\u002dLeste\"},{\"value\":\"tg\",\"displayValue\":\"Togo\"},{\"value\":\"tk\",\"displayValue\":\"Tokelau\"},{\"value\":\"to\",\"displayValue\":\"Tonga\"},{\"value\":\"tt\",\"displayValue\":\"Trinidad and Tobago\"},{\"value\":\"tn\",\"displayValue\":\"Tunisia\"},{\"value\":\"tr\",\"displayValue\":\"Turkey\"},{\"value\":\"tm\",\"displayValue\":\"Turkmenistan\"},{\"value\":\"tc\",\"displayValue\":\"Turks and Caicos 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href=\\\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_groups_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eall of Linkedin\\u003c/a\\u003e\",\"search_all_members_or_linkedin_i18n\":\"\\u003ca href=\\\"http://www.linkedin.com/vsearch/p?rsid=3346798021396963377369&trk=vsrp_people_nores_searchallmembers&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eSearch All LinkedIn members\\u003c/a\\u003e or \\u003ca href=\\\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_people_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eall of Linkedin\\u003c/a\\u003e\",\"peopleVerticalSelected\":true,\"more_i18n\":\"More…\",\"search_all_jobs_or_linkedin_i18n\":\"\\u003ca href=\\\"http://www.linkedin.com/vsearch/j?rsid=3346798021396963377369&trk=vsrp_jobs_nores_searchalljobs&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eSearch all jobs\\u003c/a\\u003e or \\u003ca href=\\\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_jobs_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eall of Linkedin\\u003c/a\\u003e\",\"federated_search_link_universities\":\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_universities_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\",\"teamlink_optin_dialog_title_i18n\":\"Teamlink Settings\",\"search_all_universities_or_linkedin_i18n\":\"\\u003ca href=\\\"http://www.linkedin.com/vsearch/e?rsid=3346798021396963377369&trk=vsrp_universities_nores_searchalluniversities&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eSearch all universities\\u003c/a\\u003e or \\u003ca href=\\\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_universities_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eall of Linkedin\\u003c/a\\u003e\",\"content_search_link_base\":\"/vsearch/ic\",\"federated_search_link_base\":\"/vsearch/f\",\"i18n_company_size\":\"Company size\",\"i18n_recently_joined\":\"Recently 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Salaries are not necessarily endorsed by companies who post jobs on LinkedIn, and actual compensation may vary.\",\"lix_lazyRightRailUpsellAd\":\"control\",\"facetUpsellType\":\"default\",\"i18n_payscale_logo\":\"Payscale Logo\",\"link_inbox_messages_search_1\":\"http://www.linkedin.com/inbox/messages/search?trk=vsrp_inbox_sel&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Atrans_nav\",\"federated_search_link\":\"http://www.linkedin.com/vsearch/f?orig=TRNV&rsid=3346798021396963377369&trk=vsrp_all_sel&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Atrans_nav\",\"search_all_linkedin_groups_i18n\":\"\\u003ca href=\\\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_groups_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eSearch All\\u003c/a\\u003e of LinkedIn\",\"company_search_link\":\"http://www.linkedin.com/vsearch/c?orig=TRNV&rsid=3346798021396963377369&trk=vsrp_companies_sel&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Atrans_nav\",\"anchor_premium_upsell_upgrade\":{\"attributeMap\":{\"class\":\"spotlight\\u002dbutton\",\"href\":\"http://www.linkedin.com/mnyfe/subscriptionv2?trk=fps_srch_hovercard\"}},\"job_search_link_base\":\"/vsearch/j\",\"people_search_link\":\"http://www.linkedin.com/vsearch/p?orig=TRNV&rsid=3346798021396963377369&trk=vsrp_people_sel&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Atrans_nav\",\"company_search_link_base\":\"/vsearch/c\",\"people_search_no_res_link\":\"http://www.linkedin.com/vsearch/p?rsid=3346798021396963377369&trk=vsrp_people_nores_searchallmembers&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\",\"i18n_years_of_experience\":\"Years of experience\",\"search_all_linkedin_universities_i18n\":\"\\u003ca href=\\\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_universities_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eSearch All\\u003c/a\\u003e of LinkedIn\",\"school_search_link_base\":\"/vsearch/e\",\"i18n_seniority_level\":\"Seniority level\",\"link_premium_upsell\":\"/vsearch/ad?width=175&height=350&zone=voltron_people_search_internal_jsp&iframe=false&upsell=true\",\"i18n_search_all_linkedin_influencer_content\":\"\\u003ca href=\\\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_influencer_content_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eSearch All\\u003c/a\\u003e of LinkedIn\",\"i18n_search_all_influencer_content_or_linkedin\":\"\\u003ca href=\\\"http://www.linkedin.com/vsearch/ic?rsid=3346798021396963377369&trk=vsrp_influencer_content_nores_searchallinfluencercontent&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eSearch all articles\\u003c/a\\u003e or \\u003ca href=\\\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_influencer_content_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eall of Linkedin\\u003c/a\\u003e\",\"federated_search_link_content\":\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_influencer_content_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\",\"i18n_shared_groups\":\"Shared groups\",\"i18n_upgrade_button\":\"\\u003ca class=\\\"spotlight\\u002dbutton\\\" href=\\\"http://www.linkedin.com/mnyfe/subscriptionv2?trk=fps_srch_hovercard\\\"\\u003eUpgrade\\u003c/a\\u003e\",\"influencer_content_search_no_res_link\":\"http://www.linkedin.com/vsearch/ic?rsid=3346798021396963377369&trk=vsrp_influencer_content_nores_searchallinfluencercontent&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\",\"federated_search_link_groups\":\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_groups_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\",\"content_search_no_res_link\":\"http://www.linkedin.com/vsearch/ic?rsid=3346798021396963377369&trk=vsrp_influencer_content_nores_searchallinfluencercontent&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\",\"federated_search_link_jobs\":\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_jobs_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\",\"i18n_search_all_content_or_linkedin\":\"\\u003ca href=\\\"http://www.linkedin.com/vsearch/ic?rsid=3346798021396963377369&trk=vsrp_influencer_content_nores_searchallinfluencercontent&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eSearch all posts\\u003c/a\\u003e or \\u003ca href=\\\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_influencer_content_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eall of Linkedin\\u003c/a\\u003e\",\"lix_subsPremiumUpsellHoverCard\":\"on\",\"search_all_linkedin_jobs_i18n\":\"\\u003ca href=\\\"http://www.linkedin.com/vsearch/f?rsid=3346798021396963377369&trk=vsrp_jobs_nores_searchall&trkInfo=VSRPsearchId%3A3346798021396963377369%2CVSRPcmpt%3Ano_results\\\"\\u003eSearch All\\u003c/a\\u003e of LinkedIn\"},\"lix_removeSimplyHired\":\"control\",\"i18n_unlike\":\"Unlike\",\"learn_more_persist_link\":\"http://www.linkedin.com/mnyfe/subscriptionv2?displayProducts=track%3Avsrp_upsell_learnmore&family=general\",\"i18n_secondary_actions\":\"Secondary Actions\",\"i18n_location\":\"Location\",\"i18n_save_person\":\"Save\",\"i18n_company_size\":\"Company Size\",\"i18n_view_prof_of_poster\":\"View profile of the job poster &#187\",\"i18n_see_more\":\"See more\",\"i18n_following_edu_badge\":\"[Following]\",\"i18n_members_only\":\"Members Only\",\"lix_lazyBottomAdV2\":\"control\",\"i18n_search_within\":\"Search Within\",\"i18n_student_count\":\"Students and Alumni Count\",\"i18n_sorry_no_sim_group_results\":\"Sorry, there are no similar groups at the moment.\",\"lix_enableKatyInboxIntro\":\"control\",\"i18n_sorry_no_sim_university_results\":\"Sorry, there are no similar universities at the moment.\",\"i18n_follow_influencer\":\"Follow 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"prompt_number": 18
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{
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| gpl-2.0 |
whitead/numerical_stats | unit_8/hw_2018/Homework_8.ipynb | 1 | 2709 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Homework 8\n",
"====\n",
"#### CHE 116: Numerical Methods and Statistics\n",
"\n",
"3/22/2018\n",
"\n",
"----"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.CLT Concepts (8 Points)\n",
"====\n",
"\n",
"1. If you sum together 20 numbers sampled from a binomial distribution and 10 from a Poisson distribution, how is your sum distribted?\n",
"\n",
"2. If you sample 25 numbers from *different* beta distributions, how will each of the numbers be distributed?\n",
"\n",
"3. Assume a HW grade is determined as the average of 3 HW assignments. How is the HW grade distributed?\n",
"\n",
"4. You measure the height of 3 people. What distribution will the uncertainty of the mean of the heights follow?\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Confidence Interval (16 Points)\n",
"===\n",
"\n",
"Report the given confidence interval for error in the mean using the data in the next cell and describe in words what the confidence interval is for each example. 4 points each\n",
"\n",
"1. 80% Double. \n",
"2. 99% Upper ( a value such that the mean lies above that value 99% of the time)\n",
"3. 95% Double\n",
"4. Redo part 3 with a known standard deviation of 2\n",
"\n",
"`\n",
"data_1 = [0.41,2.69,3.82,0.42,1.20]\n",
"`\n",
"\n",
"`\n",
"data_2 = [5.07,2.79,1.24,6.50,3.17,3.59,5.42,4.10,1.26,0.54,1.22,4.43,3.83,0.93,3.45,5.24,3.51,4.64,0.65,3.27,2.41,4.31,4.15,2.24,2.30,3.3]\n",
"`\n",
"\n",
"`\n",
"data_3 = [5.62,2.34,2.76,2.80,1.15,5.19,-0.91]\n",
"`\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Confidence Intervals (8 Points)\n",
"====\n",
"\n",
"State what distribution and its parameters for each of the following cases. 2 points each.\n",
"\n",
"1. $P(\\mu - \\bar{x})$, $\\sigma = 2.4$, $N = 4$\n",
"2. $P(\\mu)$, $\\bar{x} = 11$, $\\sigma_x = 3.2$, $N = 11$\n",
"3. $P(\\mu)$, $\\bar{x} = -3$, $\\sigma_x = 2.1$, $N = 35$\n",
"4. $P(\\mu)$, $\\bar{x} = 6$, $\\sigma = 11$, $N = 30$"
]
}
],
"metadata": {
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"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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| gpl-3.0 |
NeuroDataDesign/pan-synapse | pipeline_1/background/connectLib_revised.md.ipynb | 1 | 579820 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# connectLib Pipeline\n",
"\n",
"## Introduction\n",
"The connectLib Pipeline filters out the background noise of an n-dimensional image and then segments the resulting image into groups of data type Cluster presented in list-form. The pipeline uses Otsu's Binarization to filter out the background noise. Next, Connected Components clusters the remaining foreground. We then remove outlier clusters using the Interquartile Range Rule. These outliers result from the filtered background which gets labeled as one large cluster. Since we know this background cluster is large, we just need to threshold our clusters so that the upper outlier volumes get removed. The final step is to coregister our clusters with the raw image. This is a consequence from the PLOS Pipeline (see PLOS_Pipeline_Revised.md) which degrades the original clusters."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulation Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Easy Simulation:\n",
"Our simulated data will be a 100x100x100 volume with a voxel intensity distribution approximately the same as that of the true image volumes (i.e., 98% noise, 2% synapse). The synapse voxels will be grouped together in clusters as they would in the true data. Based on research into the true size of synapses, these synthetic synapse clusters will be given area of ~.2 microns ^3, or about 27 voxels (assuming the synthetic data here and the real world data have identical resolutions). We will differeniate the background from the foreground in this simulation by assigning intensity values. Background voxels will be assigned a value from 0-10,000; foreground points will be given a value of 60,000. After the data goes through the pipeLine, I will gauge performance based on the following:\n",
"\n",
"1. average volume of synapses (should be about 27 voxels) \n",
"2. volumetric density of data (should be about 2% of the data)\n",
"\n",
"We believe our pipeline will yield perfect results on this simulated data. This is because the main filtering from Otsu's Binarization requires the distribution of voxel values to be bimodal. That is, there is a clear differentiation between background and foreground. \n",
"\n",
"\n",
"### Easy Simulation Code"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sys\n",
"sys.path.insert(0,'../code/functions/')\n",
"from random import randrange as rand\n",
"from skimage.measure import label\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from mpl_toolkits.mplot3d import Axes3D\n",
"import pickle\n",
"\n",
"def generatePointSet():\n",
" center = (rand(0, 99), rand(0, 99), rand(0, 99))\n",
" toPopulate = []\n",
" for z in range(-1, 2):\n",
" for y in range(-1, 2):\n",
" for x in range(-1, 2):\n",
" curPoint = (center[0]+z, center[1]+y, center[2]+x)\n",
" #only populate valid points\n",
" valid = True\n",
" for dim in range(3):\n",
" if curPoint[dim] < 0 or curPoint[dim] >= 100:\n",
" valid = False\n",
" if valid:\n",
" toPopulate.append(curPoint)\n",
" return set(toPopulate)\n",
" \n",
"def generateTestVolume():\n",
" #create a test volume\n",
" volume = np.zeros((100, 100, 100))\n",
" myPointSet = set()\n",
" for _ in range(rand(500, 800)):\n",
" potentialPointSet = generatePointSet()\n",
" #be sure there is no overlap\n",
" while len(myPointSet.intersection(potentialPointSet)) > 0:\n",
" potentialPointSet = generatePointSet()\n",
" for elem in potentialPointSet:\n",
" myPointSet.add(elem)\n",
" #populate the true volume\n",
" for elem in myPointSet:\n",
" volume[elem[0], elem[1], elem[2]] = 60000\n",
" #introduce noise\n",
" noiseVolume = np.copy(volume)\n",
" for z in range(noiseVolume.shape[0]):\n",
" for y in range(noiseVolume.shape[1]):\n",
" for x in range(noiseVolume.shape[2]):\n",
" if not (z, y, x) in myPointSet:\n",
" noiseVolume[z][y][x] = rand(0, 10000)\n",
" return volume, noiseVolume\n",
"\n",
"randIm = generateTestVolume()\n",
"foreground = randIm[0]\n",
"combinedIm = randIm[1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**What We Expect Our Simulation Data Will Look Like:**\n",
"The above code should generate a 100x100x100 volume and populate it with various, non-intersectting pointsets (representing foreground synpases). When the foreground is generated, the volume will then be introduced to random background noise which will fill the rest of the volume. \n",
"\n",
"### Easy Simulation Plots"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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DNmGiDqiqSiQSIRwOEwgEiEQicdsfWq1WrFar/r+iKEcNGQ0fPpwxY8bQr18/iouLefnl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\nuFgrt7JYLH5JHr7M8ob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nbJ4wZroJsGdVMBMPSzDLyMhg7ty5fPbZZ0RFRfH888/b5c1E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\nzBPdA4GVAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x102db13d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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nP/85Vq9ejU2bNqGsrAwnnngibrvtNgwZMsRIo6GhAZdccgmeeeYZuFwunHnm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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x106152ed0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#displaying the random clusters\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"z, y, x = foreground.nonzero()\n",
"ax.scatter(x, y, z, zdir='z', c='r')\n",
"plt.title('Random Foreground')\n",
"plt.show()\n",
"\n",
"#displaying the noise\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"z, y, x = combinedIm.nonzero()\n",
"ax.scatter(x, y, z, zdir='z', c='r')\n",
"plt.title('Random Noise + Foreground')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Why Our Simulation is Correct:** Real microscopic images of synapses usually contain a majority of background noise and relatively few synapse clusters. As shown above, the generated test volume follows this expectation. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Difficult Simulation\n",
"We will now simulate data where our algorithm will not perform well on. We will generate a 100x100x100 test volume populated with background and foreground voxels containing the same intensity. Since the distribution of voxels is now unimodal (no clear difference between background and foreground), our filtering algorithm should not work well. However, the intensity values will not appear in our matplotlib plots. Therefore, our difficult simulation will appear to be the same as the Easy Simulation, but should fail after it goes through the connectLib pipeline.\n",
"\n",
"### Difficult Simulation Code and Plot"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def generateDifficultTestVolume():\n",
" #create a test volume\n",
" volume = np.zeros((100, 100, 100))\n",
" myPointSet = set()\n",
" for _ in range(rand(500, 800)):\n",
" potentialPointSet = generatePointSet()\n",
" #be sure there is no overlap\n",
" while len(myPointSet.intersection(potentialPointSet)) > 0:\n",
" potentialPointSet = generatePointSet()\n",
" for elem in potentialPointSet:\n",
" myPointSet.add(elem)\n",
" #populate the true volume\n",
" for elem in myPointSet:\n",
" volume[elem[0], elem[1], elem[2]] = 60000\n",
" #introduce noise\n",
" noiseVolume = np.copy(volume)\n",
" for z in range(noiseVolume.shape[0]):\n",
" for y in range(noiseVolume.shape[1]):\n",
" for x in range(noiseVolume.shape[2]):\n",
" if not (z, y, x) in myPointSet:\n",
" noiseVolume[z][y][x] = 60000\n",
" return volume, noiseVolume\n",
"\n",
"randImHard = generateDifficultTestVolume()\n",
"foregroundHard = randImHard[0]\n",
"combinedImHard = randImHard[1]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Xy6VbSElJSdhstogxZDm2KZecrM2CDcd75qHjsc41EUuoo4OK5G8tWqiNDljj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LQcEdnYi3F3ejzkNG1K6Y4d9EJAozkCWEOAbrEYm0tKeO/NNxl38CB/mzGDzmeeye79+wmWlZGC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\nd+7MihUrtHM2adKEKVOmMHPmzFI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4OOHk5CQWL14sEhMTxbZt20T16tXFjh07tDXh4eHi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\nFf8PRPxWHhSUxQIAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x102dc6410>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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nP/85Vq9ejU2bNqGsrAwnnngibrvtNgwZMsRIo6GhAZdccgmeeeYZuFwunHnm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\nrwYgK491f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HK5jEwmUzMXWuSll17CunXrsGbNGjyxbh2e++1v8cq2bRgql8MxNlmTV68p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\nZQSznipkLdQzHeJ7l2S4SMjFS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lY/wTb8WQEOfwqU6Nc3aNKEf3/zjVllLwuSvNTr0js4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\ndiWRlCv+HxnYKC3O1pL0AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x102dc6b10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#displaying the random clusters\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"z, y, x = foregroundHard.nonzero()\n",
"ax.scatter(x, y, z, zdir='z', c='r')\n",
"plt.title('Random Foreground')\n",
"plt.show()\n",
"\n",
"#displaying the noise\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111, projection='3d')\n",
"z, y, x = combinedImHard.nonzero()\n",
"ax.scatter(x, y, z, zdir='z', c='r')\n",
"plt.title('Random Noise + Foreground')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Simulation Analysis\n",
"\n",
"## Pseudocode\n",
"\n",
"**Inputs: ** 3D image array that has been processed through plosLib pipeline, raw image file that hasn't been through plosLib\n",
"\n",
"**Outputs: ** List of synapse clusters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"####Pseudocode: Will not run!####\n",
"\n",
"#Step 1 Otsu's Binarization to threshold out background noise intensity to 0.\n",
"for(each 2D image slice in 3D plos_image): \n",
" threshold_otsu on slice #uses Otsu's Binarization to threshold background noise to 0. \n",
"return thresholded_image\n",
"\n",
"#Step 2 Cluster foreground using connected components\n",
"connected_components on thresholded_image #labels and clusters 'connected' regions in foreground \n",
"for(each labeled region): \n",
" MAKE Cluster object #instance that contains voxel members that made up labeled region \n",
" plos_ClusterList.append(Cluster) #list of synapse/foreground clusters \n",
"return plos_ClusterList\n",
"\n",
"#Step 3 Use Naive Fencing (IQR Range Rule) to remove large background cluster that formed \n",
"IQR = getIQR(plos_ClusterList.getVolumes()) #calculate IQR of Cluster volumes\n",
"UpperOutlierFence = 75thpercentile(plos_ClusterList.getVolumes()) + 1.5*IQR #get upper volume threshold (third quartile + 1.5*IQR) \n",
"for (Cluster in plos_ClusterList):\n",
" if (Cluster.getVolume() > UpperOutlierFence) #if volume is considered an upper outlier, remove it\n",
" plos_ClusterList.remove(Cluster) \n",
"\n",
"#Step 4 Coregister Degraded clusters found above with Raw clusters\n",
"threshold_otsu on raw_image #Thresholds raw image background\n",
"rawClusterList = connected_components on thresholded_raw_image #Clusters raw image\n",
"for raw_cluster in rawClusterList: \n",
" for plos_cluster in plos_ClusterList:\n",
" if plos_cluster in raw_cluster: #if degraded cluster is contained in the raw cluster\n",
" actualClusterList.append(raw_cluster) #add raw cluster to actual Cluster list.\n",
" \n",
"return actualClusterList"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Algorithm Code"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from skimage.filters import threshold_otsu\n",
"from skimage.measure import label\n",
"from cluster import Cluster\n",
"import numpy as np\n",
"import cv2\n",
"import plosLib as pLib\n",
"\n",
"### Step 1: Threshold the image using Otsu Binarization \n",
"def otsuVox(argVox):\n",
" probVox = np.nan_to_num(argVox)\n",
" bianVox = np.zeros_like(probVox)\n",
" for zIndex, curSlice in enumerate(probVox):\n",
" #if the array contains all the same values\n",
" if np.max(curSlice) == np.min(curSlice):\n",
" #otsu thresh will fail here, leave bianVox as all 0's\n",
" continue\n",
" thresh = threshold_otsu(curSlice)\n",
" bianVox[zIndex] = curSlice > thresh\n",
" return bianVox\n",
"\n",
"### Step 2: Cluster foreground using Connected Components\n",
"def connectedComponents(voxel):\n",
" labelMap = label(voxel)\n",
" clusterList = []\n",
" #plus 1 since max label should be included\n",
" for uniqueLabel in range(0, np.max(labelMap)+1):\n",
" memberList = [list(elem) for elem in zip(*np.where(labelMap == uniqueLabel))]\n",
" if not len(memberList) == 0:\n",
" clusterList.append(Cluster(memberList))\n",
" return clusterList\n",
"\n",
"### Step 3: Remove outlier clusters using IRQ Rule\n",
"def thresholdByVolumePercentile(clusterList):\n",
" #putting the plosPipeline clusters volumes in a list\n",
" plosClusterVolList =[]\n",
" for cluster in (range(len(clusterList))):\n",
" plosClusterVolList.append(clusterList[cluster].getVolume())\n",
"\n",
" #finding the upper outlier fence\n",
" Q3 = np.percentile(plosClusterVolList, 75)\n",
" Q1 = np.percentile(plosClusterVolList, 25)\n",
" IQR = Q3 - Q1\n",
" upperThreshFence = Q3 + 1.5*IQR\n",
"\n",
" #filtering out the background cluster\n",
" upperThreshClusterList = []\n",
" for cluster in (range(len(clusterList))):\n",
" if clusterList[cluster].getVolume() < upperThreshFence:\n",
" upperThreshClusterList.append(clusterList[cluster])\n",
"\n",
" return upperThreshClusterList\n",
"\n",
"### Step 4: Coregister clusters with raw data.\n",
"def clusterCoregister(plosClusterList, rawClusterList):\n",
" #creating a list of all the member indices of the plos cluster list\n",
" plosClusterMemberList = []\n",
" for cluster in range(len(plosClusterList)):\n",
" plosClusterMemberList.extend(plosClusterList[cluster].members)\n",
"\n",
" #creating a list of all the clusters without any decay\n",
" finalClusterList =[]\n",
" for rawCluster in range(len(rawClusterList)):\n",
" for index in range(len(plosClusterMemberList)):\n",
" if ((plosClusterMemberList[index] in rawClusterList[rawCluster].members) and (not(rawClusterList[rawCluster] in finalClusterList))):\n",
" finalClusterList.append(rawClusterList[rawCluster])\n",
"\n",
" return finalClusterList\n",
"\n",
"########## Complete Pipeline ##########\n",
"def completePipeline(image):\n",
" #Plos Pipeline Results\n",
" plosOut = pLib.pipeline(image)\n",
" #Otsu's Binarization Thresholding\n",
" bianOut = otsuVox(plosOut)\n",
" #Connected Components\n",
" connectList = connectedComponents(bianOut)\n",
" #Remove outlier clusters\n",
" threshClusterList = thresholdByVolumePercentile(connectList)\n",
" #finding the clusters without plosPipeline - lists the entire clusters\n",
" bianRawOut = otsuVox(image)\n",
" clusterRawList = connectedComponents(bianRawOut)\n",
" #coregistering with raw data\n",
" clusters = clusterCoregister(threshClusterList, clusterRawList)\n",
" return clusters"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Easy Simulation Analysis\n",
"**What We Expect**\n",
"As previously mentioned, we believe the pipeline will work very well on the easy simulation (See Simulation Data: Easy Simulation for explanation).\n",
"\n",
"**Generate Easy Simulation Data:** See Simulation Data Above.\n",
"\n",
"### Pipeline Run on Easy Data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"completeClusterMemberList = completePipeline(combinedIm)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Easy Simulation Results "
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"### Get Cluster Volumes\n",
"def getClusterVolumes(clusterList):\n",
" completeClusterVolumes = []\n",
" for cluster in clusterList:\n",
" completeClusterVolumes.append(cluster.getVolume())\n",
" return completeClusterVolumes"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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8gszMbCmSNJI0VuQrUZjiamYVGnMi6VClJbPn5ulmW3aSdzelpY9fVFpqu95S1hdo8buM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\nKuX/A3AAQGjA+cidAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d80a890>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import mouseVis as mv\n",
"\n",
"#plotting results\n",
"completeClusterVolumes = getClusterVolumes(completeClusterMemberList)\n",
"mv.generateHist(completeClusterVolumes, title = 'Cluster Volumes for Easy Simulation', bins = 25, xaxis = 'Volumes', yaxis = 'Relative Frequency')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Performance Metrics:\n",
"We will be judging our algorithm's performance through two metrics: **average cluster volume** and **cluster density per volume**. This is based off of the 2 parameters we used to generate the test volume (see Simulation Data: Easy Simulation).\n",
"\n",
"If our algorithm was successful, the average volume of detected synapse clusters should be equal to the average volume of the total foreground clusters that we generated. That is, our pipeline labeled synapses into correctly sized clusters (27 voxels). \n",
"\n",
"Cluster density basically returns how many clusters were detected given a certain volume size. This is to show how many of the synapse clusters our algorithm was actually able to label. If the algorithm performs correctly, the relative number of synapses clusters per volume should equal around 2% (the volumetric density of synapses we generated in the test volume). "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#test stats\n",
"\n",
"# get actual cluster volumes from foreground (for 'Expected' values)\n",
"def getForegroundClusterVols(foreground):\n",
" foregroundClusterList = connectedComponents(foreground)\n",
" del foregroundClusterList[0] #background cluster\n",
" foregroundClusterVols = []\n",
" for cluster in foregroundClusterList:\n",
" foregroundClusterVols.append(cluster.getVolume())\n",
" return foregroundClusterVols\n",
" \n",
"def getAverageMetric(coClusterVols, foreClusterVols):\n",
" #no clusters found\n",
" if (len(coClusterVols)==0):\n",
" avgClusterVol = 0\n",
" else:\n",
" #average volume of detected clusters\n",
" avgClusterVol = np.mean(coClusterVols)\n",
" #average volume of total foreground clusters\n",
" avgExpectedVol = np.mean(foreClusterVols)\n",
" print 'Average Volume'\n",
" print \"\\tExpected: \" + str(avgExpectedVol) + '\\tActual: ' + str(avgClusterVol)\n",
" return avgExpectedVol, avgClusterVol\n",
"\n",
"def getDensityMetric(coClusterVols, foreClusterVols):\n",
" #no clusters found\n",
" if (len(coClusterVols)==0):\n",
" coClusterVols.append(0)\n",
" print 'Cluster Density of Data By Volume'\n",
" print \"\\tExpected: \" + str(np.sum(foreClusterVols)/(100*100*100.0)) + '\\tActual: ' + str(np.sum(coClusterVols)/(100*100*100.0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quantify Performance for Easy Simulation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"foregroundClusterVols = getForegroundClusterVols(foreground)\n",
"getAverageMetric(completeClusterVolumes, foregroundClusterVols)\n",
"getDensityMetric(completeClusterVolumes, foregroundClusterVols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As shown above, our connectLib pipeline worked extremely well on the easy simulation. The small difference between the actual and expected values come from the generated synapse point sets. Foreground synapses can potentially be adjacent to each other in the test volume. Connected Components will label the multiple, connected synapses as one cluster, which explains the cluster volumes at roughly 56 (2 synapses) and 81 (3 synapses) [See Histogram in Easy Simulation Results]. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Difficult Simulation Analysis\n",
"**What We Expect:** Since Otsu's Binarization depends on a bimodal distribution of voxel intensities, the background should not get thresholded for the difficult simulation. Furthermore, since all the voxels are identical in terms of intensity, connectedComponents should label the entire volume as just one cluster.\n",
"\n",
"**Generate Difficult Simulation Data:** See Simulate Data: Difficult Simulation.\n",
"\n",
"\n",
"### Pipeline Run on Difficult Data:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.py:1728: RuntimeWarning:\n",
"\n",
"invalid value encountered in double_scalars\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n"
]
}
],
"source": [
"completeClusterMemberListHard = completePipeline(combinedImHard)\n",
"print len(completeClusterMemberListHard)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Difficult Simulation Results:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of Clusters: 1\n",
"Cluster Volume: 1000000\n",
"Coregistered Clusters: 0\n"
]
}
],
"source": [
"#Plos Pipeline Results\n",
"plosOut = pLib.pipeline(combinedImHard)\n",
"#Otsu's Binarization Thresholding\n",
"bianOut = otsuVox(plosOut)\n",
"#Connected Components\n",
"connectList = connectedComponents(bianOut)\n",
"#get total volume for hard simulation clusters\n",
"totalClusterHard = []\n",
"for cluster in connectList:\n",
" totalClusterHard.append(cluster.getVolume())\n",
"#get coregistered (complete) cluster volumes\n",
"completeClusterVolumesHard = getClusterVolumes(completeClusterMemberListHard)\n",
"\n",
"print 'Number of Clusters: ' + str(len(totalClusterHard))\n",
"print 'Cluster Volume: ' + str(totalClusterHard[0])\n",
"print 'Coregistered Clusters: ' + str(len(completeClusterMemberListHard))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Performance Metrics\n",
"See Easy Simulation Analysis: Performance Metrics."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Quantify Performance for Difficult Simulation"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Volume\n",
"\tExpected: 28.854\tActual: 0\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.014427\tActual: 0.0\n"
]
}
],
"source": [
"foregroundClusterVolsHard = getForegroundClusterVols(foregroundHard)\n",
"getAverageMetric(completeClusterVolumesHard, foregroundClusterVolsHard)\n",
"getDensityMetric(completeClusterVolumesHard, foregroundClusterVolsHard)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As predicted, the foreground and background was combined into one cluster through the connectLib Pipeline (see Results). This large cluster does not coregister with any of the original foreground clusters. Clearly, our pipeline performed very poorly on the difficult simulation as zero clusters were actually detected. This ultimately proves our earlier thesis that the connectLib pipeline is dependent on the foreground and background voxels having significantly different intensities."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Verify Simulation Analysis\n",
"\n",
"Repeat Easy and Hard simulation analysis 10 times each. "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Average Volume\n",
"\tExpected: 28.4134615385\tActual: 28.84359401\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.01773\tActual: 0.017335\n",
"Average Volume\n",
"\tExpected: 27.7397260274\tActual: 0\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.014175\tActual: 0.0\n",
"Average Volume\n",
"\tExpected: 28.5643879173\tActual: 28.8886986301\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.017967\tActual: 0.016871\n",
"Average Volume\n",
"\tExpected: 29.0643356643\tActual: 0\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.020781\tActual: 0.0\n",
"Average Volume\n",
"\tExpected: 29.8421052632\tActual: 29.8653250774\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.020412\tActual: 0.019293\n",
"Average Volume\n",
"\tExpected: 28.2398703404\tActual: 0\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.017424\tActual: 0.0\n",
"Average Volume\n",
"\tExpected: 28.2857142857\tActual: 28.541322314\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.014652\tActual: 0.013814\n",
"Average Volume\n",
"\tExpected: 28.4954268293\tActual: 0\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.018693\tActual: 0.0\n",
"Average Volume\n",
"\tExpected: 29.3209169054\tActual: 29.7013372957\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.020466\tActual: 0.019989\n",
"Average Volume\n",
"\tExpected: 28.3928571429\tActual: 0\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.016695\tActual: 0.0\n",
"Average Volume\n",
"\tExpected: 28.9078549849\tActual: 29.5523809524\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.019137\tActual: 0.018618\n",
"Average Volume\n",
"\tExpected: 28.8849056604\tActual: 0\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.015309\tActual: 0.0\n",
"Average Volume\n",
"\tExpected: 28.6306179775\tActual: 29.0573529412\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.020385\tActual: 0.019759\n",
"Average Volume\n",
"\tExpected: 28.3795180723\tActual: 0\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.014133\tActual: 0.0\n",
"Average Volume\n",
"\tExpected: 28.740234375\tActual: 29.2116182573\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.014715\tActual: 0.01408\n",
"Average Volume\n",
"\tExpected: 28.9358974359\tActual: 0\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.020313\tActual: 0.0\n",
"Average Volume\n",
"\tExpected: 28.585089141\tActual: 28.5339130435\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.017637\tActual: 0.016407\n",
"Average Volume\n",
"\tExpected: 28.7681415929\tActual: 0\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.016254\tActual: 0.0\n",
"Average Volume\n",
"\tExpected: 28.8755129959\tActual: 29.1942028986\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.021108\tActual: 0.020144\n",
"Average Volume\n",
"\tExpected: 28.4411302983\tActual: 0\n",
"Cluster Density of Data By Volume\n",
"\tExpected: 0.018117\tActual: 0.0\n"
]
}
],
"source": [
"easySimulationVolumes = []\n",
"hardSimulationVolumes = []\n",
"\n",
"for i in range(10):\n",
" #Easy Simulation\n",
" randIm = generateTestVolume()\n",
" foreground = randIm[0]\n",
" combinedIm = randIm[1]\n",
" completeClusterMemberList = completePipeline(combinedIm)\n",
" completeClusterVolumes = getClusterVolumes(completeClusterMemberList)\n",
" foregroundClusterVols = getForegroundClusterVols(foreground)\n",
" easySimulationVolumes.append(getAverageMetric(completeClusterVolumes, foregroundClusterVols))\n",
" getDensityMetric(completeClusterVolumes, foregroundClusterVols)\n",
" \n",
" #Hard Simulation\n",
" randImHard = generateDifficultTestVolume()\n",
" foregroundHard = randImHard[0]\n",
" combinedImHard = randImHard[1]\n",
" completeClusterMemberListHard = completePipeline(combinedImHard)\n",
" completeClusterVolumesHard = getClusterVolumes(completeClusterMemberListHard)\n",
" foregroundClusterVolsHard = getForegroundClusterVols(foregroundHard)\n",
" hardSimulationVolumes.append(getAverageMetric(completeClusterVolumesHard, foregroundClusterVolsHard))\n",
" getDensityMetric(completeClusterVolumesHard, foregroundClusterVolsHard)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plotting Expected and Average Cluster Volumes for each easy simulation. \n",
"\n",
"Red = Expected Average Volume\n",
"Blue = Observed Average Volume "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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KLaNxg2pOmxmRPJXxGNJAc2vIBkgjDR2PpDbScPG/KvfCiLhe0q3Za/6QveZx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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d6351d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#separate expected and actual values into separate indices\n",
"esv = [list(t) for t in zip(*easySimulationVolumes)]\n",
"#outlier\n",
"del esv[0][6]\n",
"del esv [1][6]\n",
"\n",
"fig = plt.figure()\n",
"plt.title('Easy Simulation: Average and Expected Cluster Volumes (10 Trials)')\n",
"plt.xlabel('Simulation #')\n",
"plt.ylabel('Volume (voxels)')\n",
"x = np.arange(9)\n",
"plt.scatter(x, esv[0], c='r')\n",
"plt.scatter(x, esv[1], c='b')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Plotting Expected and Average Cluster Volumes for each difficult simulation. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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NjpfbwHmZLRE34Wli1CRgpOe1nU56NtZc0jPw3h8Rl0h6Len5Vl8gPafxfuC3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\nuwVVdBWpUQwAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d18a6d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"hsv = [list(t) for t in zip(*hardSimulationVolumes)]\n",
"fig = plt.figure()\n",
"plt.title('Difficult Simulation: Average and Expected Cluster Volumes (10 Trials)')\n",
"plt.xlabel('Simulation #')\n",
"plt.ylabel('Volume (voxels)')\n",
"x = np.arange(10)\n",
"plt.scatter(x, hsv[0], c='r')\n",
"plt.scatter(x, hsv[1], c='b')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## Summary of Simulation Analysis\n",
"\n",
"Our difficult and easy simulation data demonstrates how our connectLib pipeline is dependent on how different the background and foreground voxel intensity values are. When the background and foreground are not distinguishable, the connectLib cannot threshold and filter out the background clusters, thus creating one large cluster combining all the voxels in the volume. Thus, essentially no synpases (clusters) can be detected correctly. On the other hand, if the foreground voxels are very distinguishable from the background noise (easy simulation), our connectLib pipeline works extremely well. For the easy simulations, 100% of the background noise was filtered out and almost all of the foreground point sets (representing synapses) were clustered correctly. The only errors were from adjacent 'synapses' that were clustered together. "
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Real Data\n",
"\n",
"Our sample data will come from different slices (z = 5) of a tiff image (3D). The tiff file is a photon microscope image of a mouse brain. The dimensions of our data will be 1024 x 1024 x 5 voxels^3 (x,y,z axis respectively). \n",
"\n",
"### Displaying Real Data"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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4a7P8QU35l4EbRonvy8Dver2n1EsnR7Z5JrBFj+VfRB1geit1HMpTu9c5Tmy93odVqbfN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\nte7/A/KP/4WgZkEXAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11d189e10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pickle\n",
"\n",
"realData = pickle.load(open('../data/realDataRaw_t0.synth'))\n",
"realDataSection = realData[5: 10]\n",
"\n",
"plosDataSection = pLib.pipeline(realDataSection)\n",
"mv.generateHist(plosDataSection, bins = 50, title = \"Voxel Intensity Distribution after PLOS\", xaxis = 'Relative Voxel Intensity', yaxis = 'Frequency')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Predicting Performance:\n",
"Mouse brains have a lot more activity than be portrayed in our simulated data. There are different captured cell types and a wide variation of background/foreground noise. Our Naive Fencing method and Otsu's Binarization could potentially not be enough to produce clean synapse clusters. Because of this added complexity present in mouse brain images, we believe our connectLib pipeline might not work perfectly on the real data. What is more concerning is that the distribution of voxel intensities is unimodal. The foreground does not appear to be significantly different from the background. Thus, Otsu's Binarization might not threshold the background successfully. \n",
"\n",
"### connectLib Algorithm Run on Real Data"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running\n"
]
}
],
"source": [
"print 'Running'\n",
"realClusterList = completePipeline(plosDataSection)\n",
"realClusterVols = getClusterVolumes(realClusterList)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Results"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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PP7r3ofxDNzOzQUrSNqSxWp+IwhQIZrZ8DJgxX5IOVrqNyPx8CfC2vcR/XNLMHH9bnsm6\nHHOcpMeUbt9wtaSNS+vXkXSx0i1H5ubL0FcvxWwh6dp8nIckHVlav5nS7WBmKd1uou7YoVZfn5lZ\nhb5A6h27ordAM+u7AVF8SdqLdMn4saRxLbeRzsfXHWQsaRypu/ts0qmey0m3YNmsEHMU6dYbBwDb\nkbrEp5bmXJpMOg0znjRIdCfSpHi1faxJuux/FmnyvSNJ9+Tbv7CPYaTTWEeRTm30+fWZmVVB0u6S\n/gvYj3Rz9Zd728bM+m5AnHaUdCNwU0Qclp+LNP7htIg4qU78z0hjCj5YWHYDcGtEfDE/fwz4bkSc\nkp+vRRqj8emIuETSGNK5+bERcWuOmUC6p9x6keYHOog0DmB05HucSfo26dYnrxR6hRxmAadExGl9\neX1mZlWQ9AhpYs+rgM9Ez3PRmVmH9HvPl6SVSRMG/r62LFJFOI10lVc941h6nhpIPVTj8j43AkaX\n9vks6RYctX3uAMytFV7ZNNJVYNsXYq6NpW8uOxXYRFJTMyG3+frMzJa7iFg/ItaIiD1deJlVp9+L\nL9LVUUNJvVJFj5MKqHpG9xI/ilRE9RQzmjRVwCsiTbj3dCmm3j6gcW5l7bw+MzMz61IDeZ4v0drE\njM3EdyKmNpdOX8/XNjxOnsV6AmmepJf6eBwzM7PBZDXStD1TI+Kf/ZxLXQOh+HqKNF/KqNLykSzb\nW1Qzp5f4OaTiZlRpHyNJ897UYpaa7TzPkLxOXtfTceght7J2Xt8EWry3mpmZmS3lk6QL6wacfi++\nIs2uPJ10xeGV8MqA9PGkCRrruaHO+l3yciJilqQ5Oeb2vM+1SGO5flDYx3BJWxfGfY0nFW03F2JO\nkDQ0/nUPsF1JE+HNW46v70GAiy66iDFjxjRzGMsOP/xwTjnllP5OY4XiNmuP2611brP2uN1aM3Pm\nTPbZZx/41z03B5x+L76yk4ELcpFyM+k+ZcOAnwDk28HMjoiv5vhTgWskHUG6OnEiaVD75wv7/D5w\njKS/k34Ax5NueHoFQETcLWkqcHa+qnEV4HRgSkTUer4mA18n3VbmRGBz0szmr9y/Kw+o34xUtK0C\nvE7SlsDzEXF/M6+vjpcAxowZwzbbbNNE81nN2muv7TZrkdusPW631rnN2uN2a9uAHbYzIIqvPPXD\nCOA40um5vwITIt0IFmA9YFEh/gZJE4Fv5sd9pOkf7irEnCRpGGneruGkWzrsFhELCofem3SrkWmk\nW2tcSqGwiohn8/QTZ5Bmf34KmBQR5xb2sS7pVGZt/NaX8+Ma4N1Nvj4zMzMbJAZE8QUQEWcCZzZY\n9+46y35Bur9ZT/ucRLrfV6P1z5Du6dXTPv4GvLOH9Q/RxFWjPb0+MzMzGzwGwlQTZmZmZoOGiy/r\nKhMnTuzvFFY4brP2uN1a5zZrj9ut+wyI2wvZ0iRtA0yfPn26B1mamZm1YMaMGYwdOxbS7QNn9Hc+\n9bjny8zMzKxCLr7MzMzMKuTiy8zMzKxCA2aqCVvWqaeeyqhR5bsSJePGjePDH/5wxRmZmZlZX7n4\nGsAmT/5/SKsts3zJknksWfI/LFq0iCFD3HlpZma2InHxNYAtWvRLoN7VjucD+1WcjZmZmXWCu03M\nzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxC\nLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zM\nzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTi\ny8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzM\nKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCLr7M\nzMzMKuTiy8zMzKxCLr7MzMzMKuTiy8zMzKxCA6b4knSwpFmS5ku6UdK2vcR/XNLMHH+bpN3qxBwn\n6TFJL0q6WtLGpfXrSLpY0jxJcyWdI2n1UswWkq7Nx3lI0pGt5iJpdUlnSHok53KnpANaayEzMzPr\nBgOi+JK0F/A94Fhga+A2YKqkEQ3ixwGTgbOBrYDLgcslbVaIOQr4EnAAsB3wQt7nKoVdTQbGAOOB\n9wM7AWcV9rEmMBWYBWwDHAlMkrR/K7kApwC7AnsDmwLfB86Q9IGmG8nMzMy6woAovoDDgbMi4sKI\nuBs4EHgR2K9B/GHAbyPi5Ii4JyKOBWaQiq1izPER8auIuAP4FLAu8CEASWOACcDnIuKWiLgeOAT4\nhKTReR/7ACvnmJkRcQlwGnBEi7mMAy6IiOsi4uGIOJtUYG7XelOZmZnZiqzfiy9JKwNjgd/XlkVE\nANNIRUs94/L6oqm1eEkbAaNL+3wWuKmwzx2AuRFxa2Ef04AAti/EXBsRi0rH2UTS2s3kkl0PfFDS\nujm/nYF/z3FmZmY2iPR78QWMAIYCj5eWP04qoOoZ3Uv8KFIR1VPMaOCJ4sqIWAw8XYqptw+aiCnm\nfggwE5gtaQFwFXBwRPy5zmszMzOzLrZSfyfQA5EKqE7GdyJGTcYU1x9K6k37APAwaWzZmZIei4g/\nNN7N4cDapWUTezismZnZ4DFlyhSmTJmy1LJ58+b1UzbNGwjF11PAYlJvVdFIlu1RqpnTS/wcUgE0\nqrSPkcCthZiRxR1IGgqsk9f1dJxir1qPuUhaDfgmsEdE/C6vv0PS1sCXgR6Kr1NI4/zLzm+8iZmZ\n2SAxceJEJk5culNixowZjB07tp8yak6/n3aMiIXAdNIVhwBIUn5+fYPNbijGZ7vk5UTELFJRVNzn\nWqTep+sL+xiei6Ca8aSi7eZCzE65KKvZFbgnIuYVYhrmQhqwvzLL9pQtZgC0v5mZmVVroHz4nwx8\nQdKnJG0K/AgYBvwEQNKFkr5ViD8V2E3SEZI2kTSJNGj/jELM94FjJO0uaXPgQmA2cAVAvqpyKnC2\npG0lvR04HZgSEbWer8nAAuA8SZvlKTEOJU2L0VQuEfEccA3wXUnvlLShpM+Qrr68rG/NZmZmZiua\ngXDakYi4JM/pdRzpFN5fgQkR8WQOWQ9YVIi/QdJE0um8bwL3kU7r3VWIOUnSMNK8XcOB64DdImJB\n4dB7k4qkacAS4FLS1BG1fTwraUKOuYV0inRSRJzbSi7AXsC3gYuAVwMPAUdHxI/baS8zMzNbcSnN\n6mADiaRtgOnpbGyjMV/7sXjxYoYMGSidl2ZmZv2vMOZrbETM6O986vEnt5mZmVmFXHyZmZmZVcjF\nl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZ\nVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZ\nmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVajl4kvSMZLWWx7JmJmZmXW7dnq+PgE8\nKGmqpD0lrdLppMzMzMy6VcvFV0S8BRgH/B34IfAPSadL2rrTyZmZmZl1m7bGfEXEXyLiYOC1wEHA\nxsDNkm6VdLCkNTuZpJmZmVm36OuA+wAWA0vy8xeB/wQekfSxPu7bzMzMrOu0VXxJ2lLS94HHgB8A\ndwGbR8TbgTcCk4AzOpWkmZmZWbdo52rHW4FbgDGkU47rR8SREXE3QEQEcBEwspOJmpmZmXWDldrY\n5kpgj4h4uFFARDwlaeX20zIzMzPrTi0XXxFxbJNxi1tPx8zMzKy7tXPa8X8lfaXO8iMlTelMWmZm\nZmbdqZ0B9zsDv6uz/Hd5nZmZmZk10E7xtSbwcp3lC4C1+5aOmZmZWXdrp/i6E/h4neV7Anf3LR0z\nMzOz7tbO1Y4nAD+X9AbgD3nZeGAf0n0fzczMzKyBdq52vFzSR4H/JhVcLwJ/A3aLiN93OD8zMzOz\nrtJOzxcRcSVpvi8zMzMza0FbxReApJWAEZTGjUXEY31NyszMzKxbtVx8SXojcA6wI6DiKtKNtod2\nJjUzMzOz7tNOz9dPSIXWh4F/kAouMzMzM2tCO8XX1sC2ETGz08mYmZmZdbt25vm6B1in04mYmZmZ\nDQbtFF//CZwk6R2S1pY0rPjodIJmZmZm3aSd0461iVWvabDeA+7NzMzMGmin+Nql41mYmZmZDRLt\nzHDvWezNzMzM2tTOmC8kjZP0E0nXSlo3L/ukpLd1Nj0zMzOz7tJy8SXpw6RxXwFsB6yWV72adL9H\nMzMzM2ugnZ6vrwEHRcRngYWF5X8CxnYkKzMzM7Mu1U7xtSnwxzrL5wHD+5aOmZmZWXdrp/iaA7yx\nzvK3AQ+0m4ikgyXNkjRf0o2Stu0l/uOSZub42yTtVifmOEmPSXpR0tWSNi6tX0fSxZLmSZor6RxJ\nq5ditshj2+ZLekjSkW3mMkbSFZKekfS8pJskrdd8C5mZmVk3aKf4Ohc4VdJY0rivUZL2Av4HOKud\nJPL23wOOJd2+6DZgqqQRDeLHAZOBs4GtgMuByyVtVog5CvgScABpbNoLeZ+rFHY1GRgDjAfeD+xU\nfA2S1gSmArOAbYAjgUmS9m8xlzcC1wF35WNsDhwPvNRCM5mZmVkXUERr98WWJODrwFeAV+XFC4BT\nIuLotpKQbgRuiojDCsd4BDgtIk6qE/8zYFhEfLCw7Abg1oj4Yn7+GPDdiDglP18LeBz4dERcImkM\ncCcwNiJuzTETgN8A60XEHEkHkYqk0RGxKMd8G9gjIjZrIZcpwIKI+HST7bENMB2mk2q+svOB/Vi8\neDFDhrR1waqZmVlXmjFjBmPHjoX0+T6jv/Opp+VP7ki+AfwbqafnHcDIPhReK5MG6r8yf1ikinAa\nMK7BZuPy+qKptXhJGwGjS/t8FripsM8dgLm1wiubRurN274Qc22t8CocZxNJazeZi0i9avdJ+p2k\nx/Np1T0avDYzMzPrYm13m0TESxFxe0Rcnwubdo0g3ZLo8dLyx0kFVD2je4kfRSqieooZDTxRXBkR\ni4GnSzH19kETMbX1I4E1gKOAq0h3CPglcJmkHeu+OjMzM+taLc9wL+lqUmFTV0Ts2qeMCofq6Tht\nxnciRk3G1NbXCtzLI+K0/P3teULaA0ljwRo4HFi7tGxiD4c1MzMbPKZMmcKUKVOWWjZv3rx+yqZ5\n7dzb8e7S85VJpx83BS5qY39PAYtJvVVFI1m2R6lmTi/xc0gF0KjSPkYCtxZiRhZ3IGkosE5e19Nx\nir1qveXyFLAImFmKmQm8fZlXtpRTaDzmy8zMbHCbOHEiEycu3SlRGPM1YLUz5uuQ0uPAiNgBOJ10\nRWGr+1tIGlk+vrYsj5MaD1zfYLMbivHZLnk5ETGLVBQV97kWaSzX9YV9DJe0dWEf40lF282FmJ1y\nUVazK3BPRMwrxPSUy0LgL8AmpZg3AQ81eH1mZmbWpTp5qdwFwP69RtV3MvAFSZ+StCnwI2AY8BMA\nSRdK+lYh/lRgN0lHSNpE0iTSoP0zCjHfB46RtLukzYELgdnAFQARcTdpYPzZkraV9HZSATklImo9\nX5NJV3KeJ2mzPCXGoaRpMVrJ5bvAXpL2l/RGSV8CPgD8oM32MjMzsxVUO6cdG9mOVKi0LE/9MAI4\njnQK76/AhIh4MoesRzp1V4u/QdJE4Jv5cR9p+oe7CjEnSRpGmrdrOGls1W4RUcxxb1KRNA1YAlwK\nHFbYx7N5+okzgFtIpxAnRcS5LeZyuaQDga+SirV7gI9ExA3ttJeZmZmtuNqZ5+uS8iLgtaRpGb4V\nEV/vUG6Dluf5MjMza8+KMM9XOz1fL5eeLwFuJBVeV/U9JTMzM7Pu1XLxFRH7Lo9EzMzMzAYDn7My\nMzMzq1A7k6w+SZOTn0bEyN6jzMzMzAaPdsZ8nQj8N+kKwdrVeuNIc119C5jbmdTMzMzMuk87xdf2\nwLGFW+UAIOlQ4F0R8ZGOZGZmZmbWhdoZ87Ub6QbRZVeRZn83MzMzswbaKb6eJs3OXvYBfMrRzMzM\nrEftnHaTBD9HAAAeuUlEQVT8BnCWpHcCN5EG3+9AKr4O7GBuZmZmZl2nnXm+zpU0k3Qbnr1JM9zf\nRRrv9ecO52dmZmbWVdq6t2NEXA9c3+FczMzMzLpeW5OsStpQ0iRJF0oamZftKmlMZ9MzMzMz6y4t\nF1+SdgTuBN4J7AWskVeNBY7rXGpmZmZm3aednq8TgUkRsTOwoLD896SB92ZmZmbWQDvF1xbApXWW\nPwG8pm/pmJmZmXW3doqvecDoOsu3BB7tWzpmZmZm3a2d4ut/ge9Ieg35BtuStgf+B7iog7mZmZmZ\ndZ12iq+jgQeAx0iD7e8iTTtxC3B851IzMzMz6z7tTLL6MvBZSd8gjf9aA5gREXd3OjkzMzOzbtNS\n8SVpZeAO4EMRMRN4cHkkZWZmZtatWjrtGBELgTXJY73MzMzMrDXtjPn6IXCkpKGdTsbMzMys27Vz\nb8ctgAnArpJuB14oroyIPTuRmJmZmVk3aqf4egm4otOJmJmZmQ0G7VztuO/ySMTMzMxsMGh6zJek\nd0tqp6fMzMzMzLJWBtxfDby69kTSjZJe1/mUzMzMzLpXK8WXSs/fDKzawVzMzMzMul47U02YmZmZ\nWZtaKb6CpSdXLT83MzMzs160MoBewO8lLcrPhwG/krSgGBQR23QqOTMzM7Nu00rx9Y3Sc8/1ZWZm\nZtaipouviCgXX2ZmZmbWIg+4NzMzM6uQiy8zMzOzCrn4MjMzM6uQiy8zMzOzCvWp+JK0WqcSMTMz\nMxsMWi6+JA2R9DVJjwLPS9ooLz9e0uc6nqGZmZlZF2mn5+sY4DPAV4DiBKt3APt3ICczMzOzrtVO\n8fUp4AsRcTGwuLD8NmDTjmRlZmZm1qXaKb5eB/y9wb5W7ls6ZmZmZt2tneLrLmDHOss/Btzat3TM\nzMzMulsr93asOQ64QNLrSMXbRyRtQjod+YFOJmdmZmbWbVru+YqIK0hF1nuAF0jF2Bhg94i4urPp\nmZmZmXWXdnq+iIg/Abt0OBczMzOzrtfOPF9nS3rn8kjGzMzMrNu1M+B+FDBV0iOSTpK0ZaeTMjMz\nM+tW7Yz5+iAwGjge2A6YIelOSUdL2qDTCZqZmZl1k7bu7RgRz0TEjyPiXcAGwE9IVzve37nUzMzM\nzLpPX2+svTLwVmB7YEPg8Q7kZGZmZta12iq+JO0s6WxSsXUB8BywO7B+u4lIOljSLEnzJd0oadte\n4j8uaWaOv03SbnVijpP0mKQXJV0taePS+nUkXSxpnqS5ks6RtHopZgtJ1+bjPCTpyHZyKcSeJWmJ\npEN7bxUzMzPrNu1c7TgbuAp4DXAAMCoiPhsR0yJiSTtJSNoL+B5wLLA16T6RUyWNaBA/DpgMnA1s\nBVwOXC5ps0LMUcCXco7bkeYkmypplcKuJpPmKBsPvB/YCTirsI81ganALGAb4EhgkqT9CzG95lKI\n/VDO5dEmm8bMzMy6TDs9X8cB60bEhyLi5xHxUgfyOBw4KyIujIi7gQOBF4H9GsQfBvw2Ik6OiHsi\n4lhgBqnYKsYcHxG/iog7SGPS1gU+BCBpDDAB+FxE3BIR1wOHAJ+QNDrvYx/S/So/FxEzI+IS4DTg\niBZzId8R4DRgb2BRyy1kZmZmXaGdqx1/HBFzO5VAHjc2Fvh94RgBTAPGNdhsXF5fNLUWL2kj0hWZ\nxX0+C9xU2OcOwNyIKN6PchoQpDFstZhrI6JYLE0FNpG0djO55HwEXAicFBEzG7wmMzMzGwSamuFe\n0mXAZyLi2fx9QxHxkRZzGAEMZdnB+o8DmzTYZnSD+FqP1ShSEdVTzGjgieLKiFgs6elSzAN19lFb\nN6+JXAD+C1gQEWc0eD1mZmY2SDR7e6F5pGIG4NnC98uTWjxOM/GdiFGTMQEgaSxwKGksm5mZmQ1y\nTRVfEfHZwvef6XAOTwGLSb1VRSNpPHXFnF7i55AKoFGlfYwEbi3EjCzuQNJQYJ28rqfjFHvVesvl\nHaSLEx5JZx+B1NN3sqT/iIiNGrxG0lC4tUvLJjYONzMzG0SmTJnClClTllo2b968fsqmeS3fWFvS\nH4CPRMQzpeVrAZdHxLtb2V9ELJQ0nXTF4ZV5X8rPT2uw2Q111u+SlxMRsyTNyTG3F/LbHvhBYR/D\nJW1dGPc1nlS03VyIOUHS0IhYnJftCtwTEfMKMQ1zIY31urqU///l5ec3eH3ZKaSLLMt62czMzGwQ\nmDhxIhMnLt0pMWPGDMaOHdtPGTWn5eILeBewSp3lqwE7tpnHycAFuQi7mdTlM4w0cz6SLgRmR8RX\nc/ypwDWSjgB+Q+oOGgt8vrDP7wPHSPo78CDpdkizgSsAIuJuSVOBsyUdlF/T6cCUiKj1fE0Gvg6c\nJ+lEYHPSKcTDCsfpMZd8ccJSFyhIWgjMiYj72mksMzMzW3E1XXxJ2qLwdLPCdAyQTqO9lzbnr4qI\nS/KcXseRTuH9FZgQEU/mkPUoTM8QETdImgh8Mz/uA/aIiLsKMSdJGkaat2s4cB2wW0QsKBx6b+AM\n0tWKS4BLKRRW+QKDCTnmFtIp0kkRcW4rudR7yU03jpmZmXUVpVkdmgiUlvCvokF1QuYDh0TEeR3K\nbdCStA0wHabT+LTjfixevJghQ/p0hygzM7OuUjjtODYiZvR3PvW0ctrxDaSi6wHSLO1PFtYtAJ4o\njIsyMzMzszqaLr4i4qH8rbtazMzMzNrUzoB7APK9C19PafB9RFzZ16TMzMzMulU7U01sBPySdOVf\n8K/xX7XxYEM7k5qZmZlZ92nnFOKpwCzSVYkvAm8GdiJdDfiujmVmZmZm1oXaOe04Dnh3RDyZr4Bc\nEhF/knQ0aaJR30bHzMzMrIF2er6GAs/n758C1s3fP0TjG2GbmZmZGe31fN0BbEGacuIm4CuSFgBf\nyMvMzMzMrIF2iq8TgNXz918Hfk2aPf6fwF4dysvMzMysK7VcfEXE1ML3fwc2lfRqYG40O12+mZmZ\n2SDV9jxfRRHxdCf2Y2ZmZtbtmiq+JF3W7A4j4iPtp2NmZmbW3Zrt+Zq3XLMwMzMzGySaKr4i4rPL\nOxEzMzOzwaCtm2RLWknSeyQdIGnNvGxdSWt0Nj0zMzOz7tLOvR03AH5Huqn2qsDVwHPAUfn5gZ1M\n0MzMzKybtHtvx1uAdYD5heW/BMZ3IikzMzOzbtXOVBPvAN4eEQskFZc/CLyuE0mZmZmZdat27+04\ntM7y9UinH83MzMysgXaKr/8D/qPwPPJA+28AV3UkKzMzM7Mu1c5px/8Epkq6C1gNmAz8O/AUMLGD\nuZmZmZl1nXbu7Thb0pakm2hvCawBnAtcHBHze9zYzMzMbJBr696OEbEIuDg/XiFpWES82InEzMzM\nzLpRW5OslklaTdJ/Ag90Yn9mZmZm3arp4kvSqpK+LekWSddL+lBe/llS0fUfwCnLKU8zMzOzrtDK\nacfjgAOAacDbgJ9LOg8YBxwB/DwiFnc+RTMzM7Pu0Urx9XHgUxFxpaS3ALcDKwNbRkQsl+zMzMzM\nukwrY77WA6YDRMQdwMvAKS68zMzMzJrXSvE1FFhQeL4IeL6z6ZiZmZl1t1ZOOwr4iaSX8/PVgB9J\neqEYFBEf6VRyZmZmZt2mleLrgtLzizqZiJmZmdlg0HTxFRGfXZ6JmJmZmQ0GHZlk1czMzMya4+LL\nzMzMrEIuvszMzMwq5OLLzMzMrEIuvszMzMwq5OLLzMzMrEIuvszMzMwq5OLLzMzMrEIuvszMzMwq\n5OLLzMzMrEIuvszMzMwq5OLLzMzMrEIuvszMzMwq5OLLzMzMrEIuvszMzMwq5OLLzMzMrEIuvszM\nzMwq5OLLzMzMrEIuvszMzMwqNGCKL0kHS5olab6kGyVt20v8xyXNzPG3SdqtTsxxkh6T9KKkqyVt\nXFq/jqSLJc2TNFfSOZJWL8VsIenafJyHJB3ZSi6SVpJ0oqTbJT0v6VFJF0h6beutZGZmZiu6AVF8\nSdoL+B5wLLA1cBswVdKIBvHjgMnA2cBWwOXA5ZI2K8QcBXwJOADYDngh73OVwq4mA2OA8cD7gZ2A\nswr7WBOYCswCtgGOBCZJ2r+FXIbl5d/Ir+3DwCbAFa20kZmZmXUHRUR/54CkG4GbIuKw/FzAI8Bp\nEXFSnfifAcMi4oOFZTcAt0bEF/Pzx4DvRsQp+flawOPApyPiEkljgDuBsRFxa46ZAPwGWC8i5kg6\nCDgeGB0Ri3LMt4E9ImKzZnOpk/9bgZuADSJidp312wDTYTqp5is7H9iPxYsXM2TIgKifzczMBoQZ\nM2YwduxYSJ/vM/o7n3r6/ZNb0srAWOD3tWWRKsJpwLgGm43L64um1uIlbQSMLu3zWVLBU9vnDsDc\nWuGVTQMC2L4Qc22t8CocZxNJazeTSwPD83Ge6SHGzMzMulC/F1/ACGAoqVeq6HFSAVXP6F7iR5GK\nm55iRgNPFFdGxGLg6VJMvX3QREzd3CWtCnwHmBwRz9eLMTMzs+41EIqvRkQqoDoZ34kYNRmzzHpJ\nKwE/z+vqnpI0MzOz7rZSfycAPAUsJvVWFY1k2R6lmjm9xM8hFUCjSvsYCdxaiBlZ3IGkocA6eV1P\nxyn2qvWWS23ftcJrfeDdzfV6HQ6sXVo2sffNzMzMBoEpU6YwZcqUpZbNmzevn7JpXr8XXxGxUNJ0\n0hWHV8IrA+7HA6c12OyGOut3ycuJiFmS5uSY2/M+1yKN5fpBYR/DJW1dGPc1nlS03VyIOUHS0HxK\nEmBX4J6ImFeIaZhLPnat8NoI2Dki5vbWLskpNB5wb2ZmNrhNnDiRiROX7pQoDLgfsAbKaceTgS9I\n+pSkTYEfkaZo+AmApAslfasQfyqwm6QjJG0iaRJp0P4ZhZjvA8dI2l3S5sCFwGzyFA8RcTdpYPzZ\nkraV9HbgdGBKRNR6viYDC4DzJG2Wp8Q4lDQtRlO55N60X5CqqH2AlSWNyo+V+9huZmZmtoLp954v\ngDz1wwjgONIpvL8CEyLiyRyyHrCoEH+DpInAN/PjPtL0D3cVYk6SNIw0b9dw4Dpgt4hYUDj03qQi\naRqwBLgUOKywj2fz9BNnALeQTpFOiohzW8hlPeAD+fu/5q+1MWE7A9e22FxmZma2AhsQ83zZ0jzP\nl5mZWXs8z5eZmZmZLcXFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZ\nVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZ\nmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmF\nXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZ\nmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjF\nl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZ\nVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVcjFl5mZmVmFXHyZmZmZVWjAFF+SDpY0S9J8\nSTdK2raX+I9Lmpnjb5O0W52Y4yQ9JulFSVdL2ri0fh1JF0uaJ2mupHMkrV6K2ULStfk4D0k6cnnk\nYp0xZcqU/k5hheM2a4/brXVus/a43brPgCi+JO0FfA84FtgauA2YKmlEg/hxwGTgbGAr4HLgckmb\nFWKOAr4EHABsB7yQ97lKYVeTgTHAeOD9wE7AWYV9rAlMBWYB2wBHApMk7b8ccrEO8B+p1rnN2uN2\na53brD1ut+4zIIov4HDgrIi4MCLuBg4EXgT2axB/GPDbiDg5Iu6JiGOBGaQCpxhzfET8KiLuAD4F\nrAt8CEDSGGAC8LmIuCUirgcOAT4haXTexz7AyjlmZkRcApwGHNHJXMzMzGzw6PfiS9LKwFjg97Vl\nERHANGBcg83G5fVFU2vxkjYCRpf2+SxwU2GfOwBzI+LWwj6mAQFsX4i5NiIWlY6ziaS1O5iLmZmZ\nDRIr9XcCwAhgKPB4afnjwCYNthndIL7WYzWKVET1FDMaeKK4MiIWS3q6FPNAnX3U1s3rUC4NzGyw\n/MGeNzMzs0Hh4Ycf5qmnnuoxZsSIEbz+9a+vKCNrxkAovhoRqWjpZHwnYtRkTF+Os1r6sk/DjddZ\n5zVMnjwZSXXXDxkyhCVLljTcvrf1ndhHf6yfPXs2F1988YDNbyCuL7bZQM1xIK6fPXs2U6ZMGbD5\nNbO+6hzqvdf6uw1WhPWNfkefeuopjjzyv1i48KWG2wOssspqXHbZpbz2ta/tMa5bzJz5SsfFav2Z\nR08GQvH1FLCY1ENUNJJle4tq5vQSP4dU3Iwq7WMkcGshZmRxB5KGAuvkdT0dp9iT1YlcyjZssPwV\nc+c+yb777ttb2KC0zz6Ni1arz23Wnr333ru/U1jh+L3Wnr6024IFL/GBD3ygg9msMDYEru/vJOrp\n9+IrIhZKmk664vBKAKXunPGkwe313FBn/S55ORExS9KcHHN73udapLFcPyjsY7ikrQvjvsaTCqWb\nCzEnSBoaEYvzsl2BeyJiXgdzKZsKfJJ0frHnf2nMzMysaDVS4TW1n/NoSGlsez8nIe0JXECaiuFm\n0tWPHwM2jYgnJV0IzI6Ir+b4ccA1wH8BvwEm5u+3iYi7csxXgKOAz5CKmOOBNwNvjogFOeYqUg/U\nQcAqwHnAzRGxb16/FnA3cDVwIrA5cC5wWESc28lczMzMbHDo954vgIi4JM/pdRzp9NxfgQkR8WQO\nWQ9YVIi/QdJE4Jv5cR+wR63YyTEnSRpGmrdrOHAdsFup2NkbOIN0teIS4FLStBC1fTwraUKOuYV0\ninRSrfDqcC5mZmY2CAyIni8zMzOzwaLf5/kyMzMzG0xcfJmZmZlVyMXXANTqTcYHKkk7SrpS0qOS\nlkj6YJ2YFebm583k0leSjpZ0s6RnJT0u6ZeS3lSKWVXSDyQ9Jek5SZdKKk+bsr6k30h6QdIcSSdJ\nGlKKeZek6ZJeknSvpE/XyafH92KncukLSQfmn+m8/Lhe0ns7nWO3tFc9+X23RNLJnc61m9pN0rG5\nnYqPuwrr3WYNSFpX0k9zPi/m39ltSjGD5/MgIvwYQA9gL9L0Ep8CNiUN0n8aGNHfubXxWt5Luoji\nQ6S53D5YWn9Ufm27A28h3ZT8fmCVQsxvSffKfCvwNuBe4KLC+jWBf5Culh0D7Em6cfn+hZhxwELS\nPTk3Ab4BvAxs1slcOtRmVwH75teyOfBr0hWyryrE/DAveyfpRvTXA9cV1g8B/ka6zHpz0j1MnwBO\nKMRsCDwPnJTb5ODcRru08l7sRC4daLP35/faxvlxQv75jnF7NdV+25Lu5HErcLLfZz221bGkKYNe\nQ7pSfiTwardZr+02HJgFnEO6neAGwHuANxRiBtXnwXL5ZfajT2/SG4FTC88FzAa+0t+59fF1LWHZ\n4usx4PDC87WA+cCe+fmYvN3WhZgJpCtfR+fnB5GuQl2pEPNt4K7C858BV5aOfQNwZidzWU7tNiIf\n9x2FvF4GPlyI2STHbJef75b/uBT/EB8AzK21E2nqlNtLx5oCXNXse7FTuSyndvsn8Fm3V6/ttAZw\nD/Bu4I/k4svt1rC9jgVmNFjnNmvcbt8BruklZlB9Hvi04wCi9m4yvkKS9AZWrJufN5PL8jA8H+Pp\n/HwsaYqYYq73AA+Xcv1bRBRv+DYVWJs0v1wtpqc2aea9+NYO5dIxkoZI+gQwjPQH1e3Vsx8Av4qI\nP5SWdyrXbmy3f1caSnG/pIskrZ+X+73W2O7ALZIuURpOMUPS/rWVg/HzwMXXwNLTTcZ7uQn3Cmc0\nbd78nFSIFGPq7YMmYvp8I/ZSLh0lScD3gT/Fv+aNGw0syH8Mesq13TZZS9KqNPdeHNWhXPpM0lsk\nPUf6b/9M0n/8d+P2aigXqVsBR9dZ3alcu63dbiRNlj0BOBB4A3BtHuvj91pjG5F6pe4h3SXmR8Bp\nkmr3TBp0nwcDYpJV61WrNxlfka0oNz9vJaZdZwKbAe9oIrbZPHprk2ZiOtUmnWy3u4EtST2FHwUu\nlLRTD/GDur0krUcq7HeJiIWtbNpkHl3ZbhFRvF3NHZJuBh4ijS1qdCu4Qd1m2RDS3WO+lp/fJunN\npILsoh6269rPA/d8DSzt3GR8RVW84XhR+abkVd/8vJ1cOv6zkXQG8D7gXRHxWGHVHGAVpVtf9ZRr\n+bWMKqxrFDMSeDbSnReaeS/2NZeOtVtELIqIByJiRkT8N3Ab6W4Vbq/6xpIGjU+XtFDSQtLA7MMk\nLcjHWtXt1rNI9/i9l3Shh99rjf0DmFlaNhN4fSGXQfV54OJrAMn/gdZuMg4sdZPxAXln9nZFxCzS\nG7j4Wms3HK+91ldufl7YtN7Nz3fKb/yaRjc/L1rq5ud9zOWm5l51c3LhtQewc0Q8XFo9nTSos5jr\nm0h/xIq5bq50y66aXYF5/OsPYL022ZV/tUkz78W+5nIXy88QYNUO5Nit7TWNdIXbVqQewy1Jt1C7\nqPD9wj7k2q3tthRJawBvJA3Q9nutsT+TBvwXbULqNRycnwd9vYrBj84+SN3X81n6EuJ/Aq/p79za\neC2rk/6Qb0W6MuQ/8vP18/qv5Ne2O+mD4HLSvTGLl/NeRfog2BZ4O2nMwE8L69ci/eG7gHSKbi/S\nZdqfK8SMAxbwr0uLJ5FOERQvLe5zLh1qszNJVxvtSPrPq/ZYrRQzC3gXqQfjzyx7CfltpEuhtyCN\nT3kcOL4Qs2FupxNzm3wxt9F7WnkvdiKXDrTZN0mnZjcgXRb+bdIHz7vdXi214ytXO7rdGrbRd4Gd\n8nvtbcDV+Tj/5jbrsd3eShqPeTSpWN0beA74RCFmUH0eLNdfZj/afqN+kTQ/y3xShf3W/s6pzdfx\nTlLRtbj0OK8QMyn/srxIuuJk49I+hpP+G59HKkrOBoaVYjYHrsn7eBj4cp1cPkoaFzSfNE/PhDox\nfc6lA21Wr70WA58qxKwKnE46/fAc8HNgZGk/65PmCHue9Af1RGBInZ/P9Nwm9wH7tvpe7FQufWyz\nc0jzVM0n/cf6f+TCy+3VUjv+gaWLL7fbsnlOIU3pMJ/0t2YyS89V5TZr3HbvI/3tfRG4E9ivTswk\nBsnngW+sbWZmZlYhj/kyMzMzq5CLLzMzM7MKufgyMzMzq5CLLzMzM7MKufgyMzMzq5CLLzMzM7MK\nufgyMzMzq5CLLzMzM7MKufgyMwMkXSfppP7Ow8y6n4svM1uhSbpS0m8brNtR0hJJb6k6LzOzRlx8\nmdmK7lzgPZJeV2fdZ4G/RMQdFedkZtaQiy8zW9H9mnTz4E8XF0paHfgY6cbbSNpZ0l8kvSTpUUkn\nSFK9HUoamnvM3lda/pykvfP3b8wxH5X0J0kvSrpJ0kaSdpA0Pcf/WtI6pf0cIGmmpPmS7pT0hcK6\nVST9UNJjef0Dkr7ciYYys4HBxZeZrdAiYjFwIfCZ0qo9SX/jfiZpfeA3wJ+ALYCDgQOBozuQwiTg\nWGCb/HwK8E3gIGBHYNMcA4CkTwP/DRyV1x0DfFvSxBxyBDAB+CjwJmBf4OEO5GlmA8RK/Z2AmVkH\nnAccKWmniLg2L/sM8POIeE7SMcD9EXF4XndvLsi+AXyrj8c+MSJ+DyDpNFIhuFNE3JyXnQ/sVYif\nBBweEVfm5w9J2gI4gFS4rQ/cGxE35PWP9DE/Mxtg3PNlZiu8iLgHuB7YD0DSxqRep/NyyKZ5fdGf\ngbUlje7j4f9W+P7x/PWO0rKROa81gQ2AC/IpyeckPUfqBdsox58PbCfpbknflzS+j/mZ2QDj4svM\nusW5wEclrUEaaP/3iLgurxMQpfjaeK/y8uKy8piwemcLFtbZrrys9rd2zfz1M8CWhcdbSMUiEXEL\nqUD7OjAM+IWkyXWOa2YrKBdfZtYtLgGWAHuTxkmdW1h3F/D2UvzbgWci4vHSciJiCf+/fftViSCM\nwjD+nKTBYNVq0m7xEjSLxSbYFMRmsq2gQUQQg0GjVyAGu2gQL0AR/NMsrsF0DN8I6wriGj6W5fnB\nlJkz882U4eVlBl6Bsa99ETEJDHWP9nKDmflMacImMvOua3vomHvLzNPMXG6eZ6EJlZIGgN98SRoI\nmfkeEadAi9IwnXQc3gdWImIXOACmKM3Szi+XvABWI+KKErpafG+04Gcz9hebwHZEtIFzYBiYBkYy\ncy8i1infed008/PAU2a2/7GWpD5k8yVpkBwBo8BZZr587czMR2AWmKGEmn1KCNvqOLe7xVoDnil/\nSB5TwtdH10xPzVdzL4eUPyGXgFtKyFsE7puRNrABXAOXwDgw1+s6kvpXZPb87pAkSdI/2XxJkiRV\nZPiSJEmqyPAlSZJUkeFLkiSpIsOXJElSRYYvSZKkigxfkiRJFRm+JEmSKjJ8SZIkVWT4kiRJqsjw\nJUmSVJHhS5IkqaJPuz69a9TBhbAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x124313e90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"mv.generateHist(realClusterVols, title = 'Cluster Volumes for Easy Simulation', bins = 50, xaxis = 'Volumes', yaxis = 'Relative Frequency')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[5203323, 652, 28, 52, 13, 9, 9, 24, 28, 11, 2, 3, 24, 87, 20, 4, 18, 4, 4, 26, 19, 14, 14, 27, 32, 5, 35, 28, 9, 10, 23, 38, 64, 48, 42, 19, 21, 12, 10, 115, 117, 7, 41, 22, 17, 28, 22, 15, 23, 36, 95, 44, 131, 30, 9, 20, 25, 174, 13, 134, 12, 29, 7, 377, 7, 16, 21, 779, 22, 12, 28, 14, 28, 7, 21, 26, 34, 16, 11, 9, 26, 243, 9, 25, 200, 50, 13, 4, 13, 10, 15, 40, 11, 16, 58, 227, 50, 60, 10, 46, 13, 3, 44, 7, 45, 17, 13, 5, 51, 13, 9, 13, 23, 26, 56, 16, 35, 13, 12, 26, 93, 5, 9, 35, 10, 70, 41, 17, 208, 7, 29, 20, 4, 10, 55, 603, 7, 9, 89, 10, 220, 14, 9, 45, 39, 52, 6, 14, 4, 33, 17, 12, 14, 7, 50, 48, 39, 15, 8, 29, 28, 24, 66, 717, 83, 2532, 50, 6, 10, 10, 12, 21, 10, 7, 9, 7, 6, 9, 38, 15, 13, 30, 6, 169, 11, 36, 12, 22, 104, 36, 21, 40, 4, 44, 42, 7, 19, 192, 33, 34, 36, 20, 24, 19, 11, 22, 10, 12, 42, 45, 36, 8, 8, 12, 39, 12, 27, 7, 184, 6, 13, 35, 21, 15, 22, 37, 30, 12, 16, 32, 25, 18, 32, 26, 32, 12, 26, 12, 22, 17, 14, 26, 25, 4, 13, 37, 4, 13, 96, 9, 38, 16, 10, 37, 51, 36, 8, 16, 56, 10, 16, 22, 10, 38, 11, 43, 9, 7, 3880, 26, 31, 43, 58, 16, 17, 74, 46, 26, 23, 13, 352, 4, 10, 26, 27, 10, 10, 119, 12, 8, 4, 8, 3, 1, 4, 4, 2, 20, 3, 6, 5, 6, 7, 6, 5, 5, 10, 3, 1, 3, 13, 2, 3, 2, 3, 4, 6, 5, 4, 2, 2, 4, 11, 7, 3, 2, 7, 4, 2, 1, 1, 5, 2, 4, 3, 19, 7, 1, 4, 2, 2, 1, 3, 13, 12, 1, 2, 3, 3, 11, 5, 8, 1, 2, 2, 12, 5, 1, 8, 3, 5, 1, 1, 11, 10, 2, 2, 15, 2, 8, 2, 16, 3, 5, 6, 2, 19, 2, 2, 2, 4, 3, 12, 3, 7, 6, 2, 2, 4, 1, 6, 3, 3, 3, 2, 19, 3, 42, 3, 1, 1, 5, 10, 17, 7, 1, 4, 2, 5, 5, 4, 3, 9, 4, 2, 10, 1, 10, 2, 3, 4, 2, 1, 39, 2, 17, 4, 11, 5, 3, 2, 5, 2, 11, 7, 7, 3, 6, 5, 4, 12, 10, 4, 1, 10, 6, 6, 6, 2, 4, 2, 2, 13, 11, 3, 9, 7, 3, 16, 17, 5, 3, 7, 12, 2, 3, 11, 3, 2, 5, 5, 5, 8, 7, 5, 12, 6, 28, 13, 1, 3, 4, 9, 3, 3, 2, 9, 4, 3, 6, 4, 5, 2, 3, 6, 3, 3, 10, 17, 13, 4, 17, 1, 10, 6, 1, 10, 5, 4, 1, 9, 14, 4, 3, 1, 4, 6, 9, 3, 2, 1, 2, 16, 2, 4, 4, 9, 2, 9, 4, 4, 4, 2, 21, 5, 29, 2, 20, 15, 8, 14, 5, 3, 3, 8, 4, 15, 2, 18, 3]\n"
]
}
],
"source": [
"print realClusterVols\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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T1F8KnAgcDTwEOAU4JckDBqpdABwI/CHwx7RWoE8n+b1JxS1JkubfOPcGehCw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\nZrIiSZJ67f8HvcF/XSZU8H8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d1ecb50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"del realClusterVols[0]\n",
"mv.generateHist(realClusterVols, title = 'Cluster Volumes for Easy Simulation', axisStart = 0, axisEnd = 200, bins = 25, xaxis = 'Volumes', yaxis = 'Relative Frequency')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Potential Corrections to connectLib Pipeline\n",
"\n",
"Because the distribution of intensities is not clearly bimodal, a simple binary threshold, with the lower 98% of voxel intensities getting thresholded to 0, might be a better method for filtering background noise than Otsu's Method. Furthermore, there is actually more issue in filtering out additional foreground noise that are not synapses. These elements such as glial cells still get clustered and labeled as synapses but are not (as you can tell by the voxel volume). "
]
},
{
"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
}
| apache-2.0 |
mathnathan/notebooks | Not so Magic of Bootstrapping.ipynb | 1 | 24631 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"# True variance parameter of population\n",
"true_mean = 0.0\n",
"true_var = 2.6341\n",
"\n",
"# Sample for calculating variance estimate\n",
"sample_size = 100\n",
"sample = np.random.normal(true_mean, true_var**0.5, sample_size)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean Estimate Error: -0.187516027399\n",
"Variance Estimate Error: -0.233122275687\n"
]
}
],
"source": [
"print('Mean Estimate Error: ', sample.mean() - true_mean)\n",
"print('Variance Estimate Error: ', sample.var() - true_var)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"num_resamples = 1000\n",
"resample_sizes = np.arange(2,99)\n",
"bootstrap_vars = []\n",
"bootstrap_means = []\n",
"for resample_size in resample_sizes:\n",
" bootstrap_var = 0\n",
" bootstrap_mean = 0\n",
" for i in range(num_resamples):\n",
" resample = np.random.choice(sample, resample_size, replace=True)\n",
" bootstrap_var += resample.var()/num_resamples\n",
" bootstrap_mean += resample.mean()/num_resamples\n",
" bootstrap_vars.append(bootstrap_var)\n",
" bootstrap_means.append(bootstrap_mean)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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pVY2WCC3QF5uSyS8pq7cT289PePaq0zhvYCdemzmKDmGt6lxfKdXyaHNWC/Th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\nmkSUUkp5TJOIUkopj2kSUUop5bH/DzcMBZsycQRjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7efd6d80cef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = plt.subplot(111)\n",
"ax.plot(np.array(bootstrap_vars)-true_var, label='Var')\n",
"ax.plot(np.array(bootstrap_means)-true_mean, label='Mean')\n",
"ax.set_ylabel('Estimate Error')\n",
"ax.set_xlabel('Resample Size')\n",
"ax.legend()\n",
"plt.show()"
]
},
{
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
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